Air pollution benefits from reduced on-road activity due to COVID-19 in the United States.

On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.

Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.

BACKGROUND: A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. OBJECTIVE: In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. METHOD: We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. RESULTS: The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE: Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds.

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion.

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R2 from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R2 values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R2 for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study.

Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.

Enhancing the local air quality benefits of roadside green infrastructure using low-cost, impermeable, solid structures (LISS).

Communities located in near-road environments face adverse health effects due to elevated exposures to traffic-related air pollution (TRAP). While the use of a combination of solid structures (i.e. sound walls) and vegetation barriers can be an effective TRAP mitigation tool, installing these barriers can also present challenges to local communities. Sound walls are costly, and building these structures often requires the involvement of federal, state, and local permitting agencies. In this paper, we proposed that the use of low-cost, impermeable, solid structures (LISS), e.g., an impermeable thin wooden, plastic or metal fence, combined with vegetation can provide an effective option for local communities to improve near-road air quality due to lower costs and easier implementation. We conducted Large Eddy Simulations (LES) for different design scenarios of LISS and vegetation barriers under various conditions. Our results indicate that (i) combining LISS and vegetation is more effective than either alone, (ii) combining a less dense vegetation and LISS can be as effective as a dense vegetation barrier, (iii) In certain scenarios, depending on wind speed and particle size, vegetation barriers alone might lead to elevated pollutant concentrations; however, combining LISS with vegetation can mitigate those negative impacts, (iv) placing LISS closer to the freeway and in front of the vegetation barrier enhances vertical dispersion of pollutants, and (v) increasing LISS height promotes pollutant concentration reduction. These design recommendations can be used by urban planners, developers, and local community leaders to evaluate and implement green infrastructure to mitigate TRAP.

Identifying air pollution source impacts in urban communities using mobile monitoring.

With increasing population, rapid urbanization, and increased migration to cities, the local impacts of increasing transportation and industrial-related air pollution are of growing concern worldwide. Elevated air pollution concentrations near these types of sources have been linked to adverse health effects including acute and chronic respiratory and cardiovascular diseases. Mobile monitoring has proven to be a useful technique to characterize spatial variability of air pollution in urban areas and pollution concentration gradients from specific sources. A study was conducted in the Kansas City, Kansas (USA) metropolitan area using mobile monitoring to characterize the spatial variability and gradients of air pollutants to identify the contribution of multiple sources on community-level air quality in a complex urban environment. Measurements focused on nitrogen dioxide (NO2), black carbon (BC), and ultrafine particulate matter (UFP). Mobile monitoring showed that median concentrations of these pollutants ranged by up to a factor of three between the communities, with individual measurements ranging over an order of magnitude within the community. Evaluating these air quality measurements with wind direction data highlighted the influence of specific and combinations of air pollution sources on these elevated concentrations, which can provide valuable information to environmental and public health officials in prioritizing and implementing cost-effect air quality management strategies to reduce exposures for urban populations.

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization.

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.

Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone.

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application ("app") that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

A new bottom-up emissions estimation approach for aircraft sources in support of air quality modelling for community-scale assessments around airports.

Transportation infrastructure (including roadway traffic, ports, and airports) is critical to the nation's economy. With a growing economy, aircraft activity is expected to grow across the world. In the US, airport-related emissions, while generally small, are not an insignificant source of air pollution and related adverse health effects. However, currently there is a lack of tools that can easily be applied to study near-source pollution and explore the benefits of improvements to air quality and exposures. Screening-level air quality modelling is a useful tool for examining urban-scale air quality impacts of airport operations. Spatially-resolved aircraft emissions are needed for the screening-level modelling. In order to create spatially-resolved aircraft emissions, we developed a bottom-up emissions estimation methodology that includes data from a global chorded inventory dataset from the aviation environmental design tool (AEDT). The initial implementation of this method was performed for Los Angeles International Airport (LAX). This paper describes a new emissions estimation methodology for aircraft emissions in support of community-scale assessments of air quality around airports and presents an illustration of its application at the Los Angeles International Airport during the LAX 2011/2012 Air Quality Source Apportionment Study.

The effects of roadside vegetation characteristics on local, near-road air quality.

