Based on the driving force-pressure-state-impact-response (DPSIR) model, 19 indicators were selected to construct a comparison between Zhuhai and Macao, two adjacent cities at the estuary of the Pearl River in China, which have different development models and water resource sustainable development strategies. Factors that may affect the sustainability of water resources were screened and placed according to the relationships of the five subsystems in the DPSIR model, establishing a sustainable evaluation model for water resources in the two cities. The results analyzed by Principal Component Analysis and Entropy methods showed that (1) Zhuhai City was greatly affected by the driving force, while Macao was greatly affected by the state system from 2012 to 2021. (2) From the trend changes, it can be seen that, in recent years, the water resources of the two cities have been moving towards sustainable development, and the management and protection of water resources have achieved remarkable results. From the evaluation results, it can be seen that implementing urban water-saving activities, strengthening the proportion of environmental water conservancy, public measures in public investment, upgrading sewage treatment machinery to improve sewage treatment rates, and other measures can effectively improve the current situation of water resources in both regions. In the future, the Zhuhai and Macao cities may continue to face a series of water resource pressures brought on by socio-economic developments. Therefore, an active adjustment of the development of the measurement of controlling wastewater discharge and saving water resources was proposed, adhering to the direction of sustainable development, and ensuring the benign development of socio-economic conditions and the ecological environment. This study can provide data to support regional water resource security and policy formulation with different political systems.

Anthropogenic activities have had a great impact on the characteristics of watershed pollution. The Pearl River basin is the third largest river in China, but it has been affected by eutrophication impact for a long time. This study comprehensively evaluated the nutrient pollution characteristics of the Pearl River basin from 2016 to 2018. The result shows that the ratio of total nitrogen (TN) and total phosphorus (TP), TN/TP was 24.7 of the Pearl River basin, which indicated that phosphorus was the restrictive factor for eutrophication problems. The limitation of TP maybe caused that TN is difficult to decrease through denitrification and nitrification. The fluxes of TN and TP remained stable in recent 20 years, and the flux of TN and TP transferred from the Pearl River basin to the ocean was 6.86×105 and 2.84×104 t in 2017, respectively. The Pearl River estuary had the largest discharge, accounting for more than 40% of the Pearl River basin. The TN in the West River and North River had a high pollution load, while TP pollution in the East River was very serious. It is necessary to establish an effective mechanism to control nitrogen and phosphorus. A large amount of nitrogen and phosphorus caused the decrease of dissolved oxygen (DO) and the increase of chemical oxygen demand (COD), which aggravated eutrophication. This study of nutrient elements fluxes and distribution in the Pearl River Basin are one of the important prerequisites for clarifying the causes of eutrophication, providing data and theoretical support for further water pollution control and water environmental protection in the future, and also providing a basis for pollution control decisions.

Non-point source pollution (NSP) and runoff intensities and distribution are primarily affected by landscape structure and composition. Multiple causalities hinder our ability to determine significant variables that influence NSP. Therefore, we developed an approach that integrates the Soil and Water Assessment Tool (SWAT), random forest regression model, redundancy analysis, and correlation coefficient to assess the role of landscape structure on runoff and NSP in the Dongsheng basin. We used R to calculate landscape metrics and the SWAT to simulate NSP loads from 1990 to 2019. redundancy analysis (RDA), random forest, and Pearson correlation were used to analyze the relationships among landscape metrics and NSP variables. The largest patch index (LPI) shows a significant negative correlation with NSP, with an R2 of −0.58 for TP and TN and −0.62 for sediment load. The findings indicate that landscapes with larger patch sizes, a high number of patches, and aggregation of patches largely influence pollution distribution. Overall, the results suggest that the role of landscape patterns on NSP outweighs that of runoff. Moreover, the findings infer that the aggregation and connectivity of forest patches contribute to the decline in NSP load and vice versa for cropland cover. Thus, for sustainable watershed management, it is crucial to encourage unfragmented landscapes, especially pollutant-intercepting landcovers such as forests.

Coastal industrial parks are areas where chemical production activities are concentrated and play an extremely important role in economic development. The properties of chemical raw materials and products determine that chemical industrial parks are more dangerous than other types, and preventing groundwater pollution in chemical industrial parks has become an important task in environmental protection work. This study used self-organizing maps, entropy weight water quality index method, and K-means clustering method to analyze groundwater chemical data during the wet and dry seasons. Selecting 16 hydrochemical indicators, a study was conducted on 36 sampling points each during the wet and dry seasons. Based on SOM and K-means, the groundwater components were divided into four clusters, and the chemical characteristics of the four clusters during different periods were analyzed. The water quality difference between the wet and dry seasons was quantified through water quality evaluation. The results showed that the groundwater quality in the study area was sensitive to seasonal changes, and overall, the water quality was worse during the dry season. Comparing the characteristics of the four clusters, it was found that Cluster IV was the most severely invaded by seawater and had the poorest water quality. The spatial concentration in the lower right corner of the research area further proves that the SOM clustering analysis method can efficiently classify and achieve the effect of small intra-cluster differences and significant inter cluster differences, providing a scientific basis for the zoning management and protection of groundwater resources.