Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates

Low level exposure to hydrogen sulfide: a review of emissions, community exposure, health effects, and exposure guidelines

Hydrogen sulfide (H2S) is a toxic gas that is well-known for its acute health risks in occupational settings, but less is known about effects of chronic and low-level exposures. This critical review investigates toxicological and experimental studies, exposure sources, standards, and epidemiological studies pertaining to chronic exposure to H2S from both natural and anthropogenic sources. H2S releases, while poorly documented, appear to have increased in recent years from oil and gas and possibly other facilities. Chronic exposures below 10 ppm have long been associated with odor aversion, ocular, nasal, respiratory and neurological effects. However, exposure to much lower levels, below 0.03 ppm (30 ppb), has been associated with increased prevalence of neurological effects, and increments below 0.001 ppm (1 ppb) in H2S concentrations have been associated with ocular, nasal, and respiratory effects. Many of the studies in the epidemiological literature are limited by exposure measurement error, co-pollutant exposures and potential confounding, small sample size, and concerns of representativeness, and studies have yet to consider vulnerable populations. Long-term community-based studies are needed to confirm the low concentration findings and to refine exposure guidelines. Revised guidelines that incorporate both short- and long-term limits are needed to protect communities, especially sensitive populations living near H2S sources.

Exposure assessment of PM2.5 in temple premises and crematoriums in Kanpur, India

Regular use of incense and earthen lamps in temples leads to the release of particulate matter (PM), airborne flecks, and gaseous pollutants. Similarly, the cremation of dead bodies using timber and other accessories such as incense, organic chemicals containing carbon, and clothes generates air pollutants. It is currently unclear how much emissions and exposure these activities may lead. This work attempts to fill this gap in our understanding by assessing the associated emissions of PM_2.5 and the corresponding exposure. Ten temples and two cremation grounds were considered for the sampling of PM_2.5. The average PM_2.5 concentration at the ten temples and the two crematoriums was found to be 658.30 ± 112.63 µg/m³ and 1043.50 ± 191.63 µg/m³, respectively. The range of real-time PM_2.5 data obtained from the nearest twelve stations located in the vicinity was 113-191 µg/m³. The exposure assessment in terms of deposition dose was carried out using the ICRP model. The maximum and minimum total respiratory deposition dose rate for PM_2.5 for temples was 175.75 µg/min and 101.15 µg/min, respectively. For crematoriums, the maximum and minimum value of same was 252.3 µg/min and 194.31 µg/min, respectively, for an exposure period of 10 min.

Air quality monitoring and measurement in an urban airshed: Contextualizing datasets from the Detroit Michigan area from 1952 to 2020

With urban air quality being a pressing public health concern, community members are becoming increasingly engaged in determining the links between air quality and human health. Although new measurement tools such as low-cost sensors make local data more accessible, a better understanding of gaps in regional datasets is needed to develop effective metropolitan-scale solutions. Using scoping review methodology, we compiled 214 published journal articles and grey literature reports of air quality data from the Detroit, Michigan area from 1952 through 2020. This critical scoping review focuses on air quality datasets, but related topics such as health studies and community-based participatory science studies were examined from the included articles. Most of these publications were peer-reviewed journal articles published after 2001. Particulate matter, nitrous oxides, and sulfur dioxide were the most commonly studied air pollutants, and asthma was the most frequently associated health outcome paired with air pollution datasets. Few publications reported methods for community-based participatory science. This critical scoping review establishes a foundation of historical air quality data for the Detroit metropolitan area and a set of evaluation criteria that can be replicated in other urban centers. This foundation enables future detailed analysis of air quality datasets and showcases strategies for implementing effective community science programs and monitoring efforts.

Quality of automatic geocoding tools: a study using addresses from hospital record files in Temuco, Chile

Abstract: Automatic geocoding methods have become popular in recent years, facilitating the study of the association between health outcomes and the place of living. However, rather few studies have evaluated geocoding quality, with most of them being performed in the US and Europe. This article aims to compare the quality of three automatic online geocoding tools against a reference method. A subsample of 300 handwritten addresses from hospital records was geocoded using Bing, Google Earth, and Google Maps. Match rates were higher (> 80%) for Google Maps and Google Earth compared with Bing. However, the accuracy of the addresses was better for Bing with a larger proportion (> 70%) of addresses with positional errors below 20m. Generally, performance did not vary for each method for different socioeconomic status. Overall, the methods showed an acceptable, but heterogeneous performance, which may be a warning against the use of automatic methods without assessing quality in other municipalities, particularly in Chile and Latin America.

