From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Air pollution mortality benefits of sustained COVID-19 mobility restrictions in Australian cities.

OBJECTIVES: Emissions from road traffic, power generation and industry were substantially reduced during pandemic lockdown periods globally. Thus, we analysed reductions in traffic-related air pollution in Australian capital cities during March-April 2020 and then modelled the mortality benefits that could be realised if similar reductions were sustained by structural policy interventions. STUDY DESIGN: Satellite, air pollution monitor and land use observations were used to estimate ground-level nitrogen dioxide (NO2) concentrations in all Australian capital cities during: (a) a typical year with no prolonged air pollution events; (b) a hypothetical sustained reduction in NO2 equivalent to the COVID-19 lockdowns. METHODS: We use the WHO recommended NO2 exposure-response coefficient for mortality (1.023, 95 % CI: 1.008-1.037, per 10 µg/m3 annual average) to assess gains in life expectancy and population-wide years of life from reduced exposure to traffic-related air pollution. RESULTS: We attribute 1.1 % of deaths to anthropogenic NO2 exposures in Australian cities, corresponding to a total of 13,340 years of life lost annually. Although COVID-19-related reductions in NO2 varied widely between Australian cities during April 2020, equivalent and sustained reductions in NO2 emissions could reduce NO2-attributable deaths by 27 %, resulting in 3348 years of life gained annually. CONCLUSIONS: COVID-19 mobility restrictions reduced NO2 emissions and population-wide exposures in Australian cities. When sustained to the same extent by policy interventions that reduce fossil fuel consumption by favouring the uptake of electric vehicles, active travel and public transport, the health, mortality and economic benefits will be measurable in Australian cities.

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area.

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO2. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 µm2 cm-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 µm2 cm-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO2) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM2.5.

Characterizing emission factors and oxidative potential of motorcycle emissions in a real-world tunnel environment.

Transportation emissions significantly affect human health, air quality, and climate in urban areas. This study conducted experiments in an urban tunnel in Taipei, Taiwan, to characterize vehicle emissions under real driving conditions, providing emission factors of PM2.5, eBC, CO, and CO2. By applying multiple linear regression, it derives individual emission factors for heavy-duty vehicles (HDVs), light-duty vehicles (LDVs), and motorcycles (MCs). Additionally, the oxidative potential using dithiothreitol assay (OPDTT) was established to understand PM2.5 toxicity. Results showed HDVs dominated PM2.5 and eBC concentrations, while LDVs and MCs influenced CO and CO2 levels. The CO emission factor for transportation inside the tunnel was found to be higher than those in previous studies, likely owing to the increased fraction of MCs, which generally emit higher CO levels. Among the three vehicle types, HDVs exhibited the highest PM2.5 and eBC emission factors, while CO and CO2 levels were relatively higher for LDVs and MCs. The OPDTTm demonstrated that fresh traffic emissions were less toxic than aged aerosols, but higher OPDTTv indicated the impact on human health cannot be ignored. This study updates emission factors for various vehicle types, aiding in accurate assessment of transportation emissions' effects on air quality and human health, and providing a guideline for formulating mitigation strategies.

Long-term residential exposure to source-specific particulate matter and incidence of diabetes mellitus - A cohort study in northern Sweden.

Diabetes mellitus (DM) incidence have been assessed in connection with air pollution exposure in several studies; however, few have investigated associations with source-specific local emissions. This study aims to estimate the risk of DM incidence associated with source-specific air pollution in a Swedish cohort with relatively low exposure. Individuals in the Västerbotten intervention programme cohort were followed until either a DM diagnosis or initiation of treatment with glucose-lowering medication occurred. Dispersion models with high spatial resolution were used to estimate annual mean concentrations of particulate matter (PM) with aerodynamic diameter ≤10 µm (PM10) and ≤2.5 µm (PM2.5) at individual addresses. Hazard ratios were estimated using Cox regression models in relation to moving averages 1-5 years preceding the outcome. During the study period, 1479 incident cases of DM were observed during 261,703 person-years of follow-up. Increased incidence of DM was observed in association with PM10 (4% [95% CI: -54-137%] per 10 µg/m3), PM10-traffic (2% [95% CI: -6-11%] per 1 µg/m3) and PM2.5-exhaust (11% [95% CI: -39-103%] per 1 µg/m3). A negative association was found for both PM2.5 (-18% [95% CI: -99-66%] per 5 µg/m3), but only in the 2nd exposure tertile (-10% [95% CI: -25-9%] compared to the first tertile), and PM2.5-woodburning (-30% [95% CI: -49-4%] per 1 µg/m3). In two-pollutant models including PM2.5-woodburning, there was an 11% [95% CI: -11-38%], 6% [95% CI: -16-34%], 13% [95% CI: -7-36%] and 17% [95% CI: 4-41%] higher risk in the 3rd tertile of PM10, PM2.5, PM10-traffic and PM2.5-exhaust, respectively, compared to the 1st. Although the results lacked in precision they are generally in line with the current evidence detailing particulate matter air pollution from traffic as an environmental risk factor for DM.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m3 to 12.283 µg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.

