A novel nonlinear direct-mapping approach for multiple time scale driving force analysis of surface water quality variations under intense human interference.

ABSTRACT

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.