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Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as the vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using the Theil–Sen median trend analysis and Mann–Kendall test during 2000–2019. The influence of natural and anthropogenic factors on the driving mechanism of the spatial pattern of NDVI was explored by the Multiscale Weighted Regression (MGWR) model. Additionally, we employed the Boosted Regression Tree (BRT) model to explore their contribution to NDVI. Then, the MGWR model was utilized to predict future greenness patterns based on precipitation and temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for the period 2021–2100. The results showed that: (1) the NDVI of Hainan Island forests significantly increased from 2000 to 2019, with an average increase rate of 0.0026/year. (2) the R2 of the MGWR model was 0.93, which is more effective than the OLS model (R2 = 0.42) in explaining the spatial relationship. The spatial regression coefficients of the NDVI with temperature ranged from −10.05 to 0.8 (p < 0.05). Similarly, the coefficients of Gross Domestic Product (GDP) with the NDVI varied between −5.98 and 3.28 (p < 0.05); (3) The natural factors played the most dominant role in influencing vegetation activities as a result of the relative contributions of 83.2% of forest NDVI changes (16.8% contributed by anthropogenic activities). (4) under SSP119, SSP245, and SSP585 from 2021 to 2100, the NDVI is projected to have an overall decreasing pattern under all scenarios. This study reveals the trend of greenness change and the spatial relationship with natural and anthropogenic factors, which can guide the medium and long-term dynamic monitoring and evaluation of tropical forests on Hainan Island. [ABSTRACT FROM AUTHOR]

The intensification of anthropogenic activities (agriculture, industry, and exploitation of water resources) during urbanization has posed significant challenges to the aquatic environment, particularly in karst regions. Karst aquifers are highly susceptible to surface contaminants and exhibit minimal natural remediation capabilities. Our understanding of the anthropogenic activities involved in these sensitive karst systems remains limited. To address this gap, we conducted a comprehensive study, collecting 285 groundwater samples in Feicheng, northern China, from 1996 to 2015. The overexploitation of karst groundwater has resulted in several concerns. The whole dataset was classified into four groups according to land use. Water quality assessments revealed a yearly decline, particularly in industrial and agricultural areas. The water chemistry transitioned from Ca-Mg-HCO3 to Ca-Mg-HCO3-SO4. Such evolution was attributed to natural hydrogeochemical processes, atmospheric precipitation, and anthropogenic inputs. Natural factors included water-rock interactions (the mineral dissolution) and ion exchange. Absolute principal component scores with multiple linear regression (APCS-MLR) were used to quantitatively estimate the sources of pollution. The results showed that hydrogeological settings (recharge, runoff, and discharge) were crucial in the hydrochemistry evolution of karst groundwater systems. In agricultural areas, it is recognized that much of the NO3− accumulation in aquifers came from upstream inputs in the groundwater system, not just irrigation and fertilization. Urban areas were affected by Cl− pollution, primarily due to domestic waste. Industrial regions of recharge zones were more susceptible to atmospheric precipitation and industrial waste, with pollutants infiltrating through rainfall and degrading water quality. Mining areas exhibit higher SO42− and lower pH due to the oxidation of sulfur-containing minerals. Therefore, the rapid response and low self-purification capacity of groundwater in karst regions necessitate caution in urban planning to mitigate impacts on these fragile systems. [ABSTRACT FROM AUTHOR]

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables. [ABSTRACT FROM AUTHOR]

Transitional waters (TWs) are relevant ecological and economical ecosystems that include estuaries, deltas, bays, wetlands, marshes, coastal lakes, and coastal lagoons and play a central role in providing food, protecting coastal environments, and regulating nutrients. However, human activities such as industrialization, urbanization, tourism, and agriculture are threatening these ecosystems, which results in contamination and habitat degradation. Therefore, it is essential to evaluate contamination in TW to develop effective management and protection strategies. This study analyses the application of geospatial technologies (GTS) for monitoring and predicting contaminant distribution in TW. Cartography, interpolation, complex spatial methods, and remote sensing were applied to assess contamination profiles by heavy metals, and persistent organic compounds, and analyze contamination indices or some physicochemical water parameters. It is concluded that integrating environmental and demographic data with GTS would help to identify critical points of contamination and promote ecosystem resilience to ensure long-term health and human well-being. This review comprehensively analyzes the methods, indicators, and indices used to assess contamination in transitional waters in conjunction with GTS. It offers a valuable foundation for planning future research on pollution in these types of waters or other similar water bodies worldwide. [ABSTRACT FROM AUTHOR]

In recent decades, China has experienced substantial climate change and significant vegetation greenness due to the extensive implementation of artificial ecological restoration programs. However, the quantitative contributions of climatic and anthropogenic drivers to the national variations in associated evapotranspiration (ET) and gross primary productivity (GPP) over China at different climate zoning sub-regions remain unclear. Based on the analysis of climate factor and vegetation disturbance trends created by anthropogenic activities, this study constructed a remote sensing-based ecological model consisting of Penman–Monteith–Leuning (PML) and light use efficiency (LUE) components. The proposed model simulated the spatiotemporal changes in ET and GPP between 1999 and 2018 over China. The contributions of climatic factors and anthropogenic activities to ET and GPP variations were quantitatively calculated by ridge regression. The results show that (1) both interannual ET and GPP markedly increased, by 1.32 mm yr−1 and 8.01 g C m−2 yr−1, respectively; (2) vegetation changes due to anthropogenic disturbance made the dominant contribution to GPP variations over China, while the dominant factor influencing ET changes differed by sub-region due to the joint effects of vegetation and climate; (3) temperature and precipitation positively affected ET, while wind speed, humidity, and solar radiation negatively contributed to ET in most parts of Mainland China. These findings may provide a workable, scientific reference for further ecological restoration decision-making processes in China. [ABSTRACT FROM AUTHOR]