Traffic and meteorological impacts on near-road air quality: summary of methods and trends from the Raleigh Near-Road Study

Analysis of PM2.5, black carbon, and trace metals measurements from the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS)

Communities near transportation sources can be impacted by higher concentrations of particulate matter (PM) and other air pollutants. Few studies have reported on air quality in complex urban environments with multiple transportation sources. To better understand these environments, the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS) was conducted in three neighborhoods in Southeast Kansas City, Kansas. This area has several emissions sources including transportation (railyards, vehicles, diesel trucks), light industry, commercial facilities, and residential areas. Stationary samples were collected for 1-year (October 24, 2017, to October 31, 2018) at six sites using traditional sampling methods and lower-cost air sensor packages. This work examines PM less than 2.5 μm in diameter (PM2.5), black carbon (BC), and trace metals data collected during KC-TRAQS. PM2.5 filter samples showed the highest 24-h mean concentrations (9.34 μg/m3) at the sites located within 20-50 m of the railyard. Mean 24-h PM2.5 concentrations, ranging from 7.96 to 9.34 μg/m3, at all sites were lower than that of the nearby regulatory site (9.83 μg/m3). Daily maximum PM2.5 concentrations were higher at the KC-TRAQS sites (ranging from 25.31 to 43.76 μg/m3) compared to the regulatory site (20.50 μg/m3), suggesting short-duration impacts of localized emissions sources. Across the KC-TRAQS sites, 24-h averaged PM2.5 concentrations from the sensor package (P-POD) ranged from 3.24 to 5.69 µg/m3 showing that, out-of-the-box, the PM sensor underestimated the reference concentrations. KC-TRAQS was supplemented by elemental and organic carbon (EC/OC) and trace metal analysis of filter samples. The EC/OC data suggested the presence of secondary organic aerosol formation, with the highest mean concentrations observed at the site within 20 m of the railyard. Trace metals data showed daily, monthly, and seasonal variations for iron, copper, zinc, chromium, and nickel, with elevated concentrations occurring during the summer at most of the sites.Implications: This work reports on findings from a year-long air quality study in Southeast Kansas City, Kansas to understand micro-scale air quality in neighborhoods impacted by multiple emissions sources such as transportation sources (including a large railyard operation), light industry, commercial facilities, and residential areas. While dozens of studies have reported on air quality near roadways, this work will provide more information on PM2.5, black carbon, and trace metals concentrations near other transportation sources in particular railyards. This work can also inform additional field studies near railyards.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework

BACKGROUND

Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.

OBJECTIVES

To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.

METHODS

We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations.

RESULTS

The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.

DISCUSSION

The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Examination of the Performance of a Three-Phase Atmospheric Turbulence Model for Line-Source Dispersion Modeling Using Multiple Air Quality Datasets

One of the weaknesses of current line-source models for predicting downwind concentrations from mobile sources is accounting for the dispersion of effluents. Most of the investigators in the field have taken different approaches over the last 50 years, ranging from the use of Pasquill–Gifford (P-G) dispersion curves to the use of equations based on atmospheric turbulence for point source dispersion. Madiraju and Kumar (2021) proposed a three-phase turbulence (TPT) model using the key features of mobile source dispersion that appear in the existing literature. This paper examines the performance of line-source models using an updated TPT model. The generic dispersion equations were considered from the SLINE 1.1, CALINE 4, ADMS, and SLSM models. Multiple air quality field data sets collected by other investigators near the roadways were used during this study. These include field data collected from the Idaho Falls Tracer Experiment 2008 (used as the dataset to compare with the initial model), the CALTRANS Highway 99 Tracer experiment, and the Raleigh 2006 experiment. The predicted concentrations were grouped under unstable and stable atmospheric conditions. The evaluation of the model was performed using several statistical parameters such as FB, NMSE, R2, MG, VG, MSLE, and MAPE. The results indicate that the ADMS and SLINE 1.1 models perform better than CALINE4 and SLSM. SLINE 1.1 tends to overpredict for stable atmospheric conditions and underpredict for unstable atmospheric conditions. A trial test was performed to implement the TPT model in the basic line-source model (SLSM). The results indicate that the majority (FB, NMSE, R2, and MSLE) of the indicators have improved and are in the satisfactory range of a good model performance level.

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.

Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches

Excessive traffic pollutant emissions in high-density cities result in thermal discomfort and are associated with devastating health impacts. In this study, an improved data analytic framework that combines geo-processing techniques, social habits of local citizens like traffic patterns and working schedule and district-wise building morphologies was established to retrieve street-level traffic NOx and PM2.5 emissions in all 18 districts of Hong Kong. The identification of possible human activity regions further visualizes the intersection between emission sources and human mobility. The updated spatial distribution of traffic emission could serve as good indicators for better air quality management, as well as the planning of social infrastructures in the neighborhood environment. Further, geo-processed traffic emission figures can systematically be distributed to respective districts via mathematical means, while the correlations of NOx and mortality within different case studies range from 0.371 to 0.783, while varying from 0.509 to 0.754 for PM2.5, with some assumptions imposed in our study. Outlying districts and good practices of maintaining an environmentally friendly transportation network were also identified and analyzed via statistical means. This newly developed data-driven framework of allocating and quantifying traffic emission could possibly be extended to other dense and heavily polluted cities, with the aim of enhancing health monitoring campaigns and relevant policy implementations.

