Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China

Cross-sectional analysis of socioeconomic drivers of PM2.5 pollution in emerging SAARC economies

Within the intricate interplay of socio-economic, natural and anthropogenic factors, haze pollution stands as a stark emblem of environmental degradation, particularly in the South Asian Association for Regional Cooperation (SAARC) region. Despite significant efforts to mitigate greenhouse gas emissions, several SAARC nations consistently rank among the world's most polluted. Addressing this critical research gap, this study employs robust econometric methodologies to elucidate the dynamics of haze pollution across SAARC countries from 1998 to 2020. These methodologies include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) estimator, Panel two-stage least squares (TSLS), Feasible Generalized Least Squares (FGLS) and Dumitrescu-Hurlin (D-H) causality test. The analysis reveals a statistically significant cointegrating relationship between PM2.5 and economic indicators, with economic development and consumption expenditure exhibiting positive associations and rainfall demonstrating a mitigating effect. Furthermore, a bidirectional causality is established between temperature and economic growth, both influencing PM2.5 concentrations. These findings emphasize the crucial role of evidence-based policy strategies in curbing air pollution. Based on these insights, recommendations focus on prioritizing green economic paradigms, intensifying forest conservation efforts, fostering the adoption of eco-friendly energy technologies in manufacturing and proactively implementing climate-sensitive policies. By embracing these recommendations, SAARC nations can formulate comprehensive and sustainable approaches to combat air pollution, paving the way for a healthier atmospheric environment for their citizens.

A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers

Quantifying drivers contributing to air quality improvements is crucial for pollution prevention and optimizing local policies. Despite advances in machine learning for air quality analysis, their limited interpretability hinders attribution on global and local scales, vital for informed city management. Our study introduces an innovative framework quantifying socioeconomic and natural impacts on mitigation of particulate matter pollution in 31 Chinese major cities from 2014 to 2021. Two indices, formulated based on the additivity of Shapley additive explanations, are proposed to measure driver contributions globally and locally. Our analysis explores the self-contained and interactive effects of these drivers on particulate levels, pinpointing critical threshold values where these drivers trigger shifts in particulate matter levels. It is revealed that SO2, NOx, and dust emission reductions collectively account for 51.58 % and 51.96 % of PM2.5 and PM10 decreases at the global level. Moreover, our findings unveil a significant heterogeneity in driver contributions to pollutant mitigation across distinct cities, which can be instrumental in crafting location-specific policy recommendations.

Impact of comprehensive air pollution control policies on six criteria air pollutants and acute myocardial infarction morbidity, Weifang, China: A quasi-experimental study

Comprehensive air pollution control policies may reduce pollutant emissions. However, the impact on disease morbidity of the change for the concentration of air pollutants following the policies has been insufficiently studied. We aim to assess the impact of comprehensive air pollution control policies on the levels of six criteria air pollutants and acute myocardial infarction (AMI) morbidity in Weifang, China. This study performed an interrupted time series analysis. The linear model with spline terms and generalized additive quasi-Poisson model were used to estimate the immediate change from 2016 to 2019 in the daily concentration of six air pollutants (PM2.5, PM10, SO2, NO2, O3, and, CO) and AMI incident cases (Age ≥35) associated with the implementation of air pollution control policies in Weifang, respectively. After the implementation of air pollution control policies, air quality in Weifang had been improved. Specifically, the daily concentrations of PM2.5, PM10, SO2, and, CO immediately decreased by 27.9 % (95 % CI: 6.6 % to 44.3 %), 32.9 % (95 % CI: 17.5 % to 45.5 %), 14.6 % (95 % CI: 0.4 % to 26.8 %), and 33.9 % (95 % CI: 22.0 % to 44.0 %), respectively. In addition, the policies implementation was also associate with the immediate decline in the AMI morbidity (-6.5 %, 95 % CI: -10.4 % to -2.3 %). And subgroup analyses indicate that the health effects of the policy intervention were only observed in female (-9.4 %, 95 % CI: -14.4 % to -4.2 %) and those aged ≥65 years (-10.5 %, 95 % CI: -14.6 % to -6.2 %). During the final 20 months of the study period, the policy intervention was estimated to prevent 1603 (95 % CI: 574 to 2587) cases of incident AMI in Weifang. Our results provide strong rationale that the policy intervention significantly reduced ambient pollutant concentrations and AMI morbidity, which highlighted the importance for a comprehensive and rigorous air pollution control policy in regions with severe air pollution.

Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.

Synergistic relationship and interact driving factors of pollution and carbon reduction in the Yangtze River Delta urban agglomeration, China

The urban agglomeration is the most concentrated region of economy, population, and industry. It is also the key area of carbon emissions (CE) and air pollution management. CE and air pollution have the possibility of collaborative governance due to the same root and the same source of them. To achieve the goal of sustainable development, it is important to study the coordinated relationship of CE and atmosphere pollutants in urban agglomerations. However, most researches have ignored the synergistic relationship between CE and air pollutants. Furthermore, there is limited current study on the driving factors of the synergistic relationship between air pollutants and CE. To fill these research gaps, we first explore the spatial-temporal evolvement law of CE and PM2.5 utilizing satellite remote sensing data sets. Secondly, we analyze the synergistic relationship of CE and PM2.5 in the Yangtze River Delta (YRD) urban agglomeration using the coupling coordination degree (CCD) model from 2000 to 2020. At last, we further study the influencing factors of the synergistic relationship of CE and PM2.5 based on the geo-detector model. The findings display that (1) in 2020, the total CE in the YRD urban agglomeration is 2.24 billion tons, accounting for 22.5% of China, but its growth rate has gradually dropped to 7.25%. Besides, the PM2.5 concentration shows a waving upward-downward tendency. In 2020, the range of higher PM2.5 regions significantly decreased, and air quality gradually improved. (2) The CCD of PM2.5 and CE is at the coordination level in general (CCD > 0.6) between 2000 and 2020, which can realize the coordinated governance of pollution and carbon reduction. (3) Digital elevation model (DEM), topographic relief (RDLS), and population density have a higher degree of influence on the synergistic relationship between CE and PM2.5. Besides, the interaction of topographic and socio-economic factors is the main driving factor between the two. This paper can provide a referral for decision-makers to synergistically make plans for pollution and carbon reduction and facilitate the sustainable development of urban agglomerations.

