How does vehicle emission control policy affect air pollution emissions? Evidence from Hainan Province, China

Sources and human health risks associated with potentially toxic elements (PTEs) in urban dust: A global perspective

Long-term exposure to urban dust containing potentially toxic elements (PTEs) poses detrimental impacts on human health. However, studies estimating human health risks in urban dusts from a global perspective are scarce. We evaluated data for twelve PTEs in urban dusts across 59 countries from 463 published articles, including their concentrations, input sources, and probabilistic risks to human health. We found that 34.1 and 60.3% of those investigated urban dusts have been heavily contaminated with As and Cd, respectively. The input of PTEs was significantly correlated with economic structure due to emissions of industrial activities and traffic emissions being the major sources. Based on the Monte Carlo simulation, we found that the mean hazard index below the safe threshold (1.0) could still cause non-negligible risks to human health. Arsenic and Cr were the major PTEs threatening human health, and relatively high risk levels were observed in cities in China, Korea, Chile, Malaysia, and Australia. Importantly, our analysis suggested that PTEs threaten the health of approximately 92 million adults and 280 million children worldwide. Overall, our study provides important foundational understanding and guidance for policy decision-making to reduce the potential risks associated with PTE exposure and to promote sustainable development of urban economies.

Fugitive road dust particulate matter emission inventory for India: A field campaign in 32 Indian cities

This research delves into the pivotal issue of road dust emissions and their profound ramifications on air quality across diverse regions of India. In pursuit of this objective, the study initiated a comprehensive field campaign to estimate silt loading (sL) values and evaluate the distribution of vehicles at 259 locations spanning 32 Indian cities. Remarkable disparities in sL values were observed across different road types and states. Notably, sites in Rajasthan, characterized by its arid Aravalli range and industrial activities, emerged as stark outliers, exhibiting significantly elevated sL values (up to 137 g/m2) compared to their counterparts. The regional analysis goes further to elucidate the relation between climatic conditions, topography, and silt loading. As a broader trend, roads in North India have higher sL values in contrast to those in South India. Further, a comprehensive particulate matter road dust emission inventory for the entire India in the year 2022 was developed using the vehicle registration data from 1352 road transport offices nationwide, in conjunction with the data from the field campaign concerning sL values and vehicle counts. Specific states such as Rajasthan, Uttar Pradesh, Maharashtra, Karnataka, and Gujarat emerged as the predominant contributors to road dust emissions. These states not only exhibit elevated sL values, but also account for a substantial proportion of the total registered vehicles in India, thereby underscoring the pressing imperative for effective mitigation measures. Weather Research and Forecasting coupled with chemistry (WRF-Chem) simulations, using this emission inventory, reveal that PM2.5 concentrations stemming from road dust exceed the World Health Organization guidelines in 55 % of the states across India. Further analysis delineates that more than 10,000 lives are annually lost due to PM2.5 pollution attributable to road dust in India, with the potential to salvage 10 % of these lives by paving all roads throughout the country.

Proposal of policies based on temporal-spatial dynamic characteristics and co-benefits of CO2 and air pollutants from vehicles in Shanghai, China

In megacities, vehicle emissions face urgent challenges related to air pollution and CO2 control. To achieve the refinement of vehicle control policies for the co-control of air pollutants and CO2, this study established a vehicle emission inventory with high spatial and temporal resolution based on the hourly traffic flow in Shanghai and analyzed the spatial and temporal distribution characteristics of the real-time vehicle emissions. Meanwhile, a policy evaluation framework was constructed by combining pollutant emission predictions with quantitative co-control effect assessments. The results indicated that spatio-temporal variations in different air pollutants and CO2 could mainly be attributed to primary contributing vehicle types. The pollutants (CO2, CO and VOCs) primarily contributed by private cars exhibited a bimodal pattern in 24-h time series and their spatial distribution was concentrated in the urban city center. The spatial distribution of NOx and PM primarily contributed by heavy trucks was still obvious on non-urban center areas. Furthermore, the results of synergistic effect analysis revealed that the alternative energy replacement scenario demonstrated the most significant potential for the co-control. Based on temporal-spatial and co-benefit analysis, the precise control policy of vehicle emissions can be established through time-, region-, and model-control. This study provides references and research methods for the formulation of the vehicle refinement control policies in worldwide megacities.

Impact of economic policy uncertainty and renewable energy on environmental quality: testing the LCC hypothesis for fast growing economies

This study investigates the influence of economic policy uncertainty and trade openness on load capacity factor for fast growing countries for time period of 1996-2019. The empirical outcomes verify the presence of the LCC hypothesis in fast growing economies. Results also show that economic policy uncertainty reduces environmental quality for lower quantiles, whereas renewable energy consumption is a useful tool for improving environmental quality. Moreover, the negative sign of the coefficient of trade openness demonstrates that the current pattern of trade is not providing the desired outcomes. Based on these empirical findings, we suggest a comprehensive policy framework to attain the targets of SDG 07 (renewable energy), SDG 08 (economic growth), and SDG 13 (climate action).