Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland

Zhang, Chaosheng; Luo, Lin; Xu, Weilin; Ledwith, Valerie

doi:10.1016/j.scitotenv.2008.03.011

Cited by 310 publications

(168 citation statements)

References 18 publications

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“…I is formally equivalent to the regression coefficient in a regression of Wy on y. A high positive local Moran's I value implies that the location under study has similarly high or low values as its neighbors, thus the locations are spatial clusters (Zhang et al, 2008) (low-high outliers) (Figure 2-b).…”

Section: Robust Distance-based Local Spatial Outlier and Cluster Idenmentioning

confidence: 99%

Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area

Nguyen

Hung

et al. 2016

Bulletin of the Mineral Research and Exploration

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Spatial clusters and spatial outliers play an important role in the study of the spatial distribution patterns of geochemical data. They characterize the fundamental properties of mineralization processes, the spatial distribution of mineral deposits, and ore element concentrations in mineral districts. In this study, a new method for the study of spatial distribution patterns of multivariate data is proposed based on a combination of robust Mahalanobis distance and local Moran's I i . In order to construct the spatial matrix, the Moran's I spatial correlogram was first used to determine the range. The robust Mahalanobis distances were then computed for an association of elements. Finally, local Moran's I i statistics was used to measure the degree of spatial association and discover the spatial distribution patterns of associations of Cu, Au, Mo, Ag, Pb, Zn, As, and Sb elements including spatial clusters and spatial outliers. Spatial patterns were analyzed at six different spatial scales (2km, 4 km, 6 km, 8 km, 10 km and 12 km) for both the raw data and Box-Cox transformed data. The results show that identified spatial cluster and spatial outlier areas using local Moran's I i and the robust Mahalanobis accord the objective reality and have a good conformity with known deposits in the study area.

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Section: Robust Distance-based Local Spatial Outlier and Cluster Idenmentioning

confidence: 99%

Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area

Nguyen

Hung

et al. 2016

Bulletin of the Mineral Research and Exploration

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“…The weight W ij can also be determined using a distance band: samples within a distance band are given the same weight, while those outside the distance band are given the weight of 0 (Zhang et al, 2008). The latter weighting method is used in GeoDa software in its current version (v. 0.95i, Spatial Analysis Laboratory, 2007).…”

Section: Spatial-outlier and Spatial-cluster Analysesmentioning

confidence: 99%

“…When using local Moran's I index to detect spatial outliers and spatial clusters, the results were affected by the definition of weight function, data transformation, and existence of extreme values (Zhang et al, 2008). Therefore, the above three factors were taken into consideration in order to obtain reliable and stable results.…”

Section: Spatial-outlier and Spatial-cluster Analysesmentioning

confidence: 99%

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Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Tunney

Zhang

2010

Z. Pflanzenernähr. Bodenk.

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The spatial variation of soil test P (STP) in grassland soils is becoming important because of the use of STP as a basis for policies such as the recently EU-introduced Nitrate Directive. This research investigates the spatial variation of soil P in grazed grassland plots with a long-term (38 y) experiment. A total of 326 soil samples (including 14 samples from an adjacent grasswood buffer zone) were collected based on a 10 × 10 m 2 grid system. The samples were measured for STP and other nutrients. The results were analyzed using conventional statistics, geostatistics, and a geographic information system (GIS). Soil test P concentrations followed a lognormal distribution, with a median of 5.30 mg L -1 and a geometric mean of 5.35 mg L -1 . Statistically significant (p < 0.01) positive correlation between STP and pH was found. Spatial clusters and spatial outliers were detected using the local Moran's I index (a local indicator of spatial association) and were mapped using GIS. An obvious low-value spatial-cluster area was observed on the plots that received zero-P fertilizer application from 1968 to 1998 and a large high-value spatial-cluster area was found on the relatively high-P fertilizer application plots (15 kg ha -1 y -1 ). The local Moran's I index was also effective in detecting spatial outliers, especially at locations close to spatial-cluster areas. To obtain a reliable and stable spatial structure, semivariogram of soil-P data was produced after elimination of spatial outliers. A spherical model with a nugget effect was chosen to fit the experimental semivariogram. The spatial-distribution map of soil P was produced using the kriging interpolation method. The interpolated distribution map was dominated by medium STP values, ranging from 3 mg to 8 mg L -1 . An evidently low-P-value area was present in the upper side of the study area, as zero or short-term P fertilizer was applied on the plots. Meanwhile, high-P-value area was located mainly on the plots receiving 15 kg P ha -1 y -1 (for 38 y) as these plots accumulated excess P after a long-term P-fertilizer spreading. The high-or low-value patterns were in line with the spatial clusters. Geostatistics, combined with GIS and the local spatial autocorrelation index, provides a useful tool for analyzing the spatial variation in soil nutrients.

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“…Where will be the most susceptible locations? It is imperative to learn how the pollutants are distributed in the urban settings (Zhang et al, 2008;Schröder, 2006).…”

Section: Introductionmentioning

confidence: 99%

A GIS technology based potential eco-risk assessment of metals in urban soils in Beijing, China

Wang

Bai

Chen

et al. 2012

Environmental Pollution

102

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a b s t r a c tEcological risks of heavy metals in urban soils were evaluated using Beijing, China as an example. Cadmium, Cu, Zn, Pb, Cr and Ni contents of 233 surface soils sampled by 1 min latitude Â 1 min longitude grid were used to identify their spatial distribution patterns and potential emission sources. Throughout the city, longer the duration of urbanization greater was the accumulations of heavy metals especially, Cd, Cu, Pb, and Zn. The soil Zn mainly came from the wears of vehicular tires. Point source emissions of heavy metals were few and far in the downwind southeeast quadrant of Beijing. The calculated risk indices showed potential median eco-risks in the ancient central city. No potential high eco-risk due to soil-borne heavy metals was found. The potential medium eco-risk areas in Beijing would expand from the initial 24 to 110 km 2 if soil pH were to reduce by 0.5 units in anticipation.

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Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland

Cited by 310 publications

References 18 publications

Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area

Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

A GIS technology based potential eco-risk assessment of metals in urban soils in Beijing, China

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