Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling

Spatial-temporal evolution characteristics of PM2.5 and its driving mechanism: spatially explicit insights from Shanxi Province, China

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies.

Ambient air pollution and the health-related quality of life of older adults: Evidence from Shandong China

Ambient air pollution is a major public health concern impacting all aspects of human health. There is a lack of studies on the impact of ambient air pollution on health-related quality of life (HRQoL) of older Chinese adults. Our study answers two questions: How concentrations of ambient air pollutants are associated with HRQoL among older adults in China and, second, what are the possible mechanisms through which ambient air pollution affects HRQoL. From the 2018 National Health Service Survey, we sampled 5717 aged 65 years or older residents for the eastern province of Shandong, China. Data on individual exposures to PM_2.5 and PM₁₀ (particulate matter with diameter less than or equal to 2.5 μm and 10 μm) and sulfur dioxide (SO₂) were collected from the ChinaHighAirPollutants (CHAP) datasets. Mixed-effects Tobit regression models and mixed-effects ordered Probit regression models were employed to examine the associations of long-term exposure to ambient air pollution with the European Quality of Life 5 Dimensions 3 Level Version (EQ-5D-3L) scale comprising mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Socioeconomic, demographic and behavioral factors relating to HRQoL were also examined. The results show that for each 1 μg/m³ increase, EQ-5D-3L scores fell 0.002 for PM_2.5; 0.001 for PM₁₀ and 0.002 for SO₂. Long term exposure to PM_2.5, PM₁₀ and SO₂ were also associated with increased prevalence of pain/discomfort and anxiety/depression. The reduced HRQoL effects of ambient air pollution were exacerbated by higher socioeconomic status (affluent, urban and higher level of education). Our findings suggested that HRQoL of older Chinese adults was not only associated with demographic, socioeconomic, and health-related factors, but also negatively correlated with air pollution, especially through increased pain/discomfort and anxiety/depression. The paper proposes policy recommendations.

Spatiotemporal variations and mechanism of PM2.5 pollution in urban area: The case of Guiyang, Guizhou, China

PM_2.5 has been a hot concern in the recent decade. Many studies have focused on metropolises or those areas with poor air quality, but the PM_2.5 of more widespread areas is less considered. Considering the challenges of rapid economic growth and environmental problems against a developing region, we took Guiyang as a study case to assess the spatiotemporal variations and mechanism of PM2.5 pollution in an urban area from 2000 to 2020 in an extended sense. Based on PM_2.5 concentration data from 14 monitoring points in Guiyang, spatiotemporal variations and formation mechanism were assessed using wavelet, moving maximal information coefficients, and spatial correlation analysis. The urban Nighttime light data was selected to evaluate the impacts of socioeconomic factors on PM2.5 concentration using spatial correlation analysis. Further, wavelet and statistical analysis were adopted to analyze multi-dimensional temporal variations of PM_2.5 hourly concentration and the relationship with pressure, temperature, vapor pressure, relative humidity, wind, and visibility. The PM_2.5 hourly concentration was obtained from the monitoring points in downtown Guiyang according to data continuity and availability. PM_2.5 had different temporal variations at daily, monthly, seasonal, and annual levels, and interannual variation was the most obvious. The temperature was the main factor leading to the interannual temporal variation of PM_2.5. Wind and pressure were more significant for the responses of a shorter period variation with -0.76 and -0.80 of the minimum of correlation coefficient, respectively. Meanwhile, human activities significantly influenced spatiotemporal variations of PM_2.5. A spatial correlation analysis between PM2.5 and the related influencing factors from 2000 to 2018 was implemented based on a geographic information system. Besides, the landcovers within a buffer zone with a radius of 1 km on 14 monitoring points were visually interpreted to analyze the relationship between PM_2.5 and landcovers. Moreover, multivariate wavelet coherence analysis revealed the PM2.5 interaction among monitoring points. The PM_2.5 concentration in Guiyang dropped from 49 μg/m³ in 2012 to about 27 μg/m³ in 2018, and the air quality greatly improved. As in most cities, Guiyang has a significant PM_2.5 pollution island effect, with traffic and building land density contributing to higher PM_2.5 concentrations. There were some typical nonlinear spatiotemporal variations between PM_2.5 and its influencing factors, and these variations varied with the selected scale.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM_2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM_2.5, and meteorological and socioeconomic impacts of PM_2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM_2.5 concentration in 2020 was 32.73 μg/m³ existing a U-shaped variation pattern, which has decreased by 6.38 μg/m³ compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM_2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM_2.5 concentrations. The lowest PM_2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM_2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM_2.5 concentrations, with precipitation has the greatest impact on PM_2.5, which are beneficial for reducing PM_2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM_2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM_2.5.

