Real time AI-monitoring systems for monitoring and forecasting of air pollution in Russia

Title : Real time AI-monitoring systems for monitoring and forecasting of air pollution in Russia

Abstract:

The challenges of balancing industrial growth with environmental safety measures have never been more critical than nowadays. Traditional monitoring methods, unfortunately, often did not support us with enough data to prevent or predict ecological crises. We are able only to eliminate the consequences. The report examines new Russian real-time monitoring system, which includes Iot-sensors, mathematic prognostic model. Small sensors detect a wide range of chemicals, for example, NH3, CO, NO2, CH4, SO2, O3, PM2.5, and PM10. IT-system collects data provide real-time information about pollutants concentration. We use the FLEXPART Lagrangian particle dispersion model, powered by high-resolution WRF (Weather Research and Forecasting) meteorological data. This synergy enables highly accurate environmental forecasting with a lead-time of up to 48 hours. The user is able to choose the technological capacity and predict future impact to air, or reduce air pollution before the issues arise. IT-system enables environmental control agencies to identify potential pollution sources. This system facilitates the investigation of resident complaints and helps to mitigate social tension by providing transparent, evidence-based information on air quality and its origins. These advancements in monitoring allow for dynamic pollution control, yielding substantial improvements in ambient air quality within industrial zones in Russia: Nizhnij Novgorod, arctic zone.

Biography:

Daria Klishina holds a Master’s level degree in Environmental Impact Assessment from Kaliningrad State Technical University. Ms. Klishina serves as a Lead Expert in the development of advanced IT systems for real-time environmental monitoring and ecological impact prediction. Her technical contributions to the field are evidenced by her status as a co-author on two patents related to IT systems for ecological monitoring. Her primary research focus lies at the intersection of technology and corporate responsibility. Specifically, she investigates how transparent data platforms can be leveraged within ESG frameworks to streamline ESG reporting and foster trust between industrial entities and their stakeholders.