Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

Chen, Hao; Li, Xinwei; Li, Loretta Y.; Gao, Tianning; Chang, Yuyu

doi:10.1016/j.scitotenv.2014.03.026

Cited by 132 publications

(57 citation statements)

References 42 publications

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“…Details regarding the sources of metal elements are presented in Section 3.2. Table 2 compares our results with previous measurements of the metals in PM 2.5 samples collected from various sites, including rural, urban, industrial, forest, suburban, mountain, and island sites cities in China, Europe and United States [28,31,33,59,60]. Overall, the concentrations of dust-related metal elements in suburban Nanjing were at similar levels compared with those from some rural sites in foreign countries like Switzerland, Los Angles, USA, but with higher concentrations of some heavy metals derived from vehicle and industrial emissions, although levels of these metals appear to be lower than those in a few cities, such as Xi'an, Ji'nan and Zhuzhou [33].…”

Section: Annual Characteristic Of Metal Elementsmentioning

confidence: 65%

Seasonal Variations and Sources of 17 Aerosol Metal Elements in Suburban Nanjing, China

Chen

et al. 2016

Atmosphere

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Abstract:In this work, the seasonal variations and sources of trace metal elements in atmospheric fine aerosols (PM 2.5 ) were investigated for a year-long field campaign from July 2012 to June 2013, conducted in suburban Nanjing, eastern China, at a site adjacent to an industry zone. The PM 2.5 samples collected across four seasons were analyzed for 17 metal elements, namely, Sodium (Na), Magnesium (Mg), Aluminum (Al), Vanadium (V), Chromium (Cr), Manganese (Mn), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Selenium (Se), Strontium (Sr), Cadmium (Cd), Barium (Ba), Lead (Pb), Molybdenum (Mo), and Antimony (Sb) using an inductively coupled plasma mass spectrometry (ICP-MS). We found that the total concentration of all 17 metal elements was 1.23 µg/m 3 , on average accounting for 1.0% of the total PM 2.5 mass. For our data, mass concentrations of Al, Cd, Ba were highest in summer, Mg, Cu, Zn, Se, Pb peaked in autumn, Cr, Mn, Ni, As, Sr, Sb increased significantly in winter, while the concentrations of Na, V, Mo were at their highest levels in spring. Air mass back trajectory analysis suggested that air parcels that arrived at the site originated from four dominant regions (Japan, yellow sea and bohai; Southeast of China, the Pacific Ocean; Southwest of Jiangsu and Anhui province; Northern Asia inland and Mongolia region), in particular, the one from Northern Asia inland and Mongolia contained the highest concentrations of As, Sb, Sr, and was predominant in winter. Positive matrix factorization (PMF) analyses revealed that the industrial emission is the largest contributor (34%) of the observed metal elements, followed by traffic (25%), soil dust (19%), coal combustion (10%), incineration of electronic waste (9%), and a minor unknown source (3%). In addition, we have also investigated the morphology and composition of particles by using the scanning electron microscopy (SEM)/energy-dispersive spectrometry (EDS) techniques, and identified particles from coal burning sources, etc., similar to the PMF results.

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Section: Annual Characteristic Of Metal Elementsmentioning

confidence: 65%

Seasonal Variations and Sources of 17 Aerosol Metal Elements in Suburban Nanjing, China

Chen

et al. 2016

Atmosphere

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“…Significant positive correlation was found between Ni and Cu (r = 0.75, P < 0.05) and Zn and Cr (r = 0.88, P < 0.05). Correlations between heavy (Rafiei et al 2010;Bekteshi & Bara 2013), but in some cases provide only interesting information regarding the source and the pathways of the metals (Chen et al 2014). No significant correlation was found for arsenic.…”

Section: Resultsmentioning

confidence: 99%

Assessment of soil heavy metal pollution in a former mining area - before and after the end of mining activities

Demková¹,

Jezný²,

Bobuľská³

2017

Soil Water Res.

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Demková L., Jezný T., Bobuľská L. (2017): Assessment of soil heavy metal pollution in a former mining area -before and after the end of mining activities. Soil & Water Res., 12: 229−236.Toxicity and persistence of heavy metals, which are accumulated in the environment as the result of diverse industrial activities, represent serious environmental problem worldwide. The intense mineral extraction in mining areas has produced a large amount of waste material and tailings, which release toxic elements to the environment. The aim of the study was to determine in two time horizons (1997, 2015) the heavy metal contents of samples derived from ten sampling sites located in the former mining area of Central Spiš (Slovakia). In order to compare the level of contamination, the contamination factor (C i f ), degree of contamination (C d ), and pollution load index (PLI) were computed. Spearman's correlation coefficient was used in order to detect the relationships among heavy metals. A serious situation was found for Hg, Zn, and Cd, which exceeded limit values at all sampling sites within both studied years. In 1997, the average values of contamination factor have shown very high contamination with all studied heavy metals, and moderate contamination with Co. In 2015, the study area was classified as very highly contaminated with As, Hg, Zn, Cu, considerably contaminated with Ni, Cr, Pb, and Cd, while Co contamination was not detected. Since 1997 till 2015 the pollution load index decreased by about 38%, nevertheless even then almost all sampling sites were classified as heavily polluted. Despite the fact that mining activities were stopped or limited at the beginning of the 21 st century, the presence of heavy metals in soils remains at a serious level. The high level of contamination is a result of heavy metal persistence and non-biodegradability.

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“…There are a number of studies on statistical methods to estimate soil parameters and assist in environmental management (i.e., McBratney and Odeh, 1997;Chen et al, 2014;Khaledian et al, 2016a;Hosseini et al, 2017) including CEC using general linear models (Seybold et al, 2005), multiple linear regression (Shabani and Norouzi, 2015;Khaledian et al, 2016b), PLS and stepwise regression (Khaledian et al, 2016a), adaptive network-based fuzzy inference system (ANFIS) and artificial neural networks (ANNs) , and genetic expression programming (GEP) and multivariate adaptive regression splines (MARS) (Emamgolizadeh et al, 2015). However, these studies have not modeled CEC using advanced statistical methods such as algorithms (particle swarm optimization (PSO) and monotone regression tool (MONANOVA)) and compared the performance of these methods, which would represent an advance in the current knowledge.…”

Section: Introductionmentioning

confidence: 99%

Modeling soil cation exchange capacity in multiple countries

Khaledian

Brevik

Pereira

et al. 2017

CATENA

102

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A B S T R A C TCation exchange capacity (CEC), as an important indicator for soil quality, represents soil's ability to hold positively charged ions. We attempted to predict CEC using different statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R 2 = 0.82 and Root Mean Squared Error (RMSE) = 6.32) and ANNs (R 2 = 0.82 and RMSE = 5.53) were the best models to estimate CEC, PSO (R 2 = 0.80 and RMSE = 5.54) and PCR (R 2 = 0.70 and RMSE = 6.48) also worked well and the results were very similar to each other. While the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations, clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available.

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Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

Cited by 132 publications

References 42 publications

Seasonal Variations and Sources of 17 Aerosol Metal Elements in Suburban Nanjing, China

Seasonal Variations and Sources of 17 Aerosol Metal Elements in Suburban Nanjing, China

Assessment of soil heavy metal pollution in a former mining area - before and after the end of mining activities

Modeling soil cation exchange capacity in multiple countries

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