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Background: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monitoring stations may be unable to predict and characterize fine-scale TRAP, especially in complex urban environments influenced by various features. This study aims to estimate fine-scale (50 m) concentrations of nitrogen oxides (NO and NO₂) in Hong Kong using a deep learning (DL) structured model. Methods: We collected data from mobile air quality sensors on buses and crowd-sourced Google real-time traffic status as a proxy for real-time traffic emissions. Our DL model was compared with existing machine learning models to assess performance improvements. Using an interpretable machine learning method, we hierarchically evaluated the global, local, and interaction effects for different features. Results: Our DL model outperformed existing machine learning models, achieving R2 values of 0.72 for NO and 0.69 for NO₂. The incorporation of traffic status as a key predictor improved model performance by 9% to 17%. The interpretable machine learning method revealed the importance of traffic-related features and their pairwise interactions. Conclusion: The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.

Ammonia (NH 3 ) measurements were performed with a mobile platform deploying a cavity ring-down spectroscopy NH 3 analyzer in Beijing. The transect and loop sampling strategy revealed that the Beijing urban area is more strongly affected by NH 3 emissions than surrounding areas. Although average enhancements of on-road NH 3 were small compared to background levels, traffic emissions clearly dominated city enhancements of NH 3 , carbon dioxide (CO 2 ), acetaldehyde and acetone. Increments of on-road NH 3 ranged between 5.1 ppb and 11.4 ppb in urban areas, representing an enhancement of 20.6 % to 47.9 % over the urban background. The vehicle NH 3 :CO 2 emission ratio was 0.26 ppb/ppm, about a factor of 1.5 higher than the value derived from the available emission inventory. The obtained NH 3 emission factor was approximately 306.9 mg/kg. If the annual gasoline consumption in Beijing is accurate, annual NH 3 emissions from vehicles are estimated at 1.5 Gg. The influx and outflux of NH 3 in Beijing during monitoring periods fluctuated due to variations of wind direction (WD), wind speed (WS), and planetary boundary layer height (PBLH). Net fluxes at the 4 th Ring Road were larger than zero, suggesting that local emissions were important in urban Beijing. Negative net fluxes at the 6 th Ring Road reveal a large amount of NH 3 transported from agricultural regions south of Beijing lost during transport across the city, for example by deposition or particle formation in the city. Our analyses have important implications for regional NH 3 emission estimates and for improving vehicular NH 3 emission inventory allocations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The high-spatial-resolution distributions of the mass concentration and chemical composition of submicron particulate matter (PM 1 ) across four different functional districts in Lanzhou, a typical northwestern city in China, were studied during the winter haze pollution period using an on-road real-time mobile monitoring system. The purpose of this study is to characterize the spatial variation in the sources and chemical formation of aerosols at the intra-urban scale. A higher PM 1 mass concentration (63.0 μg m -3 ) was observed in an industrially influenced district (XG) with major contributions (70.4%) from three secondary inorganic species (sulfate, nitrate, and ammonium) and two oxygenated organic aerosol (OOA) components with different oxygenation levels. Compared with the densely populated district (CG), sulfate and more-oxidized OOA were the two most distinct contributors to the elevated PM 1 mass in XG during the daytime (30.9% in XG vs. 17.5% in CG), whereas nitrate and less-oxidized OOA dominated (41.4% in XG vs. 30.6% in CG) during the nighttime. A lower PM 1 mass (44.3 μg m -3 ) was observed in CG and was contributed predominantly by primary organic aerosols emitted from traffic, cooking, and heating activities. The chemical formation mechanisms of secondary PM 1 species in the two different districts during the daytime and nighttime are further examined, which indicated the important photochemical formations of nitrate in CG but sulfate in XG during the daytime, whereas favorable aqueous-phase formations of nitrate and LO-OOA in both districts during the nighttime. The stronger atmospheric oxidation capability might be a key factor leading to the more significant formations of secondary species in XG than CG. These results illustrate city-scale aerosol loading and chemical processes and are useful for local policy makers to develop differentiated and efficient mitigation strategies for the improvement of air quality in Lanzhou., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

