Quantifying the drivers of PM2.5 variation in Shenyang, China: A factor decomposition model. (1st December 2022) - Guo, Xiaodan; Xiao, Bowen; Su, Hongyan
Abstract: Taking variations in PM2.5 as indicators for assessing the performance of authority in air quality management will probably lead to misjudgment, as PM2.5 concentration is affected not only by anthropogenic emissions but also by uncontrollable circumstances. To solve this problem, we proposed a decomposition method to attribute the variations in PM2.5 to the contributions of meteorological conditions, cross-regional transports of pollutants, secondary aerosols, and local emissions. This method estimated the relationship between PM2.5 concentration and the various influencing factors using a semi-parametric generalized additive model. A case study was conducted in Shenyang, a heavily polluted city in northeast China, based on up to 595, 000 hourly data samples from 2014 to 2017. The decomposition results indicated that the average PM2.5 in 2017 decreased by 39.80% compared with 2014, far exceeding the government's target of 15%, but only 11.79% of the decrease was benefited from the control of local emissions. The severe pollution event that occurred in November 2015 was induced by the combination of massive emissions from heating and meteorological conditions conducive to pollutant accumulation. Furthermore, the approach we proposed can be extended to any location that has monitoring data on air pollutant concentrations and meteorological conditions. Highlights: Causes of PM2.5 variation was decomposed into subjective and objective ones. The effect of meteorological condition on PM2.5 was analyzed quantitatively. Semi-parametric generalized additive model was used to predict PM2.5 concentration. An appraisal method of objectively evaluating air-quality changes was proposed. The effect of air-quality management in Shenyang was proved to be overestimated.