Near-road Site Research Articles

Characterization of the short-term temporal variability of road dust chemical mixtures and meteorological profiles in a near-road urban site in British Columbia

ABSTRACT Springtime road dust is a common air quality concern for northern latitudes communities. Traction material, brake wear, tire wear, and other particles entrained in snow during winter become re-suspended into the air as the snow melts. Characterization of within-season variability of road dust composition and weather conditions is critical to understanding its acute health impacts. Sixty 24-hour samples of PM10 were collected with a high-volume sampler at a near-road site in Prince George, British Columbia. Near-road samples were collected intermittently in winter and daily in spring. All samples were analyzed for 18 trace elements. We used data from a nearby regulatory monitor to estimate daily coarse fraction (PM10–2.5). Proportion of PM10–2.5 in PM10 was used to classify days as high or low road dust based on a threshold of 65% PM10–2.5. We identified clusters of near-road samples based on weather parameters and PM10 components using Bayesian profile regression (BPR). Twenty-one near-road PM10 samples were classified as high road dust days and 34 as low road dust days. Concentrations of total trace elements and specific trace elements were significantly higher on high road dust days (aluminum, chromium, iron, lead, tin, vanadium, and zinc). High road dust days also had low relative humidity and precipitation and higher temperature and air pressure. We identified five clusters of near-road PM10 mixtures that suggested an interaction between temperature and humidity for road dust impacts. Samples from a near-road site indicated days highly affected by springtime road dust are distinct from other days based on particulate mixtures and meteorological factors. Higher loading of trace element mixtures in PM10 on high road dust days has important implications for acute toxicity of road dust. Relationships between road dust and weather may facilitate further research into health effects of road dust as the climate changes. Implications: Non-tailpipe emissions driven by springtime road dust in northern latitude communities is increasing in importance for air pollution control and improving our understanding of the health effects of chemical mixtures from particulate matter exposure. High-volume samples from a near-road site indicated that days affected by springtime road dust are substantively different from other days with respect to particulate matter mixture composition and meteorological drivers. The high load of trace elements in PM10 on high road dust days has important implications for the acute toxicity of inhaled air and subsequent health effects. The complex relationships between road dust and weather identified in this study may facilitate further research on the health effects of chemical mixtures related to road dust while also highlighting potential changes in this unique form of air pollution as the climate changes.

Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature

Vehicles are a major source of anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NOx), and black carbon (BC). CO and NOx are known to be harmful to human health and contribute to ozone formation, while BC absorbs solar radiation that contributes to global warming and also has negative impacts on human health and visibility. Travel restrictions implemented during the COVID-19 pandemic provide researchers the opportunity to study the impact of large, on-road traffic reductions on local air quality. Traffic counts collected along Interstate-95, a major eight-lane highway in Maryland (US), reveal a 60% decrease in passenger car totals and an 8.6% (combination-unit) and 21% (single-unit) decrease in truck traffic counts in April 2020 relative to prior Aprils. The decrease in total on-road vehicles led to the near-elimination in stop-and-go traffic and a 14% increase in the mean vehicle speed during April 2020. Ambient near-road (NR) BC, CO, NOx, and carbon dioxide (CO2) measurements were used to determine vehicular emission ratios (ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO, ΔNOx/ΔCO2, and ΔCO/ΔCO2), with each ratio defined as the slope value of a linear regression performed on the concentrations of two pollutants within an hour. A decrease of up to a factor of two in ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO2, and in the fraction of on-road diesel vehicles from weekdays to weekends shows diesel vehicles to be the dominant source of BC and NOx emissions at this NR site. We estimate up to a 70% reduction in BC emissions in April 2020 compared to earlier years, and attribute much of this to lower diesel BC emissions resulting from improvements in traffic flow and fewer instances of acceleration and braking. Future efforts to reduce vehicular BC emissions should focus on improving traffic flow or turbocharger lag within diesel engines. Inferred BC emissions from the NR site also depend on ambient temperature, with an increase of 54% in ΔBC/ΔCO from −5 to 20 °C during the cold season, similar to previous studies that reported increasing BC emissions with rising temperature. The default setting of MOVES3, the current version of the mobile emission model used by the US EPA, does not adjust hot-running BC emissions for ambient temperature. Future work will focus on improving the accuracy of mobile emissions in air quality modeling by incorporating the effects of temperature and traffic flow in the system used to generate mobile emissions input for commonly used air quality models.

