On-road Concentrations Research Articles

Roadside PM2.5 concentrations measured with low-cost sensors and student science in Arba Minch, Ethiopia

Exposure to PM2.5 poses one of the biggest health threats, with traffic and biomass burning as dominant sources in urban areas of low-income countries. In Ethiopia, the combination of these two sources suggests a high roadside exposure. Because of a lack of resources for data collection, only few studies were conducted on roadside exposure in Ethiopia. Using low-cost sensors and student science could partially remedy this lack of resources. Students collected PM2.5 data in Arba Minch at four stationary locations and inside two public transport tricycles during a period of six weeks with self-made low-cost sensors. Data was analyzed to gain insight into concentration levels, temporal variation, spatial variation, and difference between next to the road and on-road concentrations. Average concentrations ranged from 13-36 µg/m3. Concentrations were highest during morning hours (42 ± 12 for hours 6:00-10:00, versus 20 ± 1 and 32 ± 4 for 10:00-17:00 and 17:00-21:00, respectively), and concentrations were highest at the local bus station (36.2 µg/m3). On-road concentrations showed the highest variation and were on average higher than concentrations next to the road (33 ± 25 and 30 ± 22 µg/m3 versus 23.3 ± 18 and 22.6 ± 18 µg/m3). On a daily average level, concentrations at different locations showed a high correlation (R2 0.8-0.95) amongst each other. This suggests the possibility to interpolate concentrations from one location to other locations. Moreover, the PM2.5 concentrations exceeded air quality guidelines. In Ethiopia, more than ten cities have higher populations and traffic flows than Arba Minch. In those cities, similar or higher exceedances are expected. With this study as an example, other universities could likewise conduct research with low-cost sensors and student science in their cities. Cooperation across course instructors and universities in applying these methods will increase the insight in PM2.5 exposure in Ethiopian cities.
 Keywords: ambient air pollution; traffic; Sensirion SPS30; student measurements; Particulate Matter

Multi-season mobile monitoring campaign of on-road air pollution in Bengaluru, India: High-resolution mapping and estimation of quasi-emission factors

Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitoring is sparse. Here, we report results from a ~ year-long mobile monitoring campaign of on-road concentrations of black carbon (BC), ultrafine particles (UFP), and carbon dioxide (CO2) in Bengaluru, India. The study route included 150 unique kms (average: ~22 repeat measurements per monitored road segment). After cleaning the data for known instrument artifacts and sensitivities, we generated 30 m high-resolution stable ‘data only’ spatial maps of BC, UFP, and CO2 for the study route. For the urban residential areas, the mean BC levels for residential roads, arterials, and highways were ~ 10, 22, and 56 μg m−3, respectively. A similar pattern (highways being characterized by highest pollution levels) was also observed for UFP and CO2. Using the data from repeat measurements, we carried out a Monte Carlo subsampling analysis to understand the minimum number of repeat measures to generate stable maps of pollution in the city. Leveraging the simultaneous nature of the measurements, we also mapped the quasi-emission factors (QEF) of the pollutants under investigation. The current study is the first multi-season mobile monitoring exercise conducted in a low or middle -income country (LMIC) urban setting that oversampled the study route and investigated the optimum number of repeat rides required to achieve representative pollution spatial patterns characterized with high precision and low bias. Finally, the results are discussed in the context of technical aspects of the campaign, limitations, and their policy relevance for our study location and for other locations. Given the day-to-day variability in the pollution levels, the presence of dynamic and unorganized sources, and active government pollution mitigation policies, multi-year mobile measurement campaigns would help test the long-term representativeness of the current results.

