Utilizing In Situ Ultraviolet-Visual Spectroscopy to Measure Nutrients and Sediment Concentrations in Stormwater Runoff

J. Houle, Daniel R. Macadam, T. Ballestero, T. Puls
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引用次数: 1

Abstract

: The capacity to collect meaningful data to estimate stormwater runoff water quality and subsequent system removal performance is key to selecting the appropriate solutions to protect water resources. Historically, grab sampling and automated composite sampling approaches have been used with training and comprehensive quality assurance protocols to produce defensible data. Innovative approaches use real-time ultraviolet-visual spectrometry (UV-Vis) can provide a powerful tool for understanding pollutant loading regionally. This study found that real-time UV-Vis sensing is a potential new tool for understanding stormwater effluent pollutant dynamics. Researchers compared data from real-time sensing using UV-Vis spectrometers to develop calibration curves for predicting pollutant concentrations in stormwater flows. Results from paired laboratory data and raw spectral data established calibrations for the stormwater runoff composition and support further investigations of the use of this technology to predict in situ concentrations of total Kjeldahl nitrogen (TKN), dissolved organic carbon (DOC), total phosphorus (TP), total suspended solids (TSS), total nitrogen (TN), and nitrate as nitrogen (NO 3 − N) resulted in robust models with R 2 values in the range 0.99 – 0.93. Using partial least squares regression (PLSR) methods, the study demonstrated a strong correlation between concentrations generated by the raw absorbance data across the full available spectrum (220 to 730 nm). These results indicate the potential for developing specific stormwater calibration curves for pollutants of interest representative of stormwater runoff. Collectively, these results indicate that real-time UV-Vis spectrometers can redefine stormwater control monitoring by potentially delivering more accurate, more repeatable laboratory quality data instantaneously, with greater efficiency. DOI: 10.1061/JSWBAY.0000994. © 2022 American Society of Engineers. Practical Applications: Results from single grab samples of stormwater events are insufficient to develop estimates of storm event mean concentrations (EMCs). Commonly grab samples are taken during the rising limb of a hydrograph and used to characterize first flush phe-nomena. It is now known that first flush is a simple way to think about pollutant build-up and wash-off dynamics; however, it is a more theoretical concept because actual runoff concentrations for different pollutants vary due to many watershed and pollutant characteristics. Analyzing individual samples may be helpful to identify trends in loading rates of various constituents over a storm event and their changing intensities, but this significantly adds to laboratory costs and personnel hours to manage and process each event. Composite samples, on the other hand, offer affordable analytics and management but represent average pollutant concentrations that cannot be further discretized. These methods coupled with the difficulty of predicting dynamic storm variations in real time necessary to appropriately sample the storm leads to masked representation of what is happening in real time. The results of this paper introduce a real-time analytical method for stormwater chemistry assessment that bridges many of the contemporary sampling pitfalls. Regressions were within typical acceptable ranges allowed in stormwater chemistry analytical results from certified labs. These results encourage advancing the use of these technologies with greater efficiency and at a lower overall cost than conventionally available methodologies.
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利用原位紫外光谱法测量雨水径流中养分和沉积物浓度
收集有意义的数据以估计雨水径流水质和随后的系统去除性能的能力是选择适当的解决方案以保护水资源的关键。从历史上看,抓取采样和自动复合采样方法已与培训和综合质量保证协议一起使用,以产生可辩护的数据。采用实时紫外-可见光谱(UV-Vis)的创新方法可以为了解污染物的区域负荷提供有力的工具。本研究发现实时UV-Vis传感是了解雨水排放污染物动态的潜在新工具。研究人员比较了使用紫外可见光谱仪的实时传感数据,从而开发了用于预测暴雨水流中污染物浓度的校准曲线。配对的实验室数据和原始光谱数据的结果建立了雨水径流组成的校准,并支持使用该技术预测总凯氏定氮(TKN)、溶解有机碳(DOC)、总磷(TP)、总悬浮固体(TSS)、总氮(TN)和硝态氮(no3−N)的原位浓度的进一步研究,得到了r2值在0.99 - 0.93范围内的可靠模型。使用偏最小二乘回归(PLSR)方法,该研究证明了在整个可用光谱(220至730 nm)中由原始吸光度数据产生的浓度之间存在很强的相关性。这些结果表明,有可能为代表雨水径流的污染物制定特定的雨水校准曲线。总的来说,这些结果表明,实时紫外-可见光谱仪可以通过提供更准确、更可重复的实验室质量数据,以更高的效率,重新定义雨水控制监测。DOI: 10.1061 / JSWBAY.0000994。©2022美国工程师学会。实际应用:单次采集暴雨事件样本的结果不足以对暴雨事件平均浓度(EMCs)进行估计。通常,抓拍样本是在水流曲线上升段采集的,用于描述首次冲水现象。现在我们知道,第一次冲洗是考虑污染物积聚和冲洗动力学的一种简单方法;然而,这是一个更理论化的概念,因为不同污染物的实际径流浓度因许多流域和污染物特征而异。分析单个样品可能有助于确定风暴事件中各种成分的加载率趋势及其变化强度,但这明显增加了实验室成本和人员时间来管理和处理每个事件。复合样品,另一方面,提供负担得起的分析和管理,但代表平均污染物浓度,不能进一步离散。这些方法加上实时预测风暴动态变化的困难,这是对风暴进行适当采样所必需的,导致对实时发生的情况的掩盖表示。本文的结果介绍了一种用于雨水化学评估的实时分析方法,该方法弥补了许多当代采样缺陷。回归在经认证实验室的雨水化学分析结果允许的典型可接受范围内。这些结果鼓励以比传统方法更高的效率和更低的总成本推进这些技术的使用。
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3.80
自引率
15.80%
发文量
37
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