Abstract

Ambient air TSP concentrations, dry deposition fluxes and particulate-bound mercury (Hg(p)) concentrations were measured and analyzed at a complex (traffic, residential and commercial) site. Zhang and He’s model^[1 Zhang, L.; He, Z. Technical Note: An Empirical Algorithm Estimating Dry Deposition Velocity of Fine, Coarse and Giant Particles. Atmos. Chem. Phys. 2014, 14, 3729–3737. DOI: 10.5194/acp-14-3729-2014.[Crossref], [Web of Science ®] , [Google Scholar]^] was used to predict the dry deposition fluxes of ambient air particulates and Hg(p) at this complex site. The results revealed that October had the highest mean particulate concentration and lowest Hp(p) concentration and dry deposition flux. The mean calculated dry deposition fluxes of PM_2.5 and PM_2.5-10 accounted for 1%–2% and 0.06%–5% of the average total calculated dry deposition particle flux, respectively. The average calculated particle dry depositions flux of PM₁₀₊, accounted for 93%–99% of the average total calculated dry depositions particle flux. Finally, the model of Zhang and He underestimated the ambient air dry depositions fluxes of both particulates and Hg(p) for all particles sizes (PM_2.5, PM_2.5-10, PM₁₀⁺) at the mixed site in this study. Better results concerning the dry deposition fluxes of pollutants were obtained as the particles size increased.

摘要

在一个复杂的（交通、住宅和商业）场地测量和分析了环境空气 TSP 浓度、干沉降通量和颗粒结合汞 (Hg(p)) 浓度。张和何的模型^{[ 1} 张，L。He, Z. 技术说明：估算细颗粒、粗颗粒和巨颗粒的干沉积速度的经验算法。大气。化学 物理。 2014 , 14 , 3729 – 3737。DOI：10.5194/acp-14-3729-2014。[交叉引用]，[万维网科学®]  ，[谷歌学术] ^]被用来在这个复杂的网站来预测环境空气微粒和Hg（p）的干沉积通量。结果显示，10 月份的平均颗粒物浓度最高，而 Hp(p) 浓度和干沉降通量最低。PM_2.5和 PM的平均计算干沉降通量_2.5-10分别占平均总计算干沉降颗粒通量的 1%–2% 和 0.06%–5%。PM ₁₀₊的平均计算颗粒干沉降通量占平均总计算干沉降颗粒通量的93%–99%。最后，Zhang 和 He 的模型低估了本研究中混合场地所有粒径（PM _2.5、PM _2.5-10、PM ₁₀ ⁺）的颗粒物和 Hg(p) 的环境空气干沉降通量。随着颗粒尺寸的增加，污染物的干沉降通量得到了更好的结果。