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 PM2.5 concentration and its reductions in China from the years of 2014-2017. In terms of the impact on PM2.5 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 PM2.5 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 PM2.5 concentration levels. Multiple factors should thus be taken into account when any new control policies are going to be established.

China\'s energy use has increased significantly in recent years with the country\'s rapid economic growth and large-scale urbanization. Therefore, air pollution has become a major issue. In this study, we conducted spatial autocorrelation and spatial panel regression analyses of sulfur dioxide (SO2) and nitrogen oxide (NOX) emissions using the panel data of 31 provincial-level administrative units in China during the period 2011-2017 to comprehensively understand the factors affecting air pollutant emissions. This study contributes to the literature by considering comprehensive factors and spatial effects in the panel-data econometric framework of the whole country of China. The analysis of spatial characteristics shows that during the study period, pollutant emissions in China declined, although emissions in northern regions were still relatively high. Furthermore, SO2 and NOX emissions showed significant positive spatial autocorrelations. The results of a fixed-effect spatial lag model showed that both socioeconomic and natural factors were statistically significant for air pollutant emissions, although the degree differed by the type of pollutant. The population, the urbanization rate, the share of added value of secondary industry, and heating and cooling degree days positively affected emissions, while population density, per-capita gross regional product, precipitation, and relative humidity negatively affected emissions. Based on these results, we have put forward suggestions to address the issue of air pollution and achieve environmental sustainability, such as the promotion of regional cooperation and a transition of the economic structure.

The rapid development of urbanization and agriculture poses serious impacts on groundwater in arid and semi-arid areas, which typically have high groundwater depletion rates. In this study, chemical and isotopic analyses combined with different data interpretation methods (diagrams, bivariate analyses, principal component analysis (PCA), and hierarchical cluster analysis (HCA)) were used to identify the major factors controlling groundwater chemistry in an arid and semi-arid region of North China. Sixty-four groundwater samples (35 from unconfined aquifer, 29 from confined aquifer) were collected in Baotou City, North China, and 17 chemical variables were detected for each sample. The complex hydrochemical types in unconfined groundwater (e.g., HCO3-Ca·Mg, HCO3·Cl-Na·Mg, SO4-Na·Mg, and Cl·SO4-Na types) may be related to anthropogenic activities, while the main hydrochemical types in confined groundwater are HCO3-Ca·Mg, HCO3-Na·Mg, HCO3·Cl-Na·Ca, SO4·HCO3-Na·Mg, and Cl·SO4-Na types. Three component models for unconfined and confined groundwater were revealed using PCA, which explained approximately 79.69% and 80.68% of the data variance, respectively, providing a deeper insight into groundwater composition controlled by geochemistry and anthropogenic activities. Three clusters were yielded from HCA. The factors and identified clusters were verified with hydrochemical investigations. Among the natural factors, the main hydrochemical processes involve the dissolution of various minerals (halite, gypsum, feldspar, fluorite, mirabilite, biotite, dolomite, and calcite), cation exchange, evaporation, and mixing. The anthropogenic factors include domestic sewage intrusion and agricultural activities, which are most likely to lead to further declines in groundwater quality. These findings may be useful for improving groundwater resource management for sustainable development in arid and semi-arid areas.

Global change, which refers to large-scale changes in the earth system and human society, has been changing the outbreak and transmission mode of many infectious diseases. Climate change affects infectious diseases directly and indirectly. Meteorological factors including temperature, precipitation, humidity and radiation influence infectious disease by modulating pathogen, host and transmission pathways. Meteorological disasters such as droughts and floods directly impact the outbreak and transmission of infectious diseases. Climate change indirectly impacts infectious diseases by altering the ecological system, including its underlying surface and vegetation distribution. In addition, anthropogenic activities are a driving force for climate change and an indirect forcing of infectious disease transmission. International travel and rural-urban migration are a root cause of infectious disease transmission. Rapid urbanization along with poor infrastructure and high disease risk in the rural-urban fringe has been changing the pattern of disease outbreaks and mortality. Land use changes, such as agricultural expansion and deforestation, have already changed the transmission of infectious disease. Accelerated air, road and rail transportation development may not only increase the transmission speed of outbreaks, but also enlarge the scope of transmission area. In addition, more frequent trade and other economic activities will also increase the potential risks of disease outbreaks and facilitate the spread of infectious diseases.

China has experienced severe acid rain pollution during the past decades due to excessive sulfur oxides (SO2) and nitrous oxides (NOx) 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.

BACKGROUND: We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control.
METHODS: GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter.
RESULTS: (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011.
CONCLUSIONS: The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation.

An understanding of land-use change and its drivers in agroecosystems is important when developing adaptations to future environmental and socioeconomic pressures. Agricultural abandonment occurs worldwide with multiple potentially positive and negative consequences; however, the main factors causing agricultural abandonment in a country i.e., at the macro scale, have not been identified. We hypothesized that socio-environmental factors driving agricultural abandonment could be summarized comprehensively into two, namely \"natural\" and \"social\", and the relative importance of these differs among regions. To test this postulate, we analyzed the factors currently leading to agricultural abandonment considering ten natural environment variables (e.g., temperature) and five social variables (e.g., number of farmers) using the random forest machine learning method after dividing Japan into eight regions. Our results showed that agricultural abandonment was driven by various socio-environmental factors, and the main factors leading to agricultural abandonment differed among regions, especially in Hokkaido in northern Japan. Hokkaido has a relatively large area of concentrated farmland, and abandonment might have resulted from the effectiveness of cultivation under specific climate factors, whereas the other regions have relatively small areas of farmland with many elderly part-time farmers. In such regions, abandonment might have been caused by the decreasing numbers of potential farmers. Thus, two different drivers of agricultural abandonment were found: inefficient cultivation and decreasing numbers of farmers. Therefore, agricultural abandonment cannot be prevented by adopting a single method or policy. Agricultural abandonment is a significant problem not only for food production but also for several ecosystem services. Governments and decision-makers should develop effective strategies to prevent further abandonment to ensure sustainable future management of agro-ecosystems.