Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods

Lin, Yu‐Pin; Cheng, Bai You; Chu, Hsin; Chang, Tsun-Kuo; Yu, Hsiu‐Ying

doi:10.1016/j.geoderma.2011.05.004

Cited by 56 publications

(17 citation statements)

References 41 publications

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“…Geostatistics, multivariate methods and Geographic Information System (GIS) mapping are powerful analysis tools that have been used in numerous studies for determination of spatial distribution, and behavior of pollutants in urban areas (Gong et al, 2010;Guagliardi et al, 2012;Imperato et al, 2003;Lee et al, 2006;Morton-Bermea et al, 2009;Rodriguez-Salazar et al, 2011;Yuan et al, 2013). Kriging is one of the most commonly applied interpolation methods in environmental studies (Buttafuoco et al, 2010;Dayani and Mohammad, 2010;Hani and Kariminejad, 2010;Khalil et al, 2013;Lin et al, 2011;Xie et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of metals in urban soil of Novi Sad, Serbia: GIS based approach

Mihailović

Budinski-Petković

Popov

et al. 2015

Journal of Geochemical Exploration

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Section: Introductionmentioning

confidence: 99%

Spatial distribution of metals in urban soil of Novi Sad, Serbia: GIS based approach

Mihailović

Budinski-Petković

Popov

et al. 2015

Journal of Geochemical Exploration

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“…The IK interpolator estimates the probability that the concentration of a pollutant exceeds a specific threshold value at a given location (Lin et al, 2011). In this work, the IK estimator was used in order to produce a contamination risk map that represent the iso-probability that β-HCH milk content Z(u) exceeds the specified threshold value (zk) of 0.0005 mg/kg.…”

Section: { } Indicator Krigingmentioning

confidence: 99%

“…Among other techniques, indicator kriging (IK) is a geostatistical tool which estimates the probability of a contaminant to exceed specific threshold concentrations in not sampled locations on the basis of data from surrounding areas, thus providing a probability map of spatial contamination (Van Meirvenne and Goovaerts, 2001;Brus et al, 2002;Ciotoli et al, 2007;Huang et al, 2007;Chu et al, 2010). On the other hand, classical statistical methods such as logistic regression (LR) combined with GIS have been used for the same purpose based on the presence of some environmental predictive factors (Liu et al, 2005;Twarakavi and Kaluarachchi, 2005;Lin et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A spatial, statistical approach to map the risk of milk contamination by β-hexachlorocyclohexane in dairy farms

et al. 2013

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Abstract. In May 2005, beta-hexachlorocyclohexane (β-HCH) was found in a sample of bovine bulk milk from a farm in the Sacco River valley (Latium region, central Italy). The primary source of contamination was suspected to be industrial discharge into the environment with the Sacco River as the main mean of dispersion. Since then, a surveillance programme on bulk milk of the local farms was carried out by the veterinary services. In order to estimate the spatial probability of β-HCH contamination of milk produced in the Sacco River valley and draw probability maps of contamination, probability maps of β-HCH values in milk were estimated by indicator kriging (IK), a geo-statistical estimator, and traditional logistic regression (LR) combined with a geographical information systems approach. The former technique produces a spatial view of probabilities above a specific threshold at non-sampled locations on the basis of observed values in the area, while LR gives the probabilities in specific locations on the basis of certain environmental predictors, namely the distance from the river, the distance from the pollution site, the elevation above the river level and the intrinsic vulnerability of hydro-geological formations. Based on the β-HCH data from 2005 in the Sacco River valley, the two techniques resulted in similar maps of high risk of milk contamination. However, unlike the IK method, the LR model was capable of estimating coefficients that could be used in case of future pollution episodes. The approach presented produces probability maps and define highrisk areas already in the early stages of an emergency before sampling operations have been carried out.

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“…Spatial error models and geographically-weighted regressions have been performed to analyze the relationship between heavy metal concentrations and possible influencing factors, and they are considered to be more powerful tools for exploring spatial heterogeneity and have a better goodness-of-fit than conventional linear regression models [9,16]. However, apart from the lack of consideration for the soil background values, the results from single heavy metal regression with land use information are used for source apportionment and pollution management rather than evaluating pollutant release based on differing land use types [9,16,18]. Heavy metal pollution is triggered by wastewater irrigation, applying fertilizer, household refuse disposal, and other practices carried out on or around the cultivated land.…”

Section: Introductionmentioning

confidence: 99%

Impacts from Land Use Pattern on Spatial Distribution of Cultivated Soil Heavy Metal Pollution in Typical Rural-Urban Fringe of Northeast China

Wang

et al. 2017

IJERPH

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Under rapid urban sprawl in Northeast China, land conversions are not only encroaching on the quantity of cultivated lands, but also posing a great threat to black soil conservation and food security. This study’s aim is to explore the spatial relationship between comprehensive cultivated soil heavy metal pollution and peri-urban land use patterns in the black soil region. We applied spatial lag regression to analyze the relationship between PLI (pollution load index) and influencing factors of land use by taking suburban cultivated land of Changchun Kuancheng District as an empirical case. The results indicate the following: (1) Similar spatial distribution characteristics are detected between Pb, Cu, and Zn, between Cr and Ni, and between Hg and Cd. The Yitong River catchment in the central region, and the residential community of Lanjia County in the west, are the main hotspots for eight heavy metals and PLI. Beihu Wetland Park, with a larger-area distribution of ecological land in the southeast, has low level for both heavy metal concentrations and PLI values. Spatial distribution characteristics of cultivated heavy metals are related to types of surrounding land use and industry; (2) Spatial lag regression has a better fit for PLI than the ordinary least squares regression. The regression results indicate the inverse relationship between heavy metal pollution degree and distance from long-standing residential land and surface water. Following rapid urban land expansion and a longer accumulation period, residential land sprawl is going to threaten cultivated land with heavy metal pollution in the suburban black soil region, and cultivated land irrigated with urban river water in the suburbs will have a higher tendency for heavy metal pollution.

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Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods

Cited by 56 publications

References 41 publications

Spatial distribution of metals in urban soil of Novi Sad, Serbia: GIS based approach

Spatial distribution of metals in urban soil of Novi Sad, Serbia: GIS based approach

A spatial, statistical approach to map the risk of milk contamination by β-hexachlorocyclohexane in dairy farms

Impacts from Land Use Pattern on Spatial Distribution of Cultivated Soil Heavy Metal Pollution in Typical Rural-Urban Fringe of Northeast China

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