Evaluation of potential health risk of arsenic-affected groundwater using indicator kriging and dose response model

Lee, Jin‐Jing; Jang, Cheng‐Shin; Wang, Sheng Wei; Liu, Chen‐Wuing

doi:10.1016/j.scitotenv.2007.06.021

Cited by 80 publications

(23 citation statements)

References 35 publications

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“…For example, criteria can be developed and parameters measured and integrated to evaluate if a land area is suitable for development of irrigated agriculture. The MVIK method has been used to map the distribution of soil nutrients [10], define zones of soil pollutants [11], evaluate chemical health risks of groundwater [12], evaluate soil quality indices [6,13], classify soil degradation in agricultural lands [14], and assess groundwater quality for irrigated agriculture [15].…”

Section: Introductionmentioning

confidence: 99%

Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

Smith

Halvorson

2011

Applied and Environmental Soil Science

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Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK) procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO 2 > microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

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Section: Introductionmentioning

confidence: 99%

Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

Smith

Halvorson

2011

Applied and Environmental Soil Science

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“…Probabilities of values of unsampled points excluding specific thresholds are also obtained. Lee et al (2007) used indicator kriging for the evaluation of potential health risk of arsenic-affected groundwater in northeastern Taiwan. The experimental variogram is fitted with a theoretical model.…”

Section: Applications Of Geostatistics In the Study Of Arsenicmentioning

confidence: 99%

Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Ghosh

Parial

2014

Recent Trends in Modelling of Environmental Contaminants

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The arsenic-related groundwater problem is threatening huge areas in different countries like India, Bangladesh, China, Chile, Mexico etc. Arsenic contamination in ground water affects human health through its direct and indirect consumption. In order to combat this problem, an in-depth understanding of the background process of arsenic contamination is needed. Remote Sensing and Geographic Information System (GIS) offer a suitable solution for understanding and mapping the extent of the arsenic-infected areas. Various thematic maps on geomorphology, landuse/landcover, hydrology etc. can be prepared by interpreting remote sensing data, i.e. satellite imagery of the concerned area. These thematic maps can be further utilized to explore and discover relationships between that particular theme and the distribution of arsenic. Digital elevation models prepared using photogrammetric techniques utilizing Remote Sensing data may help in understanding the nature of distribution of arsenic-contaminated areas. GIS helps capturing, retrieving and analysing geospatial data. It recreates a real-world scenario and helps in analysing and creating new relevant information. In arsenic affected areas, people are exposed to arsenic contamination through its direct consumption with drinking water as well as through consumption of crops contaminated due to irrigation with arsenic rich groundwater. Risk and vulnerability assessment of such population can be done using GIS. Web-based GIS offers a unique opportunity of crowdsourcing and development of participatory public GIS. It can help in generation and analysis of distribution and assessment of impact of arsenic contamination and evolving mitigation strategy. Geostatistical methods help in interpolating the degree of arsenic contamination in unsampled locations as well as modelling uncertainty about unknown values. Initially experimental semivariograms are constructed and are fitted with suitable models like spherical, Gaussian etc. Various techniques of kriging are employed to interpolate level of arsenic contamination in unsampled 198 A. R. Ghosh and K. Parial locations using parameters obtained from variography. Zonation of arsenic contamination levels helps in the assessment of risk and vulnerability of residents of arsenic-contaminated areas and the development of efficient mitigation strategies.

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“…Alguns pesquisadores têm trabalhado a aplicação de krigagem indicativa (BASTANTE et al, 2005;SALGUEIRO et al, 2008;LEE et al, 2007), de krigagem fatorial e da cokrigagem. Neste trabalho,…”

Section: Introductionunclassified

Estatística descritiva e estacionaridade em variáveis geoquímicas ambientais

Assumpção

Hadlich²

2017

Eng. Sanit. Ambient.

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of soils and sediments collected in the field. We also determined the values of pH, salinity, total nitrogen, phosphorus, organic matter and particle size.Statistical analyzes, semivariogram development, ordinary kriging use and, lastly, cross validation were performed to measure the uncertainty of the previous measurement of data. It was found, in this work, by means of the variograms that although data were ordinary, they showed stationarity. In addition, the parameter of descriptive statistics that mostly correlates directly with the ordinary kriging is variance.

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Evaluation of potential health risk of arsenic-affected groundwater using indicator kriging and dose response model

Cited by 80 publications

References 35 publications

Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arsenic Contamination in Groundwater

Estatística descritiva e estacionaridade em variáveis geoquímicas ambientais

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