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

Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site

INTRODUCTION

Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development.

The key pollutants that are the key focus of this work include nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM2.5; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O3). NOx, CO, and BC are tracers of vehicle emissions and dispersion. PM2.5 is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM2.5 and UFP concentrations. O3 concentrations are influenced by interaction with NOx near the roadway. Nitrogen dioxide (NO2), CO, PM2.5, and O3 are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work.

METHODS

The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection.

The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM2.5 at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites.

RESULTS

We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system.

At the freeway site, we measured the spatial gradient of NOx, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants.

At the urban site, we measured daily average concentrations of nitric oxide (NO), NOx, O3, and PM2.5 at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O3, PM2.5, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution.

Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R2) was 0.61 for hourly NOx concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NOx at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NOx at the freeway site and for UFPs and O3 at the urban site pedestrian transect.

CONCLUSIONS

Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

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.

Seasonal Variation, Chemical Composition, and PMF-Derived Sources Identification of Traffic-Related PM1, PM2.5, and PM2.5-10 in the Air Quality Management Region of Žilina, Slovakia

Particulate matter (PM) air pollution in the urban environment is mainly related to the presence of potential sources throughout the year. Road transport is one of the most important sources of PM in the urban environment, because it directly affects pedestrians. PM measurements were performed in the city of Žilina, Slovakia, at various road-traffic-related measurement stations over the course of several years. This paper evaluates changes in the concentration of the fine fraction (PM_2.5), the ultrafine fraction (PM₁), and the coarse fraction (PM_2.5–10) over time. PM concentrations were measured by reference gravimetric method. Significant changes in PM concentrations over time due to the diversification of pollution sources and other, secondary factors can be observed from the analysis of the measured data. PM samples were subjected to chemical analysis inductively coupled plasma mass spectrometry (ICP-MS) to determine the concentrations of elements (Mg, Al, Ca, Cr, Cu, Fe, Cd, Sb, Ba, Pb, Ni, and Zn). The seasonal variation of elements was evaluated, and the sources of PM_2.5, PM₁, and PM_2.5–10 were estimated using principal component analysis (PCA) and positive matrix factorization (PMF). PM_2.5 (maximum concentration of 148.95 µg/m³ over 24 h) and PM₁ (maximum concentration of 110.51 µg/m³ over 24 h) showed the highest concentrations during the heating season, together with the elements Cd, Pb, and Zn, which showed a significant presence in these fractions. On the other hand, PM_2.5–10 (maximum concentration of 38.17 µg/m³ over 24 h) was significantly related to the elements Cu, Sb, Ba, Ca, Cr, Fe, Mg, and Al. High correlation coefficients (r ≥ 0.8) were found for the elements Mg, Ca, Fe, Al, Cd, Pb, and Zn in the PM₁ fraction, Cd, Pb, and Zn in PM_2.5, and Ba, Sb, Fe, Cu, Cr, Mg, Al, and Ca in PM_2.5–10. Using PMF analysis, three major sources of PM (abrasion from tires and brakes, road dust resuspension/winter salting, and combustion processes) were identified for the PM_2.5 and PM₁ fractions, as well as for the coarse PM_2.5–10 fraction. This study reveals the importance of non-exhaust PM emissions in the urban environment.

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including CO,NO,NO2,NOx, and O3. We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

Size-resolved particle emission factors of vehicular traffic derived from urban eddy covariance measurements

Road traffic is one of the major sources of atmospheric particles in urban areas. Modelling the contribution of this source to urban pollution requires reliable estimates of emission factors possibly resolved in size. In this work, size-resolved particle emission factors (EFs) of the mixed vehicle fleet were measured in the urban area of Lecce (south-eastern Italy). The approach used is based on vertical fluxes measured with eddy-covariance, counting of vehicles, and estimation of footprints. Results show that the average EF in number (range 0.009-3 μm) was 2.2*10¹⁴ #/Veh km, being dominated by ultrafine particles (D_p < 0.25 μm) due to exhaust emissions. EF number size distribution decreases with particle size. A reduction of more than four orders of magnitude was observed at D_p ≅ 0.9 μm. EF mass size distribution reaches a maximum around D_p ≅ 0.3 μm then decreases until D_p ≅ 0.9 μm. For larger particles EF in mass increases for the influence of non-exhaust emissions. Average emission factor of PM₁ was 56 mg/Veh Km and that of PM_2.5 was 63 mg/Veh Km. A comparison of measurements taken in 2010 and 2015 in the same area shows a decreasing trend of the average total EF in number of about 56%, likely as a consequence of the increased use of new generation vehicles following more restrictive limits for particle emissions.

A hybrid modeling framework to estimate pollutant concentrations and exposures in near road environments

Traffic related air pollution is one of the major local sources of pollution challenging most urban populations. Current air quality modeling approaches can estimate the concentrations of air pollutants on either regional or local scales but cannot effectively estimate concentrations from the combination of regional and local sources at both local and regional scales simultaneously. This study describes a hybrid modeling framework, HYCAMR, combining a regional model, CAMx, and a local-scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species at high temporal (hourly) and spatial (40 m) resolution. HYCAMR utilizes all the chemical and physical processes available in CAMx and the Particulate Matter Source Apportionment Technology (PSAT) tool to estimate concentrations from both onroad and nonroad emission sources. HYCAMR employs R-LINE, to estimate the normalized dispersion of pollutant mass from onroad emission sources, from primary and secondary roads, at high resolution. Applying R-LINE for one day per month using average daily meteorology yields seasonally-resolved spatial dispersion profiles at low computational cost. Combining the R-LINE spatial dispersion profile with CAMx concentration estimates yields an estimate of the combined concentrations for a range of pollutants at high spatial and temporal resolution. In three major cities in Connecticut, HYCAMR shows strong temporal and seasonal variability in NOx, PM_2.5, and elemental carbon (EC) concentrations. This study evaluates HYCAMR year 2011 estimates of NO₂ and PM_2.5 against two sources: satellite-based estimates at coarse resolution and regression model estimates at census block group resolution. In this evaluation, HYCAMR demonstrates improved agreement with the land-use regression modeling and mixed agreement with satellite-based estimates when compared to the regional CAMx estimates.

