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

ABSTRACT: Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the

SUBMITTER: Qiu M 

PROVIDER: S-EPMC9957566 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature