Recently, our team led by Professor Zhang Zhen has made new progress in the rapid identification and prediction of arsenic forms. This study focuses on emergency response monitoring of arsenic pollution and develops a novel fluorescence sensor array based on machine learning algorithms for rapid identification and prediction of multiple arsenic forms (AsIII, AsV, MMAV, and DMAV). This study provides a reliable, fast, and intelligent arsenic speciation analysis platform, which provides a powerful tool for water quality assessment and emergency response. The related achievements were published in Environmental Science&Technology under the title "Enabling Emergency Response to Arsenic Contamination: Simulation and Rapid Identification of Arsenic Speciation by Machine Learning Driven Fluorescent Sensor Array". Jiangsu University is the first completion unit, our young teacher Wei Dali is the first author, and Professor Zhang Zhen is the corresponding author.