Assessment of land use efect, mapping of human health risks and chemometric analysis of potential toxic elements in topsoils of Aran‑o‑Bidgol, Iran

نویسندگانروح اله میرزایی محمد آبادی,ندا روان خواه,سعید معصوم,انوار اسدی,آرمین سروشیان
نشریهEnvironmental Geochemistry and Health
ضریب تاثیر (IF)ثبت نشده
نوع مقالهFull Paper
تاریخ انتشار2023-08-03
رتبه نشریهعلمی - پژوهشی
نوع نشریهالکترونیکی
کشور محل چاپایران
نمایه نشریهJCR

چکیده مقاله

This study examines topsoil contamina-tion in Aran-o-Bidgol urban region of central Iran, with a focus on potentially toxic elements (PTEs). A total of 135 topsoil samples in diferent land types were characterized, ranging from areas with agricul-tural farms, desert, industrial and residential activ-ity, and brick kilns. The average concentrations of Cd, Pb, Cu, Ni, Cr, Co, Fe, Zn, and Mn were 0.72, 11.41, 14.82, 29.87, 51.13, 106.69, 8741.87, 48.59, and 346.42  mg  kg −1 , respectively, which all exceed the local background levels. The results reveal that land use signifcantly afected PTE concentrations. Cr, Co, Mn, and Fe concentrations in soils of residen-tial and brick kiln areas were especially high. In con-trast, concentrations of Cu, Ni, and Zn were higher in agricultural and residential areas. Risk assessment analysis showed that the sum of toxic units for PTEs for brick kilns (1.72), residential (1.82), and agricul-tural (1.79) areas exceeded those of other land types and that Ni and Cr contributed the most to the high toxic risk index values. Both carcinogenic and non-carcinogenic risk indices of PTEs in soils were within an acceptable limit, except for the cancer risk of Ni (3.52E−04) and Cr (3.00E−04) among children. The spatial hazard index and carcinogenic health risk of PTEs showed that samples from the southwestern parts of the study area might pose signifcant health problems to adults and children. This study demon-strates how combining diferent techniques can help spatially characterize PTE accumulation and protect populations at risk.

tags: Land use · Heavy metals · Counterpropagation artifcial neural networks · Health risk assessment · Chemometrics