Comparison of methods for addressing the point-to-area data transformation to make data suitable for environmental, health and socio-economic studies

Meng, Yuting; Cave, Mark; Zhang, Chaosheng

doi:10.1016/j.scitotenv.2019.06.452

Cited by 14 publications

(4 citation statements)

References 66 publications

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“…While the absence of input data normality is usually negated by introducing the logarithmic transformation [63], as was the case for TC, the additional lack of spatial autocorrelation for TN disabled the accurate kriging interpolation. This property was also a frequent occurrence in previous studies [64][65][66], indicating the necessity of a framework that does not require such prerequisites. Since value distributions of TC and TN represent a highly common case, a universal approach to soil prediction should be resistant to these properties, a criterion which was not met by the OK and RK geostatistical approaches in this study.…”

Section: Discussionmentioning

confidence: 55%

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

et al. 2022

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With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping.

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Section: Discussionmentioning

confidence: 55%

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

et al. 2022

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“…Spatial distribution patterns of heavy metals hazard index were described using ArcGIS 10.2 developed by ESRI. Recently, ordinary kriging and inverse distance weight (IDW) have been commonly used interpolation methods to describe the spatial distribution of heavy metals [MENG et al 2019]. Interpolation kriging was drawn for the non-carcinogenic HI distribution pattern for adults and children.…”

Section: Spatial Distribution Patterns and Data Analysismentioning

confidence: 99%

Journal of Water and Land Development

2022

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This study explains water quality in terms of seven heavy metals in the Upstream Citarum River and analyses human health risk (non-carcinogenic risk) for adults and children. Water samples were collected from five sampling locations along the Upstream Citarum River, i.e. from Majalaya Sub-District to Dayeuhkolot Sub-District. The contents of heavy metals were analysed by the Atomic Absorption Spectrometer (AAS) variant 240 FS. The results of the analysis showed that the pollution index value, which was categorised as slightly polluted from the highest to the lowest value, was as follows: location 4 (4.220) > location 1 (3.764) > location 2 (3.219) > location 5 (2.967) > location 3 (2.800). Values of the hazard index (HI) for adults and children were as follows: Pb > Cr > Cd > Zn > Ni > Co > Cu. Pb and Cr have HI values greater than 1. This indicates that these metals can have a negative impact on public health. The HI in the ingestion pathway was greater than that of the dermal pathway, and the HI value for children was greater than that for adults. Further research is needed regarding the health risks from groundwater around the area which is used directly by the community because river water and groundwater systems are interconnected through streambeds.

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“…The quality of the sampling set also affects the choice of the interpolation method. The kriging interpolation is more sophisticated than other methods, however, the limited number of sampling points, non-regular datasets, and clustered location of points could affect the quality of the interpolated outputs [57]. In the case of poorly distributed or few sampling points within the study area, then the application of the IDW method is better [58].…”

Section: Spatial Analysis and Modelling In The Geographical Informati...mentioning

confidence: 99%

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

et al. 2021

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Understanding the magnitude and distribution of the mixes of the near-ground carbon dioxide (CO2) components spatially (related to the surface characteristics) and temporally (over seasonal timescales) is critical to evaluating present and future climate impacts. Thus, the application of in situ measurement approaches, combined with the spatial interpolation methods, will help to explore variations in source contribution to the total CO2 mixing ratios in the urban atmosphere. This study presents the spatial characteristic and temporal trend of atmospheric CO2 levels observed within the city of Wroclaw, Poland for the July 2017–August 2018 period. The seasonal variability of atmospheric CO2 around the city was directly measured at the selected sites using flask sampling with a Picarro G2201-I Cavity Ring-Down Spectroscopy (CRDS) technique. The current work aimed at determining the accuracy of the interpolation techniques and adjusting the interpolation parameters for estimating the magnitude of CO2 time series/seasonal variability in terms of limited observations during the vegetation and non-vegetation periods. The objective was to evaluate how different interpolation methods will affect the assessment of air pollutant levels in the urban environment and identify the optimal sampling strategy. The study discusses the schemes for optimization of the interpolation results that may be adopted in areas where no observations are available, which is based on the kriging error predictions for an appropriate spatial density of measurement locations. Finally, the interpolation results were extended regarding the average prediction bias by exploring additional experimental configurations and introducing the limitation of the future sampling strategy on the seasonal representation of the CO2 levels in the urban area.

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Comparison of methods for addressing the point-to-area data transformation to make data suitable for environmental, health and socio-economic studies

Cited by 14 publications

References 66 publications

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

Journal of Water and Land Development

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

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