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Source Apportionment of Water Pollution in Nan-kan River Using Receptor Models and Fingerprint Identification
Receptor models,Source apportionment,Wastewater database,Similarity indicator,Fingerprint matching,
|Publication Year :||2020|
研究中以桃園市南崁溪為目標河川，規劃河川水體為受體點，流域周邊事業廢水為污染源進行兩梯次採樣，在第二梯次採樣中，將安排一河川污染熱區—大檜溪橋進行30天之連續監測。本研究除了檢測該地區水質表現外，亦由52項元素之分析結果進行化學質量平衡模式 (EV-CMB) 與正矩陣因子模式 (PMF) 之解析，而針對PMF解析結果，需進一步透過相似性指標與指紋圖譜資料庫進行比對，辨識可能之污染事業別。
在兩梯次河川水樣分析結果中，全流域之「河川污染指標 (River Pollution Index, RPI)」皆屬於中度至嚴重污染，而屬嚴重污染之採樣點中，上游樂善寺主要受林口工業區之事業廢水影響，下游則由於生活污水及事業廢水雙重影響導致污染，另外，由於，該流域過去曾有Cu濃度超標之事件，桃園市政府亦於該流域進行Cu之總量管制，因此，特別針對Cu元素之污染情形與來源進行探討。在兩梯次河川水樣分析結果中，下游採樣點之Cu濃度皆高於環保署訂定之保護人體健康相關環境基準值0.03 mg/L，此外，在污染熱區之30天連續監測中，Cu之金屬指標值 (Metal Index, MI) 亦多數大於1，顯示可能造成健康之危害。
在相似性指標測試結果中，指標COD (coefficient of divergence) 與各事業別之比對正確率相較 SID (standardised identity distance) 與PD (Pearson distance) 為佳，因此，後續由COD指標輔以SID和PD進行比對。
針對污染熱區，以60筆連續監測資料進行正矩陣因子模式 (Positive Matrix Factorization, PMF) 分析，推估可能污染源為6個，此一結果與資料庫中各事業指紋資料進行比對，並且與高污染事件日中EV-CMB之解析結果交互探討後，推測6個可能之污染源分別為食品製造業 (Factor 1、5)、光電材料及元件製造業 (Factor 2)、污水處理業 (Factor 3)、未納管污水 (Factor 4) 以及電鍍業 (Factor6)，其中，Factor 4為高污染事件日之主要貢獻者，另外，在Cu之各來源貢獻比例部分，PMF與EV-CMB有相似之解析結果。
River water quality is closely related to population health. However, due to the fluidity of pollutants and limited resources of environmental protection agencies, it is not easy to trace the sources of pollution. Therefore, this research attempts to use receptor models which are based on the concept of conservation of mass to develop the method of source identification for water pollution.
This study took Nan-kan river as an example. The sampling sites at water body were seen as receptors and the industrial wastewater along the river were sources of pollution. For the second batch of sampling, the pollution hot zone, Daguixi bridge, was continuously monitored for 30 days. Apart from testing the water quality in this area, the analysis results of 52 elements were also used in Chemical Mass Balance (EV-CMB) and Positive Matrix Factorization (PMF) modeling. The similarity comparison for PMF modeling results were further carried out to identify possible pollution sources by industrial categories.
Based on the analysis results of river samples, the 'River Pollution Index (RPI)' showed that the entire river was moderately-polluted to severely-polluted. The upstream Leshan Temple was mainly affected by the industrial wastewater from the Linkou industrial park, and the downstream sites were influenced by domestic sewage and business wastewater. Besides, due to there have been cases where the concentration of copper exceeded the standard in the past, and the “Total Copper Discharge Control” by Taoyuan City Government, the sources of copper were investigated further. For samples collected from the two batches, the copper concentration at downstream sampling sites was relatively high, and was above the environmental protection standards set by the Environmental Protection Agency for protection of human health (0.03 mg/L). In addition, during the 30-day monitoring period at the hot zone, the metal index (MI) for Cu was mostly greater than 1, which was seemed to be hazardous for health.
The EV-CMB modeling revealed that the wafer manufacturing and semiconductor manufacturing had a greater contribution at midstream and downstream sites. Besides, the results showed that unmanaged sewage at the Daguixi Bridge had an apparent contribution to the second batch sampling. As for the source of copper pollution in the downstream areas, the printed circuit board manufacturing industry was the main contributor. At the Daguixi bridge, the impact of unmanaged sewage was the biggest. In the part of continuous monitoring data, the main contribution of unmanaged sewage was identified on the three severely-polluted days, 08/30, 09/16, and 09/17.
According to the test results of the similarity indicators, the accuracy of the coefficient of divergence (COD) in each industrial category was better than that of standardised identity distance (SID) and Pearson distance (PD). Therefore, the COD indicator was supplemented by the SID and PD indicators for comparison.
For pollution hot zone, PMF modeling results based on the continuous monitoring data showed that there were six possible sources of pollution. These 6 factors were compared with the fingerprints of each industry in the database. And after interactive discussion with the analysis results of the EV-CMB on the severely-polluted day, 6 possible pollution sources were estimated to be food manufacturing (Factor 1 and Factor 5), optoelectronic materials and components manufacturing (Factor 2), wastewater treatment (Factor 3), unmanaged sewage (Factor 4) and electroplating industry (Factor 6). Among them, Factor 4 was the main contributor to the severely- polluted event. Also, the analysis results of PMF and EV-CMB on the proportion of Cu contribution were similar.
Based on the above discussion, the pollution pattern in the Nan-kan River is dominated by domestic sewage and this result is consistent with previous studies. In the analysis of the two receptor models, the larger contributors all point to the same pollution source. In the application of the receptor model, the advantage of EV-CMB is that it does not require a high number of environmental data, but if it is not possible to fully acquire the source of pollution in the area, it will affect the quality of the analysis. At this time, the PMF model will help to identify sudden pollution events. However, PMF requires a certain number of data to be analyzed. Therefore, if there is a breakthrough in continuous river monitoring, it will be beneficial to understand the sudden pollution. To take this advantage, the continuous expansion of the fingerprint database can make the identification more accurate.
|Appears in Collections:||環境與職業健康科學研究所|
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