Assessment and Characterization of Alkylated PAHs in Selected Sites across Canada

Alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), dibenzothiophenes (DBTs), and unsubstituted polycyclic aromatic hydrocarbons (PAHs) are naturally present in fossil fuels. Thus, they can be considered as candidates for markers of pollution from petrogenic emissions such as those from traffic. Consequently, ambient air concentrations of alkyl-PAHs, DBTs, and PAHs at selected ambient air monitoring sites of various types (residential, near-road, urban-industrial, agricultural) in Montréal, Toronto, Hamilton, Edmonton, and Simcoe, were evaluated from 2015 to 2016 to study their profiles, trends, and assess potential primary emission source types. Alkyl-PAHs were the prevailing species at all sites and were most elevated at the high-traffic impacted near-road site in Toronto which was also accompanied by the highest unsubstituted PAH concentrations. Comparison of relative abundance ratios of alkyl-PAH and PAH groupings suggests that the profile differences amongst sites were small. Source attribution with cluster grouping suggested similar emission sources of alkyl-PAH and PAH at all sites, with the exception of Hamilton which was particularly impacted by additional emission sources of PAHs. The Principal Component Analysis further indicated distinct PAC profiles at HWY401 and HMT that have the same variability of “heavy PACs” but differ in “medium mass PAHs” sources. Seasonality affected the bulk species trends (alkylated naphthalenes, fluorenes, and phenanthrenes/anthracenes), especially at sites with lower concentrations of these species. This study findings confirm a notable contribution of traffic emissions to alkyl-PAH levels in urban ambient air at the studied Canadian sites, and show that enhanced speciation of alkyl-PAHs provides more data on ambient air quality and additional health risks, and can also help distinguish petrogenic-influenced sources from other sources.

Characterization of winter air pollutant gradients near a major highway

Traffic-related air pollutants (TRAP) including nitric oxide (NO), nitrogen oxide (NOx), carbon monoxide (CO), ultrafine particles (UFP), black carbon (BC), and fine particulate matter (PM2.5) were simultaneously measured at near-road sites located at 10 m (NR10) and 150 m (NR150) from the same side of a busy highway to provide insights into the influence of winter time meteorology on exposure to TRAP near major roads. The spatial variabilities of TRAP were examined for ambient temperatures ranging from −11 °C to +19 °C under downwind, upwind, and stagnant air conditions. The downwind TRAP concentrations at NR10 were higher than the upwind concentrations by a factor of 1.4 for CO to 13 for NO. Despite steep downwind reductions of 38 % to 75 % within 150 m, the downwind concentrations at NR150 were still well above upwind concentrations. Near-road concentrations of NOx and UFP increased as ambient temperatures decreased due to elevated emissions of NOx and UFP from vehicles under colder temperatures. Traffic-related PM2.5 sources were identified using hourly PM2.5 chemical components including organic/inorganic aerosol and trace metals at both sites. The downwind concentrations of primary PM2.5 species related to tailpipe and non-tailpipe emissions at NR10 were substantially higher than the upwind concentrations by a factor of 4 and 32, respectively. Traffic-related PM2.5 sources accounted for almost half of total PM2.5 mass under downwind conditions, leading to a rapid change of PM2.5 chemical composition. Under stagnant air conditions, the concentrations of most TRAP and related PM2.5 including tailpipe emissions, secondary nitrate, and organic aerosol were comparable to, or even greater than, the downwind concentrations under windy conditions, especially at NR150. This study demonstrates that stagnant air conditions further widen the traffic-influenced area and people living near major roadways may experience increased risks from elevated exposure to traffic emissions during cold and stagnant winter conditions.

Insights into Elemental Composition and Sources of Fine and Coarse Particulate Matter in Dense Traffic Areas in Toronto and Vancouver, Canada.