Variation of ambient air pollutants concentration over Lucknow city, trajectories and dispersion analysis using HYSPLIT4.0

The present study deals with the analysis of daily average concentrations of respirable suspended particulate matter (RSPM- PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) at seven monitoring stations namely, Hazratganj, Talkatora, Mahanagar, Aliganj, Sarai Mali Khan, Gomtinagar, and Ansal TC in Lucknow city from 2016 to 2020. The analysis shows that the annual average concentration of RSPM varies from 148.74 to 323.05 µg/m3, SO2 varies from 7.11 to 8.94 µg/m3 and NO2 from 23.52 to 31.86 µg/m3 at all the locations. From the analysis of seasonal variation, it is found that the minimum concentration of RSPM found to be 81.59 µg/m3 in monsoon and maximum concentration was found to be 447.47 µg/m3 in post-monsoon. However, the seasonal variation of SO2 was found in the range of 5.55 to 10.94 µg/m3 and NO2 in the range of 20.23 to 38.40 µg/m3, which are below the prescribed standards. The pollution level decreased to some extent due to the COVID-19 lockdown in the year 2020 but not below the prescribed standard for RSPM. The levels of PM10 in Lucknow are not reducing despite the government of India banning industries and adopting other safeguards within the city. The Trajectory and Dispersion study using the HYSPLIT4.0 model shows insufficient local pollution control, and pollutants are carried from adjacent locations due to the wind blowing from north-west direction to keep daily pollution levels over the standards prescribed by Central Pollution Control Board (i.e., 100 µg/m3). The peak concentration of RSPM is recorded to be 323.05 µg/m3 for the year 2017 at the Hazratganj monitoring station. Over the study region, wavelet analysis of monthly averaged values of PM10 data sets at all seven stations revealed that the presence of semi-annual and annual periodicity. The findings reveal that controlling of particulate matter pollution in the city is a significant concern and has an alarming situation as compared to SO2 and NO2 pollutants.

Area-Based Geocoding: An Approach to Exposure Assessment Incorporating Positional Uncertainty

While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case-control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point-based method along with a novel area-based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low-certainty locations. In contrast, area-based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point-based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

Uncertainty in geospatial health: challenges and opportunities ahead

PURPOSE

Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.

METHODS

We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.

RESULTS

We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health.

CONCLUSIONS

Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

Short-term impacts of air pollutants in three megacities of India during COVID-19 lockdown

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14-March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30-60% and 52-80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period.

Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

Health effects associated with proximity to waste collection points in Beitbridge Municipality, Zimbabwe

Population growth, urbanisation and economic development have led to the increasing generation of municipal solid waste while environmentally sustainable management remains a challenge the world over. This study sought to investigate health effects associated with proximity to waste collection points in Beitbridge Municipality, Zimbabwe. A cross-sectional study was undertaken to compare the occurrence of disease among the residents living within different distances from the waste collection points (50 m, 100 m, 150 m, 200 m, 250 m, 300 m and above 300 m). A handheld GPS device was used to collect coordinates of the location for the purposes of mapping. The Fishers Exact test and the Multiple Logistic Regression model conducted (on STATA V 13 SE) to determine the association between different variables and the occurrence of health effects. Questionnaires were administered to 700 stratified randomly selected respondents. Five refuse collection points and spatial distribution of health conditions were mapped at Dulibadzimu high-density suburb. The overall response rate was 98% and females constituted the majority of respondents (58%). Most of these respondents were aged between 26 and 35 years of age and were involved in informal trading (35%). Reported health conditions were diarrhoea, dyspnoea, dry cough, eye irritation and asthma. Distance, waste collection point, level of education, nature of occupation and sex were significant contributors to the prevalence of health effects associated with exposure to waste. Exposure to waste is a serious health concern in Beitbridge. Local authority is encouraged to abolish these waste collection points and invest more on conventional waste management systems in partnership with different stakeholders.

