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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 (PM 2.5 ) and unimodal (PM 10 ) 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 PM 2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM 2.5 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., (© 2024. The Author(s).)

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 (PM 2.5 ) 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 PM 2.5 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 PM 2.5 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 PM 2.5 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., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.)

Road traffic represents the dominant source of air pollution in urban street canyons. Local wind conditions greatly impacts the dispersion of these pollutants, yet street trees complicate ventilation in such settings. This case study adopts a novel modelling framework to account for dynamic traffic and wind conditions to identify the optimal street tree configuration that prevents a deterioration in air quality. Measurement data from a shallow to moderately deep street canyon (average 0.5 H/W aspect ratio and four lanes of 1-way traffic) in Dublin, Ireland was used for model calibration. The computational fluid dynamics (CFD) models were used to examine scenarios of dynamic traffic flows within each traffic lane with respect to its impact on local PM 2.5 concentrations on adjacent footpaths, segmenting air quality monitoring results based on different wind conditions for model calibration. The monitoring campaign identified higher PM 2.5 concentrations on the leeward (north) footpath, with average differences of 14.1 % (2.15 μg/m 3 ) for early evening peaks. The modelling results demonstrated how street trees negatively impacted air quality on the windward footpath in parallel wind conditions regardless of leaf area density (LAD) or tree spacing, with mixed results observed on the leeward footpath in varying traffic flows and wind speeds. Perpendicular wind direction models and high wind speed exacerbated poor air quality on the windward footpath for all tree spacing models, while improving the air quality on the leeward footpath. The findings advise against planting high-LAD trees in this type of street, with a minimum of 20 m spacing for low-LAD trees to balance reducing local air pollution and ventilation capacity in the street. This study highlights the complexities of those in key decision-marking roles and demonstrates the need to adopt a transparent framework to ensure adequate modelling evidence can inform tree planting in city streets., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mengyi Jin reports financial support was provided by National Natural Science Foundation of China. Prashant Kumar reports financial support was provided by Engineering and Physical Sciences Research Council. Prashant Kumar reports financial support was provided by Natural Environment Research Council. n/a If there are other authors, they 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 The Authors. Published by Elsevier B.V. All rights reserved.)

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., 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.)

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 COVID-19 pandemic has been argued to be the 'great equaliser', but, in fact, ethnically and racially segregated communities are bearing a disproportionate burden from the disease. Although more people have been infected and died from the disease among these minority communities, still fewer people in these communities are complying with the suggested public health measures like social distancing. The factors contributing to these ramifications remain a long-lasting debate, in part due to the contested theories between ethnic stratification and ethnic community. To offer empirical evidence to this theoretical debate, we tracked public social-distancing behaviours from mobile phone devices across urban census tracts in the United States and employed a difference-in-difference model to examine the impact of racial/ethnic segregation on these behaviours. Specifically, we focussed on non-Hispanic Black and Hispanic communities at the neighbourhood level from three principal dimensions of ethnic segregation, namely, evenness, exposure, and concentration. Our results suggest that (1) the high ethnic diversity index can decrease social-distancing behaviours and (2) the high dissimilarity between ethnic minorities and non-Hispanic Whites can increase social-distancing behavior; (3) the high interaction index can decrease social-distancing behaviours; and (4) the high concentration of ethnic minorities can increase travel distance and non-home time but decrease work behaviours. The findings of this study shed new light on public health behaviours among minority communities and offer empirical knowledge for policymakers to better inform just and evidence-based public health orders., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© Urban Studies Journal Limited 2021.)

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.)