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, an improved understanding of traffic impacts on particle concentrations and their components could help mitigate air pollution. In this study, the characteristics and sources of trace elements in PM2.5 (fine), and PM10-2.5 (coarse), were investigated in dense traffic areas in Toronto and Vancouver, Canada, from 2015–2017. At nearby urban background sites, 24-h integrated PM samples were also concurrently collected. The PM2.5 and PM10-2.5 masses, and a number of elements (i.e., Fe, Ba, Cu, Sb, Zn, Cr), showed clear increases at each near-road site, related to the traffic emissions resulting from resuspension and/or abrasion sources. The trace elements showed a clear partitioning trend between PM2.5 and PM10-2.5, thus reflecting the origin of some of these elements. The application of positive matrix factorization (PMF) to the combined fine and coarse metal data (86 total), with 24 observations at each site, was used to determine the contribution of different sources to the total metal concentrations in fine and coarse PM. Four major sources were identified by the PMF model, including two traffic non-exhaust (crustal/road dust, brake/tire wear) sources, along with regional and local industrial sources. Source apportionment indicated that the resuspended crustal/road dust factor was the dominant contributor to the total coarse-bound trace element (i.e., Fe, Ti, Ba, Cu, Zn, Sb, Cr) concentrations produced by vehicular exhaust and non-exhaust traffic-related processes that have been deposited onto the surface. The second non-exhaust factor related to brake/tire wear abrasion accounted for a considerable portion of the fine and coarse elemental (i.e., Ba, Fe, Cu, Zn, Sb) mass at both near-road sites. Regional and local industry contributed mostly to the fine elemental (i.e., S, As, Se, Cd, Pb) concentrations. Overall, the results show that non-exhaust traffic-related processes were major contributors to the various redox-active metal species (i.e., Fe, Cu) in both PM fractions. In addition, a substantial proportion of these metals in PM2.5 was water-soluble, which is an important contributor to the formation of reactive oxygen species and, thus, may lead to oxidative damage to cells in the human body. It appears that controlling traffic non-exhaust-related metals emissions, in the absence of significant point sources in the area, could have a pronounced effect on the redox activity of PM, with broad implications for the protection of public health.

Characterizing Nitrogen Oxides and Fine Particulate Matter near Major Highways in the United States Using the National Near-Road Monitoring Network.

As part of the United States Environmental Protection Agency's 2010 Nitrogen Dioxide (NO2) National Ambient Air Quality Standards (NAAQS) review, a national network of near-road sites was established to characterize pollutant behavior, interaction, and dispersion in the ambient near-road environment. Using spatial interpolation to estimate the near-road concentration increments of NO2 and particulate matter with an aerodynamic diameter of 2.5 μm and less (PM2.5) relative to nearby non-near-road monitors, we found that the 2013-2018 national average increment is 6.9 ppb and 1.0 μg m-3 for NO2 and PM2.5, respectively. Analyses of the hourly near-road NO2, nitric oxide (NO), and PM2.5 increments showed distinct diurnal cycles; the NO2 increment peaks at ∼9 ppb during the early afternoon (2-4 pm local time) while the NO and PM2.5 increments peak during the morning rush hour (5-8 am local time) at 25 ppb and 1.8 μg m-3 for NO and PM2.5, respectively. Although long-term trends are not yet available for this network of sites, a similar analysis of the NO2 and PM2.5 increment at a quasi-near-road site outside of the official network in Elizabeth, NJ showed gradual decreases in the increment over time since the mid-2000s.

Assessment of the Near-Road (monitoring) Network including comparison with nearby monitors within U.S. cities

Emissions from on-road mobile sources have historically been an important anthropogenic contributor to ambient air pollution leading to high levels of air pollution near major roadways. The U.S. EPA recently implemented the Near-Road (monitoring) Network to measure NO2 concentrations by high-traffic roadways in urban centers throughout the U.S., as these locations were believed to characterize worst-case human exposures to traffic-related air pollutants. Many near-road sites also include PM2.5 and CO measurements, which along with the NO2 observations, were compared in a pairwise manner against non-near-road monitors located within the city-scale boundary. After controlling for primary emissions from the target highways, we found the PM2.5 concentration difference (i.e. near-road concentration minus non-near-road site concentration) between the near-road and non-near-road urban sites to be δ = 0.42 µg m−3( H0: µdiff = 0; Ha: µdiff > 0 (µnon-near-road > µnear-road); p = 0.051; α = 0.05, 95% CI: −0.08–0.90 µg m−3, n = 35 comparisons). NO2 and CO levels were on average higher at the near-road sites compared to the non-near-road urban sites by 5.0 (95% CI: 3.4–6.5) ppb (n = 44 comparisons) and 9.2 × 10−2( 95% CI: 0.04–0.14) ppm (n = 42 comparisons), respectively. The average PM2.5 difference found here is 5%, and at 14 of the 35 (∼40%) urban monitor comparisons and 28 of the 72 (∼39%) overall comparisons, PM2.5 is actually higher at the non-near-road site relative to its near-road pair. Cleaner vehicle fleets, formation of secondary PM from on-road emissions occurring downwind (i.e. away from the road), decreased secondary organic aerosol (SOA) formation rates in the near-road environment, the prevalence of other low-volume vehicular and local, non-vehicular sources of emissions at the non-near-road sites (e.g. railyards, truck yards, ports, biomass-fueled heating, backyard barbecuing, and commercial cooking, etc) and local meteorology (e.g. wind speed and wind direction) explain this finding. The wintertime PM2.5 concentration difference was higher than the other seasons, likely a result of higher primary PM2.5 tailpipe emissions and lower temperatures that both reduced near-road PM volatility and decreased photochemical activity resulting in lower SOA production at the urban scale. Further, all near-road NO2 and CO concentrations were below the annual and hourly NAAQS, while eight (most of which were in wildfire-prone locations) of the 94 PM2.5 sites used in this study were above the annual National Ambient Air Quality Standards. In addition, strong agreement with both annual average daily traffic and fleet-equivalent AADT were found for near-road NO2 and CO concentrations, while weaker, but still positive relationships were found for near-road PM2.5 levels. Lastly, same observational data was used to assess on-road mobile source emission estimates from the EPA National Emission Inventory, and analysis of the observations are in rough agreement with the current ratio of NOx to CO emissions from on-road mobile sources.

