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The concepts of resilience and sustainability are becoming popular in disaster planning and management. However, there is an absence of mutual considerations of the two concepts from an integrated perspective to promote post-disaster livelihood, particularly in the relocated rural communities. To fill the research gap, this paper explores the factors and relationships of resilient rural livelihoods (RRL) and sustainable rural livelihoods (SRL) in resettlement communities after a major disaster. Specifically, we construct a theoretical model that integrates resources, strategies, and outcomes of RRL and SRL in the recovery phase. We use a dataset for household-level recovery after the Jiangsu Tornado (2016) in China to validate the theoretical model with a structural equation modeling (SEM) approach. Results show that government-driven, recovery-concentrated policies of "resilience" may not promote the long-term sustainability of rural household livelihoods because financial capital and institutional participation are negatively associated with the community's self-reliance for future disaster recovery. The tangible assets are likely to make people more confident in disaster preparedness, while the intangible assets lack such an impact and even make the relocated households concerned about future disasters. For RRL outcomes, financial capital and socio-political capital can enhance the recovery, while human capital and livelihood strategies have negative effects. In addition, natural capital and financial capital positively affect household recovery, but the human capital remains a negative contributor. These findings clearly reveal the general patterns of rural livelihoods in relocated communities after a disaster and provide insights into potential measures to promote sustainable and resilient rural livelihoods. [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]

This paper reports the impacts of economic analysis results on sea-level rise adaptation decision making with different economic analysis methods. The methodology was applied to Hillsborough County, Florida. A general conclusion is that partial shoreline protection should be implemented to reduce the potential impacts of sea-level rise on important land use, then transportation infrastructure is preferred to be protected or accommodated, and finally managed relocation should be adopted. More specifically, the results show that the best adaptation strategy is shoreline protection plus transportation infrastructure accommodation; the length of shoreline protection plays an important role in the economic analysis results, and shoreline protection and accommodation adaptation strategies for all areas are not recommended because of either high costs or low benefits; the value of travel time saving and spatial autocorrelation play important roles in the economic analysis results of accommodation strategy, which highlights the importance of including indirect economic factors and spatial autocorrelation impacts when making sea-level rise adaptation decisions. [ABSTRACT FROM AUTHOR]

This paper provides a comparative overview of urban transport in the world's two most populous countries: China and India. Cities in both countries are suffering from severe and worsening transport problems: air pollution, noise, traffic injuries and fatalities, congestion, parking shortages, energy use, and a lack of mobility for the poor. The urban transport crisis in China and India results from continuing population growth, urbanization, suburban sprawl, rising incomes, and skyrocketing motor vehicle ownership and use. This paper critically assesses government policies in each country and suggests a range of specific improvements. It advocates a slowdown in the massive roadway investment in recent years and a shift in emphasis to expanding and improving public transport, cycling, and walking facilities. While continued growth in motor vehicle use is inevitable, China and India should restrict motor vehicle use in congested city centres and increase taxes, fees, and charges to reflect the enormous social and environmental costs of motor vehicle use. At the same time, much stricter regulations should be imposed on manufacturers to produce cleaner, more energy-efficient, quieter, and safer cars, motorcycles, buses, and trucks. Mitigating the many social and environmental impacts of rising motorization is obviously important for the future well-being of Chinese and Indian cities. It is also crucial for the future of the rest of the world. Unless the problems of motorization in China and India can be effectively dealt with, the world faces sharp increases in greenhouse gases, accelerating climate change, and rapid depletion of a range of non-renewable resources. [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]

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]

Natural disasters, intentional attacks, and operational incidents are posing increasing threats on rail transit network. The vulnerability of rail transit network becomes an important concern of researchers and rail managers. This paper proposes a station-based accessibility approach addressing passenger flow and land use characteristics in rail transit network vulnerability analysis. Land use variables are measured as the independency degree on rail transit. The reduction ratio of network accessibility before and after incidents is calculated to measure the potential consequences. Based on results of comparisons with existing methods with the help of an example problem, the proposed accessibility measure demonstrates better and more reasonable results as not only the rail network and passenger flow but also the land use and travel alternative variables interacting with rail transit are accounted. The proposed method is then applied to Shanghai metro network. The data for analysis include rail transit network data, passenger flow data, and land use data around stations. Results indicate that the vulnerability of rail transit network is jointly affected by its network topology, passenger flow, and land use variables. Unbalanced land use, high plot ratio, and the less travel alternatives will increase the dependency of land on rail transit travel, leading to high network vulnerability once disrupted. Results of this work will inform rail transit managers of the degree of network vulnerability and critical stations and links as well as the land use dependency on vulnerability. Findings of this study may have implications not only for the planning of other transit modes to enhance the resilience of public transit network in vulnerable areas but also for the land use development around rail stations. [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. [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]

