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Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises. This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sustainability. The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks. It includes production, consumption, and transportation agents, attributing economic features to supply chain components and capturing their interactions. It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors. By coupling high spatial–temporal resolution firm-level data from 18 916 chemical enterprises, this study investigates the economic and environmental impacts of an environmental policy resulting in the closure of 1800 chemical enterprises over three years. The results revealed a significant economic loss of 25.8 billion USD, ranging from 23.8 billion to 31.8 billion USD. Notably, over 80% of this loss was attributed to supply chain propagation. Counterfactual analyses indicated that implementing a staggered shutdown strategy prevented 18.8% of supply chain losses, highlighting the importance of a gradual policy implementation to prevent abrupt supply chain disruptions. Furthermore, the study highlights the effectiveness of a multi-objective policy design in reducing economic losses (about 29%) and environmental risks (about 40%), substantially enhancing the efficiency of the environmental policy. The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

In recent years, overwhelming evidence has emphasized the crucial role of inflammation in the pathogenesis of PD. However, the exact mechanisms by which inflammation damages dopaminergic neurons in PD are still unclear. Therefore, we generated a MPTP-induced PD mouse model and performed spatial transcriptomic sequencing to provide more insight into the process of PD development at specific brain regions. Our results indicate that the pathological changes of PD are mainly manifested in the midbrain, especially in the substantia nigra region, with significant reductions in oligodendrocytes and Agt-labeled astrocytes and an increase in Gfap-labeled astrocytes. Macrophages displayed an increasing trend in the PD environment, indicating a pattern of immune modulation induced by PD. Moreover, pathway analysis revealed significant impairments in ion migration ability, abnormal Ca2+ channels, cAMP signaling, and synaptic damage in PD. Significant downregulation of Mt1 and Mt2 and upregulation of Atp1b2, Gpi1, and Cox6a1 in PD further underscored the occurrence of intense inflammation and immune alterations. On the basis of these findings, we have validated the significant accumulation of Ca2+ in the midbrain tissue in the PD environment by measuring its content. Additionally, we have demonstrated a close association between the reduction of dopaminergic neurons, represented by the midbrain region, and ferroptosis by evaluating the iron content, malondialdehyde (MDA) levels, and the protein expression of GPX4 and TH in the tissue. We propose the hypothesis that PD-related inflammation and immune changes can induce neuronal and oligodendrocyte damage through the induction of ferroptosis, thereby further accelerating the progression of PD.

Abstract Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.

Abstract The Belt and Road Initiative (BRI) makes headway in constructing railway projects invested by China, yet related environmental impacts are rarely discussed. Here we quantify the carbon footprint of railway project construction (RPC) under the BRI and identify the drivers behind their variations to find possible carbon reduction pathways. Our results show that China‐invested overseas RPC in BRI countries induced 2,095.2 Mt CO2 emissions through the global supply chains during 2008–2017. Specifically, 73% of the carbon footprint relies on supply chains in BRI countries, and 27% spills over to other regions serving as the suppliers of global resources. Upgrading key industries' technological levels in BRI countries to China's average level can mitigate the carbon footprint by 77%. Once all industries achieve China's average technological level, nearly 90% of the carbon footprint can be avoided. The findings potentially provide valuable insights into achieving sustainable project construction in cross‐regional cooperation and promoting green development of the BRI.

Summary: Background: Metropolitan areas have complex trade linkages internally and externally. This complexity stimulates the unequal spatial transfer of environmental health consequences, economy, and employment embodied in internal trade or trade with the outside regions, resulting in unequal exchange. Existing research has rarely discussed this issue at a refined scale, hindering targeted inequity alleviation policies. Methods: We conducted a mixed-methods study, focusing on the most polluted metropolitan area in the world, the Beijing-Tianjin-Hebei region of China, and developed an integrated modelling framework to downscale the analysis of the trade-driven unequal transfer of PM2·5- related premature deaths, value added, and job opportunities to the city scale within Beijing-Tianjin-Hebei. The study couples a nested Multi-Regional Input-Output model table containing data from 13 Beijing-Tianjin-Hebei cities and 28 outer provinces in 2017 with a bottom-up emission inventory, value added and employment statistical data, the Weather Research and Forecasting-Comprehensive Air Quality Model with Extensions, the Global Exposure Mortality Model, and human capital methods. We also constructed two indices measuring unequal exchanges between PM2·5-related deaths and economic and employment gains embodied in trades between cities in Beijing-Tianjin-Hebei and trades with outside regions. Findings: The Beijing-Tianjin-Hebei region as a single entity shifted 14 985 (95% CI 12 800–16 948) net deaths to regions outside the Beijing-Tianjin-Hebei through trade, most of which occurred in the central region of China. The industrial-based peripheral Beijing-Tianjin-Hebei cities suffered the most serious inequities when trading with other Beijing-Tianjin-Hebei cities and outside regions. While gaining equivalent local jobs, these industrial-based peripheral cities had 250% higher PM2·5-related deaths (10·2 PM2·5-related deaths for obtaining 1000 local jobs) than core cities (2·9 PM2·5-related deaths for obtaining 1000 local jobs) and 57·7% higher PM2·5-related deaths than agricultural-based peripheral cities (6·5 PM2·5-related deaths for obtaining 1000 local jobs). While gaining equivalent value added, industrial-based peripheral cities had 50·6% higher PM2·5-related deaths (¥13·9 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added) than core cities (¥9·2 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added) and 67·4% higher PM2·5-related deaths than agricultural-based peripheral cities (¥8·3 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added). Interpretation: Treating metropolitan areas as a single entity obscured internal heterogeneity, potentially misleading policy makers into imposing strict regulations on the whole metropolitan area to alleviate the inequities it posed on outside regions. However, several peripheral Beijing-Tianjin-Hebei cities were disadvantaged in their trade with core Beijing-Tianjin-Hebei cities and outside regions. Therefore, policies should be tailored for particular cities within metropolitan areas. Future targeted policies should include, but not be limited to, making ecological compensations and incorporating the environment and health costs into the price of pollution-intensive goods and services. Funding: National Key Research and Devlopment Program of China, National Natural Science Foundation of China, and Jiangsu Natural Science Foundation

