Instituto Hidrográfico ; R. Trinas 49 , 1200-615 Lisboa , Portugal.
Centro de Química Estrutural - Faculdade de Ciências da Universidade de Lisboa , Edifício C8, Campo Grande , 1749-016 Lisboa , Portugal.
Anal Chem. 2019 May 7;91(9):5698-5705. doi: 10.1021/acs.analchem.8b05781. Epub 2019 Apr 19.
The assessment of river water pollution trends is affected by the seasonal variation of river conditions, the variability of pollution sources, the heterogeneity of pollutants distribution, the representativeness/uncertainty of sampling, and the uncertainty of sample analysis. This work presents a methodology to model the uncertainty of river water sampling based on available information about the spatial distribution of the studied parameter in the river. The uncertainty from "single sampling" (SS) or by production of a composite sample by mixing m subsamples collected randomly (RS) or in a line that crosses the sampling circle (LS) was studied. This methodology was applied to the determination of nutrients (NO , NO, PO, and SiO) in an area of the Tagus River estuary with a range of about 350 m. This methodology can be applied to the determination of the mean value of other parameters in other river areas requiring a previous study of system heterogeneity. The spatial distribution of nutrients in the studied river area was characterized from the analysis of 10 samples collected at known geographical coordinates. The system heterogeneity was described by a three-dimensional ( x, y, z) surface with x and y variables for samples positions and z variable representing the measured nutrient levels. The randomization of this surface for the uncertainty of coordinates and repeatability of nutrient concentration measurement, using Monte Carlo simulations, allowed estimation of the uncertainty of the three sampling strategies: SS, RS, and LS. The uncertainty from RS and LS is equivalent and significantly smaller than that from SS when at least three subsamples are mixed in the composite sample. The sampling relative standard uncertainty ranged from 0.31% to 4.4%, producing nutrient concentration estimates in the river area with a relative expanded uncertainty from 5.9% to 10% with approximately 95% confidence level (coverage factor of 2). The used spreadsheet is available as Supporting Information.
河流水体污染趋势的评估受到河流条件季节性变化、污染源变化性、污染物分布异质性、采样代表性/不确定性以及样品分析不确定性的影响。本工作提出了一种基于河流中研究参数空间分布的可用信息来模拟河流水样不确定性的方法。研究了“单次采样”（SS）或通过混合 m 个随机采集的子样（RS）或沿穿过采样圆的线采集的混合子样（LS）产生复合样的不确定性。该方法应用于塔古斯河河口地区的营养物质（NO 3 -、NO 2 -、PO 4 3- 和 SiO 2 ）的测定，该地区的范围约为 350 m。该方法可应用于其他河流区域中其他参数平均值的测定，这些区域需要对系统异质性进行预先研究。通过对在已知地理坐标处采集的 10 个样本的分析，对研究河流区域的营养物质空间分布进行了描述。系统异质性由具有 x 和 y 变量的三维（x、y、z）表面描述，用于样本位置，z 变量代表测量的营养水平。使用蒙特卡罗模拟对该表面进行随机化，以估计坐标和重复测量营养浓度的不确定性，从而可以估算三种采样策略（SS、RS 和 LS）的不确定性。当在复合样中混合至少三个子样时，RS 和 LS 的不确定性与 SS 相当，且显著更小。采样相对标准不确定度范围为 0.31%至 4.4%，在河流区域内产生的营养物浓度估计值的相对扩展不确定度范围为 5.9%至 10%，置信水平约为 95%（覆盖因子为 2）。所用的电子表格可作为支持信息提供。
