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High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data

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† Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712 United States
‡ Environmental Defense Fund, New York, New York 10010 United States
§ School of Population and Public Health, University of British Columbia, Vancouver V6T 1Z3 Canada
∥ Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720 United States
⊥ Aclima, Inc., 10 Lombard St., San Francisco, California 94111 United States
# Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195 United States
∇ Institute for Risk Assessment Science, Utrecht University, Utrecht 3584 CM Netherlands
*E-mail: [email protected]
Cite this: Environ. Sci. Technol. 2017, 51, 12, 6999–7008
Publication Date (Web):June 5, 2017
//doi.org/10.1021/acs.est.7b00891
Copyright © 2017 American Chemical Society
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Abstract

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Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

1 Introduction

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Air pollution is a major global risk factor for ill-health and death.(1-4) Air pollution measurements are crucial for epidemiology and air quality management, but the extent of ground-based air pollution observations is limited.(5, 6) For many developing-country regions, especially in populous parts of Asia and Africa, robust air quality monitoring is largely absent.(5) Even for high-income regions, ambient monitors are generally sparsely sited. For the 60% of the U.S. census urban areas with continuous regulatory monitoring, there are a mean of ∼2–5 monitors per million people and 1000 km2 (Supporting Information (SI) Table S1). However, primary air pollutant concentrations in cities can vary sharply over short distances (∼0.01–1 km) owing to unevenly distributed emissions sources, dilution, and physicochemical transformations.(7-10) Such gradients are not well represented with routine ambient measurements, but have important implications for exposure assessment, epidemiology, air quality management, and environmental equity.(10-13)
Advances in air pollution exposure assessment techniques over the past two decades have helped address limitations of (i) data coverage and (ii) spatial resolution that are associated with central-site ambient monitoring.(10, 14) These methods include satellite remote sensing (RS), chemical transport models (CTMs), land-use regression (LUR) models, and direct personal exposure measurements.(14) Each of these approaches has distinct advantages and limitations. Satellite RS instruments and CTMs are spatially coarse (>1–10 km resolution), and cannot characterize the fine-scale gradients (10–300 m) that drive population exposure to local emissions such as traffic. Satellites are unable to measure some pollutants of key health concern (e.g., black carbon, ultrafine particles). Dispersion models and CTMs are only as reliable as their underlying emissions inventories, and thus cannot reveal unexpected sources. Empirical and geostatistical models such as LUR and kriging can estimate concentrations at high spatial resolution.(10, 14-17) However, these models provide limited temporal information, often require local training data sets to be available or collected, and struggle to predict the tails of air pollution distributions, especially where idiosyncratic local sources exist. Alternative methods to measure how air pollution concentrations vary within cities and neighborhoods could complement these existing techniques and fill important data gaps.(18, 19)
We explore here the potential of routine mobile monitoring with fleet vehicles for measuring time-integrated concentrations at high spatial resolution. The use of vehicles for mobile air pollution sampling dates to the 1970s or earlier,(20) and has been common since the early 2000s.(12, 13, 21-27) However, even purpose-built “mobile labs” are rarely deployed for extended periods, and often depend on trained research staff as drivers.(25-28) As a result, few mobile monitoring data sets have sufficient repetition frequency or statistical power to precisely reveal consistent long-term spatial patterns over wide areas. We consider here an alternative approach, equipping professionally driven fleet vehicles with air quality instruments, repeatedly sampling every street of an urban area, and applying data reduction algorithms to distill stable long-term spatial patterns from time-resolved data. This approach has been implemented for public transportation vehicles along fixed routes, including trams in Karlsruhe and Zürich.(24, 29) We report here on a 1 year pilot study in the San Francisco Bay Area using extensive route-free driving representative of a broad sample of city streets. In addition to testing the feasibility of this approach at scale, we explicitly aimed to “overdesign” our sampling scheme to enable quantitative characterization of trade-offs between sampling frequency and measurement precision/accuracy. (A similar oversampling approach has been tested on shorter cycling routes in the U.S. and Belgium).(15, 30, 31) We demonstrate that our measurement approach is capable of precisely (±10–20% of median) revealing urban pollution gradients at very fine ∼30 m scales, or 104–105× greater spatial resolution than with urban-ambient monitors. Our results corroborate current understandings and also provide new insights into the spatial variability of urban air pollution.

2 Materials and Methods

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In brief, we equipped two Google Street View (SV) mapping vehicles with an Aclima environmental intelligence fast-response pollution measurement and data integration platform (Aclima, Inc., San Francisco, CA). Using these vehicles, we repeatedly measured weekday daytime concentrations of black carbon particles (BC), NO, and NO2 over 1 year for every road within diverse residential, commercial and industrial areas of Oakland, CA. BC, NO, and NO2 are key health relevant pollutants whose sources include vehicular traffic, shipping, industrial combustion, cooking, and heating. The large resulting data set (3 × 106 1-Hz measurements within a 30 km2 area; 24 000 total vehicle-km on 750 road-km) is unique among mobile monitoring studies in its very high coverage density and repeat-visit frequency (average of 31 days and 200 1-Hz measurements for each 30 m of road, SI Figure S2). Through data reduction and bootstrap resampling(32) algorithms, we compute median ± standard error (SE) annual daytime concentrations for ∼21 000 unique road segments. While instantaneous pollution levels vary rapidly in on-road environments, by repeatedly sampling each road and computing long-term medians, we obtained stable, precise (±10–20%) estimates of time-integrated pollution at fine 30 m spatial scales. Overall, these data reduction methods produce maps of systematic spatial variation in long-term air pollution that are robust to the stochastic temporal variability in the underlying 1-Hz data.

2.1 Measurement Platform

Two Google Street View (SV) vehicles were equipped with the Aclima Ei measurement and data acquisition platform. This platform provides data management, quality control, and visualization functions, facilitating extensive, routine measurements. In the present configuration, monitors employed were fast-response (1 Hz) laboratory-grade analyzers: BC was measured using a photoacoustic extinctiometer, NO was measured using chemiluminescence, and NO2 was measured using cavity-attenuation phase-shift spectroscopy. The inlet system was designed to minimize self-sampling and particle sampling losses. Further details of the instrumentation design, routine calibrations, and QA/QC protocols/algorithms are provided as SI.

2.2 Study Area Description and Sampling Protocol

Sampling during the 1 year study duration emphasized three main areas within Oakland, CA: West Oakland (WO, ∼10 km2), Downtown Oakland (DT, ∼5 km2), and East Oakland (EO, ∼15 km2). WO is bounded by major interstate highways (I-880, I-980, I-580), the fifth-largest U.S. container port, and associated rail and trucking facilities. Residential blocks are interspersed with industries in this lower-income neighborhood. DT has mixed residential and commercial mid- and high-rise buildings. EO is divided between industrial and mixed-income residential areas.
We undertook mobile monitoring on weekdays from May 28, 2015 to May 14, 2016. During routine operation, cars left garages in either Mountain View, CA or San Francisco, CA at around 9:00 AM local time and drove to Oakland, CA for ∼6–8 h of driving. Barring operational constraints (e.g., maintenance), two cars were operated simultaneously on each driving day.
Drivers were provided daily driving assignments consisting of polygons (∼1–5 km2) in which the driver was tasked with driving every road at least once. Drivers were instructed to drive in the normal flow of traffic. Mean speeds were ∼25 km h–1 on surface streets and ∼88 km h–1 on highways. Routine sampling was conducted during all seasons. Vehicles were operated without regard to weather, except during 1–2 exceptional storms that precluded safe driving. The prevailing wind direction for the study area was from the west (∼85% of all study hours; median wind speed 4 m s–1). A total of 2.7 M 1-Hz observations, or 800 h, on ∼16 000 unique 30 m road segments were collected in core WO+DT+EO domain. Additional monitoring incorporated interstate highways linking the study areas and car maintenance garages (∼300 k 1-Hz measurements on ∼4500 unique 30 m road segments). Further information about the study domain and operational considerations are provided as SI, including detailed maps (Figure S1–S2).

2.3 Data Reduction

We developed a series of data reduction algorithms to convert our data set of ∼3 million instantaneous observations into estimates of median annual weekday concentrations for individual 30-m road segments. First, we accounted for the possible biases of daily diurnal variation in urban-background concentrations by applying an hourly multiplicative adjustment factor to our 1-Hz mobile data based on ambient concentrations at a regulatory fixed-site monitor in WO.(15, 23, 30, 33) This adjustment approach makes the implicit assumption that temporal patterns are spatially invariant. This common assumption is conceptually imperfect but may be acceptable for well-mixed daytime hours.(15, 23, 30, 33) In any case, the temporal adjustment had only a minor (±10%) impact on the observed spatial patterns in long-term concentrations, likely because the numerous repeated measurements were already balanced in space and time.
Next, we employed a “snapping” procedure to assign each 1-Hz measurement to the nearest 30 m road segment on the basis of measured GPS coordinates, thereby allowing repeated measurements along the same road to be analyzed as a group.(15) We chose to use 30 m road segments for analysis after weighing considerations of spatial resolution (smaller segments show more spatial variability) and sample size (outliers and GPS errors have proportionally greater influence when data are “thinly sliced”). A virtue of this data reduction step is that it ensures that road segments with a high number of raw 1-Hz samples (e.g., owing to low average speeds) are not over-represented in our spatial analyses: regardless of the number of 1-Hz samples, a single point estimate of concentration is produced for each road segment. We restrict our analysis to include only the ∼21 000 road segments in our study domain with at least 10 unique days of measurement between 8 am and 6:59 pm local time between 28 May 2015 and 18 May 2016. The typical road segment contains ∼150–250 individual 1-Hz samples collected on ∼20–50 distinct days. The SI provides further details on the performance of our “snapping” and temporal adjustment algorithms (see Figures S4–S5).
For each of the ∼21 000 road segments, we computed distributional parameters of repeated 1-Hz observations. Similar to some recent mobile monitoring studies,(15, 30) but unlike others,(28) we explicitly chose not to filter out “peak” 1-Hz concentration measurements resulting from encounters with other vehicles’ exhaust plumes in assessing spatial patterns. The frequency with which plumes occur in space and time provides valuable information about the conditions that give rise to on-road concentrations.(30) We evaluated both the median and mean as metrics of central-tendency. In initial study design, we had two a priori concerns about the possible use of the mean as a reliable metric: First, that a small number of extreme outlier observations (true concentration “peaks” or rare spurious measurements) might bias our estimation of “typical” concentrations for each road segment, and second, that if we under-sampled the tails of a skewed concentration distribution, we might not obtain stable means. Also, for a log-normal or similarly skewed distribution, median is generally a more appropriate measure of central tendency than is mean. Accordingly, we chose to use median concentrations as our core estimate of central tendency for 30 m road segments, and evaluated means in sensitivity analyses.
We used a set of bootstrap resampling procedures to quantify the effect of sample-to-sample variability and sampling error on the precision of central-tendency 30 m concentration estimates. For each individual road segment, we computed the standardized error (SE) of the median and mean concentration. As a metric of precision, we consider normalized SE: the ratio of the standard error of the median (mean) concentration to the median (mean) concentration itself (SI Table S4). On average, the normalized SE of individual 30 m median (mean) concentrations was low: respectively 20% (21%), 16% (15%), and 9% (8%) for BC, NO, and NO2. Road segment mean concentrations were well correlated with medians (mean > median owing to positive skew, r2 ∼ 0.6–0.9). In sum, for our sample size, central-tendency concentrations can be estimated with good precision and minimal sampling error from stochastic variability. For large groups of road segments (e.g., all residential streets), we estimate the precision of the central tendency to be within ≪1% of the normalized SE.
The SI provides further information on bootstrapping and precision calculations, comparisons between mean and median metrics, and summary tables of raw and reduced data (SI Tables S2–S3). The distributions of all ∼21 000 30 m-median road segment concentrations for NO, NO2, and BC are positively skewed, but based on a Kolmogorov–Smirnov test do not conform to a single parametric distribution (SI Figure S6).

2.4 Stability Analysis: Monte Carlo Subsampling

We utilize a subsampling analysis to investigate whether a less intensive program of repeated monitoring can reproduce the long-term pollution patterns observed from the full data set.(15, 30, 31) A specific question of interest here is whether there is a point of “diminishing return,” beyond which additional repeat driving provides relatively little added information.
We use Monte Carlo simulation to repeatedly subsample our full data set to systematically introduce fewer days of driving for analysis. Briefly, we randomly sample without replacement 1 ≤ N ≤ 50 unique drive days at each location from our full data set. Within our core domain, the mean road segment has 31 days of sampling (10% trimmed range: 17–49 days). For each value of N, we performed 250 random draws to produce 250 subsampled “maps” of 30 m median concentrations. For road segments with fewer than N days of sampling, the “subsampling” effectively includes all observations collected over the duration of our campaign. Thus, the subsampled maps converge to the result of the full data set as N approaches the total number of unique driving days for the full measurement campaign. We compute three metrics to compare the performance of each subsampled concentration map to that of our full data set. First, as a metric of precision, we computed the r2 between each subsampled map and the corresponding full data set of 30 m median concentrations. Second, as a metric of bias, we compute the normalized root-mean-square error (normalized RMSE, or equivalently, the coefficient of variation of the RMSE, CV-RMSE) as the square root of the mean of the squared difference between each subsampled 30 m median concentration and its corresponding median from the full data set. Third, as a metric of the temporal stability of subsampled spatial patterns, we computed the intraclass correlation (ICC) of each subsampled iteration, grouped by 30 m road segments. For this application, ICC values of 0.75–1 reflect large and systematic spatial differences, with low residual temporal variability at each location (see Section S1.5). We find that spatial variability dominates the total variability in our data set, with ICC values in the range of 0.8–0.95 for all pollutants (see SI Table S5), indicating that our observed long-term spatial patterns are robust to stochastic variability among individual 1-Hz samples.

2.5 Data Mining: Spatial Patterns

We conducted two “data mining” investigations to explore the determinants of the spatial patterns that are revealed by our mobile monitoring data, specifically (i) near-highway distance-decay relationships and (ii) the effect of local, transient sources on spatial patterns.
Distance-decay relationships for pollutant concentrations near major roadways have been documented in the literature,(8, 34, 35) relying predominantly on experiments along carefully selected near-highway upwind-downwind transects.(35) Here, we demonstrate a “big data” approach that provides complementary information, using our full mobile data set to characterize time-averaged, isotropic (i.e., direction-independent) daytime distance-decay relationships. For every road segment within WO and DT, we computed a distance-to-nearest-highway parameter d (“spatial lag”). We bin all road segments by d, and compute the median concentration as a function of spatial lag using 50 m bins. For each pollutant we estimate a three-parameter exponential distance-decay relationship for the mean highway normalized concentration ratio as a function of spatial lag. The SI provides further detail on this fitting approach.
To investigate how localized or transient emissions sources contribute to the on-road concentrations measured by our mobile monitors, we employed a peak detection algorithm.(25, 28, 36, 37) The basic conceptual approach uses a moving-window function to identify a localized concentration baseline, and then attributes the difference between the measured value and the baseline to short-duration concentration “peaks”. We applied a 10 s moving average filter to the entire input data set to prevent spurious extreme low values in the raw data (e.g., instrument noise) from influencing the baseline determination. We established the concentration baseline for each second in this 10 s smoothed data set as the smaller of two quantities: the instantaneous value in the data set, or the p = fifth percentile concentration within a moving window of t = 120 s. By selecting the fifth percentile, the algorithm identifies a value that is representative of the cleanest local conditions, which are more influenced by the urban background than by localized sources. We chose t = 120 s to ensure that the time window was longer than the typical traffic light cycle, but short enough that the baseline concentrations on surface streets was established using measurements collected within ∼1 km of each location (30 km/h for 2 min = 1 km).
After establishing the baseline concentration, we derived the peak fraction parameter PF by computing (i) the mean baseline value over all repeated observations, and (ii) the mean 10s-smoothed observed concentration. Then, we compute PF as 1.0 minus the ratio of the mean baseline value to the mean observed concentration. Thus, PF is an indicator of the contribution to mean observed levels on each road segment of concentrations above the mean local baseline. While PF is inherently a semiquantitative indicator metric that is somewhat influenced by parameter choice, it usefully reveals spatial patterns in the density and magnitude of local, transient emissions sources. As a sensitivity analysis, we computed PF where the moving window period t was doubled to 4 min. For that analysis, overall spatial patterns in PF were nearly identical, but with an upward shift of 5–10% in PF: a longer window results in somewhat lower and more spatially homogeneous baseline concentrations.

