Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools

Abstract

Abstract. In this study, three methods, i.e., the random forest (RF) algorithm, boosted regression trees (BRTs) and the improved complete ensemble empirical-mode decomposition with adaptive noise (ICEEMDAN), were adopted for investigating emission-driven interannual variations in concentrations of air pollutants including PM2.5, PM10, O3, NO2, CO, SO2 and NO2 + O3 monitored in six cities in South China from May 2014 to April 2021. The first two methods were used to calculate the deweathered hourly concentrations, and the third one was used to calculate decomposed hourly residuals. To constrain the uncertainties in the calculated deweathered or decomposed hourly values, a self-developed method was applied to calculate the range of the deweathered percentage changes (DePCs) of air pollutant concentrations on an annual scale (each year covers May to the next April). These four methods were combined together to generate emission-driven trends and percentage changes (PCs) during the 7-year period. Consistent trends between the RF-deweathered and BRT-deweathered concentrations and the ICEEMDAN-decomposed residuals of an air pollutant in a city were obtained in approximately 70 % of a total of 42 cases (for seven pollutants in six cities), but consistent PCs calculated from the three methods, defined as the standard deviation being smaller than 10 % of the corresponding mean absolute value, were obtained in only approximately 30 % of all the cases. The remaining cases with inconsistent trends and/or PCs indicated large uncertainties produced by one or more of the three methods. The calculated PCs from the deweathered concentrations and decomposed residuals were thus combined with the corresponding range of DePCs calculated from the self-developed method to gain the robust range of DePCs where applicable. Based on the robust range of DePCs, we identified significant decreasing trends in PM2.5 concentration from 2014 to 2020 in Guangzhou and Shenzhen, which were mainly caused by the reduced air pollutant emissions and to a much lesser extent by weather perturbations. A decreasing or probably decreasing emission-driven trend was identified in Haikou and Sanya with inconsistent PCs, and a stable or no trend was identified in Zhanjiang with positive PCs. For O3, a significant increasing trend from 2014 to 2020 was identified in Zhanjiang, Shenzhen, Guangzhou and Haikou. An increasing trend in NO2 + O3 was also identified in Zhanjiang and Guangzhou and an increasing or probably increasing trend in Haikou, suggesting the contributions from enhanced formation of O3. The calculated PCs from using different methods implied that the emission changes in O3 precursors and the associated atmospheric chemistry likely played a dominant role than did the perturbations from varying weather conditions. Results from this study also demonstrated the necessity of combining multiple decoupling methods in generating emission-driven trends in atmospheric pollutants.

An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models

“…11). Additionally, these statistical methods assume that the variation of air pollutant is a linear sum of meteorological and emission changes 18,42 . Therefore, the influence of meteorological and emission changes on air pollutants can be cleanly separated from each other 19 .…”

“…As an alternative, TSM and ML have been widely used to normalize the weather on air pollutants. It should be noted that none of the existing methods is perfect in decoupling the impacts of emissions and meteorology on air pollutant variations 42 . The performance and comparability of different methods should therefore be assessed.…”

“…The performance and comparability of different methods should therefore be assessed. Intra-comparisons between TSM [41][42][43][44] or intercomparisons between TSM, ML, and CTMs 40,[45][46][47] , however, are less reported 39,40 . One of the biggest challenges is the lack of simultaneous CTM results as a reference.…”

Associations of interannual variation of Summer Tropospheric Ozone with Western Pacific Subtropical High in China from 1999 to 2017

“…Meteorological parameters affecting surface O3 evolution include but are not limited to winds, air temperature, relative humidity, and solar radiation (Ma et al, 2021). While anthropogenic factors play vital roles in ozone formation, meteorological factors determine, to a significant extent, the changes and evolution in O3 concentrations (Ding et al, 2019;Li et al, 2019Lin et al, 2021Lin et al, , 2022. Meteorological conditions modulate O3 concentrations through atmospheric transport and affect natural emissions from biological sources and chemical reaction rates (Fu et al, 2019;Lu et al, 2019).…”