Spatiotemporal dynamics and impacts of socioeconomic and natural conditions on PM2.5 in the Yangtze River Economic Belt

Spatio-temporal differentiation characteristics and the influencing factors of PM2.5 emissions from coal consumption in Central Plains Urban Agglomeration

Central Plains urban agglomeration (CPUA) had developed rapidly, but its air pollution was also serious. Despite advances in study on China's PM2.5 emissions from coal consumption (CC), the differentiation characteristics and the affecting variables of PM2.5 in CPUA required further investigation. This paper computed the PM2.5 emissions of each city from 2000 to 2020 using CC data from CPUA, evaluated its spatio-temporal fluctuation characteristics using the spatial autocorrelation and analyzed its influencing factors by combining various indicators through the spatial Durbin model (SDM). The results verified that: (1) There was a trend of rapid increase of PM2.5 emissions from CC; (2) The Moran's I of the PM2.5 emissions from CC showed a significant agglomeration effect; (3) PM2.5 emissions from CC had a strong spillover effect. The recommendations were in this following: (1) The urban pollution regulation and the pace of industrial green transformation should be Strengthened; (2) Close linkages between cities should be established and attention should be paid to pollution management; (3) The spillover of PM2.5 emissions from CC should be lessened and development of environmental governance technology should be enhanced.

Impact of urban space on PM2.5 distribution: A multiscale and seasonal study in the Yangtze River Delta urban agglomeration

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities.

Spatiotemporal heterogeneity of the relationships between PM2.5 concentrations and their drivers in China's coastal ports

PM2.5 is one of the primary air pollutants that affect air quality and threat human health in the port areas. To prevent and control air pollution, it is essential to understand the spatiotemporal distributions of PM2.5 concentrations and their key drivers in ports. 19 coastal ports of China are selected to examine the spatiotemporal distributions of PM2.5 concentrations during 2013-2020. The annual average PM2.5 concentration decreases from 61.03 μg/m3 to 30.17 μg/m3, with an average decrease rate of 51.57%. Significant spatial autocorrelation exists among PM2.5 concentrations of ports. The result of the geographically and temporally weighted regression (GTWR) model shows significant spatiotemporal heterogeneity in the effects of meteorological and socioeconomic factors on PM2.5 concentrations. The effects of boundary layer height on PM2.5 concentrations are found to be negative in most ports, with a stronger effect found in the Pearl River Delta, Yangtze River Delta and some ports of the Bohai Rim Area. The total precipitation shows negative effects on PM2.5 concentrations, with the strongest effect found in ports of the Southeast Coast. The effects of surface pressure on PM2.5 concentrations are positive, with stronger effects found in Beibu Gulf Port and Zhanjiang Port. The effects of wind speed on PM2.5 concentrations generally increase from south to north. Cargo throughput shows strong and positive effects on PM2.5 concentrations in ports of Bohai Rim Area; the positive effects found in Beibu Gulf Port increased from 2013 to 2018 and decreased since 2019. The positive effects of GDP and nighttime light on PM2.5 concentrations gradually decrease and turn negative from south to north. Understandings obtained from this study can potentially support the prevention and control of air pollution in China's coastal ports.

Spatiotemporal retrieval and feature analysis of air pollution episodes

Air pollution has inevitably come along with the economic development of human society. How to balance economic growth with a sustainable environment has been a global concern. The ambient PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) is particularly life-threatening because these tiny aerosols could be inhaled into the human respiration system and cause millions of premature deaths every year. The focus of most relevant research has been placed on apportionment of pollutants and the forecast of PM2.5 concentration measures. However, the spatiotemporal variations of pollution regions and their relationships to local factors are not much contemplated in the literature. These local factors include, at least, land terrain, meteorological conditions and anthropogenic activities. In this paper, we propose an interactive analysis platform for spatiotemporal retrieval and feature analysis of air pollution episodes. A domain expert can interact with the platform by specifying the episode analysis intention considering various local factors to reach the analysis goals. The analysis platform consists of two main components. The first component offers a query-by-sketch function where the domain expert can search similar pollution episodes by sketching the spatial relationship between the pollution regions and the land objects. The second component helps the domain expert choose a retrieved episode to conduct spatiotemporal feature analysis in a time span. The integrated platform automatically searches the episodes most resembling the domain expert's original sketch and detects when and where the episode emerges and diminishes. These functions are helpful for domain experts to infer insights into how local factors result in particular pollution episodes.