The perception of climate change as an abstract and distant concept hinders climate action. Previous studies suggest that individuals may conflate climate change and local air pollution due to misconceptions, as these environmental issues share similar emission sources and mitigation strategies. However, it remains uncertain whether leveraging the public’s high perception of air pollution can lead to increased climate change awareness and supportive behaviors. In this study, we surveyed 1236 Chinese online customers to explore the influence of air pollution perception on climate change perception and willingness to pay (WTP) for supporting climate actions. Structural equation modeling results revealed that higher air pollution perception, which is connected to an individual’s living environment and health status, is significantly associated with higher climate change perception. Despite this link, higher climate change perception does not necessarily translate to higher WTP for climate actions. However, air pollution perception can indirectly increase WTP for climate actions by fostering avoidance behavior intentions. For practical implications, our findings suggest that an emphasis on the co-benefits of climate change mitigation efforts for improving air quality may help lead to greater policy support and understanding. Additionally, promoting individual behavioral changes requires connecting these issues to personal experiences and highlighting tangible benefits such as improved health and well-being.

Understanding the inequality of PM _2.5 -related health is crucial for promoting health, building a just society, and advancing multiple Sustainable Development Goals goals. However, previous research has predominantly concentrated on PM _2.5 exposure inequality, neglecting varied prompt responses and protective behaviors against it. Here, we established the relationship between short-term healthcare expenditure and PM _2.5 concentration using the number and amount of healthcare transactions across all healthcare categories based on the Union Pay data. We also assessed daily city-specific PM _2.5 -related mortality and healthcare expenditures and evaluated their inequalities among cities according to the income inequality index, the Gini coefficient. The results show that short-term exposure to PM _2.5 leads to severe physiological and health-related economic burdens on Chinese residents. From 2017 to 2019, 77.8 (34.5–121.1) thousand deaths were attributed to daily PM _2.5 , with healthcare expenditures reaching 93.7 (69.1–118.3) billion Chinese Yuan. Additionally, there were significant inequalities in PM _2.5 -related mortality and healthcare expenditures among cities. The inequality index for PM _2.5 -related healthcare expenditures was 0.53, while the inequality index for PM _2.5 -related mortality was 0.13. The greater inequality in healthcare expenditures than in mortality, implying inadequate healthcare resources amplify the health inequality related to PM _2.5 exposure. 28.6% of Chinese cities lacked affordable healthcare resources to address the high physiological burden attributable to PM _2.5 . Our multidimensional exploration is essential for formulating effective policies addressing environmental health inequality. Focusing on these cities with disproportionate challenges is crucial for creating a more equitable and sustainable society.

Abstract The accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short‐term memory network of delay prediction with an attention mechanism (LSTM‐AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM‐AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM‐AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM‐AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM‐AM combined with runway and apron flow control can reduce or eliminate delays of one flight.

Background: The aim of this study was to evaluate the long-term sequelae and cognitive profiles resulting from severe hand, foot, and mouth disease (HFMD) with central nervous system (CNS) involvement. Methods: 294 HFMD cases were included in a retrospective follow-up study. Physical examinations were conducted. The Chinese Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI-IV) was used to assess intelligence. Results: 58 mild HFMD cases and 99 severe HFMD cases with mild CNS involvement did not present any neurological sequelae. In comparison, the sequelae incidence for severe HFMD with more severe CNS complications was 50.0%. The proportion of full-scale intelligence quotient (FSIQ) impairment was 45.0%. In the 2:6–3:11 age group, severe HFMD with more severe CNS complications and lower maternal education level were risk factors for verbal comprehension disorder. Urban–rural residence and lower paternal education level were risk factors for FSIQ disorder. Furthermore, in the 4:0–6:11 age group, severe HFMD with more severe CNS complication was a risk factor for visual spatial disorder and fluid reasoning disorder. Lower paternal education level was a risk factor for FSIQ disorder. Conclusion: Early assessment and intervention among severe HFMD patients with more severe CNS involvement at a very young age will prove beneficial for their future performance.

Aerosol particle size has a crucial impact on the environment and public health. Current satellite-based regression models focus on the total amount of particles and are limited by surface observations. This study proposes an algorithm to derive the long-term normalized volume size distribution (VSD) of aerosol particles, which is independent of ground measurements. The size distribution and aerosol optical depth of Multi-angle Imaging SpectroRadiometer (MISR) components are employed. We find the estimated MISR VSD is consistent with Aerosol Robotic Network (AERONET) observations, with R = 0.56, 0.54, 0.59, and 0.68 for daily, monthly, seasonal, and annual levels. The stratified validations of radius, stations, and years further confirm the stable performance of derived VSD (R = 0.28 to 0.73). The application of the random forest model demonstrates the potential improvements of predicted VSD by 10-fold cross-validation R = 0.86 at the monthly level. We apply MISR VSD to quantify the normalized volume of fractional aerosol particles at a resolution of 0.2° × 0.2° during 2004 to 2016 in China. We also calculate the proportion of small and medium particles to indicate the contribution of anthropogenic aerosols. The highest ratios are concentrated in the northeastern regions especially during winter while relatively lower in the Taklamakan Desert of western China. The case study demonstrates that the application of MISR data can yield valuable and resolved size distributions of aerosol particles.