Assessment of mobile source air toxics in an Environmental Justice Denver community adjacent to a freeway

Air pollutant concentrations are often higher near major roadways than in the surrounding environments owing to emissions from on-road mobile sources. In this study, we quantified the gradient in black carbon (BC) and air toxics concentrations from the I-70 freeway in the Elyria-Swansea environmental justice neighborhood in Denver, Colorado, during three measurement campaigns in 2017-2018. The average hourly upwind-downwind gradient of BC concentrations from the roadway was 500-800 ng/m³, equal to an increment of approximately 30-80% above local background levels within 180 m of the freeway. When integrated over all wind directions, the gradients were smaller, approximately 150-300 ng/m³ (~11-18%) over the course of nearly four months of measurements. No statistically significant gradient in air toxics (e.g., benzene, formaldehyde, etc.) was found, likely because the uncertainties in the mean concentrations were larger than the magnitude of the gradient (<25%). This finding is in contrast to some earlier studies in which small gradients of benzene and other VOCs were found. We estimate that sample sizes of at least 100 individual measurements would have been required to estimate mean concentrations with sufficient certainty to quantify gradients on the order of ±10% uncertainty. These gradient estimates are smaller than those found in previous studies over the past two decades; more stringent emissions standards, the local fleet age distribution, and/or the steady turnover of the vehicle fleet may be reducing the overall impact of roadway emissions on near-road communities. Implications: Gradients of near-road pollution may be declining in the near-road environment as tailpipe emissions from the vehicle fleet continue to decrease. Near-road concentration gradients of mobile source air toxics, including benzene, 1,3-butadiene, and ethylbenzene, will require higher sample sizes to quantify as emissions continue to decline.

High-Efficiency Air Cleaning Reduces Indoor Traffic-Related Air Pollution and Alters Indoor Air Chemistry in a Near-Roadway School

Schools in proximity to roadways expose students to traffic-related air pollution (TRAP). We investigate impacts of air-cleaning on indoor TRAP levels and indoor chemistry in a renovated school adjacent an interstate highway. We monitor air pollutants pre- and post-renovation and quantify efficiency of particle (MERV8 and 16 filters) and gas (functionalized activated carbon) air-cleaning. Time-resolved measurements show air-cleaning systems are effective, with in situ particle removal efficiency >94% across 10 nm to 10 μm. Activated carbon removed BTEX and NO₂ with variability in removal efficiency. Over eight months of monitoring, NO₂ removal efficiency was 96% initially and decreased to 61%; and BTEX removal efficiency was >80% or increased to >80%. Air-cleaning reduced indoor TRAP to below or near urban background. Air-cleaning systems suppressed indoor chemistry by reducing indoor levels of oxidants (NO₂, O₃) and reactive organics of indoor origin. When the air cleaning system was inactive, our data show that indoor SOA formation within the school was elevated. Loss rates of NO₂ and O₃ through the air-cleaning system were ∼1.5-2.4 h^-1 and ∼2.3 h^-1, respectively. Air-cleaning was 83% and 69% efficient, respectively, in removing monoterpenes and isoprene. By suppressing precursors, scaling calculations show air-cleaning prevented ∼3.4 mg/h of indoor SOA formation due to indoor ozone-monoterpene chemistry. For comparison, we estimate that filtration removed ∼130 mg/h of PM_0.01-0.3.

Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles

Spatial variability in real-world on-road tailpipe light-duty gasoline vehicle nitrogen oxides, hydrocarbon, carbon monoxide, and carbon dioxide emission rates, the locations of emissions hotspots, and factors that explain spatial variability are quantified. A sample of 205 vehicles were measured on four predefined round-trip study routes using Portable Emission Measurement Systems. The trips on each route were divided into segments, averaging 1/4 mile in length. Segment-average emission rates were estimated based on measured 1 Hz emission rates. Emission hotspots are defined as segments with ≥90th percentile of segment-average emission rates. The hotspots have average emission rates 2-4 times greater, depending on the pollutant, than other segments. Hotspots are of heterogeneous characteristics including road attributes and vehicle activity metrics. For example, some hotspots were on arterial roads with an upstream signalized intersection and positive road grade, whereas some hotspots were on interstates with positive grade. Vehicle activity metrics, including average vehicle specific power and relative positive acceleration, help identify the hotspots. To reliably identify a fleet-average hotspot, data are needed for at least 36-130 vehicles, depending on the pollutant.

Combining Chemometrics and Sensors: Toward New Applications in Monitoring and Environmental Analysis

For many years, an extensive array of chemometric methods have provided a platform upon which a quantitative description of environmental conditions can be obtained. Applying chemometric methods to environmental data allows us to identify and describe the interrelations between certain environmental drivers. They also provide an insight into the interrelationships between these drivers and afford us a greater understanding of the potential impact that these drivers can place upon the environment. However, an effective marriage of these two systems has not been performed. Therefore, it is the aim of this review to highlight the advantages of using chemometrics and sensors to identify hidden trends in environmental parameters, which allow the state of the environment to be effectively monitored. Despite the combination of chemometrics and sensors, to capture new developments and applications in the field of environmental sciences, these methods have not been extensively used. Importantly, although different parameters and monitoring procedures are required for different environments (e.g., air, water, soil), they are not distinct, separate entities. Contemporary developments in the use of chemometrics afford us the ability to predict changes in different aspects of the environment using instrumental methods. This review also provides an insight into the prevailing trends and the future of environmental sensing, highlighting that chemometrics can be used to enhance our ability to monitor the environment. This enhanced ability to monitor environmental conditions and to predict trends would be beneficial to government and research agencies in their ability to develop environmental policies and analysis procedures.

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.

Near-Road Traffic-Related Air Pollution: Resuspended PM2.5 from Highways and Arterials

Recent studies suggest that the transportation sector is a major contributor to fine particulate matter (PM_2.5) in urban areas. A growing body of literature indicates PM_2.5 exposure can lead to adverse health effects, and that PM_2.5 concentrations are often elevated close to roadways. The transportation sector produces PM_2.5 emissions from combustion, brake wear, tire wear, and resuspended dust. Traffic-related resuspended dust is particulate matter, previously deposited on the surface of roadways that becomes resuspended into the air by the movement of traffic. The objective of this study was to use regulatory guidelines to model the contribution of resuspended dust to near-road traffic-related PM_2.5 concentrations. The U.S. Environmental Protection Agency (EPA) guidelines for quantitative hotspot analysis were used to predict traffic-related PM_2.5 concentrations for a small network in Dallas, Texas. Results show that the inclusion of resuspended dust in the emission and dispersion modeling chain increases prediction of near-road PM_2.5 concentrations by up to 74%. The results also suggest elevated PM_2.5 concentrations near arterial roads. Our results are discussed in the context of human exposure to traffic-related air pollution.

