Articles | Volume 22, issue 16
Research article
19 Aug 2022
Research article |  | 19 Aug 2022

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Minghao Qiu, Corwin Zigler, and Noelle E. Selin

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Cited articles

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Short summary
Evaluating impacts of emission changes on air quality requires accounting for meteorological variability. Many studies use simple regression methods to correct for meteorology, but little is known about their performance. Using cases in the US and China, we show that widely used regression models do not perform well and can lead to biased estimates of emission-driven trends. We propose a novel machine learning method with lower bias and provide recommendations to policymakers and researchers.
Final-revised paper