Unfrozen water content is a significant hydro-thermal property in numerical modeling in cold regions. Although numerous models have been developed to mimic the variation of unfrozen water content with subzero temperature, comprehensive evaluation of unfrozen water content models is scarce. This study collected a total of 29 models and divided them into four categories, namely, theoretical models, soil water characteristic curve (SWCC)-based models, empirical models, and estimation models. These models were evaluated with 1278 experimental points from 16 studies covering multiple soil types, including 24 clays, 18 silty clays, 7 silts, 19 sands, and 10 sandstones. Root mean square error and average deviations were applied to judge the performance of these models. Most unfrozen water content models can well simulate the relationship between unfrozen water content and subzero temperature. Among the aforementioned four categories of unfrozen water content models, Lizhm et al. model, Fredlund and Xing (C=1)-Wen model, Kozlowski empirical model, and Kozlowski estimation model performed best in their respective categories. Compared to the rest three categories, estimation models can be applied to predict the variation of unfrozen water content with subzero temperature by some easy-to-obtain soil physical parameters and provide guidance for the development of unfrozen water content models.

Soil freezing characteristic curve (SFCC) defines the relationship between unfrozen water content and subzero temperature. The SFCC is widely applied to estimate the soil properties. The current study explored the effects of initial water content, dry density, soil type and desalination on the SFCC during a freezing-thawing process. It showed that the initial water content, soil type and desalination had great impacts on SFCC, while the dry density had an insignificant effect on SFCC. Hysteresis behavior was observed for all the soil samples. The hysteresis behavior was obvious in the temperature range of -5°C < T < 0, but not obvious in the temperature range of T ≤ -10 °C. A new equation containing freezing/thawing point was proposed to model the SFCC. The new model and four SFCC models were evaluated with measured data in this study. It showed that the new model performed best among these models. The new model accurately mimics the SFCC and is continuous near the freezing/thawing point and 0 °C. It can be easily incorporated into numerical algorithms for coupled heat and mass transfer in cold regions.

Vitamin D metabolism is essential in aging and can be affected by multiple environmental factors. However, most studies conducted single exposure analyses. We aim to assess the individual and combined effects of ultraviolet (UV) radiation, residential greenness, fine particulate matter (PM2.5), and ozone (O3) on vitamin D levels in a national cohort study of older adults in China. We used the 2012 and 2014 Chinese Longitudinal Healthy Longevity Survey data, and measured the environmental exposure in the same year. We interpolated the UV radiation from monitoring stations, measured residential greenness through satellite-derived Normalized Difference Vegetation Index (NDVI), modeled PM2.5 with satellite data, and estimated O3 using machine learning. We dichotomized serum 25-hydroxy vitamin D (25(OH)D), the primary circulating form of vitamin D, into non-deficiency (≥50 nmol/L) and deficiency (

In this paper, the macroscopic traffic states and network traffic dynamics during the Qingming Festival holiday are explored with the Macroscopic Fundament Diagram (MFD), including the weekday before the holiday (WBH), the day of Qingming Festival (DQF), and the ordinary weekday (OW). The network is heterogeneous on the densities’ distribution, and the congested areas are different in location and size. Normalized Cut (Ncut) algorithm is used to partition the heterogeneous network into multiple homogeneous subregions. The MFD of each subregion is distributed within the critical density on the WBH, and the high-density region moves from the city center to the periphery. On the DQF, high-density areas are on the central and west of the urban network. The congested branch appears on the MFD of the subregion. Then, two calibrated dynamic models, are applied to analyze the network evolution based on the partitioning results. On the basis of the calibrated model, several control strategies are proposed to relieve regional congestion on holidays. According to the simulation results, congestion on the DQF can be alleviated by controlling the external inflow ratio of subregion 2 or limiting the amount of traffic entering subregion 2 from the outside.

Environmental science is an interdisciplinary science developed in the process of understanding and solving ecological and environmental problems. In order to tackle these problems, environmental science research is expected to reveal the source, behavior, fate, exposure, and risks of pollutants in the environment and develop potential solutions to control pollution. It provides the scientific basis for decision-makers to establish environmental and economic policies, and promote concerted efforts for the sustainable development of society. Here, we articulate the development patterns, challenges, and future research needs of environmental science in China based on literature review and expert panel discussion. Environmental science research has evolved significantly in the past decade with an increasing diversity of environmental pollutants and health impacts, new technologies and methods, deepening fusion of multiple disciplines, and emerging solutions for pollution control. Its future development relies on the advances in our knowledge on the fate and transport of pollutants, regional environmental processes, ecotoxicological effects, environmental exposure and health effects, environmental analysis and monitoring, source control and reduction, environmental remediation, as well as environmental risk management. For each of these fields, we summarize the significant challenges and highlight the research demands for China. Based on the status quo of China's environmental science research and future needs, we provide recommendations to promote its future development, including encouraging innovation and interdisciplinary research, providing decision support for national needs, encouraging international collaboration, and improving collaboration mechanisms.

