Modeling and Monitoring of Air Quality: From Data to Predictions

Dear Colleagues,

Spatial modeling of air quality relies on diverse environmental and meteorological datasets to analyze and predict pollution levels across different regions. By integrating data from meteorological stations, remote sensing technologies, and sensor networks, these models evaluate the transport, transformation, and dispersion of pollutants such as nitrogen dioxide, sulfur dioxide, ozone, and particulate matter. Computational simulations provide insights into pollutant distribution and trends, facilitating early warning systems and policy interventions. Despite advancements in predictive modeling, challenges remain, including the need for more comprehensive data integration, the inclusion of emerging pollutants, and the expansion of models into underrepresented regions. Strengthening interdisciplinary approaches and leveraging artificial intelligence can further enhance the accuracy and applicability of air quality assessments, contributing to improved urban air quality management and public health outcomes.

Dr. Agnieszka Rorat
Guest Editor

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