Indian Institute of Technology Kharagpur, WB 721302, India.
Bangladesh Agricultural University, Mymensingh 2202, Bangladesh.
Environ Int. 2021 Aug;153:106529. doi: 10.1016/j.envint.2021.106529. Epub 2021 Mar 27.
High levels of arsenic in drinking water and food materials continue to pose a global health challenge. Over 127 million people alone in Bangladesh (BD) and West Bengal (WB) state of India are exposed to elevated levels of arsenic in drinking water. Despite decades of research and outreach, arsenic awareness in communities continue to be low. Specifically, very few studies reported arsenic awareness among low-income farming communities. A comprehensive approach to assess arsenic awareness is a key step in identifying research and development priorities so that appropriate stakeholder engagement may be designed to tackle arsenic menace. In this study, we developed a comprehensive arsenic awareness index (CAAI) and identified key awareness drivers (KADs) of arsenic to help evaluate farmers' preferences in dealing with arsenic in the environment. The CAAI and KADs were developed using a questionnaire survey in conjunction with ten machine learning (ML) models coupled with a hybrid feature selection approach. Two questionnaire surveys comprising of 73 questions covering health, water and community, and food were conducted in arsenic-affected areas of WB and BD. Comparison of CAAIs showed that the BD farmers were generally more arsenic-aware (CAAI = 7.7) than WB farmers (CAAI = 6.8). Interestingly, the reverse was true for the awareness linked to arsenic in the food chain. Application of hybrid feature selection identified 15 KADs, which included factors related to stakeholder interventions and cropping practices instead of commonly perceived factors such as age, gender and income. Among ML algorithms, classification and regression trees and single C5.0 tree could estimate CAAIs with an average accuracy of 84%. Both communities agreed on policy changes on water testing and clean water supply. The CAAI and KADs combination revealed a contrasting arsenic awareness between the two farming communities, albeit their cultural similarities. Specifically, our study shows the need for increasing awareness of risks through the food chain in BD, whereas awareness campaigns should be strengthened to raise overall awareness in WB possibly through media channels as deemed effective in BD.
饮用水和食物中高浓度的砷仍然是一个全球性的健康挑战。仅在孟加拉国(BD)和印度西孟加拉邦(WB),就有超过 1.27 亿人饮用的水中砷含量超标。尽管经过几十年的研究和宣传,社区的砷意识仍然很低。具体来说,很少有研究报告低收人群体的砷意识。评估砷意识的综合方法是确定研究和发展重点的关键步骤,以便设计适当的利益相关者参与来解决砷的威胁。在这项研究中,我们开发了一个综合的砷意识指数(CAAI),并确定了砷的关键意识驱动因素(KADs),以帮助评估农民在环境中处理砷时的偏好。CAAI 和 KADs 是通过结合十个机器学习(ML)模型的问卷调查和混合特征选择方法开发的。在 WB 和 BD 的砷污染地区进行了两次问卷调查,共 73 个问题,涵盖健康、水和社区以及食物。CAAI 的比较表明,BD 的农民通常比 WB 的农民更有砷意识(CAAI=7.7)。有趣的是,在食物链中与砷有关的意识方面,情况正好相反。混合特征选择的应用确定了 15 个 KADs,其中包括与利益相关者干预和种植实践相关的因素,而不是通常认为的年龄、性别和收入等因素。在 ML 算法中,分类和回归树以及单个 C5.0 树可以以 84%的平均准确率估计 CAAI。两个社区都同意对水测试和清洁水供应进行政策改革。CAAI 和 KADs 的组合揭示了这两个农民社区之间砷意识的对比,尽管它们具有文化相似性。具体来说,我们的研究表明,需要通过食物链提高 BD 地区对风险的认识,而 WB 地区可能需要通过媒体渠道加强宣传活动,以提高整体认识,因为在 BD 地区,媒体渠道被认为是有效的。