3 Results and Discussion

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3.1 Spatial Patterns and Hotspots

Our mobile monitoring data set paints a vivid picture of within-urban pollution patterns, revealing remarkable and stable heterogeneity in daytime weekday pollutant concentrations (Figure 1, Figure S7). Figure 1b illustrates fine-scale variability in pollution for an indicative 0.6 km2 industrial/residential zone in West Oakland (WO). Within this small area, time integrated 30-m median primary pollutant concentrations vary by >5× within individual city blocks, and by >8× overall, as the result of local emissions sources and traffic congestion. A surprising feature throughout our data set is the ubiquity of persistent daytime “hotspots” (length <100 m), tentatively classified as locations where concentrations of multiple pollutants exceed nearby ambient levels by 50% or more. Figure 2 presents data and plausible causes for an indicative set of hotspots based on analysis of Google Street View imagery. Examples include intersections along a major truck route, vehicle repair facilities, and industrial sites with associated truck traffic. As one indication of a localized source, most hotspots show sharp spatial “peaks” for multiple pollutants. (For the full data set, spatial patterns of 30 m-median concentrations for the three pollutants are moderately correlated, r2: BC-NO = 0.71, NO-NO2 = 0.57, BC-NO2 = 0.50). The SI provides further detail on the hotspot identification scheme.

Figure 1

Figure 1. High-resolution mapping of time-integrated concentrations. Annual median daytime concentrations for 30 m-length road segments based on 1 year of repeated driving for a 16 km2 domain in West Oakland [WO] and Downtown (a), and for a 0.6 km2 industrial-residential area in WO (b). Median ± SE concentrations are tabulated by road type in c. Annual median daytime ambient concentrations Camb at a regulatory fixed-site monitor in WO are plotted as shaded stars. Localized hotspots in b correspond to major intersections, industries, and businesses with truck traffic, and are interspersed with lower-income housing (see aerial image). Locations of hotspots are similar among pollutants. d, Distributions of 780 1-Hz NO measurements for a transect of eight 30 m road segments (see b, from point X to Y) to illustrate relationship between 1-Hz samples (∼100 per segment over 1 year) and plotted long-term medians (colored bars, blue horizontal lines). Elevated levels near midpoint of transect are associated with operations at a metal recycler (see Figure 2). Wind rose data are provided in SI Figure S1, and show consistent westerly winds. Imagery © 2016 Google, map data © 2016 Google.

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Figure 2

Figure 2. Illustrative pollution hotspots. a. Street View and aerial imagery of the metals recycling cluster highlighted in Figure 4a–c. Frequent heavy-duty and medium-duty truck traffic is evident in repeated Street View images. b,c. Multipollutant hotspots (i.e., prominent local concentration outliers) were identified from BC, NO, and NO2 median concentrations as described in SI. Twelve illustrative hotspots labeled A–L here, overlaid on the 30 m BC map in b for context. List in c enumerates possible emissions sources for each illustrative hotspot, with the following classification scheme for each pollutant: (+) indicates a prominent localized hotspot or cluster of roads where concentrations are sharply elevated above nearby background levels, (∼) indicates a less prominent hotspot or cluster with moderately elevated levels, and (×) indicates the absence of a clearly discernible hotspot. Imagery © 2016 Google, map data © 2016 Google.

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To demonstrate how hotspots consistently emerge from time-resolved data, Figure 1d shows long-term median and 1-Hz concentrations for the 240 m transect labeled X–Y in Figure 1b. Over the short length of this transect, time-integrated median NO levels rise and fall 4×. Concentrations are highest directly in front of the entrance to a metals recycling facility, where the 1-Hz distribution exhibits a large number of high NO concentrations. SV and aerial photos show a high volume of trucks and heavy equipment operating at this facility (Figure 2).
At broader spatial scales, pollutant levels differ substantially among three major road classes: highways, arterial roads, and residential streets (Figure 1). Median weekday daytime concentrations for WO and Downtown Oakland (DT) residential streets are approximately consistent with daytime observations at a regulatory fixed-site monitor in West Oakland (Figure 1c, road vs ambient: 0.41 ± 0.01 vs 0.50 μg m–3 BC, 5.3 ± 0.11 vs 3.5 ppb NO, 13.2 ± 0.18 vs 10.4 ppb NO2). In contrast, concentrations on busier streets and highways are substantially elevated above urban-background levels. For BC, median highway [arterial] concentrations exceed those on residential streets by a factor of 2.7 [1.3]; for NO by a factor of 4.8 [1.9]; and for NO2 by a factor of 1.8 [1.3]. Atypically high pollutant concentrations for a given road class are evident in several areas of Figure 1, especially for NO and BC. Median concentrations on city-designated truck routes linking highways to industrial areas are 1.9–3.6× higher than on other surface streets (SI Table S6). We observe consistently higher BC and NO levels (1.5–2.0× higher) on I-880 (high truck density) than on I-580 (trucks prohibited, see SI and Figure S8).

3.2 Distance-Decay Relationships

Between the extremes of polluted freeways and cleaner residential streets, concentrations on average follow “distance-decay” relationships similar to those observed elsewhere.(8, 9, 34, 35) An unconstrained three parameter exponential model, C(d) = α + β exp (−3d/k), reproduces the concentration-distance relationship C(d) with high fidelity (r2 ≥ 0.96, see Figure 3). Here, the isotropic parameter d reflects the distance to highways (m), the urban background parameter α represents concentrations far-from-highway (d → ∞), the near-road parameter β represents the concentration increment resulting from proximity to the highway, and the decay parameter k governs the spatial scale over which concentrations relax to α. For all pollutants, estimated values of (α + β) ∼ 1.0, indicating that the combined contribution of the urban background and the near-highway increment approaches the levels observed on highways.

Figure 3

Figure 3. Decay of concentrations from major highways into city streets for WO and DT. a. Plotted points represent the ratio of median concentrations at a given distance from highways (d, “spatial lag”) to median on-highway concentrations; error bars present standard error from bootstrap resampling. An unconstrained three parameter exponential model reproduces observed decay relationships with high fidelity. Here, the parameter α represents the ratio of urban-background to highway concentrations (d → ∞), β represents the additional increment in pollution at near-highway conditions, and the decay parameter k governs the spatial scale of the decay process. The value of α is intermediate for BC (primary, conserved pollutant); lower for NO (consumed rapidly during daytime by reaction with O3) and higher for NO2 (elevated background from regional secondary photochemical conversion from NO). Data in SI demonstrate that parameter estimates are consistent among alternative fitting approaches. b. Distance-to-highway metric d for surface streets in WO and DT, computed based on the harmonic mean distance of each surface street segment to closest portion of the four major highways in the domain (see SI). Map data © 2016 Google.

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Results in Figure 3 represent average weekday daytime decay patterns. These results reflect the average surface-street to highway concentration ratio as a function of d; as averages, they do not explore temporal differences (traffic, meteorology), the impact of wind direction, or heterogeneity among individual road segments. Interestingly, the fitted values of the parameters α and β correspond closely (within ±5%) to the results of the comprehensive meta-analysis of Karner et al. (2009).(8) Consistent with expectations about sources and atmospheric dynamics, our observed distance-decay relationships are sharpest for NO, intermediate for BC, and most shallow for NO2. BC and NO are primary air pollutants from combustion sources. Unlike BC, NO is rapidly depleted during daylight hours by reaction with ozone (NO + O3→ NO2, τ ∼ 1–5 min).(38) Because most (but not all(39, 40)) daytime NO2 is formed through secondary chemistry, spatial patterns of NO2 tend to be more homogeneous than NO or BC. Further analysis of the robustness of our decay fitting approach and comparison with Karner et al. is provided as SI.

3.3 Localized Concentration Peaks

To further elucidate determinants of spatial patterns in air pollution, we mined the time-resolved data that underlie long-term median concentrations. The time evolution of concentrations collected by a mobile monitor reflects the superposition of (i) spatiotemporally localized concentration peaks from primary emissions and (ii) a comparatively less variable urban background condition.(28, 37)Figure 4a illustrates the application of our baseline-and-peak decomposition algorithm to a 10 min excerpt from our WO mobile monitoring data. Four coincident NO and NO2 peaks in this time series correspond in space (Figure 4b,c) to roads on the perimeter of the metals recycling facility illustrated in Figure 1b, demonstrating how spatially localized peaks are superimposed onto the urban background. For measurements at each 30 m road segment, we estimate the fractional contribution of localized sources to observed conditions (“peak fraction”, PF) as 1.0 minus the ratio of the mean 30-m baseline concentration to mean 30-m observed concentration (Figure 4d–f). (Thus, at any given location, the observed 30 m median concentration is algebraically equivalent to the sum of the baseline plus peak contributions). Median values of PF are highest for NO and BC (∼65%), indicating the predominant contribution of transient localized sources (e.g., traffic, industrial facilities). Spatial variations in PF values (Figure 4d) reveal that the urban background dominates NO levels on low-traffic residential streets (PF ∼ 25–40%), whereas transient peaks govern concentrations on arterials (PF ∼ 60–80%) and near industrial facilities. In contrast, for NO2, PF is consistently low (∼20%), reflecting the high baseline contribution from photochemistry. Future work could explore whether PF and other derived metrics (e.g., pollutant ratios) may be useful in representing the “freshness” of emissions to inform epidemiological analyses of multipollutant mixtures.

Figure 4

Figure 4. Identification of localized concentration peaks. a. Example 10 min time series of NO and NO2 on afternoon of 4/22/2016. Baseline-fitting algorithm decomposes measurements (solid traces) into an ambient baseline component (dashed lines) and a high-frequency component indicative of localized pollutant sources (“peaks”, difference between observation and baseline). Peak fraction PF indicates contribution of peaks to total sampled mass. PF is high for NO (low baseline, sharp peaks), and low for NO2 (elevated ambient levels from photochemistry). Temporal progress along route indicated by blue-white-red color scale in a, and mapped in space in b. The drive route for these 10 min is a 4 km sequence of right-hand turns. As indicated by the time color scale in a and b, the starred NO peaks are spatially concentrated around a single city block with a scrap metal plant (marked × in b and c, cf. Figure 1b and Figure 2a). c, Spatial concentration profile for this example period. d,e,f. Application of peak-separation algorithm to entire data set. d,e. Blue-green-red color scale for PF quantifies fraction of mean concentration at each 30 m road segment attributable to transient peaks. f. Median PF values by road class. Imagery © 2016 Google, map data © 2016 Google.

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Taken in combination, our findings suggest that consistent spatial variability of air pollution exists at much finer scales than is generally detected using conventional measurements and predicted by models. Because hotspots are revealed through repeated measurement, this approach may be useful for evaluating the fine-scale performance of LUR and other models that use spatial surrogate data. We demonstrate that weekday, daytime primary pollutant levels persistently vary by a factor of 2–8× or more within neighborhoods and individual city blocks. For the median road segment, the absolute concentration difference from the single WO ambient regulatory monitor is 32% (BC), 61% (NO), and 35% (NO2) during weekday daytime hours. Sparse monitoring networks may therefore provide an imprecise approximation and underestimate the true variability in heterogeneous population exposures.
The implications of these findings for population exposure and public health depend in part on the degree to which on-road concentrations can represent exposures. Many epidemiological studies use predicted home-address concentrations as a proxy for long-term personal exposures. These predicted home-address concentrations are often based upon models derived from roadside measurements given the ease of access to roadsides (e.g., utility poles) for monitoring. Recent research suggests that on-road concentrations can be well correlated with residential address exposures, even when on-road concentrations are higher than surrounding off-road areas owing to proximate traffic emissions.(17, 41) Future work could validate the predictive power of routine mobile monitoring data using either residential fixed-site or personal exposure measurements.

3.4 Scaling and Future Prospects

Through systematic subsampling of our weekday daytime measurement data set, we find that 10–20 drive days (or 2–3× fewer data than collected here) are sufficient to reproduce key spatial patterns with good precision and low bias (Figure 5). The following trends hold: A small number of drive days (N < 5) typically results in a poor approximation of long-term spatial patterns from the full data set, with generally low precision (r2) and high bias (CV-RMSE). However, each additional sampling day, across a year, results in a substantial improvement in r2 and CV-RMSE. For our data set, diminishing returns for improvement in r2 set in at ∼10–20 drive days, with mean r2 for BC and NO approaching 0.7 after 10 days of driving, and approaching 0.9 after 20–25 days of driving. Mean values of ICC (SI Figure S9) are generally high for 10 or fewer drive days, indicating that stochastic temporal variability from a small number of drive days does not obscure an overall spatially dominated pollution pattern. Similar conclusions on the number of sampling days required for convergence to stable patterns have recently been reported in studies using repeated bicycle-based BC and PM measurements.(15, 30, 31) Our sampling was restricted to weekday daytime conditions; in general, spatial patterns may differ at other times (nights, weekends, holidays).(42) Future work may reveal whether similar scaling considerations hold over a broader range of conditions, and in locales with less consistent meteorology than Oakland.

Figure 5

Figure 5. Scaling analysis through systematic subsampling. Using the systematic subsampling algorithm described in Section 2.4, we investigated the relationship between number of drive days and metrics of precision and bias. a. Mean subsampled r2 as a function of 30 m-median road segment concentrations relative to the full data set, plotted as function the number of unique drive days for BC, NO, and NO2. See SI for details of the subsampling algorithm and r2 calculations. b. Mean subsampled coefficient of variation of root mean squared errors (CV-RMSE) versus the number of unique drive days for BC, NO, and NO2.

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This study demonstrates a straightforward approach for dramatically increasing the spatial resolution with which air pollution is measured. The instrumentation costs of this technique using reference-grade monitors are perhaps comparable to those of traditional ambient monitoring. However, this routine mobile monitoring approach can provide several orders of magnitude more spatial information, albeit with lower temporal resolution. Future developments in fast-response low-cost sensor technologies could lower the costs of this approach. Moreover, in future studies, a considerably less intensive sampling scheme may provide similar results (Figure 5). Here, we employed Google SV vehicles to enable future analyses of contemporaneous 3D imagery, but other vehicle fleets (e.g., taxis, delivery vehicles, public transit) could routinely collect analogous data by repeatedly driving fixed or variable routes within a city. To facilitate scaling, our data reduction algorithms could be easily automated. Equipping ∼500 such vehicles could enable high-resolution mapping of the 25 largest U.S. urban areas, which in combination account for ∼50% [36%] of the U.S. urban [total] population. Likewise, prior experience(12) suggests that this approach could be extended to global megacities (world’s 20 largest cities have 480 M people, >10% of world urban population(43)) and to the thousands of other cities where air quality management is impaired by an absence of robust monitoring infrastructure. Future research could explore whether this approach can effectively be translated to such settings.
Routine availability of high-resolution air quality data in all major urban areas could have transformative implications for environmental management, air pollution science, epidemiology, public awareness, and policy. By highlighting localized pollution hotspots, these data may identify new opportunities for pollution control. Street-level air quality data can complement, challenge, and validate other diverse air quality data sets, including regulatory data, CTM outputs, land-use regression predictions, and remotely sensed observations. In turn, this refinement can help address exposure misclassification in epidemiological studies.(44, 45) Through combination with personal GPS data on smartphone applications, rich “personal exposure analytics” become possible,(46, 47) which could inform epidemiological studies and alter personal behavior, much as real-time traffic data now inform individual driving patterns. Broader societal consequences of the public awareness enabled by high-resolution pollution maps might include shifts in urban land-use decisions, regulatory actions, and in the political economy of environmental “riskscapes”.

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b00891.

  • Text, figures and tables with detailed information on experimental methods, QA/QC procedures, sampling protocols, data reduction/analysis techniques, supplemental analyses, and a data dictionary (PDF)

  • High-resolution concentration data (XLS)

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  • Corresponding Author
  • Authors
    • Kyle P. Messier - Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712 United States;  Environmental Defense Fund, New York, New York 10010 United States
    • Shahzad Gani - Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712 United States
    • Michael Brauer - School of Population and Public Health, University of British Columbia, Vancouver V6T 1Z3 Canada
    • Thomas W. Kirchstetter - Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720 United States
    • Melissa M. Lunden - Aclima, Inc., 10 Lombard St., San Francisco, California 94111 United States
    • Julian D. Marshall - Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195 United States
    • Christopher J. Portier - Environmental Defense Fund, New York, New York 10010 United States
    • Roel C.H. Vermeulen - Institute for Risk Assessment Science, Utrecht University, Utrecht 3584 CM Netherlands
    • Steven P. Hamburg - Environmental Defense Fund, New York, New York 10010 United States
  • Notes

    The authors declare no competing financial interest.

