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Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
Atmospheric Chemistry and Physics
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 meteorologicaldoi:10.5194/acp-22-10551-2022 pmid:36845997 pmcid:PMC9957566 fatcat:upbr5l6tdvadxglzgiw5qtkjmm