Source apportionment of PM2.5 at two Seattle chemical speciation sites

ABSTRACT Positive Matrix Factorization analysis of PM2.5 chemical speciation data collected from 2015–2017 at Washington State Department of Ecology’s urban NCore (Beacon Hill) and near-road (10th and Weller) sites found similar PM2.5 sources at both sites. Identified factors were associated with gasoline exhaust, diesel exhaust, aged and fresh sea salt, crustal, nitrate-rich, sulfur-rich, unidentified urban, zinc-rich, residual fuel oil, and wood smoke. Factors associated with vehicle emissions were the highest contributing sources at both sites. Gasoline exhaust emissions comprised 26% and 21% of identified sources at Beacon Hill and 10th and Weller, respectively. Diesel exhaust emissions comprised 29% of identified sources at 10th and Weller but only 3% of identified sources at Beacon Hill. Correlation of the diesel exhaust factor with measured concentrations of black carbon and nitrogen oxides at 10th and Weller suggests a method to predict PM2.5 from diesel exhaust without a full chemical speciation analysis. While most PM2.5 sources exhibit minimal change over time, primary PM2.5 from gasoline emissions is increasing on average 0.18 µg m−3 per year in Seattle. Implications This study utilizes Positive Matrix Factorization to determine PM2.5 sources from chemical speciation measurements at two urban Seattle sites from 2015-2017. The paper reports PM2.5 source trends, and extends previous source apportionment analyses in Seattle to the present day. The study also quantifies diesel PM2.5 at a near-road site, and describes a predictive model that allows estimation of the contribution of diesel PM2.5 to the total measured PM2.5 at near-road sites across the country without a full chemical speciation analysis.

Using near-road observations of CO, NOy, and CO2 to investigate emissions from vehicles: Evidence for an impact of ambient temperature and specific humidity

Abstract Vehicles are a significant source of carbon monoxide (CO) and nitrogen oxides (NOx), two harmful pollutants and precursors to ozone formation. Previous studies have shown that emissions of NOx in the US EPA's National Emissions Inventory (NEI) are overestimated relative to observations in the summer and possibly for an annual average. Here we use measurements of CO, NOx, carbon dioxide (CO2), and meteorological variables collected at a near-road (NR) site along I-95 in Howard County, Maryland, during the cold months of 2016 and 2017, to infer ΔCO/ΔNOx, ΔCO2/ΔNOx, and ΔCO2/ΔCO emission ratios from vehicular running exhaust and their sensitivity to temperature and specific humidity. We also use aircraft observations of CO, NOx, and meteorological variables collected during the 2011 summertime DISCOVER-AQ campaign over the Baltimore-Washington region to analyze the impact of temperature and humidity on ΔCO/ΔNOx ratios, which integrate anthropogenic and biogenic sources in the urban area. Overall, we find a strong, statistically significant increase of 113% in ΔCO/ΔNOx and of 112% in ΔCO2/ΔNOx from −5 to 25 °C at the I-95 NR site, indicating a decrease of approximately 50% in emissions of NOx as air warms, linked primarily to diesel-powered trucks. Temperature sensitivity of pollution control equipment on diesel vehicles may contribute to this trend. Results are robust when using several different techniques for calculating emission ratios. The sensitivity of vehicular emissions of NOx to specific humidity is much weaker and cannot solely explain the trend with temperature. The aircraft data show a similar increase of 114% in ΔCO/ΔNOx from 25 °C to 34 °C, with a weaker sensitivity to specific humidity. In comparison to the NR observations, ΔCO/ΔNOx output from the MOtor Vehicle Emission Simulator (MOVES) with default settings, used to simulate mobile emissions for air quality models and in the NEI, showed a smaller increase for ΔCO/ΔNOx of 41% over the temperature range −5 to 25 °C. The increase in ΔCO/ΔNOx from MOVES is due to an increase in emissions of CO by 23% and a decrease in emissions of NOx by 11% over −5 to 25 °C, which is less than the observed decrease in NOx. Our study suggests that the overestimate in emissions of NOx in the NEI previously reported using summertime observations may be corrected in part by accounting for the temperature sensitivity of mobile NOx running emissions within MOVES. Future work will focus on improving MOVES by adjusting parameters controlling the impact of temperature and humidity on emissions to better represent the behavior of real-world vehicular emissions.

