Empirical Bayesian kriging implementation and usage

Gribov, Alexander; Krivoruchko, Konstantin

doi:10.1016/j.scitotenv.2020.137290

Cited by 145 publications

(58 citation statements)

References 36 publications

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“…Given the mean level of air pollutants and the total confirmed new cases for 17 cities and provinces, the Kriging predicting model which considers the autocorrelation was applied to produce the prediction of the surface 16 . The general formula for the Kriging interpolation is as follows:

\overset{Z}{true} (s_{0}) = \sum_{i = 1}^{N} λ_{i} Z (s_{i})

where

Z (s_{i})

is the established value at the

i^{th}

location,

λ_{i}

is the unknown weight for the measurement at the neighborhood data of the

i^{th}

location, and

s_{0}

is the predicted location, and

N

is the number of measurements.…”

Section: Methodsmentioning

confidence: 99%

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Ambient air pollution, meteorology, and COVID‐19 infection in Korea

Hoang

Tran

2020

Journal of Medical Virology

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The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID‐19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID‐19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID‐19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0‐14; OR = 1.09; 95% CI = 1.03‐1.15) increase of COVID‐19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO 2 (lag 0‐7, lag 0.14, and lag 0‐21) was significantly associated with increases of COVID‐19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02‐1.25), 1.19 (1.09‐1.30), and 1.30 (1.19‐1.41), respectively. A 0.1 ppm increase in CO (lag 0‐21) was associated with the increase in COVID‐19 confirmed cases (OR = 1.10, 95% CI = 1.04‐1.16). There was a positive association between per 0.001 ppm of SO 2 concentration (lag 0, lag 0‐7, and lag 0‐14) and COVID‐19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04‐1.22), 1.20 (1.11‐1.31), and 1.15 (1.07‐1.25), respectively. There were significantly temporal associations between temperature, NO 2 , CO, and SO 2 concentrations and daily COVID‐19 confirmed cases in Korea.

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\overset{Z}{true} (s_{0}) = \sum_{i = 1}^{N} λ_{i} Z (s_{i})

where

Z (s_{i})

is the established value at the

i^{th}

location,

λ_{i}

is the unknown weight for the measurement at the neighborhood data of the

i^{th}

location, and

s_{0}

is the predicted location, and

N

is the number of measurements.…”

Section: Methodsmentioning

confidence: 99%

“…Given the mean level of air pollutants and the total confirmed new cases for 17 cities and provinces, the Kriging predicting model which considers the autocorrelation was applied to produce the prediction of the surface. 16 The general formula for the Kriging interpolation is as follows:…”

Section: Statistical Analysis 221 | Descriptive Analysismentioning

confidence: 99%

Ambient air pollution, meteorology, and COVID‐19 infection in Korea

Hoang

Tran

2020

Journal of Medical Virology

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“…CASCADE provides interpolated GIS raster files (GEOtiff; coordinate system WGS 1984 Arctic Polar Stereographic) for OC content, δ 13 C-OC and for Δ 14 C-OC in surface sediments across the Arctic Ocean. OC data was mapped in ArcGIS 10.6 and interpolated to a resolution of 5x5 km per grid cell using the Empirical Bayesian Kriging function (EBK; Gribov and Krivoruchko, 2020) in the commercially-available ArcGIS 10.8 software package (ESRI). Kriging builds on the assumption that two points located in proximity are more similar than two points further distant and creates a gridded surface of predicted values using an empirical semivariogram model.…”

Section: Data Interpolationmentioning

confidence: 99%

CASCADE – The Circum-Arctic Sediment CArbon DatabasE

Martens¹,

Romankevich²,

Semiletov³

et al. 2020

Preprint

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Abstract. Biogeochemical cycling in the extensive shelf seas and in the interior basins of the semi-enclosed Arctic Ocean are strongly influenced by land-ocean transport of carbon and other elements. The Arctic carbon cycle system is also inherently connected with the climate, and thus vulnerable to environmental and climate changes. Sediments of the Arctic Ocean are an active and integral part in Arctic biogeochemical cycling, and provide the opportunity to study present and historical input and fate of organic matter (e.g., through permafrost thawing). To compare differences between the Arctic regions and to study Arctic biogeochemical budgets, comprehensive sedimentary records are required. To this end, the Circum-Arctic Sediment CArbon DatabasE (CASCADE) was established to curate data primarily on concentrations of organic carbon (OC) and OC isotopes (δ13C, Δ14C), yet also on total N (TN) as well as of terrigenous biomarkers and other sediment geochemical and physical properties drawn both from the published literature and from earlier unpublished records through an extensive international community collaboration. This paper describes the establishment, structure and current status of CASCADE. This first public version includes OC concentrations in surface sediments at 4244 oceanographic stations including 2317 with TN concentrations, 1555 with δ13C-OC values, 268 with Δ14C-OC values and 653 records with quantified terrigenous biomarkers (high molecular weight n-alkanes, n-alkanoic acids and lignin phenols) distributed over the shelves and the central basins of the Arctic Ocean. CASCADE also includes data from 326 sediment cores, retrieved by shallow box- or multi-coring and deep gravity/piston coring, as well as sea-bottom drilling. The comprehensive dataset reveals several large-scale features, including clear differences in both OC content and isotope-based diagnostics of OC sources between the shelf sea recipients. This indicates, for instance, the release of strongly pre-aged terrigenous OC to the East Siberian Arctic shelf and younger terrigenous OC to the Kara Sea and thus provides clues about land-ocean transport of material released by thawing permafrost. CASCADE enables synoptic analysis of OC in Arctic Ocean sediments and facilitates a wide array of future empirical and modelling studies of the Arctic carbon cycle. CASCADE is openly and freely available online (https://doi.org/10.17043/cascade; Martens et al., 2020b), is provided in various machine-readable data formats (data tables, GIS shapefile, GIS raster), and also provides ways for contributing data for future CASCADE versions. CASCADE will be continuously updated with newly published and contributed data over the foreseeable future as part of the database management of the Bolin Centre for Climate Research at Stockholm University.

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“…When the data coverage is poor, EBK integrates an a priori background mean. EBK differs from other kriging methods by including the error introduced by estimating the underlying semivariogram [71]. Detailed information on EBK can be found in Krivoruchko (2012) [44].…”

Section: Ebk Methodsmentioning

confidence: 99%

“…In the PTE estimation results produced by the EBK algorithm, we detected a more dramatic PTE variation in the study area. This is because, unlike in OK, in EBK, the stochastic spatial process is represented locally as a stationary or non-stationary random field, so the prediction varies more strongly [71,85]. In addition, the sample size is another important factor to affect the interpolation accuracy [86][87][88].…”

Section: Spatial Prediction Accuracy Using Different Interpolation Mementioning

confidence: 99%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

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Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

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Empirical Bayesian kriging implementation and usage

Cited by 145 publications

References 36 publications

Ambient air pollution, meteorology, and COVID‐19 infection in Korea

Ambient air pollution, meteorology, and COVID‐19 infection in Korea

CASCADE – The Circum-Arctic Sediment CArbon DatabasE

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

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