Optimum urban runoff pollution control based on dynamic load calculation and effective control units identification – a case study in a highly urbanized basin in China

Abstract

In the context of urban environmental challenges, urban runoff pollution, originating as a fundamental source of water pollution, remains a significant issue due to its widespread occurrence and sporadic release, which complicates precise monitoring and effective mitigation efforts. This study is dedicated to investigating prominent urban runoff pollution challenges in a highly urbanized basin located within the Guangdong-Hong Kong-Macao Greater Bay Area. Through a combination of field monitoring, statistical analyses, and modeling techniques, we present a comprehensive framework tailored to optimize control strategies for urban runoff pollution. The findings reveal variations in Event Mean Concentrations (EMCs) of ammonia nitrogen, ranging from 0.28 to 7.65 mg L-1, with distinct disparities observed across different rain events and land use categories. We delineated 431 drainage districts and organized them into 12 clusters based on similar discharge characteristics, which facilitates the establishment of a prioritized pollution control hierarchy. Subsequently, both the spatial and temporal load distributions were analyzed using EMC calculations, allowing us to determine a prioritized pollution control scheme that ranks units from level 1 to level 12. A 1-d mathematical model of target urbanized basin linking distributively discharged runoff pollution and dynamic river quality improvement has been established, and the dynamic simulation results ultimately demonstrate the importance sequence of managing runoff pollution discharges from 63 identified drainage districts, which accounts for 46.8% of the total urban runoff pollution load. This investigation provides a powerful research tool for urban runoff pollution control within extensive urban areas, offering valuable insights for the practical implementation of pollution control initiatives.