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Tracking sensitive source areas of different weather pollution types using GRAPES-CUACE adjoint model
- Wang, Chao, An, Xingqin, Zhai, Shixian, Hou, Qing, Sun, Zhaobin
- Atmospheric environment 2018 v.175 pp. 154-166
- atmospheric chemistry, control methods, models, particulates, pollution, weather, China
- In this study, the sustained pollution processes were selected during which daily PM2.5 concentration exceeded 75 μg/m³ for three days continuously based on the hourly data of Beijing observation sites from July 2012 to December 2015. Using the China Meteorological Administration (CMA) MICAPS meteorological processing system, synoptic situation during PM2.5 pollution processes was classified into five weather types: low pressure and weak high pressure alternating control, weak high pressure, low pressure control, high rear, and uniform pressure field. Then, we chose the representative pollution cases corresponding to each type, adopted the GRAPES-CUACE adjoint model tracking the sensitive source areas of the five types, and analyzed the critical discharge periods of Beijing and neighboring provinces as well as their contribution to the PM2.5 peak concentration in Beijing. The results showed that the local source plays the main theme in the 30 h before the objective time, and prior to 72 h before the objective time contribution of local sources for the five pollution types are 37.5%, 25.0%, 39.4%, 31.2%, and 42.4%, respectively; the Hebei source contributes constantly in the 57 h ahead of the objective time with the contribution proportion ranging from 37% to 64%; the contribution period and rate of Tianjin and Shanxi sources are shorter and smaller. Based on the adjoint sensitivity analysis, we further discussed the effect of emission reduction control measures in different types, finding that the effect of local source reduction in the first 20 h of the objective time is better, and if the local source is reduced 50% within 72 h before the objective time, the decline rates of PM2.5 in the five types are 11.6%, 9.4%, 13.8%, 9.9% and 15.2% respectively. And the reduction effect of the neighboring sources is better within the 3–57 h before the objective time.