Fifteen Years of Community Exposure to Heavy-Duty Emissions: Capturing Disparities over Space and Time.

Disparities in exposure to traffic-related air pollution have been widely reported. However, little work has been done to simultaneously assess the impact of various vehicle types on populations of different socioeconomic/ethnic backgrounds. In this study, we employed an extreme gradient-boosting approach to spatially distribute light-duty vehicle (LDV) and heavy-duty truck emissions across the city of Toronto from 2006 to 2020. We examined associations between these emissions and different marginalization indices across this time span. Despite a large decrease in traffic emissions, disparities in exposure to traffic-related air pollution persisted over time. Populations with high residential instability, high ethnic concentration, and high material deprivation were found to reside in regions with significantly higher truck and LDV emissions. In fact, the gap in exposure to traffic emissions between the most residentially unstable populations and the least residentially unstable populations worsened over time, with trucks being the larger contributor to these disparities. Our data also indicate that the number of trucks and truck emissions increased substantially between 2019 and 2020 whilst LDVs decreased. Our results suggest that improvements in vehicle emission technologies are not sufficient to tackle disparities in exposure to traffic-related air pollution.

Use of historical mapping to understand sources of soil-lead contamination: Case study of Santa Ana, CA.

This paper investigates the historical sources of soil-lead contamination in Santa Ana, California. Even though dangerous levels of soil-lead have been found in a wide variety of communities across the United States, public health institutions lack clarity on the historical origins of these crises. This study uses geo-spatial data collected through archival research to estimate the impact of two potential sources of lead contamination in the past -- lead-paint and leaded gasoline. It examines, through a combination of statistical and historical methods, the association between lead concentrations in contemporary soil samples and patterns in the evolution of the city's physical features, such as the growth of urbanized areas and the historical flow of traffic. We emphasize the value of historical data collected through archival research for understanding the sources of environmental lead, particularly leaded gasoline, which our study found to be the most likely and most prominent contributor to soil-lead in Santa Ana's environment. This research contributes to environmental-justice advocacy efforts to reframe lead poisoning as a systemic environmental issue and outlines the path forward to community-level remediation strategies.

Long-term characterization of roadside air pollutants in urban Beijing and associated public health implications.

Road traffic constitutes a major source of air pollutants in urban Beijing, which are responsible for substantial premature mortality. A series of policies and regulations has led to appreciable traffic emission reductions in recent decades. To shed light on long-term (2014-2020) roadside air pollution and assess the efficacy of traffic control measures and their effects on public health, this study quantitatively evaluated changes in the concentrations of six key air pollutants (PM2.5, PM10, NO2, SO2, CO and O3) measured at 5 roadside and 12 urban background monitoring stations in Beijing. We found that the annual mean concentrations of these air pollutants were remarkably reduced by 47%-71% from 2014 to 2020, while the concurrent ozone concentration increased by 17.4%. In addition, we observed reductions in the roadside increments in PM2.5, NO2, SO2 and CO of 54.8%, 29.8%, 20.6%, and 59.1%, respectively, indicating the high effectiveness of new vehicle standard (China V and VI) implementation in Beijing. The premature deaths due to traffic emissions were estimated to be 8379 and 1908 cases in 2014 and 2020, respectively. The impact of NO2 from road traffic relative to PM2.5 on premature mortality was comparable to that of traffic-related PM2.5 emissions. The public health effect of SO2 originating from traffic was markedly lower than that of PM2.5. The results indicated that a reduction in traffic-related NO2 could likely yield the greatest benefits for public health.

Health risks of heavy metal contamination in road surface dusts from selected major roads in East London, South Africa.

