Revealing Insights into Air Pollution: Latest Research
Revealing Insights into Air Pollution: Latest Research
by Cong Cao, LCSSP Postdoc
May 31, 2024
Are you curious about the latest developments in the field of environmental economics? If so, you might be interested in my latest research, "How to Better Predict the Effect of Urban Traffic and Weather on Air Pollution? Norwegian Evidence from Machine Learning Approaches" which delves into the complex relationship between traffic volume, air pollution, and meteorological conditions. This paper was published in a leading journal of behavioral economics, the "Journal of Economic Behavior & Organization".
In this paper, I employ state-of-the-art machine learning methodologies to unravel the mysteries surrounding air pollution dynamics. My study, set against the backdrop of Oslo, Norway, harnesses hourly traffic volume, air quality data, and meteorological parameters to shed light on this pressing issue.
In this article, I use state-of-the-art machine learning methods to shed light on this pressing issue by utilizing hourly traffic volume, air quality data, and meteorological parameters in the context of Oslo, Norway. The key to my research is finding the most effective way to predict air pollution levels. In contrast to past research, my findings suggest that traditional models outperform machine learning algorithms in this regard. By carefully analyzing six data sets from the entire year of 2019, I demonstrate that the autoregressive integrated moving average model with exogenous input variables and the autoregressive moving average dynamic linear model excels in predicting air pollution levels. Furthermore, by taking into account the intricate set of influencing factors, my research proposes transportation policy recommendations to mitigate the environmental consequences of transportation.
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