Abstract Particulate matter (PM) released from industrial emissions could cause health problems, which are strongly associated with the chemical composition of PMs. This study aimed to estimate the unknown contribution of two iron industries using a known iron plant via sensitivity analysis. For this purpose, a comprehensive analysis was carried out on the characteristics of known iron plant, accessible sampling locations between the known plant and other two iron industries (unknown contributors), and in the heart of affected residential areas. A validated dispersion/deposition model was developed based on the gathered pieces of evidence of the known source. Analysis of the known processes and dustfalls classified the shape of known iron particles into seven main categories, including single and agglomerated of both sharp- and soft-edged fine (PM2.5 – less than 2.5 μm in aerodynamic diameter) and coarse (greater than PM2.5) iron particles. The high contribution of fine iron particles in the main stacks (more than 78.4%), very low contribution in the process samples (less than 10% in raw feed and around one-third of iron particles before the furnace) and almost insignificant contribution in the in-site dustfalls proved the transfer of fine iron particles to residential areas. However, elevated temperature after rotary Kiln resulted in the formation of agglomerated soft-edged fine/coarse particles. Therefore, PM2.5 was considered as the main atmospheric iron size fraction during the model development. The highest iron concentration (1.34 μg m−3) was observed in the area close to the two iron industries. Although the model showed under-prediction considering only plant sources, sensitivity analysis revealed the contribution of other iron industries in under debate areas. The daily concentration of atmospheric particles measured through this study was within the guidelines proposed by the regulatory bodies, however, the iron contents were found high in some locations highlighting the need for detailed regulatory control.

中文翻译：

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使用敏感性分析表征钢铁工业在大气铁排放中的未知贡献

摘要 工业排放中释放的颗粒物 (PM) 会导致健康问题，这与 PM 的化学成分密切相关。本研究旨在通过敏感性分析使用已知的钢铁厂估计两个钢铁行业的未知贡献。为此，对已知钢铁厂的特征、已知工厂与其他两个钢铁工业（未知贡献者）之间的可访问采样位置以及受影响居民区的中心进行了综合分析。基于收集到的已知来源的证据，开发了经过验证的分散/沉积模型。对已知过程和降尘的分析将已知铁颗粒的形状分为七个主要类别，包括单个和聚集的锋利和软边细（PM2.5 – 空气动力学直径小于 2.5 微米）和粗（大于 PM2.5）铁颗粒。主烟道中细铁颗粒的贡献很高（超过 78.4%），在工艺样品中的贡献非常低（原料进料中不到 10%，炉前铁颗粒约占三分之一）和几乎不显着的贡献现场降尘证明细铁颗粒向居民区转移。然而，回转窑后升高的温度导致形成附聚的软边细/粗颗粒。因此，在模型开发过程中，PM2.5 被认为是主要的大气铁粒度分数。在靠近两个铁工业的区域观察到最高的铁浓度（1.34 μg m-3）。尽管该模型仅考虑植物来源显示出预测不足，但敏感性分析揭示了其他钢铁行业在争议领域的贡献。通过这项研究测量的大气颗粒日浓度在监管机构提出的指导方针范围内，但是，在某些地方发现铁含量很高，这突出了详细监管控制的必要性。