The efforts of China to combat air pollution during the period of 2015-2018: A case study assessing the environmental, health and economic benefits in the Beijing-Tianjin-Hebei and surrounding "2 + 26" regions

During the period of 2015-2018, Chinese government had made great efforts to mitigate air pollutants, such as air quality monitoring, energy structure adjustment, and pollutant emission reduction from industry, transportation and household sectors. With the special investment of 152 billion Chinese Yuan (CNY) in the Beijing-Tianjin-Hebei (BTH) and surrounding "2 + 26" regions, the annual local concentrations of PM_2.5, PM₁₀, SO₂ and NO₂ decreased from 77, 132, 38 and 46 μg/m³ to 60, 109, 20 and 43 μg/m³. It was estimated that the improvement in air quality avoided 27,021 (95 % CIs 12,548-39,738) premature deaths attributed to air pollution exposure based on an exposure-response function, including 45 %, 17 % and 15 % of cardiopulmonary, lung cancer and respiratory morality cases. Air pollution reduction was also effective in reducing work time loss, which reduced the total working time loss by 3.8 × 10⁷ (95 % CIs 1.8 × 10⁷-5.6 × 10⁷) h, and the per capita working time loss by 0.28 (95 % CIs 0.13-0.41) h/capita by 2018. From the economic aspect, air pollution control actions in those regions saved 95.6 (95 % CIs 44.2-141) billion CNY economic loss by using the value of statistical life (VSL). The total benefit-cost ratio was 63.7 % (95 % CIs 29.4 %-93.7 %). The cost-effectiveness in Beijing and Tianjin were relatively low due to the regional contribution from other cities of the air pollution transmission channel. Despite the uncertainties, the results clearly show the significance of the environmental, health and economic benefits of actions in the BTH and surrounding "2 + 26" regions for combating air pollution.

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.

Spatial temporal patterns and driving factors of industrial pollution and structures in the Yangtze River Economic Belt

The conflict between industrial development and environmental pollution is global. This study quantitatively analyzes the temporal and spatial changes, spatial effects and determinants of industrial pollution discharge in the Yangtze River Economic Belt through two main indicators: wastewater and SO₂. Analyze the spatial coupling relationship between industrial agglomeration and pollution emission and the characteristics of industrial structure in different regions. The analysis shows that industrial emissions in the Yangtze River Economic Belt first increased and then decreased during the period 2003-2019. Industrial pollution spread from large to small and medium cities and shifted from downstream to upstream. Moreover, a positive correlation exists between industrial pollution discharge and per capita GDP, secondary industry proportion, population density, and energy use. Meanwhile, scientific and technological progress and environmental regulations are associated with industrial pollution reduction. Since the Yangtze River Economic Belt was still in the industrialization stage and had not yet reached a turning point on the Environmental Kuznets Curve. The "pollution refuge" phenomenon was evident in the Belt, where underdeveloped areas in the central and western regions accommodated portions of highly polluting industries from the eastern areas through "regional competition" and "policy depression." The industrial agglomeration and pollution antagonistic zones were dominated by polluting industries; environmental risks were greatest in these areas. The upstream and downstream of the YREB play the negative and positive environmental externalities of industrial agglomeration, respectively. Thus, differential control measures should be formulated according to different regions, industrial pollutants, and polluting industries to improve environmental quality.

