How Physics-Based Machine Learning Unveils the Link Between Rising Heating Demand and Increased Air Pollution in Norwegian Cities
How Physics-Based Machine Learning Unveils the Link Between Rising Heating Demand and Increased Air Pollution in Norwegian Cities
by Cong Cao, LCSSP Postdoc
May 31, 2024
Introduction: Our latest paper, "Physics-based machine learning reveals rising heating demand heightens air pollution in Norwegian cities", just released on arXiv, reveals how growing heating demand affects air pollution levels in Norwegian cities. The research, led by Dr. Cong Cao, Dr. Ramit Debnath, and Flintridge Foundation Professor R. Michael Alvarez, this study employs an arsenal of analytical techniques, including Physics-based Machine Learning (PBML) and traditional regression models, to unravel the complexities of atmospheric pollution dynamics.
Dive deeper: We use regression modeling and machine learning methods such as K-means clustering, hierarchical clustering, and random forest techniques. We delved into a decade-long (2009-2018) dataset that includes daily traffic patterns, meteorological conditions, and air quality indicators in three prominent urban centers in Norway.
Key findings: Our results reveal a significant correlation between an increase in the number of hot days and rising levels of air pollution. This finding highlights the profound impact of increased heating activity on the atmospheric composition of Norwegian cities. It is worth noting that the application of physics-based machine learning outperforms traditional long short-term memory (LSTM) methods in accurately predicting air pollution trends.
Implications for policy and practice: Our study provides actionable insights for policymakers addressing the twin challenges of climate change and air quality management. By elucidating the link between heating demand and air pollution, we provide stakeholders with empirical evidence to inform data-driven policy interventions. With a deeper understanding of climate and air quality interactions, policymakers can develop more effective strategies aimed at mitigating environmental degradation and safeguarding public health.
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