Does Administrative Division Adjustment promote low-carbon city development? Empirical evidence from the "Revoke County to Urban District" in China

The carbon emission reduction in city regions as a result of the optimization of urban spatial layout is crucial for combating global warming and has garnered widespread attention in recent years. There is little evidence, however, that a specific spatial optimization technique has a substantial effect on urban spatial layout and carbon dioxide (CO₂) emissions. As an effective tool of hierarchical network governance in China, Administrative Division Adjustment (ADA) has the potential to achieve this goal, due to its redistributive effects on urban space resources. Therefore, we utilize the "Revoke County to Urban District" (CTD)-one of the common and typical ADA policies-as a case study to examine its environmental implications, based on the mediation mechanism of urban spatial layout. The empirical results from a panel dataset of 285 prefecture-level and above cities in China indicate that the CTD will reduce urban CO₂ emissions, especially in low administrative levels (low-rank), non-resource based (RB), non-key environmental protection (KEP), midwestern and northwestern cities. And the additional mediation mechanisms demonstrate that the environmental benefits of the CTD in China are attributed to the optimization of urban spatial layout, which reduces CO₂ emissions by improving public transportation and limiting urban sprawl.

Multidimensional effects of urbanization on PM2.5 concentration in China

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM_2.5 and ignored the impact of multidimensional urbanization on PM_2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM_2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM_2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM_2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM_2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM_2.5 concentration has significant regional differences in China. Spatially, the PM_2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM_2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM_2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM_2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM_2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China

This study investigated the energy poverty spatiotemporal interaction characteristics and socioeconomic determinants in rural China from 2000 to 2015 using exploratory time-space data analysis and a geographical detector model. We obtained the following results. (1) The overall trend of energy poverty in China's rural areas was "rising first and then declining", and the evolution trend of energy poverty in the three regions formed a "central-west-east" stepwise decreasing pattern. (2) There was a dynamic local spatial dependence and unstable spatial evolution process, and the spatial agglomeration of rural energy poverty in China had a relatively higher path dependence and locked spatial characteristics. (3) The provinces with negative connections were mainly concentrated in the central and western regions. Anhui and Henan, Inner Mongolia and Jilin, Jilin and Heilongjiang, Hebei and Shanxi, and Liaoning and Jilin constituted a strong synergistic growth period. (4) From a long-term perspective, the disposable income of rural residents had the greatest determinant power on rural energy poverty, followed by per capita GDP, rural labor education level, regulatory agencies, and energy investment. In addition, our findings showed that the selected driving factors all had enhanced effects on rural energy poverty in China through interaction effects.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Spatiotemporal heterogeneity of PM2.5 and its driving difference comparison associated with urbanization in China's multiple urban agglomerations

The development of urban agglomeration further deteriorates the air pollution status arising from urbanization. However, disparities in the urbanization process across different urban agglomerations may shape unique regional air pollution characters, and further complicate its driving mechanism. In this study, 11 urban agglomerations with different urbanization levels in China thus were chosen as the case study areas, to explore the spatiotemporal heterogeneity of PM2.5 and its potential driving difference related to the urbanization process from a multi-urban agglomeration perspective. The ground-monitored PM2.5 data and socioeconomic panel data (2015-2018) were processed using multiple statistical analysis methods, and the main findings of this study can be generated as followed: (1) significant spatial heterogeneity characteristics of PM2.5 pollution were recognized across the study area. And even though obvious improvement in the air quality during the study period, PM2.5 concentration remains at a high level for most of the urban agglomerations. (2) Urbanization process has a substantial contribution to regional PM2.5 pollution, and significant differences of the urbanization factors on PM2.5 concentration across the urban agglomerations assigned with various urbanization levels were emphasized. The significance of this study is to provide insight into the relationship of the urbanization process on PM2.5 pollution in different urban agglomerations and to offer a scientific basis for regional cooperation for air pollution regulation among multiple urban agglomerations.