The Impacts of Urban Form on PM2.5 Concentrations: A Regional Analysis of Cities in China from 2000 to 2015

The urban form (e.g., city size, shape, scale, density, etc.) can impact the air quality and public health. However, few studies have been conducted to assess the relationship between the urban form and PM2.5 concentrations on a regional scale and long-term basis in China. In this study, we explored the impact of the urban form on the PM2.5 concentrations in four different regions (i.e., northeast, central, east, western) across China for the years 2000, 2005, 2010, and 2015. Five landscape metrics were classified into three characteristics of the urban form (compactness, shape complexity, and urban expansion) using high-resolution remote-sensing data. With considerations given to regional differences, panel-data models and city-level panel data were used to calculate the impact of the urban form on the PM2.5 concentrations. The results of the study indicate that urban expansion is positively correlated with the PM2.5 concentrations across China, with the only exception being the country’s western region, which suggests that urban extension is conducive to increasing the PM2.5 levels in relatively developed regions. Meanwhile, the positive relationship between the irregularity of cities and the PM2.5 concentrations indicates that reducing the urban shape complexity will help to mitigate PM2.5 pollution. Moreover, urban compactness, which mainly refers to the landscape-division-index values, proved to have a negative effect on the PM2.5 concentrations, suggesting that the optimization of urban spatial compactness could reduce PM2.5 levels. The findings of this study are beneficial for a better understanding of the intensity and direction of the effect of the urban form on PM2.5 concentrations.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m³ increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.

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.

Spatiotemporal variations of air pollution and population exposure in Shandong Province, eastern China, 2014-2018

To clarify the characteristics and interannual variation of air pollution since the implementation of China's clean air actions, hourly in situ measurements of six gaseous and particulate criteria pollutants at 100 sites in Shandong Province were studied during 2014-2018. General decreasing trends in the concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO were observed, while O₃ increased continuously. In 2018, the annual average PM_2.5, PM₁₀, NO₂, SO₂, and CO concentration in Shandong was 50, 100, 35, 16 μg m^-3, and 1.5 mg m^-3, representing decreases of 39%, 30%, 24%, 73%, and 35% from 2014, respectively. These decreases occurred throughout the province. Seven "2 + 26" cities (in Beijing-Tianjin-Hebei and its surrounds) in western Shandong had higher average concentrations and greater reductions than other areas. In contrast, O₃ concentration rose, with occurrences of the 90^th percentile of all daily maximum 8-h averages increasing by 12% from 159 to 181 μg m^-3, during 2014-2018. From May to September, O₃ pollution dominated as the sole primary pollutant on non-attainment days, and PM_2.5 contributed to more than 90% of polluted days in wintertime months. Population exposures were investigated based on high-resolution monitoring data and population distribution, and high exposure to pollution was displayed. The population-weighted exposure to PM_2.5 in Shandong was 50 μg m^-3, a decrease of 33%. Eighty-nine percentage of the provincial population was exposed to PM_2.5 > 35 μg m^-3, while for 99.2% of population in the seven "2 + 26" cities, PM_2.5 exposure exceeded 50 μg m^-3.

WITHDRAWN: Analysis of distribution characteristics of PM2.5 and health risk appraisal in northeast china through the geographically weighted regression model

Ahead of Print article withdrawn by publisher.

