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Liu A, Qu C, Zhang J, Sun W, Shi C, Lima A, De Vivo B, Huang H, Palmisano M, Guarino A, Qi S, Albanese S. Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 913:169498. [PMID: 38154632 DOI: 10.1016/j.scitotenv.2023.169498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/28/2023] [Accepted: 12/17/2023] [Indexed: 12/30/2023]
Abstract
There is yet no scientific consensus, and for now, on how to choose the optimal interpolation method and its parameters for mapping soil-borne organic pollutants. Take the polychlorinated biphenyls (PCBs) for instance, we present the comparison of some classic interpolation methods using a high-resolution soil monitoring database. The results showed that empirical Bayesian kriging (EBK) has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in inverse distance weighting (IDW) is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW's RMSE, but slightly affected in ordinary kriging (OK), while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.
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Affiliation(s)
- Ao Liu
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
| | - Chengkai Qu
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China.
| | - Jiaquan Zhang
- Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
| | - Wen Sun
- Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China
| | - Changhe Shi
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
| | - Annamaria Lima
- Department of Earth Sciences, Environment and Resources, University of Naples Federico II, Naples 80125, Italy
| | - Benedetto De Vivo
- Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China; Pegaso On-Line University, Naples 80132, Italy
| | - Huanfang Huang
- State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510535, China
| | - Maurizio Palmisano
- Experimental Research Center, National Research Council, Benevento 82100, Italy
| | - Annalise Guarino
- Department of Earth Sciences, Environment and Resources, University of Naples Federico II, Naples 80125, Italy
| | - Shihua Qi
- State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China
| | - Stefano Albanese
- Department of Earth Sciences, Environment and Resources, University of Naples Federico II, Naples 80125, Italy
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Mouat JS, Li X, Neier K, Zhu Y, Mordaunt CE, La Merrill MA, Lehmler HJ, Jones MP, Lein PJ, Schmidt RJ, LaSalle JM. Networks of placental DNA methylation correlate with maternal serum PCB concentrations and child neurodevelopment. ENVIRONMENTAL RESEARCH 2023; 220:115227. [PMID: 36608759 PMCID: PMC10518186 DOI: 10.1016/j.envres.2023.115227] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Gestational exposure to polychlorinated biphenyls (PCBs) has been associated with elevated risk for neurodevelopmental disorders. Placental epigenetics may serve as a potential mechanism of risk or marker of altered placental function. Prior studies have associated differential placental DNA methylation with maternal PCB exposure or with increased risk of autism spectrum disorder (ASD). However, sequencing-based placental methylomes have not previously been tested for simultaneous associations with maternal PCB levels and child neurodevelopmental outcomes. OBJECTIVES We aimed to identify placental DNA methylation patterns associated with maternal PCB levels and child neurodevelopmental outcomes in the high-risk ASD MARBLES cohort. METHODS We measured 209 PCB congeners in 104 maternal serum samples collected at delivery. We identified networks of DNA methylation from 147 placenta samples using the Comethyl R package, which performs weighted gene correlation network analysis for whole genome bisulfite sequencing data. We tested placental DNA methylation modules for association with maternal serum PCB levels, child neurodevelopment, and other participant traits. RESULTS PCBs 153 + 168, 170, 180 + 193, and 187 were detected in over 50% of maternal serum samples and were highly correlated with one another. Consistent with previous findings, maternal age was the strongest predictor of serum PCB levels, alongside year of sample collection, pre-pregnancy BMI, and polyunsaturated fatty acid levels. Twenty seven modules of placental DNA methylation were identified, including five which significantly correlated with one or more PCBs, and four which correlated with child neurodevelopment. Two modules associated with maternal PCB levels as well as child neurodevelopment, and mapped to CSMD1 and AUTS2, genes previously implicated in ASD and identified as differentially methylated regions in mouse brain and placenta following gestational PCB exposure. CONCLUSIONS Placental DNA co-methylation modules were associated with maternal PCBs and child neurodevelopment. Methylation of CSMD1 and AUTS2 could be markers of altered placental function and/or ASD risk following maternal PCB exposure.
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Affiliation(s)
- Julia S Mouat
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA; Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Genome Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA
| | - Xueshu Li
- Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Kari Neier
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA; Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Genome Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA
| | - Yihui Zhu
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA; Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Genome Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA
| | - Charles E Mordaunt
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA; Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Genome Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA
| | - Michele A La Merrill
- Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Department of Environmental Toxicology, University of California, Davis, CA, USA
| | - Hans-Joachim Lehmler
- Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Michael P Jones
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Pamela J Lein
- Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA; Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Rebecca J Schmidt
- Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA; Department of Public Health Sciences, School of Medicine, University of California, Davis, CA, USA
| | - Janine M LaSalle
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA; Perinatal Origins of Disparities Center, University of California, Davis, CA, USA; Genome Center, University of California, Davis, CA, USA; MIND Institute, University of California, Davis, CA, USA.
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