Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK.
Environ Sci Process Impacts. 2014 Jul;16(7):1676-91. doi: 10.1039/c4em00100a.
We evaluate different frequencies of riverine nutrient concentration measurement to interpret diffuse pollution in agricultural catchments. We focus on three nutrient fractions, nitrate-nitrogen (NO3-N), total reactive phosphorus (TRP) and total phosphorus (TP) observed using conventional remote laboratory-based, low-frequency sampling and automated, in situ high-frequency monitoring. We demonstrate the value of low-frequency routine nutrient monitoring in providing long-term data on changes in surface water and groundwater nutrient concentrations. By contrast, automated high-frequency nutrient observations provide insight into the fine temporal structure of nutrient dynamics in response to a full spectrum of flow dynamics. We found good agreement between concurrent in situ and laboratory-based determinations for nitrate-nitrogen (Pearson's R = 0.93, p < 0.01). For phosphorus fractions: TP (R = 0.84, p < 0.01) and TRP (R = 0.79, p < 0.01) the relationships were poorer due to the underestimation of P fractions observed in situ and storage-related changes of grab samples. A detailed comparison between concurrent nutrient data obtained by the hourly in situ automated monitoring and weekly-to-fortnightly grab sampling reveals a significant information loss at the extreme range of nutrient concentration for low-frequency sampling.
我们评估了不同频率的河流水体营养浓度测量方法,以解释农业流域中的扩散污染。我们重点关注三种养分组分:使用常规远程实验室基础、低频采样和自动化、原位高频监测方法观测到的硝酸盐氮(NO3-N)、总活性磷(TRP)和总磷(TP)。我们展示了低频常规养分监测在提供地表水和地下水养分浓度变化的长期数据方面的价值。相比之下,自动化高频养分观测可深入了解养分动态在响应全频谱水流动态方面的精细时间结构。我们发现原位和基于实验室的硝酸盐氮测定值之间具有良好的一致性(Pearson's R = 0.93,p < 0.01)。对于磷组分:TP(R = 0.84,p < 0.01)和 TRP(R = 0.79,p < 0.01),由于原位观测到的磷组分低估以及采集样本的存储相关变化,这些关系较差。通过对每小时原位自动化监测和每周至两周采集的同步养分数据进行详细比较,发现低频采样在养分浓度极值范围内存在显著的信息损失。