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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 “increment” and evaluated the effects of changes in meteorology and traffic. The Denver near-road site consistently had the highest PM2.5 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 PM2.5 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 PM2.5 concentrations than nearby sites, there was no clear correlation between traffic conditions (vehicle speed, fleet mix) and the high PM2.5 concentrations.

National Assessment of Near-Road Air Quality in 2016: Multi-Year Pollutant Trends and Estimation of Near-Road PM2.5 Increment

Continuous monitoring of PM2.5, NO2, and other pollutants occurs at near-road air quality monitoring locations throughout the United States, as required by Environmental Protection Agency (EPA) regulations. Near-road monitoring sites were set up from 2014 through 2016, with 68 NO2 monitors in 2016, 31 of which also monitored PM2.5. Annual and multi-year statistics of NO2 and PM2.5 concentrations were compared with the National Ambient Air Quality Standards. A PM2.5“increment”—the difference between concentrations measured in the near-road environment and those concentrations measured at nearby sites—was estimated. Several methods were used to select nearby sites from which to calculate “background” concentrations: (1) sites within 25 km, 50 km, and 100 km of the near-road site, and sites whose 24 h PM2.5 concentrations were well correlated with those at the near-road site; (2) sites within 40 km of the near-road site, where PM2.5 concentrations were interpolated using an inverse distance weighting; and (3) a site in the “upwind” direction from the near-road site. The mean PM2.5 increment across all near-road sites ranged from +0.6 to +1.1 μg/m3 (or 6–10% of the average PM2.5), depending on the method. Understanding the variation in the PM2.5 increment can be useful to assess calculations of PM2.5 background, which is required by EPA under various regulatory frameworks. The PM2.5 increment generally decreased with increasing distance from monitoring site to roadway, and increased with increasing traffic volume.

Temporal and spatial variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust emissions

The contribution of traffic-related particulate matter (PM2.5, particles smaller than 2.5 μm in diameter) sources can vary temporally and spatially, which may disproportionately contribute to health outcomes. Furthermore, non-exhaust emissions are a growing concern due to the high concentrations of redox active metals that can be present. The temporal and spatial variabilities of traffic-related PM2.5 sources were investigated in this study by comparing source contributions between two near-road sites. In order to identify local PM2.5 sources with greater temporal and spatial resolution, receptor modeling was performed for hourly-resolved organics, inorganic ions, trace elements, and black carbon in PM2.5 simultaneously measured at downtown and highway sites located within 15 m of a major roadway and highway, respectively, in Toronto. The source apportionment study revealed that traffic-related PM2.5 sources were mainly from exhaust emissions (9%–19% of PM2.5) and non-exhaust emissions including brake wear (2%–6%) and resuspension of road dust (3%–4%). The traffic-related sources exhibited strong diurnal and spatial variabilities, whereas no spatial and temporal differences were observed for the largest PM2.5 contributors, oxidized organic aerosol and secondary sulphate. During morning rush hours, the overall contribution of traffic exhaust and non-exhaust emissions were elevated up to 35%–48% of total PM2.5 mass, which was found to be the largest PM2.5 source at the highway site and the second largest contributor in the downtown area. Furthermore, the contribution of traffic-related sources at the highway site was higher than at the downtown site by a factor of 2–3, suggesting that exposure to traffic-related emissions varies greatly in space and time. Nearly one-third of the traffic-related source contributions were associated with non-exhaust emissions from brake wear and road dust resuspension in the urban environment. Elevated levels of non-exhaust sources were correlated with the number of heavy-duty vehicles, rather than total traffic volume. Although the contribution of brake wear and road dust sources to total PM2.5 mass was relatively low, non-exhaust emissions contributed a substantial fraction of trace elements, especially for Ba (74–79%), Cu (66–71%), and Mn (53–65%) in the urban atmosphere.