Viaduct is a ubiquitous transportation infrastructure in the congested megacities worldwide to improve the accessibility and capacity of urban transportation network. However, there is a lack of understanding of the impacts of the interplay between viaduct-ground emissions and viaduct-canyon configurations on the particle distribution in urban street canyon. To fill the research gap, we conducted vertical measurements of particle number concentrations (PNCs) at different heights of "street canyon along a viaduct" to reveal effect of viaduct on the vertical distribution of PNCs in street canyon. Observation results indicated that the vertical profiles of PNCs exhibited bimodal distribution patterns, which were more significant for coarse particles than fine particles. The one peak appeared at ground level and the other at the viaduct height, indicating the impacts of "double" emission sources (i.e., the emissions on the ground and viaduct) and the hindrance of viaduct to particle diffusion. We further modelled the role of viaduct in street canyon through Computational Fluid Dynamics (CFD) simulations to reveal the vertical distribution of particles under different viaduct-canyon configurations and discern the contributions of viaduct and ground emissions to the particle distribution. Simulation results showed that viaduct changed airflow field and turbulence structure and elevated particle concentrations in street canyon while the optimized viaduct-canyon configurations including higher viaduct height (12 > 10 > 8 m), smaller aspect ratio (0.5 > 0.67 > 1), and shorter centerline distance (0 > 1 > 2 m) between canyon and viaduct could bring better dispersion conditions and lower particle concentrations. Additionally, ground emissions contributed more to the vertical distribution of particles on the leeward side of street canyon than viaduct emissions while the windward side displayed the opposite characteristics to the leeward side. These findings revealed the general patterns of particle diffusion in viaduct-canyon configurations and provided implications into viaduct design and traffic management to alleviate local particulate pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that can have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO 2 concentration decreased by approximately 50%, while O 3 concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO 2 concentration decreased exponentially, whereas O 3 concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy., Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02351-7., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interests regarding the publication of this article., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management., (© 2021 Society for Risk Analysis.)

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.

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19., (© 2022 Elsevier Ltd. All rights reserved.)

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/m 3 ) 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., Competing Interests: 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., (© 2021 Elsevier Ltd. All rights reserved.)

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 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., Competing Interests: 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., (© 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.

The availability of portable nephelometers has improved assessment of exposure to atmospheric particles at a high resolution regarding space and time. However, nephelometer performance has seldom been evaluated for outdoor measurements, especially in Chinese cities. During 37 days of measurements at four outdoor sites in Shanghai, we assessed a popular nephelometer called SidePak (TSI Inc., USA) for PM 1.0 , PM 2.5 and PM 10 mass measurements and compared them to US federal reference methods (FRMs) based on different measurement principles. The nephelometer showed high measurement precision and stability and was strongly correlated with FRMs, making it superior to the portable light scattering monitors reported in the past and thus indicating the maturity of this principle. The nephelometer measurements overestimated all those of FRMs by a factor of two, which is higher than in evaluations in other international cities. This overestimation showed a descending order for PM 1.0 (2.9-fold), PM 2.5 (2.2-fold) and PM 10 (1.9-fold) relative to the FRMs of tapered element oscillating microbalance or beta attenuation combined with nephelometry, based on whole samples. Sites that are far from direct pollution sources showed very good agreement between the nephelometer and FRMs for PM 2.5 mass measurements, while, by comparison, the roadside site showed a lower SidePak/FRM PM 2.5 ratio, which is likely due to higher abundance of elemental carbon in roadside particles. Relative humidity (RH) was shown to be a key factor that distorted the measurement of the nephelometer. An empirical formula incorporating an RH adjustment developed to correct the nephelometer could produce a reasonable result, even across the various sites. This study demonstrates the great potential of the nephelometer for outdoor particle mass measurements, but for accurate and comparable data, a site-specific calibration is strongly recommended before using.

Recent studies have suggested that rail transit not only facilitates urban growth but also promotes urban agglomeration. Yet research that links industrial agglomeration with rail transit is scant-what types of industries are likely to cluster near rail stations? To what extent can rail transit access be seen as having an influence on industrial agglomeration? And how do these interactions vary as rail transit proximity increases? To answer these and related questions, we investigate the relationship between industrial agglomeration and rail transit in the Dallas-Fort Worth metropolitan area using the Longitudinal Employer Dynamics (LEHD) employment data from 2014 at the census block level. First, we use the Local Indicator of Spatial Association statistics (LISA) tests to identify industrial agglomeration patterns within the study area. We then use logistics models to reveal the relationship between rail transit proximity and industrial agglomeration. Our study finds that the impacts of rail transit on industrial agglomeration, in terms of magnitudes and signs, are mixed across industries. The varying results suggest that the benefits of rail transit access exhibit considerable demand from certain industry sectors including the manufacturing, knowledge, and services industries, while exerting weaker forces in pulling agglomeration in its immediate environs among other industries (including the retail trade sector). In practice, these results are useful for justifying evidence-based rail transit planning.

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., (Copyright © 2017 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.)

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.)

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.