Near-road Vehicle Emissions Air Quality Monitoring for Exposure Modeling

Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO₂), are analyzed at two roadside monitors in Atlanta, GA located within 325m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO₂ concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

Vertical and Horizontal Profiles of Particulate Matter and Black Carbon Near Elevated Highways Based on Unmanned Aerial Vehicle Monitoring

Highways passing through cities cause additional pollution inside the city. However, most of the current studies are using ground-based monitoring technologies, which make it difficult to capture the dispersion patterns of pollutants near elevated highways or transportation interchanges. The purpose of this study is to discover short-term three-dimensional variations in traffic-related pollutants based on unmanned aerial vehicles. The monitoring locations are at suburban elevated highway and transportation interchanges. The monitoring parameters include the particle number concentration (PN), particle mass concentration (PM), and black carbon (BC). The vertical profiles showed that most air pollutants increased significantly with the height of the elevated highways. Compared with the ground level, PNs increased by 54%–248% and BC increased by 201%. The decline rate of particle concentrations decreased with the increase of height and remained stable after 120 m. Furthermore, the R2 heatmap for regressions between each altitude showed that the linear relationship between 0–120 m was higher than that of other altitudes. In horizontal profiles, PNs spread to 100 m and then began to decline, BC began to decay rapidly after 50 m, but PMs varied less. After crossing another highway, PNs increased by 69–289%, PMs by 7–28%, and BC by 101%. Furthermore, the formation of new particles was observed at both locations as PN3 increased with distance within 100 m from the highway. This paper fills in the void of three-dimensional in situ monitoring near elevated highways, and can help develop and refine a three-dimensional traffic-related air pollution dispersion model and assess the impacts of transportation facilities on the urban environment.

Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness

Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO₂), ozone (O₃), methane (CH₄) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from <10 % for gases to <25 % for BC and PN at 1-s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that U.S. EPA classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by <1 to 10 kilometers (km). A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1-km scale differences in mean NO₂ and O₃ concentrations up to 117 % and 46 %, respectively, of mean values during a two-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100 - 300m, corresponding to 1-minute averages.

A meta-analysis of selected near-road air pollutants based on concentration decay rates

Traffic-related air pollution has been associated with various health risks for human populations living near roadways. Understanding the relationship between traffic density and dispersion of vehicle-released air pollutants is important for assessing human exposure to near-road air pollutants. We performed a literature survey targeting publications containing measurement data of traffic-related air pollutants near roads with distance information on their concentration distribution. Concentration decay rates over down-wind distance away from major roads were calculated for black carbon (BC), carbon monoxide (CO) and nitrogen oxides (NO₂ or NO_x) and meta-data analysis on these rates was performed. These analyses showed metadata-based exponential decay rates of 0.0026, 0.0019, 0.0004, and 0.0027 m⁻¹ for BC, CO, NO₂ and NOx, respectively. Using these measurement data-based decay rates, concentrations for BC, CO, NO₂ and NO_x over various near-road distances were predicted. These results are useful for enhancing exposure modeling and thus more reliably assessing the health risk of exposure to near road air pollution.

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.

Conditions Leading to Elevated PM2.5 at Near-Road Monitoring Sites: Case Studies in Denver and Indianapolis

We examined two near-road monitoring sites where the daily PM_2.5 readings were among the highest of any near-road monitoring location in the U.S. during 2014–2016: Denver, Colorado, in February 2014 and Indianapolis, Indiana, in November 2016. At the Denver site, which had the highest measured U.S. near-road 24-hr PM_2.5 concentrations in 2014, concentrations exceeded the daily National Ambient Air Quality Standards (NAAQS) on three days during one week in 2014; the Indianapolis site had the second-highest number of daily exceedances of any near-road site in 2016 and the highest 3-year average PM_2.5 of any near-road site during 2014–2016. Both sites had hourly pollutant, meteorological, and traffic data available, making them ideal for case studies. For both locations, we compared air pollution observations at the near-road site to observations at other sites in the urban area to calculate the near-road PM_2.5 “increment” and evaluated the effects of changes in meteorology and traffic. The Denver near-road site consistently had the highest PM_2.5 values in the Denver area, and was typically highest when winds were near-downwind, rather than directly downwind, to the freeway. Complex Denver site conditions (near-road buildings and roadway alignment) likely contributed to higher PM_2.5 concentrations. The increment at Indianapolis was also highest under near-downwind, rather than directly downwind, conditions. At both sites, while the near-road site often had higher PM_2.5 concentrations than nearby sites, there was no clear correlation between traffic conditions (vehicle speed, fleet mix) and the high PM_2.5 concentrations.

Vehicle Ammonia Emissions Measured in An Urban Environment in Sydney, Australia, Using Open Path Fourier Transform Infra-Red Spectroscopy

Airborne particulate matter (PM) is a major health risk in urban settings. Ammonia (NH3) from vehicle exhaust is an under-recognised ingredient in the formation of inorganic PM and there remains a shortage of data to properly quantify the role of NH3 from vehicles in PM formation. An Open-path Fourier transform infra-red (OP-FTIR) spectrometer measured atmospheric NH3, carbon monoxide (CO) and carbon dioxide (CO2) at high temporal resolution (5 min) in Western Sydney over 11 months. The oxides of nitrogen (NO2 and NO; NOx) and sulphur dioxide (SO2) were measured at an adjacent air quality monitoring station. NH3 levels were maxima in the morning and evening coincident with peak traffic. During peak traffic NH3:CO ratio ranged from 0.018 to 0.022 ppbv:ppbv. Results were compared with the Greater Metropolitan Region 2008 (GMR2008) emissions inventory. Measured NH3:CO was higher during peak traffic times than the GMR2008 emissions estimates, indicating an underestimation of vehicle NH3 emissions in the inventory. Measurements also indicated the urban atmosphere was NH3 rich for the formation of ammonium sulphate ((NH4)2SO4) particulate was SO2 limited while the formation of ammonium nitrate (NH4NO3) was NH3 limited. Any reduction in NOx emissions with improved catalytic converter efficiency will be accompanied by an increase in NH3 production and potentially with an increase in NH4NO3 particulate.

Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations

To quantify the fine-scale spatial variations and local source impacts of urban ultrafine particle (UFP) concentrations, we conducted 3-6 weeks of continuous measurements of particle number (a proxy for UFP) and other air pollutant (CO, NO₂, and PM_2.5) concentrations at 32 sites in Pittsburgh, Pennsylvania during the winters of 2017 and 2018. Sites were selected to span a range of urban land use attributes, including urban background, near local and arterial roads, traffic intersections, urban street canyon, near-highway, near large industrial source, and restaurant density. The spatial variations in urban particle number concentrations varied by about a factor of three. Particle number concentrations are 2-3 times more spatially heterogeneous than PM_2.5 mass. The observed order of spatial heterogeneity is UFP > NO₂ > CO > PM_2.5. On average, particle number concentrations near local roads with a cluster of restaurants and near arterial roads are roughly two times higher than the urban background. Particle number concentrations in the urban street canyon, downwind of a major highway, and near large industrial sources are 2-4 times higher than background concentrations. While traffic is known as an important contributor to particle number concentrations, restaurants and industrial emissions also contribute significantly to spatial variations in Pittsburgh. Particle size distribution measurements using a mobile laboratory show that the local spatial variations in particle number concentrations are dictated by concentrations of particles smaller than 50 nm. A large fraction of urban residents (e.g., ~50%) in Pittsburgh live near local sources and are therefore exposed to 50%-300% higher particle number concentrations than urban background location. These locally emitted particles may have greater health effects than background particles.

Magnetite fine particle and nanoparticle environmental contamination from industrial uses of coal

Recently it has been shown that there are two types of magnetite particles in the human brain, some, which occur naturally and are jagged in appearance, and others that arise from industrial sources, such as coal fired power plants, and are spherical. In order to confirm the latter, the magnetic component of coal ash is first purified and characterized by XRD, showing that it is magnetite with an average particle size of 211 nm. Studies confirm the coal ash magnetic behavior, and that the magnetite is not bound to the other components of coal ash but exist as an isolatable component. SEM studies confirm that in the process of burning coal at very high temperatures for industrial uses, the magnetite formed is spherically shaped, as recent studies of brain tissues of highly exposed urban residents have found. As such, the use of coal for industrial applications such as coking in the production of steel and in power plants is indicated to be a major source of the spherical magnetic combustion-associated magnetite fine particle and nanoparticle environmental pollution. The capacity of these magnetic particles to penetrate and damage the blood-brain-barrier and the early development of Alzheimer's disease hallmarks in exposed populations calls for detail analysis of magnetic fine and nanoparticle distribution across the world. Summation: Industrial coal usage produces spherical magnetic particles and nanoparticles, identical to those associated with neurological disorders.

Preparation Technique and Properties of Nano-TiO2 Photocatalytic Coatings for Asphalt Pavement

According to the characteristics of asphalt pavement, a kind of nano-TiO2 photocatalytic coating was prepared by using the emulsified asphalt as the carrier. All of its properties met the technical requirements. An exhaust gas degradation test device and its test steps were developed. The evaluation indexes, cumulative degradation rate, and degradation efficiency, were put forward. From the two aspects of the nano-TiO2 content in photocatalytic coatings and the spraying amount of photocatalytic coatings in the surface of slabs (300 mm × 300 mm), the exhaust gas degradation effects, the performances of skid resistance, and the water permeability of asphalt mixture were analyzed. The test results showed that the cumulative degradation rate of exhaust gas was better when nano-TiO2 content was increased in the range of 0–8% and the spraying amount was changed in the range of 0–333.3 g/m2. In practical engineering applications, the anti-skid performance of asphalt pavement can be satisfied when the spraying amount of photocatalytic coating was limited to under 550 g/m2. The spraying amount of nano-TiO2 photocatalytic coating had little effect on the water permeability of the asphalt mixture. Therefore, 8% nano-TiO2 content in the coating and a 400 g/m2 spraying amount were finally recommended based on the photocatalytic properties, as well as for economic reasons.

Сontamination of urban soils with heavy metals in Moscow as affected by building development

Building development in cities creates a geochemical heterogeneity via redistributing the atmospheric fluxes of pollutants and forming sedimentation zones in urban soils and other depositing media. However, the influence of buildings on the urban environment pollution is poorly understood. The aim of this study is to evaluate the barrier functions of urban development by means of a joint analysis of the contents of heavy metals and metalloids in the upper horizon of urban soils, their physicochemical properties, and the parameters of the buildings. The soil-geochemical survey was performed in the residential area of the Moscow's Eastern Administrative District (Russia). The parameters of the buildings near sampling points were determined via processing data from the OpenStreetMap database, 2GIS databases and GeoEye-1 satellite image. A high level of soil contamination with Cd, W, Bi, Zn, As, Cr, Sb, Pb, Cu was revealed, depending on building parameters. A protective function of the buildings for yards is manifested in the decreasing concentrations of As, Cd, Co, Cr, Mo, Ni, Pb, Sb, Sn, W by 1.2-3 times at distances of <23-36 m from the buildings with their total area ≥660 m² and the height ≥7.5-21 m. An opposite effect which enhances concentrations of Bi, Cd, Co, Cr, Cu, Mo, Pb, Sb, Sn, W, Zn by 1.2-1.9 times is seen in "well-shaped" yards acting as traps under similar distances and heights, but at their average area ≥118-323 m², and total area ≥323-1300 m². The impact of these two building patterns on the soil contamination is only seen for certain directions of atmospheric flows. Buildings located in the northwestern sector relative to the sampling point protect the latter from the aerial pollution.

Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption

Traffic source emission inventories for the rapidly growing West African urban cities are necessary for better characterization of local vehicle emissions released into the atmosphere of these cities. This study is based on local field measurements in Yopougon (Abidjan, Côte d’Ivoire) in 2016; a site representative of anthropogenic activities in West African cities. The measurements provided data on vehicle type and age, traveling time, fuel type, and estimated amount of fuel consumption. The data revealed high traffic flow of personal cars on highways, boulevards, and backstreets, whereas high flows of intra-communal sedan taxis were observed on main and secondary roads. In addition, the highest daily fuel consumption value of 56 L·day−1 was recorded for heavy vehicles, while the lowest value of 15 L·day−1 was recorded for personal cars using gasoline. This study is important for the improvement of uncertainties related to the different databases used to estimate emissions either in national or international reports. This work provides useful information for future studies on urban air quality, climate, and health impact assessments in African cities. It may also be useful for policy makers to support implementation of emission reduction policies in West African cities.