Acknowledgment

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We gratefully acknowledge the contributions of K. Tuxen-Bettman, A. Raman, R. Moore, L. Vincent, C. Owens, D. Herzl, O. Puryear, A. Teste, M. Gordon, B. Beveridge, L. Clark, M. Chu Baird, C. Ely, A. Roy, J. Choi, R. Alvarez, S. Fruin, D. Holstius, P. Martien, and the Google Street View and Aclima mobile platform teams. Funding was provided by a grant from Signe Ostby and Scott Cook to Environmental Defense Fund. JSA was supported by a Google Earth Engine Research Award, MML is employed by Aclima, Inc., and TWK has been an investigator on unrelated research sponsored by Aclima. Data will be made available in an interactive online archive.

References

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    Marshall, Julian D.; Nethery, Elizabeth; Brauer, Michael
    Atmospheric Environment (2008), 42 (6), 1359-1369CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    An important component of air quality management and health risk assessment is improved by understanding of spatial and temporal variability in pollutant concns. We compare, for Vancouver, Canada, three approaches for estg. within-urban spatiotemporal variability in ambient concns.: spatial interpolation of monitoring data; an empirical/statistical model based on geog. analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (std. deviation) are modest. LUR and CMAQ perform well in predicting concns. at monitoring sites (av. abs. bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resoln.; LUR (monitors) offers the greatest (least) spatial resoln. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concns., the findings may depend on which estn. method is employed.
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  11. 11
    Morello-Frosch, R.; Pastor, M.; Sadd, J. Environmental justice and Southern California’s “riskscape”: the distribution of air toxics exposures and health risks among diverse communities Urban Affairs Review 2001, 36, 551– 578 DOI: 10.1177/10780870122184993
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  12. 12
    Apte, J. S.; Kirchstetter, T. W.; Reich, A. H.; Deshpande, S. J.; Kaushik, G.; Chel, A.; Marshall, J. D.; Nazaroff, W. W. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India Atmos. Environ. 2011, 45, 4470– 4480 DOI: 10.1016/j.atmosenv.2011.05.028
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    12
    Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India
    Apte, Joshua S.; Kirchstetter, Thomas W.; Reich, Alexander H.; Deshpande, Shyam J.; Kaushik, Geetanjali; Chel, Arvind; Marshall, Julian D.; Nazaroff, William W.
    Atmospheric Environment (2011), 45 (26), 4470-4480CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    Concns. of air pollutants from vehicles are elevated along roadways, indicating that human exposure in transportation microenvironments may not be adequately characterized by centrally located monitors. Results are reported from ∼180 h of real-time measurements of fine particle and black carbon mass concn. (PM2.5, BC) and ultrafine particle no. concn. (PN) inside a common vehicle, the auto-rickshaw, in New Delhi, India. Measured exposure concns. are much higher in this study (geometric mean for ∼60 trip-averaged concns.: 190 μg m-3 PM2.5, 42 μg m-3 BC, 280 × 103 particles cm-3; GSD ∼1.3 for all three pollutants) than reported for transportation microenvironments in other megacities. In-vehicle concns. exceeded simultaneously measured ambient levels by 1.5× for PM2.5, 3.6× for BC, and 8.4× for PN. Short-duration peak concns. (averaging time: 10 s), attributable to exhaust plumes of nearby vehicles, were greater than 300 μg m-3 for PM2.5, 85 μg m-3 for BC, and 650 × 103 particles cm-3 for PN. The incremental increase of within-vehicle concn. above ambient levels-which is attributed to in- and near-roadway emission sources-accounted for 30%, 68% and 86% of time-averaged in-vehicle PM2.5, BC and PN concns., resp. Based on these results, it is established that one's exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high-income countries. This study illuminates an environmental health concern that may be common in many populous, low-income cities.
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  13. 13
    Boogaard, H.; Kos, G. P. A.; Weijers, E. P.; Janssen, N. A. H.; Fischer, P. H.; van der Zee, S. C.; de Hartog, J. J.; Hoek, G. Contrast in air pollution components between major streets and background locations: Particulate matter mass, black carbon, elemental composition, nitrogen oxide and ultrafine particle number Atmos. Environ. 2010, 45, 650– 658 DOI: 10.1016/j.atmosenv.2010.10.033
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    Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models J. Exposure Anal. Environ. Epidemiol. 2005, 15, 185– 204 DOI: 10.1038/sj.jea.7500388
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    14
    A review and evaluation of intraurban air pollution exposure models
    Jerrett, Michael; Arain, Altaf; Kanaroglou, Pavlos; Beckerman, Bernardo; Potoglou, Dimitri; Sahsuvaroglu, Talar; Morrison, Jason; Giovis, Chris
    Journal of Exposure Analysis and Environmental Epidemiology (2005), 15 (2), 185-204CODEN: JEAEE9; ISSN:1053-4245. (Nature Publishing Group)
    A review. The development of models to assess air pollution exposures within cities for assignment to subjects in health studies was identified as a priority area for future research. This paper reviews models for assessing intra-urban exposure under 6 classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorol. models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. The modeling procedures and results are enriched with applied examples from Hamilton, Canada. In addn., we qual. evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space anal. will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
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  15. 15
    Hankey, S.; Marshall, J. D. Land use regression models of on-road particulate air pollution (particle number, black carbon, PM2.5, particle size) using mobile monitoring Environ. Sci. Technol. 2015, 49, 9194– 9202 DOI: 10.1021/acs.est.5b01209
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    15
    Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring
    Hankey, Steve; Marshall, Julian D.
    Environmental Science & Technology (2015), 49 (15), 9194-9202CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Land use regression (LUR) models typically use fixed-site monitoring; this work used mobile monitoring as a cost-effective alternative for LUR model development. Bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, Minnesota, was used to build LUR models for particulate concns. (particle no. [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). A total of 1224 sep. LUR models were developed and examd. by varying pollutant, time-of-day, and time-series data spatiotemporal smoothing method. Base-case LUR models had modest goodness-of-fit (adjusted R2, ∼0.5 PN; ∼0.4 PM2.5; 0.35 BC; ∼0.25 particle size), low bias (<4%) and abs. bias (2-18%), and included predictor variables which captured proximity to and d. of emission sources. Measurement spatial d. resulted in a large model-building dataset (n = 1101 concn. ests.); ∼25% of buffer variables were selected at spatial scales <100 m, suggesting on-road particle concns. change on small spatial scales. LUR model R2 improved as sampling runs were completed, with diminishing benefits after ∼40 h data collection. Spatial auto-correlation of model residuals indicated the models performed poorly where emission source spatiotemporal resoln. (i.e., traffic congestion) was poor. Results suggested LUR modeling from mobile measurements is possible, but more work could usefully inform best practices.
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  16. 16
    Reyes, J. M.; Serre, M. L. An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources Environ. Sci. Technol. 2014, 48, 1736– 1744 DOI: 10.1021/es4040528
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    16
    An LUR/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary Sources
    Reyes, Jeanette M.; Serre, Marc L.
    Environmental Science & Technology (2014), 48 (3), 1736-1744CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Knowledge of particulate matter concns. <2.5 μm in diam. (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiol. studies need accurate exposure ests. to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) sep. to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to est. PM2.5 across the United States from 1999 to 2009. A cross-validation was done to det. the improvement of the est. due to the LUR incorporation into BME. These results were applied to known diseases to det. predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error redn. of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568 090 and 306 316 deaths, resp., across the United States from 1999 to 2007.
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  17. 17
    Kerckhoffs, J.; Hoek, G.; Messier, K. P.; Brunekreef, B.; Meliefste, K.; Klompmaker, J. O.; Vermeulen, R. Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ. Sci. Technol. 2016, 50, 12894– 12902 DOI: 10.1021/acs.est.6b03476
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    17
    Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model
    Kerckhoffs, Jules; Hoek, Gerard; Messier, Kyle P.; Brunekreef, Bert; Meliefste, Kees; Klompmaker, Jochem O.; Vermeulen, Roel
    Environmental Science & Technology (2016), 50 (23), 12894-12902CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultra-fine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concn. predictions. This work compared LUR models based on stationary (30 min) and mobile UFP and BC measurements in a single campaign. An elec. car collected repeated stationary and mobile measurements in Amsterdam and Rotterdam, Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. The main comparison was based on predicted concns. of mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 = 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on av., 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, resp. LUR models based on mobile and stationary monitoring predicted highly correlated UFP and BC concn. surfaces; predicted concns. based on mobile measurements were systematically higher.
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  18. 18
    Snyder, E. G.; Watkins, T. H.; Solomon, P. A.; Thoma, E. D.; Williams, R. W.; Hagler, G. S. W.; Shelow, D.; Hindin, D. A.; Kilaru, V. J.; Preuss, P. W. The changing paradigm of air pollution monitoring Environ. Sci. Technol. 2013, 47, 11369– 11377 DOI: 10.1021/es4022602
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    18
    The Changing Paradigm of Air Pollution Monitoring
    Snyder, Emily G.; Watkins, Timothy H.; Solomon, Paul A.; Thoma, Eben D.; Williams, Ronald W.; Hagler, Gayle S. W.; Shelow, David; Hindin, David A.; Kilaru, Vasu J.; Preuss, Peter W.
    Environmental Science & Technology (2013), 47 (20), 11369-11377CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    The air pollution monitoring paradigm is rapidly changing due to recent advances in: development of portable, lower-cost air pollution sensors which report data in near-real time at high-time resoln.; increased computational and visualization capabilities; and wireless communication/infrastructure. It is possible these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities necessary tools to understand their environmental exposure; these individual and community-based data strategies can be developed to reduce pollution exposure and to understand links to health indicators. Topics discussed include: current state of sensor science; supplementing routine ambient air monitoring networks; expanding the conversation with communities and citizens; enhancing source compliance monitoring; monitoring personal exposure; challenges; and opportunities for solns.: a changing role for government.
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  19. 19
    West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L. What we breathe impacts our health: Improving understanding of the link between air pollution and health Environ. Sci. Technol. 2016, 50, 4895– 4904 DOI: 10.1021/acs.est.5b03827
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    19
    "What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health"
    West, J. Jason; Cohen, Aaron; Dentener, Frank; Brunekreef, Bert; Zhu, Tong; Armstrong, Ben; Bell, Michelle L.; Brauer, Michael; Carmichael, Gregory; Costa, Dan L.; Dockery, Douglas W.; Kleeman, Michael; Krzyzanowski, Michal; Kunzli, Nino; Liousse, Catherine; Lung, Shih-Chun Candice; Martin, Randall V.; Poschl, Ulrich; Pope, C. Arden; Roberts, James M.; Russell, Armistead G.; Wiedinmyer, Christine
    Environmental Science & Technology (2016), 50 (10), 4895-4904CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chem. species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chem. and phys. properties of complex air pollutant mixts., and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiol. and toxicol. research, and to better quantify the effects of specific source sectors and mitigation strategies.
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  20. 20
    Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H. Characterization of California aerosols--I. Size distributions of freeway aerosol Atmos. Environ. 1975, 9, 463– 482 DOI: 10.1016/0004-6981(75)90107-9
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    20
    Characterization of California aerosols. I. Size distributions of freeway aerosol
    Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H.
    Atmospheric Environment (1967-1989) (1975), 9 (5), 463-82CODEN: ATENBP; ISSN:0004-6981.
    Simultaneously with the taking of filter and impactor samples for chem. anal., the aerosol particle size distribution was measured with continuous instruments over the particle size range from ∼0.003-40μ. From comparisons of measurements when the wind was directly from the freeway with measurements when the wind was blowing toward the freeway, it was possible to calc. by difference the direct contribution of the freeway traffic to the aerosol mixt. Morning rush-hr traffic contributes ∼17.1 μ3/cm3 to the aerosol vol., predominantly in the particle size range <0.15μ. The freeway aerosol size distribution exhibits a typical strong combustion mode at ∼0.02 μ particle size.
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  21. 21
    Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles Atmos. Environ. 2005, 39, 3597– 3610 DOI: 10.1016/j.atmosenv.2005.02.034
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    21
    Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles
    Westerdahl, Dane; Fruin, Scott; Sax, Todd; Fine, Philip M.; Sioutas, Constantinos
    Atmospheric Environment (2005), 39 (20), 3597-3610CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)
    Recent health studies have reported that ultrafine particles (UFP) (<0.1 μm in diam.) may be responsible for some of the adverse health effects attributed to particulate matter. In urban areas UFP are produced by combustion sources such as vehicle exhaust, and by secondary formation in the atm. While UFP can be monitored, few studies have explored the impact of local primary sources in urban areas (including mobile sources on freeways) on the temporal and spatial distribution of UFP. This paper describes the integration of multiple monitoring technologies on a mobile platform designed to characterize UFP and assocd. pollutants, and the application of this platform in a study of UFP no. concns. and size distributions in Los Angeles. Monitoring technologies included 2 condensation particle counters (TSI Model 3007 and TSI 3022A) and scanning mobility particle sizers for UFP. Real-time measurements made of NOx (by chemiluminescence), black C (BC) (by light absorption), particulate matter-phase PAH (by UV ionization), and particle length (by diffusional charging) showed high correlations with UFP nos., (r2 = 0.78 for NO, 0.76 for BC, 0.69 for PAH, and 0.88 for particle length). Av. concns. of UFP and related pollutants varied by location, road type, and truck traffic vols., suggesting a relation between these concns. and truck traffic d.
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  22. 22
    Hudda, N.; Gould, T.; Hartin, K.; Larson, T. V.; Fruin, S. A. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind Environ. Sci. Technol. 2014, 48, 6628– 6635 DOI: 10.1021/es5001566
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    22
    Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km Downwind
    Hudda, Neelakshi; Gould, Tim; Hartin, Kris; Larson, Timothy V.; Fruin, Scott A.
    Environmental Science & Technology (2014), 48 (12), 6628-6635CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    This work measured the spatial pattern of particle no. (PN) concns. downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle which enabled coverage of larger areas than allowed by traditional stationary measurements. LAX emissions adversely affected air quality much farther than reported in previous studies. At least a 2-fold increase in PN concns. over un-impacted baseline PN concns. was measured for most daytime hours in an ∼60 km2 area which extended 16 km (10 mi) downwind, and a 4- to 5-fold increase 8-10 km (5-6 mi) downwind. Locations of max. PN concns. were aligned to eastern, downwind jet trajectories during prevailing westerly winds; 8 km downwind concns. exceeded 75,000 particles/cm3, more than the av. freeway PN concn. in Los Angeles. During infrequent northerly winds, the impact area remained large, but shifted to south of the airport. A freeway length which would cause an impact equiv. to that measured in this work (i.e., PN concn. increases weighted by impacted area) was estd. to be 280-790 km. The total freeway length in Los Angeles is 1500 km. Results suggested airport emissions are a major source of PN in Los Angeles and are of the same general magnitude as the entire urban freeway network. They also indicated major airport air quality impact areas may be seriously underestimated.
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  23. 23
    Larson, T.; Henderson, S. B.; Brauer, M. Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression Environ. Sci. Technol. 2009, 43, 4672– 4678 DOI: 10.1021/es803068e
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    23
    Mobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use Regression
    Larson, Timothy; Henderson, Sarah B.; Brauer, Michael
    Environmental Science & Technology (2009), 43 (13), 4672-4678CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Land use regression (LUR) is used to map air pollutant concn. spatial variability for risk assessment, epidemiol., and air quality management. Conventional LUR requires long-term measurements at multiple sites, so application to particulate matter has been limited. Mobile monitoring characterized spatial variability in carbon black concns. for LUR modeling. A particle soot absorption photometer in a moving vehicle measured the absorption coeff. (σap) in summer during peak afternoon traffic at 39 sites. LUR modeled the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10-s measurements for each site. Model performance (detd. by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, resp.) than for the mean, 75th, and 90th percentiles (0.51, 0.55, and 0.54, resp.). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean σap from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate development of LUR models for particulate matter.
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  24. 24
    Hasenfratz, D.; Saukh, O.; Walser, C.; Hueglin, C.; Fierz, M.; Arn, T.; Beutel, J.; Thiele, L. Deriving high-resolution urban air pollution maps using mobile sensor nodes Pervasive and Mobile Computing 2015, 16 (Part B) 268– 285 DOI: 10.1016/j.pmcj.2014.11.008
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    Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U. A mobile pollutant measurement laboratory-measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution Atmos. Environ. 2002, 36, 5569– 5579 DOI: 10.1016/S1352-2310(02)00694-5
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    25
    A mobile pollutant measurement laboratory - measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution
    Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U.
    Atmospheric Environment (2002), 36 (36-37), 5569-5579CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science Ltd.)
    A mobile pollutant measurement lab. was designed and built at the Paul Scherrer Institute (Switzerland) for the measurement of on-road ambient concns. of a large set of trace gases and aerosol parameters with high time resoln. (<15 s for most instruments), along with geog. and meteorol. information. This approach allowed for pollutant level measurements both near traffic (e.g. in urban areas or on freeways/main roads) and at rural locations far away from traffic, within short periods of time and at different times of day and year. Such measurements were performed on a regular base during the project year of gas phase and aerosol measurements (YOGAM). This paper presents data measured in the Zurich (Switzerland) area on a late autumn day (6 Nov.) in 2001. The local urban particle background easily reached 50,000 cm-3, with addnl. peak particle no. concns. of ≤400,000 cm-3. The regional background of the total particle no. concn. was not found to significantly correlate with the distance to traffic and anthropogenic emissions of CO and NOx. On the other hand, this correlation was significant for the no. concn. of particles in the size range 50-150 nm, indicating that the particle no. concn. in this size range is a better traffic indicator than the total no. concn. Particle no. size distribution measurements showed that daytime urban ambient air is dominated by high no. concns. of ultrafine particles (nanoparticles) with diams. <50 nm, which are immediately formed by traffic exhaust and thus belong to the primary emissions. However, significant variation of the nanoparticle mode was also obsd. in no. size distributions measured in rural areas both at daytime and nighttime, suggesting that nanoparticles are not exclusively formed by primary traffic emissions. While urban daytime total no. concns. were increased by a factor of 10 compared to the nighttime background, corresponding factors for total surface area and total vol. concns. were 2 and 1.5, resp.
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  26. 26
    Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aaalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T. ″Sniffer″ - a novel tool for chasing vehicles and measuring traffic pollutants Atmos. Environ. 2004, 38, 3625– 3635 DOI: 10.1016/j.atmosenv.2004.03.047
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    "Sniffer"-a novel tool for chasing vehicles and measuring traffic pollutants
    Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T.; Hillamo, R. E.
    Atmospheric Environment (2004), 38 (22), 3625-3635CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science B.V.)
