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Dr. P. Sumalatha is an Assistant Professor in the Department of Basic Sciences and Humanities at Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India. She is involved in teaching and academic activities in basic sciences and humanities. Her contribution focuses on multimodal data fusion for air quality monitoring and pollution prediction.
Air pollution constitutes a significant environmental issue that impacts public health, ecological viability, urban growth, and overall quality of life. Traditional air quality monitoring systems frequently rely on stationary monitoring stations and singular pollutant assessments, localised pollution incidents, or the intricate interactions between meteorological factors, vehicular emissions, industrial operations, and atmospheric dynamics. Through the understanding of interconnections among various supplementary sources, multimodal AI can facilitate pollutant concentration assessment, short-term pollution prediction, hotspot detection, and proactive alerts. This chapter delineates an extensive framework for the monitoring of air quality across many modalities and the forecasting of pollution, encompassing data collection, and implementation. The section additionally presents mathematical equations for multimodal representation learning and pollutant prediction, as well as an experimental approach employing regression, and spatial assessment measures. The suggested framework establishes a basis for creating intelligent and data-centric air quality control systems.