Roadside vegetation has been shown to impact downwind, near-road air quality, with some studies identifying reductions in air pollution concentrations and others indicating increases in pollutant levels when vegetation is present. These widely contradictory results have resulted in confusion regarding the capability of vegetative barriers to mitigate near-road air pollution, which numerous studies have associated with significant adverse human health effects. Roadside vegetation studies have investigated the impact of many different types and conditions of vegetation barriers and urban forests, including preserved, existing vegetation stands usually consisting of mixtures of trees and shrubs or plantings of individual trees. A study was conducted along a highway with differing vegetation characteristics to identify if and how the changing characteristics affected downwind air quality. The results indicated that roadside vegetation needed to be of sufficient height, thickness, and coverage to achieve downwind air pollutant reductions. A vegetation stand which was highly porous and contained large gaps within the stand structure had increased downwind pollutant concentrations. These field study results were consistent with other studies that the roadside vegetation could lead to reductions in average, downwind pollutant concentrations by as much as 50% when this vegetation was thick with no gaps or openings. However, the presence of highly porous vegetation with gaps resulted in similar or sometimes higher concentrations than measured in a clearing with no vegetation. The combination of air quality and meteorological measurements indicated that the vegetation affects downwind pollutant concentrations through attenuation of meteorological and vehicle-induced turbulence as air passes through the vegetation, enhanced mixing as portions of the traffic pollution plume are blocked and forced over the vegetation, and through particulate deposition onto leaf and branch surfaces. Computational fluid dynamic modeling highlighted that density of the vegetation barrier affects pollutant levels, with a leaf area density of 3.0 m2 m-3 or higher needed to ensure downwind pollutant reductions for airborne particulate matter. These results show that roadside bushes and trees can be preserved or planted along highways and other localized pollution sources to mitigate air quality and human health impacts near the source if the planting adheres to important characteristics of height, thickness, and density with full coverage from the ground to the top of the canopy. The results also highlight the importance of planting denser vegetation and maintaining the integrity and structure of these vegetation barriers to achieve pollution reductions and not contribute to unintended increases in downwind air pollutant concentrations.

The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS): Integration of Low-Cost Sensors and Reference Grade Monitoring in a Complex Metropolitan Area. Part 1: Overview of the Project.

Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 µm (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 µg/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 µg/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.

A web-based screening tool for near-port air quality assessments.

The Community model for near-PORT applications (C-PORT) is a screening tool with an intended purpose of calculating differences in annual averaged concentration patterns and relative contributions of various source categories over the spatial domain within about 10 km of the port. C-PORT can inform decision-makers and concerned citizens about local air quality due to mobile source emissions related to commercial port activities. It allows users to visualize and evaluate different planning scenarios, helping them identify the best alternatives for making long-term decisions that protect community health and sustainability. The web-based, easy-to-use interface currently includes data from 21 seaports primarily in the Southeastern U.S., and has a map-based interface based on Google Maps. The tool was developed to visualize and assess changes in air quality due to changes in emissions and/or meteorology in order to analyze development scenarios, and is not intended to support or replace any regulatory models or programs.

Near-port air quality assessment utilizing a mobile measurement approach.

Mobile monitoring is a strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA's Geospatial Measurement of Air Pollution (GMAP) vehicle - an all-electric vehicle is outfitted with a number of measurement devices to record real-time concentrations of particulate matter and gaseous pollutants - was used to map air pollution levels near the Port of Charleston in South Carolina. High-resolution monitoring was performed along driving routes near several port terminals and rail yard facilities, recording geospatial coordinates and concentrations of pollutants including black carbon, size-resolved particle count ranging from ultrafine to coarse (6 nm-20 µm), carbon monoxide, and nitrogen dioxide. Additionally, a portable meteorological station was used to characterize local conditions. The primary objective of this work was to characterize the impact of port facilities on local scale air quality. The study determined that elevated concentration measurements of black carbon and PM correlated to periods of increased port activity and a significant elevation in concentration was observed downwind of ports. However, limitations in study design prevented a more complete analysis of the port effect.

Evaluation and development of tools to quantify the impacts of roadside vegetation barriers on near-road air quality.

Traffic emissions are associated with the elevation of health risks of people living close to highways. Roadside vegetation barriers have the potential of reducing these risks by decreasing near-road air pollution concentrations. However, while we understand the mechanisms that determine the mitigation caused by solid barriers, we still have questions about how vegetative barriers affect dispersion. The US EPA conducted several field experiments to understand the effects of vegetation barriers on dispersion of pollutants near roadways (e.g., 2008 North Carolina study and 2014 California study) that indicate the reduction of near-road pollutant concentrations can be up to 30% due to the barrier effects. The results of these field studies are being used to develop and evaluate dispersion models that account for the effects of near-road vegetative barriers. These models can be used for evaluating the effectiveness of vegetation barriers as a potential mitigation strategy to reduce exposure to traffic-related pollutants and their associated adverse health effects. This paper presents the results of the analysis of the field studies and discusses dispersion models being used to describe the data in order to simulate the effects of near-road barriers and to develop recommendations for model improvements.

The effects of vegetation barriers on near-road ultrafine particle number and carbon monoxide concentrations.

