太子河干流总氮与氨氮水质参数反演及时空变化研究OA北大核心CHSSCDCSTPCD

The Taizi River is one of the most important water systems in Liaoning Province.With the increasing attention to the water environment,the water environment quality of Taizi River Basin is improving year by year,and its research di-rection is gradually shifting from"governance"to"monitoring and governance".It has become a more urgent task to im-prove the timeline of water quality parameter monitoring of Taizi River.As such,the objective of this study is to implement a real-time monitoring system that can precisely track the levels of total nitrogen(TN)and ammonia nitrogen(NH4+-N)in the Taizi River Basin.This study evaluated the relationship between different Landsat 8 remote sensing image bands and water quality data from 10 monitoring sites over a five-year period(2014-2019)by utilizing the analysis of linear correla-tion.As a result,a highly optimized BP neural network model was established.Through this model,the spatio-temporal distribution of TN and NH4+-N in the main stream of Taizi River from 2014 to 2019 was successfully inverted,and the pre-diction efficiency of the model was significantly high.The BP neural network model demonstrated a superior accuracy,with a coefficient of determination(R2)of 0.777 and 0.550,respectively,and a root mean square error(RMSE)of 1.464 and 0.667 mg·L-1 for TN and NH4+-N,respectively.The findings of this study indicate that the TN and NH4+-N water quality parameters in the Taizi River Basin exhibited an overall positive trend from 2014 to 2019.Specifically,the overall NH4+-N concentration was in Class Ⅲ of the Environmental Quality Standards for Surface Waters,whereas TN concentration re-mained consistently at Class V water standards throughout the year.Analysis of the spatial distribution of TN and NH4+-N highlighted significant variability across the region.Elevated water quality was observed in the upper reaches,followed by a reduction in quality in the mid-sections,and severely degraded water quality in the lower reaches,ranging from Xiaolinzi to Tangmazhai.Ultimately,the results demonstrate the effectiveness and efficiency of this approach in achieving compelling research outcomes.These findings provide valuable insights for future applications of the BP neural network optimization model in water quality research and management.