    To measure traffic pollutants with high temporal and spatial resoln. under real conditions a mobile lab. was designed and built in Helsinki Polytechnic in close co-operation with the University of Helsinki. The equipment of the van provides gas phase measurements of CO and NOx, no. size distribution measurements of fine and ultrafine particles by an elec. low pressure impactor, an ultrafine condensation particle counter and a scanning mobility particle sizer. Two inlet systems, one above the windshield and the other above the bumper, enable chasing of different type of vehicles. Also, meteorol. and geog. parameters are recorded. This paper introduces the construction and tech. details of the van, and presents data from the measurements performed during an LIPIKA campaign on the highway in Helsinki. Approx. 90% of the total particle no. concn. was due to particles smaller than 50 nm on the highway in Helsinki. The peak concns. exceeded often 200,000 particles cm-3 and reached sometimes a value of 106 cm-3. Typical size distribution of fine particles possessed bimodal structure with the modal mean diams. of 15-20 nm and ∼150 nm. Atm. dispersion of traffic pollutions were measured by moving away from the highway along the wind direction. At a distance of 120-140 m from the source the concns. were dild. to one-tenth from the values at 9 m from the source.
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  27. 27
    Kolb, C.; Herndon, S. C.; McManus, J. B.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D.; Jayne, J. T.; Canagaratna, M. R.; Worsnop, D. R. Mobile laboratory with rapid response instruments for real-time measurement of urban and regional trace gas and particulate distributions and emissions source characteristics Environ. Sci. Technol. 2004, 38, 5694– 5703 DOI: 10.1021/es030718p
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    27
    Mobile Laboratory with Rapid Response Instruments for Real-Time Measurements of Urban and Regional Trace Gas and Particulate Distributions and Emission Source Characteristics
    Kolb, Charles E.; Herndon, Scott C.; McManus, J. Barry; Shorter, Joanne H.; Zahniser, Mark S.; Nelson, David D.; Jayne, John T.; Canagaratna, Manjula R.; Worsnop, Douglas R.
    Environmental Science and Technology (2004), 38 (21), 5694-5703CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Recent technol. advances have allowed the development of robust, relatively compact, low power, rapid response (∼1 s) instruments with sufficient sensitivity and specificity to quantify many trace gases and aerosol particle components in the ambient atm. Suites of such instruments can be deployed on mobile platforms to study atm. processes, map concn. distributions of atm. pollutants, and det. the compn. and intensities of emission sources. A mobile lab. contg. innovative tunable IR laser differential absorption spectroscopy (TILDAS) instruments to measure selected trace gas concns. at sub parts-per-billion levels and an aerosol mass spectrometer (AMS) to measure size resolved distributions of the non-refractory chem. components of fine airborne particles as well as selected com. fast response instruments and position/velocity sensors is described. Examples of the range of measurement strategies that can be undertaken using this mobile lab. are discussed, and samples of measurement data are presented.
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  28. 28
    Brantley, H. L.; Hagler, G. S. W.; Kimbrough, E. S.; Williams, R. W.; Mukerjee, S.; Neas, L. M. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends Atmos. Meas. Tech. 2014, 7, 2169– 2183 DOI: 10.5194/amt-7-2169-2014
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    Hagemann, R.; Corsmeier, U.; Kottmeier, C.; Rinke, R.; Wieser, A.; Vogel, B. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM’ Atmos. Environ. 2014, 94, 341– 352 DOI: 10.1016/j.atmosenv.2014.05.051
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    Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory 'AERO-TRAM'
    Hagemann, Rowell; Corsmeier, Ulrich; Kottmeier, Christoph; Rinke, Rayk; Wieser, Andreas; Vogel, Bernhard
    Atmospheric Environment (2014), 94 (), 341-352CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    For the first time in Germany, we obtained high-resoln. spatial distributions of particle nos. and nitrogen oxides in an urban agglomeration using a tram system. In comparison to particle nos. the NOx concn. decreased much faster with a significantly steeper gradient when going from the inner city to the surrounding area. In case of NOx the decrease was 70% while for particle no. concn. it was only 50%. We found an area in the rural surrounding with a second increase of particle nos. without simultaneous enhanced NOx levels. The source of the high particle nos. could be ascribed to industry emissions about 5-10 km away. The mean spatial distribution of particle no. concn. depended on wind direction, wind velocity and boundary layer stability. The dependency was particularly strong in the rural area affected by industrial emissions, where individual wind directions led to concn. differences of up to 25%. The particulate concn. was 40% higher during low wind velocities (1-5 m s-1) than during high wind velocities (>5 m s-1). We obsd. similar findings for the impact of boundary layer stability on particle nos. concn. Particle pollution was 40% higher for stable stratification compared to neutral or unstable cases.
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  30. 30
    Van den Bossche, J.; Peters, J.; Verwaeren, J.; Botteldooren, D.; Theunis, J.; De Baets, B. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset Atmos. Environ. 2015, 105, 148– 161 DOI: 10.1016/j.atmosenv.2015.01.017
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    Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset
    Van den Bossche, Joris; Peters, Jan; Verwaeren, Jan; Botteldooren, Dick; Theunis, Jan; De Baets, Bernard
    Atmospheric Environment (2015), 105 (), 148-161CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    Mobile monitoring is increasingly used as an addnl. tool to acquire air quality data at a high spatial resoln. However, given the high temporal variability of urban air quality, a limited no. of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodol. to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concns. aggregated at a resoln. of 20 m are obsd. Mapping at such a high resoln. is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalization, depending on the location 24-94 repeated measurement runs (median of 41) are required to map the BC concns. at a 50 m resoln. with an uncertainty of 25%. When relaxing the uncertainty to 50%, these nos. reduce to 5-11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resoln., and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient no. of repetitions in relation to the desired reliability and spatial resoln. Specific data processing methods such as background normalization and event detection have to be applied.
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    Peters, J.; Theunis, J.; Van Poppel, M.; Berghmans, P. Monitoring PM10 and ultrafine particles in urban environments using mobile measurements Aerosol Air Qual. Res. 2013, 13, 509– 522 DOI: 10.4209/aaqr.2012.06.0152
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    Efron, B. Bootstrap methods: another look at the jackknife Ann. Stat. 1979, 7, 1– 26 DOI: 10.1214/aos/1176344552
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    Dons, E.; Int Panis, L.; Van Poppel, M.; Theunis, J.; Wets, G. Personal exposure to black carbon in transport microenvironments Atmos. Environ. 2012, 55, 392– 398 DOI: 10.1016/j.atmosenv.2012.03.020
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    Personal exposure to Black Carbon in transport microenvironments
    Dons, Evi; Int Panis, Luc; Van Poppel, Martine; Theunis, Jan; Wets, Geert
    Atmospheric Environment (2012), 55 (), 392-398CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    We evaluated personal exposure of 62 individuals to the air pollutant Black Carbon, using 13 portable aethalometers while keeping detailed records of their time-activity pattern and whereabouts. Concns. encountered in transport are studied in depth and related to trip motives. The evaluation comprises more than 1500 trips with different transport modes. Measurements were spread over two seasons. Results show that 6% of the time is spent in transport, but it accounts for 21% of personal exposure to Black Carbon and approx. 30% of inhaled dose. Concns. in transport were 2-5 times higher compared to concns. encountered at home. Exposure was highest for car drivers, and car and bus passengers. Concns. of Black Carbon were only half as much when traveling by bike or on foot; when incorporating breathing rates, dose was found to be twice as high for active modes. Lowest in transport' concns. were measured in trains, but nevertheless these concns. are double the concns. measured at home. Two thirds of the trips are car trips, and those trips showed a large spread in concns. In-car concns. are higher during peak hours compared to off-peak, and are elevated on weekdays compared to Saturdays and even more so on Sundays. These findings result in significantly higher exposure during car commute trips (motive Work'), and lower concns. for trips with motive Social and leisure'. Because of the many factors influencing exposure in transport, travel time is not a good predictor of integrated personal exposure or inhaled dose.
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  34. 34
    Zhou, Y.; Levy, J. I. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis BMC Public Health 2007, 7, 89 DOI: 10.1186/1471-2458-7-89
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    Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis
    Zhou Ying; Levy Jonathan I
    BMC public health (2007), 7 (), 89 ISSN:.
    BACKGROUND: There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. METHODS: We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. RESULTS: We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100-400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200-500 m for nitrogen dioxide and 100-300 m for ultrafine particle counts. CONCLUSION: First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize.
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  35. 35
    Zhu, Y.; Hinds, W. C.; Kim, S.; Sioutas, C. Concentration and size distribution of ultrafine particles near a major highway J. Air Waste Manage. Assoc. 2002, 52, 1032– 1042 DOI: 10.1080/10473289.2002.10470842
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    35
    Concentration and size distribution of ultrafine particles near a major highway
    Zhu Yifang; Hinds William C; Kim Seongheon; Sioutas Constantinos
    Journal of the Air & Waste Management Association (1995) (2002), 52 (9), 1032-42 ISSN:1096-2247.
    Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diameter <0.1 microm) in an urban environment, yet little is known about the concentration and size distribution of ultrafine particles in the vicinity of major highways. In the present study, particle number concentration and size distribution in the size range from 6 to 220 nm were measured by a condensation particle counter (CPC) and a scanning mobility particle sizer (SMPS), respectively. Measurements were taken 30, 60, 90, 150, and 300 m downwind, and 300 m upwind, from Interstate 405 at the Los Angeles National Cemetery. At each sampling location, concentrations of CO, black carbon (BC), and particle mass were also measured by a Dasibi CO monitor, an aethalometer, and a DataRam, respectively. The range of average concentration of CO, BC, total particle number, and mass concentration at 30 m was 1.7-2.2 ppm, 3.4-10.0 microg/m3, 1.3-2.0 x 10(5)/cm3, and 30.2-64.6 microg/m3, respectively. For the conditions of these measurements, relative concentrations of CO, BC, and particle number tracked each other well as distance from the freeway increased. Particle number concentration (6-220 nm) decreased exponentially with downwind distance from the freeway. Data showed that both atmospheric dispersion and coagulation contributed to the rapid decrease in particle number concentration and change in particle size distribution with increasing distance from the freeway. Average traffic flow during the sampling periods was 13,900 vehicles/hr. Ninety-three percent of vehicles were gasoline-powered cars or light trucks. The measured number concentration tracked traffic flow well. Thirty meters downwind from the freeway, three distinct ultrafine modes were observed with geometric mean diameters of 13, 27, and 65 nm. The smallest mode, with a peak concentration of 1.6 x 10(5)/cm3, disappeared at distances greater than 90 m from the freeway. Ultrafine particle number concentration measured 300 m downwind from the freeway was indistinguishable from upwind background concentration. These data may be used to estimate exposure to ultrafine particles in the vicinity of major highways.
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  36. 36
    Both, A. F.; Balakrishnan, A.; Joseph, B.; Marshall, J. D. Spatiotemporal aspects of real-time PM2.5: Low- and middle-income neighborhoods in Bangalore, India Environ. Sci. Technol. 2011, 45, 5629– 5636 DOI: 10.1021/es104331w
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    36
    Spatiotemporal Aspects of Real-Time PM2.5: Low- and Middle-Income Neighborhoods in Bangalore, India
    Both, Adam F.; Balakrishnan, Arun; Joseph, Bobby; Marshall, Julian D.
    Environmental Science & Technology (2011), 45 (13), 5629-5636CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    We measured outdoor fine particulate matter (PM2.5) concns. in a low- and a nearby middle-income neighborhood in Bangalore, India. Each neighborhood included sampling locations near and not near a major road. One-minute av. concns. were recorded for 168 days during Sept. 2008 to May 2009 using a gravimetric-cor. nephelometer. We also measured wind speed and direction, and PM2.5 concn. as a function of distance from road. Av. concns. are 21-46% higher in the low- than in the middle-income neighborhood, and exhibit differing spatiotemporal patterns. For example, in the middle-income neighborhood, median concns. are higher near-road than not near-road (56 vs. 50 μg m-3); in the low-income neighborhood, the reverse holds (68 μg m-3 near-road, 74 μg m-3 not near-road), likely because of within-neighborhood residential emissions (e.g., cooking; trash combustion). A moving-av. subtraction method used to infer local- vs. urban-scale emissions confirms that local emissions are greater in the low-income neighborhood than in the middle-income neighborhood; however, relative contributions from local sources vary by time-of-day. Real-time relative humidity correction factors are important for accurately interpreting real-time nephelometer data.
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    Watson, J. G.; Chow, J. C. Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer J. Air Waste Manage. Assoc. 2001, 51, 1522– 1528 DOI: 10.1080/10473289.2001.10464379
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    37
    Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer
    Watson, John G.; Chow, Judith C.
    Journal of the Air & Waste Management Association (2001), 51 (11), 1522-1528CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
    A successive moving av. subtraction method was developed and applied to black carbon measured over 5-min intervals at a downtown site near many small emitters and at a suburban residential site within the urban plume, but distant from specific emitters. Short-duration pulses assumed to originate from nearby sources were subtracted from concns. at each site and were summed to est. middle-scale (0.1-1 km) contributions. The difference of the remaining baselines at urban and suburban monitors was interpreted as the contribution to the downtown monitor from source emissions mixed over a neighborhood scale (1-5 km). Baseline at the suburban site was interpreted as the contribution of the mixt. of black carbon sources for the entire city. When applied to 24-day periods from Feb. and Mar. 1997 in Mexico City, the anal. showed 65% of the 24-h black carbon was part of the urban mixt.; 23% originated in the neighborhood surrounding the monitor and 12% was contributed from nearby sources. Results indicate a fixed-site monitor can reasonably represent exposure in the surrounding neighborhood, even when many local sources, e.g., diesel vehicle exhaust, affects the monitor.
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    Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed.; Wiley-Interscience: Hoboken, NJ, 2006.
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    Dallmann, T. R.; Harley, R. A.; Kirchstetter, T. W. Effects of diesel particle filter retrofits and accelerated fleet turnover on drayage truck emissions at the Port of Oakland Environ. Sci. Technol. 2011, 45, 10773– 10779 DOI: 10.1021/es202609q
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    39
    Effects of Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage Truck Emissions at the Port of Oakland
    Dallmann, Timothy R.; Harley, Robert A.; Kirchstetter, Thomas W.
    Environmental Science & Technology (2011), 45 (24), 10773-10779CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Heavy-duty diesel drayage trucks have a disproportionate impact on the air quality of communities surrounding major freight-handling facilities. In an attempt to mitigate this impact, the state of California has mandated new emission control requirements for drayage trucks accessing ports and rail yards in the state beginning in 2010. This control rule prompted an accelerated diesel particle filter (DPF) retrofit and truck replacement program at the Port of Oakland. The impact of this program was evaluated by measuring emission factor distributions for diesel trucks operating at the Port of Oakland prior to and following the implementation of the emission control rule. Emission factors for black carbon (BC) and oxides of nitrogen (NOx) were quantified in terms of grams of pollutant emitted per kg of fuel burned using a carbon balance method. Concns. of these species along with carbon dioxide were measured in the exhaust plumes of individual diesel trucks as they drove by en route to the Port. A comparison of emissions measured before and after the implementation of the truck retrofit/replacement rule shows a 54 ± 11% redn. in the fleet-av. BC emission factor, accompanied by a shift to a more highly skewed emission factor distribution. Although only particulate matter mass redns. were required in the first year of the program, a significant redn. in the fleet-av. NOx emission factor (41 ± 5%) was obsd., most likely due to the replacement of older trucks with new ones.
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    Jenkin, M. E. Analysis of sources and partitioning of oxidant in the UK—Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London Atmos. Environ. 2004, 38, 5131– 5138 DOI: 10.1016/j.atmosenv.2004.05.055
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    40
    Analysis of sources and partitioning of oxidant in the UK-Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London
    Jenkin, Michael E.
    Atmospheric Environment (2004), 38 (30), 5131-5138CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)
    Hourly mean concn. data for NO, NO2, and O3 at Marylebone Rd, an urban curb-side site in London, UK, were used to assess diurnal and seasonal dependence of oxidant sources and their origins. Obsd. oxidant (OX, defined as NO2 + O3) concns. were interpreted in terms of a the sum of a NOx-independent regional contribution and a linearly NOx-dependent local contribution. The former is believed to be equal to the background O3 concn.; the latter is likely to be dominated by NO2 emissions from road transport at the study site. Derived regional OX concns. displayed a significant seasonal variation with a springtime max. of ∼43 ppb in Apr. Results were broadly similar to those reported for background O3 at low altitude sites in northwestern Europe. A strong diurnal variation in local OX contribution was obsd. throughout the year, with highest concns. (typically ∼0.11 ppb/ppb NOx) in daytime. Diurnal profiles averaged over periods of the year when the UK operates under Greenwich Mean and British summer times, demonstrated a clear temporal shift, consistent with the local OX contribution due to primary NO2 emissions from road transport. In conjunction with traffic flow statistics and assocd. NOx emissions ests., results suggested primary NO2 from diesel-fueled vehicles accounted for 0.996 v-0·6 diesel NOx emissions, by vol., where v = mean vehicle speed in km/h (range, 30-60 km/h). This corresponded to 11.8 ± 1.2% NOx emissions integrated over the av. diurnal cycle for conditions at Marylebone Rd. Results also suggested primary NO2 emissions from gasoline-fueled vehicles were far less important, with an upper limit NO2:NOx emission ratio of <3%.
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    Riley, E. A.; Schaal, L.; Sasakura, M.; Crampton, R.; Gould, T. R.; Hartin, K.; Sheppard, L.; Larson, T.; Simpson, C. D.; Yost, M. G. Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution Atmos. Environ. 2016, 132, 229– 239 DOI: 10.1016/j.atmosenv.2016.03.001
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    Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution
    Riley, Erin A.; Schaal, LaNae; Sasakura, Miyoko; Crampton, Robert; Gould, Timothy R.; Hartin, Kris; Sheppard, Lianne; Larson, Timothy; Simpson, Christopher D.; Yost, Michael G.
    Atmospheric Environment (2016), 132 (), 229-239CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
    Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term avs. To investigate the stationarity of short-term mobile monitoring measurements, we calcd. long term medians derived from a mobile monitoring campaign that also employed 2-wk integrated passive sampler detectors (PSD) for NOx, Ozone, and nine volatile org. compds. at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 min at a resoln. of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-wk PSD data and observe correlations between mobile NOx measurements and PSD NOx measurements in both summer and winter (Pearson's r = 0.84 and 0.48, resp.). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NOx was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r = 0.8), and intermodal (1-3 μm) particle counts (r = 0.73). Principal Component Anal. of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles of traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies.
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    Hu, S.; Fruin, S.; Kozawa, K.; Mara, S.; Paulson, S. E.; Winer, A. M. A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours Atmos. Environ. 2009, 43, 2541– 2549 DOI: 10.1016/j.atmosenv.2009.02.033
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    A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours
    Hu, Shishan; Fruin, Scott; Kozawa, Kathleen; Mara, Steve; Paulson, Suzanne E.; Winer, Arthur M.
    Atmospheric Environment (2009), 43 (16), 2541-2549CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
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  • Abstract

