Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities

arXiv:2405.04716v1 Announce Type: cross
Abstract: 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 …

abstract air pollution air quality analyze arxiv change cities climate climate change clustering cs.ai cs.cy cs.lg cs.ne deep learning demand hierarchical influence interactions k-means physics pollution quality random regression study type