Environmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA, Wageningen, the Netherlands.
Environmental Systems Analysis Group, Wageningen University, P.O. Box 47, 6700 AA, Wageningen, the Netherlands.
Water Res. 2019 Feb 1;149:202-214. doi: 10.1016/j.watres.2018.10.069. Epub 2018 Oct 30.
Cryptosporidium is a leading cause of diarrhoea and infant mortality worldwide. A better understanding of the sources, fate and transport of Cryptosporidium via rivers is important for effective management of waterborne transmission, especially in the developing world. We present GloWPa-Crypto C1, the first global, spatially explicit model that computes Cryptosporidium concentrations in rivers, implemented on a 0.5 × 0.5° grid and monthly time step. To this end, we first modelled Cryptosporidium inputs to rivers from human faeces and animal manure. Next, we use modelled hydrology from a grid-based macroscale hydrological model (the Variable Infiltration Capacity model). Oocyst transport through the river network is modelled using a routing model, accounting for temperature- and solar radiation-dependent decay and sedimentation along the way. Monthly average oocyst concentrations are predicted to range from 10 to 10 oocysts L in most places. Critical regions ('hotspots') with high concentrations include densely populated areas in India, China, Pakistan and Bangladesh, Nigeria, Algeria and South Africa, Mexico, Venezuela and some coastal areas of Brazil, several countries in Western and Eastern Europe (incl. The UK, Belgium and Macedonia), and the Middle East. Point sources (human faeces) appears to be a more dominant source of pollution than diffuse sources (mainly animal manure) in most world regions. Validation shows that GloWPa-Crypto medians are mostly within the range of observed concentrations. The model generally produces concentrations that are 1.5-2 log10 higher than the observations. This is likely predominantly due to the absence of recovery efficiency of the observations, which are therefore likely too low. Goodness of fit statistics are reasonable. Sensitivity analysis showed that the model is most sensitive to changes in input oocyst loads. GloWPa-Crypto C1 paves the way for many new opportunities at the global scale, including scenario analysis to investigate the impact of global change and management options on oocysts concentrations in rivers, and risk analysis to investigate human health risk.
隐孢子虫是全球范围内导致腹泻和婴儿死亡的主要原因之一。更好地了解隐孢子虫通过河流的来源、命运和传输对于有效管理水传播至关重要,特别是在发展中国家。我们提出了 GloWPa-Crypto C1,这是第一个全球、空间明确的模型,用于计算河流中的隐孢子虫浓度,在 0.5×0.5°网格和每月时间步长上实施。为此,我们首先从人类粪便和动物粪便建模隐孢子虫输入到河流中。接下来,我们使用基于网格的宏观水文模型(可变入渗能力模型)建模的水文模型。使用路由模型模拟卵囊在河流网络中的传输,考虑到温度和太阳辐射相关的衰减以及沿途的沉积。预测每月平均卵囊浓度在大多数地方范围从 10 到 10 个卵囊/L。高浓度的关键区域(“热点”)包括印度、中国、巴基斯坦和孟加拉国、尼日利亚、阿尔及利亚和南非、墨西哥、委内瑞拉以及巴西一些沿海地区、西欧和东欧的几个国家(包括英国、比利时和马其顿)以及中东。在大多数世界区域,点源(人类粪便)似乎比扩散源(主要是动物粪便)更成为污染的主要来源。验证表明,GloWPa-Crypto 的中位数大多在观察到的浓度范围内。该模型通常产生的浓度比观察到的浓度高 1.5-2 个对数。这主要可能是由于观察到的回收率缺失,因此可能过低。拟合优度统计数据是合理的。敏感性分析表明,该模型对输入卵囊负荷的变化最敏感。GloWPa-Crypto C1 为全球范围内的许多新机会铺平了道路,包括情景分析,以调查全球变化和管理选项对河流中卵囊浓度的影响,以及风险分析,以调查人类健康风险。