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Investigating the Combined Use of Enrichment Factor and Weather Research and Forecasting (WRF) Modelling for Precipitation Sample Source Identification: A Case Study in North Carolina, USA
Archives of Environmental Contamination and Toxicology ( IF 3.7 ) Pub Date : 2021-04-16 , DOI: 10.1007/s00244-021-00843-1
Elif Yavuz , S. Levent Kuzu , Gürdal Kanat , Nedim Vardar

Pollutants emitted into the air not only have local effect but can also affect areas further from the source. The goal of this study was to assess a method for identifying the sources of element pollution in rainwater using enrichment factors supported by Weather Research and Forecasting (WRF) model. In this study, we collected nineteen rainwater samples at the two locations of Durham and Chimney Ridge in North Carolina, USA in July of 2014. The samples were analyzed for pH, conductivity and levels of major ions and a range of trace elements. These data showed that the pH of precipitation ranged between 3.91 and 6.65, with an average value of 4.98. The average electrical conductivity was 15.58 and 17.7 μS/cm for rainwater collected at Durham and Chimney Ridge, respectively. The lowest concentration of the elements analyzed was for thorium (Th) with an average concentration of 0.002 ppb, whereas the highest elemental concentration was for calcium (Ca) with an average concentration of 980.3 ppb. Enrichment factors for trace elements were assessed within three different groups as: (1) rarely enriched, (2) significantly enriched, and (3) highly enriched. Copper (Cu), zinc (Zn), arsenic (As), molybdenum (Mo), silver (Ag), cadmium (Cd), and lead (Pb) were highly enriched trace elements. The wind fields acquired by the WRF model indicated the probable contamination sources. Source identification indicated that the highest contribution of elements to precipitation was from industry. The results showed that the combined use of enrichment factors and the WRF model can be used to identify the sources of pollutants in precipitation samples.



中文翻译:

研究富集因子与天气研究与预报(WRF)建模的结合使用,以进行降水样品源识别:美国北卡罗来纳州的一个案例研究

排放到空气中的污染物不仅会产生局部影响,还会影响到远离源头的区域。这项研究的目的是评估一种使用天气研究和预报(WRF)模型支持的富集因子来识别雨水中元素污染源的方法。在这项研究中,我们于2014年7月在美国北卡罗莱纳州的达勒姆和烟囱岭两个地方收集了19个雨水样品。分析了样品的pH,电导率和主要离子水平以及一系列微量元素。这些数据表明沉淀的pH值在3.91至6.65之间,平均值为4.98。在达勒姆和烟囱岭收集的雨水的平均电导率分别为15.58和17.7μS/ cm。所分析元素的最低浓度为for(Th),平均浓度为0.002 ppb,而最高元素浓度为钙(Ca),平均浓度为980.3 ppb。在三个不同的组中评估了微量元素的富集因子:(1)很少富集;(2)显着富集;(3)高富集。铜(Cu),锌(Zn),砷(As),钼(Mo),银(Ag),镉(Cd)和铅(Pb)是高度富集的微量元素。WRF模型获得的风场表明了可能的污染源。资料来源表明,元素对降水的最大贡献来自工业。

更新日期:2021-04-16
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