Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Zhang, Chaosheng; Ya, Tang; Luo, Lin; Xu, Weilin

doi:10.1016/j.envpol.2009.05.044

Cited by 60 publications

(21 citation statements)

References 11 publications

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“…Kriging with antecedent data transformation is commonly termed transGaussian kriging (e.g., Cressie 1993;Schabenberger and Gotway 2005). It has previously been used in applications outside QPE such as for the mapping of hake abundance (Jardim and Ribeiro 2008) or mapping of Pb concentration in soils (Zhang et al 2009). In the context of geostatistical radar-gauge combination, some studies have used a prescribed data transformation: for example, Schuurmans et al (2007) use a square root transformation in an application with situations of abundant daily rainfall, and Verworn and Haberlandt (2011) use a log transformation for selected flood cases.…”

Section: Introductionmentioning

confidence: 99%

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

Erdin

Frei

Künsch

2012

Journal of Hydrometeorology

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Geostatistics provides a popular framework for deriving high-resolution quantitative precipitation estimates (QPE) by combining radar and rain gauge data. However, the skewed and heteroscedastic nature of precipitation is in contradiction to assumptions of classical geostatistics. This study examines the potential of trans-Gaussian kriging to overcome this contradiction. Combination experiments are undertaken with kriging with external drift (KED) using several settings of the Box-Cox transformation. Hourly precipitation data in Switzerland for the year 2008 serve as test bed to compare KED with and without transformation. The impact of transformation is examined with regard to compliance with model assumptions, accuracy of the point estimate, and reliability of the probabilistic estimate. Data transformation improves the compliance with model assumptions, but some level of contradiction remains in situations with many dry gauges. Very similar point estimates are found for KED with untransformed and appropriately transformed data. However, care is needed to avoid excessive transformation (close to log) because this can introduce a positive bias. Strong benefits from transformation are found for the probabilistic estimate, which is rendered positively skewed, sensitive to precipitation amount, and quantitatively more reliable. Without transformation, 44% of all precipitation observations larger than 5 mm h 21 are considered as extremely unlikely by the probabilistic estimate in the test application. Transformation reduces this rate to 4%. Although transformation cannot fully remedy the complications for geostatistics in radar-gauge combination, it seems a useful procedure if realistic and reliable estimation uncertainties are desired, such as for the stochastic simulation of QPE ensembles.

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Section: Introductionmentioning

confidence: 99%

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

Erdin

Frei

Künsch

2012

Journal of Hydrometeorology

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“…Earlier studies have adopted Morans I analysis to identify pollution hotspots and spatial outliers based on results of soil surveys3536. The geographically weighted regression (GWR) method, a spatial form of linear regression, analyzes spatial relationships between variables37. Better than the classical regression method, GWR can be used for exploring spatially varied relationship between soil contaminants and the factors that influence the extent of contamination, such as population distribution, traffic distribution, urban development levels, soil properties, and land-use types.…”

mentioning

confidence: 99%

Spatial Analysis of PAHs in Soils along an Urban–Suburban–Rural Gradient: scale effect, distribution patterns, diffusion and influencing factors

Peng

Wang

Chen

2016

Sci Rep

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Spatial statistical methods including Cokriging interpolation, Morans I analysis, and geographically weighted regression (GWR) were used for studying the spatial characteristics of polycyclic aromatic hydrocarbon (PAH) accumulation in urban, suburban, and rural soils of Beijing. The concentrations of PAHs decreased spatially as the level of urbanization decreased. Generally, PAHs in soil showed two spatial patterns on the regional scale: (1) regional baseline depositions with a radius of 16.5 km related to the level of urbanization and (2) isolated pockets of soil contaminated with PAHs were found up to around 3.5 km from industrial point sources. In the urban areas, soil PAHs showed high spatial heterogeneity on the block scale, which was probably related to vegetation cover, land use, and physical soil disturbance. The distribution of total PAHs in urban blocks was unrelated to the indicators of the intensity of anthropogenic activity, namely population density, light intensity at night, and road density, but was significantly related to the same indicators in the suburban and rural areas. The moving averages of molecular ratios suggested that PAHs in the suburban and rural soils were a mix of local emissions and diffusion from urban areas.

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“…It is expected that the methodologies demonstrated in this study can be applied in other datasets of soil science to analyze ''points of interest'', such as the soil pollution studies, as the High-Low outliers may imply the potential contamination of heavy metals (Zhang et al 2009). It should also be noted that if there is no evidence to show that outliers are erroneous such as laboratory errors, they had better be put back during the interpolation stage of kriging to honour their existence.…”

Section: Spatial Structure Analysesmentioning

confidence: 98%

“…Since the bins (class intervals) used to count the frequencies are equally distributed, the outlying values are located far away from the majority of values, which can be visually identified (Zhang et al 2009).…”

Section: Histogram and Box Plotmentioning

confidence: 99%

“…These outliers are also named ''global outliers'' (Lalor and Zhang 2001). Compared to the global outliers, local outliers can be judged based on the comparison of values in question with their neighboring values which are spatially located in a certain distance, and such outliers can be regarded as ''spatial outliers'' or ''local outliers'' (Zhang et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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Outlier identification of soil phosphorus and its implication for spatial structure modeling

Zhao

Zhang

et al. 2015

Precision Agric

Self Cite

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Outliers are classified as global outliers and spatial outliers. Up to now, there is little information about the outliers especially the spatial outliers and their influence on the spatial structure modeling of soil properties. A total of 537 soil samples were collected based on a 30 9 30 m grid in a permanent dairy farm in southeast Ireland. Graphic methods of histogram and box plot combined with Moran's I were applied to detect the outliers of soil phosphorus (P). Sixteen outliers (5 global outliers and 11 spatial outliers) of soil P were found in the study area. Compared to the raw data, the data with global outliers excluded always had the larger global Moran's I value indicating a stronger spatial autocorrelation. Clear spatial clusters (High-High and Low-Low clusters) were observed based on local Moran's I. The High-High spatial clusters were located around the main farm yard and near the traffic route due to more intensive management by farmers. The Low-Low spatial clusters were mainly close to the river. For these areas, P fertilizer or slurry should be applied for healthy grass growth. The dataset with outliers excluded had a reliable semi-variogram model with a low nugget/sill ratio (32.4 %), which was closed to its corresponding transformed data (30.5 %). The cross-validation results revealed that the dataset without outliers had the strongest linear regression model (r = 0.768), indicating that the outliers played an important role in the spatial structure modeling.

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Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Cited by 60 publications

References 11 publications

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

Spatial Analysis of PAHs in Soils along an Urban–Suburban–Rural Gradient: scale effect, distribution patterns, diffusion and influencing factors

Outlier identification of soil phosphorus and its implication for spatial structure modeling

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