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO 2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO 2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US. • Integrating engine characteristics with the MOVES model can improve model performance. • Integrating machine learning with MOVES performs well in prediction of vehicle CO 2 and NO emission. • The proposed emission model provides a possibility for calculation rather than costly an experiment in countries outside the USA. [ABSTRACT FROM AUTHOR]

On-road vehicle has been a prominent emission source, hence a key target of control for environment, health, and climate concerns. While considerable efforts have been made to investigate criteria pollutants such as adverse gaseous and particulate emissions across the world, there is limited systematic research to characterize on-road carbon emissions, especially as countries strive to set and achieve various climate goals (i.e., carbon neutrality, carbon peaking by a certain year, etc.) through cleaner vehicle technologies such as the latest vehicle emission standard, China VI (2020). This study used the portable emission measurement system (PEMS) to reveal the real-world emissions from two light-duty gasoline vehicles, one meeting the China V standard and the other China VI. The PEMS measurements were performed secondly in urban and suburban areas respectively, and finally yielded 10 h of rich and finely resolved emissions data. In addition of single-factor analysis, this study employed two machine learning models, stepwise regression and light gradient boosting machine (LightGBM), to investigate the coupling effects among vehicle/traffic parameters and identify driving behavior, engine condition, and external environment as key determining factors that affect vehicular carbon emissions. In particular, engine load percent and engine speed were confirmed to be the most dominant factors affecting vehicular carbon emissions under both China V and China VI standards, explaining ∼60% of variability in carbon emissions. As a result, vehicle specific power (VSP) is derived from vehicle travel parameters and used as a surrogate for carbon emission estimation. The external facility and environmental factors turned out significant as well in affecting vehicle carbon emissions: carbon emissions from the same vehicle were significantly higher in an urban setting then in suburban areas, mostly due to the more aggressive driving patterns in congested urban traffic. Travelling under relatively better traffic conditions on expressways gave the lowest per-distance emission factors of CO 2 , CO, and NOx. Comparing between the China V and VI vehicle, our PEMS data show significant carbon emission reduction benefits (15.9% in CO 2 , 28.8% in CO) as we move to the new China VI standard, although such benefit was not observed in NOx emissions. • CO 2 emission rate illustrated a higher relevance to engine characteristics, followed by driving characteristics. • China VI policy for light-duty gasoline vehicles show lower carbon emission rates than China V policy. • Aggressive driving behaviors results in higher CO 2 emission rates. [ABSTRACT FROM AUTHOR]

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM 2.5 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. PM 2.5 and NO 2 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 PM 2.5 and NO 2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM 2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO 2 model. Respectively, both PM 2.5 and NO 2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO 2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM 2.5 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 PM 2.5 (0.88) being higher than NO 2 (0.62). The LUR was then used to produce continuous concentration fields for NO 2 and PM 2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM 2.5 studies in New York and Beijing show that Shanghai PM 2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions. [ABSTRACT FROM AUTHOR]

The vertical distribution patterns of PM 2.5 (particulate matters with diameters ≤2.5 μm) are crucial for understanding the aggregation, dispersion, and regional transport of PM 2.5. However, due to the measurement limitation, the vertical observational data o is difficult to be obtained and is relatively insufficient at present stage. With this consideration, two electric-fueled rotary-wing unmanned aerial vehicles (UAVs) are used in this study to reveal the vertical distribution of PM 2.5 in Shanghai of China. Totally 53 vertical profiles of PM 2.5 concentration and meteorological factors are collected to reveal the vertical distribution pattern of PM 2.5. The results indicate that PM 2.5 vertical concentration, especially the maximum of the PM 2.5 vertical concentration, shows a good correlation with surface PM 2.5 concentration, which means it is possible to use surface PM 2.5 concentration to extrapolate PM 2.5 vertical concentration. In terms of meteorological factors, low wind speed and northerly (or northwesterly) wind direction significantly contribute to the accumulation of high concentration PM 2.5 in Shanghai during the winter. Furthermore, the low height of the planetary boundary layer (PBL) facilitates the accumulation of pollutants near the ground, but it is not imperative for the vertically high concentration of PM 2.5 , since vertically high PM 2.5 concentration exists within high PBL as well. It has been concluded that low PBL height is not mandatory for well vertical mixing within it, for the vertically even distribution of PM 2.5 occurred under both low and high PBL. In terms of the effect the temperature, although temperature makes a significant contribution to the inverse distribution of PM 2.5 , no significant linear correlation was found between temperature and PM 2.5 vertical distribution. The findings in this study could help to understand the vertical distribution patterns of PM 2.5 during the winter, and provide more evidence of how other factors impact these patterns. [Display omitted] • The accuracy in measuring PM 2.5 by the UAV platform was evaluated. • Week northerly wind are desideratum for high PM 2.5 concentration. • Low PBL accounts for high surface PM 2.5 concentration in low altitude. • Vertically, there is no significant linear correlation between temperature and PM 2.5. [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 (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.)

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 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.

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.