Novel method for industrial sewage outfall detection: Water pollution monitoring based on web crawler and remote sensing interpretation techniques

Abstract

Sewage outfalls are the ideal locations for water pollution monitoring as they are the final gate that pollutants must pass before entering rivers and oceans. Strengthening the supervision and control of industrial sewage outfalls has become the most important task in water pollution control. Most rivers in China are highly polluted, especially those flowing through cities. It is difficult to investigate, monitor, trace, and remediate sewage outfalls because information regarding the distribution of sewage outfalls into rivers is incomplete and the existing methods of monitoring sewage outfalls into rivers are outdated. This paper proposes an innovative method for feasible and efficient acquisition of sewage outfall data through a combination of web crawler and remote sensing interpretation technologies, employing data from the Luan River Basin as a testing site. We crawled industrial information and spatial location data on the Internet and obtained the locations of industrial sewage outfalls by interpreting remote sensing images. We modeled the distribution of sewage inflow from major industries into the tributaries of the Luan River Basin. We verified the accuracy and reliability of the developed method by comparing its results with actual data collected during our field investigation. Sample field tests confirmed the high accuracy (89%) of the proposed method, indicating its successful application in the Luan River Basin. We believe that the proposed method can be used to verify and supervise sewage outfalls in other locations in the future, especially owing to its improved acquisition system, which ensures feasible and efficient measurements without the need for manual investigation. This study provides a scientific basis for decision-making with regard to sustainable development.