Enhancing Models and Measurements of Traffic-Related Air Pollutants for Health Studies Using Dispersion Modeling and Bayesian Data Fusion

INTRODUCTION

The adverse health effects associated with exposure to traffic-related air pollutants (TRAPs) remain a key public health issue. Often, exposure assessments have not represented the small-scale variation and elevated concentrations found near major roads and in urban settings. This research explores approaches aimed at improving exposure estimates of TRAPs that can reduce exposure measurement error when used in health studies. We consider dispersion models designed specifically for the near-road environment, as well as spatiotemporal and data fusion models. These approaches are implemented and evaluated utilizing data collected in recent modeling, monitoring, and epidemiological studies conducted in Detroit, Michigan.

APPROACH

Dispersion models, which estimate near-road pollutant concentrations and individual exposures based on first principles - and in particular, high fidelity models - can provide great flexibility and theoretical strength. They can represent the spatial variability of TRAP concentrations at locations not measured by conventional and spatially sparse air quality monitoring networks. A number of enhancements to dispersion modeling and mobile on-road emissions inventories were considered, including the representation of link-based road networks and updated estimates of temporal allocation of traffic activity, emission factors, and meteorological inputs. The recently developed Research LINE-source model (RLINE), a Gaussian line-source dispersion model specifically designed for the near-road environment, was used in an operational evaluation that compared predicted concentrations of nitrogen oxides (NOx), carbon monoxide (CO), and PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter) with observed concentrations at air quality monitoring stations located near high-traffic roads. Spatiotemporal and data fusion models provided additional and complementary approaches for estimating TRAP exposures. We formulated both nonstationary universal kriging models that exploit the spatial correlation in the monitoring data, and data fusion models that leverage the information contained in both the monitoring data and the output of numerical models, specifically RLINE. These models were evaluated using observations of nitric oxide (NO), NOx, black carbon (BC), and PM2.5 monitored along transects crossing major roads in Detroit. We also examined model assumptions, including the appropriateness of the covariance functions, errors in RLINE outputs, and the effects of jointly modeling two pollutants and using an updated emission inventory.

RESULTS

For CO and NOx, dispersion model performance was best when monitoring sites were close to major roads, during downwind conditions, during weekdays, and during certain seasons. The ability to discern local and particularly the traffic-related portion of PM2.5 was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain sources (e.g., area, fugitive) and some processes (e.g., formation of secondary aerosols). Sensitivity analyses of alternative meteorological inputs and updated emission factors showed some performance gain when using local (on-site) meteorological data and updated inventories. Overall, the operational evaluation suggested RLINE's usefulness for estimating spatially and temporally resolved exposure estimates. The application of the universal kriging models confirmed that wind speed and direction are important drivers of nonstationarity in pollutant concentrations, and that these models can predict exposure estimates that have lower prediction errors than do stationary model counterparts. The application of the Bayesian data fusion models suggested that the RLINE output had a spatially varying additive bias for NOx and PM2.5 and provided little additional information for NOx, besides what is already contained in traffic and geographical information system (GIS) covariates, but had improved estimates of PM2.5 concentrations. Results of the nonstationary Bayesian data fusion model that used RLINE output across a field spanning the measurement sites were similar to a regression-based Bayesian data fusion approach that used only RLINE output at the monitoring locations, with the latter being computationally less burdensome. Using the regression-based Bayesian data fusion model, we found that RLINE with the updated emission inventory provided results that were more useful for estimating NOx concentration at unmonitored sites, but the updated emission inventory did not improve predictions of PM2.5 concentrations. Joint modeling of NOx and PM2.5 was not useful, a result of differences in RLINE's utility in predicting PM2.5 and NOx - useful for the former, but not for the latter - and differences in the spatial dependence structures of the two pollutants. Overall, information provided by RLINE was shown to have the potential to improve spatiotemporal estimates of TRAP concentrations.

