Excessive levels of Fe, Mn and As are the main factors affecting groundwater quality in Songliao plain, northeast China. However, there are few studies on the source and mobilization mechanisms of Fe, Mn and As in the groundwater of Northeastern China. This study takes Shuangliao city in the middle of Songliao plain as an example, where the source and mobilization mechanisms of iron, manganese and arsenic in groundwater in the study area were analyzed by statistical methods and spatial analysis. The results show that the source of Fe and Mn in the groundwater of the platform is the iron and manganese nodules in the clay layer, while, in the river valley plain, it originates from the soil and the whole aquifer. The TDS, fluctuation in groundwater levels and the residence time are the important factors affecting the content of Fe and Mn in groundwater. The dissolution of iron and manganese minerals causes arsenic adsorbed on them to be released into groundwater. This study provides a basis for the rational utilization of groundwater and protection of people’s health in areas with high iron, manganese and arsenic contents.

A landslide is a typical geomorphological phenomenon associated with the regular cycles of erosion in tropical climates occurring in hilly and mountainous terrain. Awgu, Southeast Nigeria, has suffered a severe landslide disaster, and no one has studied the landslide susceptibility in the study area using an advanced model. This study evaluated and compared the application of three machine learning algorithms, namely, extreme gradient boosting (Xgboost), Random Forest (RF), and Naïve Bayes (NB), for a landslide susceptibility assessment in Awgu, Southeast Nigeria. A hazard assessment was conducted through a field investigation, remote sensing, and a consultation of past literature reviews, and 56 previous landslide locations were prepared from various data sources. A total of 10 conditioning factors were extracted from various databases and converted into a raster. Before modeling the landslide susceptibility, the information gain ratio (IGR) was used to select and quantitatively describe the predictive ability of the conditioning factors. The Pearson correlation coefficient was used to judge the correlation between 10 conditioning factors. In this study, rainfall is the most significant factor with respect to landslide distribution and occurrence. The confusion matrix, the area under the receiver operating characteristic curve (AUROC), was used to validate and compare the models. According to the AUROC results, the prediction accuracy for the RF, NB, and XGBOOST models are 0.918, 0.916, and 0.902, respectively. This current study can support the landslide susceptibility assessment of Awgu, Southeast Nigeria, and can provide a reference for other areas with the same conditions.

Increasing trends of groundwater and soil salinization, as well as desertification, is characteristic of many arid and semi-arid regions under climatic and anthropogenic influences. This has led to the implementation of management strategies to protect the ecological environment. Changling County in Northeast China is a typical semi-arid area that has experienced these changes. Thus, management strategies such as the “Three North Shelterbelt Project” which involves planting trees to reduce wind speed and halt desertification, and the Changling local alkaline land restoration project, from the year 2000, involving fencing of grasslands have been implemented in the area. Premised on the dynamic nature of the ecological environmental problems, this study was undertaken to assess the spatio-temporal evolution of the ecological environment using hydro-geochemical, spatial, remote sensing, and statistical techniques from the year 2001 to 2019. It was found that groundwater salinity was stable within the period due to groundwater exploitation that declined depth to groundwater table (DWT) thus reducing the impact of evaporation concentration of salts in groundwater. Salinized land area increased by about 6706 ha at a rate of 0.06%/year as a result of the reduction in the size of water bodies and swampland as the declining water table exposed shallow water to more evaporation. The effect of the conversion of water bodies and swamplands to salinized land is believed to overshadow the climatic influence of decreased evaporation-precipitation ratio that normally decreases soil salinization. Most of the study area was stable in terms of desertification (98.22%, 56,3497 ha) as significantly degraded lands covered only 0.03% (148 ha) of the area while 1.67% (9556 ha) had significantly increased vegetation, respectively. Precipitation had an insignificant relationship with desertification with irrigation believed to be the main driver of significant vegetation improvement. Water-saving irrigation practices and the growing of salt-tolerant or semi-tolerant crop species are recommended to maximize food production while stemming the environmental degradation trend due to declining DWT.

Monitoring landscape ecological pressure on river eutrophication requires spatially explicit, temporally comparable indicators derived from remote sensing data. This study develops the Net Ecological Pressure Index (NEPI _ws) to quantify watershed-scale ecological pressure on eutrophication based on the balance between agricultural and forest domination and ranged from −1 to 1,where positive values reveal domination of agriculture ecological pressure over forests ecological buffering, while negative values reveal the opposite domination pattern. Using 30 m land-use/land-cover data for 2010 and 2024, the spatio-temporal dynamics of NEPI _ws was analyzed for 25 1^st-order trib-utaries in the Three Gorges Reservoir Region, including 13 on the northern bank and 12 on the southern bank across reservoir sensitive, sub-sensitive, and non-sensitive zones to account for interaction with backwater effects. Results showed a clear spatial pat-tern, with NEPI ws increasing on both banks as backwater sensitivity decreased farther from the dam. Cumulative NEPI _bank of both banks revealed higher value on the north-ern bank in the non-sensitive and sub-sensitive zones, while showed higher value on the southern bank in the sensitive zone. From 2010 to 2024, the cumulative NEPI bank exhibited contrasting temporal trends. In the sensitive zone, cumulative NEPI _bank in-creased by 0.0896 on the southern bank, 2.05 times the increase on the northern bank 0.0436. In the sub-sensitive zone, NEPI _bank declined more on the northern bank (−0.105) than on the southern bank (−0.0401). In the non-sensitive zone, NEPI _bank de-creased on the northern bank (−0.0641) but increased on the southern bank (0.0242). Overall, NEPI effectively captured spatio‑temporal variations in watershed‑scale eco-logical pressure on tributary eutrophication using Earth observation data. Its stand-ardized and transferable formulation enables comparative assessment, eco‑functional zoning and spatial prioritization of eutrophication management in large reservoir sys-tems, demonstrating the value of remote sensing data for integrated water resources management in mixed agricultural–forest landscapes.