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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. [ABSTRACT FROM AUTHOR]

Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China. [ABSTRACT FROM AUTHOR]