Metal contamination of rice supplies in Asia

最近，亚洲各地的大米供应受到重金属污染的问题引起了公众的广泛争论。特别讨论了两种元素：砷（As）（严格来说是类金属而不是金属）和镉（Cd）。大米被认为是人类饮食中砷的主要来源之一，长期接触砷会导致皮肤癌、肺癌、膀胱癌、前列腺癌以及心脏病。在亚洲的许多地方都发现了砷污染稻田的现象，特别是在恒河和其他从喜马拉雅山脉流出的河流的人口密集的三角洲，这些河流的岩石中含有砷。由于与氧化还原条件有关的土壤化学变化，砷在土壤中的溶解度——以及它被植物根系吸收的容易程度——在土壤因种植水稻而被淹时增加。因此，大米和砷之间有特殊的联系。镉也是一种主要致癌物和慢性肾毒素。水稻土壤中镉的来源包括母质岩石物质、贱金属开采和水稻水源上游的工业排放。与砷相反，镉的溶解度在土壤因种植水稻而淹水时趋于降低，这又与氧化还原化学的变化有关。但相反，如果土壤在生长季节被排干，例如在亚洲各地广泛使用的节水灌溉系统，则镉的溶解度和植物吸收量往往会增加。因此，人们担心节水灌溉技术可能会加剧镉污染的风险。人们还担心，为了人类营养，对锌等微量营养素吸收能力增强的新品种水稻也可能吸收更多的镉。然而，亚洲各地这些金属污染风险的程度尚不清楚。由于控制金属吸收的过程和变量的复杂组合，在不同的水稻环境中进行预测是困难的。在缺乏对问题的严重程度的可靠评估的情况下，很难优先考虑研究开发减少污染风险的技术，尽管有希望通过水稻育种和农学管理来做到这一点。因此，迫切需要更好地估计亚洲不同水稻环境中的污染风险。在这个项目中，我们建议开发基于空间数据分析和建模的方法来做到这一点。我们将开发基于环境、管理和遗传变量的空间解析数据的水稻污染水平预测模型。为此，我们将使用最新的空间数据分析和建模技术。为了发展我们的模式，我们将在孟加拉国和菲律宾进行试点研究，在这些研究中，我们将对相关地理区域的污染及其驱动因素进行调查。我们选择孟加拉国和菲律宾的原因与这些国家的相关空间数据资源的可用性以及这些国家对水稻污染研究的有利政治条件有关。我们将努力通过项目小组在目标国家内和跨科学学科的广泛伙伴关系网络，广泛传播项目发现和我们将制定的通用方法。

There has recently been much public debate about contamination of rice supplies across Asia with heavy metals. Two elements in particular are discussed: arsenic (As) (which is strictly a metalloid not a metal) and cadmium (Cd). Rice is thought to be one of the main sources of As in the human diet, and chronic As exposure is linked to cancers of the skin, lung, bladder and prostate as well as heart disease. Contamination of rice fields with As has been found in many parts of Asia, especially in the heavily-populated deltas of the Ganges and other rivers draining from the Himalayas whose rocks contain it. The solubility of As in soils - and hence the ease with which it is absorbed by plant roots - increases when a soil is flooded for paddy rice production because of changes in soil chemistry linked to redox conditions. Hence the particular link between rice and As. Cadmium is also a primary carcinogen and a chronic nephrotoxin. Sources of Cd in rice soils include parent rock materials and base-metal mining and industrial discharges up-stream of rice field water supplies. In contrast to As, the solubility of Cd tends to decrease when a soil is flooded for rice production, again linked to changes in redox chemistry. But conversely, if the soil is drained during the growing season, as for example in water-saving irrigation systems that are becoming widespread in various parts of Asia, Cd solubility and hence plant uptake tend to increase. So there is concern that water-saving irrigation technologies may exacerbate Cd contamination risks. There is also concern that new varieties of rice with enhanced uptake of micro-nutrients such as zinc, for the sake of human nutrition, may also take up more Cd. However, the extent of these metal contamination risks across Asia is not well understood. Because of the complex mix of processes and variables controlling metal uptake, making predictions across diverse rice environments is difficult. In the absence of reliable assessments of the extents the problem, it is difficult to prioritise research to develop technologies to lessen contamination risks, though there are promising possibilities for doing this through rice breeding and agronomic management. There is therefore a pressing need for better estimates of contamination risks in the different rice environments across Asia.In this project we propose to develop methods with which to do this based on spatial data analysis and modelling. We will develop models for predicting contamination levels in rice grain based on spatially-resolved data on environmental, management and genetic variables. We will use the latest spatial data analysis and modelling techniques for this. To develop our models, we will make pilot studies in Bangladesh and the Philippines, in which we will conduct surveys of contamination and its drivers over relevant geographic areas. Our reasons for choosing Bangladesh and the Philippines are related to the availabilities of relevant spatial data resources in these countries, and favourable political conditions for studies on rice contamination in these countries. We will seek to widely disseminate the project findings and the generic methods we will develop through the project team's wide network of partnerships within target countries and across scientific disciplines.