Abstract With the continuous occurrence of natural disasters, natural hazard triggered technological accident (Natech) risks also follow. At present, many countries have performed much research on Natech risks. However, there is still a lack of Natech research at the regional or watershed level in China. The Yangtze River Economic Belt (YREB) is not only an industrially intensive development area but also an area with frequent natural disasters. In this study, we selected the YREB as a typical case to study the Natech risk triggered by floods, geological disasters, and typhoons at the regional or watershed level. Four types of risk indicators representing risk sources, natural hazard factors, control levels, and vulnerabilities were developed to assess the spatial patterns of the Natech risks of the YREB. The results show that the Natech risk triggered by floods and typhoons is more serious in eastern area and central area than in western zone and that the Natech risk triggered by geological disasters is more serious in the west part. Approximately 7.85% of the areas are at relatively high-risk and above the Natech risk level based on the comprehensive assessment of three types of Natech risks. The combined population of these areas accounts for approximately 15.67% of the whole YREB, and the combined GDP accounts for approximately 25.41%. It can be predicted that the occurrence of Natech risks in these areas will cause serious harm to both the people and the economy. This work will provide the basis and key management direction for Natech risk management in the YREB.

Abstract Battery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real‐world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.

The dual-carbon strategy advocates a green, environmentally friendly, and low-carbon lifestyle. In the field of transportation, electric vehicles (EVs) have been regarded as an effective solution to reduce carbon emissions and to conserve energy. Developing a reasonable charging guidance scheme for users is a feasible way to solve problems, such as the range anxiety of EV users, and has a great application value for the promotion of EVs in the future. In practical situations, how to develop charging induction schemes for users that better meet their needs according to the type of user and their multi-dimensional preferences is the focus of this paper. To this end, this study utilized charging behavioral data to investigate the multi-dimensional charging preference of users based on the collaborative filtering algorithm. Then, a multi-objective optimization model was established based on the preference degree of each charging station and the integrated travel cost. An NSGA-III framework was used to design the algorithm to solve the proposed model. The algorithm was tested using simulation experiments that were designed based on the road network and charging stations in Beijing. The final result is an experimental analysis of the weight matrices for the three different preferences of minimum energy consumption cost, minimum time cost, and minimum fee cost, which yields a difference of about 4.4% between the optimal energy consumption cost and the maximum energy cost, about 2.9% between the optimal time cost and the maximum time cost, and about 10% between the optimal fee cost and the maximum fee cost under these three different preferences, respectively. The proposed multi-objective optimization model is able to provide users with reliable charging station selection by incorporating their personalized charging preference characteristics and charge guidance schemes.

The outbreak of the COVID-19 pandemic has raised concerns about the use of public transport, with a surge in people considering personal car usage. However, owning private cars is costly and wasteful of resources. Electric car-sharing (ECS) is considered a safer and more private mode of transportation compared with public transportation. The COVID-19 pandemic has affected transport on transportation policies and travel willingness. What is the effect of the COVID-19 pandemic on CS travel, especially considering the safety issues during the COVID-19 pandemic? This study analyses the differences in the travel characteristics of private car owners and nonowners while using CS under the influence of the COVID-19 pandemic. Quantitative analysis during four months before and four months after the outbreak of the COVID-19 pandemic is conducted based on CS order data in Lanzhou, China. It was found that the number of CS orders fell by 55.8% during the COVID-19 pandemic. Travel behavior during the pandemic is different from that before the outbreak of the pandemic. Additionally, both private car owners and nonowners use CS while having differences in travel characteristics. Based on the results, business suggestions are introduced on the distribution of vehicles to help improve the profit of CS operators.

Ambient particulate matter pollution has been linked to impaired cognitive performance, but the effect of ambient ozone exposure on cognitive function remains largely unknown. We examined the association of long-term ozone exposure with the risk of cognitive impairment among a national representative cohort of 9,544 Chinese older adults (aged 65 years and over) with baseline normal cognition from the Chinese Longitudinal Healthy Longevity Survey (2005–2018). The ozone exposure of each participant was measured by annual mean ozone concentrations for the county of residence. Cognitive function was assessed by the Chinese version of the Mini-Mental State Examination (MMSE). We defined cognitive impairment as an MMSE score below 18 points accompanied by an MMSE score that declined ≥ 4 points from baseline. Cox proportional hazards models were applied to explore the association of ozone exposure with cognitive impairment. During the mean follow-up time of 6.5 years, 2,601 older adults developed cognitive impairment. Each 10-μg/m3 increase in annual mean ozone exposure was associated with a 10.4% increased risk of cognitive impairment. The exposure–response relationship between ozone exposure and risk of cognitive impairment showed a linear trend. Sensitivity analyses revealed the association to be robust. We found that older adults from Eastern, Central, and Southern China were particularly susceptible. Our results show that ozone is a risk factor for late-life cognitive decline. Reducing ambient ozone pollution may help delay the onset of cognitive impairment among older adults.