The study assessed the concentration and health hazards of Cd, Mn, Pb, Ni and Zn in 54 road surface dust samples. Heavy metal concentrations descended as follows: Mn > Zn > Pb > Ni > Cd and the concentrations were 316.22, 161.24, 88.76, 22.69 and 0.21. Health hazard assessment showed that the modes of exposure descended in the sequence of ingestion > dermal contact > inhalation for children and dermal contact > ingestion > inhalation for adults. The hazard index for both populations was >1 suggesting susceptibility to non-cancer risks. The carcinogenic risk for Ni and Cd was below 1 × 10-4 suggesting a negligible risk of cancer for both populations. The study suggests that more research studies on HM contamination in road dusts are to be undertaken in the country to develop HM pollution threshold values in road dusts and to account for pollution in city centers.

An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India.

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi.

Traffic restrictions during COVID-19 lockdown improve air quality and reduce metal biodeposition in tree leaves.

The coronavirus disease (COVID-19) has had a great global impact on human health, the life of people, and economies all over the world. However, in general, COVID-19´s effect on air quality has been positive due to the restrictions on social and economic activity. This study aimed to assess the impact on air quality and metal deposition of actions taken to reduce mobility in 2020 in two different urban locations. For this purpose, we analysed air pollution (NO2, NO, NOx, SO2, CO, PM10, O3) and metal accumulation in leaves of Tilia cordata collected from April to September 2020 in two cities in northern Spain (Pamplona-PA and San Sebastián-SS). We compared their values with data from the previous year (2019) (in which there were no mobility restrictions) obtained under an identical experimental design. We found that metal accumulation was mostly lower during 2020 (compared with 2019), and lockdown caused significant reductions in urban air pollution. Nitrogen oxides decreased by 33%-44%, CO by 24%-38%, and PM10 by 16%-24%. The contents of traffic-related metals were significantly reduced in both studied cities. More specifically, significant decreases in metals related to tyre and brake wear (Zn, Fe, and Cu) and road dust resuspension (Al, Ti, Fe, Mn, and Ca) were observed. With these results, we conclude that the main reason for the improvement in urban air pollutants and metals was the reduction in the use of cars due to COVID-19 lockdown. In addition, we offer some evidence indicating the suitability of T. cordata leaves as a tool for biomonitoring metal accumulation. This information is relevant for future use by the scientific community and policy makers to implement measures to reduce traffic air pollution in urban areas and to improve environmental and human health.

Proximity of schools to roads and students' academic performance: A cross-sectional study in the Federal District, Brazil.

Investigations of the educational implications of children's exposure to air pollutants at school are crucial to enhance our understanding of the hazards for children. Most of the existing literature is based on studies performed in North America and Europe. Further investigation is required in low- and middle-income countries, where there are important challenges related to public health, transportation, environment, and education sector. In response, in this present study, we studied the association between proximity of schools to roads and the academic achievement of the students in the Federal District, Brazil. We accessed academic achievement data at the student level. The data consist of 256 schools (all the public schools in the FD) and a total of 344,175 students (all the students enrolled in the public schools in the FD in 2017-2020). We analyzed the association between the length of all roads within buffers around schools and student-level academic performance using mixed-effects regression models. After adjustments for several covariates, the results of the primary analysis indicate that the presence of roads surrounding schools is negatively associated with student-level academic performance in the FD. This association varies significantly depending on the buffer size surrounding schools. We found that the highest effects occur in the first buffer, with 250 m. While in the first buffer we estimated that an increase of 1 km of length of roads around schools was associated with a statistically significant decrease of 0.011 (95%CI: 0.008; 0.013) points in students' grades (students' academic performance varies from 0 to 10), in the buffer of 1 km we found a decrease of 0.002 (95%CI: 0.002; 0.002) points in the student-level academic performance. Findings from our investigation provide support for the creation of effective health, educational and urban planning policies for local intervention in the FD. This is essential to improve the environmental quality surrounding schools to protect children from exposure to environmental hazards.

Characterizing the Performance of a Compact BTEX GC-PID for Near-Real Time Analysis and Field Deployment.

