Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, an...
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Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (wrf-cuace) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 mu gm-3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in & Uuml;r & uuml;mqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 mu gm-3 and 29.10 mu gm-3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation-observation error was relatively small;however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emissi
The impact of Arctic Oscillation (AO) anomalies on winter PM2.5 variability in China was investigated using a numerical modeling system (wrf-cuace). The model results showed that the influence of AO anomalies on winte...
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The impact of Arctic Oscillation (AO) anomalies on winter PM2.5 variability in China was investigated using a numerical modeling system (wrf-cuace). The model results showed that the influence of AO anomalies on winter PM2.5 concentration was mainly concentrated in eastern China, especially in Central China (CEN), Beijing-Tianjin Hebei (BTH), the Yangtze River Delta (YRD), and Pearl River Delta (PRD) and was mostly consistent with the conclusions of a previous analysis using haze data. Winter PM2.5 concentrations in CEN and BTH increased under abnormally high AO and decreased under abnormally low AO due to the subsequent changes in specific meteorological conditions, such as temperature, wind speed, and boundary layer height. Winter PM2.5 decreased in the YRD and PRD in both abnormally high and low AO years due to more favorable vertical transport conditions and regional transport capacity compared with those of other regions. In addition to meteorological factors, AO anomalies also impacted PM2.5 depositions in winter, with more apparent effects in southern China. It is found that AO had a larger impact on dry deposition than on wet deposition, and dry deposition was a dominant factor affecting PM2.5 concentrations in CEN. (C) 2021 Elsevier B.V. All rights reserved.
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