The high moisture content and perishable organic waste of municipal solid waste (MSW) in China have caused the severe odor nuisance to be one of the crucial reasons for resident complaints. Understanding the environmental risks of odorous compounds lays the foundations for resolving the problems. This study collected concentration data of 86 odorous compounds in five types of MSW processing facilities/equipment which can well represent the whole process of MSW stream, including waste bins and transfer stations for collection, compost plants and anaerobic digestion plants for utilization, and landfills for final disposal. The results revealed that the occupational health risks of odorants were not fully consistent with the compound concentrations and olfactory annoyance. Higher odorous compound concentrations and more severe olfactory annoyance can be found in the MSW utilization and disposal facilities, but the occupational carcinogenic risk (2.79 × 10−5–1.12 × 10−3) was non-negligible along the whole MSW stream. Aromatic hydrocarbons and halogenated hydrocarbons were crucial contributors to the carcinogenic risk of odorous compounds emission from these facilities. Particularly for estimating the adverse impact range of MSW facilities, the carcinogenic risk was the most critical factor, implying impact distance of ∼1.5 km for MSW transfer station and ∼5 km for landfill, and even higher for the regions (such as southwest China) with lower wind speed and higher atmospheric stability. In addition to current regulations, another 5 compounds (acetaldehyde, 1,3,5-trimethylbenzene, 1,2-dichloroethane, acrolein, and benzyl chloride) that displayed high carcinogenic risks were suggested to be concerned. This study provided insights for the policymakers regarding MSW odors management, especially underscoring the importance of considering the health risks of odorous compounds.

Due to the large amount of waste generated by urban construction, the transportation of construction waste has a significant impact on urban traffic. Understanding the transportation trajectory of garbage trucks can improve the management of transportation routes and reduce traffic accidents. This study analyzes electric waybill and state data of garbage trucks to identify hot nodes of construction waste transportation, where the volume of garbage trucks is relatively high. Management should strengthen the hot nodes to reduce traffic accidents. First, several machine learning methods are used to improve the prediction accuracy of electric waybill generation, where the garbage truck recorded on the electric waybill is regarded as a working truck. Second, the transportation trajectory of working trucks is extracted, and its spatiotemporal characteristics are further analyzed. Hot nodes are found based on density clustering. Finally, a case study is conducted based on the Shenzhen construction waste transportation system. The results show that the XGBoost model can improve the accuracy of the generation of waybill to 90.5% compared with the decision tree model, random forest, and GBDT. Moreover, the density clustering model can discover the hot nodes of construction waste transportation. Considering the minimum number of samples and the neighborhood radius, the clustering number is determined as 100. The ratio of noise points is determined as 0.79. The results can provide decision support for the management of electronic waybill and garbage truck transportation.

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

Abstract China has pledged to cap its carbon emission by 2030 and achieve carbon neutrality by 2060, making knowledge about how the Chinese general public understands climate change crucial and timely. This article reports findings from surveys of climate change perceptions in six Chinese cities (∼40 million people). We identify 10 distinct mental images and 37 subcategories that represent a wide spectrum of perceptions of climate change among the Chinese public. The results reveal that people tend to conflate climate change with air pollution and seasonal weather changes. Although skepticism is not prominent, voices for action are also limited. Furthermore, climate change perceptions are heterogenous across regions and demographic groups. Respondents from developed cities are less likely to conflate climate change with local weather. People living in polluted regions tend to equate climate change with air pollution. Well‐educated, high‐income, and young residents are more aware of the scientific dimensions of climate change and its consequences. Females and the elderly think more about health implications and how to adapt. Compared to Western countries, opinions about climate change in China are less polarized and controversial, probably due to different political realities and media framings. This study provides an updated picture of climate change perceptions among the Chinese general public and recommends targeted and multi‐level communication strategies for policymakers.

Abstract Background Cervical cancer is a major public health concern in China. We report the end‐of‐study results of a phase II/III trial to assess the efficacy, immunogenicity, and safety of the AS04‐human papillomavirus (HPV)‐16/18 vaccine in Chinese women aged 18‐25 years followed for up to 72 months after first vaccination. Results of approximately 57 months following first vaccination have been previously reported. Methods Healthy 18‐25‐year‐old women (N = 6051) were randomized (1:1) to receive three doses of AS04‐HPV‐16/18 vaccine or Al(OH)3 (control) at Months 0‐1‐6. Vaccine efficacy against HPV‐16/18 infection and cervical intraepithelial neoplasia (CIN), cross‐protective vaccine efficacy against infections and lesions associated with nonvaccine oncogenic HPV types, immunogenicity, and safety were assessed. Efficacy was assessed in the according‐to‐protocol efficacy (ATP‐E) cohort (vaccine N = 2888; control N = 2892), total vaccinated cohort for efficacy (TVC‐E; vaccine N = 2987; control N = 2985) and TVC‐naïve (vaccine N = 1660; control N = 1587). Results In initially HPV‐16/18 seronegative/DNA‐negative women, vaccine efficacy against HPV‐16/18‐associated CIN grade 2 or worse was 87.3% (95% CI: 5.5, 99.7) in the ATP‐E, 88.7% (95% CI: 18.5, 99.7) in the TVC‐E, and 100% (95% CI: 17.9, 100) in the TVC‐naïve. Cross‐protective efficacy against incident infection with HPV‐31, HPV‐33 and HPV‐45 was 59.6% (95% CI: 39.4, 73.5), 42.7% (95% CI: 15.6, 61.6), and 54.8% (95% CI: 19.3, 75.6), respectively (ATP‐E). At Month 72, >95% of initially seronegative women who received HPV vaccine in the ATP cohort for immunogenicity (N = 664) remained seropositive for anti‐HPV‐16/18 antibodies; anti‐HPV‐16 and anti‐HPV‐18 geometric mean titers were 678.1 EU/mL (95% CI: 552.9, 831.5) and 343.7 EU/mL (95% CI: 291.9, 404.8), respectively. Serious adverse events were infrequent (1.9% vaccine group [N = 3026]; 2.7% control group [N = 3025]). Three and zero women died in the control group and the vaccine group respectively. New onset autoimmune disease was reported in two women in the vaccine group and two in the control group. Conclusions This is the first large‐scale randomized clinical trial of HPV vaccination in China. High and sustained vaccine efficacy against HPV‐16/18‐associated infection and cervical lesions was demonstrated up to Month 72. The vaccine had an acceptable safety profile. Combined with screening, prophylactic HPV vaccination could potentially reduce the high burden of HPV infection and cervical cancer in China. Trial registration NCT00779766.

