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Below the boundary layer, the air pollutants have been confirmed to present the decreasing trend with the height in most situaitons. However, the disperiosn rate of air pollutants in the vertical profile is rarely investigated in detail, especially through in-situ measurement. With this consideration, we employed an unmanned aerial vehicle equipped with portable monitoring equipments to scrutinize the vertical distribution of PM 2.5 . Based on the original data, we found that PM 2.5 concentration decreases gradually with altitude below the boundary layer and demonstrated an obvious linear correlation. Therefore, the vertical distribution of PM 2.5 was quantified by representing the distribution of PM 2.5 with the slope of PM 2.5 vertical distribution. We used backward trajectories to reveal the causes of outliers (PM 2.5 increasing with altitude), and found that PM 2.5 in the high altitude came from the southwest. Besides, the relationship between the vertical distribution of PM 2.5 and various meteorological factors was investigated using stepwise regression analysis. The results show that the four meteorological factors most strongly correlated with the slope values are: (a) the difference in relative humidity between the ground and the air; (b) the difference in temperature between the ground and the air; (c) the height of the boundary layer; and (d) the wind speed. The slope values increase with increasing the difference in relative humidity between ground and air and the difference in temperature between the ground and the air, and decrease with increasing boundary layer height and wind speed. According to the Random Forest calculations, the ground-to-air relative humidity difference is the most important at 0.718; the wind speed is the least important at 0.053; and the ground-to-air temperature difference and boundary layer height are 0.140 and 0.088, respectively., 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 © 2024 Elsevier Ltd. All rights reserved.)

Vehicles generally move smoothly and with high speeds on elevated roads, thereby producing specific traffic-related carbon emissions in contrast to ground roads. Hence, a portable emission measurement system was adopted to determine traffic-related carbon emissions. The on-road measurement results revealed that the instantaneous emissions of CO 2 and CO from elevated vehicles were 17.8% and 21.9% higher than those from ground vehicles, respectively. Based on it, the vehicle specific power was confirmed to exhibit a positive exponential relationship with instantaneous CO 2 and CO emissions. In addition to carbon emissions, carbon concentrations on roads were simultaneously measured. The average CO 2 and CO emissions on elevated roads in urban areas were 1.2% and 6.9% higher than those on ground roads, individually. Finally, a numerical simulation was performed, and the results verified that elevated roads could deteriorate the air quality on ground roads but improve the air quality above them. What ought to be paid attention to is that the elevated roads present varied traffic behaviour and cause particular carbon emissions, indicating that comprehensive consideration and further balance among the traffic-related carbon emissions are necessary when building elevated roads to alleviate the traffic congestion in urban areas., 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 © 2023 Elsevier Inc. All rights reserved.)

The implementation of short-term traffic restriction policies (TRPs) during major events positively influences the traffic emission reduction. However, few studies explore the impact of diesel vehicle emissions on air quality during short-term TRP. In particular, the intertwined influences of short-term TRP and Spring Festival remains unclear. Based on Beijing 2022 Olympic Games, this study analyzed the spatiotemporal changes in urban air quality and diesel vehicle emission during short-term TRP. The results showed that the TRPs and Spring Festival contributed equally to the improvement of air quality and reduction of diesel vehicle emissions. The "interruption-recovery" pattern of short-term TRPs is characterized by a longer duration and a slower decline/recovery rate. Additionally, the individual contribution rate of diesel vehicle emissions to urban air pollutants was 15-20 % higher than that of meteorological factors during short-term TRPs., 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 © 2023 Elsevier B.V. All rights reserved.)

Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring networks (AQMN), and hence exhibit low precision in regional prediction tasks. With this consideration, we proposed a novel deep learning-based hybrid model of Res-GCN-BiLSTM combining the residual neural network (ResNet), graph convolutional network (GCN), and bidirectional long short-term memory (BiLSTM), for predicting short-term regional NO 2 and O 3 concentrations. Auto-correlation analysis and cluster analysis were first utilized to reveal the inherent temporal and spatial properties respectively. They demonstrated that there existed temporal daily periodicity and spatial similarity in AQMN. Then the identified spatiotemporal properties were sufficiently leveraged, and monitoring network topological information, as well as auxiliary pollutants and meteorology were also adaptively integrated into the model. The hourly observed data from 51 air quality monitoring stations and meteorological data in Shanghai were employed to evaluate it. Results show that the Res-GCN-BiLSTM model was better adapted to the pollutant characteristics and improved the prediction accuracy, with nearly 11% and 17% improvements in mean absolute error for NO 2 and O 3 , respectively compared to the best performing baseline model. Among the three types of monitoring stations, traffic monitoring stations performed the best for O 3 , but the worst for NO 2 , mainly due to the impacts of intensive traffic emissions and the titration reaction. These findings illustrate that the hybrid architecture is more suitable for regional pollutant concentration., 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 © 2023 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic and the corresponding lockdown measures have been confirmed to reduce the air pollution in major megacities worldwide. Especially at some monitoring hotspots, NO 2 has been verified to show a significant decrease. However, the diffusion pattern of these hotspots in responding to COVID-19 is not clearly understood at present stage. Hence, we selected Beijing, a typical megacity with the strictest lockdown measures during COVID-19 period, as the studied city and attempted to discover the NO 2 diffusion process through complex network method. The improved metrics derived from the topological structure of the network were adopted to describe the performance of diffusion. Primarily, we found evidences that COVID-19 had significant effects on the spatial diffusion distribution due to combined effect of changed human activities and meteorological conditions. Besides, to further quantify the impacts of disturbance caused by different lockdown measures, we discussed the evolutionary diffusion patterns from lockdown period to recovery period. The results displayed that the difference between normal operation and pandemic operation firstly increased at the cutoff of lockdown measures but then declined after the implement of recovery measures. The source areas had greater vulnerability and lower resilience than receptors areas. Furthermore, based on the conclusion that the diffusion pattern changed during different periods, we explored the key stations on the path of diffusion process to further gain information. These findings could provide references for comprehending spatiotemporal pattern on city scale, which might be help for high-resolution air pollution mapping and prediction., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (DCRNN) model that fully considers the influence of geographic distance and dominant wind direction on the regional variations in air quality through different combinations of directed and undirected graphs. The hourly fine particulate matter (PM 2.5 ) and ozone data from 123 air quality monitoring stations in the Yangtze River Delta, China are used to evaluate the performance of the DCRNN model in the regional prediction of PM 2.5 and ozone concentrations. Results show that the proposed DCRNN model outperforms the baseline models in prediction accuracy. Compared with the undirected graph model, the directed graph model considering the effects of wind direction performs better in 24 h predictions of pollutant concentrations. In addition, more accurate forecasts of both PM 2.5 and ozone are found at a regional level where monitoring stations are distributed densely rather than sparsely. Therefore, the proposed model can assist environmental researchers to further improve the technologies of air quality forecasts and could also serve as tools for environmental policymakers to implement pollution control measures.

In roadside environments, commuters are exposed to a high level of traffic-related pollution. Despite vegetation is often used to mitigate air pollution in road environments, its air quality impacts are complex and could be both positive or negative depending on specific conditions. This study conducted field measurements to assess the air quality impacts of roadside vegetation. Three common street vegetation configurations (dense vegetation, porous vegetation, and clearing) were selected and the concentrations of size-resolved particles and black carbon were measured. Results show that dense vegetation formed an accumulation area of particle pollutants on the sidewalk and bikeway, which was attributable to the increased deposition of pollutants. Compared with porous vegetation, the increase in particle concentrations before dense vegetation was 0-35% on the sidewalk (closer to vegetation) and 0-6% on the bikeway. Due to high homogeneity, fine particles (0.3-1 μm) showed low variability among different sample points, while coarse particles (>1 μm) showed high variability and presented a significant increase in concentration before dense vegetation. Porous vegetation showed weak interception effects on pollutants, and the particle concentrations before porous vegetation were close to those in the clearing. The horizontal decay of particle concentrations in porous and dense vegetation showed that particle pollutants were difficult to penetrate dense vegetation, which concentrations of particles presented a pronounced increase in the front part (0-5 m) of dense vegetation but also showed a large drop across it. These results suggest that vegetation serves as a good filter to clean the air and could improve the air quality away from the vegetation but could also worsen the air quality close to the vegetation. This study provides an insight into the environmental impacts of roadside vegetation, which could have practical implications in air pollution abatement., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM 2.5 ) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM 2.5 , the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM 2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

