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Prolonged exposure to PM 2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM 2.5 -related mortality due to contributions from key factors - PM 2.5 concentration, population exposure, and healthcare levels - for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM 2.5 -related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM 2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (-42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM 2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Many cities across the world face the challenge of severe fine particulate matter (PM 2.5 ) pollution. Among the many factors that affect PM 2.5 pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM 2.5 concentrations. Based on PM 2.5 raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM 2.5 pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM 2.5 pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM 2.5 concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM 2.5 concentrations. The further north and west that cities were, the more dependent PM 2.5 pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The air quality issues caused by extreme haze episodes in China have become increasingly serious in recent years. In particular, fine particulate matter (PM 2.5 ) has become the major component of haze with many adverse impacts and has therefore become of great concern to scientists, government, and the general public in China. This study investigates the spatiotemporal variation in PM 2.5 in 269 Chinese cities from 2015 to 2016 and its associations with socioeconomic factors to identify the possible strategies for PM 2.5 pollution mitigation. Specifically, we first quantified the spatial pattern of PM 2.5 concentrations in both 2015 and 2016, and then changes between the two years. Next, we examined the relationship between socioeconomic factors and PM 2.5 concentrations and changes. The results showed that most cities in eastern China experienced decreases in PM 2.5 concentration, although most of these cities already had high PM 2.5 pollution level. Cities with low PM 2.5 concentrations experienced increases in PM 2.5 concentrations and were mostly located in southern and southwestern China. The PM 2.5 concentration was the highest in winter, followed by in spring, autumn and summer; for changes in PM 2.5 concentrations, the highest magnitude of decrease occurred in summer, followed by the decreases in winter, autumn and spring. Cities with high PM 2.5 concentrations tended to be clustered, but the clustered characteristics were not clearly related to the changes in PM 2.5 concentrations. The relationship between PM 2.5 concentration and urban size was an inverse U-shaped curve, suggesting the existence of the Environmental Kuznets Curve for air quality in China. Population density and secondary industry share are the keys factors relating to air pollution control. In comparison to other cities, most moderately developed cities had a greater magnitude of decrease in PM 2.5 concentrations and the key factor for pollution improvement was industrial structure; however, smaller cities tended to have a greater increase in PM 2.5 concentrations and population density was the most important influencing factor. As a result, for air pollution control in China, specific regulations should be carried out according to different regions and different developmental stages based on the locations of cities., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Cities in developing countries worldwide are facing severe fine particulate matter (PM 2.5) pollution. Numerous studies have been conducted to investigate how biophysical and socioeconomic factors affect PM 2.5 concentration. However, most of these studies have typically focused on biophysical and socioeconomic factors separately rather than taking an integrated social-ecological perspective. Cities are widely recognized social-ecological systems, and such an integrative perspective is crucially important for understanding the generation and mitigation of PM 2.5 pollutants. In this study, by considering socioeconomic, biophysical, and meteorological factors simultaneously, we took a comprehensive social-ecological perspective to assess the impacts of these factors on PM 2.5 concentrations. Correlation analysis and variance partitioning were used to quantify the impacts and relative importance of the factors. The analysis was conducted across mainland China focusing on 88 major cities based on socio-economic data, monitoring data of air quality and meteorological conditions, and land cover data. The statistical analysis results revealed that: (1) The biophysical structure and socio-economic factors significantly affect urban PM 2.5 levels year round, but the impacts of meteorological factors are significant only in winter. The biophysical structure factors and the socio-economic ones contribute equally to the variations in PM 2.5 concentration. (2) The effects of the factors vary by region. In northern China, the socio-economic factors together with meteorological factors contribute the most to the variation of PM 2.5 concentration. In southern China, however, the unique effect of meteorological factors is dominant in affecting the level of PM 2.5. (3) Air temperature has a positive relationship with the concentration of PM 2.5 in northern China, but a negative relationship in southern China, which might indicate the varied dominant precursors of PM 2.5 between the two regions. This study extends our understanding of the effects of social-ecological factors on urban PM 2.5 pollution and underscores the importance of differentiated strategies for air pollution mitigation in different regions, and in different seasons, which provides a scientific basis for sustainable development from the perspective of urban air pollution mitigation. [Display omitted] • Biophysical structure, socioeconomic and meteorological factors all affected PM 2.5 • In northern China, Social economy and meteorology together affected PM 2.5 • In southern China, the meteorological factors dominated the variation of PM 2.5 • Temperature had contradictory effects on PM 2.5 between Northern and Southern China. [ABSTRACT FROM AUTHOR]