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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.)

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.)

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.)

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]

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]

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]

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]