Network function virtualization (NFV) brought significant flexibility.However,such flexibility came with considerable compromises,since virtual machine carried monolithic functions could introduce significant performance overhead.A novel high-performance and programmable framework called MicroNF was proposed,which combines programmable hardware infrastructure and traditional software infrastructure in NFV to achieve both high performance and flexibility.In particular,microservice,a new design approach in software architecture,was leveraged by MicroNF to re-architect NFV to enable functional reusability among services and improve performance.MicroNF was implemented in a test bed based on OpenStack and ONetCard.Experimental results show that MicroNF reduces the forwarding latency of a service chain by an average of 70% compared with DPDK-based software implementation.

Human infections with vaccinia virus (VACV), mostly from laboratory accidents or contact with infected animals, have occurred since smallpox was eradicated in 1980. No recent cases have been reported in China. We report on an outbreak of VACV from occupational exposure to rabbit skins inoculated with VACV.

Background: Reducing cadmium (Cd) exposure in Cd-polluted areas in Asia is urgently needed given the toxic effects of Cd. The short-term and long-term benefits of lowering Cd exposure are unknown because of its long half-life in the body. Objectives: We aimed to investigate whether an intervention with low-Cd rice in a contaminated area of China reduced urinary Cd (UCd) levels and improved blood pressure and kidney function outcomes compared to no-intervention in consumers of high-Cd rice in the same region. Methods: 106 non-smoking subjects were divided into three treatment groups: the intervention group (replacing homegrown high-Cd rice with market low-Cd rice, n = 34), the non-intervention group (continue eating high-Cd rice, n = 36) and the control group (continued eating low-Cd rice they have been eating for years, n = 36). The intervention period lasted for almost 8 months, during which participants were visited on up to 4 occasions and UCd, systolic and diastolic blood pressure (SBP, DBP), kidney function biomarkers (β2-microglobulin and N-acetyl-β-D-glucosaminidase) were measured. Results: After 3 months, the geometric mean UCd in the intervention (Int) group decreased significantly by 0.32 μg/g (p = 0.007), while changes were not significant in the non-intervention (non-Int) group (0.13 μg/g, p = 0.95) or the control group (−0.01 μg/g, p = 0.52). UCd in the Int group remained lower than in the non-Int group but higher than in the Control group through the end of follow up. DBP in the Int group decreased significantly from 80 mm Hg at month three (p = 0.03) and stayed around 74 mm Hg for the remainder of the study. SBP also decreased in the Int group but with variations similar to those observed in the other two groups. The two kidney biomarkers showed variations without a clear pattern. Conclusion: This study suggested that UCd reflected both short-term (

An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles.

We conducted a cross-sectional study with 395 completely matched student samples enrolled from a public primary school in Nanjing of eastern China, including questionnaires, blood samples, growth indexes and school performances, all of which were used for the analysis of variance (ANOVA) and general linear model (GLM). The results showed that factors, such as gender, age, parents’ education, residential passive smoking and picky eaters, had significant impacts on the blood lead levels (BPbs). As for the linear and non-linear dose-response relationship between BPbs and erythrocyte parameters, we found a positive association between BPbs and red blood cell count (RBC count) and mean corpuscular hemoglobin concentration (MCHC) but a negative association between BPbs and hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH). When BPbs increased by 10 μg/L, the RBC count increased by 0.18 × 1012/L, while HGB and HCT decreased by 1.19 g/L and 0.41% for boys, respectively. As for girls, corresponding increases in RBC count was 0.05 × 1012/L, while HGB and HCT decreased by 0.82 g/L and 0.23%. Meanwhile, for both boys and girls, MCHC increased by 2.55 g/L, while MCV and MCH levels decreased by 0.41 fL and 0.12 pg each. Furthermore, a remarkable adverse effect (p

In recent years, ground-level ozone has become a severe ambient pollutant in major urban areas of China, which has adverse impacts on population health. However, in-situ measurements of the ozone concentration before 2013 in China are quite scarce, which cannot facilitate the assessment of the long-term trends and effects of ozone pollution. In this study, we used daily maximum 8-hour average (MDA8) ozone observations from 2013 to 2017 combined with concurrent ozone retrievals, aerosol reanalysis, meteorological parameters, and land-use data to establish a nationwide MDA8 prediction model based on the eXtreme Gradient Boosting (XGBoost) algorithm. The model achieves high prediction accuracy compared with other studies, with R2 values for the by-year, site-based, and sample-based cross-validation (CV) schemes of 0.61, 0.64, and 0.78, respectively, at the daily level. External testing with regional measurements from 2005 to 2012 and nationwide data in 2018 have shown that the model is robust and reliable for historical data prediction, with external model testing R2 values ranging from 0.60 to 0.87 at the month level in different years. Using the final estimator, we obtained nationwide monthly mean ozone concentrations from 2005 to 2012 and daily MDA8 ozone concentrations from 2013 to 2017 at a resolution of 0.1° × 0.1°. According to the average number of days exceeding the standard and the average of the 90th percentile of the MDA8 ozone concentrations, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta, the Pearl River Delta, the Jianghan Plain, the Sichuan Basin, and the Northeast Plain regions were identified as pollution hotspots. During the research period, the overall ozone levels fluctuated slightly, and their trends were not spatially continuous. There was a significant increasing trend in the BTH region by 1.37 (95% CI: 0.46,2.29) μg/m3/year between 2013 and 2017. In 2017, 26.24% of the population lived in areas exceeding the Chinese grade II national air quality standard, which shows that ozone pollution has posed an obvious threat to population health in China. Our products will provide reliable support for future long-term nationwide health impact studies and policy-making for pollution control and prevention.

