Contribution of climate/meteorology to winter haze pollution in the Fenwei Plain, China

Abstract

In recent years, anthropogenic emissions in the Fenwei Plain (FWP) have decreased; however, haze pollution remains a serious issue. This study explored the possible reasons for this enduring problem in terms of climate and meteorology. Firstly, the contribution of climate and meteorology to haze pollution in the FWP was quantified using a best fit model and differences in key meteorological parameters were analysed over several time periods. Key climate factors were identified using a relative importance test and correlation analysis, and the adjusted optimal subset model (AOSM) was used to predict the number of winter haze days for the entire winter and for individual winter months, respectively. Results showed that the average minimum contribution (CONave) of climate/meteorology to winter haze pollution was 24.3% from 1984 to 2016 and the modulating role of climate and meteorological conditions increased significantly after 2010 (CONave = 55.8%). This was attributed to a significant decrease (increase) in sea level pressure and surface wind speed (500 hPa geopotential height and surface temperature) in the FWP. The explained variance (R2) and mean absolute error (MAE) of the models for the entire winter period were 88% and 3.54 days, respectively. For predictions in November, December, January, and February, the R2 was above 71% and the MAE was below 1.83 days. Furthermore, independent predictions for 2018 showed that the bias in the monthly models was lower by 5 days compared to the entire winter model. The results of a recycling independent test indicated that all the models evaluated had excellent stability, proving that the climate factors chosen by the AOSM do affect haze pollution in the FWP.

Predicting gridded winter PM2.5concentration in the east of China

“…Seasonal prediction focused on predicting long-term trends and interannual-decadal variations 1-3 months in ad-vance . Because of the limitation of short observational period, many previous studies employed the number of haze days as a proxy of PM 2.5 pollution to build statistical prediction models (Yin and Wang, 2016a;Yin et al, 2017;Dong et al, 2021;Zhao et al, 2021;Chang et al, 2021). Since 2020, several high-resolution PM 2.5 reanalysis datasets have been successively released, which greatly increased the possibility for direct seasonal prediction of PM 2.5 concentration that is more familiar to decision makers and the public (Yin et al, 2021).…”

“…3c, g). The Fenwei Plain was highly polluted and gained great attention in recent years, while the other two centers have relatively better air quality (Zhao et al, 2021). The October snow depth DY in eastern Siberia (CC with PC3 = −0.65; Fig.…”

“…Besides analysis of climate mechanisms, the number of haze days was also used as a proxy predictand of PM 2.5 pollution. Taking advantage of the memory effect in slow-varying climate forcings (e.g., sea surface temperature and sea ice), the number of haze days was successfully predicted in North China (Yin and Wang, 2016a;Yin et al, 2017), Yangtze River Delta (Dong et al, 2021) and Fenwei Plain (Zhao et al, 2021). Chang et al (2021) used regional stratospheric warming over northeastern Asia in November to predict haze pollution in the Sichuan Basin for 5-7 weeks.…”

Interannual Variation and Statistical Prediction of Summer Dry and Hot Days in South China from 1970 to 2018

“…In this study, we use the key climate factors, EASM and IOBW, finally identified in the previous section, as the predictor's input to the multiple linear regression model to predict the interannual trend of DH days. By using the adjusted optimal subset model (AOSM, Zhao et al [61] ), the prediction model of the number of DH days in summer in South China is established as follows:…”