Effects of agricultural land types on microplastic abundance: A nationwide meta-analysis in China

Microplastics (MPs) accumulation in agricultural land that possibly poses threats to food security and human health has recently attracted increasing attention. Land use type probably is a key factor that drives the contamination level of soil MPs. Nevertheless, few studies have performed large-scale systematic analysis of the effects in different agricultural land soils on the MPs abundance. In this study, we constructed a national MPs dataset comprising 321 observations from 28 articles, summarised the current status of microplastic pollution in five agricultural land types in China through meta-analysis, and investigated the effects and key factors of agricultural land types on microplastic abundance. The results showed that among the existing soil microplastic research, vegetable soils maintained a higher environmental exposure distribution than other agricultural lands, and with the most common trend being vegetable land > orchard land > cropland > grassland. By combining agricultural practices, demographic economic factors and geographical factors, a potential impact identification method based on subgroup analysis was established. The findings demonstrated that agricultural film mulch significantly increased soil MPs abundance, especially in orchards. Increased population and economy (carbon emissions and PM2.5 concentrations) add MPs abundance in all kinds of agricultural lands. And the significant changes of effect sizes in high-latitude and mid-altitude areas suggested that geographical space differences exerted a certain degree of impact on the soil distribution of MPs. By the proposed method, different levels of MPs risk areas in agricultural soils can be more reasonably and effectively identified, which will provide type-specific policies technical and theoretical support for the precise management of MPs in agricultural land soils.

Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China

With the continuous promotion of urbanization in China, air pollution problems have become increasingly prominent in recent years. Various factors, such as emissions, meteorology, and physical and chemical reactions, jointly affect the severity of PM_2.5 pollution to a large extent. This study selected five meteorological variables (planetary boundary layer height (PBLH), wind speed (WS), temperature(T), water vapor mixing ratio(Q), and precipitation (PCP)) for perturbation, and 21 different scenarios were set up. In this study, the effects of changes in a single meteorological variable on the pollutants produced in the area were represented by subtracting the baseline scenario (i.e., without perturbation of meteorological variables) simulated in January 2017 separately from each post-disturbance scenario. The results showed that Handan (HD) has the highest annual mean PM_2.5 concentration of 85.75 μg/m³ in 2017, while all cities in study area exceeded the secondary concentration limit of urban atmospheric particulate matter. The correlation coefficient (R) between the simulation values of models and the actual monitoring values ranges from 0.41 to 0.74, indicating good model performance and acceptable simulation errors. PBLH (±10%-±20%), WS(±10%-±20%), and PCP(±10%-±20%) all showed a single adverse effect among the five meteorological variables, meaning that a reduction in these three factors led to an increase in PM_2.5 concentrations. However, T (±1 K-±1.5 K) and Q (±10%-±20%) could indicate a positive impact under certain conditions. From the sensitivity calculations of single meteorological variables, it is clear that WS, PBLH, and PCP show a highly linear trend in all cities at the 0.01 level of significance. The hypothesis that T changes linearly in 10 cities in the study area is valid, while for Q, the hypothesis that Q changes linearly only occurs in Shijiazhuang and Baoding. When different meteorological variables are disturbed, there are significant spatial differences in the main affected areas of PM_2.5 concentrations. By discussing the impact of meteorological variable disturbance on air quality in critically polluted cities in China, this study identified the meteorological variables that can substantially affect PM_2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures.

Haze pollution and urbanization promotion in China: How to understand their spatial interaction?

Can promoting urbanization and controlling haze pollution result in a win-win situation? Based on panel data from 287 prefecture-level cities in China, this paper uses the three-stage least-squares estimator method(3SLS) and generalized space three-stage least-squares estimator method (GS3SLS) to study the spatial interaction between haze pollution and urbanization. The results show the following: (1) There is a spatial interaction between haze pollution and urbanization. On the whole, haze pollution and urbanization have a typical inverted U-shaped relationship. (2) Haze and urbanization show different relationships in different regions. The haze pollution in the area left of the Hu Line has a linear relationship with urbanization. (3) In addition to haze, urbanization also has a spatial spillover effect. When the haze pollution in the surrounding areas increases, the haze pollution in the area will also increase, but the level of urbanization will increase. When the level of urbanization in the surrounding areas increases, it will promote the level of urbanization in the local area and alleviate the haze pollution in the local area. (4) Tertiary industry, greening, FDI and precipitation can help alleviate haze pollution. FDI and the level of urbanization have a U-shaped relationship. In addition, industry, transportation, population density, economic level and market scale can promote regional urbanization.

Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China

In the context of serious urban air pollution and limited land resources, it is important to understand the environmental value of ecological land. Previous studies focused mostly on the effectiveness of a particular type of green space or the total amount of ecological land on PM_2.5 and have rarely analyzed the association between ecological land structure and PM_2.5 systematically and quantitatively. Therefore, we took 277 cities in China as an example, comprehensively compared the results of different models, and selected a spatial Durbin model using time-fixed effects to dissect the degree of influence of ecological land and different land types within it on PM_2.5. The urban ecological land use structure was closely related to PM_2.5, and the higher the proportion of ecological land use was, the lower the PM_2.5. The degree and direction of influence of different types of land functions within ecological land on PM_2.5 differed, with forests, shrubs, and grasslands causing a weakening impact on PM_2.5, while wetlands and waters did not have a weakening role. The degree of reduction of PM_2.5 by a single type of ecological land was significantly smaller than that by a composite type of ecological land. Green space should be comprehensively considered, designed and adjusted in urban planning to continuously optimize the ecological spatial structure, increase landscape diversity and maximize ecological benefits. The findings of this study help with exploring the effects of land use structure under the goal-oriented control of air pollution and provide theoretical reference and decision-making support for formulating precise air pollution control policies and optimizing the spatial development of national land.