Spatiotemporal variations in PM 2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM 2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM 2.5 . Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM 2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM 2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM 2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM 2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM 2.5 , this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM 2.5 , and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The unfavorable locations of underground infrastructures and poor ventilation facilities can result in the deterioration of enclosed air quality. Some researchers have studied air quality and ventilation measures in different types of underground buildings. However, few studies have investigated the pollution in pedestrian passageways connecting underground structures. Hence, in this paper, we attempted to investigate the spatial distribution of fine particulate matter (PM 2.5 ) in underground passageways. First, measurements were designed and conducted in a pedestrian passageway beneath the Shanghai South Railway Station, Shanghai, China. Second, numerical simulations were performed based on computational fluid dynamic (CFD) technology. Finally, the numerical simulations were extended to examine impacts of the ventilation measures on PM 2.5 concentration with different inlet positions and air velocity in underground passageways. The simulation results showed good agreement with the experimental data, and the numerical model was validated to be an effective method to investigate the spatial distribution of PM 2.5 in underground passageways. Results suggest that building additional entrances is an advisable method for improving air quality in the underground passageways of the Shanghai South Railway Station, while jet fans are not recommended. Findings of this study offer suggestions for mitigating PM 2.5 pollution in underground passageways.

Potential utilities of instrumented lightweight unmanned aerial vehicles (UAVs) to quickly characterize tropospheric ozone pollution and meteorological factors including air temperature and relative humidity at three-dimensional scales are highlighted in this study. Both vertical and horizontal variations of ozone within the 1000 m lower troposphere at a local area of 4 × 4 km 2 are investigated during summer and autumn times. Results from field measurements show that the UAV platform has a sufficient reliability and precision in capturing spatiotemporal variations of ozone and meteorological factors. The results also reveal that ozone vertical variation is mainly linked to the vertical distribution patterns of air temperature and the horizontal transport of air masses from other regions. In addition, significant horizontal variations of ozone are also observed at different levels. Without major exhaust sources, ozone horizontal variation has a strong correlation with the vertical convection intensity of air masses within the lower troposphere. Higher air temperatures are usually related to lower ozone horizontal variations at the localized area, whereas underlying surface diversity has a week influence. Three-dimensional ozone maps are obtained using an interpolation method based on UAV collected samples, which are capable of clearly demonstrating the diurnal evolution processes of ozone within the 1000 m lower troposphere., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM2.5 and NO2 concentrations at 35-45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM2.5 and NO2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO2 model. Respectively, both PM2.5 and NO2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM2.5 (0.88) being higher than NO2 (0.62). The LUR was then used to produce continuous concentration fields for NO2 and PM2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM2.5 studies in New York and Beijing show that Shanghai PM2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions., (Copyright © 2015. Published by Elsevier B.V.)

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks. [ABSTRACT FROM AUTHOR]

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures. [ABSTRACT FROM AUTHOR]

Transportation-related risk factors are a major source of morbidity and mortality in China, where the expansion of road networks and surges in personal vehicle ownership are having profound effects on public health. Road traffic injuries and fatalities have increased alongside increased use of motorised transport in China, and accident injury risk is aggravated by inadequate emergency response systems and trauma care. National air quality standards and emission control technologies are having a positive effect on air quality, but persistent air pollution is increasingly attributable to a growing and outdated vehicle fleet and to famously congested roads. Urban design favours motorised transport, and physical activity and its associated health benefits are hindered by poor urban infrastructure. Transport emissions of greenhouse gases contribute substantially to regional and global climate change, which compound public health risks from multiple factors. Despite these complex challenges, technological advances and innovations in planning and policy stand to make China a leader in sustainable, healthy transportation.

Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world's busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port's interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation. [ABSTRACT FROM AUTHOR]

Abstract Previous studies on air pollution exposure have mostly focused on the urban, regional, national and global scales, but the outcomes cannot well support risk assessments in urban communities. To determine how the urban form and meteorology influence the air pollution distribution at a neighborhood scale (2 km*2 km), we performed a fine-scale investigation of a typical air pollutant (i.e., PM 2.5) by mobile measurements in two communities in the inner and outer cities of Shanghai, China. The PM 2.5 mass concentrations and potential impacting factors, such as PM 2.5 background levels, road networks, traffic volumes, meteorological parameters, building heights and land use types, were collected at a 10 m spatial resolution, and their relationships were analyzed using both a generalized additive model (GAM) and land use regression (LUR). The modeling results showed that the GAM outperformed LUR in both study areas, with a higher adjusted R2 and a lower RMSE. The PM 2.5 was found to have drastic variations at the neighborhood scale, which was primarily driven by spatial patterns of the PM 2.5 background levels and traffic volumes. The GAM-based PM 2.5 concentration surface clearly disclosed the heterogeneous variation of the PM 2.5 mass concentrations in each community, and demonstrated the prominent influence from the nearby highway, especially in the central urban area. This study provides the first empirical evidence of the exposure heterogeneity of the air pollution on a neighborhood scale, which can assist planners and policymakers in evaluating planning strategies with a full consideration of reducing local air pollution. Highlights • Mobile measurements of PM 2.5 concentrations are performed in two neighborhoods of Shanghai. • Generalized additive models outperform land use regressions for assessing PM 2.5 variations. • PM 2.5 of high values are mainly distributed alongside highways up to 100–300 m. • Building height with wind speed primarily determines PM 2.5 variations in the central urban area. • PM 2.5 variations in the suburban area are mainly impacted by regional emission sources. [ABSTRACT FROM AUTHOR]

Abstract Air quality in megacities and its correlation with urban form have become a priority, since numerous cities worldwide are encountering a development bottleneck due to air pollution. This paper analyzed the spatial variations in 8 air pollutants among 18 ambient air monitoring stations across Shanghai based on continuously hourly mass concentration data from 28 days in February 2017. In each of the 18 stations, we created a buffer zone with a radius of 1 km, and derived 18 quantified features of the urban form including points of interest and environmental information by the geographic information system and the Baidu Map program interface. Then, the 18 stations were clustered into 3-level groups by using the k -nearest neighbor method according to every pollutant and urban form. Results showed that the spatial variation differed per pollutant, as did urban form. PM 2.5 concentrations were high in the western and low in the eastern of Shanghai, which can be explained by regional influences and the distance from East China Sea. PM 10 showed a relatively high level in the developed urban areas, and high buildings with a similar height in these areas significantly restrain the dispersion of PM 10. The spatial variations of the 6 gaseous pollutants mainly depend on local human activities and transport-related emissions. The distance to primary road, standard deviation of building floors, and average building floors were the top 3 urban form features influencing the spatial variations of all pollutants. Wind ventilation was identified as a critical index for air quality-oriented urban design. Highlights • Spatial variability is analyzed for 8 air pollutants among 18 monitoring stations. • The cluster analysis is performed to correlate air pollutants and urban form. • Spatial variation of PM 2.5 relies critically on regional impacts and the distance to East China Sea. • High buildings with a similar height significantly restrain the dispersion of PM 10. • Gaseous pollutants strongly correlate to local activities and transport-related emissions. [ABSTRACT FROM AUTHOR]

A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM2.5 concentrations. Significant stratified layers of ozone and PM2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM2.5 in the planetary boundary layer (PBL). The NO and NO2 concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM2.5. The strongest effect on ozone was related to air temperature, while PM2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM2.5 pollutions at a localized area. [ABSTRACT FROM AUTHOR]

Road traffic pollution has become a major source of pollution in the city. As a result, the residents of near-road neighborhood are inevitably exposed to high air pollution. However, due to lack of the measured data especially in vertical direction, the distribution of traffic-related air pollutants (TRAP) in three-dimension in community along the road is not understand in details. Hence, in this paper, we use an unmanned aerial vehicle (UAV) to reveal the impact of the TRAP on the community. Firstly, an in-situ observation was adopted using UAV and portable air pollutants sensors to realize three-dimensional monitoring of air pollutant distribution from traffic in near-road area. Secondly, a statistical analysis was conducted to reveal near-road PM 1 distribution in different wind directions and time periods based on 14 days' observation. And results showed that PM 1 concentration dropped 44% after 5 rows of high residence buildings beside an urban expressway while slightly fluctuated in the open field next to the residence community. Thirdly, a model based on CALINE4 was established to verify the PM 1 concentration in the neighborhood, providing quantitative data support and theoretical basis for the planning and design of neighborhood to reduce pollutants' concentration level. We found the UAV platform is quite applicable in field measurement, and that traffic emissions provide most PM 1 source in near-road area, while wind and building layout have a great influence on its distribution pattern. This research approach has the potential to be further investigated not only to support the design and development of urban building projects, but also to aid the location choice and alignment of new transportation infrastructures from the perspective of mitigating their environmental impacts. • UAV platform is used to realize three-dimensional distribution of air pollutant in near-road area. • Spatial distribution of PM 1 is dependent with wind directions and traffic emission intensity. • PM 1 in community exhibited linear variations related to the distance of expressway. [ABSTRACT FROM AUTHOR]

