Multiple Regression Analysis of Low Visibility Focusing on Severe Haze-Fog Pollution in Various Regions of China
Authors: Liu Zhaodong , Wang Hong , Peng Yue , Zhang Wenjie , Zhao Mengchu 
Published: January 2022

Visibility degradation is a pervasive environmental problem in winter in China and its prediction accuracy is therefore important, especially in low visibility conditions. However, current visibility parameterization algorithms tend to overestimate low visibility (<5 km) during haze–fog events. The key point of low visibility calculation and prediction depends on a reasonable understanding of the correlation between visibility, PM2.5 concentration, and relative humidity (RH). Using the observations of PM2.5 concentration and meteorology from December 2016 to February 2017, under different RH levels, the relative contribution differences of PM2.5 concentrations and RH to visibility degradation are investigated in depth. On this basis, new multiple nonlinear regressions for low visibility are developed for eight regions of China. The results show that under relatively low RH conditions (<80% or 85%), PM2.5 concentration plays a leading role in visibility changes in China. With the increase in RH (80–90% or 85–95%), the PM2.5 concentration corresponding to the visibility of 10 and 5 km decreases and the contribution of RH becomes increasingly important. When the RH grows to >95%, a relatively low PM2.5 concentration could also lead to visibility decreasing to <5 km. Within this range, the PM2.5 concentration corresponding to the visibility of 5 km in Central China (CC), Sichuan Basin (SCB), and Yangtze River Delta (YRD) is approximately 50, 50, and 30 μg m−3, and that in Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZP) is approximately 125 μg m−3, respectively. Specifically, based on these contribution differences, new multiple nonlinear regression equations of visibility, PM2.5 concentration, temperature, and dew point temperature of the eight regions (Scheme A) are established respectively after grouping the datasets by setting different RH levels (BTH, GZP, and North Eastern China (NEC): RH < 80%, 80 ≤ RH < 90% and RH ≥ 90%; CC, SCB, YRD, and South China Coastal (SCC): RH < 85%, 85 ≤ RH < 95% and RH ≥ 95%; Xinjiang (XJ): RH < 90% and RH ≥ 90%). According to the previous regression methods, we directly established the multiple regression models between visibility and the same factors as a comparison (Scheme B). Statistical results show that the advantage of Scheme A for 5 and 3 km evaluation is more significant compared with Scheme B. For the five low visibility regions (BTH, GZP, CC, SCB, and YRD), RMSEs of Scheme A under visibility <5 and 3 km are 0.77–1.01 and 0.48–0.95 km, 16–43 and 24–57% lower than those of Scheme B, respectively. Moreover, Scheme A reproduced the winter visibility in BTH, GZP, CC, SCB, YRD, and SCC from 2016 to 2020 well. The MAEs, MBs, and RMSEs under visibility < 5 km are 0.44–1.41, −1.33–1.24, and 0.58–2.36 km, respectively. Overall, Scheme A is confirmed to be reliable and applicable for low visibility prediction in many regions of China. This study provides a new visibility parameterization algorithm for the haze–fog numerical prediction system.