Numerous studies have shown that people living in near-roadway communities (within 100 m of the road) are exposed to high ultrafine particle (UFP) number concentrations, which may be associated with adverse health effects. Vegetation barriers have been shown to affect pollutant transport via particle deposition to leaves and altering the dispersion of emission plumes, which in turn would modify the exposure of near-roadway communities to traffic-related UFPs. In this study, both stationary (equipped with a Scanning Mobility Particle Sizer, SMPS) and mobile (equipped with Fast Mobility Particle Sizer, FMPS) measurements were conducted to investigate the effects of vegetation barriers on downwind UFP (particle diameters ranging from 14 to 102 nm) concentrations at two sites in North Carolina, USA. One site had mainly deciduous vegetation while the other was primarily coniferous; both sites have a nearby open field without the vegetation barriers along the same stretch of limited access road, which served as a reference. During downwind conditions (traffic emissions transported towards the vegetation barrier) and when the wind speed was above or equal to 0.5m/s, field measurements indicated that vegetation barriers with full foliage reduced UFP and CO concentrations by 37.7-63.6% and 23.6-56.1%, respectively. When the test was repeated at the same sites during winter periods when deciduous foliage was reduced, the deciduous barrier during winter showed no significant change in UFP concentration before and after the barrier. Results from the stationary (using SMPS) and mobile (using FMPS) measurements for UFP total number concentrations generally agreed to within 20%.

Roadside vegetation barrier designs to mitigate near-road air pollution impacts.

With increasing evidence that exposures to air pollution near large roadways increases risks of a number of adverse human health effects, identifying methods to reduce these exposures has become a public health priority. Roadside vegetation barriers have shown the potential to reduce near-road air pollution concentrations; however, the characteristics of these barriers needed to ensure pollution reductions are not well understood. Designing vegetation barriers to mitigate near-road air pollution requires a mechanistic understanding of how barrier configurations affect the transport of traffic-related air pollutants. We first evaluated the performance of the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model with Large Eddy Simulation (LES) to capture the effects of vegetation barriers on near-road air quality, compared against field data. Next, CTAG with LES was employed to explore the effects of six conceptual roadside vegetation/solid barrier configurations on near-road size-resolved particle concentrations, governed by dispersion and deposition. Two potentially viable design options are revealed: a) a wide vegetation barrier with high Leaf Area Density (LAD), and b) vegetation-solid barrier combinations, i.e., planting trees next to a solid barrier. Both designs reduce downwind particle concentrations significantly. The findings presented in the study will assist urban planning and forestry organizations with evaluating different green infrastructure design options.

Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies.

Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, because people spend more time indoors, using ambient concentration to represent exposure may cause error. To quantify the associated exposure error, we computed a series of six different hourly-based exposure metrics at 16,095 Census blocks of three Counties in North Carolina for CO, NO(x), PM(2.5), and elemental carbon (EC) during 2012. These metrics include ambient background concentration from space-time ordinary kriging (STOK), ambient on-road concentration from the Research LINE source dispersion model (R-LINE), a hybrid concentration combining STOK and R-LINE, and their associated indoor concentrations from an indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the standard, the comparison showed that outdoor STOK metrics yielded large error at both population (67% to 93%) and individual level (average bias between -10% to 95%). For pollutants with significant contribution from on-road emission (EC and NO(x)), the on-road based indoor metric performs the best at the population level (error less than 52%). At the individual level, however, the STOK-based indoor concentration performs the best (average bias below 30%). For PM(2.5), due to the relatively low contribution from on-road emission (7%), STOK-based indoor metric performs the best at both population (error below 40%) and individual level (error below 25%). The results of the study will help future epidemiology studies to select appropriate exposure metric and reduce potential bias in exposure characterization.

A modeling framework for characterizing near-road air pollutant concentration at community scales.

In this study, we combine information from transportation network, traffic emissions, and dispersion model to develop a framework to inform exposure estimates for traffic-related air pollutants (TRAPs) with a high spatial resolution. A Research LINE source dispersion model (R-LINE) is used to model multiple TRAPs from roadways at Census-block level for two U.S. regions. We used a novel Space/Time Ordinary Kriging (STOK) approach that uses data from monitoring networks to provide urban background concentrations. To reduce the computational burden, we developed and applied the METeorologically-weighted Averaging for Risk and Exposure (METARE) approach with R-LINE, where a set of selected meteorological data and annual average daily traffic (AADT) are used to obtain annual averages. Compared with explicit modeling, using METARE reduces CPU-time by 88-fold (46.8h versus 32min), while still retaining accuracy of exposure estimates. We show two examples in the Piedmont region in North Carolina (~105,000 receptors) and Portland, Maine (~7000 receptors) to characterize near-road air quality. Concentrations for NOx, PM2.5, and benzene in Portland drop by over 40% within 200m away from the roadway. The concentration drop in North Carolina is less than that in Portland, as previously shown in an observation-based study, showing the robustness of our approach. Heavy-duty diesel vehicles (HDDV) contribute over 55% of NOx and PM2.5 near interstate highways, while light-duty gasoline vehicles (LDGV) contribute over 50% of benzene to urban areas where multiple roadways intersect. Normalized mean error (NME) between explicit modeling and METARE in Portland ranges from 12.6 to 14.5% and normalized mean bias (NMB) ranges from -12.9 to -11.2%. When considering a static emission rate (i.e. the emission does not have temporal variability), both NME and NMB improved (10.5% and -9.5%). Modeled concentrations in Detroit, Michigan at an array of near-road monitors are within a factor of 2 of observed values for CO but not NOx.