• Vertical PM1 distributions are revealed by instrumented UAVs. • PM1 shows significant horizontal decay while penetrating vegetation. • PM1 exhibits vertical unimodal distribution at site with vegetation barrier. • Vegetation barriers reduce PM1 on ground and in high-rise area vertically. • Dense vegetation has a stronger lifting effect on particles than porous vegetation. The increasing knowledge in the distributions of air pollutants influenced by vegetation could facilitate reducing human exposure in residential areas. However, the distribution patterns of submicron particles (PM 1) in residential areas have not been clearly recognized, particularly for vertical distribution at high altitudes, which are closely associated with the exposure level of residents living in high-rise buildings. Thus, instrumented unmanned aerial vehicles (UAVs) are used to characterize the vertical distribution of PM 1 concentrations in near-road residential areas, including sites with dense, porous, and no vegetation barriers. Results show that PM 1 concentrations present unimodal distributions vertically at all scenarios with vegetation barriers, and the highest concentration in the vertical direction appears near the vegetation canopy, rather than on the ground. The particle mitigation under the influence of vegetation is weak in near-road residential areas, and the concentrations behind dense vegetation and porous vegetation decrease by 10.1% and 4.9%, respectively. Moreover, computational fluid dynamics simulations verified by field measurement are conducted to investigate the effects of vegetation density and height on PM 1 distribution. Effective countermeasures are proposed to support urban planning and design to alleviate air pollution in residential areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%–48.8% drop in the concentration of NO 2 has been observed in the four metropolises compared with the same period in 2018–2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO 2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality. • The interruption and recovery of air pollution during the COVID-19 was evaluated. • Regression Discontinuity Design (RDD) has been used for statistical analysis. • Data of four major Chinese cites were utilized for characterizing and modelling. • The year-on-year NO 2 and PM 2.5 showed 25.3%–48.8% and 23.1%–46.2% drop separately. [ABSTRACT FROM AUTHOR]