This work explores the characteristics of the usage behaviour of station-based car-sharing users based on the actual operation data from a car-sharing company in Gansu, China. We analyse the characteristics of the users’ demands, such as usage frequency and order quantity, for a day with 24 1 h time intervals. Results show that most car-sharing users are young and middle-aged men with a low reuse rate. The distribution of users’ usage during weekdays shows noticeable morning and evening peaks. We define two attributes, namely, the latent ratio and persistence ratio, as classification indicators to understand the user diversity and heterogeneity thoroughly. We apply the k-means clustering algorithm to group the users into four categories, namely, lost, early loyal, late loyal, and motivated users. The usage characteristics of lost users, including maximum rental time and travel distance, minimum percentage of same pickup and return station, and low percentage of locals, have noticeable differences from those of the other users. Late loyal users have lower rental time and travel distance than those of the other users. This manifestation is in line with the short-term lease of shared cars to complete short- and medium-distance travel design concepts. We also propose a model that predicts the driver cluster based on the decision tree. Numerical tests indicate that the accuracy is 91.61% when the user category is predicted four months in advance using the observation-to-judgment period ratio of 3 : 1. The results in this study can support enterprises in user management.

An airport gate is the core resource of an airport operation, which is an important place for passengers to get on and off the aircraft and for maintaining aircraft. It is the prerequisite for other related dispatch. Effective and reasonable allocation of gates can reduce airport operating costs and increase passenger satisfaction. Therefore, an airport gate assignment problem (AGAP) needs to be urgently solved in the actual operation of the airport. In this paper, considering the actual operation of the airport, we formulate an integer programming model for AGAP by considering multiple constraints. The model aims to maximize the number of passengers on flights parked at the gate. A tabu search-based algorithm is designed to solve the problem. In the process of algorithm design, an effective initial solution is obtained. A unique neighborhood structure and search strategy for tabu search are designed. The algorithm can adapt to the dynamic scheduling of airports. Finally, tests are performed using actual airport data selected from Kunming Changshui International Airport in China. The experimental results indicate that the proposed method can enhance the local search ability and global search ability and get satisfactory results in a limited time. These results provide an effective support for the actual gate assignment in airport operations.

Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.

Respiratory, and cardio-cerebrovascular health-related diseases significantly threaten human health and together with air pollution form a complex pathophysiological system. Other complex biological systems show that increased variance and autocorrelations in time series may act as valid early warning signals for critical transitions. On population level, we determined the likelihood that increased variance and autocorrelation of hospital visit on cardiopulmonary disease preceded critical transitions in population health by human-pollution interactions. We investigated long-term hospital visits from a hospital in Nanjing City, China during 2006–2016 for the most important cardiopulmonary diseases likely to be influenced by air pollution: cerebrovascular accident disease (CVAD), coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), lung cancer disease (LCD), and the grouped categories of respiratory system disease (RESD) and cardio-cerebrovascular system disease (CCD). The time series of standard deviations (SDs) and autocorrelation at-lag-1 (AR-1) were studied as potential Early-Warning Indicators (EWIs) of transitions in population health. Elevated SDs provided an early warning for critical transitions in visit for LCD and overall CCD and CVAD, for the period of 2012–2013, after which a real transition of increased visit occurred for these disease categories. Statistical testing showed that these SDs were significantly increased (p

Abstract Battery electric logistics vehicles (BELVs) reduce transportation costs and air pollution unlike conventional logistics vehicles. However, the limited driving range of BELVs creates new problems for logistics transport. Accurate driving distance estimation of BELVs can help logistics companies determine transport strategies and alleviate the range anxiety of drivers. Based on mass data from BELVs operating in Beijing, China, this study uses a practical and effective data-based modeling method, regression analysis, to establish the data-based model of driving distance estimation. During the modeling process, a nonlinear relation between percentage of energy consumption per kilometer and driving speed is explored based on the experimental data. After determining the model variables, the model frame of driving distance in consideration of driving speed and state of charge is established. The forgetting factor recursive least-squares algorithm is applied to estimate the parameter values of the model. Verification results confirm the feasibility of the model and show that the model errors are small. The proposed model is also used to explore the economical driving speed of BELVs.

Software-based sequential service chains in network function virtualization (NFV) could introduce significant performance overhead.Current acceleration efforts for NFV mainly target on optimizing each component of the sequential service chain.However,based on the statistics from real world enterprise networks,it was observed that 53.8% network function (NF) pairs could work in parallel.In particular,41.5% NF pairs could be parallelized without causing extra resource overhead.NFP was presented,a high performance framework,that innovatively enabled network function parallelism to improve NFV performance.NFP consisted of three logical components.Firstly,NFP provided a policy specification scheme for operators to intuitively describe sequential or parallel NF chaining intents.Secondly,NFP orchestrator intelligently identified NF dependency and automatically compiled the policies into high performance service graphs.Thirdly,NFP infrastructure performed light-weight packet copying,distributed parallel packet delivery,and load-balanced merging of packet copies to support NF parallelism.An NFP prototype based on DPDK in Linux containers was implemented.The evaluation results show that NFP achieves significant latency reduction for real world service chains.