Does air pollution contribute to urban-rural disparity in male lung cancer diseases in China?

It remains unknown whether exposure to ambient air pollution can be a mediator linking socioeconomic indicator to health outcome. The present study aims to examine the mediation effect of PM2.5 air pollution on the association between urban-rural division and the incidence (mortality) rate of male lung cancer. We performed a nationwide analysis in 353 counties (districts) of China between 2006 and 2015. A structural equation model was developed to determine the mediation effect of exposure to PM2.5. We also tested whether the findings of the mediation effect of exposure to PM2.5 are sensitive to the controls of smoking factors and additional air pollutant, and PM2.5 exposures with different lag structures. According to the results, we found that exposure to PM2.5 significantly mediated the association between urban-rural division and the incidence rate of male lung cancer. Specifically, there were significant associations between urban-rural division, exposure to PM2.5, and the incidence rate of male lung cancer, with PM2.5 exposure accounting for 29.80% of total urban-rural difference in incidence rates of male lung cancer. A similar pattern of results was observed for the mortality rate of male lung cancer. That is, there was a significant mediation effect by PM2.5 on the association of the mortality rate with urban-rural division. The findings of exposure to PM2.5 as a mediator were robust in the three sensitivity analyses. In conclusion, urban-rural difference in exposures to PM2.5 may be a potential factor that contributes to urban-rural disparity in male lung cancer diseases in China. The findings inform that air pollution management and control may be effective measures to alleviate the great difference in male lung cancer diseases between urban and rural areas in China.

Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China

In recent years, with the continuous advancement of China's urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of "U" shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM_2.5 showed an "inverted U-shaped" quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM_2.5 and PM₁₀ were significantly positively correlated with NO₂, SO₂, CO and air pressure, and significantly negatively correlated with O₃ and air temperature. Wind speed was significantly negatively correlated with PM_2.5, and significantly positively correlated with PM₁₀. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM_2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities.

Assessing the impact of digital financial inclusion on PM2.5 concentration: evidence from China

Digital finance as a new technology-driven business model shortens the distance between borrowers and lenders. Economic research finds that digital finance promotes economic efficiency by reducing transaction costs, information asymmetry, and inequality. Digital finance is an energy-intensive industry; therefore, increased efficiency in the industry should yield environmental benefits. We examine the externality of digital finance on air pollution. By analyzing data on digital financial inclusion and fine particulate matter concentration in China, we demonstrate using a dynamic panel data model that the development of digital finance damages the environment. However, after incorporating a threshold effect into a kink model, we determine that digital finance reduces pollution when its development exceeds a certain level. The results suggest that a high level of digital finance development not only increases economic growth but also improves air quality; this result provides novel insight into the relationship between economic growth and the environment.

Urban Expansion in China: Spatiotemporal Dynamics and Determinants

China’s urbanization has attracted many scholars’ attention due to its significant impact on socioeconomic sustainability. Many studies have explored the spatial pattern and effects of the factors influencing urban expansion. However, the spatiotemporal dynamics integrating spatial and temporal dimensions and the spatial scales of the influencing factors are always ignored. This study applied the framework of exploratory space–time data analysis (ESTDA) to investigate the spatiotemporal dynamics of urban expansion across 342 cities in China from 1990 to 2017 and, further, used multiscale geographically weighted regression (MGWR) to estimate the effects of influencing factors on urban expansion. We found that urban expansion had an obvious south–north division, and yet the effects of influencing factors usually showed an east–west division. We also found that the dynamic local spatial dependency of urban expansion was accompanied by a volatile coevolution process and inclined to transfer from heterogeneity to homogeneity, and homogeneity tended to be stable. The coevolution of urban expansion between cities and other neighboring ones became stronger with increases in time and regional integration. These findings support the use of customized urban planning for specific regions in different spatial dependence to improve land-use efficiency and coordinate regional development.