At the crosswalk of urban traffic intersections, vehicles frequently stop with their engines idling adjacent to it during the red-light period and accelerate with intensified emissions during the green-light period. As a result, pedestrians are inevitably at a higher risk of exposure to near-source traffic-related air pollution while walking on the crosswalk or waiting on the roadside. Therefore, this study focuses on pedestrian exposure to particulate matter on the crosswalk at urban intersections. Firstly, mobile measurements at varied locations of the crosswalk were carried out to reveal the spatial difference of particles on the whole crosswalk. The results show that the concentrations of submicron and coarse particles are higher on the parking crosswalk than that on the non-parking crosswalk. Secondly, mobile measurements at varied heights of crosswalk were carried out to discover the vertical difference of particles on the crosswalk. Particulate concentration is higher at the height of 1.5 m than that at 0.5 m. Thirdly, fixed-site measurements were conducted on the roadside to make a comparison with the crosswalk. Particulate concentration is found to be nearly 6% higher on the crosswalk than that on the roadside. Finally, pedestrian exposure to particulate matter in varied trips on the crosswalk is investigated and the exposure in trips with a 10s–20s delay after the onset of a walk signal is lower than that in other situations. The results can help fully recognize the particulate pollution at intersections and better understand the particulate exposure of pedestrians on crosswalk at urban intersections. [Display omitted] • Submicron and coarse particles on parking crosswalk is higher than non-parking area. • Submicron and coarse particles at height of 1.5 m on crosswalk is higher than that at 0.5 m. • Spatio-temporal variation of PM 10 on crosswalk is distinguished with that of PM 1. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has significantly inhibited global economic growth and impacted the environment. Some evidence suggests that lockdown strategies have significantly reduced traffic-related air pollution (TRAP) in regions across the world. However, the impact of COVID-19 on TRAP on roadside is still not clearly understood. In this study, we assessed the influence of the COVID-19 lockdown on the levels of traffic-related air pollutants in Shanghai. The pollution data from two types of monitoring stations—roadside stations and non-roadside stations were compared and evaluated. The results show that NO 2 , PM 2.5 , PM 10 , and SO 2 had reduced by ~30–40% at each station during the COVID-19 pandemic in contrast to 2018–2019. CO showed a moderate decline of 28.8% at roadside stations and 16.4% at non-roadside stations. In contrast, O 3 concentrations increased by 30.2% at roadside stations and 5.7% at non-roadside stations. This result could be resulted from the declined NOx emissions from vehicles, which lowered O 3 titration. Full lockdown measures resulted in the highest reduction of primary pollutants by 34–48% in roadside stations and 18–50% in non-roadside stations. The increase in O 3 levels was also the most significant during full lockdown by 64% in roadside stations and 33% in non-roadside stations due to the largest decrease in NO 2 precursors, which promote O 3 formation. Additionally, Spearman's rank correlation coefficients between NO 2 and other pollutants significantly decreased, while the values between NO 2 and O 3 increased at roadside stations. [Display omitted] • NO 2 , PM 2.5 , PM 10 and SO 2 reduced by ~30–40% in shanghai during the COVID-19 pandemic in contrast to 2018–2019. • CO showed a decline of 28.8% on roadside stations and 16.4% at non-roadside stations. • Decline of NOx from vehicles leads to an increase of O 3 on roadside (30.2%) and non-roadside (5.7%). • Full lockdown measures resulted in the highest reduction of primary pollutants. [ABSTRACT FROM AUTHOR]

Typical air pollution events involving ozone (O 3) and PM 2.5 occurred frequently in China, while the fine-scale pollution variation, especially at a neighborhood level (2 km*2 km), is complex and still not clear. To assess how urban form and meteorology influence neighborhood air pollution distribution, this study took the Minhang district in Shanghai, as experimental cases, and performed a neighborhood-scale investigation on O 3 and PM 2.5 by using mobile measurements. Both land-use regression model and decision tree model were used to examine the relationship between air pollutant concentration and influenced variables. As the decision tree model captured the linear and non-linear relationship between variables, it was demonstrated that explained more variations of O 3 and PM 2.5 concentrations than the LUR model. The results also showed that O 3 concentrations were mainly affected by meteorological factors while PM 2.5 concentrations were more heavily determined by background level and residential area. Both O 3 and PM 2.5 showed a significant correlation with air temperature, traffic volume, building height, and green space. Interestingly, green spaces were negatively correlated with the PM 2.5 variations, which was almost the opposite to that of O 3. With the superiority to the discrete observation, the decision tree model based concentration surfaces clearly revealed the heterogeneity of O 3 and PM 2.5 distributions. This study not only preliminarily identifies the impacts of land-use type and meteorological factors on the spatial patterns of O 3 and PM 2.5 , but also provides a possible alternative method for assessing the neighborhood air pollution in the future. • Mobile measurements are performed on PM 2.5 /O 3 variations in two neighborhoods in Shanghai. • Decision tree models performed well in assessing both pollutant (PM 2.5 /O 3) variations. • The concentrations of O 3 were mainly affected by the meteorological factors. • The concentrations of PM 2.5 was more heavily impacted by background concentration and residential area. • Green space area was negatively correlated with the PM 2.5 variations, which was almost opposite compared with that of O 3. [ABSTRACT FROM AUTHOR]

