Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils

Lv, Jianshu; Yang, Liu; Zhang, Zulu; Dai, Jierui

doi:10.1016/j.jhazmat.2013.07.065

Cited by 170 publications

(62 citation statements)

References 56 publications

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“…As a classical prediction method, MLSR is widely used to predict soil properties and the interaction between analysis variables by considering many factors [40,41]. For instance, Ishii et al [42] used the MLSR method by considering all relevant information, eliminating irrelevant factors, simplifying equations, reducing errors, adding variables to the model one by one, and analyzing the explanatory variables with F-test.…”

Section: Multiple Stepwise Linear Regressionsmentioning

confidence: 99%

“…The MLSR model introduced each variable into the model one by one, and F-test was used to analyze and explain the contribution of each variable to the prediction [43]. In the process of modeling, environment variables were introduced into the model one by one, and unimportant environment variables were proposed by using t-test [41]. During the whole iteration process, the iteration was stopped until there were no environment variables that could be added and deleted in the modeling process.…”

Section: Multiple Stepwise Linear Regressionsmentioning

confidence: 99%

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Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

et al. 2020

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Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

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Section: Multiple Stepwise Linear Regressionsmentioning

confidence: 99%

Section: Multiple Stepwise Linear Regressionsmentioning

confidence: 99%

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

et al. 2020

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“…Soil sites were chosen in mid-August 2014 by using a uniform grid method in the study area. In total, 54 soil sampling sites were collected, including 5 sites respectively in tideland and halophyte land in coastal wetlands, and 44 sites (industrial land (10), forestland (6), residential land (7), aquaculture land (6), cropland (8), and vegetable land (7)) in the areas reclaimed for 30 years. Both surface (0-20 cm) and subsurface (20-40 cm) samples were collected from each sampling site and recorded using GPS ( Figure 2).…”

Section: Sampling Design and Chemical Analysismentioning

confidence: 99%

Enrichment, Source Apportionment and Health Risk Assessment of Soil Potentially Harmful Elements Associated with Different Land Use in Coastal Tidelands Reclamation Area, Eastern China

Xin-jian

Huang

Xie

et al. 2020

IJERPH

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Coastal tidelands are important ecological habitat resources and valuable resources for agricultural land reclamation. Enrichment of potentially harmful elements (PHEs) in soil caused by anthropogenic activity is an important factor implicated in the ecological deterioration of soil in China. A total of 54 soil sample sites were selected from a 30-year reclaimed tideland and an adjoining coastal wetland. Descriptive and multivariate statistical analyses were performed to describe the enrichment, source, health risk status of eight PHEs (As, Co, Cr, Cu, Mn, Ni, Pb, and Zn) after long-term reclamation. Results indicated that after 30 years of reclamation, most soil PHEs are slightly enriched, whereas no serious threat of environmental pollution was observed. In the reclamation area, the enrichment of PHEs in the aquaculture land, industrial land, and cropland was relatively high compared with other land use types, such as tideland and halophyte land. The source analysis divided the PHEs into five categories: (1) Cu; (2) Co and Mn; (3) Cr; (4) As and Pb; (5) Zn and Ni. Cu was completely derived from natural parent materials and other elements were governed by both weathering of parent rock and human activities, including agricultural activities, industrial production, and transportation emissions. The health risk assessment showed that the soil PHEs potentially had no non-carcinogenic risk to the public, but there was an acceptable probability to have cancer due to Cr and As. Meanwhile, children are more susceptible to harm from the PHEs in soil than adults. According to the economic and social development situation in the coastal region, it is necessary to pay attention to the environmental threats of PHEs enrichment.

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“…In this study, the approach followed for determining the subset of variable selection problem is the forward stepwise regression [24,25,26], this method is simple to define. We began with no variables in the model; we selected the variable that had the highest R-Squared.…”

Section: Variables Selectionmentioning

confidence: 99%

Quantitative structure-property relationship study of alcohols water solubility based on a new model combined modified autocorrelation method and electro-topological indices

Jermouni¹,

Nohair²

2020

Preprint

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In this study, structure water solubility modeling was performed to describe a set of 50 of aliphatic alcohols in a Quantitative Structure-Property Relationship model by developing of two descriptors types based on multifunctional autocorrelation method, which gives a general description of whole molecule; and electro-topological descriptors. The index combines the topological nature with electronic state of the atom. The Modified Autocorrelation Method was used in structure–property relationships to describe the local environment of the hydroxyl group. For the statistical studies, Multiple Linear Regression, Artificial Neural Networks and Principal Components Analysis were used. The approach efficiency approach was evaluated through the predictive ability of models by leave-p-Out cross-validation method. The coefficient of determination and errors of descriptors combination calculated respectively by multiple linear regression and artificial neural networks were r= 0.99, s = 0.18 and r = 0.99, s = 0.32. In order to simplify components computation, the molecules were coded by means of SMILES system and stored as input files. The results showed that aliphatic alcohols solubility is dominated by the shape and molecule branching, also the electro-topological descriptors had a considered model effect.

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Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multi-scale variability of heavy metals in soils

Cited by 170 publications

References 56 publications

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

Enrichment, Source Apportionment and Health Risk Assessment of Soil Potentially Harmful Elements Associated with Different Land Use in Coastal Tidelands Reclamation Area, Eastern China

Quantitative structure-property relationship study of alcohols water solubility based on a new model combined modified autocorrelation method and electro-topological indices

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