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首页> 外文期刊>Water Science and Technology >Occurrence and fate of Ascaris lumbricoides ova in biosolids in Victoria, Australia: a human health risk assessment of biosolids storage periods
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Occurrence and fate of Ascaris lumbricoides ova in biosolids in Victoria, Australia: a human health risk assessment of biosolids storage periods

机译:澳大利亚维多利亚生物溶胶中蛔虫炎的发生和命运:生物溶解期的人类健康风险评估

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Reuse of sewage biosolids in Victoria, Australia, typically involves mesophilic anaerobic digestion followed by air-drying and long-term storage to ensure removal of ova of soil-transmitted helminths (STH) such as Ascaris lumbricoides. Long-term storage degrades the biosolids' agronomic quality due to the loss of key plant nutrients and takes up large areas of storage space. The impact of varying biosolids holding times and other processes on STH using Ascaris as the reference STH pathogen was examined in this study using a quantitative risk analysis approach. Risk modelling of the potential human health impacts from the presence of Ascaris ova in biosolids was undertaken for discrete holding periods of 1, 2 and 3 years. Modelling showed that to meet the WHO 1 mu DALY . person(-1) . year(-1) disease burdens guideline for limiting exposure category, a biosolids storage period of 1.24 years or 2.1 years would be required, depending on the data source of ova shedding rates per worm (Bangladesh or Nigeria, respectively). The soil exposure and salad/root vegetable consumption models included a number of variables with moderate to high degrees of uncertainty. Monte Carlo simulation was used to assess the effect of uncertainty in model input variables and to assist in highlighting areas for further research.
机译:澳大利亚维多利亚污水生物糖浆中的重复使用通常涉及嗜可能的厌氧消化,然后涉及风干和长期储存,以确保除去土壤传播的蠕虫(STH)的OVA,例如蛔虫。长期储存由于关键植物营养物质的丧失而降低了生物溶解的农艺质量,并占用了大面积的存储空间。在本研究中,使用定量风险分析方法,研究了使用蛔虫作为参考属病原体的改变的生物糖浆持续时间和其他方法对STH的影响。对生物溶解中蛔虫炎的存在潜在的人体健康影响的风险建模对于离散的持有期为1,2和3年。建模表明,满足何慕达利。人(-1)。年份(-1)疾病负担限制曝光类别的指南,需要1.24岁或2.1年的生物溶解期,这取决于每蠕虫的OVA脱落率的数据来源(孟加拉国或尼日利亚)。土壤暴露和沙拉/根蔬菜消耗模型包括许多变量,具有中等至高的不确定性。 Monte Carlo仿真用于评估不确定性在模型输入变量中的效果,并有助于突出区域进行进一步研究。

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