The construction and examination of social vulnerability and its effects on PM2.5 globally: combining spatial econometric modeling and geographically weighted regression

Fine particulate matter (PM2.5) is of widespread concern, as it poses a serious impact on economic development and human health. Although the influence of socioeconomic factors on PM2.5 has been studied, the constitution and the effect analysis of social vulnerability to PM2.5 remain unclear. In this study, a comprehensive theoretical framework with appropriate indicators for social vulnerability to PM2.5 was constructed. Using spatial autocorrelation analysis, a positive global spatial autocorrelation and notable local spatial cluster relationships were identified. Spatial econometric modeling and geographically weighted regression modeling were performed to explore the cause-effect relationship of social vulnerability to PM2.5. The spatial error model indicated that population and education inequality in the sensitivity dimension caused a significant positive impact on PM2.5, and biocapacity and social governance in the capacity dimension strongly contributed to the decrease of PM2.5 globally. The geographically weighted regression model revealed spatial heterogeneity in the effects of the social vulnerability variables on PM2.5 among countries. These empirical results can provide policymakers with a new perspective on social vulnerability as it relates to PM2.5 governance and targeted environmental pollution management.

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.

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.

Analysis on the Characteristics of Air Pollution in China during the COVID-19 Outbreak

The COVID-19 pandemic poses a serious global threat to human health. In China, the government immediately implemented lockdown measures to curb the spread of this virus. These measures severely affected transportation and industrial production across the country, resulting in a significant change in the concentration of air pollutants. In this study, the Euclidean distance method was used to select the most similar meteorological field during the COVID-19 lockdown period. Changes in the concentration of air pollutants in China were analyzed under similar meteorological background conditions. Results indicate that, compared with data from 2015–2019, air quality in China significantly improved; with the exception of ozone (O3), the concentration of major air pollutants declined. Compared with baseline conditions, the reduction of air pollutants in China from 25 January to 22 February 2020 (Period 2) was the most significant. In particular, NO2 decreased by 41.7% in the Yangtze River Delta. In Period 2, the reduction of air pollutants in areas other than Hubei gradually decreased, but the reduction of NO2 in Wuhan reached 61.92%, and the reduction of air pollutants in various regions after February 23 was significantly reduced. By excluding the influence of meteorological factors and calculating the contribution of human activities to atmospheric pollutants by linear fitting, in Period 2 the effect of artificial controls on NO2 in Wuhan attained 30.66%, and reached 48.17% from 23 February to 23 March (Period 3). Results from this investigation provides effective theoretical support for pollution prevention and control in China.

A Clustering Framework to Reveal the Structural Effect Mechanisms of Natural and Social Factors on PM2.5 Concentrations in China

Understanding the mechanisms of various factors that affect PM2.5 can assist in the development of scientific measures to improve air quality. Nevertheless, existing research has concentrated on exploring local effect mechanisms, while structural effect mechanisms at regional or national scales have scarcely been analysed. Consequently, this study presents an analytical framework for elucidating the structural effect mechanisms of associated factors on PM2.5. Geographically and temporally weighted regression was used to explore the local effect mechanisms. This was followed by spatial clustering analysis to reveal these mechanisms by detecting their aggregation patterns. In the analysis, datasets for annual mean PM2.5 and socio-economic factors in China from 1999 to 2016 were employed. Urban population, gross industrial output, and sulphur dioxide emissions were identified as factors affecting changes in PM2.5 concentrations. These three factors had both negative and positive effects, while the gross industrial output had the largest coefficient variation degree. Three geographically related factors exhibited different impacts on PM2.5 concentrations in most of mainland China. These factors were the urban population roughly west of the Heihe-Tengchong line, gross industrial output primarily in southwestern China, and sulphur dioxide emissions primarily in southern China.

Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018

Fine particulate matter (PM_2.5) is closely related to the air quality and public health. Numerous models have been introduced to simulate the PM_2.5 concentrations at large scale based on remote sensing and auxiliary data. However, the data precision provided by these models are inadequate for epidemiology and pollutant exposure studies at medium or small scale. The present study aims to calibrate PM_2.5 concentrations at 1 km resolution scale across China during 2015-2018 based on monitoring station data and auxiliary data using a novel geographically and temporally weighted regression model (GTWR). The cross-validation (CV) method and the geographically weighted regression (GWR) model are conducted for validation and cross-comparison. Additionally, the spatial autocorrelation and slope analysis methods are implemented to detect the spatiotemporal dynamic of PM_2.5 concentrations. A sample-based CV of the GTWR model demonstrates an acceptable precision with a coefficient of determination equal to 0.67, a root-mean-square error of 10.32 μg/m³, and a mean prediction error of-6.56 μg/m³. This result proves that the GTWR model can simulate PM_2.5 concentrations at a higher spatial resolution and accuracy across China than some previous models. Besides, the heterogeneity and spatiotemporal dynamic of PM_2.5 concentrations are obvious, that is, the High-High (H-H) agglomeration areas with strong haze pollution were mainly concentrated in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Chengdu-Chongqing (CY), and Guanzhong Plain (GZP). In addition, the PM_2.5 concentrations are undergoing a decreasing trend in most of the study area, and the decrease in the BTH is dramatic. The results of the present study are helpful for calibrating and detecting the spatiotemporal dynamic of PM_2.5 concentrations and useful for the government to make decisions about decreasing haze pollution in urban agglomeration scale.

Spatial distribution and determinants of PM2.5 in China's cities: fresh evidence from IDW and GWR

While numerous studies have explored the spatial patterns and underlying causes of PM_2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM_2.5 factors. In order to enrich this research field, we collected PM_2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM_2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM_2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM_2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM_2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM_2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM_2.5. When the per capita income reaches 47,000 yuan, the PM_2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM_2.5 governance measures.

Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial-temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai'an, and was distributed stably at the border between Jining and Tai'an; (3) The global spatial autocorrelation experienced a changing process of "decline-growth-decline", and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

The contributions of socioeconomic and natural factors to the acid deposition over China

China has experienced severe acid rain pollution during the past decades due to excessive sulfur oxides (SO₂) and nitrous oxides (NO_x) emissions, which further caused lake acidification, biodiversity losses and climate change. Although the major sources of acid deposition have been clarified previously, the contributions of socioeconomic (natural) factors to the regional acid deposition remained unknown. Therefore, a series of valuable data including socioeconomic (natural) variables and measured pH value in the rainwater at the city level were collected to identify the key factors influencing the rainwater pH value at the national and the regional scale using the spatial econometric model/geographical detector technique and geographical weight regression (GWR) model, respectively. The results showed that the annual mean pH value in the rainwater in China was 6.54 ± 0.72. The rainwater pH in winter (6.01 ± 0.41) was significantly lower than those observed during summer (6.74 ± 0.64), spring (6.71 ± 0.71) and autumn (6.71 ± 0.69). The spatial econometric model indicated that socioeconomic indicators including per capita gross industrial production (GIP), ratio of built-up area to the urban land (RBU), foreign direct investment (FDI), SO₂ emission, and meteorological factors of annual mean precipitation (AMP), and annual mean relative humidity (AMRH) were the main factors for the acid deposition. The geographical detector technique implied that the power of determinants were in the order of AMRH (10.00%) = AMP (10.00%) > SO₂ emission (8.51%) > FDI (8.32%) > RBU (7.64%) > per capita GIP (7.00%). The GWR implied that GIP, FDI, and SO₂ emission made relatively higher contribution to acid deposition in East China relative to other regions owning to the huge population and the higher energy consumption. The higher rainfall amount and RH in Southeast China significantly increased the pollutant deposition fluxes and promoted the heterogeneous transformations of precursors of acid rain, respectively. The findings herein shed light upon the socioeconomic forces for the acid deposition in China for the first time and provided the new information for government sectors to control the acid rain pollution in the future.

Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

A novel framework for decomposing PM2.5 variation and demographic change effects on human exposure using satellite observations

Human exposure to PM_2.5, represented by population-weighted mean PM_2.5 concentration (c_ρ), declines under three conditions: (1) mean PM_2.5 concentration declines, (2) PM_2.5 concentration within urban areas goes through more of a decrease than within rural areas, or (3) city planning relocates people into cleaner areas. Decomposing these effects on human exposure is essential to guide future environmental policies. The lack of ground PM_2.5 observations limits the assessment of human exposure to PM_2.5 over China. This study proposed a novel diagnostic framework using satellite observations to decompose the variation in c_ρ resulting from change in the mean PM_2.5 concentration, spatial difference in PM_2.5 change, and demographic change. In this framework, we decomposed c_ρ into mean PM_2.5 concentration (c₀) and pollution-population-coincidence induced PM_2.5 exposure (PPCE). We then used this framework to decompose the variation in c_ρ over China within three recent Five-Year Plans (FYPs) (2001-2015). The results showed that the decline in c₀ reduced c_ρ in most provinces within the eleventh and twelfth FYPs. The spatial difference in PM_2.5 change reduced the PPCE and c_ρ in most provinces within the tenth and twelfth FYPs, with the most substantial reduction rate of -3.64 μg m^-3·yr^-1 in Tianjin within the twelfth FYP. Rural-to-urban migration resulting from rapid urbanization, however, increased the PPCE and c_ρ (by as much as 0.22 μg m^-3·yr^-1) in all provinces except Taiwan within all three FYPs. The demographic change reduced c_ρ in Taiwan because of the migration of population into less polluted areas. To better reduce human exposure, it is recommended that control efforts further target populous residential areas and urbanization planning relocates people into less polluted areas. Our decomposition framework paves a new way to decompose the human exposure to other air pollutants in China and other regions.

The contributions of socioeconomic indicators to global PM2.5 based on the hybrid method of spatial econometric model and geographical and temporal weighted regression

PM_2.5 pollution poses a negative effect on human health and economic growth. However, the major socioeconomic driving forces of global PM_2.5 pollution during a long-term period remained unclear. In this study, we explored the potential association between socioeconomic indicators and the PM_2.5 level worldwide using a spatial econometric model coupled with a geographical and temporal weighted regression (GTWR). The results suggested that renewable energy consumption ratio, per capita gross domestic production (GDP), per capita CO₂ emission, urban population ratio, and fossil fuel consumption ratio were major factors responsible for the global PM_2.5 pollution. The impacts of socioeconomic indicators on the PM_2.5 level varied with the income-level and time. Fossil fuel consumption ratio, per capita CO₂ emission, urban population ratio were major contributors for severe PM_2.5 pollution in the developing countries (e.g., China and India). Further, these impacts have become more remarkable in recent years. Per capita GDP still played a crucial role on the PM_2.5 pollution in India, indicating that energy-intensive industries were major contributors to its economic growth, thereby leading to the higher PM_2.5 concentration in India. However, China has strode across the inflection of Environmental Kuznets Curve (EKC) as a whole and decreased the reliance on the secondary industries. Compared with the developing countries, the impacts of socioeconomic indicators on PM_2.5 pollution in most of the developed countries remained relatively stable and weak, implicating that fossil fuel consumption and urbanization were not major contributors for local PM_2.5 level. The findings of this study clarified major contributors for PM_2.5 pollution, and provided scientific basis for mitigating the PM_2.5 pollution.

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.

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

Does the path of technological progress matter in mitigating China's PM2.5 concentrations? Evidence from three urban agglomerations in China

Technological progress is widely considered to play an important role in reducing air pollution. While growing literature has explored the effects of technological progress on environmental quality, fewer studies have considered the varied effects exerted by different technological progress paths on PM_2.5 concentrations. This paper explored the relationship between two different kinds of technological progress (indigenous innovation and technology diffusion) and PM_2.5 concentrations. Indigenous innovation was in this study considered to be composed by research and development investment (R&D) and import technology (IM); technology diffusion was represented by foreign direction investment (FDI) and export learning effect represent (EX). A panel data model was employed in order to explore the varied impact of these different technological progress paths on PM_2.5 concentrations, using data for 48 cities located in China's three most developed urban agglomerations (Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta), for the period 2000-2015. The results reveal that without control variables, FDI had a significant negative impact on PM_2.5 levels in Beijing-Tianjin-Hebei, and EX a significant positive impact. FDI, R&D, and EX were found to positively correlate with PM_2.5 levels in the Yangtze River Delta. In Pearl River Delta, R&D presented a significant negative relationship. The findings of this study provide decision makers and industry managers with a scientific basis from which to approach the task of mitigating PM_2.5 concentrations through technological progress.

