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Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India
Wu, R., Podgorski, J., Berg, M., & Polya, D. A. (2021). Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India. Environmental Geochemistry and Health, 43, 2649-2664. https://doi.org/10.1007/s10653-020-00655-7
Comparative methods for predicting cyanide pollution in artisanal small-scale gold mining catchment by using logistic regression and kriging with GIS
Razanamahandry, L. C., Digbeu, P. M., Andrianisa, H. A., Karoui, H., Podgorski, J., Manikandan, E., … Yacouba, H. (2020). Comparative methods for predicting cyanide pollution in artisanal small-scale gold mining catchment by using logistic regression and kriging with GIS. African Journal of Science, Technology, Innovation and Development, 12(3), 287-295. https://doi.org/10.1080/20421338.2020.1734325
Adaption to climate change: a case study of two agricultural systems from Kenya
Stefanovic, J. O., Yang, H., Zhou, Y., Kamali, B., & Ogalleh, S. A. (2017). Adaption to climate change: a case study of two agricultural systems from Kenya. Climate and Development, 11(4), 319-337. https://doi.org/10.1080/17565529.2017.1411241
Coupling predicted model of arsenic in groundwater with endemic arsenism occurrence in Shanxi Province, Northern China
Zhang, Q., Rodriguez-Lado, L., Liu, J., Johnson, C. A., Zheng, Q., & Sun, G. (2013). Coupling predicted model of arsenic in groundwater with endemic arsenism occurrence in Shanxi Province, Northern China. Journal of Hazardous Materials, 262, 1147-1153. https://doi.org/10.1016/j.jhazmat.2013.02.017
Use of qualitative environmental and phenotypic variables in the context of allele distribution models: detecting signatures of selection in the genome of Lake Victoria cichlids
Joost, S., Kalbermatten, M., Bezault, E., & Seehausen, O. (2012). Use of qualitative environmental and phenotypic variables in the context of allele distribution models: detecting signatures of selection in the genome of Lake Victoria cichlids. In F. Pompanon & A. Bonin (Eds.), Methods in molecular biology: Vol. 888. Data production and analysis in population genomics. Methods and protocols (pp. 295-314). https://doi.org/10.1007/978-1-61779-870-2_17
Predicting the risk of arsenic contaminated groundwater in Shanxi Province, Northern China
Zhang, Q., Rodríguez-Lado, L., Johnson, C. A., Xue, H., Shi, J., Zheng, Q., & Sun, G. (2012). Predicting the risk of arsenic contaminated groundwater in Shanxi Province, Northern China. Environmental Pollution, 165, 118-123. https://doi.org/10.1016/j.envpol.2012.02.020
A comparison of different rule-based statistical models for modeling geogenic groundwater contamination
Amini, M., Abbaspour, K. C., & Johnson, C. A. (2010). A comparison of different rule-based statistical models for modeling geogenic groundwater contamination. Environmental Modelling and Software, 25(12), 1650-1657. https://doi.org/10.1016/j.envsoft.2010.05.014
Modeling large scale geogenic contamination of groundwater, combination of geochemical expertise and statistical techniques
Amini, M., Johnson, A., Abbaspour, K. C., & Mueller, K. (2009). Modeling large scale geogenic contamination of groundwater, combination of geochemical expertise and statistical techniques. R. S. Anderssen, R. D. Braddock, & L. T. H. Newham (Eds.) (pp. 4100-4106). Presented at the 18th world IMACS / MODSIM congress. .
Predictive assessment of fish health and fish kills in the Neuse River Estuary using elicited expert judgment
Borsuk, M. E. (2004). Predictive assessment of fish health and fish kills in the Neuse River Estuary using elicited expert judgment. Human and Ecological Risk Assessment, 10(2), 415-434. https://doi.org/10.1080/10807030490438454