Angzu Cai

Mr. Angzu Cai

Hebei University of eEngineering

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 September 2021 in Atmosphere
Verified
Journal Article
Open access
PDF file
ACAngzu Cai
Angzu Cai
HZHaixia Zhang
Haixia Zhang
LWLitao Wang
Litao Wang
QWQing Wang
Qing Wang
XWXiaoqi Wu
Xiaoqi Wu
In order to determine the pollution sources and human health risks of metal elements in PM2.5, samples were collected by a large flow particulate matter sampler in the four seasons in 2013, 2015, and 2017 (January, April, July, and October). The mass concentrations of 10 metals (Ti, V, Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The sources of heavy metals were identified by Unmix, and the potential non-carcinogenic/carcinogenic risk was evaluated. The influences of local and regional sources were also explored during the high-carcinogenic risk period (HCRP). The wind field and 72 h backward trajectories were performed to identify the potential local and regional sources in HCRP. The results showed that the average annual concentrations of PM2.5 in the urban area of Handan city were 105.14, 91.18, and 65.85 μg/m3 in 2013, 2015, and 2017, respectively. The average daily concentrations of the metals in PM2.5 in January were higher than that of April, July, and October. The average mass concentrations of the 10 heavy metal elements in PM2.5 were 698.26, 486.92, and 456.94 ng·m−3 in 2013, 2015, and 2017, respectively. The main sources of the metals in PM2.5 were soil dust sources, vehicular emissions, coal burning, and industrial activities. The carcinogenic risks of Cr and As were above 1 × 10−6 over the three years. Wind direction analysis showed that the potential local sources were heavy industry enterprises and the economic development zone. The backward trajectory analysis indicated that PM2.5 long transported from Shandong, Henan, and the surrounding cities of Handan had quite an impact on the heavy metals contained in the atmosphere of the studied area. The health risk assessment results demonstrated that the trend for non-carcinogenic risk declined, and there was no non-carcinogenic risk in 2017. However, the carcinogenic risk levels were high over the three years, particularly in January.

15 October 2021 in Applied Sciences
Verified
Journal Article
Open access
PDF file
HZHaixia Zhang
Haixia Zhang
ACAngzu Cai
Angzu Cai
XWXiaojian Wang
Xiaojian Wang
LWLitao Wang
Litao Wang
QWQing Wang
Qing Wang
Yingqun MaYingqun Ma
Yingqun Ma
Soil-heavy metals are potentially harmful to the ecosystem and human health. Quantifying heavy metals sources is conducive to pollution control. In this study, 64 surface-soil samples were collected in Handan city. Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined; then, their spatial distribution in the sampling area was drawn by ArcGIS. The pollution index (PI) method, geo-accumulation index (Igeo) method, Nemerow integrated pollution index (NIPI) and pollution load index (PLI) were used to evaluate the pollution level of heavy metals in surface soil; then, an ecological and health risk assessment of soil-heavy metals was carried out. Combined with the spatial distribution, correlation analysis, cluster analysis, PCA and PMF model, the pollution sources of heavy metals in soil were identified and apportioned. The results showed that the average content of Cd was nearly ten times that of the background limit, which was the most serious among the studied metals. In terms of non-carcinogenic risk, Cr had the highest value, followed by Pb. In terms of carcinogenic risk, Cd, Cr, and Ni had an acceptable or tolerable risk. Three pollution sources were identified by cluster analysis and PCA, including traffic sources with Cu, Pb and Cd as main loads, industrial sources with Mn, Cd and Zn as main loads, and natural sources with Cr and Ni as main loads. The PMF model analyzed three main factors: traffic source (17.61%), natural source (28.62%) and industrial source (53.77%). The source categories and the main load elements obtained from the source apportionment results were consistent with the source identification results.