Current status and future challenges of chlorobenzenes pollution in soil and groundwater (CBsPSG) in the twenty-first century: a bibliometric analysis.

The global industrial structure had undertaken significant changes since the twenty-first century, making a severe problem of chlorobenzene pollution in soil and groundwater (CBsPSG). CBsPSG receives increasing attention due to the high toxicity, persistence, and bioaccumulation of chlorobenzenes. To date, despite the gravity of this issue, no bibliometric analysis (BA) of CBsPSG does exist. This study fills up the gap by conducting a BA of 395 articles related to CBsPSG from the Web of Science Core Collection database using CiteSpace. Based on a comprehensive analysis of various aspects, including time-related, related disciplines, keywords, journal contribution, author productivity, and institute and country distribution, the status, development, and hotspots of research in the field were shown visually and statistically. Moreover, this study has also delved into the environmental behavior and remediation techniques of CBsPSG. In addition, four challenges (unequal research development, insufficient cooperation, deeply mechanism research, and developing new technologies) have been identified, and corresponding suggestions have been proposed for the future development of research in the field. Afterwards, the limitations of BA were discussed. This work provides a powerful insight into CBsPSG, enabling to quickly identify the hotspot and direction of future studies by relevant researchers.

Designing dynamic groundwater management strategies through a composite groundwater vulnerability model: Integrating human-related parameters into the DRASTIC model using LightGBM regression and SHAP analysis.

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of -0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from -0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas.

Assessment of groundwater vulnerability by applying the improved DRASTIC model: a case in Guyuan City, Ningxia, China.

Groundwater is the main source of production and living in most arid and semi-arid areas, and it plays an increasingly critical role in achieving local urban development. There is a serious issue regarding the contradiction between urban development and groundwater protection. In this study, we used three different models to assess the groundwater vulnerability of Guyuan City, including DRASTIC model, analytical hierarchy process-DRASTIC model (AHP-DRASTIC) and variable weight theory-DRASTIC model (VW-DRASTIC). The groundwater vulnerability index (GVI) of the study area was calculated in ArcGIS. Based on the magnitude of GVI, the groundwater vulnerability was classified into five classes: very high, high, medium, low, and very low using the natural breakpoint method, and the groundwater vulnerability map (GVM) of the study area was drawn. In order to validate the accuracy of groundwater vulnerability, the Spearman correlation coefficient was used, and the results showed that the VW-DRASTIC model performed best among the three models (ρ=0.83). The improved VW-DRASTIC model shows that the variable weight model effectively improves the accuracy of the DRASTIC model, which is more suitable for the study area. Finally, based on the results of GVM combined with the distribution of F- and urban development planning, suggestions were proposed for further sustainable groundwater management. This study provides a scientific basis for groundwater management in Guyuan City, which can be an example for similar areas, particularly in arid and semi-arid areas.

Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China.

China has been subject to rapid urban expansion and afforestation since the economic reform in 1978. However, the influence of land use and cover changes (LUCCs) and human activities on landslide occurrence is often ignored in landslide susceptibility mapping and zonation (LSMZ). In this study, Enshi City, China, was selected as the study area because of dramatic LUCCs during the last two decades. This study divided landslide affecting factors (AFs) into base affecting factors (BAFs) and land-related affecting factors (LAFs), and 15 landslide susceptibility maps were created by three different types of models. The results showed that the combination 6 of heuristic multi-layer perceptron model with LAFs (HMLP-LAFC6) model obtained the highest model performance. In addition, any factor combinations of HMLP-LAF model outperformed other two types of models, and the use of land use and cover (LULC) in different periods as well as LUCCs may significantly impact the model performance. Given that land policy adjustments are normally core drivers of LUCC in China, a land planning based LSMZ framework was proposed, which is suitable for LSMZ in rapid LUCC regions with radical land policies. Finally, this paper strongly suggests developing more hybrid models that coupling dynamic AFs, clarifying the quantitative boundaries of time-irrelevant and dynamic AFs, increasing the accuracy of LULC prediction, and improving the abilities of bilateral understanding for effective, integrated, and systematic management of land planning and landslide hazards.

Combining the classic vulnerability index and affinity propagation clustering algorithm to assess the intrinsic aquifer vulnerability of coastal aquifers on an integrated scale.

In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultural pollutants. Groundwater management requires a comprehensive analysis of both horizontal and vertical pollution in coastal aquifers. In this paper, Intrinsic Aquifer Vulnerability (IAV) was assessed on an integrated scale using two classic IAV models (DRASTIC and GALDIT) separately based on a GIS database. Hydrogeological parameters from two classic IAV models were clustered using affinity propagation (AP) clustering algorithm, and silhouette coefficients were used to determine the optimal classification result. In our application, the objects of the AP algorithm are 3320 units divided from the whole study area with 500 m*500 m precision. A comparison of all four outputs in AP-DRASTIC shows that the clustering results of the 4-classification yielded the best silhouette coefficient of 0.406 out of all four. Cluster 4, which comprises 21% of the area, had relatively low level of groundwater contamination, despite its high level of vulnerability as indicated by the classic DRASTIC index. In the second level of vulnerability Cluster 3, 53.8% of all water samples were found to be contaminated, indicating a greater level of nitrate contamination. With respect to AP-GALDIT, the silhouette coefficient for result 7-classification reaches the highest value of 0.343. There was a high level of vulnerability identified in Clusters 2, 4 and 5 (34.7% of the study area) relating to the classic GALDIT index. The concentration of chloride in all water samples obtained in these areas was extremely high. Groundwater management should be addressed by AP-DRASTIC results on anthropogenic activity/contamination control, and by AP-GALDIT results on groundwater extraction limitation. Overall, this method allows for the evaluation of IAV in other coastal areas on an integrated scale, facilitating the development of groundwater management strategies based on a better understanding of the aquifer's essential characteristics.

Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China.

At present, landslide susceptibility assessment (LSA) based on the characteristics of landslides in different areas is an effective prevention measure for landslide management. In Enshi County, China, the landslides are mainly triggered by high-intensity rainfall, which causes a large number of casualties and economic losses every year. In order to effectively control the landslide occurrence in Enshi County and mitigate the damages caused by the landslide. In this study, eight indicators were selected as assessment indicators for LSA in Enshi County. The analytic hierarchy process (AHP) model, information value (IV) model and analytic hierarchy process-information value (AHP-IV) model were, respectively, applied to assess the landslide distribution of landslides in the rainy season (RS) and non-rainy season (NRS). Based on the three models, the study area was classified into five levels of landslide susceptibility, including very high susceptibility, high susceptibility, medium susceptibility, low susceptibility, and very low susceptibility. The receiver operating characteristic (ROC) curve was applied to verify the model accuracy. The results showed that the AHP-IV model (ROC = 0.7716) was more suitable in RS, and the IV model (ROC = 0.8237) was the most appropriate model in NRS. Finally, combined with the results of landslide susceptibility in RS and NRS, an integrated landslide susceptibility map was proposed, involving year-round high susceptibility, RS high susceptibility, NRS high susceptibility and year-round low susceptibility. The integrated landslide susceptibility results provide a more detailed division in terms of the different time periods in a year, which is beneficial for the government to efficiently allocate landslide management funds and propose effective landslide management strategies. Additionally, the focused arrangement of monitoring works in landslide-prone areas enable collect landslide information efficiently, which is helpful for the subsequent landslide preventive management.