Incorporating the impact of roadside barrier effects on dispersion into AERMOD

ABSTRACT This paper focuses on the impact of solid barriers located upwind of a highway in reducing vehicle related concentrations that occur downwind of the roadway, compared to a highway without barriers. Measurements made in the United States Environmental Protection Agency’s meteorological wind tunnel show that the mitigating impact of an upwind barrier is comparable to that of a downwind barrier. Upwind barriers lead to reductions in pollution concentrations by drawing emissions in from the highway toward the barrier. The emissions are then entrained into the flow above the recirculation zone and dispersed vertically as they are advected downwind. This upwind transport of vehicle emissions leads to concentrations at the center of the roadways that are roughly 200–300% higher than those measured on roadways with downwind barriers. This difference between on-road concentrations indicates that although both types of barriers mitigate the impact of vehicle emissions downwind of a roadway, the upwind barrier may create adverse air quality impacts for the people on the road. We have formulated a semiempirical dispersion model that incorporates the physics revealed by the wind tunnel measurements. This model improves upon a model proposed by Ahangar et al. (2017) by adjusting the wind speed to get a more realistic plume dispersion just downwind of the upwind barrier and also by providing vertical profiles of concentrations in addition to ground-level concentrations. The upwind barrier model proposed in this paper and the downwind barrier model described in Francisco et al. (2022) have been incorporated into AERMOD (version 21112) as a nonregulatory option, including the new two-barrier option when modeling both barriers on the same roadway. Implications: Our paper presents an air dispersion model algorithm for modeling the effect of upwind noise barriers on dispersion of traffic-related emissions from roadways, which was incorporated into EPA’s AERMOD and then evaluated using observations from a wind tunnel experiment. The results are compared and contrasted with results from both a no-barrier case and downwind barrier cases. This manuscript expands on previously published work analyzing the effect of barrier height and source-to-barrier distance on downwind dispersion (Atmos. Pollut. Res., 13:101385, 2022, https://doi.org/10.1016/j.apr.2022.101385). The current manuscript uses the same wind tunnel setup as reported there, but focuses on a different subset of cases, namely the upwind barrier cases, when developing dispersion model algorithms to simulate the observed effects. We believe the evaluations of the vertical profiles from the wind tunnel study, development, and incorporation of the upwind barrier algorithms into AERMOD, and model evaluation of these new algorithms are significant contributions to understanding the effects of these commonly used roadside barriers

On-board measurements using two successive vehicles to assess in-cabin concentrations of on-road pollutants

Gaseous and particulate pollutants are produced in large quantities from road vehicles. These traffic-related air pollutants can infiltrate the car-cabins exposing drivers and passengers to toxic substances with serious adverse health effects. The infiltration process depends on many parameters, those from outside of the vehicle (e.g. traffic intensity, meteorology, road type) and those depending on the vehicle itself such as the ventilation and/or the vehicle speed/age. In the present study, we undertook on-road measurements to assess the impact of ventilation and vehicles spacing (proximity effect) on in-cabin to on-road (I/O) concentration ratios of nitrogen oxides (NOx) and ultrafine particles (UFP). The novelty of the present work is that two successive vehicles have been used to circumvent the influence of the emissions of the upstream vehicle on the downstream test vehicle. We show that the distance to the upstream vehicle influences significantly the (I/O) ratios. Overall, the results show that car cabin is less polluted when the ventilation is set to recirculation mode and keeping a distance to the upstream car corresponding to twice its height in non-idle conditions.

Characterizing in-cabin air quality and vehicular air filtering performance for passenger cars in China

The rapidly growing vehicle population has become a crucial contributor to severe air-pollution and public health issues. In urban areas, vehicles have become one of the important sources of air pollutants such as nitrogen oxides and fine particulate matter (PM2.5). In particular, the on-road concentrations of traffic-related air pollutants (TRAPs) are typically many times higher than normal ambient concentrations, potentially leading to high in-vehicle exposure levels to TRAPs. Limited studies have focused on the variability in in-vehicle concentrations of TRAPs and linked the pollution level to both out-cabin conditions and the in-cabin filtration performance during real-world travels. Therefore, this study measured on-roadway, in-cabin concentrations of PM2.5 and carbon dioxide (CO2) by using well-calibrated low-cost sensors during various conditions. Our results indicate that, although in-cabin PM2.5 concentrations are correlated to out-cabin PM2.5 concentration levels, the control efficiency would be affected by the ventilation mode and the adoption of vehicular filtration device. The PM2.5 reduction efficiencies could achieve 45% and 77% for in-use and new filters made by vehicle manufacturers respectively, with the average CO2 concentration remained at a safe level (<800 ppm) under the in-vehicle outside air ventilation. The application of a high-efficiency cabin air (HECA) filter can further enhance the PM2.5 filtration efficiency up to 85–96%, indicating the significance of advanced cabin air filtration technology for improving in-cabin air quality and reducing health risk of Chinese drivers.

NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential

Traffic related nitrogen dioxide (NO2) poses a serious environmental and health risk factor in the urban environment. Drivers and vehicle occupants in general may have acute exposure to NO2 levels. In order to identify key controllable measures to reduce vehicle occupant's exposure, this study measures NO2 exposure inside ten different vehicles under real world driving conditions and applies a targeted intervention by replacing previously used filters with new standard pollen and new activated carbon cabin filters. The study also evaluates the efficiency of the latter as a function of duration of use. The mean in-vehicle NO2 exposure across the tested vehicles, driving the same route under comparable traffic and ambient air quality conditions, was 50.8 ± 32.7 μg/m3 for the new standard pollen filter tests and 9.2 ± 8.6 μg/m3 for the new activated carbon filter tests. When implementing the new activated carbon filters, overall we observed significant (p < 0.05) reductions by 87 % on average (range 80 - 94.2 %) in the in-vehicle NO2 levels compared to the on-road concentrations. We further found that the activated carbon filter NO2 removal efficiency drops by 6.8 ± 0.6 % per month; showing a faster decay in removal efficiency after the first 6 months of use. These results offer novel insights into how the general population can control and reduce their exposure to traffic related NO2. The use and regular replacement of activated carbon cabin air filters represents a relatively inexpensive method to significantly reduce in-vehicle NO2 exposure.

Mobile-monitoring of Black Carbon and Ultrafine particles in Bangalore, India

INTRODUCTION--Mobile monitoring can effectively capture spatial patterns in on-road exposure. Here, we report measured on-road concentrations of black carbon and ultrafine particles in urban and peri-urban Bangalore, India.METHODS--Our mobile platform, a CNG car, was equipped with an aethalometer for black carbon (BC) and a condensation particle counter for ultrafine particles (UFP). We sampled on roadways in four parts of Bangalore: a central business district, a residential urban neighborhood, a peri-urban neighborhood, and along an urban-rural transect; in total, we collected ~400 h (~5000 km) of on-road data. Our study design involved oversampling each road segment (n=25 repeated samples per road). We then quantify, via subsequent subsampling, the data requirements for robustly estimating time-integrated concentrations. We computed average concentration for each 30m road segment and computed mean and median concentrations for each region.RESULTS--Mean on-road concentrations were highest in the central business district (BC: 50 µg/m3; UFP: 145,000 cm-3), intermediate in the urban residential neighborhood (32 µg/m3; 87,000 cm-3), and lowest in the peri-urban neighborhood (29 µg/m3; 30,000 cm-3). Median values are 2% - 38% less than mean. The urban-rural transect reveals analogous spatial patterns: average concentrations decline when going from downtown to the peri-urban area; spatial gradients are larger for UFP than BC. Subsampling analyses reveal how the robustness of estimated mean concentrations varies with number of observations and the timing of those observations, and how that relationship varies in space.CONCLUSION--Average on-road concentrations decline from downtown to the peri-urban area; spatial gradients are larger for UFP than BC. Our subsampling analyses inform how many visits are needed to obtain robust estimates of long-term average concentrations, as well as how that result varies in space and time.

