Data-Driven Atmospheric Modeling for Air Quality Assessment and Human Health Risk Analysis in Urban Environments

Rapid industrial growth and urbanisation have resulted in a drastic deterioration of air quality, posing a grave threat to human health as well as environmental sustainability. Despite considerable progress, existing atmospheric models often find it difficult to reflect the nonlinear relationships between meteorological parameters, pollutant dispersion and associated human health hazards in rapidly urbanising regions. Understanding the complex interplay between meteorological conditions and atmospheric pollution dispersion is a fundamental requirement for accurate air quality assessment and effective mitigation measures.
This work proposes a data-driven atmospheric modelling framework for the assessment of urban air quality and possible human health concerns based on meteorological observations, pollutant concentration measurements and environmental datasets. This novel framework blends computational and statistical tools to investigate temporal and spatial variations of important air pollutants including particulate matter (PM2.5 and PM10), nitrogen oxides (NOx), and ground-level ozone (O3).
The data-driven modeling approach is applied to establish the relations between meteorological variables (temperature, humidity, wind speed, wind direction) and the pollutant concentrations and to predict future air quality conditions. The modeling framework captures the nonlinear atmospheric behavior and provides insight into the pollutant transport and dispersion under various environmental conditions. The developed approach helps identify the periods and locations of high pollution levels and related health risks and, thus, contributes to environmental monitoring and decision support. The proposed framework can be applied in early warning systems, urban planning, and evidence-based environmental policy development for the improvement of public health and promotion of sustainable smart city management.