A comparison of mobile and stationary air quality measurements in Lublin, Poland during the COVID-19 lockdown in 2020 and in a comparable period in 2017 has demonstrated that a substantial decrease of the traffic intensity by more than 50%, especially during certain times of the day in the lockdown period has only been partially reflected in the air quality improvement in the city. Mobile measurements carried out during six runs within a 24-hour period in 2017 and 2020 indicated a decrease of the average PM 2.5 and PM 10 concentrations by ~ 30% and ~14%, respectively. In turn, stationary measurement results obtained for the same periods demonstrated their increase by respectively ~35% and ~106% and a decrease in the average NO 2 , NO x , C 6 H 6 and CO concentrations. This could have been impacted by meteorological factors and emissions from other, non-traffic-related sources, mainly from residential coal burning. The changes in the vehicle fleet structure could also have played a role., Competing Interests: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Introduction: In 2020, due to the outbreak of COVID-19, there has been an unprecedented decrease in road traffic in almost all urbanized areas around the globe. This has undoubtedly affected the ambient air quality., Methods: In this study mobile and fixed-site measurements of aerosol particle concentrations in the ambient air in one of the busiest streets in Lublin, a mid-sized city in Central Europe (Poland) during the COVID-19 lockdown in the spring of 2020 were performed. Based on the measurements particle doses received by road users during different times of the day were assessed. The obtained results were compared with corresponding pre-COVID-19 measurements also performed in the spring which were available only from 2017., Results: During lockdown the mass concentration of traffic-related submicrometer PM 1 particles and number concentration of ultrafine PN 0.1 particles was significantly reduced. This resulted in a decrease of doses inhaled by road users as well as of particle doses deposited in their respiratory tracks. The greatest reductions of respectively over 2 times and over 5 times were observed during the day for total particles and traffic-related particles. Smaller reductions indicating the existence of relatively intensive non-traffic emissions were reported at night., Conclusions: Substantial decrease in traffic intensity in the city caused by lockdown restrictions resulted in a significant reduction in the concentration of vehicle-generated particles in the ambient air. This in turn could have resulted in smaller doses inhaled by the inhabitants, specifically road users, which should have a positive impact on their health., Competing Interests: The author declares no conflict of interest., (© 2021 The Author.)

Baseline mobile surveys of methane sources using vehicle-mounted instruments have been performed in the Fylde and Ryedale regions of Northern England over the 2016-19 period around proposed unconventional (shale) gas extraction sites. The aim was to identify and characterise methane sources ahead of hydraulically fractured shale gas extraction in the area around drilling sites. This allows a potential additional source of emissions to atmosphere to be readily distinguished from adjacent sources, should gas production take place. The surveys have used ethane:methane (C2:C1) ratios to separate combustion, thermogenic gas and biogenic sources. Sample collection of source plumes followed by high precision δ 13 C analysis of methane, to separate and isotopically characterise sources, adds additional biogenic source distinction between active and closed landfills, and ruminant eructations from manure. The surveys show that both drill sites and adjacent fixed monitoring sites have cow barns and gas network pipeline leaks as sources of methane within a 1 km range. These two sources are readily separated by isotopes (δ 13 C of -67 to -58‰ for barns, compared to -43 to -39‰ for gas leaks), and ethane:methane ratios (<0.001 for barns, compared to >0.05 for gas leaks). Under a well-mixed daytime atmospheric boundary layer these sources are generally detectable as above baseline elevations up to 100 m downwind for gas leaks and up to 500 m downwind for populated cow barns. It is considered that careful analysis of these proxies for unconventional production gas, if and when available, will allow any fugitive emissions from operations to be distinguished from surrounding sources., (Copyright © 2019 The Author(s). Published by Elsevier B.V. All rights reserved.)