Polycentric agglomeration and haze pollution: evidence from China

Polycentric agglomeration has gradually become a salient feature of rapid growth in urbanization in China. Using province-level balanced panel data over the period 2000-18, we examine the impact of polycentric agglomeration on haze pollution and its mechanism of action. The results show that the impact of polycentric agglomeration on haze pollution exhibits a significant inverted U-shaped feature. Nevertheless, except for a few provinces where polycentric agglomeration exceeds the turning point, the degree of polycentric concentration in most provinces lies to the left of the turning point. Further, a mediating effect model illustrates that industrial structure rationalization and technological progress are effective paths through which polycentric agglomeration affects haze pollution. Finally, we demonstrate that the effect of polycentric agglomeration on haze pollution is influenced by transportation and communication infrastructure; improved transportation and communication infrastructure contributes to the haze control effect of polycentric agglomeration.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Elucidating the characteristics and influencing mechanisms of PM_2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM_2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM_2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM_2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM_2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM_2.5 concentrations present a U-shaped distribution. The PM_2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM_2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM_2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM_2.5 concentrations in winter. The effect of anthropogenic emissions on PM_2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM_2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM_2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM_2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Do anthropogenic and natural factors elevate the haze pollution in the South Asian countries? Evidence from long-term cointegration and VECM causality estimation

Anthropogenic and natural factors lead to substantial environmental degradation. This shift is aligned with the country's overall development, resulting in high demand for energy resources and a dramatic shift in human activities that contribute to haze pollution. Some of the countries in the South Asian region are ranked between one and twenty on the list of countries with the highest levels of PM2.5 pollution. The member countries have taken many steps to tackle global warming, but concern about haze pollution was found limited. Moreover, very little research was conducted on haze pollution, which led us to conduct this research in this region. This study used the panel data from 1998 to 2018 and a set of econometric models like long-term cointegrating relationship, fully modified ordinary least squares, and vector error-correction model Granger causality tests to examine the major drivers like anthropogenic and natural factors that might elevate haze pollution. Furthermore, our empirical results depict that (1) there is a long-term cointegrating relation between haze and the factors studied. (2) Energy consumption, urbanisation, and economic growth are the primary drivers of environmental degradation. (3) Rainfall has the most substantial influence on reducing haze pollution. The study concluded that (a) if the countries continue to develop at the same pace, all factors studied will continue to drive haze pollution to rise. (b) A decrease in PM2.5 pollution requires improvements in regional rainfall through vegetation, reducing reliance on fossil fuel-based energy sources, and increasing environmental education. (c) Slowing down the drive for urbanisation would not be cost-effective in reducing haze pollution in the region in the short run. Thus, reducing haze by adjusting the factors studied would not be easy in the short run and require the careful adoption of long-term policies.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective

Background

Fine particulate matter (PM_2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM_2.5 pollution is controversial in China.

Methods

In this study, we explored spatial-temporal characteristics and driving factors of PM_2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR).

Results

PM_2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m^-3 a^-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM_2.5 concentrations. The driving force of socioeconomic factors on PM_2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM_2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM_2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013.

Conclusion

We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM_2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM_2.5 emission and put forward effective policy to alleviate haze pollution.

The spatial heterogeneity of the impact of PM2.5 on domestic tourism flows in China

As haze pollution intensifies, its impact on tourism is becoming increasingly obvious. However, limited studies have analyzed the impacts of haze pollution on tourism. To explore the contribution rate and impact of PM_2.5 pollution on tourism flows, panel data on 341 prefecture-level cities in China from 2001 to 2015 were used. The results illustrated that the changes in PM_2.5 pollution and domestic tourism flows showed a similar partial-most anti-phase main spatial pattern in space, as well as other spatial patterns of PM_2.5. From a regional perspective, the contribution rate of PM_2.5 to domestic tourism flows was less than that of traditional factors, such as GDP, GDP_500, and 45A, but larger than that of the Airport factor. The contribution rate of the interaction between PM_2.5 and 45A on domestic tourism flows was the largest. From a local perspective, PM_2.5 pollution had a negative impact on domestic tourism flows in northern China, while it had a positive impact in other regions. The classic environmental Kuznets curve (EKC) hypothesis showed applicability to the Chinese tourism industry, and the is of great significance for comprehensively understanding the impact of PM_2.5 pollution on tourism flows and for promoting the sustainable development of domestic tourism.

Ventilation Capacities of Chinese Industrial Cities and Their Influence on the Concentration of NO2

Most cities in China, especially industrial cities, are facing severe air pollution, which affects the health of the residents and the development of cities. One of the most effective ways to alleviate air pollution is to improve the urban ventilation environment; however, few studies have focused on the relationship between them. The Frontal Area Index (FAI) can reflect the obstructive effect of buildings on wind. It is influenced by urban architectural form and is an attribute of the city itself that can be used to accurately measure the ventilation capacity or ventilation potential of the city. Here, the FAIs of 45 industrial cities of different sizes in different climatic zones in China were computed, and the relationship between the FAI and the concentration of typical pollutants, i.e., NO2, were analyzed. It was found that (1) the FAIs of most of the industrial cities in China were less than 0.45, indicating that most of the industrial cities in China have excellent and good ventilation capacities; (2) there were significant differences in the ventilation capacities of different cities, and the ventilation capacity decreased from the temperate to the tropical climate zone and increased from large to small cities; (3) there was a significant difference in the ventilation capacity in winter and summer, indicating that that with the exception of building height and building density, wind direction was also the main influencing factor of FAI; (4) the concentration of NO2 was significantly correlated with the FAI, and the relative contribution of the FAI to the NO2 concentration was stable at approximately 9% and was generally higher than other socioeconomic factors. There was a turning point in the influence of the FAI on the NO2 concentration (0.18 < FAI < 0.49), below which the FAI had a strong influence on the NO2 concentration, and above which the influence of the FAI became weaker. The results of this study can provide guidance for suppressing urban air pollution through urban planning.

Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China

Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

The Cause of China's Haze Pollution: City Level Evidence Based on the Extended STIRPAT Model

Based on the extended STIRPAT model, this paper examines social and economic factors regarding PM_2.5 concentration intensity in 255 Chinese cities from 2007 to 2016, and includes quantile regressions to analyze the different effects of these factors among cities of various sizes. The results indicate that: (1) during 2007–2016, urban PM_2.5 concentration exhibited declining trends in fluctuations concerning the development of the urban economy, accompanied by uncertainty under different city types; (2) population size has a significant effect on propelling PM_2.5 concentration; (3) the effect of structure reformation on PM_2.5 concentration is evident among cities with different populations and levels of economic development; and (4) foreign investment and scientific technology can significantly reduce PM_2.5 emission concentration in cities. Accordingly, local governments not only endeavor to further control population size, but should implement a recycling economy, and devise a viable urban industrial structure. The city governance policies for PM_2.5 concentration reduction require re-classification according to different population scales. Cities with large populations (i.e., over 10 million) should consider reducing their energy consumption. Medium population-sized cities (between 1 million and 10 million) should indeed implement effective population (density) control policies, while cities with small populations (less than 1 million) should focus on promoting sustainable urban development to stop environmental pollution from secondary industry sources.

Distribution and sources of PM2.5-bound free silica in the atmosphere of hyper-arid regions in Hotan, North-West China

The composition of atmospheric fine particulate matter (PM_2.5) is complex and exhibits strong regional differences. Free silica (α-SiO₂) in atmospheric particulate matter is carcinogenic and is an important component of respirable particulate matter in urban areas. Measurements determined that the concentration of silicon dioxide (α-SiO₂) in PM_2.5 in the urban area of Hotan City, China, was 8.02 μg·m^-3 during the dust period and exceeded 1.77 μg·m^-3 during the non-dust period. The proportion of α-SiO₂ in PM_2.5 was 8.07% during the dust period and 2.25% during the non-dust period. Atmospheric visibility during the dust period was mainly influenced by the content of atmospheric floating dust. Analysis of α-SiO₂ pollution sources during the dust period showed that the air masses containing sand and dust originated from the desert hinterland. Following passage through oasis areas, the air mass was effectively reduced in the concentration of α-SiO₂ in PM_2.5. During the dusty period, α-SiO₂ and PM_2.5 originated from the same source in Hotan City. Moreover, wind speed was the main influencing factor for the α-SiO₂ concentration. During the non-dust period, α-SiO₂ and PM_2.5 were not from the same source of pollution.

Analysis of spatio-temporal distribution characteristics and socioeconomic drivers of urban air quality in China

Having high spatio-temporal resolution data of pollutants is critical to understand environmental pollution patterns and their mechanisms. Our research employs the hourly average concentration data on the air quality index (AQI) and its six component pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in 336 Chinese cities from 2014 to 2019. We analyze annual, seasonal, monthly, hourly, and spatial variations of different air pollutants and their socioeconomic factors. The results are as follows. (1) Air pollutants' concentration in Chinese cities decreased year by year during 2014-2019. Among the primary pollutants, PM_2.5 dominated pollution days, accounting for 38.46%, followed by PM₁₀. Monthly concentration curves of AQI, PM_2.5, NO₂, SO₂, and CO showed a U-shaped trend from January to December, while that of O₃ presented an inverted U-shaped unimodal pattern. Regarding daily variation, urban air quality tended to be worse around sunrise compared with sunset. (2) Chinese cities' air quality decreased from north to south and from inland to coastal areas. Recently, air quality has improved, and polluted areas have shrunk. The six pollutant types showed different spatial agglomeration characteristics. (3) Industrial pollution emissions were the main source of urban air pollutants. Energy-intensive industries, dominated by coal combustion, had the greatest impact on SO₂ concentration. A "pollution shelter" was established in China because foreign investment introduced more pollution-intensive industries. Thus, China has crossed the Kuznets U-curve inflection point. In addition, population agglomeration contributed the most to PM_2.5 concentration, increasing the PM_2.5 exposure risk and causing disease, and vehicle exhaust aggravated the pollution of NO₂ and CO. The higher China's per capita gross domestic product, the more significant the effect of economic development is on reducing pollutant concentration.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018

In recent years, air pollution caused by PM_2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM_2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM_2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM_2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM_2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM_2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM_2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM_2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM_2.5 pollution in China and provide an important reference for the future direction of PM_2.5 pollution control.

Does the smart city policy promote the green growth of the urban economy? Evidence from China

Urban governance is an important cornerstone in the modernization of a national governance system. The establishment of smart cities driven by digitalization will be a vital way to promote economic green and sustainable growth. By using the data of 274 prefecture-level cities in China from 2004 to 2017, we study the impact of smart city policy on economic green growth and the underlying mechanism of the impact. It is shown that the establishment of smart cities has significantly promoted the green growth of China's economy. This conclusion is further confirmed by using exogenous geographic data as instrumental variables and robustness tests, such as the quasi-experimental method of Difference in Difference with Propensity Score Matching (PAM-DID). The mechanism test shows that promoting economic growth, reducing per unit GDP energy consumption, and lowering waste emissions represent three ways for smart cities to promote green economic growth. The heterogeneity test shows that smart city policy has an obvious promotional effect on the economic green growth of both large cities and non-resource-based cities. This paper is expected to provide a reference for the urban development and economic transformation of emerging economies.

Spatial Variation of the Effect of Multidimensional Urbanization on PM2.5 Concentration in the Beijing-Tianjin-Hebei (BTH) Urban Agglomeration

Atmospheric PM_2.5 pollution has become a prominent environmental problem in China, posing considerable threat to sustainable development. The primary driver of PM_2.5 pollution in China is urbanization, and its relationship with PM_2.5 concentration has attracted considerable recent academic interest. However, the spatial heterogeneity of the effect of urbanization on PM_2.5 concentration has not been fully explored. This study sought to fill this knowledge gap by focusing on the Beijing–Tianjin–Hebei (BTH) urban agglomeration. Urbanization was decomposed into economic urbanization, population urbanization, and land urbanization, and four corresponding indicators were selected. A geographically weighted regression model revealed that the impact of multidimensional urbanization on PM_2.5 concentration varies significantly. Economically, urbanization is correlated positively and negatively with PM_2.5 concentration in northern and southern areas, respectively. Population size showed a positive correlation with PM_2.5 concentration in northwestern and northeastern areas. A negative correlation was found between urban land size and PM_2.5 concentration from central to southern regions. Urban compactness is the dominant influencing factor that is correlated positively with PM_2.5 concentration in a major part of the BTH urban agglomeration. On the basis of these findings, BTH counties were categorized with regard to local policy recommendations intended to reduce PM_2.5 concentrations.

