Analyzing environmental risk, source and spatial distribution of potentially toxic elements in dust of residential area in Xi’an urban area, China

Kabaoğlu

et al. 2021

Environ Sci Pollut Res

In the present study, the concentration levels of heavy metals such as Mn, Fe, Ni, Cu, Zn, Cr and Pb in sediment samples collected from 16 sampling locations in the Middle and Eastern Black Sea regions, Turkey was measured using energy dispersive X-ray fluorescence spectroscopy (EDXRF). Various pollution parameters and methods, such as the enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI), ecological risk index (RI), and geo-spatial distribution patterns were used to assess in detail the pollution status, ecological risks and sources of metals in sediment. The mean

Section: Discussionmentioning

confidence: 99%

Evaluation of ecological risk, source, and spatial distribution of some heavy metals in marine sediments in the Middle and Eastern Black Sea region, Turkey

Kabaoğlu

et al. 2021

Environ Sci Pollut Res

“…mining (Li et al, 2017). The coefficient of variation (CV), representing the variability of heavy metal concentrations, was defined as the ratio of standard deviation (SD) to the average of an element (Yu et al, 2021). High CV for heavy metals indicated that their concentrations were affected by the presence of the mine (Cheng et al, 2020).…”

Section: Descriptive Statistics Of Heavy Metal Concentrations In the Topsoilmentioning

confidence: 99%

“…Typically, a geographic information system (GIS) and Surfer software can be used to characterize the spatial distribution of heavy metals (Chai et al, 2021;Sun et al, 2019). Numerous indices, such as the geo-accumulation index (Igeo) (Negahban et al, 2021), pollution index (PI) (Yan g et al, 2021), Nemerow integrated pollution index (NIPI) (Guan et al, 2020) or enrichment factor (EF) (Yu et al, 2021) were applied to evaluate the ecological contamination risk. There are also many indices for health risk assessment, and a model introduced by USEPA, which includes the hazard index (HI) and cancer risk (CR) is one of the most widely applied to evaluate non-carcinogenic and carcinogenic risks to human body caused by pollution (Huang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…There are also many indices for health risk assessment, and a model introduced by USEPA, which includes the hazard index (HI) and cancer risk (CR) is one of the most widely applied to evaluate non-carcinogenic and carcinogenic risks to human body caused by pollution (Huang et al, 2021). Geostatistical models based on geographic information systems (GIS) (Yu et al, 2021) and multivariate statistical methods, e.g. principal component analysis (PCA), cluster analysis (CA) and correlation analysis are often used to qualitatively identify the origin of elements in soil (Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distribution and risk assessment of heavy metals in soil in a commonly used mining area

Xu¹,

Dai²,

Skuza³

et al. 2021

Preprint

This study investigated the spatial, vertical and fraction distribution, multivariate statistical analysis, as well as assessed pollution levels and health risks posed by heavy metals at the Wenshan mining area. There were 6 sampling points at the site, from which a total of 32 samples were collected. The eastern and south-eastern parts of the site were heavily polluted, while the western and north-western parts were less contaminated. As pollution was particularly severe in the topsoil, with the mean value of 503.31 mg kg− 1 being more than 25-fold higher than the risk screening value. As content in all layers was greater than the standard risk screening value in the three typical profiles. The proportion of various Ni, Cu and As fractions changed slightly with depth at all three sampling points, and the residual fraction percentage accounted for more or nearly 80% of the total concentration. In addition, a significantly negative correlation (p < 0.05) was detected between Ni and As (-0.85), indicating a different source of these two elements. Principal component analysis demonstrated that two PCs, i.e. PC1, attributed to human activities, e.g. mining activities and transport, and PC2, defined as natural sources, were responsible for 80.632% of the total variance. As posed severe ecological and unacceptable health risks, i.e. non-carcinogenic and carcinogenic risks both for adults and children. Therefore, a comprehensive understanding of the characteristics of mining pollutants and improved emission management in the mining process are essential to protect the environment and health of local residents.

“…Coarse sand material of road-deposited sediments is about 50% of road-deposited sediments mass [15]. The coarse particles of anthropogenic origin may contain toxic heavy metals [16][17][18][19][20]. The large size fraction material of road-deposited sediments (>100 µm) contains the mass of heavy metals within particulate matter similar to the fine fractions [21].…”

Section: Introductionmentioning

confidence: 99%

Coarse Technogenic Material in Urban Surface Deposited Sediments (USDS)

et al. 2021

In the current paper, the analysis of heavy mineral concentrate (Schlich analysis) was used to study the particles of technogenic origin in the samples of urban surface-deposited sediments (USDS). The USDS samples were collected in the residential areas of 10 Russian cities located in different economic, climatic, and geological zones: Ufa, Perm, Tyumen, Chelyabinsk, Nizhny Tagil, Magnitogorsk, Nizhny Novgorod, Rostov-on-Don, Murmansk, and Ekaterinburg. The number of technogenic particles was determined in the coarse particle size fractions of 0.1–0.25 and 0.25–1 mm. The types of technogenic particle were studied by scanning electron microscopy (SEM) analysis. The amount of technogenic material differed from city to city; the fraction of technogenic particles in the samples varied in the range from 0.01 to 0.43 with an average value of 0.18. The technogenic particles in USDS samples were represented by lithoid and granulated slag, iron and silicate microspheres, fragments of brick, paint, glass, plaster, and other household waste. Various types of technogenic particle differed in morphological characteristics as well as in chemical composition. The novelty and significance of the study comprises the following: it has been shown that technogenic particles are contained in a significant part of the USDS; the quantitative indicators of the accumulation of technogenic particles in the urban landscape have been determined; the contributions of various types of particles to the total amount of technogenic material were estimated for the urban landscape; the trends in the transformation of typomorphic elemental associations in the urban sediments associated with the material of technogenic origin were demonstrated; and the alteration trends in the USDS microelemental content were revealed, taking into account the impurities in the composition of technogenic particles.