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    Figure 1

    Figure 1. High-resolution mapping of time-integrated concentrations. Annual median daytime concentrations for 30 m-length road segments based on 1 year of repeated driving for a 16 km2 domain in West Oakland [WO] and Downtown (a), and for a 0.6 km2 industrial-residential area in WO (b). Median ± SE concentrations are tabulated by road type in c. Annual median daytime ambient concentrations Camb at a regulatory fixed-site monitor in WO are plotted as shaded stars. Localized hotspots in b correspond to major intersections, industries, and businesses with truck traffic, and are interspersed with lower-income housing (see aerial image). Locations of hotspots are similar among pollutants. d, Distributions of 780 1-Hz NO measurements for a transect of eight 30 m road segments (see b, from point X to Y) to illustrate relationship between 1-Hz samples (∼100 per segment over 1 year) and plotted long-term medians (colored bars, blue horizontal lines). Elevated levels near midpoint of transect are associated with operations at a metal recycler (see Figure 2). Wind rose data are provided in SI Figure S1, and show consistent westerly winds. Imagery © 2016 Google, map data © 2016 Google.

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    Figure 2

    Figure 2. Illustrative pollution hotspots. a. Street View and aerial imagery of the metals recycling cluster highlighted in Figure 4a–c. Frequent heavy-duty and medium-duty truck traffic is evident in repeated Street View images. b,c. Multipollutant hotspots (i.e., prominent local concentration outliers) were identified from BC, NO, and NO2 median concentrations as described in SI. Twelve illustrative hotspots labeled A–L here, overlaid on the 30 m BC map in b for context. List in c enumerates possible emissions sources for each illustrative hotspot, with the following classification scheme for each pollutant: (+) indicates a prominent localized hotspot or cluster of roads where concentrations are sharply elevated above nearby background levels, (∼) indicates a less prominent hotspot or cluster with moderately elevated levels, and (×) indicates the absence of a clearly discernible hotspot. Imagery © 2016 Google, map data © 2016 Google.

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    Figure 3

    Figure 3. Decay of concentrations from major highways into city streets for WO and DT. a. Plotted points represent the ratio of median concentrations at a given distance from highways (d, “spatial lag”) to median on-highway concentrations; error bars present standard error from bootstrap resampling. An unconstrained three parameter exponential model reproduces observed decay relationships with high fidelity. Here, the parameter α represents the ratio of urban-background to highway concentrations (d → ∞), β represents the additional increment in pollution at near-highway conditions, and the decay parameter k governs the spatial scale of the decay process. The value of α is intermediate for BC (primary, conserved pollutant); lower for NO (consumed rapidly during daytime by reaction with O3) and higher for NO2 (elevated background from regional secondary photochemical conversion from NO). Data in SI demonstrate that parameter estimates are consistent among alternative fitting approaches. b. Distance-to-highway metric d for surface streets in WO and DT, computed based on the harmonic mean distance of each surface street segment to closest portion of the four major highways in the domain (see SI). Map data © 2016 Google.

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    Figure 4

    Figure 4. Identification of localized concentration peaks. a. Example 10 min time series of NO and NO2 on afternoon of 4/22/2016. Baseline-fitting algorithm decomposes measurements (solid traces) into an ambient baseline component (dashed lines) and a high-frequency component indicative of localized pollutant sources (“peaks”, difference between observation and baseline). Peak fraction PF indicates contribution of peaks to total sampled mass. PF is high for NO (low baseline, sharp peaks), and low for NO2 (elevated ambient levels from photochemistry). Temporal progress along route indicated by blue-white-red color scale in a, and mapped in space in b. The drive route for these 10 min is a 4 km sequence of right-hand turns. As indicated by the time color scale in a and b, the starred NO peaks are spatially concentrated around a single city block with a scrap metal plant (marked × in b and c, cf. Figure 1b and Figure 2a). c, Spatial concentration profile for this example period. d,e,f. Application of peak-separation algorithm to entire data set. d,e. Blue-green-red color scale for PF quantifies fraction of mean concentration at each 30 m road segment attributable to transient peaks. f. Median PF values by road class. Imagery © 2016 Google, map data © 2016 Google.

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    Figure 5

    Figure 5. Scaling analysis through systematic subsampling. Using the systematic subsampling algorithm described in Section 2.4, we investigated the relationship between number of drive days and metrics of precision and bias. a. Mean subsampled r2 as a function of 30 m-median road segment concentrations relative to the full data set, plotted as function the number of unique drive days for BC, NO, and NO2. See SI for details of the subsampling algorithm and r2 calculations. b. Mean subsampled coefficient of variation of root mean squared errors (CV-RMSE) versus the number of unique drive days for BC, NO, and NO2.

