Xinyi Huang

Soil contamination with arsenic (As) is becoming a serious concern for living organisms. Arsenic is a nonessential metalloid for plants, humans, and other living organisms. Biochar (BC) is a very effective amendment to remediate polluted soils and it received great attention owing to its appreciable results. Arsenic toxicity negatively affects plant morph-physiological and biochemical functioning and upsurges the generation of reactive oxygen species (ROS), which negatively affect cellular structures. Arsenic toxicity also reduces seed germination and impedes plant growth by decreasing nutrient uptake, causing oxidative damage and disrupting the photosynthetic efficiency. Plants use different strategies like antioxidant defense and increased osmolyte synthesis to counteract As toxicity; nevertheless, this is not enough to counter the toxic impacts of As. Thus, applying BC has shown tremendous potential to counteract the As toxicity. Biochar application to As-polluted soils improves water uptake, maintains membrane stability and nutrient homeostasis, and increases osmolyte synthesis, gene expression, and antioxidant activities, leading to better plant performance. Additionally, BC modulates soil pH, increases nutrient availability, causes As immobilization, decreases its uptake and accumulation in plant tissues, and ensures safer production. The present review describes the sources, toxic impacts of As, and ways to lower As in the environment to decrease its toxic impacts on humans, the ecosystem, and the food chain. It concentrates on different mechanisms mediated by BC to alleviate As toxicity and remediate As-polluted soils and different research gaps that must be fulfilled in the future. Therefore, the current review will help to develop innovative strategies to minimize As uptake and accumulation and remediate As-polluted soils to reduce their impacts on humans and the environment.

As the most intuitive manifestation of land use/land cover change, the spatio-temporal evolution of landscape patterns has significant implications for optimizing regional landscape pattern and land use management. Based on multi-period remote sensing data, we selected an optimal scale (570 m) and used the geographic detector model to analyze the spatio-temporal changes in the landscape pattern of a typical hilly area (Yujiang District, Yingtan City, Jiangxi Province) in southern China. The results showed that from 2009 to 2018, the area of urban land, other construction land, rural residential land, and cultivated land expanded by 33.27%, 21.23%, 19.42%, and 1.07%, respectively. In contrast, the area of grassland, forest land, and water area shrank by 18.18%, 5.41%, and 2.19%, respectively, over the past 10 years. At the landscape level, the patch shape became more complex over time, with increased landscape fragmentation and diversity. At the class level, cultivated land, forest land, and grassland tended to be fragmented, whereas rural residential land exhibited an aggregation tendency. Slope gradient, gross regional product, and distance from major highways had a strong ability to explain the spatial differences in landscape pattern change. The results of this study enable a dynamic understanding of landscape pattern evolution in typical hilly areas in southern China and provide a reference for landscape pattern optimization in similar geomorphic settings.

Spatial land use conflicts (SLUCs) are a critical issue worldwide due to the scarcity of land resources and diversified human demand. Despite many time-series studies of SLUCs, comprehensive research on SLUCs and their driving factors over a long period remain limited. This study was conducted in the Pearl River Delta urban agglomeration, Guangdong Province, China. We constructed a landscape ecological risk assessment model to calculate annual SLUC values and analyze their spatiotemporal distribution over 30 years. K-means clustering analysis was used to cluster SLUC values for 1990–2005 and 2006–2020, yielding comprehensive conflict intensity data for each period. The major factors driving the spatial differentiation of SLUCs and their interactions in each period were identified using an optimal parameter-based geographical detector model. The results show that SLUCs varied significantly over time, with an overall decreasing trend and distinct spatial heterogeneity. Comprehensive conflict intensity for each period was characterized by low values in the peripheral regions and high values in central parts of the study area, which tended to decrease from 1990–2005 to 2006–2020. SLUCs were heavily dependent on topographical (slope and elevation) and environmental (normalized difference vegetation index) factors. Socio-economic factors (gross domestic product and population density) were also major contributors to the spatial differentiation of SLUCs. The explanatory power of multiple interacting factors on SLUCs was enhanced compared with that of individual factors. The explanatory power of the driving factors varied, and their interactions decreased over time. The results may facilitate the rational government planning of regional land use and thus effectively mitigate SLUC intensity at the macro level.