Linear Source Apportionment Using Generalized Least Squares

Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.

本研究针对荧光光谱法水质监测的应用需求，开发了适用于溶解性有机物（dissolved organic matter, DOM）高维谱图的源解析模型。本文阐述了线性源解析模型的参数估计简易方法，并阐明了该估计结果与普通最小二乘法、广义最小二乘法估计结果的关联关系。基于该最小二乘框架，本研究分析了估计结果的变异性，并提出了DOM谱图缺失元素的预测方法。通过采集自北卡罗来纳州纽斯河的荧光光谱数据，本研究验证了所得结果的实际应用价值。