Using Bayesian spatio-temporal model to determine the socio-economic and meteorological factors influencing ambient PM2.5 levels in 109 Chinese cities

OBJECTIVE

Ambient particulate pollution, especially PM_2.5, has adverse impacts on health and welfare. To manage and control PM_2.5 pollution, it is of great importance to determine the factors that affect PM_2.5 levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM_2.5 in 109 Chinese cities from January 1, 2015 to December 31, 2015.

METHODS

To evaluate potential risk factors associated with the spatial and temporal variations in PM_2.5 levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM_2.5 levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters.

RESULTS

Daily concentrations of PM_2.5 peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 μg/m³) in the Beijing-Tianjin-Hebei area. The city-level PM_2.5 was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO₂), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM_2.5 was found. The city-level and daily-level of weather conditions within a city were both associated with PM_2.5.

CONCLUSION

PM_2.5 levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM_2.5 pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.

Determinants of Urban Expansion and Spatial Heterogeneity in China

China is the world's largest developing country and its regions vary considerably. However, spatial heterogeneity in determinants of urban expansion in prefecture-level cities have not been identified. The present study explored the spatiotemporal characteristics of Chinese urban expansion and adopted a geographically weighted regression (GWR) method to determine this spatial heterogeneity. The results indicated that China experienced massive urban expansion during 1990-2015, with urban areas growing from 4.88 × 104 km2 to 1.06 × 105 km2, 46.42% of which was distributed in the eastern region. The results of the GWR model revealed the spatial heterogeneity in the determinants of urban expansion. Marketization was vital for urban expansion and had a stronger impact in the developed eastern and southern regions than in the less-developed northern and western regions. Globalization and decentralization bi-directionally affected urban expansion. The constraining effects of physical factors were limited and stronger in the developing northern region than in the developed southern region. Identifying the varying determinants of urban expansion is essential for policy-making in various regions.

AMPK activation attenuates inflammatory response to reduce ambient PM2.5-induced metabolic disorders in healthy and diabetic mice

Epidemiological and experimental studies have indicated that ambient fine particulate matter (PM_2.5) exposure is associated with the occurrence and development of metabolic disorders such as obesity and type 2 diabetes mellitus (T2DM). However, the mechanism is not clear yet, and there are few studies to explore the possible prevention measure. In this study, C57BL/6 and db/db mice were exposed to concentrated PM_2.5 or filtered air using Shanghai Meteorological and Environmental Animal Exposure System (Shanghai-METAS) for 12 weeks. From week 11, some of the mice were assigned to receive a subcutaneous injection of AMPK activator (AICAR). Lipid metabolism, glucose tolerance, insulin sensitivity and energy homeostasis were measured. Meanwhile, the respiratory, systemic and visceral fat inflammatory response was detected. The results showed that PM_2.5 exposure induced the impairments of glucose tolerance, insulin resistance, lipid metabolism disorders and disturbances of energy metabolism in both C57BL/6 and db/db mice. These impairments might be consistent with the increased respiratory, circulating and visceral adipose tissue (VAT) inflammatory response, which was characterized by the release of IL-6 and TNF-α in lung, serum and VAT. More importantly, AICAR administration led to the significant enhancement of energy metabolism, elevation of AMPK as well as the decreased IL-6 and TNF-α in VAT of PM_2.5-exposed mice, which suggesting that AMPK activation might attenuate the inflammatory responses in VAT via the inhibition of MAPKs and NFκB. The study indicated that exposure to ambient PM_2.5 under the concentration which is often seen in some developing countries could induce the occurrence of metabolic disorders in normal healthy mice and exacerbate metabolic disorders in diabetic mice. The adverse impacts of PM_2.5 on insulin sensitivity, energy homeostasis, lipid metabolism and inflammatory response were associated with AMPK inhibition. AMPK activation might inhibit PM_2.5-induced metabolic disorders via inhibition of inflammatory cytokines release. These findings suggested that AMPK activation is a potential therapy to prevent some of the metabolic disorders attributable to air pollution exposure.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O₃, wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