Black carbon (BC) has emerged as a major contributor to global climate change. Cities play an important role in global BC emission. The present study investigated the spatial pattern of in-traffic BC at a high spatial resolution in Shanghai, the commercial and financial center in Mainland China. The determinants including road network, social economic status and point-source pollutants, which may influence the BC spatial variability were also discussed. From October to December 2016, mobile monitoring was conducted to assess the BC concentrations on three sampling routes in Shanghai with a total length of 116 km. The results showed that the mean in-traffic BC among three sampling routes was 10.77 ± 3.50 μg/m 3 . BC concentrations showed a significant spatial heterogeneity. The highest BC concentrations were near industrial sources and that those high concentrations were associated with either direct emissions from the industries, freight traffic, or both. With the widely distributed polluting enterprises and high emitting vehicles, the average BC in the low urbanization areas (12.80 ± 4.54 μg/m 3 ) was 57% higher than that in the urban core (7.77 ± 2.24 μg/m 3 ). Furthermore, a land use regression (LUR) model based on mobile monitoring was developed to examine the determinants and its spatial variability of BC measurements which corresponded to 17 predictor variables, e.g. road network, land use, meteorological condition etc., in 7 buffer distances (100 m to 10 km). The variables of meteorological, socio-economical and the distance to BC point-sources were selected as the independent variables. It was found that the established LUR model could explain a proportion (68%) of the variability of BC. LUR modeling from mobile measurements was possible, but more work related to the effect of traffic regulation on BC could be helpful for informing best model practice., (Copyright © 2018 Elsevier B.V. All rights reserved.)

People living near roadways are exposed to high concentrations of ultrafine particles (UFP, diameter < 100 nm). This can result in adverse health effects such as respiratory illness and cardiovascular diseases. However, accurately characterizing the UFP number concentration requires expensive sets of instruments. The development of an UFP surrogate with cheap and convenient measures is needed. In this study, we used a mobile measurement platform with a Fast Mobility Particle Sizer (FMPS) and sound level meter to investigate the spatiotemporal relations of noise and UFP and identify the hotspots of UFP. UFP concentration levels were significantly influenced by temporal and spatial variations (p < 0.001). We proposed a Generalized Additive Models to predict UFP number concentration in the study area. The model uses noise and meteorological covariates to predict the UFP number concentrations at an industrial site in Taichung, Taiwan. During the one year sampling campaign from fall 2013 to summer 2014, mobile measurements were performed at least one week for each season, both on weekdays and weekends. The proposed model can explain 80% of deviance and has coefficient of determination (R 2 ) of 0.77. Moreover, the developed UFP model was able to adequately predict UFP concentrations, and can provide people with a convenient way to determine UFP levels. Finally, the results from this study could help facilitate the future development of noise mobile measurement., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Numerous studies have shown that people living in near-roadway communities (within 100 m of the road) are exposed to high ultrafine particle (UFP) number concentrations, which may be associated with adverse health effects. Vegetation barriers have been shown to affect pollutant transport via particle deposition to leaves and altering the dispersion of emission plumes, which in turn would modify the exposure of near-roadway communities to traffic-related UFPs. In this study, both stationary (equipped with a Scanning Mobility Particle Sizer, SMPS) and mobile (equipped with Fast Mobility Particle Sizer, FMPS) measurements were conducted to investigate the effects of vegetation barriers on downwind UFP (particle diameters ranging from 14 to 102 nm) concentrations at two sites in North Carolina, USA. One site had mainly deciduous vegetation while the other was primarily coniferous; both sites have a nearby open field without the vegetation barriers along the same stretch of limited access road, which served as a reference. During downwind conditions (traffic emissions transported towards the vegetation barrier) and when the wind speed was above or equal to 0.5m/s, field measurements indicated that vegetation barriers with full foliage reduced UFP and CO concentrations by 37.7-63.6% and 23.6-56.1%, respectively. When the test was repeated at the same sites during winter periods when deciduous foliage was reduced, the deciduous barrier during winter showed no significant change in UFP concentration before and after the barrier. Results from the stationary (using SMPS) and mobile (using FMPS) measurements for UFP total number concentrations generally agreed to within 20%., (Copyright © 2016 Elsevier B.V. All rights reserved.)