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Geogenic contamination handbook. Addressing arsenic and fluoride in drinking water
Bader, H. P., Berg, M., Bretzler, A., Gebauer, H., Huber, A. C., Hug, S. J., … Yang, H. (2015). Geogenic contamination handbook. Addressing arsenic and fluoride in drinking water. (C. A. Johnson & A. Bretzler, Eds.). Retrieved from https://www.eawag.ch/en/research/humanwelfare/drinkingwater/wrq/geogenic-contamination-handbook/
Arsenic mitigation in Bangladesh: an analysis of institutional stakeholders' opinions
Khan, N. I., & Yang, H. (2014). Arsenic mitigation in Bangladesh: an analysis of institutional stakeholders' opinions. Science of the Total Environment, 488-489, 493-504. https://doi.org/10.1016/j.scitotenv.2013.11.007
Household's willingness to pay for arsenic safe drinking water in Bangladesh
Khan, N. I., Brouwer, R., & Yang, H. (2014). Household's willingness to pay for arsenic safe drinking water in Bangladesh. Journal of Environmental Management, 143, 151-161. https://doi.org/10.1016/j.jenvman.2014.04.018
An analysis of institutional stakeholders’ opinion on arsenic mitigation in Bangladesh
Khan, N. I., & Yang, H. (2012). An analysis of institutional stakeholders’ opinion on arsenic mitigation in Bangladesh. In J. C. Ng, B. N. Noller, R. Naidu, J. Bundschuh, & P. Bhattacharya (Eds.), Arsenic in the environment. Proceedings. Understanding the geological and medical interface of arsenic. As 2012 (pp. 499-502). Retrieved from https://www.taylorfrancis.com/books/e/9780203078808/chapters/10.1201%2Fb12522-200
Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach
Wei, S., Song, J., & Khan, N. I. (2012). Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrological Processes, 26(2), 281-296. https://doi.org/10.1002/hyp.8227