Abstract

Environmental pollution is an important common health concern across the world with patterns of exposure being quite spatial and time varying. Traditional measures of pollution and epidemiological research tends to use sparse monitoring systems and single studies, which restrict their ability to resolve dynamic effects of exposure and health at large scale. To overcome these shortcomings, this research is one of the deep learning-based multimodal frameworks that incorporate remote sensing information and biomedical health records in the overall spatio-temporal analysis of environmental pollution and health risk prediction. Pollution indicators of aerosol optical depth and trace gas concentrations collected by satellites are used together with anonymized biomedical information (incidence of diseases and hospital admission history) with a deep learning architecture, which is an attention competition. The suggested model will use convolutional neural networks to learn the spatial pollution features, and temporal modeling networks to learn the dynamics of evolving exposure factors, the fusion mechanism is based on the attention, the cross-modal relationships between the environment and health data will be learned. Real-world experimental analyses on experiment datasets show the proposed multimodal approach is always better than the traditional single-modal, and statistical analysis, with a higher rate of pollution estimation and better rate of health risk prediction. The maps of spatial heat analyses also demonstrate that the model is successful in determining regions of high risks and the most exposing time periods. The findings prove that the combination of heterogeneous environmental and biomedical data with the help of advanced deep learning procedures can be used to make more sensible and valuable assessments of environmental health. The article identifies the possibility of multimodal artificial intelligence systems to assist in active environmental health monitoring, early signs of danger, and evidence-based approach to policymaking in the management of sustainable human public health.