Smaller particular matter, larger risk of female lung cancer incidence? Evidence from 436 Chinese counties

BACKGROUND

Many studies have reported the effects of PM2.5 and PM10 on human health, however, it remains unclear whether particular matter with finer particle size has a greater effect.

OBJECTIVES

This work aims to examine the varying associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10 in 436 Chinese cancer registries between 2014 and 2016.

METHODS

The effects of PM1, PM2.5 and PM10 were estimated through three regression models, respectively. Mode l only included particular matter, while Model 2 and Model 3 further controlled for time and location factors, and socioeconomic covariates, respectively. Moreover, two sensitivity analyses were performed to investigate the robustness of three particular matte effects. Then, we examined the modifying role of urban-rural division on the effects of PM1, PM2.5 and PM10, respectively.

RESULTS

The change in the incidence rate of female lung cancer relative to its mean was 5.98% (95% CI: 3.40, 8.56%) for PM1, which was larger than the values of PM2.5 and PM10 at 3.75% (95% CI: 2.33, 5.17%) and 1.57% (95% CI: 0.73, 2.41%), respectively. The effects of three particular matters were not sensitive in the two sensitivity analyses. Moreover, urban-rural division positively modified the associations of the incidence rate of female lung cancer with PM1, PM2.5 and PM10.

CONCLUSIONS

The effect on the incidence rate of female lung cancer was greater for PM1, followed by PM2.5 and PM10. There were positive modifying roles of urban-rural division on the effects of three particular matters. The finding supports the argument that finer particular matters are more harmful to human health, and also highlights the great significance to develop guidelines for PM1 control and prevention in Chinese setting.

Spatiotemporal Pattern and Driving Factors of Urban Sprawl in China

Urban sprawl is a complex phenomenon related to abnormal urbanization, and it has become a key issue of global concern. This study aimed to measure urban sprawl in China and explore its spatiotemporal patterns and driving factors. Based on 343 Chinese cities at the prefecture level and above, remote sensing-derived data from 2000 to 2017 were used to calculate the urban sprawl index (USI). The evolutionary trend and spatiotemporal pattern of urban sprawl in China were then analyzed using trend analysis and exploratory spatiotemporal data analysis, and Geodetector was applied to investigate the factors driving the changes. The results show the following. ① Moderate or high urban sprawl development occurred in China from 2000 to 2017. In terms of spatial distribution, the USI was high in northwest China and low in southeast China. ② The local spatial stability of the USI gradually decreased from southeast to northwest and northeast. USI had strong spatial dependence. No significant spatiotemporal transitions in urban sprawl were observed, and the spatial pattern was stable with strong spatial cohesion. ③ The gross regional product (GRP) of the tertiary industry, the total GRP, and investment in real estate development have been the most important factors affecting sprawl in cities at the prefecture level and above in China.

Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019

The Yellow River Economic Belt (YREB) plays an important role in China's socio-economic development and ecological security. However, this region has suffered from serious atmospheric pollution in recent years due to intense human activity. Identifying and qualifying the spatio-temporal characteristics of the region's air pollution and its driving forces would help in the formulation of effective mitigation policies. Here, the YREB's spatio-temporal characteristics of air quality were meticulously investigated using air pollution observation, synchronous meteorological, and socio-economic data from 103 cities, for the period of 2014-2019. Furthermore, the factors influencing air pollution were analyzed and qualified. Although air quality improved in the cities of the YREB following the implementation of the Air Pollution Action Plan, the region's quality index (AQI) remained higher than the national average. Annual variations of AQI in the YREB followed a U-shaped pattern, being high in autumn and winter and low in spring and summer; this U shape became shallower following improvements in air quality during autumn and winter. From 2014 to 2019, the annual average AQI values of cities in the eastern, middle, and western YREB dropped from 109.66, 111.70, and 94.65 to 92.00, 103.85, and 73.95, respectively. The air pollution trends of cities revealed obvious spatial agglomeration, and those cities with poor air quality were primarily the western cities of Shandong province, most cities in Henan province, and the eastern cities of Shanxi province. Due to the improvement of air quality in eastern cities, the pollution center of gravity moved gradually from Changzhi (113°3411"E, 36°040"N) to Linfen (110°5222″E, 36°2344″N). The results of the spatial Durbin model (SDM) indicated that air pollution had an apparent spillover effect in the YREB at the watershed scale, and that government technical expenditure, gross domestic product (GDP) per capita, population density, annual wind speed, and relative humidity all had significant negative overall effects on the AQI values of cities. The green cover rate, ratio of secondary industry, and temperature, meanwhile, all had significant positive total effects. Due to differences the natural conditions and stages of socio-economic development between the eastern, middle, and western cities of the YREB, the impact directions and functional strengths of their key factors differed greatly.

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.

Coupling relationship between construction land expansion and PM2.5 in China

Urban air pollution with PM2.5 as the main pollutant has become increasingly prominent in China since 2010. Scholars have conducted many studies on how urbanization affects PM2.5, but few concerns about the relationship between construction land (CL) expansion and PM2.5 at different scales from the perspective of expansion rate. Therefore, this study takes CL and PM2.5 data in China to describe the spatiotemporal progress of atmospheric environmental pollution and then adopts the overall and spatial coupling models to quantitatively reveal the dynamic relationship between them. The results indicate that the growth rate of PM2.5-polluted area in China was found to increase rapidly for 2000-2010 time period, followed by a continuous decline afterward. The annual average growth rates of CL area and PM2.5-polluted area within 15 years were 4.43% and 2.46%, respectively. Moreover, the barycenter distance between PM2.5 concentration and CL decreased gradually, and the two barycenters approached closer. Also, the spatial coupling coordination of CL and PM2.5 enhanced in Central, West, and East China but weakened in Northeast. Cities with a "very strong" coupling type are mainly located in the "Chongqing-Beijing" belt and the lower-middle reaches of the Yangtze River. Finally, the spatial coupling model results show that a low PM2.5 concentration is closely related to CL expansion. This study will provide a basis for cross-regional joint air pollution control and the management of heavily polluted areas in China.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Changes in Air Quality during the First-Level Response to the Covid-19 Pandemic in Shanghai Municipality, China

Ongoing rapid urban population growth world-wide has led to serious environmental problems that affect ecosystems and also lower the security and happiness of urban residents about their living environment. The most frequently reported negative impact is a deterioration in urban air quality. In this study, we performed a comprehensive assessment of the effects of the city lockdown policy in response to Covid-19 on air quality in Shanghai Municipality, China, and sought to identify a balance point between human activities and improving air quality. The first-level response (FLR) by Shanghai to control the spread of Covid-19 was to launch a lockdown, which remained in place from 24 January to 23 March, 2020. We compared airborne pollutant concentrations in different regions (downtown, suburbs) of Shanghai city in three periods (Pre-FLR, During-FLR, and Post-FLR) and in the corresponding periods in the previous year. The results showed that air quality improved significantly During-FLR compared with Pre-FLR, with the concentrations of PM2.5, PM10, SO2, NO2, and CO all decreasing significantly. The concentrations of all pollutants except O3 also decreased significantly compared with the same period in the previous year. There were also some differences in pollutant concentrations between the downtown region and the suburbs of Shanghai. However, we found that the concentrations of pollutants rebounded gradually when the restrictions on human activities ended after two months of lockdown. This study provides empirical evidence of the important effect of limiting human activities on air quality. For sustainable and clean future urban management in Shanghai and beyond, central government policy regulations requiring a low-carbon lifestyle and cleaner production in industries should be established.