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015-2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO _x, and particle number EFs (R²:0.50-0.75), with lower model performance for CO and black carbon (BC) EFs (R²:0.28-0.46). NO _x and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants.

Advice and Frequently Asked Questions (FAQs) for Citizen-Science Environmental Health Assessments

Citizen science provides quantitative results to support environmental health assessments (EHAs), but standardized approaches do not currently exist to translate findings into actionable solutions. The emergence of low-cost portable sensor technologies and proliferation of publicly available datasets provides unparalleled access to supporting evidence; yet data collection, analysis, interpretation, visualization, and communication are subjective approaches that must be tailored to a decision-making audience capable of improving environmental health. A decade of collaborative efforts and two citizen science projects contributed to three lessons learned and a set of frequently asked questions (FAQs) that address the complexities of environmental health and interpersonal relations often encountered in citizen science EHAs. Each project followed a structured step-by-step process in order to compare and contrast methods and approaches. These lessons and FAQs provide advice to translate citizen science research into actionable solutions in the context of a diverse range of environmental health issues and local stakeholders.

Why local air pollution is more than daily peaks: modelling policies in a city in order to avoid premature deaths

We use a predator-prey representation of an urban system to analyse how policy interventions can prevent the adverse effects of air pollution on people's health. The number of residents is treated as prey variable, and particulate matter that consists of particles with a diameter of up to 10 micrometres (PM10) as predator variable. This representation allows integration of population trends and the effects of environmental interventions on the average level of PM10 concentration (which establishes a baseline for the potential health burden for residents). For the case of Graz, Austria, we illustrate the insights generated regarding the interdependency of market-based and technological pollution controls, and propose an indicator that assesses the cost of delayed interventions by counting additional premature deaths caused by polluted environments.

Influential factors affecting black carbon trends at four sites of differing distance from a major highway in Las Vegas

Elevated air pollution levels adjacent to major highways are an ongoing topic of public health concern worldwide. Black carbon (BC), a component of particulate matter (PM) emitted by diesel and gasoline vehicles, was measured continuously via a filter-based light absorption technique over ~ 16 months at four different stations positioned on a perpendicular trajectory to a major highway in Las Vegas, NV. During downwind conditions (winds from the west), BC at 20 m from the highway was 32 and 60% higher than concentrations at 100 and 300 m from the roadway, respectively. Overall highest roadside (20-m site) BC concentrations were observed during the time period of 4 a.m.-8 a.m. under low-speed variable winds (3.02 μg/m³) or downwind conditions (2.84 μg/m³). The 20-m site BC concentrations under downwind conditions are 85% higher on weekday periods compared to weekends during the time period of 4 a.m.-8 a.m. Whereas total traffic volume was higher on weekdays versus weekends and differed by approximately 3% on weekdays versus weekends, similarly, the detected heavy-duty fraction was higher on weekdays versus weekends and differed by approximately 21% on weekdays versus weekend. Low wind speeds predominated during early morning hours, leading to higher BC concentrations during early morning hours despite the maximum traffic volume occurring later in the day. No noticeable impact from the airport or nearby arterial roadways was observed, with the 300-m site remaining the lowest of the four-site network when winds were from the east. Multivariate linear regression analysis revealed that heavy-duty traffic volume, light-duty traffic volume, wind speed, weekday versus weekend, surface friction velocity, ambient temperature, and the background BC concentration were significant predictors of roadside BC concentrations. Comparison of BC and PM_2.5 downwind concentration gradients indicates that the BC component contributes substantially to the PM_2.5 increase in roadside environments. These results suggest that BC is an important indicator to assess the contribution of primary traffic emissions to near-road PM_2.5 concentrations, providing opportunities to evaluate the feasibility and effectiveness of mitigation strategies.

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

To quantify the on-road PM_2.5 -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 PM_2.5 -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 PM_2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM_2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM_2.5 where the hybrid approach estimated 2.5 times more primary on-road PM_2.5 -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 PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic-related emissions.

Factors associated with NO2 and NOX concentration gradients near a highway

The objective of this research is to learn how the near-road gradient, in which NO2 and NOX (NO + NO2) concentrations are elevated, varies with changes in meteorological and traffic variables. Measurements of NO2 and NOX were obtained east of I-15 in Las Vegas and fit to functions whose slopes (dCNO2 /dx and dCNOX /dx, respectively) characterize the size of the near-road zone where NO2 and NOX concentrations from mobile sources on the highway are elevated. These metrics were used to learn about the near-road gradient by modeling dCNO2 /dx and dCNOX /dx as functions of meteorological variables (e.g., wind direction, wind speed), traffic (vehicle count), NOX concentration upwind of the road, and O3 concentration at two fixed-site ambient monitors. Generalized additive models (GAM) were used to model dCNO2 /dx and dCNOX /dx versus the independent variables because they allowed for nonlinearity of the variables being compared. When data from all wind directions were included in the analysis, variability in O3 concentration comprised the largest proportion of variability in dCNO2 /dx, followed by variability in wind direction. In a second analysis constrained to winds from the west, variability in O3 concentration remained the largest contributor to variability in dCNO2 /dx, but the relative contribution of variability in wind speed to variability in dCNO2 /dx increased relative to its contribution for the all-wind analysis. When data from all wind directions were analyzed, variability in wind direction was by far the largest contributor to variability in dCNOX /dx, with smaller contributions from hour of day and upwind NOX concentration. When only winds from the west were analyzed, variability in upwind NOX concentration, wind speed, hour of day, and traffic count all were associated with variability in dCNOX /dx. Increases in O3 concentration were associated with increased magnitude near-road dCNO2 /dx, possibly shrinking the zone of elevated concentrations occurring near roads. Wind direction parallel to the highway was also related to an increased magnitude of both dCNO2 /dx and dCNOX /dx, again likely shrinking the zone of elevated concentrations occurring near roads. Wind direction perpendicular to the road decreased the magnitude of dCNO2 /dx and dCNOX /dx and likely contributed to growth of the zone of elevated concentrations occurring near roads. Thus, variability in near-road concentrations is influenced by local meteorology and ambient O3 concentration.

