Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation

Davis, Harley T.; Aelion, C. Marjorie; McDermott, Suzanne; Lawson, Andrew

doi:10.1016/j.envpol.2009.03.021

Cited by 265 publications

(129 citation statements)

References 42 publications

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“…Several studies use this tool to show the spatial distribution of metallic pollutants in urban areas (Facchinelli et al 2001;Li et al 2001;Lee et al 2006;Zhang et al 2008;Franco-Uría et al 2009;Yang et al 2009;Davis et al 2009). Li et al (2004) produce geochemical maps of heavy metals in Kowloon's urban soils using GIS methods.…”

Section: Introductionmentioning

confidence: 97%

The study of metal contamination in urban topsoils of Mexico City using GIS

et al. 2010

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This research presents and discusses information concerning the spatial distribution of heavy metals (Pb, Cu, Zn, Ba, Co, Cr, Ni and V) in the urban environments of Mexico City using geographical information system and statistical analysis. Superficial soil samples (n = 146) were analyzed. The highest contamination indices were found in the north and center zone of the metropolitan area. In contrast, the surrounding rural fields show a lower impact grade. The higher concentrations of Pb, Cu, Zn and Ba were observed as being related to high vehicular traffic, nevertheless other elements such as Co, Cr, Ni and V do not show anthropogenic influence and their content can be attributed to the parental rock. The results are compared with previous surveys carried out in 2003 in order to evaluate temporal deposition trends. No changes were found on reported concentrations except for Cu and Zn, whose concentration has increased in later years. The results suggest that spatial distribution analysis and results in comparison with previous studies could be useful for the management and sustainable development of the metropolitan area of Mexico City.

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Section: Introductionmentioning

confidence: 97%

The study of metal contamination in urban topsoils of Mexico City using GIS

et al. 2010

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“…In order to represent regional pollution by limited ground monitoring sites, spatial interpolation was still an effective method widely used in environmental areas [11][12]. However, the research objects of spatial interpolation were mainly in soil elements [13][14] and meteorological elements [15][16], and less in atmospheric PM due to a certain number of samples being required for this method. Due to objective reasons such as the incomplete monitoring system and the high-cost of atmospheric monitoring equipment over a long period of time, it was difficult to obtain PM concentrations with the same complete time series in multiple points.…”

Section: Introductionmentioning

confidence: 99%

Temporal-Spatial Variations of Concentrations of PM10 and PM2.5 in Ambient Air

Liu

Kepeng

Xiao-dong

et al. 2016

Pol. J. Environ. Stud.

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Since the space points' average concentrations of PM obtained by air quality automatic monitoring sites were less representative of PM pollution levels in the Beijing area, it was necessary to improve the spatial resolution of PM concentrations on the basis of continuous time series. In order to solve the problem, we used one-hour average concentrations of PM from March 2013 to February 2014 obtained by monitoring sites. Firstly, concentration variations with time scale of PM 2.5 and PM 10 were researched to find out their correlations and pollution grades in continuous time series. Secondly, in order to realize the spatial distribution characteristics from points to surfaces, MATLAB spatial interpolation tools were used to predict the average concentrations of PM on any latitude-longitude grid in regional surface, then spatial interpolation on longitudes and latitudes, and the PM concentrations were researched by radial basis functions based on biharmonic green function. Finally, by constructing decision functions and sample sets, the interpolation results were tested by k-fold cross validation to analyze the error distribution between monitoring values and fitted values, and then they were compared with Kriging interpolation results realized by DACE tool in MATLAB. The results showed there were periodical variations and significant correlations on the average concentrations of PM from March 2013 to February 2014 in Beijing. The PM pollutions also had obvious regional characteristics. Interpolation results of radial basis function interpolation on PM concentrations could represent their spatial distribution in Beijing, since the method had a certain precision to improve utilization of spatial information. Moreover, the analysis showed that the main factors of PM pollution were dust storms and strong winds in spring and autumn, rainfall and the warm wet climate in summer, and cold fronts and snowfall in winter. Pollution characteristics in the Beijing area were higher in the south and lower in the north, and the pollution sources might be regional transport as well as local anthropogenic sources. The conjoint analysis on time series and spatial interpolation of concentrations had significance for further research of time-space relationship of PM, and it also provided a method for understanding regional pollution characteristics.

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“…Models that interpolate metal concentration in surface soils are often data and computer intensive, while demanding elaborate statistical analyses (Davis et al, 2009), and applied to small geographic areas. In air pollution modelling, simple linear regression, usually described as Land Use Regression (LUR), has been used to predict ambient concentrations using basic geographically derived independent variables, with comparable results to other more complex methods (Jerrett et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Modelling Arsenic and Lead Surface Soil Concentrations using Land Use Regression

Deschenes

Setton

Demers

et al. 2013

E3S Web of Conferences

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Abstract. Land Use Regression (LUR) models are increasingly used in environmental and exposure assessments to predict the concentration of contaminants in outdoor air. We explore the use of LUR as an alternative to more complex models to predict the concentration of metals in surface soil. Here, we used 55 soil samples of As and Pb collected in 1996 across British Columbia (BC), Canada by the Ministry of Environment. Predictor variables were derived for each sample site using Geographic Information System (GIS). For As (R 2 = 0.44), the resulting linear regression model includes the total length of roads (m) within 25 km, and bedrock geology. For the Pb model (R 2 =0.78), the predictor variables are the total surface area of industrial land use (m 2 ) within 5 km , the emissions of Pb (t) within 10 and 25 km, and the presence of closed mines within 50 km. The study proposes that LUR can reasonably predict the concentrations of As and Pb in surface soil over large areas.

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Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation

Cited by 265 publications

References 42 publications

The study of metal contamination in urban topsoils of Mexico City using GIS

The study of metal contamination in urban topsoils of Mexico City using GIS

Temporal-Spatial Variations of Concentrations of PM10 and PM2.5 in Ambient Air

Modelling Arsenic and Lead Surface Soil Concentrations using Land Use Regression

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