Reductions in traffic-related black carbon and ultrafine particle number concentrations in an urban neighborhood during the COVID-19 pandemic

We investigated changes in traffic-related air pollutant concentrations in an urban area during the COVID-19 pandemic. The study was conducted in a mixed commercial-residential neighborhood in Somerville (MA, USA), where traffic is the dominant source of air pollution. Measurements were made between March 27 and May 14, 2020, coinciding with a dramatic reduction in traffic (71% drop in car and 46% drop in truck traffic) due to business shutdowns and a statewide stay-at-home advisory. Indicators of fresh vehicular emissions (ultrafine particle number concentration [PNC] and black carbon [BC]) were measured with a mobile monitoring platform on an interstate highway and major and minor roadways. Our results show that depending on road class, median PNC and BC contributions from traffic were 60–68% and 22–46% lower, respectively, during the lockdown compared to pre-pandemic conditions, and corresponding reductions in total on-road concentrations were 45-69% and 22-56%, respectively. A higher BC: PNC concentration ratio was observed during the lockdown period likely indicative of the higher fraction of diesel vehicles in the fleet during the lockdown. Overall, the scale of reductions in ultrafine particle and BC concentrations was commensurate with the reductions in traffic. This natural experiment allowed us to quantify the direct impacts of reductions in traffic emissions on neighborhood-scale air quality, which are not captured by the regional regulatory-monitoring network. These results underscore the importance of measurements of appropriate proxies for traffic emissions at relevant spatial scales. Our results are useful for exposure analysis as well as city and regional planners evaluating mitigation strategies for traffic-related air pollution.

Measuring the spatial variability of black carbon in Athens during wintertime

A first assessment of the spatial variability of ambient black carbon (BC) concentrations in the Greater Area of Athens (GAA) was carried out during an intensive wintertime campaign, when ambient levels are exacerbated by increased biomass burning for residential heating. Short-term daytime BC measurements were conducted at 50 sites (traffic and urban/suburban/regional background) and on-road along 12 routes. Daytime measurements were adjusted based on BC concentrations continuously monitored at a reference site. Indicative nighttime BC ambient concentrations were also measured at several residences across the area. Daytime BC concentrations recorded an average of 2.3 μg m-3 with considerable between-site variability. Concentrations at traffic sites were significantly higher (43% on average), compared with the rest of the sites. Varying levels were observed between background site subtypes, with concentrations at urban background sites (located near the center of Athens and the port of Piraeus) being 34% and 114% higher, on average, than at suburban and regional background sites, respectively. The traffic intensity at the nearest road and the population and built density in the surrounding area of sites were recognized as important factors controlling BC levels. On-road concentration measurements (5.4 μg m-3 on average) enabled the identification of hot-spots in the road network, with peak levels encountered along motorways (13.5 μg m-3 on average). Nighttime measurements demonstrated that wintertime BC pollution, enhanced by residential biomass burning for heating, affects the entire Athens basin. The reference site in central Athens was found to be representative of the temporal variability for daytime and nighttime BC concentrations at background locations.

Variations in exposure to in-vehicle particle mass and number concentrations in different road environments

ABSTRACT Road environments significantly affect in cabin concentration of particulate matter (PM). This study conducted measurements of in-vehicle and on-road concentrations of PM10, PM2.5, PM1, and particle number (PN) in size of 0.02–1 µm, under six ventilation settings in different urban road environments (tunnels, surface roads and elevated roads). Linear regression was then used to analyze the contributions of multiple predictor variables (including on-road concentrations, temperature, relative humidity, time of day, and ventilation settings) to measured variations. On-road measurements of PM2.5, PM1, and PN concentrations from the open surface roads were 5.5%, 3.7%, and 16% lower, respectively, than those measured in tunnels, but 7.6%, 7.1% and 24% higher, respectively, than those on elevated roads. The highest on-road PM10 concentration was observed on surface roads. The time series pattern of in-vehicle particle concentrations closely tracked the on-road concentrations outside of the car and exhibited a smoother profile. Irrespective of road environment, the average I/O ratio of particles was found to be the lowest when air conditioning was on with internal recirculation, the highest purification efficiency via ventilation was obtained by switching on external air recirculation and air conditioning. Statistical models showed that on-road concentration, temperature, and ventilation setting are common factors of significance that explained 58%-80%, 64%-97%, and 87%-98% of the variations in in-vehicle PM concentrations on surface roads, on elevated roads, and in tunnels, respectively. Implications: Inside vehicles, both driver and passengers will be exposed to elevated particle concentrations. However, for in-vehicle particles, there has been no comprehensive comparative study of the three-dimensional traffic environment including tunnels surface roads and elevated roads. This study focuses on the analysis of the trends and main influencing factors of particle concentrations in different road environments. The results can provide suggestions for the driver's behavior, and provide data support for the environmental protection department to develop pollutant concentration limits within the vehicle.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Mapping Air Pollution with Google Street View Cars: Towards Efficient Mobile Monitoring

