Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina (Epagri), Rodovia Admar Gonzaga, 1.347, Itacorubi, Florianópolis, SC 88034-901, Brazil.
Centre for Environment, Fisheries & Aquaculture Science (Cefas), Weymouth Laboratory, Barrack Road, The Nothe, DT48UB, UK.
Mar Pollut Bull. 2018 Apr;129(1):284-292. doi: 10.1016/j.marpolbul.2018.02.047. Epub 2018 Mar 23.
This article describes a methodology for optimising predictive models for concentrations of faecal indicator organisms (FIOs) in coastal areas based on geographic and meteorological characteristics of upstream catchments. Concentrations of FIOs in mussels and water sampled from 50 sites in the south of Brazil from 2012 to 2013 were used to develop models to separately predict the spatial and temporal variations of FIOs. The geographical parameters used in predictive models for the spatial variation of FIOs were human population, urban area, percentage of impervious cover and total catchment area. The R of models representing catchments located within 3.1 km from the monitoring points was up to 150% higher than that for the nearest catchment. The temporal variation of FIOs was modelled considering the combined effect of meteorological parameters and different time windows. The explained variance in models based on rainfall and solar radiation increased up to 155% and 160%, respectively.
本文描述了一种基于上游集水区地理和气象特征优化沿海地区粪便指示生物（FIO）浓度预测模型的方法。使用了 2012 年至 2013 年在巴西南部 50 个地点采集的贻贝和水样中的 FIO 浓度来开发模型，以分别预测 FIO 的时空变化。用于预测 FIO 空间变化的预测模型中的地理参数有人口、城区、不透水覆盖百分比和总集水面积。代表监测点 3.1 公里范围内集水区的模型的 R 值比最近集水区的 R 值高 150%。FIO 的时间变化是通过考虑气象参数和不同时间窗口的综合影响来建模的。基于降雨和太阳辐射的模型解释方差分别增加了 155%和 160%。