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  • References

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      Brauer, Michael; Freedman, Greg; Frostad, Joseph; van Donkelaar, Aaron; Martin, Randall V.; Dentener, Frank; Dingenen, Rita van; Estep, Kara; Amini, Heresh; Apte, Joshua S.; Balakrishnan, Kalpana; Barregard, Lars; Broday, David; Feigin, Valery; Ghosh, Santu; Hopke, Philip K.; Knibbs, Luke D.; Kokubo, Yoshihiro; Liu, Yang; Ma, Stefan; Morawska, Lidia; Sangrador, Jose Luis Texcalac; Shaddick, Gavin; Anderson, H. Ross; Vos, Theo; Forouzanfar, Mohammad H.; Burnett, Richard T.; Cohen, Aaron
      Environmental Science & Technology (2016), 50 (1), 79-88CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Ambient air pollution exposure is a major risk factor for global disease. Assessing the impact of air pollution on population health and evaluating trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure ests. This work combined satellite-based ests., chem. transport model simulations, and ground measurements from 79 countries to produce global ests. of annual av. fine particle (PM2.5) and O3 concns. at 0.1° × 0.1° spatial resoln. for 5-yr intervals from 1990 to 2010 and year 2013. These ests. were used to assess population-weighted mean concns. for 1990-2013 for 188 countries. In 2013, 87% of the world population lived in areas exceeding the World Health Organization air quality guideline (10 μg/m3 PM2.5 annual av.). From 1990 to 2013, global population-weighted PM2.5 increased 20.4%, driven by trends in southern and southeastern Asia and China. Decreases in population-weighted mean PM2.5 concns. were evident in most high income countries. Population-weighted mean O3 concns. increased globally 8.9% from 1990 to 2013, with increases in most countries; modest decreases occurred in North America, parts of Europe, and several southeastern Asia countries.
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    7. 7
      Zhang, K. M.; Wexler, A. S.; Zhu, Y. F.; Hinds, W. C.; Sioutas, C. Evolution of particle number distribution near roadways. Part II: the ‘Road-to-Ambient’ process Atmos. Environ. 2004, 38, 6655– 6665 DOI: 10.1016/j.atmosenv.2004.06.044
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      7
      Evolution of particle number distribution near roadways. Part II. The 'Road-to-Ambient' process
      Zhang, K. Max; Wexler, Anthony S.; Zhu, Yi Fang; Hinds, William C.; Sioutas, Constantinos
      Atmospheric Environment (2004), 38 (38), 6655-6665CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)
      The 'road-to-ambient' evolution of particle no. distributions near the 405 and 710 freeways in Los Angeles, California, in both summer and winter, were analyzed and then simulated by a multi-component sectional aerosol dynamic model. Condensation/evapn. and diln. were demonstrated to be the major mechanisms in altering aerosol size distribution, while coagulation and deposition play minor roles. Seasonal effects were significant with winters generally less dynamic than summers. A large no. of particles grew into the >10 nm range around 30-90 m downwind of the freeways. Beyond 90 m some shrink to <10 nm range and some continued growing to >100 nm as a result of competition between partial pressure and vapor pressure. Particle compns. probably change dramatically as components adapt to decreasing gas-phase concn. due to diln., so no. distribution evolution is also an evolution of compn. As a result, people who live within about 90 m of roadways are exposed to particle sizes and compns. that others are not.
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    8. 8
      Karner, A. A.; Eisinger, D. S.; Niemeier, D. A. Near-roadway air quality: Synthesizing the findings from real-world data Environ. Sci. Technol. 2010, 44, 5334– 5344 DOI: 10.1021/es100008x
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      8
      Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data
      Karner, Alex A.; Eisinger, Douglas S.; Niemeier, Deb A.
      Environmental Science & Technology (2010), 44 (14), 5334-5344CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Despite increasing regulatory attention and literature linking roadside air pollution to health outcomes, studies on near roadway air quality have not yet been well synthesized. We employ data collected from 1978 as reported in 41 roadside monitoring studies, encompassing more than 700 air pollutant concn. measurements, published as of June 2008. Two types of normalization, background and edge-of-road, were applied to the obsd. concns. Local regression models were specified to the concn.-distance relationship and anal. of variance was used to det. the statistical significance of trends. Using an edge-of-road normalization, almost all pollutants decay to background by 115-570 m from the edge of road; using the more std. background normalization, almost all pollutants decay to background by 160-570 m from the edge of road. Differences between the normalization methods arose due to the likely bias inherent in background normalization, since some reported background values tend to under-predict (be lower than) actual background. Changes in pollutant concns. with increasing distance from the road fell into one of three groups: at least a 50% decrease in peak/edge-of-road concn. by 150 m, followed by consistent but gradual decay toward background (e.g., carbon monoxide, some ultrafine particulate matter no. concns.); consistent decay or change over the entire distance range (e.g., benzene, nitrogen dioxide); or no trend with distance (e.g., particulate matter mass concns.).
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    9. 9
      Zhu, Y. F.; Hinds, W. C.; Kim, S.; Shen, S.; Sioutas, C. Study of ultrafine particles near a major highway with heavy-duty diesel traffic Atmos. Environ. 2002, 36, 4323– 4335 DOI: 10.1016/S1352-2310(02)00354-0
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      9
      Study of ultrafine particles near a major highway with heavy-duty diesel traffic
      Zhu, Yifang; Hinds, William C.; Kim, Seongheon; Shen, Si; Sioutas, Constantinos
      Atmospheric Environment (2002), 36 (27), 4323-4335CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science Ltd.)
      Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diam. <0.1 μm) in an urban environment. Y. Zhu, et al. (2002, accepted for publication) conducted systematic measurements of ultra-fine particle concn. and size distribution near a highway dominated by gasoline vehicles. The reported study compared these measurements with those made on Interstate 710 in Los Angeles, California. The 710 freeway was selected because >25% of vehicles are heavy-duty diesel trucks. Particle no. concn. and size distribution from 6 to 220 nm were measured by a condensation particle counter and scanning mobility particle sizer, resp. Measurements were made 17, 20, 30, 90, 150, and 300 m downwind and 200 m upwind from the center of the freeway. At each sampling site, CO and black carbon (BC) concns. were also measured with a Dasibi CO monitor and an aethalometer, resp. The range of av. CO, BC, and total particulate no. concns. at 17 m was 1.9-2.6 ppm, 20.3-24.8 μg/m3, and 1.8×105-3.5×105/cm3, resp. Relative CO, BC, and particle no. concns. decreased exponentially and tracked each other well as distance from the freeway increased. Atm. dispersion and coagulation appeared to contribute to the rapid decrease in particle no. concn. and change in particle size distribution with increasing distance from the freeway. Av. traffic flow during sampling was 12,180 vehicles/h with >25% of vehicles heavy-duty diesel trucks. Ultra-fine particle no. concn. measured 300 m downwind from the freeway was indistinguishable from the upwind background concn. Data may be used to est. exposure to ultra-fine particles near major highways.
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    10. 10
      Marshall, J. D.; Nethery, E.; Brauer, M. Within-urban variability in ambient air pollution: Comparison of estimation methods Atmos. Environ. 2008, 42, 1359– 1369 DOI: 10.1016/j.atmosenv.2007.08.012
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      10
      Within-urban variability in ambient air pollution: Comparison of estimation methods
      Marshall, Julian D.; Nethery, Elizabeth; Brauer, Michael
      Atmospheric Environment (2008), 42 (6), 1359-1369CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      An important component of air quality management and health risk assessment is improved by understanding of spatial and temporal variability in pollutant concns. We compare, for Vancouver, Canada, three approaches for estg. within-urban spatiotemporal variability in ambient concns.: spatial interpolation of monitoring data; an empirical/statistical model based on geog. analyses ("land-use regression"; LUR); and an Eulerian grid model (community multiscale air quality model, CMAQ). Four pollutants are considered-nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide, and ozone-represent varying levels of spatiotemporal heterogeneity. Among the methods, differences in central tendencies (mean, median) and variability (std. deviation) are modest. LUR and CMAQ perform well in predicting concns. at monitoring sites (av. abs. bias: <50% for NO; <20% for NO2). Monitors (LUR) offer the greatest (least) temporal resoln.; LUR (monitors) offers the greatest (least) spatial resoln. Of note, the length scale of spatial variability is shorter for LUR (units: km; 0.3 for NO, 1 for NO2) than for the other approaches (3-6 for NO, 4-6 for NO2), indicating that the approaches offer different information about spatial attributes of air pollution. Results presented here suggest that for investigations incorporating spatiotemporal variability in ambient concns., the findings may depend on which estn. method is employed.
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    11. 11
      Morello-Frosch, R.; Pastor, M.; Sadd, J. Environmental justice and Southern California’s “riskscape”: the distribution of air toxics exposures and health risks among diverse communities Urban Affairs Review 2001, 36, 551– 578 DOI: 10.1177/10780870122184993
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    12. 12
      Apte, J. S.; Kirchstetter, T. W.; Reich, A. H.; Deshpande, S. J.; Kaushik, G.; Chel, A.; Marshall, J. D.; Nazaroff, W. W. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India Atmos. Environ. 2011, 45, 4470– 4480 DOI: 10.1016/j.atmosenv.2011.05.028
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      12
      Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India
      Apte, Joshua S.; Kirchstetter, Thomas W.; Reich, Alexander H.; Deshpande, Shyam J.; Kaushik, Geetanjali; Chel, Arvind; Marshall, Julian D.; Nazaroff, William W.
      Atmospheric Environment (2011), 45 (26), 4470-4480CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      Concns. of air pollutants from vehicles are elevated along roadways, indicating that human exposure in transportation microenvironments may not be adequately characterized by centrally located monitors. Results are reported from ∼180 h of real-time measurements of fine particle and black carbon mass concn. (PM2.5, BC) and ultrafine particle no. concn. (PN) inside a common vehicle, the auto-rickshaw, in New Delhi, India. Measured exposure concns. are much higher in this study (geometric mean for ∼60 trip-averaged concns.: 190 μg m-3 PM2.5, 42 μg m-3 BC, 280 × 103 particles cm-3; GSD ∼1.3 for all three pollutants) than reported for transportation microenvironments in other megacities. In-vehicle concns. exceeded simultaneously measured ambient levels by 1.5× for PM2.5, 3.6× for BC, and 8.4× for PN. Short-duration peak concns. (averaging time: 10 s), attributable to exhaust plumes of nearby vehicles, were greater than 300 μg m-3 for PM2.5, 85 μg m-3 for BC, and 650 × 103 particles cm-3 for PN. The incremental increase of within-vehicle concn. above ambient levels-which is attributed to in- and near-roadway emission sources-accounted for 30%, 68% and 86% of time-averaged in-vehicle PM2.5, BC and PN concns., resp. Based on these results, it is established that one's exposure during a daily commute by auto-rickshaw in Delhi is as least as large as full-day exposures experienced by urban residents of many high-income countries. This study illuminates an environmental health concern that may be common in many populous, low-income cities.
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    13. 13
      Boogaard, H.; Kos, G. P. A.; Weijers, E. P.; Janssen, N. A. H.; Fischer, P. H.; van der Zee, S. C.; de Hartog, J. J.; Hoek, G. Contrast in air pollution components between major streets and background locations: Particulate matter mass, black carbon, elemental composition, nitrogen oxide and ultrafine particle number Atmos. Environ. 2010, 45, 650– 658 DOI: 10.1016/j.atmosenv.2010.10.033
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    14. 14
      Jerrett, M.; Arain, A.; Kanaroglou, P.; Beckerman, B.; Potoglou, D.; Sahsuvaroglu, T.; Morrison, J.; Giovis, C. A review and evaluation of intraurban air pollution exposure models J. Exposure Anal. Environ. Epidemiol. 2005, 15, 185– 204 DOI: 10.1038/sj.jea.7500388
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      14
      A review and evaluation of intraurban air pollution exposure models
      Jerrett, Michael; Arain, Altaf; Kanaroglou, Pavlos; Beckerman, Bernardo; Potoglou, Dimitri; Sahsuvaroglu, Talar; Morrison, Jason; Giovis, Chris
      Journal of Exposure Analysis and Environmental Epidemiology (2005), 15 (2), 185-204CODEN: JEAEE9; ISSN:1053-4245. (Nature Publishing Group)
      A review. The development of models to assess air pollution exposures within cities for assignment to subjects in health studies was identified as a priority area for future research. This paper reviews models for assessing intra-urban exposure under 6 classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorol. models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. The modeling procedures and results are enriched with applied examples from Hamilton, Canada. In addn., we qual. evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space anal. will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
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    15. 15
      Hankey, S.; Marshall, J. D. Land use regression models of on-road particulate air pollution (particle number, black carbon, PM2.5, particle size) using mobile monitoring Environ. Sci. Technol. 2015, 49, 9194– 9202 DOI: 10.1021/acs.est.5b01209
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      15
      Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring
      Hankey, Steve; Marshall, Julian D.
      Environmental Science & Technology (2015), 49 (15), 9194-9202CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Land use regression (LUR) models typically use fixed-site monitoring; this work used mobile monitoring as a cost-effective alternative for LUR model development. Bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, Minnesota, was used to build LUR models for particulate concns. (particle no. [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). A total of 1224 sep. LUR models were developed and examd. by varying pollutant, time-of-day, and time-series data spatiotemporal smoothing method. Base-case LUR models had modest goodness-of-fit (adjusted R2, ∼0.5 PN; ∼0.4 PM2.5; 0.35 BC; ∼0.25 particle size), low bias (<4%) and abs. bias (2-18%), and included predictor variables which captured proximity to and d. of emission sources. Measurement spatial d. resulted in a large model-building dataset (n = 1101 concn. ests.); ∼25% of buffer variables were selected at spatial scales <100 m, suggesting on-road particle concns. change on small spatial scales. LUR model R2 improved as sampling runs were completed, with diminishing benefits after ∼40 h data collection. Spatial auto-correlation of model residuals indicated the models performed poorly where emission source spatiotemporal resoln. (i.e., traffic congestion) was poor. Results suggested LUR modeling from mobile measurements is possible, but more work could usefully inform best practices.
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    16. 16
      Reyes, J. M.; Serre, M. L. An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources Environ. Sci. Technol. 2014, 48, 1736– 1744 DOI: 10.1021/es4040528
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      16
      An LUR/BME Framework to Estimate PM2.5 Explained by on Road Mobile and Stationary Sources
      Reyes, Jeanette M.; Serre, Marc L.
      Environmental Science & Technology (2014), 48 (3), 1736-1744CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Knowledge of particulate matter concns. <2.5 μm in diam. (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiol. studies need accurate exposure ests. to properly investigate potential morbidity and mortality. Previous works have used geostatistics and land use regression (LUR) sep. to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to est. PM2.5 across the United States from 1999 to 2009. A cross-validation was done to det. the improvement of the est. due to the LUR incorporation into BME. These results were applied to known diseases to det. predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. This method showed a mean squared error redn. of over 21.89% oversimple kriging. PM2.5 explained by on road mobile emissions and stationary emissions contributed to nearly 568 090 and 306 316 deaths, resp., across the United States from 1999 to 2007.
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    17. 17
      Kerckhoffs, J.; Hoek, G.; Messier, K. P.; Brunekreef, B.; Meliefste, K.; Klompmaker, J. O.; Vermeulen, R. Comparison of ultrafine particle and black carbon concentration predictions from a mobile and short-term stationary land-use regression model Environ. Sci. Technol. 2016, 50, 12894– 12902 DOI: 10.1021/acs.est.6b03476
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      17
      Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model
      Kerckhoffs, Jules; Hoek, Gerard; Messier, Kyle P.; Brunekreef, Bert; Meliefste, Kees; Klompmaker, Jochem O.; Vermeulen, Roel
      Environmental Science & Technology (2016), 50 (23), 12894-12902CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Mobile and short-term monitoring campaigns are increasingly used to develop land use regression (LUR) models for ultra-fine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concn. predictions. This work compared LUR models based on stationary (30 min) and mobile UFP and BC measurements in a single campaign. An elec. car collected repeated stationary and mobile measurements in Amsterdam and Rotterdam, Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. The main comparison was based on predicted concns. of mobile and stationary monitoring LUR models at 12,682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 = 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on av., 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, resp. LUR models based on mobile and stationary monitoring predicted highly correlated UFP and BC concn. surfaces; predicted concns. based on mobile measurements were systematically higher.
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    18. 18
      Snyder, E. G.; Watkins, T. H.; Solomon, P. A.; Thoma, E. D.; Williams, R. W.; Hagler, G. S. W.; Shelow, D.; Hindin, D. A.; Kilaru, V. J.; Preuss, P. W. The changing paradigm of air pollution monitoring Environ. Sci. Technol. 2013, 47, 11369– 11377 DOI: 10.1021/es4022602
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      18
      The Changing Paradigm of Air Pollution Monitoring
      Snyder, Emily G.; Watkins, Timothy H.; Solomon, Paul A.; Thoma, Eben D.; Williams, Ronald W.; Hagler, Gayle S. W.; Shelow, David; Hindin, David A.; Kilaru, Vasu J.; Preuss, Peter W.
      Environmental Science & Technology (2013), 47 (20), 11369-11377CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      The air pollution monitoring paradigm is rapidly changing due to recent advances in: development of portable, lower-cost air pollution sensors which report data in near-real time at high-time resoln.; increased computational and visualization capabilities; and wireless communication/infrastructure. It is possible these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities necessary tools to understand their environmental exposure; these individual and community-based data strategies can be developed to reduce pollution exposure and to understand links to health indicators. Topics discussed include: current state of sensor science; supplementing routine ambient air monitoring networks; expanding the conversation with communities and citizens; enhancing source compliance monitoring; monitoring personal exposure; challenges; and opportunities for solns.: a changing role for government.
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    19. 19
      West, J. J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M. L.; Brauer, M.; Carmichael, G.; Costa, D. L. What we breathe impacts our health: Improving understanding of the link between air pollution and health Environ. Sci. Technol. 2016, 50, 4895– 4904 DOI: 10.1021/acs.est.5b03827
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      19
      "What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health"
      West, J. Jason; Cohen, Aaron; Dentener, Frank; Brunekreef, Bert; Zhu, Tong; Armstrong, Ben; Bell, Michelle L.; Brauer, Michael; Carmichael, Gregory; Costa, Dan L.; Dockery, Douglas W.; Kleeman, Michael; Krzyzanowski, Michal; Kunzli, Nino; Liousse, Catherine; Lung, Shih-Chun Candice; Martin, Randall V.; Poschl, Ulrich; Pope, C. Arden; Roberts, James M.; Russell, Armistead G.; Wiedinmyer, Christine
      Environmental Science & Technology (2016), 50 (10), 4895-4904CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chem. species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chem. and phys. properties of complex air pollutant mixts., and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiol. and toxicol. research, and to better quantify the effects of specific source sectors and mitigation strategies.
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    20. 20
      Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H. Characterization of California aerosols--I. Size distributions of freeway aerosol Atmos. Environ. 1975, 9, 463– 482 DOI: 10.1016/0004-6981(75)90107-9