Air pollution measurements collected through systematic mobile monitoring campaigns can provide exposure data at high spatial resolution. Approaches to minimize the amount of data and driving effort required to successfully map an urban area&amp;#8217;s air quality may help improve the scalability of this measurement approach. We explore the data requirements for mapping a city&amp;#8217;s air quality using mobile monitors utilizing &amp;#8220;data only&amp;#8221; and predictive model approaches. We used two Google Street View cars with fast-response (1 Hz) instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. This rich dataset incorporates ~ 3.6 million measurements collected repeatedly (20-50 &amp;#215;) on every city street over 2 y. We explore two alternative strategies to efficiently mapping spatial air quality patterns. First, we use a &amp;#8220;data-only&amp;#8221; approach where we attempt to minimize the number of repeated visits to each road. Second, we combine our data with a land use regression-kriging (LURK) model to predict at unobserved locations, and consider sampling schemes where only a subset of a city&amp;#8217;s roads or repeat visits are measured. In all cases, we use a Monte Carlo scheme to systematically subsample the full dataset. LURK models consistently captured general spatial trends in urban air pollution, with cross-validation R2 for log-transformed-NO and BC of 0.65 and 0.5. While LURK models did not capture the full variability of on-road concentrations, models could successfully be trained with minimal data requirements, e.g., with only a small number of repeated observations on ~20% of the roads. Data-only mapping performed poorly for &amp;lt; 4 repeated drives per road segment, but obtained surpassed the performance of LURK approach within 4 to 8 repeated drive days per road segment. Data-only mapping can have surprisingly modest data requirements. LURK models can efficiently produce valid predictions of air quality when it is not possible to sample air quality on every road.

High spatiotemporal characterization of on-road PM2.5 concentrations in high-density urban areas using mobile monitoring

Mobile air quality monitoring reports air pollutant concentrations at a high spatiotemporal resolution, enabling the characterization of heterogeneous human exposure and localized pollution hotspots. In this study, on-road concentrations of fine particulate matter (PM2.5) in a high-density urban area in Hong Kong were measured in December 2014 and January 2015 (winter) and June and July 2015 (summer) using a tramcar mobile monitoring platform. We developed a method of mapping the winter and summer on-road PM2.5 concentrations along a tramcar route at a 50-m spatial resolution, using mobile measurements. In addition, the minimum number of days required to precisely estimate on-road PM2.5 concentrations was estimated. The results showed that the on-road PM2.5 concentrations were highly correlated with PM2.5 concentrations measured at a nearby roadside air quality monitoring station (AQMS) in both winter and summer, with Pearson correlation coefficients of 0.89–0.93. The resulting maps of winter and summer on-road PM2.5 concentrations revealed small-scale spatial patterns used to identify more polluted areas. In addition, approximately 12 and 4 days were required to precisely capture spatial patterns of PM2.5 concentrations, with R2 higher than 0.6 in winter and in summer. The findings of this study offer valuable information on air pollution control and exposure reduction by highlighting localized pollution hotspots, and provide insights into the minimum sampling duration for mobile sampling campaigns.