Characterizing the evolution of physical properties and mixing state of black carbon particles: from near a major highway to the broader urban plume in Los Angeles

Abstract. Black carbon (BC) particles can have deleterious human health consequences and impact regional and global climate. Uncertainties remain in part due to incomplete knowledge on the evolution of physical properties and mixing state of black carbon from sources to the remote atmosphere. We aim to understand how “road-to-ambient” processing and longer timescale aging in an urban plume affect black carbon physical properties. Refractory black carbon (rBC) was measured during summer 2016 using a single-particle soot photometer (SP2) in two distinct environments: near a major freeway and downwind of downtown Los Angeles. The near-road measurements were made at distances ranging from 30 to 114 m downwind of Interstate 405 in Los Angeles. These results were compared with measurements performed 100 km east of Los Angeles in Redlands, California. Coatings on rBC particles were quantified using two methods. As distance from the highway increased at the near-road site, we observed decreases in rBC mass and number concentrations and increases in the number fraction of rBC particles with thick coatings (f). The latter likely occurred due to rapid processing of the highway plume and entrainment of urban background particles. Most rBC-containing particles measured near the highway were either uncoated or thinly coated. In Redlands, we found that rBC mass concentrations on weekdays were similar to those observed at the furthest measured distance from the highway (114 m). However, rBC number concentrations for the smallest measured sizes were an order of magnitude lower in Redlands than all measured distances from the highway. Observations of f indicate that values in Redlands during periods when estimated photochemical age (PCA) was highest (6–8 h) were similar to corresponding values at the furthest measured distance from the highway. This suggests that the residence time of air in the Los Angeles Basin under typical summertime conditions measured during this campaign may not be sufficient for rBC to acquire thick coatings. However, under certain meteorological conditions, f was observed to be ∼0.20 in Redlands, with coating thickness histograms showing a larger contribution of rBC particles with coating thickness > 80 nm. This occurred during a weekend day when local emissions from diesel vehicles were lower (compared to weekdays) and winds brought air from the desert regions to Redlands, both of which would increase the relative contribution of remote sources of rBC. Afternoon values of f (and O3) were found to be systematically higher on weekends than weekdays, suggesting that the “weekend effect” can create more thickly coated rBC particles presumably due to enhanced secondary organic aerosol (SOA) and reduced available rBC as condensation sites.

Air quality simulations for London using a coupled regional-to-local modelling system

Abstract. A coupled regional-to-local modelling system comprising a regional chemistry–climate model with 5 km horizontal resolution (EMEP4UK) and an urban dispersion and chemistry model with explicit road source emissions (ADMS-Urban) has been used to simulate air quality in 2012 across London. The study makes use of emission factors for NOx and NO2 and non-exhaust emission rates of PM10 and PM2.5 which have been adjusted compared to standard factors to reflect real-world emissions, with increases in total emissions of around 30 % for these species. The performance of the coupled model and each of the two component models is assessed against measurements from background and near-road sites in London using a range of metrics concerning annual averages, high hourly average concentrations and diurnal cycles. The regional model shows good performance compared to measurements for background sites for these metrics, but under-predicts concentrations of all pollutants except O3 at near-road sites due to the low resolution of input emissions and calculations. The coupled model shows good performance at both background and near-road sites, which is broadly comparable with that of the urban model that uses measured concentrations as regional background, except for PM2.5 where the under-prediction of the regional model causes the coupled model to also under-predict concentrations. Using the coupled model, it is estimated that 13 % of the area of London exceeded the EU limit value of 40 µg m−3 for annual average NO2 in 2012, whilst areas of exceedances of the annual average limit values of 40 and 25 µg m−3 for PM10 and PM2.5 respectively were negligible.

2350 - Field Testing a Low-Cost Sensor for Long-Term Measurement of Traffic-Related PM2.5

