Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport.
Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport.
复制标题
估计与内陆洪水事件相关的微生物风险：桥接理论和病原体运输模型。
DOI：
10.1080/10643389.2016.1269578
复制
发表时间：
2016
影响因子：
12.6
通讯作者：
Remais JV
中科院分区：
文献类型：
--
作者：
Collender PA;Cooke OC;Bryant LD;Kjeldsen TR;Remais JV
众所周知，洪水会促进传染病传播，但对与洪水相关的微生物风险的定量研究却很有限。病原体归宿和传播模型提供了一个框架来研究景观特征、水文学和水传播疾病风险之间的相互作用，但尚未针对洪水条件进行广泛开发。我们严格检查当前水文模型的能力，以表示伴随洪水而来的异常水流路径、不均匀水流深度和不稳定流速。我们研究了水动力过程与土壤和沉积物中病原体的时空变化悬浮和沉积之间的理论联系；病原体在流动中的扩散；以及影响病原体运输和持久性的成分浓度。认识到知识和建模实践方面的差距，我们提出了一个研究议程，以加强应用于内陆洪水的微生物命运和传输模型：1）开发模型，其中包含洪水源（例如厕所）的病原体排放、传输成分对病原体持久性的影响以及供应有限的病原体传输； 2）研究评估参数可识别性并比较不同程度的过程表示下的模型性能，在一系列设置中； 3) 开发遥感数据集以支持脆弱、数据匮乏地区的建模； 4) 建模者和实地研究人员之间的合作，以扩大现场有用数据的收集。
Flooding is known to facilitate infectious disease transmission, yet quantitative research on microbiological risks associated with floods has been limited. Pathogen fate and transport models provide a framework to examine interactions between landscape characteristics, hydrology, and waterborne disease risks, but have not been widely developed for flood conditions. We critically examine capabilities of current hydrological models to represent unusual flow paths, non-uniform flow depths, and unsteady flow velocities that accompany flooding. We investigate the theoretical linkages between hydrodynamic processes and spatio-temporally variable suspension and deposition of pathogens from soils and sediments; pathogen dispersion in flow; and concentrations of constituents influencing pathogen transport and persistence. Identifying gaps in knowledge and modeling practice, we propose a research agenda to strengthen microbial fate and transport modeling applied to inland floods: 1) development of models incorporating pathogen discharges from flooded sources (e.g., latrines), effects of transported constituents on pathogen persistence, and supply-limited pathogen transport; 2) studies assessing parameter identifiability and comparing model performance under varying degrees of process representation, in a range of settings; 3) development of remotely sensed datasets to support modeling of vulnerable, data-poor regions; and 4) collaboration between modelers and field-based researchers to expand the collection of useful data in situ.