Assessing and reducing fine and ultrafine particles inside Los Angeles taxis

Taxi drivers and passengers are exposed to high levels of traffic-related air pollutants, but their exposures to fine (PM2.5) and ultrafine particles (UFPs) and related mitigation strategies are rarely explored. In this study, UFP and PM2.5 concentrations were monitored concurrently inside and outside of 22 taxis under different ventilation and mitigation conditions. Under realistic working conditions (no mitigation; NM), the average UFP and PM2.5 levels inside taxis were 1.46 × 104 particles/cm3 and 26 μg/m3, respectively. When the taxi ventilation was set to outside air mode and the windows kept closed, in-cabin UFP and PM2.5 concentrations are significantly associated with on-road concentrations, driving speed, and cabin air filter usage. The average in-cabin to on-roadway (I/O) ratios for UFP and PM2.5 were reduced from 0.60 to 0.75 under NM, to 0.47 and 0.52 under the most stringent mitigation strategy of keeping the windows closed and operating a high efficiency cabin air filter (WC + HECA). Among all tested taxi models, Toyota Prius exhibited the lowest UFP and PM2.5 I/O ratios under WC + HECA. Switching cabin air filters from the originally equipped manufacturer filter (OEM) to a HECA filter reduced the UFP and PM2.5 I/O ratios most effectively in Toyota Prius taxis as well.

On-Road Air Quality Associated with Traffic Composition and Street-Canyon Ventilation: Mobile Monitoring and CFD Modeling

Mobile monitoring and computational fluid dynamics (CFD) modeling are complementary methods to examine spatio-temporal variations of air pollutant concentrations at high resolutions in urban areas. We measured nitrogen oxides (NOx), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (pPAH), and particle number (PN) concentrations in a central business district using a mobile laboratory. The analysis of correlations between the measured concentrations and traffic volumes demonstrate that high emitting vehicles (HEVs) are deterministically responsible for poor air quality in the street canyon. The determination coefficient (R2) with the HEV traffic volume is the largest for the pPAH concentration (0.79). The measured NOx and pPAH concentrations at a signalized intersection are higher than those on a road between two intersections by 24% and 25%, respectively. The CFD modeling results reveal that the signalized intersection plays a role in increasing on-road concentrations due to accelerating and idling vehicles (i.e., emission process), but also plays a countervailing role in decreasing on-road concentrations due to lateral ventilation of emitted pollutants (i.e., dispersion process). It is suggested that the number of HEVs and street-canyon ventilation, especially near a signalized intersection, need to be controlled to mitigate poor air quality in a central business district of a megacity.

Reduction of air pollution levels downwind of a road with an upwind noise barrier

We propose a dispersion model to estimate the impact of a solid noise barrier upwind of a highway on air pollution concentrations downwind of the road. The model, based on data from wind tunnel experiments conducted by Heist et al. (2009), assumes that the upwind barrier has two main effects: 1) it creates a recirculation zone behind the barrier that sweeps the emissions from the highway back towards the wall, and 2) it enhances vertical dispersion and initial mixing. By combining the upwind barrier model with the mixed wake model for a downwind barrier described in Schulte et al. (2014), we are able to model dispersion of emissions from a highway with noise barriers on both sides. The model provides a good description of measurements made in the wind tunnel. The presence of an upwind barrier causes reductions in concentrations relative to those measured downwind of a road with no barriers. The reduction can be as large as that caused by a downwind barrier if the recirculation zone covers the width of the highway. Barriers on both sides of the highway result in larger reductions downwind of the barriers than those caused by a single barrier either upwind or downwind. As expected, barrier effects are small beyond 10 barrier heights downwind of the highway. We also propose a tentative model to estimate on-road concentrations within the recirculation zone induced by the upwind barrier.

Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data.

