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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]

Severe air pollution events accompanied by high PM2.5 concentrations have been repeatedly observed in Middle-Eastern China since 2013 and decreased in recent years. The reason for this caused widespread attention. The month of January was selected to represent the winter season annual changes in the winter PM2.5 and meteorological conditions—including the upper-air meridional circulation index (MCI), winds at 700 and 850 hPa levels and surface meteorology—from 2013 to 2019. These conditions were analyzed to study the contribution of meteorology changing to the annual PM2.5 changing on the regional scale. Results show that, based on values of upper-level MCI, the years 2014, 2015, 2017, and 2019 were defined as meteorology-haze years and the years 2016 and 2018 were defined as meteorology-clean years. A change in meteorological conditions may lead to a 26% change in PM2.5 concentration between 2014 and 2013 (two meteorology-haze years) and 16–20% changes in PM2.5 concentration between meteorology-haze years and meteorology-clean years. Changes in pollutant emissions may cause 21–47% changes in PM2.5 concentration between each two meteorology-haze years. A comparison of two meteorology-clean years and pollutant emissions in 2018 may be reduced by 40% compared with 2016. Overall, changes in emissions had a greater influence on changes in PM2.5 compared with meteorological conditions. [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]

In the present study, the Morrison double-moment cloud microphysics scheme including mass and droplet number concentration of water and ice clouds is implemented into the Chinese mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). Sensitivity experiments of different cloud condensation nuclei (CCN) values are conducted to study the impacts of CCN on cloud microphysical processes and radiation processes in East China. The model evaluation shows that the simulated cloud liquid water path (CLWP) is consistent with that of the National Center for Environment Prediction (NCEP) reanalysis, and the cloud optical depth (COD) and effective radius of cloud water (Rc) are in agreement with those of the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets both in regional distribution and magnitudes. These comparisons illustrate the effectiveness of the Morrison scheme for the cloud processes in East China. For the study period of 8 to 12 October 2017, the sensitivity experiments show that with initial CCN number concentration (CCN0) increasing from 10 to 3000 cm−3, the maximum value of daily average Rc decreases by about 63%, which leads to a decrease of cloud-rain conversion rate. Moreover, the maximum value of daily average mixing ratio of cloud water (qc) increases by 133%, the maximum value of daily average mixing ratio of rain (qr) decreases by 44%, and the maximum value of daily average CLWP and COD increase by 100% and 150%, respectively. This results in about 65% increasing of the maximum value of daily average cloud downward shortwave radiative forcing (CDSRF) when CCN0 increases from 10 cm−3 to 3000 cm−3. The study indicates the important impacts of CCN on cloud properties and radiation effects. [ABSTRACT FROM AUTHOR]