Novel assessment method of heavy metal pollution in surface water: A case study of Yangping River in Lingbao City, China

Abstract

The primary purpose of this research is to understand those elements that define heavy metals contamination and to propose a novel assessment method based on principal component analysis (PCA) in the Yangping River region of Lingbao City, China. This paper makes detailed calculations regarding such factors the single-factor assessment (Pi) and Nemerow's multi-factor index (PN) of heavy metals found in the surface water of the Yangping River. The maximum values of Pi (Cd) and Pi (Pb) were determined to be 892.000 and 113.800 respectively. The maximum value of PN was calculated to be 639.836. The results of Pearson's correlation analysis, hierarchical cluster analysis, and PCA indicated heavy metal groupings as follows: Cu, Pb, Zn and As, Hg, Cd. The PCA-based pollution index (Pαn) of samplings was subsequently calculated. The relative coefficient square was valued at 0.996 between Pαn and PN, which indicated that Pαn is able to serve as a new heavy metal pollution index; not only this index able to eliminate the influence of the maximum value of Pi, but further, this index contains the principal component elements needed to evaluate heavy metal pollution levels.

Heavy metals risk assessment of water and sediments collected from selected river tributaries of the Mara River in Tanzania

“…Pollution indices are an efficient and rapid tool for producing a single value, the indicator, emanating from different parameters. Most of the indices have been employed globally to study the contamination of toxic heavy metals in river sediments [17,18]. Pollution indices are powerful tools for evaluating, developing, and conveying raw environmental data into more meaningful information for politicians and decision-makers [19].…”

Ecological risk by potentially toxic elements in surface sediments of the Lake Maracaibo (Venezuela)

“…For the interpretation of the data of PTE analysis the following methodology was used: (i) descriptive statistics (arithmetic mean, range, and standard deviation), these values were studied by means of analysis of variance (ANOVA, one way), to determine the ex-istence of significant differences between the three areas: El Tablazo Bay, Strait of Maracaibo and lake. A value of P ≤ 0.05 was considered to indicate statistical significance; and (ii) multivariate analysis, including Pearson's correlation analysis, and cluster analysis, among sampling sites (13 factors) and among PTE (12 factors), as an effective tool to identify the PTE sources [7,8,15,56]. The assumptions for these analyzes: (i) subjects were independent, (ii) the standardized residuals were normally distributed (Shapiro-Wilk test), and (iii) homoscedasticity was guaranteed (Levene test) but cosine transformations were required for data to meet this condition.…”

“…Multivariate statistical analysis, including Pearson's correlation analysis and HCA, is used to identify similar origins or geochemical characteristics between PTE when they are interrelated [7,8,15,56]. According to Pearson's analysis, significant positive correlations were found between some of them (Table 1), which could represent similar sources of natural and anthropogenic origin, while the significant negative correlation between Se and Sn (r = −0.774, P < 0.01), would indicate a contribution from different sources.…”

Assessing Heavy Metal Contamination in Oil and Gas Well Drilling Waste and Soil in Pakistan

“…(2) …where P imax is the maximum value of SPI of all tested heavy metals and P iavg is mean of the SPI of all tested heavy metals [34][35][36]. Being comprehensive pollution indices, SPI and NPI c were used to assess the heavy metal pollution levels in drilling waste discharge and the affected soil.…”

“…The statistical parameters of single pollution index (SPI) and Nemerow composite pollution index (NPI c ) for the selected heavy metals in oil and gas drilling waste discharges are summarized in Table 4. The SPI is calculated against the Pak-NEQS for each selected heavy metal in order to assess the degree of contamination of selected heavy metals in oil and gas drilling waste discharges as per SPI categorization [33][34][35] the collective pollution level of selected heavy metals in oil and gas-drilling waste discharges. The mean NPI c was 68.253, the value falling within in Class V (i.e., severely polluted) [33][34][35].…”