The increased knowledge in distribution patterns of traffic-related pollutants can help address the problems of air pollution. Utilities of instrumented unmanned aerial vehicles (UAVs) who have high mobility are an effective approach to investigate the pollutant distributions. However, UAV-based observations are mostly conducted in open suburban areas and have been rare in urban areas where present severe pollution problems. In this study, an instrumented six-rotor UAV was carried out to investigate the spatiotemporal distributions of air pollutants beside an urban arterial road in Shanghai, China. Both vertical and horizontal distributions of particulate matters (PM), carbon monoxide (CO) within the height of 120 m were investigated. Results show that the vertical fall-off of roadside CO was mainly in a height range of 0–60 m and was 0–40 m for PM. PM and CO concentrations both presented exponential decay in the vertical direction and showed more significant decay in traffic rush hours compared with that in non-rush hours. Meanwhile, the concentration of fine particles (PM 1) was mostly affected by the background concentration while the concentrations of coarse particles and CO were more related to traffic activities. Additionally, at the height of 40 m, the horizontal dispersion of CO could reach over 60 m away from the road, and the increase in pollutant concentrations was mainly in the downwind direction of the road. This study revealed the general trends of vertical and horizontal distributions of air pollutants beside an urban arterial road, which could provide implications in mitigating traffic-related air pollution. Image 1 • Distributions of traffic pollutants were investigated by instrumented UAVs. • The vertical fall-off of roadside CO and PM was mainly in 0–60 m and 0–40 m. • Roadside CO and PM concentrations presented exponential decay vertically. • The horizontal distributions of CO and PM were presented in high resolution. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations in PM 2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM 2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM 2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM 2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM 2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM 2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM 2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM 2.5 , this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM 2.5 , and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution. Image 1 • Air sensor network is arranged for environment monitoring of urban areas. • Monitoring network shows four variation patterns of the daily PM 2.5 concentration. • Spatial map reveals the pollution sources of the PM 2.5 in each variation pattern. • The entropy weight method can identify the PM 2.5 hotspots in the city. [ABSTRACT FROM AUTHOR]

Traffic restriction policy (TRP) is common practice to alleviate urban congestion and air pollution. However, effects of TRP for non-local vehicles and elevated expressways on local and regional air quality are understudied. Using air pollutant data collected by ground-based national and roadside environmental monitoring stations and field measurements, comparative research design and regression discontinuity analysis were performed to investigate the local and regional variations in air pollutant concentrations with and without the TRP. Results indicate that traffic restriction significantly decreased the local and regional concentration levels of primary gaseous pollutants (e.g., NOX and CO), particularly under more stringent TRP during China International Import Expo, which also brought an ozone increase due to the reduced effect of NOX titration. However, the TRP only exhibited limited impacts on mitigating regional particulate pollution. These findings suggest that the air quality improvements due to the non-local TRP highly depend on the types of pollutants and the spatial locations. [Display omitted] • Pollutant data from Shanghai monitoring networks are used for policy evaluation. • Comparative research design and regression discontinuity analysis are performed. • Restriction brings a sharper decline in primary gaseous pollutants than particles. • Restriction hardly improves urban air quality of Shanghai where particles dominate. • Air quality impacts highly depend on the types of pollutants and spatial locations. [ABSTRACT FROM AUTHOR]

[Display omitted] Electronic toll collection (ETC) has been designed and implemented to improve traffic efficiency and reduce traffic-related air pollution. However, the real-world impact of ETC on these factors is currently under-qualified. With this consideration, we attempted to reveal the effect of ETC on traffic efficiency and traffic pollution using portable emission measurement systems. Firstly, in contrast to the manual toll collection (MTC), ETC offer lower emissions and time savings, and there exist a quadratic relationship between emission rates and vehicle specific power. Secondly, vehicles in acceleration state emit more pollutants than other situations such as deceleration, steady cruising and idling in both ETC and MTC lanes. Thirdly, ETC lanes show 34.25% and 13.63% lower CO 2 and NO x emissions, respectively, along with a 46.55% timesaving compared to MTC lanes. Finally, the study identifies engine load percent as the primary factor impacting emissions, accounting for 60–88% of the variance. [ABSTRACT FROM AUTHOR]