Application of the Public Health Exposome Framework to Estimate Phenotypes of Resilience in a Model Ohio African-American Women's Cohort

We report integration of the United States Environmental Protection Agency's (USEPA) United States Environmental Justice Screen (EJSCREEN) database with our Public Health Exposome dataset to interrogate 9232 census blocks to model the complexity of relationships among environmental and socio-demographic variables toward estimating adverse pregnancy outcomes [low birth weight (LBW) and pre-term birth (PTB)] in all Ohio counties. Using a hill-climbing algorithm in R software, we derived a Bayesian network that mapped all controlled associations among all variables available by applying a mapping algorithm. The results revealed 17 environmental and socio-demographic variables that were represented by nodes containing 69 links accounting for a network with 32.85% density and average degree of 9.2 showing the most connected nodes in the center of the model. The model predicts that the socio-economic variables low income, minority, and under age five populations are correlated and associated with the environmental variables; particulate matter (PM_2.5) level in air, proximity to risk management facilities, and proximity to direct discharges in water are linked to PTB and LBW in 88 Ohio counties. The methodology used to derive significant associations of chemical and non-chemical stressors linked to PTB and LBW from indices of geo-coded environmental neighborhood deprivation serves as a proxy for design of an African-American women's cohort to be recruited in Ohio counties from federally qualified community health centers within the 9232 census blocks. The results have implications for the development of severity scores for endo-phenotypes of resilience based on associations and linkages for different chemical and non-chemical stressors that have been shown to moderate cardio-metabolic disease within a population health context.

Review of modelling air pollution from traffic at street-level - The state of the science

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Residential Proximity to Major Roadways and Risk of Type 2 Diabetes Mellitus: A Meta-Analysis

Research indicates that higher levels of traffic-related pollution exposure increase the risk of diabetes, but the association between road proximity and diabetes risk remains unclear. To assess and quantify the association between residential proximity to major roadways and type 2 diabetes, a systematic review and meta-analysis was performed. Embase, Medline, and Web of Science were searched for eligible studies. Using a random-effects meta-analysis, the summary relative risks (RRs) were calculated. Bayesian meta-analysis was also performed. Eight studies (6 cohort and 2 cross-sectional) with 158,576 participants were finally included. The summary unadjusted RR for type 2 diabetes associated with residential proximity to major roadways was 1.24 (95% confidence interval [CI]: 1.07–1.44, p = 0.001, I² = 48.1%). The summary adjusted RR of type 2 diabetes associated with residential proximity to major roadways was 1.12 (95% CI: 1.03–1.22, p = 0.01, I² = 17.9%). After excluding two cross-sectional studies, the summary results suggested that residential proximity to major roadways could increase type 2 diabetes risk (Adjusted RR = 1.13; 95% CI: 1.02–1.27, p = 0.025, I² = 36.6%). Bayesian meta-analysis showed that the unadjusted RR and adjusted RR of type 2 diabetes associated with residential proximity to major roadways were 1.22 (95% credibility interval: 1.06–1.55) and 1.13 (95% credibility interval: 1.01–1.31), respectively. The meta-analysis suggested that residential proximity to major roadways could significantly increase risk of type 2 diabetes, and it is an independent risk factor of type 2 diabetes. More well-designed studies are needed to further strengthen the evidence.

Particulate Matter Exposure in a Police Station Located near a Highway

People living or working near roadways have experienced an increase in cardiovascular or respiratory diseases due to vehicle emissions. Very few studies have focused on the PM exposure of highway police officers, particularly for the number concentration and size distribution of ultrafine particles (UFP). This study evaluated exposure concentrations of particulate matter (PM) in the Sinying police station near a highway located in Tainan, Taiwan, under different traffic volumes, traffic types, and shift times. We focused on periods when the wind blew from the highway toward the police station and when the wind speed was greater than or equal to 0.5 m/s. PM2.5, UFP, and PM-PAHs concentrations in the police station and an upwind reference station were measured. Results indicate that PM2.5, UFP, and PM-PAHs concentrations in the police station can be on average 1.13, 2.17, and 5.81 times more than the upwind reference station concentrations, respectively. The highest exposure level for PM2.5 and UFP was observed during the 12:00 PM–4:00 PM shift while the highest PAHs concentration was found in the 4:00 AM–8:00 AM shift. Thus, special attention needs to be given to protect police officers from exposure to high PM concentration.

Biological Monitoring of Air Pollutants and Its Influence on Human Beings

Monitoring air pollutants via plants is an economic, convenient and credible method compared with the traditional ways. Plants show different damage symptoms to different air pollutants, which can be used to determine the species of air pollutants. Besides, pollutants mass concentration scope can be estimated by the damage extent of plants and the span of polluted time. Based on the domestic and foreign research, this paper discusses the principles, mechanism, advantages and disadvantages of plant-monitoring, and exemplifies plenty of such plants and the minimum mass concentration and pollution time of the plants showing damage symptoms. Finally, this paper introduced the human health effects of air pollutants on immune function of the body, such as decrease of the body's immune function, decline of lung function, respiratory and circulatory system changes, inducing and promoting human allergic diseases, respiratory diseases and other diseases.