The impact of ambient particulate matter on hospital outpatient visits for respiratory and circulatory system disease in an urban Chinese population

There are limited evidence on the association between short-term exposure to ambient particulate matter (PM) and overall hospital outpatient visits for respiratory system disease (RESD) and cardio-cerebrovascular system disease (CCD) in high-polluted countries like China. Though previous epidemiological studies of RESD and CCD generally applied a linear relationship of the acute PM effects, it is unclear whether this linear exposure-response relationship holds in high pollution area. In this study, a time-series study during 2013 through 2016 was conducted to investigate 245,442 and 430,486 hospital visits for RESD and CCD respectively from Nanjing city, China. A combination of logistic generalized additive model (GAM) was used to evaluate the exposure-response associations. The results disclosed that a 10 μg/m³ increase in PM_2.5 and PM₁₀ concentration on the current day of exposure (lag 0) was associated with 0.36% (95% CI: -0.02%-0.73%) and 0.33% (0.07%-0.60%) increase in RESD; and 0.42% (0.00%-0.85%) and 0.37% (0.08%-0.67%) increase in CCD. The exposure-response association was approximately linear within 0-150 μg/m³ of PM concentration and non-linear across the full range of exposures. The effects of PM on RESD and CCD were sensitive to additional adjustment for co-pollutants, indicating the health effects of air pollution mixture in Nanjing city. There was no evidence of potential effect modification of RESD and CCD by season (cold and warm), age (5-64, 65-74, ≧75 years) and sex (male and female) groups. Though not statistically significant, the estimated risks in warm season were higher than those in cold season, suggesting potential synergistic effects of ambient PM pollution and temperature on triggering RESD and CCD.

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.

Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling

Whilst numerous studies have explored the spatial patterns and underlying causes of PM_2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM_2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM_2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM_2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM_2.5 in 95.60% of the cities and it correlated negatively with PM_2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM_2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM_2.5, revealing that increasing urban fragmentation was conducive to PM_2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM_2.5 governances.

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.

Spatiotemporally mapping of the relationship between NO2 pollution and urbanization for a megacity in Southwest China during 2005-2016

Nitrogen dioxide (NO₂) significantly contributes to air pollution. Long-term NO₂ exposure is harmful to human health. The NO₂ pollution in China has surpassed developed countries and attracts international attention. To understand the spatial and temporal distributions of NO₂ across Chengdu in Southwest China, a random forest (RF) model was developed based on NO₂ environmental monitoring data, the Ozone Monitoring Instrument (OMI) satellite retrievals, and geographic covariates. The RF model showed good performance with a cross validation R² of 0.77, and a root mean square error (RMSE) of 11.0 μg/m³. The ground-level NO₂ concentrations of Chengdu for 2005-2016 were predicted using the developed model with the multiyear population weighted NO₂ concentration being 41.7 ± 11.7 μg/m³. The predicted NO₂ concentrations exhibited a clear seasonal variation trend with winter being the highest and summer being the lowest. Furthermore, higher NO₂ concentrations in the downtown areas were observed than that in the rural areas indicating the former being attributed to more anthropogenic sources. The population weighted NO₂ concentrations with deseasonlization were relatively high during 2011-2013. The NO₂ concentration increased at a rate of 0.81 μg/m³/year before 2011 (43.4 ± 11.2 μg/m³) and decreased at a rate of -1.03 μg/m³/year after 2013 (44.8 ± 12.8 μg/m³).

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014⁻2017

PM_2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM_2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM_2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM_2.5 concentration changes. The results are as follows: (1) The annual average value of PM_2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM_2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM_2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m³) regulated by Chinese government. PM_2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM_2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM_2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises.

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.