Xu Danhui, Ge Baozhu, Wang Zifa, Sun Yele, Chen Yong, Ji Dongshen, Yang Ting, Ma Zhiqiang, Cheng Nianliang, Hao Jianqi, Yao Xuefeng
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Environ Pollut. 2017 Nov;230:963-973. doi: 10.1016/j.envpol.2017.07.033. Epub 2017 Jul 24.
Wet deposition is one of the most important and efficient removal mechanisms in the reduction of air pollution. As a key parameter determining wet deposition, the wet scavenging coefficient (WSC) is widely used in chemical transport models (CTMs) and reported values have large uncertainties. In this study, a high-resolution observational dataset of the soluble inorganic aerosols (SO, NO and NH, hereafter SNA) in the air and in rainwater during multiple precipitation events was collected using sequential sampling and used to estimate the below-cloud WSC in Beijing during the summer of 2014. The average concentrations of SNA in precipitation during the observational period were 7.9 mg/L, 6.2 mg/L and 4.6 mg/L, with the contributions from below-cloud scavenging constituting 56%, 61% and 47% of this, respectively. The scavenging ratios of SNA (i.e., the ratio of the concentrations in rain to concentrations in the air) were used with the height of the cloud base and the precipitation intensity to estimate the WSC. The estimated WSC of SO is comparable to that reported elsewhere. The relationship between the below-cloud WSC and the precipitation intensity followed an exponential power distribution (K=aP) for SNA. In contrast to previous studies, this study considers the differences between the chemical compositions of the SNA, with the highest WSC for NO, followed by those of SO and NH. Therefore, we recommend that CTMs include ion specific WSCs in the future.
湿沉降是减少空气污染最重要且最有效的清除机制之一。作为决定湿沉降的关键参数，湿清除系数（WSC）在化学传输模型（CTMs）中被广泛使用，但其报告值存在很大的不确定性。在本研究中，通过连续采样收集了多个降水事件期间空气中和雨水中可溶性无机气溶胶（以下简称SNA，即SO、NO和NH）的高分辨率观测数据集，并用于估算2014年夏季北京云下WSC。观测期内降水中SNA的平均浓度分别为7.9毫克/升、6.2毫克/升和4.6毫克/升，其中云下清除的贡献分别占56%、61%和47%。利用SNA的清除率（即雨水中浓度与空气中浓度的比值）以及云底高度和降水强度来估算WSC。估算出的SO的WSC与其他地方报告的值相当。云下WSC与降水强度之间的关系对于SNA遵循指数幂分布（K = aP）。与以往研究不同的是，本研究考虑了SNA化学成分之间的差异，其中NO的WSC最高，其次是SO和NH。因此，我们建议未来的CTMs纳入离子特异性WSC。
