Department of Environmental Engineering and Energy, Myongji University, 116 Myongji-ro, Cheoin-gu, Yongin, Gyeonggi-do 17058, Republic of Korea.
Department of Environmental Science, COMSATS University Islamabad, Abbottabad campus, 22060, Pakistan.
Sci Total Environ. 2019 May 1;663:764-775. doi: 10.1016/j.scitotenv.2019.01.377. Epub 2019 Jan 29.
The application and comparison of receptor modeling techniques based on ambient air quality and particulate matter increasingly being studied. However, less is known about the comparison of receptor modeling techniques using spatial runoff quality data to identify and quantify the stormwater runoff pollution. This study compared the performance of principal component analysis-multiple linear regressions (PCA-MLR) and positive matrix factorization (PMF) models on stormwater runoff data collected from a small catchment (Site 1) with urban development activity and a sub-watershed outlet (Site 2). In both sites, the PCA-MLR model identified three pollution sources, whereas PMF identified five with a detailed source mechanism including two additional sources. Furthermore, the spatial land-use land-cover (LULC) analysis results indicate that the Site 1 exhibited a rapid conversion of the native area into a built-up area over the monitoring period compared to Site 2. Based on the modeling results, domestic wastewater and soil erosion were the major source of pollution at Site 1 and Site 2, respectively. The performance evaluation statistics including Nash coefficient (0.86-0.99), % error (<-14 to 2), and coefficient of determination (R ≤ 0.99) showed better performance for the PMF model than the PCA-MLR model. Overall, the PMF receptor modeling approach was found to be more robust for the current study sites with different land use types. The findings of this study could provide a basis for further application of these receptor models and their comparison using spatial-temporal ionic and sediment related runoff monitoring data. Also, the models from this research could be combined with other receptor models on runoff quality data (e.g. CMB or UNMIX) to explore and inter-compare the outcomes, and to determine how the model results are affected by modifications to input data and model parameters. Therefore, further research is required to precisely assess the accuracy of both receptor models.
基于环境空气质量和颗粒物的受体建模技术的应用和比较越来越受到关注。然而，利用空间径流水质数据来识别和量化雨水径流污染的受体建模技术比较研究则较少。本研究比较了主成分分析-多元线性回归（PCA-MLR）和正定矩阵因子分解（PMF）模型在具有城市发展活动的小流域（站点 1）和子流域出口（站点 2）收集的雨水径流水质数据上的性能。在两个站点中，PCA-MLR 模型识别出了三个污染源，而 PMF 则识别出了五个污染源，其中包括两个额外的污染源，且详细说明了源机制。此外，空间土地利用/土地覆盖（LULC）分析结果表明，与站点 2 相比，站点 1 在监测期间，原生区迅速转变为建成区。基于建模结果，生活污水和土壤侵蚀是站点 1 和站点 2 的主要污染源。性能评估统计数据（纳什系数（0.86-0.99）、误差百分比（-14 至 2）和决定系数（R≤0.99））表明，PMF 模型的性能优于 PCA-MLR 模型。总体而言，PMF 受体建模方法在具有不同土地利用类型的当前研究站点中表现更为稳健。本研究的结果可为进一步应用这些受体模型及其在时空离子和泥沙相关径流水质监测数据上的比较提供依据。此外，该研究中的模型还可以与其他径流质量数据（如 CMB 或 UNMIX）的受体模型相结合，以探索和比较结果，并确定模型结果如何受到输入数据和模型参数修改的影响。因此，需要进一步研究以准确评估这两种受体模型的准确性。