Individual exposure to nitrogen dioxide and adverse pregnancy outcomes in Kaunas study

Several epidemiological studies have found some evidence suggesting that exposure to air pollutants during pregnancy increases the risk of adverse birth outcomes. In this cohort study, we assessed individual maternal exposure to nitrogen dioxide (NO₂) during pregnancy and examined the association between the exposure and pregnancy outcomes, such as low birth weight (LBW), term low birth weight (TLBW), small for gestational age (SGA) and preterm birth (PB). 3292 women living in Kaunas city, Lithuania, data and their singleton newborns were included in the study. Exposure to NO₂ was assigned to each individual subject during pregnancy based on residential address using an AIRVIRO dispersion model and geographic information system (GIS). The results of the logistic regression analysis showed that LBW risk increased statistically significantly with increasing exposure to NO₂. Increased maternal exposure to NO₂ tended to increase the risk for TLBW, SGA and PB.

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%.

Immunotoxicological impact of occupational and environmental nanoparticles exposure: The influence of physical, chemical, and combined characteristics of the particles

While nanotechnology is growing exponentially, the knowledge of the impact of nanoparticles (NPs) on public health and the environment is limited so far. Current nanomaterial research is focused on the applications of nanotechnology, whereas there is little information on exposure assessment and risk characterization associated with NPs. Therefore, it is essential that the factors influencing NPs associated hazards be studied. This review seeks to survey and evaluate the current literature in order to better understand the impact of both airborne and engineered NPs exposure, the mechanisms at the cellular level, and the factors influencing their immunotoxicity. In fact, NPs do have immunotoxicological significance, as immune cells in the bloodstream and tissues do act to eliminate or interact with NPs.Proper characterization of the NPs as well as understanding the processes occurring on the NPs surface when in contact with biological systems is crucial to predict or exclude toxicological effects.

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.

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21, 33, 24 and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than noncommercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity.

Childhood cancer and residential exposure to highways: a nationwide cohort study

Children living near highways are exposed to higher concentrations of traffic-related carcinogenic pollutants. Several studies reported an increased risk of childhood cancer associated with traffic exposure, but the published evidence is inconclusive. We investigated whether cancer risk is associated with proximity of residence to highways in a nation-wide cohort study including all children aged <16 years from Swiss national censuses in 1990 and 2000. Cancer incidence was investigated in time to event analyses (1990-2008) using Cox proportional hazards models and incidence density analyses (1985-2008) using Poisson regression. Adjustments were made for socio-economic factors, ionising background radiation and electromagnetic fields. In time to event analysis based on 532 cases the adjusted hazard ratio for leukaemia comparing children living <100 m from a highway with unexposed children (≥500 m) was 1.43 (95 % CI 0.79, 2.61). Results were similar in incidence density analysis including 1367 leukaemia cases (incidence rate ratio (IRR) 1.57; 95 % CI 1.09, 2.25). Associations were similar for acute lymphoblastic leukaemia (IRR 1.64; 95 % CI 1.10, 2.43) and stronger for leukaemia in children aged <5 years (IRR 1.92; 95 % CI 1.22, 3.04). Little evidence of association was found for other tumours. Our study suggests that young children living close to highways are at increased risk of developing leukaemia.

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

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

Monitoring study of the near-road PM2.5 concentrations in Maryland

The Maryland State Highway Administration (SHA) monitoring program monitored the impact of vehicular emissions on the concentrations of the fine particles smaller than 2.5 microns (PM2.5). PM2.5 concentrations were monitored in close proximity to a highway in order to determine whether traffic conditions on the roadway impact concentrations at this location. The monitoring program attempted to connect monitored concentrations with the roadway traffic exhaust or with the other sources of PM2.5. PM2.5 concentrations were collected near the Capital Beltway (I-495/I-95) in Largo, Maryland. The monitoring program was launched on May 13, 2009 and continued through the end of 2012. Two co-located monitors, one for continuous PM2.5 measurements and the other for speciation measurements, were used in this program. Meteorological and traffic information was also continuously collected at or near the monitoring site. Additionally, data from the two other monitoring locations, one at the Howard University-Beltsville, MD and one at McMillan Reservoir, DC, was used for comparison with the data collected at the SHA monitoring location. The samples collected by the speciation monitor were analyzed at the RTI and DRI Laboratories to determine the composition and the sources of the collected PM2.5 samples. Based on the apportionment analysis, the contribution of roadway sources is about 12 to 17 percent of PM2.5 at the near-road site.

IMPLICATIONS

PM2.5 monitoring at 150 m (approximately 500 feet) from a major highway in Maryland near Washington, DC, demonstrated that roadway traffic contributes to the total PM2.5 concentration near the roadway, but the contribution at such distance is small, in the order of 12-17% of the total.

Field assessment of the Village Green Project: an autonomous community air quality monitoring system

Continuous, long-term, and time-resolved measurement of outdoor air pollution has been limited by logistical hurdles and resource constraints. Measuring air pollution in more places is desired to address community concerns regarding local air quality impacts related to proximate sources, to provide data in areas lacking regional air monitoring altogether, or to support environmental awareness and education. This study integrated commercially available technologies to create the Village Green Project (VGP), a durable, solar-powered air monitoring park bench that measures real-time ozone, PM2.5, and meteorological parameters. The data are wirelessly transmitted via cellular modem to a server, where automated quality checks take place before data are provided to the public nearly instantaneously. Over 5500 h of data were successfully collected during the first ten months of pilot testing in Durham, North Carolina, with about 13 days (5.5%) of downtime because of low battery power. Additional data loss (4-14% depending on the measurement) was caused by infrequent wireless communication interruptions and instrument maintenance. The 94.5% operational time via solar power was within 1.5% of engineering calculations using historical solar data for the location. The performance of the VGP was evaluated by comparing the data to nearby air monitoring stations operating federal equivalent methods (FEM), which exhibited good agreement with the nearest benchmark FEMs for hourly ozone (r(2) = 0.79) and PM2.5 (r(2) = 0.76).

Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses

Traffic activity encompasses the number, mix, speed and acceleration of vehicles on roadways. The temporal pattern and variation of traffic activity reflects vehicle use, congestion and safety issues, and it represents a major influence on emissions and concentrations of traffic-related air pollutants. Accurate characterization of vehicle flows is critical in analyzing and modeling urban and local-scale pollutants, especially in near-road environments and traffic corridors. This study describes methods to improve the characterization of temporal variation of traffic activity. Annual, monthly, daily and hourly temporal allocation factors (TAFs), which describe the expected temporal variation in traffic activity, were developed using four years of hourly traffic activity data recorded at 14 continuous counting stations across the Detroit, Michigan, U.S. region. Five sites also provided vehicle classification. TAF-based models provide a simple means to apportion annual average estimates of traffic volume to hourly estimates. The analysis shows the need to separate TAFs for total and commercial vehicles, and weekdays, Saturdays, Sundays and observed holidays. Using either site-specific or urban-wide TAFs, nearly all of the variation in historical traffic activity at the street scale could be explained; unexplained variation was attributed to adverse weather, traffic accidents and construction. The methods and results presented in this paper can improve air quality dispersion modeling of mobile sources, and can be used to evaluate and model temporal variation in ambient air quality monitoring data and exposure estimates.

Differential effects of particulate matter upwind and downwind of an urban freeway in an allergic mouse model

Near-road exposure to air pollutants has been associated with decreased lung function and other adverse health effects in susceptible populations. This study was designed to investigate whether different types of near-road particulate matter (PM) contribute to exacerbation of allergic asthma. Samples of upwind and downwind coarse, fine, and ultrafine PM were collected using a wind direction-actuated ChemVol sampler at a single site 100 m from Interstate-96 in Detroit, MI during winter 2010/2011. Upwind PM was enriched in crustal and wood combustion sources while downwind PM was dominated by traffic sources. Control and ovalbumin (OVA)-sensitized BALB/cJ mice were exposed via oropharyngeal (OP) aspiration to 20 or 100 μg of each PM sample 2 h prior to OP challenge with OVA. In OVA-allergic mice, 100 μg of downwind coarse PM caused greater increases than downwind fine/ultrafine PM in bronchoalveolar lavage neutrophils, eosinophils, and lactate dehydrogenase. Upwind fine PM (100 μg) produced greater increases in neutrophils and eosinophils compared to other upwind size fractions. Cytokine (IL-5) levels in BAL fluid also increased markedly following 100 μg downwind coarse and downwind ultrafine PM exposures. These findings indicate coarse PM downwind and fine PM upwind of an interstate highway promote inflammation in allergic mice.

Roadside increments in PM10, NOx and NO2 concentrations observed over 2 months at a major highway in New Zealand

Continuous and simultaneous observational particulate matter (measured as PM₁₀), nitrogen dioxide (NO₂) and oxides of nitrogen (NO_x) data were captured at a kerbside site alongside a major highway in Auckland, New Zealand, and at a pair of setback sites within 250 m of the highway, day and night over 8 weeks. The three measurement sites were intended to allow emissions from the highway to be largely isolated from other sources. By filtering the data and subtracting upwind concentrations, the average roadside increment was calculated to be 1.8, 7.2 and 101.4 μg m^-3 for PM₁₀, NO₂ and NO_x, respectively, relative to a predominantly upwind setback site, and -0.1, 9.4 and 98.5 μg m^-3 for PM₁₀, NO₂ and NO_x, respectively, relative to a downwind setback site. The negative value for PM₁₀ was attributed to local evening heating sources impacting the setback site. On days when peak 24 h PM₁₀ concentrations were observed, the absolute kerbside increment was 2.1 μg m^-3. The absolute roadside 24 h average PM₁₀ increment varied diurnally, peaking (on average) at 2.4 μg m^-3 during peak traffic hours. The largest observed 24-h average PM₁₀ roadside increment was 6.9 μg m^-3 and exceeded 5 μg m^-3 on nine occasions. On each of these occasions, the daily mean wind speed was less than 2 m s^-1. The diurnally averaged difference in NO_x concentrations between the kerbside site and the setback sites clearly resembled the diurnal cycle in traffic volume, and peaked during the morning traffic peak at around 180 μg m^-3. Background NO_x concentrations were slightly higher in our study compared to a similar study in Las Vegas but absolute roadside concentrations were higher. This may be consistent with higher NO_x emission factors in Auckland, but differences in the precise distance of the monitor from the road lanes and differences in meteorology need to be considered.

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.

Changes in air quality at near-roadway schools after a major freeway expansion in Las Vegas, Nevada

Near-roadway ambient black carbon (BC) and carbon monoxide (CO) concentrations were measured at two schools adjacent to a freeway and at an urban background school 2 km from the freeway to determine the change in concentrations attributable to vehicle emissions after the three-lane expansion of U.S. Highway 95 (US 95) in Las Vegas, Nevada. Between summer 2007 and summer 2008, average weekday small-vehicle volume increased by 40% +/- 2% (standard error). Average weekday large-vehicle volume decreased by 17% +/- 5%, due to a downturn in the economy and an associated decline in goods movement. Average vehicle speed increased from 58 to 69 mph, a 16% +/- 1% increase. The authors compared BC and CO concentrations in summer 2007 with those in summer 2008 to understand what effect the expansion of the freeway may have had on ambient concentrations: BC and CO were measured 17 m north of the freeway sound wall, CO was measured 20 m south of the sound wall, and BC was measured at an urban background site 2 km south of the freeway. Between summer 2007 and summer 2008, median BC decreased at the near-road site by 40% +/- 2% and also decreased at the urban background site by 24% +/- 4%, suggesting that much of the change was due to decreases in emissions throughout Las Vegas, rather than only on US 95. CO concentrations decreased by 14% +/- 2% and 10% +/- 3% at the two near-road sites. The decrease in BC concentrations after the expansion is likely due to the decrease in medium- and heavy-duty-vehicle traffic resulting from the economic recession. The decrease in CO concentrations may be a result of improved traffic flow, despite the increase in light-duty-vehicle traffic. Implications: Monitoring of BC and CO at near-road locations in Las Vegas demonstrated the impacts of changes in traffic volume and vehicle speed on near-road concentrations. However, urban-scale declines in concentrations were larger than near-road changes due to the impacts of the economic recession that occurred contemporaneously with the freeway expansion.