In this study, we test the performance of a compact gas chromatograph with photoionization detector (GC-PID) and optimize the configuration to detect ambient (sub-ppb) levels of benzene, toluene, ethylbenzene, and xylene isomers (BTEX). The GC-PID system was designed to serve as a relatively inexpensive (~10 k USD) and field-deployable air toxic screening tool alternative to conventional benchtop GCs. The instrument uses ambient air as a carrier gas and consists of a Tenax-GR sorbent-based preconcentrator, a gas sample valve, two capillary columns, and a photoionization detector (PID) with a small footprint and low power requirement. The performance of the GC-PID has been evaluated in terms of system linearity and sensitivity in field conditions. The BTEX-GC system demonstrated the capacity to detect BTEX at levels as high as 500 ppb with a linear calibration range of 0-100 ppb. A detection limit lower than 1 ppb was found for all BTEX compounds with a sampling volume of 1 L. No significant drift in the instrument was observed. A time-varying calibration technique was established that requires minimal equipment for field operations and optimizes the sampling procedure for field measurements. With an analysis time of less than 15 min, the compact GC-PID is ideal for field deployment of background and polluted atmospheres for near-real time measurements of BTEX. The results highlight the application of the compact and easily deployable GC-PID for community monitoring and screening of air toxics.

Characterization and exposure assessment to urban air toxics across Middle Eastern and North African countries: a review.

Middle East and North African (MENA) countries over the decades are experiencing rapid industrial and infrastructural growth combined with being the global hub of oil and gas industries. These economic transformations are associated with release of air pollutants including urban air toxics (UAT) through industrial, traffic, and constructional activities into ambient urban environments. UAT concentrations levels may exacerbate in most MENA countries considering high number of vehicular traffic populations and petrochemical industries which are one of the main sources of this pollutant. Therefore, the main objective of the study is to review major findings of UAT levels in urban areas across thirteen (13) MENA countries. The study characterizes various measured UAT, assesses their concentrations in ambient environment, and identifies their major sources of emissions by reviewing more than 100 relevant UAT papers across the selected MENA countries. It was found that benzene, heavy metals, formaldehyde, and dioxin-like compounds are the most reported UAT. The study concluded that road traffic, fuel stations, and petrochemical industries were identified as the main sources of ambient UAT levels. It was further reported that most of the studies were based on short-term ambient environment with limited studies in indoor environments. Therefore, it is highly recommended that future research should focus on innovative health impact assessment and epidemiological studies from exposure to UAT levels. Also embarking on sustainable mitigation approaches through urban greenery, eco-industrial estates infrastructural developments, and renewable energy shares will reduce UAT levels and improve human health.

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.

Monitoring and assessment of cadmium, lead, zinc and copper concentrations in arable roadside soils in terms of different traffic conditions.

Quantification of the contributions from traffic source to arable roadside soil heavy metal loadings is a challenge. The contribution depends on: traffic intensity, road type and distance from the road. At a field scale (3.9 ha), 720 topsoil samples were taken. The aim of the study was to monitor and assess the impact of regional/local roads with various conditions of traffic and period of use on the distribution of Cd, Zn, Pb and Cu in the arable roadside topsoil in their vicinity. PCA indicated the occurrence of two main gradients of 11 environmental elements influencing the distribution of heavy metals in the soils of the neighbouring land. The first gradient was associated mainly with the distance from the edge of the road. The second gradient was associated with the degree of contamination of the soils and with the road type, defined by the traffic volume and period of being use. Anova reviled lack of influence of the factors for Cu contents. Unlike Cu, for Cd, Pb and Zn, the significant impact was observed for both factors and interactions between them. The concentrations of Cd, Pb and Zn, regardless of the distance from the road were 0.21-0.58 mg Cd kg-1 d.m., 13.60-41.96 mg Pb kg-1 d.m. and 40.31-63.97 mg Zn kg-1 d.m. In case of increasing distance from the road, generally the contents of Pb, Zn and Cd contents were decreasing. However, only in the case the oldest and carrying the highest traffic road was a clear, statistically significant differences noted for following distances from the road on the content of Cd, Pb and Zn. Analysis of spread gave trend curves, for Pb, Cd and Zn they were parabolas. The curves let reduce sapling distances to 65 m, 45 and 47 m for Cd, Pb and Zn, respectively.

[Air pollution and atopic eczema : Systematic review of findings from environmental epidemiological studies]. / Luftverschmutzung und atopisches Ekzem : Systematisches Review der Erkenntnisse aus umweltepidemiologischen Studien.

