  Policymakers frequently analyze air quality and climate change in isolation,
disregarding their interactions. This study explores the influence of specific
climate factors on air quality by contrasting a regression model with K-Means
Clustering, Hierarchical Clustering, and Random Forest techniques. We employ
Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine
the air pollution predictions. Our analysis utilizes ten years (2009-2018) of
daily traffic, weather, and air pollution data from three major cities in
Norway. Findings from feature selection reveal a correlation between rising
heating degree days and heightened air pollution levels, suggesting increased
heating activities in Norway are a contributing factor to worsening air
quality. PBDL demonstrates superior accuracy in air pollution predictions
compared to LSTM. This paper contributes to the growing literature on PBDL
methods for more accurate air pollution predictions using environmental
variables, aiding policymakers in formulating effective data-driven climate
policies.