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      20
      Characterization of California aerosols. I. Size distributions of freeway aerosol
      Whitby, K. T.; Clark, W. E.; Marple, V. A.; Sverdrup, G. M.; Sem, G. J.; Willeke, K.; Liu, B. Y. H.; Pui, D. Y. H.
      Atmospheric Environment (1967-1989) (1975), 9 (5), 463-82CODEN: ATENBP; ISSN:0004-6981.
      Simultaneously with the taking of filter and impactor samples for chem. anal., the aerosol particle size distribution was measured with continuous instruments over the particle size range from ∼0.003-40μ. From comparisons of measurements when the wind was directly from the freeway with measurements when the wind was blowing toward the freeway, it was possible to calc. by difference the direct contribution of the freeway traffic to the aerosol mixt. Morning rush-hr traffic contributes ∼17.1 μ3/cm3 to the aerosol vol., predominantly in the particle size range <0.15μ. The freeway aerosol size distribution exhibits a typical strong combustion mode at ∼0.02 μ particle size.
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    21. 21
      Westerdahl, D.; Fruin, S.; Sax, T.; Fine, P. M.; Sioutas, C. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles Atmos. Environ. 2005, 39, 3597– 3610 DOI: 10.1016/j.atmosenv.2005.02.034
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      21
      Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles
      Westerdahl, Dane; Fruin, Scott; Sax, Todd; Fine, Philip M.; Sioutas, Constantinos
      Atmospheric Environment (2005), 39 (20), 3597-3610CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)
      Recent health studies have reported that ultrafine particles (UFP) (<0.1 μm in diam.) may be responsible for some of the adverse health effects attributed to particulate matter. In urban areas UFP are produced by combustion sources such as vehicle exhaust, and by secondary formation in the atm. While UFP can be monitored, few studies have explored the impact of local primary sources in urban areas (including mobile sources on freeways) on the temporal and spatial distribution of UFP. This paper describes the integration of multiple monitoring technologies on a mobile platform designed to characterize UFP and assocd. pollutants, and the application of this platform in a study of UFP no. concns. and size distributions in Los Angeles. Monitoring technologies included 2 condensation particle counters (TSI Model 3007 and TSI 3022A) and scanning mobility particle sizers for UFP. Real-time measurements made of NOx (by chemiluminescence), black C (BC) (by light absorption), particulate matter-phase PAH (by UV ionization), and particle length (by diffusional charging) showed high correlations with UFP nos., (r2 = 0.78 for NO, 0.76 for BC, 0.69 for PAH, and 0.88 for particle length). Av. concns. of UFP and related pollutants varied by location, road type, and truck traffic vols., suggesting a relation between these concns. and truck traffic d.
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    22. 22
      Hudda, N.; Gould, T.; Hartin, K.; Larson, T. V.; Fruin, S. A. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind Environ. Sci. Technol. 2014, 48, 6628– 6635 DOI: 10.1021/es5001566
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      22
      Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km Downwind
      Hudda, Neelakshi; Gould, Tim; Hartin, Kris; Larson, Timothy V.; Fruin, Scott A.
      Environmental Science & Technology (2014), 48 (12), 6628-6635CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      This work measured the spatial pattern of particle no. (PN) concns. downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle which enabled coverage of larger areas than allowed by traditional stationary measurements. LAX emissions adversely affected air quality much farther than reported in previous studies. At least a 2-fold increase in PN concns. over un-impacted baseline PN concns. was measured for most daytime hours in an ∼60 km2 area which extended 16 km (10 mi) downwind, and a 4- to 5-fold increase 8-10 km (5-6 mi) downwind. Locations of max. PN concns. were aligned to eastern, downwind jet trajectories during prevailing westerly winds; 8 km downwind concns. exceeded 75,000 particles/cm3, more than the av. freeway PN concn. in Los Angeles. During infrequent northerly winds, the impact area remained large, but shifted to south of the airport. A freeway length which would cause an impact equiv. to that measured in this work (i.e., PN concn. increases weighted by impacted area) was estd. to be 280-790 km. The total freeway length in Los Angeles is 1500 km. Results suggested airport emissions are a major source of PN in Los Angeles and are of the same general magnitude as the entire urban freeway network. They also indicated major airport air quality impact areas may be seriously underestimated.
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    23. 23
      Larson, T.; Henderson, S. B.; Brauer, M. Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression Environ. Sci. Technol. 2009, 43, 4672– 4678 DOI: 10.1021/es803068e
      [ACS Full Text ACS Full Text], [CAS], Google Scholar
      23
      Mobile Monitoring of Particle Light Absorption Coefficient in an Urban Area as a Basis for Land Use Regression
      Larson, Timothy; Henderson, Sarah B.; Brauer, Michael
      Environmental Science & Technology (2009), 43 (13), 4672-4678CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Land use regression (LUR) is used to map air pollutant concn. spatial variability for risk assessment, epidemiol., and air quality management. Conventional LUR requires long-term measurements at multiple sites, so application to particulate matter has been limited. Mobile monitoring characterized spatial variability in carbon black concns. for LUR modeling. A particle soot absorption photometer in a moving vehicle measured the absorption coeff. (σap) in summer during peak afternoon traffic at 39 sites. LUR modeled the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10-s measurements for each site. Model performance (detd. by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, resp.) than for the mean, 75th, and 90th percentiles (0.51, 0.55, and 0.54, resp.). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean σap from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate development of LUR models for particulate matter.
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    24. 24
      Hasenfratz, D.; Saukh, O.; Walser, C.; Hueglin, C.; Fierz, M.; Arn, T.; Beutel, J.; Thiele, L. Deriving high-resolution urban air pollution maps using mobile sensor nodes Pervasive and Mobile Computing 2015, 16 (Part B) 268– 285 DOI: 10.1016/j.pmcj.2014.11.008
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    25. 25
      Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U. A mobile pollutant measurement laboratory-measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution Atmos. Environ. 2002, 36, 5569– 5579 DOI: 10.1016/S1352-2310(02)00694-5
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      25
      A mobile pollutant measurement laboratory - measuring gas phase and aerosol ambient concentrations with high spatial and temporal resolution
      Bukowiecki, N.; Dommen, J.; Prevot, A. S. H.; Richter, R.; Weingartner, E.; Baltensperger, U.
      Atmospheric Environment (2002), 36 (36-37), 5569-5579CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science Ltd.)
      A mobile pollutant measurement lab. was designed and built at the Paul Scherrer Institute (Switzerland) for the measurement of on-road ambient concns. of a large set of trace gases and aerosol parameters with high time resoln. (<15 s for most instruments), along with geog. and meteorol. information. This approach allowed for pollutant level measurements both near traffic (e.g. in urban areas or on freeways/main roads) and at rural locations far away from traffic, within short periods of time and at different times of day and year. Such measurements were performed on a regular base during the project year of gas phase and aerosol measurements (YOGAM). This paper presents data measured in the Zurich (Switzerland) area on a late autumn day (6 Nov.) in 2001. The local urban particle background easily reached 50,000 cm-3, with addnl. peak particle no. concns. of ≤400,000 cm-3. The regional background of the total particle no. concn. was not found to significantly correlate with the distance to traffic and anthropogenic emissions of CO and NOx. On the other hand, this correlation was significant for the no. concn. of particles in the size range 50-150 nm, indicating that the particle no. concn. in this size range is a better traffic indicator than the total no. concn. Particle no. size distribution measurements showed that daytime urban ambient air is dominated by high no. concns. of ultrafine particles (nanoparticles) with diams. <50 nm, which are immediately formed by traffic exhaust and thus belong to the primary emissions. However, significant variation of the nanoparticle mode was also obsd. in no. size distributions measured in rural areas both at daytime and nighttime, suggesting that nanoparticles are not exclusively formed by primary traffic emissions. While urban daytime total no. concns. were increased by a factor of 10 compared to the nighttime background, corresponding factors for total surface area and total vol. concns. were 2 and 1.5, resp.
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    26. 26
      Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aaalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T. ″Sniffer″ - a novel tool for chasing vehicles and measuring traffic pollutants Atmos. Environ. 2004, 38, 3625– 3635 DOI: 10.1016/j.atmosenv.2004.03.047
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      26
      "Sniffer"-a novel tool for chasing vehicles and measuring traffic pollutants
      Pirjola, L.; Parviainen, H.; Hussein, T.; Valli, A.; Hameri, K.; Aalto, P.; Virtanen, A.; Keskinen, J.; Pakkanen, T. A.; Makela, T.; Hillamo, R. E.
      Atmospheric Environment (2004), 38 (22), 3625-3635CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Science B.V.)
      To measure traffic pollutants with high temporal and spatial resoln. under real conditions a mobile lab. was designed and built in Helsinki Polytechnic in close co-operation with the University of Helsinki. The equipment of the van provides gas phase measurements of CO and NOx, no. size distribution measurements of fine and ultrafine particles by an elec. low pressure impactor, an ultrafine condensation particle counter and a scanning mobility particle sizer. Two inlet systems, one above the windshield and the other above the bumper, enable chasing of different type of vehicles. Also, meteorol. and geog. parameters are recorded. This paper introduces the construction and tech. details of the van, and presents data from the measurements performed during an LIPIKA campaign on the highway in Helsinki. Approx. 90% of the total particle no. concn. was due to particles smaller than 50 nm on the highway in Helsinki. The peak concns. exceeded often 200,000 particles cm-3 and reached sometimes a value of 106 cm-3. Typical size distribution of fine particles possessed bimodal structure with the modal mean diams. of 15-20 nm and ∼150 nm. Atm. dispersion of traffic pollutions were measured by moving away from the highway along the wind direction. At a distance of 120-140 m from the source the concns. were dild. to one-tenth from the values at 9 m from the source.
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    27. 27
      Kolb, C.; Herndon, S. C.; McManus, J. B.; Shorter, J. H.; Zahniser, M. S.; Nelson, D. D.; Jayne, J. T.; Canagaratna, M. R.; Worsnop, D. R. Mobile laboratory with rapid response instruments for real-time measurement of urban and regional trace gas and particulate distributions and emissions source characteristics Environ. Sci. Technol. 2004, 38, 5694– 5703 DOI: 10.1021/es030718p
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      27
      Mobile Laboratory with Rapid Response Instruments for Real-Time Measurements of Urban and Regional Trace Gas and Particulate Distributions and Emission Source Characteristics
      Kolb, Charles E.; Herndon, Scott C.; McManus, J. Barry; Shorter, Joanne H.; Zahniser, Mark S.; Nelson, David D.; Jayne, John T.; Canagaratna, Manjula R.; Worsnop, Douglas R.
      Environmental Science and Technology (2004), 38 (21), 5694-5703CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Recent technol. advances have allowed the development of robust, relatively compact, low power, rapid response (∼1 s) instruments with sufficient sensitivity and specificity to quantify many trace gases and aerosol particle components in the ambient atm. Suites of such instruments can be deployed on mobile platforms to study atm. processes, map concn. distributions of atm. pollutants, and det. the compn. and intensities of emission sources. A mobile lab. contg. innovative tunable IR laser differential absorption spectroscopy (TILDAS) instruments to measure selected trace gas concns. at sub parts-per-billion levels and an aerosol mass spectrometer (AMS) to measure size resolved distributions of the non-refractory chem. components of fine airborne particles as well as selected com. fast response instruments and position/velocity sensors is described. Examples of the range of measurement strategies that can be undertaken using this mobile lab. are discussed, and samples of measurement data are presented.
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    28. 28
      Brantley, H. L.; Hagler, G. S. W.; Kimbrough, E. S.; Williams, R. W.; Mukerjee, S.; Neas, L. M. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends Atmos. Meas. Tech. 2014, 7, 2169– 2183 DOI: 10.5194/amt-7-2169-2014
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      Hagemann, R.; Corsmeier, U.; Kottmeier, C.; Rinke, R.; Wieser, A.; Vogel, B. Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory ‘AERO-TRAM’ Atmos. Environ. 2014, 94, 341– 352 DOI: 10.1016/j.atmosenv.2014.05.051
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      29
      Spatial variability of particle number concentrations and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory 'AERO-TRAM'
      Hagemann, Rowell; Corsmeier, Ulrich; Kottmeier, Christoph; Rinke, Rayk; Wieser, Andreas; Vogel, Bernhard
      Atmospheric Environment (2014), 94 (), 341-352CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      For the first time in Germany, we obtained high-resoln. spatial distributions of particle nos. and nitrogen oxides in an urban agglomeration using a tram system. In comparison to particle nos. the NOx concn. decreased much faster with a significantly steeper gradient when going from the inner city to the surrounding area. In case of NOx the decrease was 70% while for particle no. concn. it was only 50%. We found an area in the rural surrounding with a second increase of particle nos. without simultaneous enhanced NOx levels. The source of the high particle nos. could be ascribed to industry emissions about 5-10 km away. The mean spatial distribution of particle no. concn. depended on wind direction, wind velocity and boundary layer stability. The dependency was particularly strong in the rural area affected by industrial emissions, where individual wind directions led to concn. differences of up to 25%. The particulate concn. was 40% higher during low wind velocities (1-5 m s-1) than during high wind velocities (>5 m s-1). We obsd. similar findings for the impact of boundary layer stability on particle nos. concn. Particle pollution was 40% higher for stable stratification compared to neutral or unstable cases.
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    30. 30
      Van den Bossche, J.; Peters, J.; Verwaeren, J.; Botteldooren, D.; Theunis, J.; De Baets, B. Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset Atmos. Environ. 2015, 105, 148– 161 DOI: 10.1016/j.atmosenv.2015.01.017
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      30
      Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset
      Van den Bossche, Joris; Peters, Jan; Verwaeren, Jan; Botteldooren, Dick; Theunis, Jan; De Baets, Bernard
      Atmospheric Environment (2015), 105 (), 148-161CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      Mobile monitoring is increasingly used as an addnl. tool to acquire air quality data at a high spatial resoln. However, given the high temporal variability of urban air quality, a limited no. of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodol. to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concns. aggregated at a resoln. of 20 m are obsd. Mapping at such a high resoln. is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalization, depending on the location 24-94 repeated measurement runs (median of 41) are required to map the BC concns. at a 50 m resoln. with an uncertainty of 25%. When relaxing the uncertainty to 50%, these nos. reduce to 5-11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resoln., and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient no. of repetitions in relation to the desired reliability and spatial resoln. Specific data processing methods such as background normalization and event detection have to be applied.
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    31. 31
      Peters, J.; Theunis, J.; Van Poppel, M.; Berghmans, P. Monitoring PM10 and ultrafine particles in urban environments using mobile measurements Aerosol Air Qual. Res. 2013, 13, 509– 522 DOI: 10.4209/aaqr.2012.06.0152
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      Efron, B. Bootstrap methods: another look at the jackknife Ann. Stat. 1979, 7, 1– 26 DOI: 10.1214/aos/1176344552
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      Dons, E.; Int Panis, L.; Van Poppel, M.; Theunis, J.; Wets, G. Personal exposure to black carbon in transport microenvironments Atmos. Environ. 2012, 55, 392– 398 DOI: 10.1016/j.atmosenv.2012.03.020
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      Personal exposure to Black Carbon in transport microenvironments
      Dons, Evi; Int Panis, Luc; Van Poppel, Martine; Theunis, Jan; Wets, Geert
      Atmospheric Environment (2012), 55 (), 392-398CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      We evaluated personal exposure of 62 individuals to the air pollutant Black Carbon, using 13 portable aethalometers while keeping detailed records of their time-activity pattern and whereabouts. Concns. encountered in transport are studied in depth and related to trip motives. The evaluation comprises more than 1500 trips with different transport modes. Measurements were spread over two seasons. Results show that 6% of the time is spent in transport, but it accounts for 21% of personal exposure to Black Carbon and approx. 30% of inhaled dose. Concns. in transport were 2-5 times higher compared to concns. encountered at home. Exposure was highest for car drivers, and car and bus passengers. Concns. of Black Carbon were only half as much when traveling by bike or on foot; when incorporating breathing rates, dose was found to be twice as high for active modes. Lowest in transport' concns. were measured in trains, but nevertheless these concns. are double the concns. measured at home. Two thirds of the trips are car trips, and those trips showed a large spread in concns. In-car concns. are higher during peak hours compared to off-peak, and are elevated on weekdays compared to Saturdays and even more so on Sundays. These findings result in significantly higher exposure during car commute trips (motive Work'), and lower concns. for trips with motive Social and leisure'. Because of the many factors influencing exposure in transport, travel time is not a good predictor of integrated personal exposure or inhaled dose.
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    34. 34
      Zhou, Y.; Levy, J. I. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis BMC Public Health 2007, 7, 89 DOI: 10.1186/1471-2458-7-89
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      34
      Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis
      Zhou Ying; Levy Jonathan I
      BMC public health (2007), 7 (), 89 ISSN:.
      BACKGROUND: There has been growing interest among exposure assessors, epidemiologists, and policymakers in the concept of "hot spots", or more broadly, the "spatial extent" of impacts from traffic-related air pollutants. This review attempts to quantitatively synthesize findings about the spatial extent under various circumstances. METHODS: We include both the peer-reviewed literature and government reports, and focus on four significant air pollutants: carbon monoxide, benzene, nitrogen oxides, and particulate matter (including both ultrafine particle counts and fine particle mass). From the identified studies, we extracted information about significant factors that would be hypothesized to influence the spatial extent within the study, such as the study type (e.g., monitoring, air dispersion modeling, GIS-based epidemiological studies), focus on concentrations or health risks, pollutant under study, background concentration, emission rate, and meteorological factors, as well as the study's implicit or explicit definition of spatial extent. We supplement this meta-analysis with results from some illustrative atmospheric dispersion modeling. RESULTS: We found that pollutant characteristics and background concentrations best explained variability in previously published spatial extent estimates, with a modifying influence of local meteorology, once some extreme values based on health risk estimates were removed from the analysis. As hypothesized, inert pollutants with high background concentrations had the largest spatial extent (often demonstrating no significant gradient), and pollutants formed in near-source chemical reactions (e.g., nitrogen dioxide) had a larger spatial extent than pollutants depleted in near-source chemical reactions or removed through coagulation processes (e.g., nitrogen oxide and ultrafine particles). Our illustrative dispersion model illustrated the complex interplay of spatial extent definitions, emission rates, background concentrations, and meteorological conditions on spatial extent estimates even for non-reactive pollutants. Our findings indicate that, provided that a health risk threshold is not imposed, the spatial extent of impact for mobile sources reviewed in this study is on the order of 100-400 m for elemental carbon or particulate matter mass concentration (excluding background concentration), 200-500 m for nitrogen dioxide and 100-300 m for ultrafine particle counts. CONCLUSION: First, to allow for meaningful comparisons across studies, it is important to state the definition of spatial extent explicitly, including the comparison method, threshold values, and whether background concentration is included. Second, the observation that the spatial extent is generally within a few hundred meters for highway or city roads demonstrates the need for high resolution modeling near the source. Finally, our findings emphasize that policymakers should be able to develop reasonable estimates of the "zone of influence" of mobile sources, provided that they can clarify the pollutant of concern, the general site characteristics, and the underlying definition of spatial extent that they wish to utilize.