BACKGROUND: Among the many risk factors for the development of atopic eczema (AE), the influence of air pollution has recently been discussed more often. A systematic review about this topic however is lacking. AIMS: Which effects of outdoor air pollution (particles, nitric oxides, sulfur dioxide, ozone or general traffic exhaust emissions) on AE can be demonstrated in a systematic analysis of available environmental epidemiologic studies? METHODS: All environmental epidemiologic studies on AE and air pollution found in the literature database PubMed were identified. The most important key figures of these studies were tabulated, the quality of evidence was graded and the studies described. RESULTS: A total of 57 studies were identified. Only one of the 15 cross-sectional studies with a large-scale exposure assessment found a significant association between AE and air pollution. In contrast 23 of 30 studies with small-scale exposure assessment found a significant association between AE and traffic related emissions-especially from trucks. Of the 30 studies, 14 were cohort studies (1 adult, 13 birth cohorts). The sole adult cohort found an association with intrinsic AE. In the East Asian cohorts (all published since 2015), an association between maternal exposure to traffic-related pollution and incidence of AE in the offspring was found. This was less clear in cohorts from Europe/US or simply not investigated. In 5/5 panel studies (all from South Korea), symptom severity of AE was found to be significantly and positively related to outdoor air pollution. CONCLUSIONS: In a systematic analysis of environmental epidemiologic studies about air pollution and AE rather good evidence was found that, based on small-scale exposure measurements, especially truck traffic emissions increased AE prevalence, while large-scale exposure to larger particles (PM10) or SO2 was without effect. Considering pathophysiologic aspects traffic exhaust emissions seem to affect both skin barrier function and activation of immune responses.

Determination of antimony and barium in UK air quality samples as indicators of non-exhaust traffic emissions.

The aim of this study was to determine concentrations of antimony and barium in air quality samples and assess the potential suitability of these metals as tracer elements for non-exhaust traffic emissions sources. Air quality filters sampled at monitoring sites around the UK for the purposes of the UK Heavy Metals Monitoring Network were subjected to microwave digestion in hydrogen peroxide and nitric acid, then analysed by ICP-MS for a suite of metals including antimony and barium. The average antimony concentration found across all the network sites was 1.84 ng m-3; the average barium concentration was 6.33 ng m-3. The range of antimony concentrations observed was 0.13-8.02 ng m-3; barium concentrations ranged from levels below the detection limit of 0.18 to 39.9 ng m-3. There are no legislative limits for antimony and barium in ambient air, but the maximum concentrations found are well below the Workplace Exposure Limits specified by the UK Health and Safety Executive. The highest concentrations were found at roadside sites situated to monitor traffic environments, supporting the suitability of antimony and barium to be considered tracer elements for traffic emissions sources. Strong correlations were observed between antimony, barium and copper, indicating they share a common traffic-related source. Based on the strong correlation with copper at urban and traffic locations, indicative annual UK atmospheric emission estimates for antimony and barium in brake and tyre wear were calculated as 6 and 19 tonnes respectively.

Development and Evaluation of the R-LINE Model Algorithms to Account for Chemical Transformation in the Near-road Environment.

With increased urbanization, there is increased mobility leading to higher amount of traffic-related activity on a global scale. Most NOx from combustion sources (about 90-95%) are emitted as NO, which is then readily converted to NO2 in the ambient air, while the remainder is emitted largely as NO2. Thus, the bulk of ambient NO2 is formed due to secondary production in the atmosphere, and which R-LINE cannot predict given that it can only model the dispersion of primary air pollutants. NO2 concentrations near major roads are appreciably higher than those measured at monitors in existing networks in urban areas, motivating a need to incorporate a mechanism in R-LINE to account for NO2 formation. To address this, we implemented three different approaches in order of increasing degrees of complexity and barrier to implementation from simplest to more complex. The first is an empirical approach based upon fitting a 4th order polynomial to existing near-road observations across the continental U.S., the second involves a simplified two-reaction chemical scheme, and the third involves a more detailed set of chemical reactions based upon the Generic Reaction Set (GRS) mechanism. All models were able to estimate more than 75% of concentrations within a factor of two of the near-road monitoring data and produced comparable performance statistics. These results indicate that the performance of the new R-LINE chemistry algorithms for predicting NO2 is comparable to other models (i.e. ADMS-Roads with GRS), both showing less than ±15% fractional bias and less than 45% normalized mean square error.