Background Health departments and urban planners have growing needs for high resolution data on urban air pollution, to quantify existing health burdens at the neighborhood scale, to identify and prioritize exposure reduction strategies for pollution hot-spots, to track progress in achieving air quality-related health improvement goals, and to assess health co-benefits of longer-term carbon mitigation strategies. To-date, however, few tools have been available to inform these high priority urban health objectives. As part of the NASA Health and Air Quality Applied Sciences Team (HAQAST) project, this study was designed to field test a unique low-cost sensor for measuring long-term concentrations of PM2.5 mass and composition at sites impacted by road traffic in three cities: Boston, New York, and San Francisco. Methods Starting in August 2017, we deployed University of North Carolina (UNC) passive samplers at 2–3 sites each in Boston, NYC and San Francisco. Sites were chosen due to availability of co-located sampling for PM2.5 using conventional, high-cost sensors, and proximity to major roadways. Each sampler had a filter placed under a fine mesh screen of 1.5 cm in diameter; particles settled gravitationally and by diffusion. We tested two sampling periods: 4 weeks and 12 weeks. Particles accumulated on the filter for as long as the sampler was left open to the air. Samplers were sent to a laboratory for analysis by electron microscopy and analyzed data was returned to us. We also obtained publically available data from high cost regulatory monitors for the sites in Boston, and compared daily averages. Results We deployed samplers on schedule and found that strong winds, particularly near highways, would occasionally disrupt the horizontal orientation of the sampler or cause the sampler to blow away; this was solved by using more sturdy mounting brackets. Initial laboratory results indicate that the 12-week sampling period provides optimal filter loadings. Sampling in 2016 and 2017 at two sites in Boston tell us that the near-road site presents higher PM2.5 annual concentrations by 0.5 to 1 ug/m3. Quantitative analyses comparing the low-cost sensor data with co-located standard devices will be carried out in the coming months as data accumulate. Conclusions Preliminary qualitative results suggest that the novel low-cost UNC sensor can be easily deployed at the community scale, with the potential to achieve broader engagement of the civil society and develop tools to enable our stakeholders to carry out health-based air quality assessments at relevant community scales.

Real world vehicle fleet emission factors: Seasonal and diurnal variations in traffic related air pollutants

Temporal variations of vehicle emissions are affected by various compounding factors in the real world. The focus of this study is to determine the effects of ambient conditions and post-tailpipe changes on traffic emissions measured in the near-road region. Emission factors allowed for the isolation of the traffic signal and accounted for effects of local meteorology and dilution. Five month-long measurement campaigns were conducted at an urban near-road site that exhibited a broad range of ambient conditions with temperatures ranging between −18 and +30 °C. Particle number emission factors were 2.0× higher in the winter relative to the summer, which was attributed to changes in particles post-tailpipe. Conversely, toluene emissions were 2.5× higher in the summer relative to the winter, attributed to changes in fuel composition. Diurnal trends of emission factors showed substantial increases in emissions during the morning rush hour for black carbon (1.9×), particle number (2.4×), and particle-bound polycyclic aromatic hydrocarbons (3.0×), affected by fleet make-up. In contrast, particle number emission factors were highest midday with mean values 3.7× higher than at night. This midday increase was attributed to particle formation or growth from local traffic emissions and showed different wind direction dependence than regional events.

A national-scale review of air pollutant concentrations measured in the U.S. near-road monitoring network during 2014 and 2015

In 2010, the U.S. Environmental Protection Agency (EPA) revised the National Ambient Air Quality Standards (NAAQS) for NO2 to include a primary health-based standard for hourly NO2, and required air quality monitoring next to major roadways in urban areas in the U.S. Requirements for near-road measurements also include carbon monoxide (CO) and particulate matter smaller than 2.5 μm in diameter (PM2.5). We performed a national-scale assessment of air pollutants measured at 81 sites in the near-road environment during the first two years (2014 and 2015) of the new measurement program. We evaluated how concentrations at these locations compared to the NAAQS, to concentrations measured at other sites within the same urban areas, and when considering their site characteristics (distance of monitor to road, traffic volume, and meteorology). We also estimated the contribution of emissions from adjacent roadways at each near-road site to the PM2.5 concentrations above the local urban background concentrations, i.e., the near-road “increment.” Hourly values of CO reached a maximum of 4.8 ppm across 31 sites in 2014 and 9.6 ppm across 47 sites in 2015, and were well below the NAAQS levels for both the 1-hr (35 ppm) and 8-hr (9 ppm) standards. Hourly concentrations of near-road NO2 reached 258 ppb across 40 sites in 2014; however, there were only two occurrences of a daily 1-hr maximum NO2 concentration above 100 ppb (the level of the hourly NO2 standard). In 2015, hourly concentrations of near-road NO2, monitored at 61 sites in 55 urban areas, reached 154 ppb. Only 0.0015% (n = 5) of hourly NO2 observations in 2015 exceeded 100 ppb. The highest annual NO2 average recorded in 2015 (29.9 ppb) occurred at the Ontario site located along I-10 in the Los Angeles, California, area and was below the level of the NO2 annual standard (53 ppb); in 2014, the highest annual mean NO2 was also observed in Los Angeles at the Anaheim site (27.1 ppb). In 2014, sites in Cincinnati, Indianapolis, and Louisville recorded annual average PM2.5 concentrations at or above 12 μg/m3 (the level of the annual standard). There were 15 occurrences in 2014 of 24-hr PM2.5 concentrations above the NAAQS level of 35 μg/m3. Annual average PM2.5 exceeded 12 μg/m3 at near-road sites in five urban areas in 2015, and there were 33 days across 12 near-road locations with 24-hr PM2.5 concentrations above 35 μg/m3. Across the near-road monitoring network, annual average PM2.5 concentrations did not have a significant relationship with traffic volume or distance between the monitor and the adjacent roadway; rather, variations in PM2.5 were mostly driven by urban-scale PM2.5, with a typically small “increment” above urban-scale concentrations due to a site's proximity to the roadway. We estimated this increment, i.e., the difference between near-road PM2.5 concentrations and the concentrations at sites in the urban area of each near-road monitor, to be on average 1.2 μg/m3 (σ = 0.3 μg/m3), with a range of −1.2 μg/m3 to 3.1 μg/m3 across the 26 sites (four of which had a negative increment). The near-road increment is on average 13% of the near-road PM2.5, and 15% of the near-road PM2.5 for sites within 20 m of the roadway.

