G. Tepanosyan, L. Sahakyan, Ch. Zhang, A. Saghatelyan;                             Applied Geochemistry,                             2019,                             104,                             pp. 116-123;                             DOI: https://doi.org/10.1016/j.apgeochem.2019.03.022                        
Soils are the main sink of pollutants in the terrestrial environment, reflecting the historical fingerprint of urban development. To provide scientifically grounded urban management and reclamation strategies for polluted sites, the spatial patterns of pollutants should be studied first by drawing a special focus on the identification of pollution hot spots. In this paper, Pb, Mo and Ti contents in Yerevan urban soils determined by the combination of X-ray fluorescence and atomic absorption spectrometry were studied using the Local Moran's I index (LMI). These elements (Pb, Mo and Ti) have different sources and a number of distance bands (650, 800, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000m and 15,000 m) were tested. This allows to reveal the optimal distance used for the identification of statistically significant spatial clusters and spatial outliers. According to the results the use of the raw data had to be excluded for elements that demonstrated highly skewed distribution whereas transformed data allowed to identify larger areas of high and low value spatial clusters. Moreover, the results of the different distance bands showed that the high and low spatial clusters became stable near the optimal distance band of 7000m in the study area. The latter constituted the 1/3 of the maximum distance between all sample pairs. At this distance, Ti LMI results reflected the natural local geology composition whereas Mo distribution was in line with the known sources of industrial pollution (“Plant of Pure Iron”). In the case of Pb, the high-value spatial clusters were in line with the built-up urban areas and spatially correlated with the prevailing northeast winds. This study confirmed the applicability of LMI in the identification of spatial clusters and outliers at the optimal distance band which might be considered also as a break point of spatial clusters expansion and a guidance for spatial boundary of possible remediation and pollution reduction applications.
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