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    35. 35
      Zhu, Y.; Hinds, W. C.; Kim, S.; Sioutas, C. Concentration and size distribution of ultrafine particles near a major highway J. Air Waste Manage. Assoc. 2002, 52, 1032– 1042 DOI: 10.1080/10473289.2002.10470842
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      35
      Concentration and size distribution of ultrafine particles near a major highway
      Zhu Yifang; Hinds William C; Kim Seongheon; Sioutas Constantinos
      Journal of the Air & Waste Management Association (1995) (2002), 52 (9), 1032-42 ISSN:1096-2247.
      Motor vehicle emissions usually constitute the most significant source of ultrafine particles (diameter <0.1 microm) in an urban environment, yet little is known about the concentration and size distribution of ultrafine particles in the vicinity of major highways. In the present study, particle number concentration and size distribution in the size range from 6 to 220 nm were measured by a condensation particle counter (CPC) and a scanning mobility particle sizer (SMPS), respectively. Measurements were taken 30, 60, 90, 150, and 300 m downwind, and 300 m upwind, from Interstate 405 at the Los Angeles National Cemetery. At each sampling location, concentrations of CO, black carbon (BC), and particle mass were also measured by a Dasibi CO monitor, an aethalometer, and a DataRam, respectively. The range of average concentration of CO, BC, total particle number, and mass concentration at 30 m was 1.7-2.2 ppm, 3.4-10.0 microg/m3, 1.3-2.0 x 10(5)/cm3, and 30.2-64.6 microg/m3, respectively. For the conditions of these measurements, relative concentrations of CO, BC, and particle number tracked each other well as distance from the freeway increased. Particle number concentration (6-220 nm) decreased exponentially with downwind distance from the freeway. Data showed that both atmospheric dispersion and coagulation contributed to the rapid decrease in particle number concentration and change in particle size distribution with increasing distance from the freeway. Average traffic flow during the sampling periods was 13,900 vehicles/hr. Ninety-three percent of vehicles were gasoline-powered cars or light trucks. The measured number concentration tracked traffic flow well. Thirty meters downwind from the freeway, three distinct ultrafine modes were observed with geometric mean diameters of 13, 27, and 65 nm. The smallest mode, with a peak concentration of 1.6 x 10(5)/cm3, disappeared at distances greater than 90 m from the freeway. Ultrafine particle number concentration measured 300 m downwind from the freeway was indistinguishable from upwind background concentration. These data may be used to estimate exposure to ultrafine particles in the vicinity of major highways.
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    36. 36
      Both, A. F.; Balakrishnan, A.; Joseph, B.; Marshall, J. D. Spatiotemporal aspects of real-time PM2.5: Low- and middle-income neighborhoods in Bangalore, India Environ. Sci. Technol. 2011, 45, 5629– 5636 DOI: 10.1021/es104331w
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      36
      Spatiotemporal Aspects of Real-Time PM2.5: Low- and Middle-Income Neighborhoods in Bangalore, India
      Both, Adam F.; Balakrishnan, Arun; Joseph, Bobby; Marshall, Julian D.
      Environmental Science & Technology (2011), 45 (13), 5629-5636CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      We measured outdoor fine particulate matter (PM2.5) concns. in a low- and a nearby middle-income neighborhood in Bangalore, India. Each neighborhood included sampling locations near and not near a major road. One-minute av. concns. were recorded for 168 days during Sept. 2008 to May 2009 using a gravimetric-cor. nephelometer. We also measured wind speed and direction, and PM2.5 concn. as a function of distance from road. Av. concns. are 21-46% higher in the low- than in the middle-income neighborhood, and exhibit differing spatiotemporal patterns. For example, in the middle-income neighborhood, median concns. are higher near-road than not near-road (56 vs. 50 μg m-3); in the low-income neighborhood, the reverse holds (68 μg m-3 near-road, 74 μg m-3 not near-road), likely because of within-neighborhood residential emissions (e.g., cooking; trash combustion). A moving-av. subtraction method used to infer local- vs. urban-scale emissions confirms that local emissions are greater in the low-income neighborhood than in the middle-income neighborhood; however, relative contributions from local sources vary by time-of-day. Real-time relative humidity correction factors are important for accurately interpreting real-time nephelometer data.
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    37. 37
      Watson, J. G.; Chow, J. C. Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer J. Air Waste Manage. Assoc. 2001, 51, 1522– 1528 DOI: 10.1080/10473289.2001.10464379
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      37
      Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer
      Watson, John G.; Chow, Judith C.
      Journal of the Air & Waste Management Association (2001), 51 (11), 1522-1528CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
      A successive moving av. subtraction method was developed and applied to black carbon measured over 5-min intervals at a downtown site near many small emitters and at a suburban residential site within the urban plume, but distant from specific emitters. Short-duration pulses assumed to originate from nearby sources were subtracted from concns. at each site and were summed to est. middle-scale (0.1-1 km) contributions. The difference of the remaining baselines at urban and suburban monitors was interpreted as the contribution to the downtown monitor from source emissions mixed over a neighborhood scale (1-5 km). Baseline at the suburban site was interpreted as the contribution of the mixt. of black carbon sources for the entire city. When applied to 24-day periods from Feb. and Mar. 1997 in Mexico City, the anal. showed 65% of the 24-h black carbon was part of the urban mixt.; 23% originated in the neighborhood surrounding the monitor and 12% was contributed from nearby sources. Results indicate a fixed-site monitor can reasonably represent exposure in the surrounding neighborhood, even when many local sources, e.g., diesel vehicle exhaust, affects the monitor.
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      Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd ed.; Wiley-Interscience: Hoboken, NJ, 2006.
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      Dallmann, T. R.; Harley, R. A.; Kirchstetter, T. W. Effects of diesel particle filter retrofits and accelerated fleet turnover on drayage truck emissions at the Port of Oakland Environ. Sci. Technol. 2011, 45, 10773– 10779 DOI: 10.1021/es202609q
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      Effects of Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage Truck Emissions at the Port of Oakland
      Dallmann, Timothy R.; Harley, Robert A.; Kirchstetter, Thomas W.
      Environmental Science & Technology (2011), 45 (24), 10773-10779CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Heavy-duty diesel drayage trucks have a disproportionate impact on the air quality of communities surrounding major freight-handling facilities. In an attempt to mitigate this impact, the state of California has mandated new emission control requirements for drayage trucks accessing ports and rail yards in the state beginning in 2010. This control rule prompted an accelerated diesel particle filter (DPF) retrofit and truck replacement program at the Port of Oakland. The impact of this program was evaluated by measuring emission factor distributions for diesel trucks operating at the Port of Oakland prior to and following the implementation of the emission control rule. Emission factors for black carbon (BC) and oxides of nitrogen (NOx) were quantified in terms of grams of pollutant emitted per kg of fuel burned using a carbon balance method. Concns. of these species along with carbon dioxide were measured in the exhaust plumes of individual diesel trucks as they drove by en route to the Port. A comparison of emissions measured before and after the implementation of the truck retrofit/replacement rule shows a 54 ± 11% redn. in the fleet-av. BC emission factor, accompanied by a shift to a more highly skewed emission factor distribution. Although only particulate matter mass redns. were required in the first year of the program, a significant redn. in the fleet-av. NOx emission factor (41 ± 5%) was obsd., most likely due to the replacement of older trucks with new ones.
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    40. 40
      Jenkin, M. E. Analysis of sources and partitioning of oxidant in the UK—Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London Atmos. Environ. 2004, 38, 5131– 5138 DOI: 10.1016/j.atmosenv.2004.05.055
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      Analysis of sources and partitioning of oxidant in the UK-Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London
      Jenkin, Michael E.
      Atmospheric Environment (2004), 38 (30), 5131-5138CODEN: AENVEQ; ISSN:1352-2310. (Elsevier B.V.)
      Hourly mean concn. data for NO, NO2, and O3 at Marylebone Rd, an urban curb-side site in London, UK, were used to assess diurnal and seasonal dependence of oxidant sources and their origins. Obsd. oxidant (OX, defined as NO2 + O3) concns. were interpreted in terms of a the sum of a NOx-independent regional contribution and a linearly NOx-dependent local contribution. The former is believed to be equal to the background O3 concn.; the latter is likely to be dominated by NO2 emissions from road transport at the study site. Derived regional OX concns. displayed a significant seasonal variation with a springtime max. of ∼43 ppb in Apr. Results were broadly similar to those reported for background O3 at low altitude sites in northwestern Europe. A strong diurnal variation in local OX contribution was obsd. throughout the year, with highest concns. (typically ∼0.11 ppb/ppb NOx) in daytime. Diurnal profiles averaged over periods of the year when the UK operates under Greenwich Mean and British summer times, demonstrated a clear temporal shift, consistent with the local OX contribution due to primary NO2 emissions from road transport. In conjunction with traffic flow statistics and assocd. NOx emissions ests., results suggested primary NO2 from diesel-fueled vehicles accounted for 0.996 v-0·6 diesel NOx emissions, by vol., where v = mean vehicle speed in km/h (range, 30-60 km/h). This corresponded to 11.8 ± 1.2% NOx emissions integrated over the av. diurnal cycle for conditions at Marylebone Rd. Results also suggested primary NO2 emissions from gasoline-fueled vehicles were far less important, with an upper limit NO2:NOx emission ratio of <3%.
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    41. 41
      Riley, E. A.; Schaal, L.; Sasakura, M.; Crampton, R.; Gould, T. R.; Hartin, K.; Sheppard, L.; Larson, T.; Simpson, C. D.; Yost, M. G. Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution Atmos. Environ. 2016, 132, 229– 239 DOI: 10.1016/j.atmosenv.2016.03.001
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      Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution
      Riley, Erin A.; Schaal, LaNae; Sasakura, Miyoko; Crampton, Robert; Gould, Timothy R.; Hartin, Kris; Sheppard, Lianne; Larson, Timothy; Simpson, Christopher D.; Yost, Michael G.
      Atmospheric Environment (2016), 132 (), 229-239CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term avs. To investigate the stationarity of short-term mobile monitoring measurements, we calcd. long term medians derived from a mobile monitoring campaign that also employed 2-wk integrated passive sampler detectors (PSD) for NOx, Ozone, and nine volatile org. compds. at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 min at a resoln. of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-wk PSD data and observe correlations between mobile NOx measurements and PSD NOx measurements in both summer and winter (Pearson's r = 0.84 and 0.48, resp.). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NOx was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r = 0.8), and intermodal (1-3 μm) particle counts (r = 0.73). Principal Component Anal. of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles of traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies.
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    42. 42
      Hu, S.; Fruin, S.; Kozawa, K.; Mara, S.; Paulson, S. E.; Winer, A. M. A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours Atmos. Environ. 2009, 43, 2541– 2549 DOI: 10.1016/j.atmosenv.2009.02.033
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      A wide area of air pollutant impact downwind of a freeway during pre-sunrise hours
      Hu, Shishan; Fruin, Scott; Kozawa, Kathleen; Mara, Steve; Paulson, Suzanne E.; Winer, Arthur M.
      Atmospheric Environment (2009), 43 (16), 2541-2549CODEN: AENVEQ; ISSN:1352-2310. (Elsevier Ltd.)
      We have obsd. a wide area of air pollutant impact downwind of a freeway during pre-sunrise hours in both winter and summer seasons. In contrast, previous studies have shown much sharper air pollutant gradients downwind of freeways, with levels above background concns. extending only 300 m downwind of roadways during the day and up to 500 m at night. In this study, real-time air pollutant concns. were measured along a 3600 m transect normal to an elevated freeway 1-2 h before sunrise using an elec. vehicle mobile platform equipped with fast-response instruments. In winter pre-sunrise hours, the peak ultrafine particle (UFP) concn. (∼95 000 cm-3) occurred immediately downwind of the freeway. However, downwind UFP concns. as high as ∼40 000 cm-3 extended at least 1200 m from the freeway, and did not reach background levels (∼15 000 cm-3) until a distance of about 2600 m. UFP concns. were also elevated over background levels up to 600 m upwind of the freeway. Other pollutants, such as NO and particle-bound polycyclic arom. hydrocarbons, exhibited similar long-distance downwind concn. gradients. In contrast, air pollutant concns. measured on the same route after sunrise, in the morning and afternoon, exhibited the typical daytime downwind decrease to background levels within ∼300 m as found in earlier studies. Although pre-sunrise traffic vols. on the freeway were much lower than daytime congestion peaks, downwind UFP concns. were significantly higher during pre-sunrise hours than during the daytime. UFP and NO concns. were also strongly correlated with traffic counts on the freeway. We assoc. these elevated pre-sunrise concns. over a wide area with a nocturnal surface temp. inversion, low wind speeds, and high relative humidity. Observation of such wide air pollutant impact area downwind of a major roadway prior to sunrise has important exposure assessment implications since it demonstrates extensive roadway impacts on residential areas during pre-sunrise hours, when most people are at home.
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      Brinkhoff, T. Major agglomerations of the world. http://citypopulation.de/world/Agglomerations.html 2016, (accessed February 14, 2017).
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      Zeger, S. L.; Thomas, D.; Dominici, F.; Samet, J. M.; Schwartz, J.; Dockery, D.; Cohen, A. Exposure measurement error in time-series studies of air pollution: concepts and consequences Environ. Health Persp. 2000, 108, 419– 426 DOI: 10.1289/ehp.00108419
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      Exposure measurement error in time-series studies of air pollution: concepts and consequences
      Zeger, Scott L.; Thomas, Duncan; Dominici, Francesca; Samet, Jonathan M.; Schwartz, Joel; Dockery, Douglas; Cohen, Aaron
      Environmental Health Perspectives (2000), 108 (5), 419-426CODEN: EVHPAZ; ISSN:0091-6765. (National Institute of Environmental Health Sciences)
      Misclassification of exposure is a well-recognized inherent limitation of epidemiol. studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estg. the relevant exposures for an individual participant in epidemiol. studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, by estg. the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiol. studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiol. studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple ests. of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum: Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter of <10 μm in aerodynamic diam. from the Particle Total Exposure Assessment Methodol. Study. Finally, we summarize open questions regarding measurement error and suggest the kind of addnl. data necessary to address them.
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    45. 45
      Sheppard, L.; Burnett, R. T.; Szpiro, A. A.; Kim, S.-Y.; Jerrett, M.; Pope, C. A.; Brunekreef, B. Confounding and exposure measurement error in air pollution epidemiology Air Qual., Atmos. Health 2012, 5, 203– 216 DOI: 10.1007/s11869-011-0140-9
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      Confounding and exposure measurement error in air pollution epidemiology
      Sheppard Lianne; Burnett Richard T; Szpiro Adam A; Kim Sun-Young; Jerrett Michael; Pope C Arden 3rd; Brunekreef Bert
      Air quality, atmosphere, & health (2012), 5 (2), 203-216 ISSN:1873-9318.
      Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.
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    46. 46
      de Nazelle, A.; Seto, E.; Donaire-Gonzalez, D.; Mendez, M.; Matamala, J.; Nieuwenhuijsen, M. J.; Jerrett, M. Improving estimates of air pollution exposure through ubiquitous sensing technologies Environ. Pollut. 2013, 176, 92– 99 DOI: 10.1016/j.envpol.2012.12.032
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      46
      Improving estimates of air pollution exposure through ubiquitous sensing technologies
      de Nazelle, Audrey; Seto, Edmund; Donaire-Gonzalez, David; Mendez, Michelle; Matamala, Jaume; Nieuwenhuijsen, Mark J.; Jerrett, Michael
      Environmental Pollution (Oxford, United Kingdom) (2013), 176 (), 92-99CODEN: ENPOEK; ISSN:0269-7491. (Elsevier Ltd.)
      Traditional methods of exposure assessment in epidemiol. studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power, or both in health effects ests. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technol. to track person-level time, geog. location, and phys. activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free-living population of 36 subjects in Barcelona, Spain. Information obtained on phys. activity and geog. location was linked to space-time air pollution mapping. We found that information from CalFit could substantially alter exposure ests. For instance, on av. travel activities accounted for 6% of time and 24% of their daily inhaled NO2. Due to the large no. of mobile phone users, this technol. potentially provides an unobtrusive means of enhancing epidemiol. exposure data at low cost.
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    47. 47
      Nyhan, M.; Grauwin, S.; Britter, R.; Misstear, B.; McNabola, A.; Laden, F.; Barrett, S. R. H.; Ratti, C. “Exposure Track”—the impact of mobile-device-based mobility patterns on quantifying population exposure to air pollution Environ. Sci. Technol. 2016, 50, 9671– 9681 DOI: 10.1021/acs.est.6b02385
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      47
      "Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution
      Nyhan, Marguerite; Grauwin, Sebastian; Britter, Rex; Misstear, Bruce; McNabola, Aonghus; Laden, Francine; Barrett, Steven R. H.; Ratti, Carlo
      Environmental Science & Technology (2016), 50 (17), 9671-9681CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Air pollution is recognized as the single largest environmental and human health threat. Many environmental epidemiol. studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure ests. at the population level have not considered spatial and temporal varying populations in the study regions. Thus, in the first study of it is kind, measured population activity patterns representing several million people were used to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yielded information concerning where and when people are present, these collective activity patterns were detd. using counts of connections to cellular networks. Population-weighted exposure to PM2.5 in New York City (NYC), i.e., Active Population Exposure, was evaluated using population activity patterns and spatiotemporal PM2.5 concns. vs. Home Population Exposure, which assumed a static population distribution using Census data. Areas of relatively higher population-weighted exposure were concd. in different districts within NYC in both scenarios, but were more centralized for the Active Population Exposure scenario. Population-weighted exposure computed in each NYC district for the Active scenario were statistically significantly (p <0.05) different from the Home scenario for most districts. Temporal variability of the Active population-weighted exposure detd. in districts were significantly different (p <0.05) during the day and at night. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiol. studies linking air quality and human health.
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