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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-232', Anonymous Referee #1, 26 Apr 2022
    • AC2: 'Reply on RC1', Minghao Qiu, 24 Jun 2022
  • RC2: 'Comment on acp-2022-232', Benjamin Wells, 11 May 2022
    • AC1: 'Reply on RC2', Minghao Qiu, 16 May 2022
      • EC1: 'Reply on AC1', Anne Perring, 24 May 2022
    • AC2: 'Reply on RC1', Minghao Qiu, 24 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Minghao Qiu on behalf of the Authors (24 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (09 Jul 2022) by Anne Perring
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