International and domestic interprovincial trade of China are entangled, but their health impacts have been treated separately in earlier studies. Here Wang. quantify the complex impacts of trade on public health across China within an integrative framework.

Abstract Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R2 = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM2.5 estimates.

With the development of software-defined networking (SDN),its scalability has become one of the most important issues of SDN.The features of SDN was studied which lead to its scalability problem when SDN was applied to large-scale network.The three main causes leading to scalability problem were discussed:control plane and data plane separation,logical centralized control and fine-grained flow control.Meanwhile,the studies which focus on the scalability of SDN from three aspects:scalability of performance,scalability of geographic and scalability of control was presented.Further,the studies on the performance evaluation of the scalability of SDN were introduced.Finally,the future work was discussed.

Thermal conductivity of soils is an important parameter in heat and water transfer analysis of soils. Here, a modified model was proposed to calculate the saturation-dependent thermal conductivity of fine-textured soils from the completely dry condition to the full water saturation. In the study, we developed a new parallel-series model for calculating the thermal conductivity of soils at the completely dry condition with a weighting parameter. The weighting parameter can be calculated using the ratio of the thermal conductivity of air to the thermal conductivity of solid grains. We also proposed a simplified equation to calculate the parameter a of the model proposed by Bi et al. using sand content and thermal conductivity of soils at the full water saturation. Substituting the new parallel-series model and the simplified equation for the parameter a into the model proposed by Bi et al., yielding the modified model. The modified model was evaluated with 14 soil samples. Results show that the modified model could well estimate the thermal conductivity from the completely dry condition to the full water saturation. Keywords: Thermal conductivity, Saturation-dependent, Parallel-series model, Weighting parameter

It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model built by the conventional multiple linear regression method is feasible to predict the driving range, but the residual errors between -3.6975 km and 3.3865 km are relatively unfaithful for real-world driving. The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods. The result of the machine learning method shows that the maximum prediction error is 1.58 km, the minimum prediction error is -1.41 km, and the average prediction error is about 0.7 km. The predictive accuracy of the gradient boosting decision tree is compared against that of the conventional approaches.

With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

The frequency and intensity of high-temperature events continue to increase, resulting in a surge of pathogenicity and mortality. People with low levels of risk perception and adaptability, such as the elderly, suffer more from high temperatures. Effective intervention measures may lead to reduced levels of high temperature-related risk. The purpose of this study was to compare changes in temperature exposure, risk perception and coping behaviors under different intervention methods. Herein we conducted three different interventions including education, subsidies for electricity and uses of spray-cooling appliances as well as collected data about temperature exposure, risk perception, and coping behaviors. Before and after the experiment, we evaluated the intervention effectiveness with a number of variables related to alerting human responses under high temperatures. We conducted nonparametric tests for paired samples and generalized linear mixed effect models. Compared with subsidy support and outdoor spray-cooling methods, education is more effective as it leads to lower levels of temperature exposure, higher levels of risk perception, and more behavioral responses. The subsidy support intervention is useful in increasing risk perception and promoting home cooling practices as well. In comparison, spray cooling barely contributes to the reduction of personal temperature exposure. The encouragement of risk-related education and continued government subsidy may prevent elderly individuals from experiencing high-temperature exposure.

Key targets of the sustainable development goals might be in contradiction to each other. For example, poverty alleviation may exacerbate air pollution by increasing production and associated emissions. This paper investigates the potential impacts of achieving different poverty eradication goals on typical air pollutants in China by capturing household consumption patterns for different income groups and locations, and linking it to China’s multi-regional input-output table and various scenarios. We find that ending extreme poverty, i.e. lifting people above the poverty line of USD 1.90 a day in 2011 purchasing power parity (PPP), increases China’s household emissions by only less than 0.6%. The contribution increases to 2.4%–4.4% when adopting the USD 3.20 PPP poverty line for lower-middle-income countries. Technical improvements in economic sectors can easily offset poverty-alleviation-induced emissions in both scenarios. Nevertheless, when moving all impoverished residents below the USD 5.50 PPP poverty line for upper-middle-income countries, household emissions in China would increase significantly by 18.5%–22.3%. Counteracting these additional emissions would require national emission intensity in production to decrease by 23.7% for SO _2 , 13.6% for NO _x , 82.1% for PM _2.5 , and 58.0% for PM _10 . Required synergies between poverty alleviation and emission reduction call for changes in household lifestyles and production.

Electric vehicles (EVs) are being increasingly adopted because of global concerns about petroleum dependence and greenhouse gas emissions. However, their limited driving range results in increased charging demands with a stochastic characteristic in real-world situations, and the charging demands should be attributed toward charging stations in time-varying road networks. To this end, this study proposes guidance strategies to provide efficient choice for charging stations and corresponding routes, and it includes the time-varying characteristic of road networks in problem formulation. Specifically, we propose two route guidance strategies from different perspectives based on the charging demand information. The first strategy focuses on the effects of the number of EVs on the charging stations’ operation, and the reachable charging stations with the fewest vehicles are selected as the heuristic suggested ones. The other strategy considers the travel cost of individual drivers and selects the charging stations nearest to the destination as heuristic suggested ones. Both strategies ensure that the selected charging stations can be reached in a time-varying road network. In addition, we carry out a simulation analysis to investigate the performance of the proposed route guidance strategies and introduce relevant insights and recommendations for the application of the strategies under various scenarios.

With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance.