Living with arsenic in the environment: An examination of current awareness of farmers in the Bengal basin using hybrid feature selection and machine learning-Reference-Cited by-同舟云学术

Living with arsenic in the environment: An examination of current awareness of farmers in the Bengal basin using hybrid feature selection and machine learning

Published:2021-08 Issue: Volume:153 Page:106529
ISSN:0160-4120
Container-title:Environment International
language:en
Short-container-title:Environment International

Author:

Mishra Debasish,Das Bhabani S.^ORCID,Sinha Tathagata,Hoque Jiaul M.,Reynolds Christian,Rafiqul Islam M.,Hossain Mahmud^ORCID,Sar Pinaki,Menon Manoj^ORCID

Funder

EPSRC

Publisher

Elsevier BV

Subject

General Environmental Science

Reference49 articles.

1. Socioeconomic status and risk for Arsenic-related skin lesions in Bangladesh;Argos;Am. J. Public Health,2007

2. Effect of cooking on arsenic concentration in rice;Atiaga;Environ. Sci. Pollut. Res.,2020

3. Environmental arsenic toxicity in West Bengal, India: A brief policy review;Basu;Indian J. Public Health,2015

4. BRAC, 2000. Combating a deadly menace: early experiences with a community-based arsenic mitigation project in Bangladesh. Research Monograph no. 16. Dhaka.

5. Carey, M., Donaldson, E., Signes-Pastor, A.J., Meharg, A.A., 2018., n.d. Dilution of rice with other gluten free grains to lower inorganic arsenic in foods for young children in response to European Union regulations provides impetus to setting stricter standards.

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