CONCLUSIONS

The study results should be interpreted and generalized cautiously given the limitations of the data used. Similar analyses in other settings are recommended for confirming and extending our findings. Still, the study highlights considerations that are relevant for exposure estimates used in health studies. The ability of a dispersion model to accurately reproduce and predict a pollutant depends on the pollutant as well as on spatial and temporal factors, such as the distance and direction from the road, time-of-day, and day-of-week. The nature and source of exposure measurement errors should be taken into consideration, particularly in health studies that take advantage of time- activity information that describes where and when individuals are exposed to pollution. Efforts to refine model inputs and improve model performance can be helpful; meteorological inputs may be the most critical. For both dispersion and spatiotemporal statistical models, sufficient and high-quality monitoring data are essential for developing and evaluating these models. Our analyses using Bayesian data fusion models confirm the presence of spatially varying errors in dispersion model outputs and allow quantification of both the magnitude and the spatial nature of these errors. This valuable information can be leveraged in health studies examining air pollution exposure as well as in studies informing regulatory responses.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Accuracy of two geocoding methods for geographic information system-based exposure assessment in epidemiological studies

BACKGROUND

Environmental exposure assessment based on Geographic Information Systems (GIS) and study participants' residential proximity to environmental exposure sources relies on the positional accuracy of subjects' residences to avoid misclassification bias. Our study compared the positional accuracy of two automatic geocoding methods to a manual reference method.

METHODS

We geocoded 4,247 address records representing the residential history (1990-2008) of 1,685 women from the French national E3N cohort living in the Rhône-Alpes region. We compared two automatic geocoding methods, a free-online geocoding service (method A) and an in-house geocoder (method B), to a reference layer created by manually relocating addresses from method A (method R). For each automatic geocoding method, positional accuracy levels were compared according to the urban/rural status of addresses and time-periods (1990-2000, 2001-2008), using Chi Square tests. Kappa statistics were performed to assess agreement of positional accuracy of both methods A and B with the reference method, overall, by time-periods and by urban/rural status of addresses.

RESULTS

Respectively 81.4% and 84.4% of addresses were geocoded to the exact address (65.1% and 61.4%) or to the street segment (16.3% and 23.0%) with methods A and B. In the reference layer, geocoding accuracy was higher in urban areas compared to rural areas (74.4% vs. 10.5% addresses geocoded to the address or interpolated address level, p < 0.0001); no difference was observed according to the period of residence. Compared to the reference method, median positional errors were 0.0 m (IQR = 0.0-37.2 m) and 26.5 m (8.0-134.8 m), with positional errors <100 m for 82.5% and 71.3% of addresses, for method A and method B respectively. Positional agreement of method A and method B with method R was 'substantial' for both methods, with kappa coefficients of 0.60 and 0.61 for methods A and B, respectively.

CONCLUSION

Our study demonstrates the feasibility of geocoding residential addresses in epidemiological studies not initially recorded for environmental exposure assessment, for both recent addresses and residence locations more than 20 years ago. Accuracy of the two automatic geocoding methods was comparable. The in-house method (B) allowed a better control of the geocoding process and was less time consuming.

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LA_eq24h, L_night, and L_den to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LA_eq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R²) was 0.68, 0.59, and 0.69 for LA_eq24h, L_night, and L_den, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LA_eq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.

High resolution spatial and temporal mapping of traffic-related air pollutants

Vehicle traffic is one of the most significant emission sources of air pollutants in urban areas. While the influence of mobile source emissions is felt throughout an urban area, concentrations from mobile emissions can be highest near major roadways. At present, information regarding the spatial and temporal patterns and the share of pollution attributable to traffic-related air pollutants is limited, in part due to concentrations that fall sharply with distance from roadways, as well as the few monitoring sites available in cities. This study uses a newly developed dispersion model (RLINE) and a spatially and temporally resolved emissions inventory to predict hourly PM_2.5 and NO_x concentrations across Detroit (MI, USA) at very high spatial resolution. Results for annual averages and high pollution days show contrasting patterns, the need for spatially resolved analyses, and the limitations of surrogate metrics like proximity or distance to roads. Data requirements, computational and modeling issues are discussed. High resolution pollutant data enable the identification of pollutant “hotspots”, “project-level” analyses of transportation options, development of exposure measures for epidemiology studies, delineation of vulnerable and susceptible populations, policy analyses examining risks and benefits of mitigation options, and the development of sustainability indicators integrating environmental, social, economic and health information.

A comparison of exposure metrics for traffic-related air pollutants: application to epidemiology studies in Detroit, Michigan

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.