As one of the most notorious invasive species, the red imported fire ant (Solenopsis invicta Buren) has many adverse impacts on biodiversity, environment, agriculture, and human health. Mapping the potential global distribution of S. invicta becomes increasingly important for the prevention and control of its invasion. By combining the most comprehensive occurrence records with an advanced machine learning method and a variety of geographical, climatic, and human factors, we have produced the potential global distribution maps of S. invicta at a spatial resolution of 5 × 5 km2. The results revealed that the potential distribution areas of S. invicta were primarily concentrated in southeastern North America, large parts of South America, East and Southeast Asia, and Central Africa. The deforested areas in Central Africa and the Indo-China Peninsula were particularly at risk from S. invicta invasion. In addition, this study found that human factors such as nighttime light and urban accessibility made considerable contributions to the boosted regression tree (BRT) model. The results provided valuable insights into the formulation of quarantine policies and prevention measures.

In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks.

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Bioenergy is expected to play a key role in achieving a future sustainable energy system. Sweet sorghum-based fuel ethanol, one of the most promising bioenergy sources in China, has been receiving considerable attention. However, the conflict between sweet sorghum development and traditional water use has not been fully considered. The article presents an integrated method for evaluating water stress from sweet sorghum-based fuel ethanol in China. The region for developing sweet sorghum was identified from the perspective of sustainable development of water resources. First, the spatial distribution of the water demand of sweet sorghum-based fuel ethanol was generated with a Decision Support System for Agrotechnology Transfer (DSSAT) model coupled with Geo-Information System (GIS). Subsequently, the surplus of water resources at the provincial scale and precipitation at the pixel scale were considered during the growth period of sweet sorghum, and the potential conflicts between the supply and demand of water resources were analyzed at regional scale monthly. Finally, the development level of sweet sorghum-based fuel ethanol was determined. The results showed that if the pressure of water consumption of sweet sorghum on regional water resources was taken into account, about 23% of the original marginal land was not suitable for development of sweet sorghum-based fuel ethanol, mainly distributed in Beijing, Hebei, Ningxia, Shandong, Shanxi, Shaanxi, and Tianjin. In future energy planning, the water demand of energy plants must be fully considered to ensure its sustainable development.

Understanding the effects of hydrological processes on solute dynamics is critical to interpret biogeochemical processes. Water chemistry and isotopic compositions of surface water (δ 18 O w and δD w) were investigated in rivers from Southwest China to study the effects of hydrological variability on biogeochemical processes. The inverse relationship between deuterium excess (d-excess) and δ 18 O w could be ascribed to nonequilibrium fractionation processes, and the slope of the Local River Water Line was much lower than the Local Meteoric Water Line, suggesting the post-precipitation evaporation pattern. The evaporation fraction (1-f) was estimated by the d-excess method, varying from 0.01 to 0.18. (1-f), was a function of water temperature and drainage mean elevation, indicating that evaporation easily occurs at high temperatures in low-elevation regions. The hydrological processes co-varied with solute dynamics in the river network, and fluid transit time and temperature were likely responsible for the co-variations. Also, we found that hydrological processes played an important role in solute dynamics through shifting the geochemical processes (e.g., enrichment, water-rock reaction, photosynthesis, and secondary mineral precipitation). This study highlights that biogeochemical processes co-vary with hydrological processes, and we suggest that investigating hydrological processes can help to understand biogeochemical processes.

Bioenergy from cassava is a promising alternative energy source for both energy supply and the mitigation of greenhouse gases. However, major global trends, such as climate change and competing landuse patterns, pose substantial risks to the sustainable development of bioenergy. The main purpose of this study was to assess the sustainable development of bioenergy from cassava, considering landuse change and climate change with a biogeochemical process model within the "water-energy-food" nexus framework. The results showed that the land resources that were suitable for the development of cassava bioenergy have continuously decreased in China since 1990. At the same time, the climate has also undergone significant changes, with temperature showing an increasing trend, and precipitation showing a decreasing trend. With the influences of both landuse change and climate change, the total bioenergy of cassava showed a downward trend. In China, the potential bioenergy production for the year 1990, 2000, and 2010 was 6075 PJ, 5974 PJ, and 4399 PJ, respectively. Compared to 1990, the bioenergy production in 2010 decreased by 1676.40 million GJ, which equals 57 million tons of standard coal. In addition, the water footprint of bioenergy from cassava was discussed. After considering changes to landuse, climate, and water footprint, it was concluded that Guangxi was the most suitable place to develop cassava bioenergy, followed by Fujian, Guangdong, and Yunnan.

Scrub typhus is a climate-sensitive and life-threatening vector-borne disease that poses a growing public health threat. Although the climate-epidemic associations of many vector-borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming.Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2-month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change.