We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Water environment response of urban water networks in the Pearl River Delta (China) under the influence of typhoon rain events
Summary
This study used artificial neural networks to model water quality parameters in the urban water network of China's Pearl River Delta region, examining how typhoon rain events affect pollutant concentrations. The research contributes to understanding how extreme weather events — which are increasing with climate change — flush pollutants including microplastics from urban environments into waterways.
Abstract Identifying water quality parameter concentrations and their drivers is important for the prevention and control of water environment pollution. In this study, we constructed an inverse model of water quality parameters based on measured water quality parameters and remote sensing spectral data for this study area using artificial neural networks. We investigated the water environment response of the urban water network in the Pearl River Delta under the influence of typhoon rain events and explored their spatial heterogeneity using a multiscale geographically weighted regression model. The results indicate that in regions with a high level of urbanization, the dissolved oxygen (DO) concentration in river water is lower, and the ammonia nitrogen (NH3-N) concentration is higher. Under the influence of typhoon rain events of varying intensities, the response of water quality parameters in the urban water network of Zhongshan City varies. The intensity of rainfall determines the impact of typhoon rain events on water quality parameter concentrations. Our results are expected to improve the understanding of water quality trends under the influence of typhoon rain events and help policymakers and planners better develop water environment control strategies during typhoon transit.
Sign in to start a discussion.
More Papers Like This
Microplastics discharged from urban drainage system: Prominent contribution of sewer overflow pollution
Researchers evaluated the abundance and distribution of microplastics in urban drainage systems in coastal Chinese cities, with a focus on sewer overflow events during storms. The study found that overflow pollution during wet weather is a prominent contributor to microplastic discharge into urban water bodies, with meteorological conditions and land use patterns significantly influencing microplastic transport and release.
Microplastic Variations in Land-Based Sources of Coastal Water Affected by Tropical Typhoon Events in Zhanjiang Bay, China
Researchers analyzed the abundance, composition, diversity, and flux of microplastics from three estuaries and one sewage outlet in Zhanjiang Bay, China, before and after tropical typhoon events, finding a 3.6-fold increase in microplastic abundance from land-based sources following typhoons. The study links increased stormwater discharge during typhoons to elevated microplastic loading in coastal waters, highlighting how climate-driven extreme weather amplifies plastic pollution.
The abundance and characteristics of microplastics in rainwater pipelines in Wuhan, China
Microplastic abundance and characteristics were analyzed in rainwater pipelines draining different land-use areas in Hongshan District, Wuhan, China. The study found microplastics throughout the rainwater drainage network, identifying urban stormwater runoff as a significant pathway for transporting microplastics from land to freshwater bodies.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.
Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.