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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96412
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor廖中明zh_TW
dc.contributor.advisorChung-Min Liaoen
dc.contributor.author王韋閔zh_TW
dc.contributor.authorWei-Min Wangen
dc.date.accessioned2025-02-13T16:21:19Z-
dc.date.available2025-02-14-
dc.date.copyright2025-02-13-
dc.date.issued2025-
dc.date.submitted2025-02-04-
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96412-
dc.description.abstract由於微型塑膠對周遭物質包含抗生素之吸附性,提供抗藥性綠膿桿菌發展之介質。此外,於各個地表水與廢水中量測出之最高濃度抗生素為抗綠膿桿菌藥物環丙沙星。以微型塑膠為棲位之抗環丙沙星綠膿桿菌極有可能以潛在暴露途徑如生食海鮮與水域活動,成為一新興且極令人擔憂之公共健康議題。然而,目前仍缺乏以機制為基礎之數理研析探討因微型塑膠吸附性引起之抗藥性病原菌發展,以及釐清棲位於人類活動產生新興環境基質之病原菌其潛在感染風險。因此,本論文訂定之目標包含四項:(1)探討微型塑膠表面周圍,因吸附性造成之環丙沙星濃度放大效應,(2)預測棲息於微型塑膠表面之綠膿桿菌其族群動態,(3)評估水域系統中因微型塑膠—抗生素共汙染所造成抗環丙沙星綠膿桿菌之發展,及(4)評估由水域活動與生食海鮮造成之增殖於微型塑膠表面抗環丙沙星綠膿桿菌感染風險。
於本論文中,被放大之微型塑膠周圍環丙沙星濃度,推導自以等溫吸附模式推估之環丙沙星劑量除以包裹於微型塑膠表面之生物膜體積。綠膿桿菌族群動態將以微型塑膠表面之環丙沙星濃度為基礎,再利用藥理動力/動態為基礎之微生物群動態模式進行模擬。接著水域環境中之生菌數將由微型塑膠上之易感/抗藥性綠膿桿菌細胞數與水中微型塑膠豐度相乘推估而得。以微生物族群發展結果與劑量反應模式為基礎,本研究進一步量化因職業/休閒水域活動與生食海鮮引起之人類感染風險,並於感染爆發前反推暴露上限。
研究結果顯示聚乙烯(PE)與聚對苯二甲酸乙二酯(PET)呈現最高之環丙沙星濃度放大效應(約105倍),其次為聚醯胺(PA)(約103倍),最低放大效應發生於聚丙烯(PP)、聚苯乙烯(PS)、及聚氯乙烯(PVC)(約102倍)。族群動態模擬指出當表面環丙沙星濃度大於0.01 mg L−1時會發生明顯之抗藥性選擇,且總族群於濃度大於2 mg L−1時受到抑制。整體而言,綜觀於中國、南亞、歐洲、及大洋洲之21個已分析之水域,已發展之抗藥性綠膿桿菌可佔其總族群10%至99%,並於水域系統中,其水中總生菌數可達10−2至104 cfu mL−1。其中西班牙之厄波羅河與中國之巢湖,呈現最高大於90%之抗藥性細菌比例。此外,總生菌數大於104 cfu mL−1則發生於中國之洪湖、鄱陽湖、及膠州灣河口。相關性分析顯示一水域中之抗藥性細菌比例,同時由環丙沙星汙染程度與微型塑膠聚合物種類組成所決定。總生菌數與微型塑膠豐度間呈正相關,顯示環境微型塑膠提供綠膿桿菌增生所需之介質。
於水域活動之定量微生物風險評估中,各類活動之年感染風險(中位數)由高至低分別為:水田耕作(約10−4–0.58)、水產養殖(約10−3–0.54)、漁業捕撈(約10−3–0.45)、職業潛水(約10−5–0.43)、休閒潛水(約10−5–0.35)、游泳(約10−5–0.29、及涉水活動(約10−6–0.13)。由於須以高頻率於汙染程度較高之水域執行,專業活動之風險明顯比休閒活動高。推估得之暴露上限顯示各種暴露途徑之感染敏感度為:眼部(約10−5–10 cfu) > 吸入(約10−3–103 cfu) > 攝入(約10–107 cfu),該結果可由不同器官之生理特徵所解釋。風險判定中,危害商數與超越風險皆顯示大部分暴露事件(約60–83%)將造成顯著感染。相對風險排序歸類顯示,各樣區中萊州灣呈現最低風險。而樣區中包含杭州灣、渤海、洞庭湖、珠海市、及膠州灣屬於中度風險,其年感染機率中位數落於10−6–10−4。而大部分剩餘樣區則呈現嚴重風險其年感染機率大於10−4。
飲食暴露風險評估中,受汙染之生蠔其生菌數可達約10–107 cfu g−1 bw,可造成每餐攝入劑量約102–108 cfu,以及近一步之年感染機率約1.50×10−6–0.98。危害商數與超越風險皆顯示生食任一產地之生蠔將造成顯著風險。更甚,本研究為每一產地之生蠔推估建議攝取量(1.99×10−5–29.42 g meal−1)與頻率(1.74×10−6–9.52 yr−1),以避免於大眾中發生感染爆發。而當中基於極低之建議值暗示強烈建議避免生食該產地之生蠔。
總結而言,本論文建議一新穎之機制法結合等溫吸附與藥理動力/動態為基礎之微生物群動態模式以評估水域系統中因微型塑膠—抗生素互動所產生之抗藥性病原菌之發展。以微生物族群發展評估為基礎,對一般水域活動與生食海鮮進行之定量微生物風險評估,強調因新興環境介質提供抗藥性病原菌增殖,其造成之健康風險已潛伏於一般大眾之中。預測性量化結果也為施政者提供水域系統管理與風險規範之建議。
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dc.description.abstractThe adsorbability of microplastics (MPs) to surrounding substrates including antibiotics can provide mediums for development of antibiotic-resistance genes carrying Pseudomonas aeruginosa. Moreover, an anti-P. aeruginosa drug, ciprofloxacin (CIP), has been measured as the highest antibiotics among surfaces and wastewaters. The MPs-niched CIP-resistant P. aeruginosa is highly likely to become an emerging public health issue with considerable concerns due to the potential exposure pathway via raw seafood diets and water activities. However, there