Evaluating heavy metals contamination in campus dust in Wuhan, the university cluster in Central China: distribution and potential human health risk analysis

Abstract

The potential health risk of heavy metals (HMs) in campus dust may threaten the health of thousands of students, teachers, and their families in Wuhan, the university cluster in Central China every day. In this research, the pollution characteristics and health risk with HMs was the first time presented in campus dust from the canteen, playground, dormitory, and school gate to date. The average HMs concentration in campus dusts ranked Pb (83.5 mg kg-1) > Cu (70.2 mg kg-1) > Zn (47.2 mg kg-1) >Cr (46.0 mg kg-1) > Ni (22.7 mg kg-1) > As (15.2 mg kg-1) > Cd (3.38 mg kg-1).The HMs would more likely to accumulate in dormitory dust and canteen dust. In the downtown area, Zn, As, and Cd had been preliminarily identified from fossil fuel combustion and natural geochemical processes. Cu and Pb would source from cooking and traffic transportation. Ni and Cr would likely reflect the contributions of natural soil weathering. Although, no significant non-carcinogenic health risks were found to students or teachers from campus dust. Their children would more likely to exposure health risks when eating in the canteen, playing on the playground, or walking around the school gate. While the incremental lifetime cancer risk values revealed respiratory intake of HMs does not pose a carcinogenic risk on the campus.

The Predominant Sources of Heavy Metals in Different Types of Fugitive Dust Determined by Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) Modeling in Southeast Hubei: A Typical Mining and Metallurgy Area in Central China

“…Researchers generally use PCA to determine the most essential factors and minimize data with minimal data loss [ 59 ]. Before the source identification, the Kaiser–Meyer–Olkin index was 0.759 and the Bartlett sphericity test result was below 0.001, indicating that the PCA model would be a suitable method for determining the principal components (PCs) of the sources of HM in fugitive dust in the study area [ 6 ]. Moreover, Liu et al classified the degrees of positive loading with loading values as strong positive loading (0.75–1.0), moderate positive loading (0.5–0.75), and weak positive loading (0.3–0.5) [ 6 ].…”

“…Eleven heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn, Fe, and Sn) were measured from all the fugitive dust samples. Following the procedure used by Liu et al [ 6 ], each sieved sample (0.2 g) was digested with HCl-HNO 3 -HF-HCLO 4 . Then, an atomic fluorescence spectrophotometer (AFS, AFS-230E) and inductively coupled plasma source-mass spectrometer (Perkin–Elmer, Elan 9000) were used to measure all the chemical elements in the digested solution.…”

“…Each principal component is uncorrelated [ 29 , 32 ]. Moreover, the mass of contaminants should not change during transportation [ 6 ]. Numerous previous studies have used PCA to identify the natural and anthropogenic contributions of contaminants by extracting the principal component information from the variables [ 34 , 35 ].…”

“…Previous studies have already revealed that fugitive dust accounts for 12–34% of particulate matter in most Chinese cities [ 4 ]. Additionally, fugitive dust easily enriches harmful elements in the environment and can cause potential human health risks, as it is a kind of small particle with a large specific surface [ 5 , 6 ]. In Tianjin, Wang et al revealed an unacceptable cancer risk from Cr from urban road fugitive dust mainly originating from traffic non-exhaust emissions [ 7 ].…”

“…Previous researchers have generally used geostatistical models, receptor models, and multivariate statistical analyses to identify HM sources [ 19 , 20 , 21 ]. Principal component analysis (PCA) has been widely used and is considered a good model for potential source distinction [ 6 ]. Anaman et al [ 22 ] employed the PCA model to determine the sources and transport routes of heavy metals (HMs) in different land-use soils.…”