Abstract：
Millions of people worldwide are affected by arsenic (As) contamination, particularly in South and Southeast Asian countries, where large-scale dependence on the usage of As-contaminated groundwater in drinking and irrigation is a familiar practice. Rice (Oryza sativa) cultivation is commonly done in South and Southeast Asian countries as a preferable crop which takes up more As than any other cereals. The present article has performed a scientific meta-data analysis and extensive bibliometric analysis to demonstrate the research trend in global rice As contamination scenario in the timeframe of 1980-2023. This study identified that China contributes most with the maximum number of publications followed by India, USA, UK and Bangladesh. The two words \'arsenic\' and \'rice\' have been identified as the most dominant keywords used by the authors, found through co-occurrence cluster analysis with author keyword association study. The comprehensive perceptive attained about the factors affecting As load in plant tissue and the nature of the micro-environment augment the contamination of rice cultivars in the region. This extensive review analyses soil parameters through meta-data regression assessment that influence and control As dynamics in soil with its further loading into rice grains and presents that As content and OM are inversely related and slightly correlated to the pH increment of the soil. Additionally, irrigation and water management practices have been found as a potential modulator of soil As concentration and bioavailability, presented through a linear fit with 95% confidence interval method.
摘要：
全球数百万人受到砷（As）污染的影响，特别是在南亚和东南亚国家，在饮用水和灌溉中大规模依赖受As污染的地下水的使用是一种熟悉的做法。水稻（Oryzasativa）的种植通常在南亚和东南亚国家作为一种较好的作物，比任何其他谷物都要多。本文进行了科学的元数据分析和广泛的文献计量分析，以证明1980-2023年全球水稻As污染情景的研究趋势。这项研究发现，中国贡献最大的出版物数量，其次是印度，美国,英国和孟加拉国。\'砷\'和\'米\'这两个词已被确定为作者使用的最主要关键词，通过共生聚类分析与作者关键词关联研究发现。对影响植物组织中砷负荷的因素和微环境性质的综合感知增加了该地区水稻品种的污染。这篇广泛的综述通过元数据回归评估分析了土壤参数，这些参数会影响和控制土壤中的As动态，并进一步加载到稻谷中，并表明As含量和OM与土壤的pH值增量成反比，并略有相关。此外,灌溉和水管理实践已被发现是土壤砷浓度和生物有效性的潜在调节剂，通过95%置信区间的线性拟合方法给出。