This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO2) and oxides of nitrogen (NOX) concentrations and near-road spatial gradients using data from a near-road monitoring network. Field data were collected in Las Vegas, NV at three monitors sited 20, 100, and 300 m from Interstate-15 between December, 2008 and January, 2010. Measurements of NO2 and NOX were integrated over 1-hour intervals and matched with meteorological data. Several mathematical transformations were tested for regressing pollutant concentrations against distance from the roadway. A logit-ln model was found to have the best fit (R2 = 94.7%) and also provided a physically realistic profile. The mathematical model used data from the near-road monitors to estimate on-road concentrations and the near-road gradient over which mobile source pollutants have concentrations elevated above background levels. Average and maximum on-road NO2 concentration estimates were 33 ppb and 105 ppb, respectively. Concentration gradients were steeper in the morning and late afternoon compared with overnight when stable conditions preclude mixing. Estimated on-road concentrations were also highest in the late afternoon. Median estimated on-road and gradient NO2 concentrations were lower during summer compared with winter, with a steeper gradient during the summer, when convective mixing occurs during a longer portion of the day On-road concentration estimates were higher for winds perpendicular to the road compared with parallel winds and for atmospheric stability with neutral-to-unstable atmospheric conditions. The concentration gradient with increasing distance from the road was estimated to be sharper for neutral-to-unstable conditions when compared with stable conditions and for parallel wind conditions compared with perpendicular winds. A regression of the NO2/NOX ratios yielded on-road ratios ranging from 0.25 to 0.35, substantially higher than the anticipated tail-pipe emissions ratios. The results from the ratios also showed that the diurnal cycle of the background NO2/NOX ratios were a driving factor in the on-road and downwind NO2/NOX ratios.

PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework.

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

Simultaneous measurements of on-road/in-vehicle nanoparticles and NOx while driving: Actual situations, passenger exposure and secondary formations

Simultaneous measurements of on-road and in-vehicle NO and NO2 levels, particle number concentrations (PNCs), and particles size distributions were performed while driving using a test vehicle equipped with real-time sensors. The results obtained on regional roads showed that heavy-duty vehicles in traffic seem to have a major impact on on-road air quality. Measurements on highways that included a 10km tunnel and a 2km uphill section of road indicated that sub-50nm particles have different features from the other species because of their higher volatility. The other species showed quite high on-road concentrations in the tunnel. In-vehicle conditions were made similar to the on-road ones by setting the air conditioning (AC) mode to the fresh air mode. The in-vehicle NO2 concentration in the tunnel was over 0.50ppmV, which is almost five times higher than the 1-hour ambient air quality standard proposed by the World Health Organization (WHO). In sections other than the tunnel, the in-vehicle NO2 concentration was almost the same as the 1-hour WHO standard. Higher on-road NO2/NOx ratios than those of exhaust gases and different behavior of sub-50nm particles from other species suggested that NO2 and sub-50nm particles were mainly due to secondary products formed by atmospheric reactions.

Influence of solid noise barriers on near-road and on-road air quality

Abstract Public health concerns regarding adverse health effects for populations spending significant amounts of time near high traffic roadways has increased substantially in recent years. Roadside features, including solid noise barriers, have been investigated as potential methods that can be implemented in a relatively short time period to reduce air pollution exposures from nearby traffic. A field study was conducted to determine the influence of noise barriers on both on-road and downwind pollutant concentrations near a large highway in Phoenix, Arizona, USA. Concentrations of nitrogen dioxide, carbon monoxide, ultrafine particles, and black carbon were measured using a mobile platform and fixed sites along two limited-access stretches of highway that contained a section of noise barrier and a section with no noise barrier at-grade with the surrounding terrain. Results of the study showed that pollutant concentrations behind the roadside barriers were significantly lower relative to those measured in the absence of barriers. The reductions ranged from 50% within 50 m from the barrier to about 30% as far as 300 m from the barrier. Reductions in pollutant concentrations generally began within the first 50 m of the barrier edge; however, concentrations were highly variable due to vehicle activity behind the barrier and along nearby urban arterial roadways. The concentrations on the highway, upwind of the barrier, varied depending on wind direction. Overall, the on-road concentrations in front of the noise barrier were similar to those measured in the absence of the barrier, contradicting previous modeling results that suggested roadside barriers increase pollutant levels on the road. Thus, this study suggests that noise barriers do reduce potential pollutant exposures for populations downwind of the road, and do not likely increase exposures to traffic-related pollutants for vehicle passengers on the highway.