Daytime atmospheric pollution has received wide attention, while the vertical structures of atmospheric pollutants at night play a crucial role in the photochemical process on the following day, which is still less reported. Focusing on Guangzhou, a megacity of South China, we established an unmanned aerial vehicle (UAV) equipped with micro detectors to collect consecutive high-resolution samples of fine particle (PM 2.5), submicron particle (PM 1.0), black carbon (BC) and ozone (O 3) concentrations in the atmosphere, as well as the air temperature (AT) and relative humidity (RH) within a 500 m altitude during nighttime from Oct. 24th to Nov. 6th, 2018. The measurements showed that PM 2.5 , PM 1.0 , and BC decreased with altitude and were influenced by the nighttime shallow planetary boundary layer (PBL) where BC was more accumulated and fluctuated. In contrast, O 3 was positively correlated with altitude. Backward trajectory clustering and Pasquill stability classification showed that advection and convection significantly influenced the vertical distribution of all pollutants, particularly particulate matter. External air masses carrying high concentrations of pollutants increased PM 1.0 and PM 2.5 levels by 145% and 455%, respectively, compared to unaffected periods. The ratio of BC to PM 2.5 indicated that local emissions had a minor role in nighttime particulate matter. Vertical transport caused by atmospheric instability reduced the differences in pollutant concentrations at various heights. Geodetector and generalized additive model showed that RH and BC accumulation in the PBL were significant factors influencing vertical changes of the secondary aerosol intensity as indicated by the ratio of PM 1.0 to PM 2.5. The joint explanation of RH and atmospheric stability with other variables such as BC is essential to understand the generation of secondary aerosols. These findings provide insights into regional and local measures to prevent and control night-time particulate matter pollution. [Display omitted] • PM 2.5 , PM 1.0 , BC, and O 3 are detected vertically at nighttime in Guangzhou. • The vertical decline is smooth for PM 2.5 and PM 1.0 but fluctuating for BC. • Compared to PM 2.5 and PM 1.0 , BC is more sensitive to PBL. • Higher BC and humidity near the PBL reduce the secondary aerosol intensity. [ABSTRACT FROM AUTHOR]

Ridesplitting, a shared mobility service, has the potential to reduce traffic-related air pollution. This study evaluates the impacts of ridesplitting on reducing different types of emissions and investigate how travel characteristics of ridesplitting affect emission reduction based on a ridesourcing dataset in Chengdu, China. First, this study quantifies the influence of ridesplitting on emissions reduction compared to single ride (i.e., non-ridesplitting) for each trip. The results indicate that a ridesplitting trip averagely reduce CO 2 , CO, NOx, and HC emissions by 34.52%, 5.98%, 33.10%, and 13.42%, respectively. Subsequently, using explainable machine learning, we quantitatively analyze how the travel characteristics of ridesplitting affect emission reduction at two levels. At the trip level, shared travel distance, shared travel time, delay, and detour are important factors for emission reduction. At the grid level, the number of orders that match co-riders within the same spatial community is more important than the total number of orders. [ABSTRACT FROM AUTHOR]

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, the improved understanding of traffic impacts on particle concentrations could help mitigate air pollution. In this study, field measurements were conducted beside an arterial road in Shanghai, China, to obtain the fine-scale temporal variations in traffic volumes, meteorological parameters, and size-resolved particle concentrations. The generalized additive model was developed to reveal their associations under different analysis intervals (time scales). Results indicate that on-road traffic greatly increased the concentration and variability of roadside particles. Variations of roadside fine particles (<1 μm, having high homogeneity) closely followed those at the background point, but background variations of coarse particles (having high heterogeneity) were negligible compared with the roadside point. Roadside particles showed high variability on the 10-s scale and presented frequent stochastic spikes above the background level. The particle variability was greatly reduced on the 60-min scale, exhibiting stable variations. The association between traffic and roadside particle variations was weak on a fine temporal scale (5-min scale). However, the association showed a pronounced increase when the analysis interval was increased (60-min scale). This is because extending the analysis interval could reduce the influence of random factors (sample randomness) and emission lagging effects on the results. Moreover, with the increase of the analysis interval, the improvement of associations between variables can be well characterized by exponential curves, which could help determine the optimal analysis interval. Additionally, heavy-duty vehicles showed a lot more significant influence on particle variations compared with that of light-duty vehicles, and should be regarded as the key governance target for on-road pollution control. These findings broaden the knowledge of traffic impacts on particle concentrations, which could provide implications in air pollution abatement. [Display omitted] • Impacts of traffic on particle variations were studied via field experiments. • The generalized additive model was used to assess the associations. • Traffic greatly increased the concentration and variability of roadside particles. • Traffic-particle associations improved with the increase of the analysis interval. • The improvement in associations can be well characterized by exponential curves. [ABSTRACT FROM AUTHOR]