Sensitivity analysis of the near-road dispersion model RLINE - An evaluation at Detroit, Michigan

The development of accurate and appropriate exposure metrics for health effect studies of traffic-related air pollutants (TRAPs) remains challenging and important given that traffic has become the dominant urban exposure source and that exposure estimates can affect estimates of associated health risk. Exposure estimates obtained using dispersion models can overcome many of the limitations of monitoring data, and such estimates have been used in several recent health studies. This study examines the sensitivity of exposure estimates produced by dispersion models to meteorological, emission and traffic allocation inputs, focusing on applications to health studies examining near-road exposures to TRAP. Daily average concentrations of CO and NOx predicted using the Research Line source model (RLINE) and a spatially and temporally resolved mobile source emissions inventory are compared to ambient measurements at near-road monitoring sites in Detroit, MI, and are used to assess the potential for exposure measurement error in cohort and population-based studies. Sensitivity of exposure estimates is assessed by comparing nominal and alternative model inputs using statistical performance evaluation metrics and three sets of receptors. The analysis shows considerable sensitivity to meteorological inputs; generally the best performance was obtained using data specific to each monitoring site. An updated emission factor database provided some improvement, particularly at near-road sites, while the use of site-specific diurnal traffic allocations did not improve performance compared to simpler default profiles. Overall, this study highlights the need for appropriate inputs, especially meteorological inputs, to dispersion models aimed at estimating near-road concentrations of TRAPs. It also highlights the potential for systematic biases that might affect analyses that use concentration predictions as exposure measures in health studies.

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16–19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Characterization of air pollutant concentrations, fleet emission factors, and dispersion near a North Carolina interstate freeway across two seasons

We present field measurement data and modeling of multiple traffic-related air pollutants during two seasons at a site adjoining Interstate 40, near Durham, North Carolina. We analyze spatial-temporal and seasonal trends and fleet-average pollutant emission factors and use our data to evaluate a line source dispersion model. Month-long measurement campaigns were performed in summer 2015 and winter 2016. Data were collected at a fixed near-road site located within 10 m from the highway edge, an upwind background site and, under favorable meteorological conditions, along downwind perpendicular transects. Measurements included the size distribution, chemical composition, and volatility of submicron particles, black carbon (BC), nitrogen oxides (NOx), meteorological conditions and traffic activity data. Results show strong seasonal and diurnal differences in spatial distribution of traffic sourced pollutants. A strong signature of vehicle emissions was observed within 100–150 m from the highway edge with significantly higher concentrations during morning. Substantially higher concentrations and less-sharp near-road gradients were observed in winter for many species. Season-specific fleet-average fuel-based emission factors for NO, NOx, BC, and particle number (PN) were derived based on up- and down-wind roadside measurements. The campaign-average NOx and PN emission factors were 20% and 300% higher in winter than summer, respectively. These results suggest that the combined effect of higher emissions and their slower downwind dispersion in winter dictate the observed higher downwind concentrations and wider highway influence zone in winter for several species. Finally, measurements of traffic data, emission factors, and pollutant concentrations were integrated to evaluate a line source dispersion model (R-LINE). The dispersion model captured the general trends in the spatial and temporal patterns in near-road concentrations. However, there was a tendency for the model to under-predict concentrations near the road in the mornings and over-predict concentrations in the evenings.