Dispersion Modeling of Traffic-Related Air Pollutant Exposures and Health Effects Among Children with Asthma in Detroit, Michigan

Vehicular traffic is a major source of ambient air pollution in urban areas. Traffic-related air pollutants, including carbon monoxide, nitrogen oxides, particulate matter less than 2.5 μm in diameter, and diesel exhaust emissions, have been associated with adverse human health effects, especially in areas near major roads. In addition to emissions from vehicles, ambient concentrations of air pollutants include contributions from stationary sources and background (or regional) sources. Although dispersion models have been widely used to evaluate air quality strategies and policies and can represent the spatial and temporal variation in environments near roads, the use of these models in health studies to estimate air pollutant exposures has been relatively limited. This paper summarizes the modeling system used to estimate exposures in the Near-Roadway Exposure and Urban Air Pollutant Study, an epidemiological study that examined 139 children with asthma or symptoms consistent with asthma, most of whom lived near major roads in Detroit, Michigan. Air pollutant concentrations were estimated with a hybrid modeling framework that included detailed inventories of mobile and stationary sources on local and regional scales; the RLINE, AERMOD, and CMAQ dispersion models; and monitored observations of pollutant concentrations. The temporal and spatial variability in emissions and exposures over the 2.5-year study period and at more than 300 home and school locations was characterized. The paper highlights issues with the development and understanding of the significance of traffic-related exposures through the use of dispersion models in urban-scale exposure assessments and epidemiology studies.

The changing paradigm of air pollution monitoring

The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.

Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification analysis

BACKGROUND

The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways.

METHODS

We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups.

RESULTS

Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups.

CONCLUSIONS

These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.

Seasonal and diurnal analysis of NO2 concentrations from a long-duration study conducted in Las Vegas, Nevada

A study, conducted in Las Vegas, NV from mid-December 2008 to mid-December 2009 along an interstate highway, collected continuous and integrated samples for a wide variety of air pollutant species including NO2 and NO(x) associated with roadway traffic. This study examined long-term trends of NO2 and NO(x) in a near-road environment compared with previous near-road studies typically lasting only a few days to months. Study results revealed concentration gradients for NO2 and NO(x) with highest absolute and average concentrations at distances closest to the roadway throughout the year. Diurnal ambient temperature changes also influenced concentrations due to atmospheric chemistry activity as well as concentration changes due to seasonal effects. These concentration gradients were observed for all wind conditions; however under downwind conditions (winds from highway), the concentration gradients are more pronounced. Higher pollutant concentrations are generally observed during low wind speed conditions especially when those winds were from the highway. Understanding long-term, seasonal variability and levels of pollutant concentrations in the near-road environment is important to researchers and decision-makers evaluating exposures and risks for near-road populations; identifying locations for future near-road monitoring sites; and determining the viability and effectiveness of mitigation strategies.

IMPLICATIONS

Population exposures to traffic emissions near roads have led to heightened public health concerns and awareness of the long-term levels and variability of these air pollutants. Epidemiological studies have lead to improved understanding of the associated risks and health effects of near road air pollutant emissions. While short-term studies provide insights on near-road air quality, longer-term trends need to be understood, especially for reactive pollutants such as NO2.

Filtration effectiveness of HVAC systems at near-roadway schools

Concern for the exposure of children attending schools located near busy roadways to toxic, traffic-related air pollutants has raised questions regarding the environmental benefits of advanced heating, ventilation, and air-conditioning (HVAC) filtration systems for near-road pollution. Levels of black carbon and gaseous pollutants were measured at three indoor classroom sites and at seven outdoor monitoring sites at Las Vegas schools. Initial HVAC filtration systems effected a 31-66% reduction in black carbon particle concentrations inside three schools compared with ambient air concentrations. After improved filtration systems were installed, black carbon particle concentrations were reduced by 74-97% inside three classrooms relative to ambient air concentrations. Average black carbon particle concentrations inside the schools with improved filtration systems were lower than typical ambient Las Vegas concentrations by 49-96%. Gaseous pollutants were higher indoors than outdoors. The higher indoor concentrations most likely originated at least partially from indoor sources, which were not targeted as part of this intervention.

PRACTICAL IMPLICATIONS

Recent literature has demonstrated adverse health effects in subjects exposed to ambient air near major roadways. Current smart growth planning and infill development often require that buildings such as schools are built near major roadways. Improving the filtration systems of a school's HVAC system was shown to decrease children's exposure to near-roadway diesel particulate matter. However, reducing exposure to the gas-phase air toxics, which primarily originated from indoor sources, may require multiple filter passes on recirculated air.

The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS): study design and methods

The Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) was designed to examine the relationship between near-roadway exposures to air pollutants and respiratory outcomes in a cohort of asthmatic children who live close to major roadways in Detroit, Michigan USA. From September 2010 to December 2012 a total of 139 children with asthma, ages 6-14, were enrolled in the study on the basis of the proximity of their home to major roadways that carried different amounts of diesel traffic. The goal of the study was to investigate the effects of traffic-associated exposures on adverse respiratory outcomes, biomolecular markers of inflammatory and oxidative stress, and how these exposures affect the frequency and severity of respiratory viral infections in a cohort of children with asthma. An integrated measurement and modeling approach was used to quantitatively estimate the contribution of traffic sources to near-roadway air pollution and evaluate predictive models for assessing the impact of near-roadway pollution on children's exposures. Two intensive field campaigns were conducted in Fall 2010 and Spring 2011 to measure a suite of air pollutants including PM2.5 mass and composition, oxides of nitrogen (NO and NO2), carbon monoxide, and black carbon indoors and outdoors of 25 participants' homes, at two area schools, and along a spatial transect adjacent to I-96, a major highway in Detroit. These data were used to evaluate and refine models to estimate air quality and exposures for each child on a daily basis for the health analyses. The study design and methods are described, and selected measurement results from the Fall 2010 field intensive are presented to illustrate the design and successful implementation of the study. These data provide evidence of roadway impacts and exposure variability between study participants that will be further explored for associations with the health measures.