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年度	111	專案性質	實驗性質
專案類別	模場試驗	研究主題	整治
申請機構	國立中興大學	申請系所	環境工程學系
專案主持人	張書奇	職等/職稱	副教授/系主任
專案中文名稱	南崁溪污染底泥整治模場試驗計畫 （結合人工智慧之多溴二苯醚整治技術研發）
中文關鍵字	底泥,整治,人工智慧,多溴二苯醚,汞
專案英文名稱	A Pilot study of the remediation of contaminated sediments – The technology development of integrated artificial-intelligence and the remediation of sediments contaminated by polybrominated diphenyl ether
英文關鍵字	Remediation, Artificial intelligence, polybrominated diphenyl ether, Mercury
執行金額	2,030,000元
執行期間	2022/7/1 至 2024/6/30
計畫中文摘要	工業發達國家之底泥污染情況相當普遍，河川下游之底泥與河口附近海水底泥均已遭受嚴重污染，其中尤以疏水性有機物及重金屬污染最為嚴重，而台灣之底泥污染物檢測濃度在全世界相關文獻報導中屢屢名列前茅。如台南安順場址海水池底泥中戴奧辛與汞之污染、南崁溪底泥中之多溴二苯醚與汞污染。這些污染也可藉由食物鏈破壞生態系及影響人體健康，急需發展經濟有效之整治技術。本研究團隊於107-108 年度計畫以相反轉技術結合微生物篩選及降解技術（ISPIE/BiRD）進行底泥整治之現地模場試驗及土壤污染整治之砂箱試驗，不僅對於已經風化之多氯聯苯污染物Aroclor 1254 與六氯苯可達98.0%以上去除率，且對土壤中DDT 可達到99.9%以上之去除率，Lindane 可達92.2%以上之去除率。為進一步優化此技術並結合人工智慧之深度機器學習技術，針對底泥中多溴二苯醚與重金屬汞污染整治技術進行2 年期之技術研發計畫，本年度為第1 年計畫。批次實驗中第2組與第9組在第84天分別降解53%和51%的BDE-209，本研究利用初始汞濃度與BDE-209之脫溴效率進行相關性分析，結果顯示在測試之範圍內，汞濃度之高低對BDE-209降解效率無顯著影響。但本研究與先前研究相比，其BDE-209去除率略有遜色，可能原因為重金屬汞濃度、海水鹽度、污染物組成與原始菌相差異。依據批次實驗之第三代定序（TGS）數據可發現能夠降解多環芳香烴化合物之菌種位於豐度前十名內。管柱實驗中，ISPIE可去除風化底泥中22%的BDE-209，於添加組中可去除15%之BDE-209。本研究之ISPIE去除效率與先前研究結果略有差距，可能為鹽度及有效溶解度影響，若加上後續BiRD去除，最佳降解組別為自然復育組(WNR)，其總去除率為56%，這與先前研究結果相似。根據管柱實驗之TGS數據可觀察出其豐度最高之菌種與批次實驗者有明顯不同，而ISPIE的確可顯著改變微生物菌群；ISPIE操作後，可發現發酵產氫菌的豐度增加，也發現降解芳香烴化合物之菌群存在。機器學習應用於TGS資料解析應屬可行，在三氯乙烯生物降解之學習效果甚佳，但囿於本計畫經費所能取得之資料集數量，在BDE-209降解資料學習上產出結果不佳。綜上可知，ISPIE/BiRD之技術用於處理高重金屬濃度底泥中之BDE-209應屬可行的。
計畫英文摘要	Sediment contamination in the developing and developed countries is a prevailing environmental problem. Sediment in lower section of rivers and in estuarine area, they are often contaminated by hydrophobic organic compounds (HOCs) and heavy metals. In the soils and sediments in Taiwan, multiple contaminants concentrations were ranked at the top in the world, for example, the dioxins and mercury in at An-Sun site in Tainan, the polybrominated diphenyl ether and mercury in the sediments of Nan-Ken River. Similarly, this situation is also quite common in the southeastern Asia. This research team in the past few years has developed a new technology for soil and sediment remediation, i.e., in situ phase inversion emulsification and biological reductive dehalogenation (ISPIE/BiRD). This method is not only very effective in removal of weathered polychlorinated biphenyls and hexachlorobenzene in river sediments but also achieved as high as 99.9% of DDT and 92.2% of Lindane in soils. This is a 2-year project. In the first year, it is expected to finish a series of batch degradation experiments and a small-scale column study. By using quantitative polymerase chain reaction (qPCR)to quantify functional genes and using third-generation gene sequencing (TGS) technology to conduct microbial community analysis to confirm the optimal reaction conditions. It is a 2-year technology research and development project. This year is the first-year plan. In the batch experiment, group 2 and group 9 degraded 53% and 51% of BDE-209, respectively, on day 84. This study used the initial mercury concentration, and the debromination efficiency of BDE-209 was used for correlation analysis. The results showed that the mercury concentration has no significant effect on the degradation efficiency in the tested range. Still, the removal rate is slightly different from previous studies, which may be the inhibition of microbial degradation efficiency caused by the mercury concentration in the Nan-Kan River. According to the third-generation sequencing (TGS) data of batch experiments, it can be found that the bacterial species capable of degrading polycyclic aromatic hydrocarbons are among the top ten in abundance. In the column experiment, ISPIE removed 22% of BDE-209 in the sediment of the weathering groups and 15% of BDE-209 in the fresh groups. The removal efficiency of ISPIE in this study is slightly different from previous studies, possibly due to the influence of salinity and effective solubility. In addition, the best degradation group for subsequent biological reduction and debromination removal is the natural restoration group (WNR). Its removal rate was 56%, which is similar to the trend of previous research results. According to the TGS data of the column experiment, it can be observed that the distribution of the bacterial species with the highest abundance is significantly different from that of the batch experiment. It was also found that ISPIE can indeed significantly change the microbial flora. After ISPIE operation, the abundance of fermentative hydrogen-producing bacteria increased. The presence of aromatic-degrading bacteria was found in the sediment of the Nan-Kan River. It should be feasible to apply machine learning to TGS data analysis, and the learning in the biodegradation of trichloroethylene is excellent but limited by the number of datasets in this study. The machine learning results on BDE-209 degradation data could be better in case of enough datasets. In summary, it is feasible to use ISPIE/BiRD technology to treat BDE-209 in sediment with high heavy metal concentration.