We are currently engaged in the management of big data generated by wastewater treatment plants, utilizing artificial intelligence for plant operations. Our efforts include employing deep learning to predict pipe failure and integrating it into an expert system for developing optimal water transmission main management strategies in multi-regional water supply facilities. We also developed a real-time aeration control system using machine learning, pattern recognition techniques, statistical tools, and empirical relations that allow 30~50% of the energy consumed for aeration. Another recent research focus involves phosphorus removal from urban and agricultural runoff using tire-derived aggregate (TDA). Our findings indicate that shredded tires can effectively remove toxic organic compounds and heavy metals. We have extended this concept to landfills by replacing leachate collection system media with shredded tires. Additionally, we applied a similar approach to golf courses to eliminate pesticides by incorporating a buffer zone filled with shredded tires. Our research encompasses a systematic approach to determining optimal operating conditions for maximizing biological phosphorus removal in high phosphorus-bearing industrial wastewater, such as dairy wastewater. Past studies have delved into the biological treatment of toxic compounds in the environment, waste treatment processes, understanding the biological phosphorus removal mechanism, developing new media for removing toxic compounds, river restoration, river water quality modeling, and the beneficial reuse of waste products.