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

Effects of indoor air pollution exposure on lung function of children in selected schools in Kigali, Rwanda

Exposure to particulate matter (PM) in schools significantly contributes to respiratory problems among children found in such learning environments. There is limited information about the health effects of exposure to PM in schools in Rwanda. The aim of this study was to assess the adverse effects of PM on children's lung function in selected schools in Kigali, Rwanda. The study was conducted in six public primary schools classified as highly or moderately exposed schools based on their proximity to pollution sources. The study involved 107 randomly selected children aged 8 to 15. The study measured the indoor air concentrations of PM2.5 and PM10 and tested the lung function of school children. Three lung function indicators including forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and peak expiratory flow (PEF) were measured. Both forms of data were collected during the dry and rainy seasons. Air quality was monitored in classrooms using Purple Air PA-II sensors, and the children's lung function was measured using a spirometer. Linear regression analysis was used to determine the associations between PM2.5 and PM10 concentrations and lung function at p = 0.05. The findings showed that the concentrations of PM2.5 were two to five times higher, and PM10 levels were about two times higher than the World Health Organization's air quality guidelines. Both PM2.5 and PM10 were associated with reduced lung function, regardless of the season. The present study shows significant adverse effects of exposure to PM10 and PM2.5 on the lung function of children in the selected schools. Hence there is the need to take measures to improve air quality and protect the health of school communities.

Synergistic effects of CO2 and air pollutants from ship emissions in Shanghai, China: Spatial-temporal characteristics, prediction assessment, policy implications

Currently, the goal of achieving net-zero emissions for ships presents a significant challenge to CO2 reduction policies. A comprehensive analysis of ship air pollutants and CO2 emissions is crucial for mitigating greenhouse effect and air pollution. To realize the overall control policies of ship emissions, this study established a high-resolution emissions inventory for air pollutants (CO, HC, NOx, PM2.5, PM10, SO2) and CO2 from 11 types of ships in Shanghai, and conducted analyses of spatiotemporal characteristics, spatial heterogeneity and consistency, and synergistic effects. Results indicated significant monthly and weekly variability in ship emissions. Due to the varying contribution rates of large ocean-going vessels, the temporal-spatial distributions of CO, HC, NOx, and CO2 revealed significant differences compared to PM2.5, PM10, and SO2. CO2 had a positive synergy in emissions with CO, HC, NOx according to the spatial heterogeneity and consistency analysis. Phasing out of old ships and implementing carbon capture technology were more conducive to CO2 reduction, while the replacement of clean energy contributed greater potential in reducing air pollutant emissions. Comprehensive mitigation measures hold effective co-benefits in air pollutants and CO2 reductions, with the synergistic effect index approaching 1. The implementation of strengthened control measures would minimize the ship emissions, achieving a 78.1% reduction in CO2, and have a positive long-term effect on co-control emission reduction. This study combines high-precision analysis, prediction assessment, and synergistic effects, providing a reference for the development of refined ship management policies in megacities worldwide.

Evaluating drivers of PM2.5 air pollution at urban scales using interpretable machine learning

Reducing urban fine particulate matter (PM2.5) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the key drivers of PM2.5 will enable the development of targeted strategies to reduce PM2.5 levels. This study introduces a machine-learning model that combines CatBoost and the Tree-Structured Parzen Estimator (TPE) to analyze PM2.5 concentration across 297 cities between 2000 and 2021. SHapley Additive exPlanations (SHAP) were employed to identify the primary factors influencing urban PM2.5 concentrations. The study revealed that the proposed model has high accuracy in predicting urban PM2.5 concentrations, achieving a coefficient of determination (R2) score of 96.44%. Socioeconomic and industrial activity are key drivers of PM2.5 concentrations. This study not only quantifies the primary factors exacerbating or alleviating pollution for each city or province during the 2000-2021 period but also evaluates the influence of operational factors such as technological and public financial expenditures. In 2000, the main contributors to pollution in four heavily polluted cities included substantial nitrogen oxide emissions, inadequate technology investments, and excessive population density and liquefied gas consumption. Due to the rapid reduction in nitrogen oxide emissions, pollution levels in these cities have improved substantially. In the future, the most effective strategies for pollution reduction in these cities will focus on controlling population density and slowing down mining development. The proposed framework serves as a robust evaluation tool and can propose tailored strategies to control PM2.5 concentrations effectively in each city.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Assessing and gauging the carbon emission efficiency in China in accordance with the sustainable development goals

In light of the growing urgency of climate change, carbon emissions reduction has emerged as a pivotal concern within global governance. In this paper, we take carbon emission efficiency (CEE) as the research object to characterize the relationship between economic, social, and environmental development in the context of the Sustainable Development Goals (SDGs). According to the regional division standard of eight comprehensive economic zones in China, this paper analyzed the spatial differences, evolutionary characteristics, and influencing factors of CEE in 257 Chinese cities over the period 2003-2019. The analysis conducted the Dagum Gini Coefficient, Markov Transition Probability Matrix, and geographically and temporally weighted regression model (GTWR). The results demonstrate that: (1) The CEE of Chinese cities exhibits an upward trajectory. (2) The inter-differences among the eight comprehensive economic zones represent the primary spatial source of CEE divergence. (3) The CEE of Chinese cities is a staged process of gradual enhancement with spatial spillover effects. (4) Environmental regulation, energy consumption intensity, and green finances are significant factors affecting CEE, and the direction and intensity of their influence have distinct spatial heterogeneity. Ultimately, this paper proposes measures to narrow the development gap between regions and enhance the CEE across the region. Meanwhile, implementing regional refinement management and formulating differentiated regional sustainable development planning.

Assessing the impact of urban road transport development on haze pollution in the Yangtze River Delta region

The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction.

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.

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).

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.