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

PM_2.5 pollution has gradually attracted people's attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM_2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM_2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM_2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NO_X of residential emissions explained 20% (q = 0.2) of the PM_2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM_2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM_2.5 pollution, improve PM_2.5 forecasting models, and formulate more precise government control policy.

Ecological determinants of effect of a free pit and fissure sealant program in Shanxi, China, 2017-2018

BACKGROUND

The aim of this study is to explore the spatial heterogeneity of the retention of PFS in children aged 7-9 years in Shanxi Province, North China and investigate the risk factors associated with PFS retention.

METHODS

In this study, 937 children aged 7-9 years from Shanxi Province, China were randomly selected, all of whom had at least one first permanent tooth sealed with PFS in 2016. The children were surveyed after 12 months (in 2017) and 24 months (in 2018). The Geo-detector model was used to explore the spatial heterogeneity of the retention rate of PFS and analyze the influence and interactions of the ecological factors on PFS.

RESULTS

3299 teeth from 937 children were analyzed. The PFS full retention rates after 12 months (in 2017) and 24 months (in 2018) were 81.6% and 75.1%, respectively. The incidence of caries of the first molar was 2.1% after 12 months and 5.4% after 24 months. The spatial heterogeneity of the PFS retention rate after 24 months was significant, which was shown as the retention rate of PFS increased from north to south after 24 months. Remarkably, the natural environmental factors exerted greater influence than the socioeconomic and medical resources factors after 12 months, where the interaction of fluorine in water (FW) had the strongest explanatory power of 52% (P < 0.05). The medical resources were important ecological factors after 24 months, and the percentage of medical technicians (PMT) had the strongest explanatory power of 70% (P < 0.05).

CONCLUSION

The natural environmental factors and medical resources factors are important ecological factors determining the spatial pattern. The government should strengthen medical and technician construction in North China, comprehensively control fluoride in water, optimize the allocation of medical resources, and promote the balanced development of regional medicine.

Evaluating the influence of land use and land cover change on fine particulate matter

Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM_2.5) has become a critical environmental issue in China. Land use and land cover (LULC) is recognized as one of the most important influence factors, however very fewer investigations have focused on the impact of LULC on PM_2.5. The influences of different LULC types and different land use and land cover change (LULCC) types on PM_2.5 are discussed. A geographically weighted regression model is used for the general analysis, and a spatial analysis method based on the geographic information system is used for a detailed analysis. The results show that LULCC has a stable influence on PM_2.5 concentration. For different LULC types, construction lands have the highest PM_2.5 concentration and woodlands have the lowest. The order of PM_2.5 concentration for the different LULC types is: construction lands > unused lands > water > farmlands >grasslands > woodlands. For different LULCC types, when high-grade land types are converted to low-grade types, the PM_2.5 concentration decreases; otherwise, the PM_2.5 concentration increases. The result of this study can provide a decision basis for regional environmental protection and regional ecological security agencies.

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM_2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM_2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM_2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM_2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM_2.5 pollution.

Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China

PM_2.5 pollution has produced adverse effects all over the world, especially in fast-developing China. PM_2.5 pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM_2.5 pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM_2.5 and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China's PM_2.5 pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM_2.5 pollution. As a decomposition item of technological progress, technical change effectively alleviates PM_2.5 pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM_2.5 pollution. Finally, in order to effectively improve China's air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM_2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM_2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM_2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM_2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Non-Linear Response of PM2.5 Pollution to Land Use Change in China

Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 μg/m3 to 31.23 μg/m3 during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m3 in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use.

Impacts of natural and socioeconomic factors on PM2.5 from 2014 to 2017

The State Council of China had issued the Air Pollution Prevention and Control Action Plan (abbreviated as "Clean Air Actions"), which ended in 2017. To evaluate the implementation effect of the clean air actions and provide the scientific basis on the future control policy, a Geographical Detector was used to quantify the impact of natural and socioeconomic factors on the PM_2.5 concentration and its reductions in China from the years of 2014-2017. In terms of the impact on PM_2.5 reduction, the industrial sulfur dioxide (SO₂) and industrial soot emissions are the only two factors shown significant influences. So the controls of industrial emission were the major policies during the implementation of the Clean Air Actions. In terms of the impact on the PM_2.5 concentrations, industrial emission was the strongest socioeconomic factor in the beginning of the Clean Air Actions, but its dominance was then declining. In contrast, the influences of population density had been enhancing and became the greatest factor in the final year. So the new control measures should focus on the urbanization regulation. In addition, the interactions between different socioeconomic factors are proved to bivariate enhance the influences on the PM_2.5 concentration levels. Multiple factors should thus be taken into account when any new control policies are going to be established.

An improved decomposition method to differentiate meteorological and anthropogenic effects on air pollution: A national study in China during the COVID-19 lockdown period

Although the effects of meteorological factors on severe air pollution have been extensively investigated, quantitative decomposition of the contributions of meteorology and anthropogenic factors remains a big challenge. The novel coronavirus disease 2019 (COVID-19) pandemic affords a unique opportunity to test decomposition method. Based on a wind decomposition method, this study outlined an improved method to differentiate complex meteorological and anthropogenic effects. The improved method was then applied to investigate the cause of unanticipated haze pollution in China during the COVID-19 lockdown period. Results from the wind decomposition method show that weakened winds increased PM_2.5 concentrations in the Beijing-Tianjin area and northeastern China (e.g., by 3.19 μg/m³ in Beijing). Using the improved decomposition method, we found that the combined meteorological effect (e.g., drastically elevated humidity levels and weakened airflow) substantially increased PM_2.5 concentrations in northern China: the most substantial increases were in the Beijing-Tianjin-Hebei region (e.g., by 26.79 μg/m³ in Beijing). On excluding the meteorological effects, PM_2.5 concentrations substantially decreased across China (e.g., by 21.84 μg/m³ in Beijing), evidencing that the strict restrictions on human activities indeed decreased PM_2.5 concentrations. The unfavorable meteorological conditions, however, overwhelmed the beneficial effects of emission reduction, causing the severe haze pollution. These results indicate that the integrated meteorological effects should be considered to differentiate the meteorological and anthropogenic effects on severe air pollution.

Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau

Grasslands are the dominant ecosystem of the Qinghai-Tibet Plateau (QTP), and they play an important role in climate regulation and represent an important ecological barrier in China. However, the spatial differentiation characteristics of net primary productivity (NPP) and normalized differential vegetation index (NDVI) and the main influencing factors that vary with grassland type on the QTP are not clear. In this study, standardized precipitation evapotranspiration index (SPEI), digital elevation model (DEM), precipitation, temperature, slope, photosynthetically active radiation (PAR) and grazing intensity were considered the driving factors. First, a grey relational degree analysis was performed to test for the quantitative relationships between NPP, NDVI and factors. Then, the geographical detector method was applied to analyze the interaction relationships of the factors. Finally, based on the geographically weighted regression (GWR) model, the influence of factors varied with grassland type on the NPP and NDVI was revealed from the perspective of spatial differentiation. The results were as follows: (1) The NPP and NDVI had roughly the same degrees of correlation with each impact factor by the grey relational degree analysis, each factor was closely related to the NPP and NDVI, and the relational degree between grazing intensity and NPP was greater than that between grazing intensity and NDVI. (2) The interaction relationships between influencing factors and NPP and NDVI varied with the grassland type and presented bivariate enhancement and nonlinear enhancement, and the interaction effects between grazing intensity and any factor on each grassland type had a greater impact on NPP. (3) The main influencing factors of the spatial heterogeneity of NPP were grazing intensity and PAR, which were "high from northeast to southwest, low from northwest to southeast" and "low in the middle and high around". The main influencing factors on the NDVI were precipitation and PAR, which were "low in the middle and high around" and "high in the north, low in the south".

Examining the impact of polycentric urban form on air pollution: evidence from China

Numerous studies on megacities have reported less air pollution in polycentric form urban than monocentric form urban. However, findings from these studies do not imply that increasing air pollution in region or country is accompanied by the expanding megacities. Using satellite night-light data, this study investigates the impact of polycentric urban form at the provincial level on PM_2.5 concentrations in China while controlling for variables of urban population size, energy consumption, and the weather. The results reveal that the PM_2.5 concentrations are reduced by 1.46% to 2.67%, with a 1% increase in polycentric urban form. The similar impact has also been observed in the South China, but larger in the Central China. Further studies show that the urban form-air pollution relationship mainly influenced by transportation distance and enhanced by rising per capita income. The findings suggest that regional planning and policies favoring polycentric urban patterns should be strengthened to alleviate air pollution.

The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model

BACKGROUND

Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change.

OBJECTIVE

Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature.

METHOD

In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM_2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015.

RESULT

Cities with serious PM_2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country's southeast, where enhanced precipitation was also shown to reduce PM_2.5 concentrations; whilst in southern and central and western regions, precipitation and PM_2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM_2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM_2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM_2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM_2.5 concentration levels in southeastern cities than in other cities in China.

CONCLUSION

PM_2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM_2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the'high-high' and 'low-low' clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread.

Spatiotemporal patterns of PM2.5 elemental composition over China and associated health risks

Trace metals in atmospheric particulate matter (PM) are a serious threat to public health. Although pollution from toxic metals has been investigated in many Chinese cities, the spatial and temporal patterns in PM_2.5 remain largely unknown. Long-term PM_2.5 field sampling in 11 cities, combined with a systemic literature survey covering 51 cities, provides the first comprehensive database of 21 PM_2.5-bound trace metals in China. Our results revealed that PM_2.5 elemental compositions varied greatly, with generally higher levels in North China, especially for crustal elements. Pollution with Cr, As, and Cd was most serious, with 61, 38, and 16 sites, respectively, surpassing national standards, including some in rural areas. Local emissions, particularly from metallurgical industries, were the dominant factors driving the distribution in polluted cities such as Hengyang, Yuncheng, and Baiyin, which are mainly in North and Central China. Elevated As, Cd, and Cr levels in Yunnan, Guizhou Province within Southwest China were attributed to the high metal content of local coal. Diverse temporal trends of various elements that differed among regions indicated the complexity of emission patterns across the country. The results demonstrated high non-carcinogenic risks for those exposed to trace metals, especially for children and residents of heavily cities highly polluted with As, Pb, or Mn. The estimated carcinogenic risks ranged from 6.61 × 10^-6 to 1.92 × 10^-4 throughout China, with As being the highest priority element for control, followed by Cr and Cd. Regional diversity in major toxic metals was also revealed, highlighting the need for regional mitigation policies to protect vulnerable populations.

Spatiotemporal variation and determinants of population's PM2.5 exposure risk in China, 1998-2017: a case study of the Beijing-Tianjin-Hebei region

PM2.5 pollution has emerged as a global human health risk. The best measure of its impact is a population's PM2.5 exposure (PPM2.5E), an index that simultaneously considers PM2.5 concentrations and population spatial density. The spatiotemporal variation of PPM2.5E over the Beijing-Tianjin-Hebei (BTH) region, which is the national capital region of China, was investigated using a Bayesian space-time model, and the influence patterns of the anthropic and geographical factors were identified using the GeoDetector model and Pearson correlation analysis. The spatial pattern of PPM2.5E maintained a stable structure over the BTH region's distinct terrain, which has been described as "high in the northwest, low in the southeast". The spatial difference of PPM2.5E intensified annually. An overall increase of 6.192 (95% CI 6.186, 6.203) ×103 μg/m3 ∙ persons/km2 per year occurred over the BTH region from 1998 to 2017. The evolution of PPM2.5E in the region can be described as "high value, high increase" and "low value, low increase", since human activities related to gross domestic product (GDP) and energy consumption (EC) were the main factors in its occurrence. GDP had the strongest explanatory power of 76% (P < 0.01), followed by EC and elevation (EL), which accounted for 61% (P < 0.01) and 40% (P < 0.01), respectively. There were four factors, proportion of secondary industry (PSI), normalized differential vegetation index (NDVI), relief amplitude (RA), and EL, associated negatively with PPM2.5E and four factors, GDP, EC, annual precipitation (AP), and annual average temperature (AAT), associated positively with PPM2.5E. Remarkably, the interaction of GDP and NDVI, which was 90%, had the greatest explanatory power for PPM2.5E ' s diffusion and impact on the BTH region.

Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression

PM_2.5 pollution is caused by multiple factors and determining how these factors affect PM_2.5 pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM_2.5 and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM_2.5 and the different factors varied with location. SO₂ emission positively affected PM_2.5, and the impact was the strongest in the Beijing-Tianjin-Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM_2.5 was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM_2.5 in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM_2.5 in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

Effects of Meteorological Factors and Anthropogenic Precursors on PM2.5 Concentrations in Cities in China

Fine particulate matter smaller than 2.5 μm (PM2.5) in size can significantly affect human health, atmospheric visibility, climate, and ecosystems. PM2.5 has become the major air pollutant in most cities of China. However, influencing factors and their interactive effects on PM2.5 concentrations remain unclear. This study used a geographic detector method to quantify the effects of anthropogenic precursors (AP) and meteorological factors on PM2.5 concentrations in cities of China. Results showed that impacts of meteorological conditions and AP on PM2.5 have significant spatio-temporal disparities. Temperature was the main influencing factor throughout the whole year, which can explain 27% of PM2.5 concentrations. Precipitation and temperature were primary impacting factors in southern and northern China, respectively, at the annual time scale. In winter, AP had stronger impacts on PM2.5 in northern China than in other seasons. Ammonia had stronger impacts on PM2.5 than other anthropogenic precursors in winter. The interaction between all factors enhanced the formation of PM2.5 concentrations. The interaction between ammonia and temperature had strongest impacts at the national scale, explaining 46% (q = 0.46) of PM2.5 concentrations. The findings comprehensively elucidated the relative importance of driving factors in PM2.5 formation, which can provide basic foundations for understanding the meteorological and anthropogenic influences on the concentration patterns of PM2.5.

Decoupling PM2.5 emissions and economic growth in China over 1998-2016: A regional investment perspective

It is crucial to decouple economic growth from environmental pollution in China. This study aims to evaluate China's decoupling level between PM_2.5 emissions and economic growth from a regional investment perspective. Using the panel data of 30 Chinese provinces for the period of 1998-2016, this study combines decomposition analysis with decoupling analysis to identify the roles of conventional factors and three novel investment factors in the mitigation and decoupling of PM_2.5 emissions in China and its four sub-regions. The results show that China's PM_2.5 emissions were weakly decoupled to economic growth during the period of 1998-2016, as well as in China's four sub-regions. At the national level, investment scale played the dominant role while investment structure had a marginal effect in mitigating the decoupling level. In contrast, emission intensity was the largest driver in promoting the decoupling effect. At the regional level, emission intensity and investment efficiency accelerated the regional decoupling level, but the coupling effect from investment scale in the western region far exceeded those in other three sub-regions. At the provincial level, the investment structure of Inner Mongolia and investment scales of Xinjiang and Inner Mongolia had the greatest impacts on PM_2.5 emission growth. Finally, several policy recommendations are raised for China to mitigate its PM_2.5 emissions.

The characteristics and mixing states of PM2.5 during a winter dust storm in Ningbo of the Yangtze River Delta, China

Dust particulates play an essential role for the nucleation, hygroscopicity and also contribute to aerosol mass. We investigated the chemical composition, size distribution and mixing states of PM_2.5 using a single-particle aerosol mass spectrometer (SPAMS), Monitor for AeRosols and Gases (MARGA), and off-line membrane sampling from 2018.1.24 to 2018.2.20 at a coastal supersite in Ningbo, a port city in Yangtze River Delta, China. During the study campaign, the eastern part of China had experienced a wide range of cooling, sandstorm, and snowfall processes. The entire sampling campaign was categorized into five sub-periods based on the levels of PM_2.5 and the ratios of PM_2.5/PM₁₀, namely clean (T1), heavy pollution (T2), light pollution (T3), dust (sandstorm) (T4) and cleaning pollution (T5) period. After comparing the average mass spectrum for each period, it shows that the primary ions, such as Ca²⁺and SiO₃^-, rarely coexist with each other within a single particle, but secondary ions generally coexist with these primary ions. Furthermore, the coexistence of each two different ions within a particle does not show distinct variation for the whole study periods. All these suggest that the absorption and partitioning of gaseous contaminants into the surface of primary aerosol through heterogeneous reactions are the major pathways of aging and growth of aerosol; and the merging of particles through collisions usually is insignificant. Although the absolute concentrations of nitrate and sulfate all increased with the PM_2.5 concentrations, the relative equivalent concentrations of NO₃^- and SO₄^2- displayed opposite trends; the relative contribution of sulfate decreased and that of nitrate increased as the increase of pollution. During the dust period, the relative equivalent concentrations of calcium and/or potassium ions in PM_2.5 are significantly higher. This study provided deep insights about the mixing states and characteristics of particulate after long-range transport and a visualization tool for aerosol study.

The Contribution Rate of Driving Factors and Their Interactions to Temperature in the Yangtze River Delta Region

Complex temperature processes are the coupling results of natural and human processes, but few studies focused on the interactive effects between natural and human systems. Based on the dataset for temperature during the period of 1980–2012, we analyzed the complexity of temperature by using the Correlation Dimension (CD) method. Then, we used the Geogdetector method to examine the effects of factors and their interactions on the temperature process in the Yangtze River Delta (YRD). The main conclusions are as follows: (1) the temperature rose 1.53 °C; and, among the dense areas of population and urban, the temperature rose the fastest. (2) The temperature process was more complicated in the sparse areas of population and urban than in the dense areas of population and urban. (3) The complexity of temperature dynamics increased along with the increase of temporal scale. To describe the temperature dynamic, at least two independent variables were needed at a daily scale, but at least three independent variables were needed at seasonal and annual scales. (4) Each driving factor did not work alone, but interacted with each other and had an enhanced effect on temperature. In addition, the interaction between economic activity and urban density had the largest influence on temperature.

Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards

BACKGROUND

Most studies on the short-term health effects of air pollution have been conducted on a daily time scale, while hourly associations remain unclear.

METHODS

We collected the hourly data of emergency ambulance calls (EACs), ambient air pollution, and meteorological variables from 2014 to 2016 in Luoyang, a central Chinese city in Henan Province. We used a generalized additive model to estimate the hourly effects of ambient air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) on EACs for all natural causes and cardiovascular and respiratory morbidity, with adjustment for potential confounding factors. We further examined the effect modification by temperature, relative humidity, wind speed, and atmospheric pressure using stratified analyses.

RESULTS

In the single-pollutant models, PM_2.5, PM₁₀, SO₂, and NO₂ were associated with an immediate increase in all-cause morbidity at 0, 0, 12, 10 h, separately, after exposure to these pollutants (excess risks: 0.19% (95% confidence interval (CI): 0.03%, 0.35%), 0.13% (95% CI: 0.02%, 0.24%), 0.28% (95% CI: 0.01%, 0.54%) and 0.52% (95% CI: 0.06%, 0.99%), respectively). These effects remained generally stable in two-pollutant models. SO₂ and NO₂ were significantly associated with an immediate increase in risk of cardiovascular morbidity, but the effects on respiratory morbidity were relatively more delayed. The stratified analyses suggested that temperature could modify the association between PM_2.5 and EACs, humidity and atmospheric pressure could modify the association between SO₂ and EACs.

CONCLUSIONS

Our study provides new evidence that higher concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ may have transiently acute effects on all-cause morbidity and subacute effects on respiratory morbidity. SO₂ and NO₂ may also have immediate effects on cardiovascular morbidity. Findings of this study have important implications for the formation of hourly air quality standards.

Socioeconomic factors and regional differences of PM2.5 health risks in China

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 μm (PM_2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM_2.5 concentrations, which further aggravate the impact of PM_2.5 pollution on population health. Using PM_2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM_2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM_2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM_2.5-health-risk and PM_2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM_2.5 health risks, and reduction of energy intensity increases PM_2.5 health risks, but the impact of the total amount of urban central heating on PM_2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM_2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM_2.5 health risks. Our research enriches PM_2.5-health-risk theory and provides some theoretical support for PM_2.5-health-risk diversity management in China.

Spatiotemporal Associations between PM2.5 and SO2 as well as NO2 in China from 2015 to 2018

Given the critical roles of nitrates and sulfates in fine particulate matter (PM_2.5) formation, we examined spatiotemporal associations between PM_2.5 and sulfur dioxide (SO₂) as well as nitrogen dioxide (NO₂) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO₂ and NO₂ to PM_2.5 was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM_2.5 and SO₂ decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM_2.5 and SO₂ loadings therein. The statistically significant coupled MCA mode between PM_2.5 and SO₂ indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing–Tianjin–Hebei region. Further statistical modeling practices revealed that the observed PM_2.5 variations in northern China could be explained largely by SO₂ rather than NO₂ therein, given the estimated relatively high importance of SO₂. In general, the evidence-based results in this study indicate a strong linkage between PM_2.5 and SO₂ in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.

Analysis of influential factors on air quality from global and local perspectives in China

Regional haze pollution has frequently occurred in China over the past several years, and this haze has hindered the development of the economy and harmed the health of people in China. Currently, several studies have analyzed the impact of different influencing factors on haze. However, few studies have comprehensively analyzed the influential factors of haze from different perspectives. In this paper, we utilized global and local regression models to explore the main influential factors on air quality index (AQI) in China from global and local perspectives. The results are as follows: (1) the AQIs of Chinese cities have significant positive spatial correlation, and higher values of AQI were typically found in Beijing-Tianjin-Hebei, Shandong, Henan, Shanxi and Shaanxi Province; (2) from a global perspective, as there is one unit of increase in the average AQI of one city's neighbors, the city's AQI will increase by 0.827 unit. An increase in the industrial structures and the number of civilian vehicles will also lead to an increase in the AQI, but the impact of precipitation is reversed; and (3) from a local perspective, there are spatial differences in the effects of different factors on the AQI. In northern China, an appropriate temperature reduction and an appropriate increase in atmospheric pressure is helpful for reducing haze pollution; however, opposing conditions are found in southern China. Compared with China's coastal cities, the increase in precipitation is more effective at reducing the AQI in inland cities. Compared with other cities, reducing the industrial structure and the number of civilian vehicles was more effective for haze management in Beijing, Tianjin, Shandong, Henan, Shanxi, and Shaanxi provinces. These results of this paper are helpful for government departments to formulate regionally differentiated governance policies regarding haze.

Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach

China has been under threat of severe haze in recent years, particularly that caused by fine particulate matter (PM_2.5). Exploring the determinants of PM_2.5 concentration is critical for improving air quality. The influencing mechanism of smog pollution is a comprehensive and systematic process affected by multiple driving factors. In this research, we collected PM_2.5 monitoring data from 292 cities across China in 2015 and employed multilevel regression models constructed using three levels to detect the physical and socioeconomic driving forces behind the PM_2.5 concentration at monthly, seasonal and spatial scales, which captured random effects both varied by season and region. The results indicated significant spatiotemporal heterogeneity in the PM_2.5 distribution, with the pollution core located in central China and northern China. The most severely haze episodes occurred in winter. Multilevel models showed that 46.40% of the variance was derived from the seasonal and spatial levels, and the models could explain a maximum of 90.7% of the PM_2.5 concentration variance. The multilevel model identified more determinant influences varying by time and region. The outcomes suggested that the impacts of temperature and relative humidity on PM_2.5 were of significant spatiotemporal heterogeneity due to the influencing mechanism differing from season and station. The variation of anthropogenic activities led to the socioeconomic influences featured a significant spatiotemporal heterogeneity. This research revealed the spatiotemporal characteristic of PM_2.5 pollution influencing mechanism from physical and perspective and provided effective strategies for restricting air pollution.