is a lack of mechanistically-based mathematical approach to explore the development of antibiotic-resistant pathogenic bacteria cased by MPs adsorption. Moreover, to understand the potential infection risk from pathogen niching on an emerging environmental medium generated from anthropogenic activities remains poorly quantified. Therefore, the objectives of this dissertation are fourfold: (i) to explore the enlarging effect on CIP concentration around MPs surface induced by adsorbability, (ii) to project the population dynamics of P. aeruginosa inhabiting on MPs surfaces, (iii) to assess the development of CIP-resistant P. aeruginosa under MPs-antibiotics coexistence among aquatic systems, and (iv) to appraise the infection risks of MPs-colonizing CIP-resistant P. aeruginosa posed through water activities and raw seafood diets.
In this dissertation, enlarged CIP concentration around MPs surface is derived from the adsorption isotherm model-estimated CIP dose divided by biofilm volume wrapping around MPs surface. A pharmacokinetic/pharmacodynamic (PK/PD)-based microbial population dynamic model is employed to simulate the population dynamics of P. aeruginosa niching on MPs on the basis of MPs surface CIP concentration. Thereafter, the total cell counts (TCCs) in aquatic environments are estimated from multiplication of cell densities of susceptible/resistant P. aeruginosa on MPs surface and MPs abundance in the water. On the basis of microbial population development outcomes with dose–response model, this study further quantified the human infection risks from occupational/recreational water activities and raw bivalve consumption, and then reversely estimated the upper limits of exposure prior to infection outbreak.
The results showed that polyethylene (PE) and polyethylene terephthalate (PET) performed the highest CIP concentration enlarging effect (~105 times), followed by polyamide (PA) (~103 times), and the lowest effects occurred in polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC) (~102 times). Simulations of population dynamics showed that an apparent resistance selection occurred when surface CIP concentration > 0.01 mg L−1, whereas the total population was inhibited at concentration > 2 mg L−1. Overall, among the 21 analyzed aquatic regions in China, South Asia, Europe, and Oceania, developed resistant P. aeruginosa occupied ~10–99% in total populations with waterborne TCCs ranging from ~10−2–104 cfu mL−1 in aquatic systems. On the other hand, Ebro River in Span, and Chaohu Lake in China showed the highest resistant proportion > 90%, whereas TCCs > 104 cfu mL−1 occurred in Honghu Lake, Poyang Lake, and Jiazhou Bay estuary of China. The correlation analysis indicated that the resistant subpopulation proportion in an aquatic area is determined by both CIP pollution level and composition of MPs polymer types. A positive correlation between TCCs and MPs abundance implicated that environmental MPs provide mediums for P. aeruginosa propagation.
In the quantitative microbial risk assessment (QMRA) for water activities, the annual infection probabilities (median) from high to low among each type of activity were: paddy farming (~10−4–0.58), aquaculture (~10−3–0.54), fishery catching (~10−3–0.45), occupational diving (~10−5–0.43), recreational diving (~10−5–0.35), swimming (~10−5–0.29), and wading activity (~10−6–0.13). Due to the necessity to be carried out in higher contaminated waters in higher frequency, the risks in occupational activities are apparently higher than the recreational. The estimated upper exposure limits showed the infection sensitivity among exposure routes: ocular (~10−5–10 cfu) > inhalation (~10−3–103 cfu) > oral (~10–107 cfu), which may be explained by the physiological characteristics between organs. In risk determination, both hazard quotient (HQ) and exceedance risk (ER) indicated that most exposure event (~60–83%) would cause significant infection. The categorization of relative risk rank exhibited that among the study regions, Laizhou Bay performed the lowest risk without significance. Regions including Hangzhou Bay, Bohai Sea, Dongting Lake, Zhuhai city, and Jiaozhou Bay belonged to moderate risk with median annual infection probabilities within 10−6–10−4. While the most remaining regions showed severe risk with annual infection > 10−4.
In risk assessment for dietary exposure, the TCCs in contaminated oysters ranged from ~10–107 cfu g−1 bw which can lead to ingested doses of ~102–108 cfu per meal, and further annual infection probability ranged from 1.50×10−6–0.98. Both HQ and ER revealed the significant risk in raw consumption of oysters from every origin. Moreover, this study estimated the suggested consumption amounts (1.99×10−5–29.42 g meal−1) and frequencies (1.74×10−6–9.52 yr−1) for oysters from each origin to prevent infection outbreak in the general public. The extremely low value indicated that raw consumption of oysters from these study regions are highly not recommend.
In conclusion, this dissertation constructed an innovative mechanistic approach incorporating the adsorption isotherms and the PK/PD-based microbial population model to assess the development of antibiotic-resistant pathogen from MPs–antibiotics interaction through aquatic systems. On the basis of the microbial population development assessment, the further conducted QMRA on common water activities and raw seafood diet highlighted the health risks lurking in the general public caused by an emerging environmental medium for antibiotic-resistant pathogen colonization. The results from predictive quantitative analysis provided recommendations for administrators on water system management and risk regulation as well.
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dc.description.tableofcontents口試委員審定書 I
謝誌 II
ABSTRACT VI
中文摘要 X
TABLE OF CONTENTS XIII
LIST OF TABLES XVIII
LIST OF FIGURES XXII
NOMENCLATURE XXVIII
CHARPTER 1. INTRODUCTION 1
CHARPTER 2. MOTIVATION AND OBJECTIVES 3
2.1. Motivation 3
2.2. Objectives 4
CHARPTER 3. LITERATURE REVIEW 5
3.1. Pseudomonas aeruginosa 5
3.1.1. General description 5
3.1.2. Virulence factor 6
3.1.3. Biofilm formation 11
3.1.4. Antibiotic resistance 14
3.2. Ciprofloxacin (CIP) in aquatic environments 17
3.3. Microplastics (MPs) adsorbability to CIP 19
3.4. Antibiotic-resistance development on MPs 20
3.5. Mechanistic models 23
3.5.1. Adsorption isotherm model 23
3.5.2. Pharmacokinetic/pharmacodynamic (PK/PD)-based P. aeruginosa population dynamics model 24
3.6. Risk assessment for environmentally relevant P. aeruginosa 26
CHARPTER 4. MATERIALS AND METHODS 27
4.1. Study framework 27
4.2. Study region data collection 31
4.3. CIP concentration estimation on MPs surface 41
4.3.1. Outline 41
4.3.2. MPs surface area estimation 44
4.3.2.1. MPs mass estimation 44
4.3.2.2. SSABET data compilation 50
4.3.3. Adsorption isotherm model 59
4.4. P. aeruginosa development assessment 67
4.4.1. PK/PD-based P. aeruginosa population dynamics model for CIP exposure 67
4.4.2. Total cell counts (TCCs) estimation for MPs-niched P. aeruginosa in aquatic environments 79
4.5. Human infection risk assessment 81
4.5.1. Framework 81
4.5.2. Exposure assessment 83
4.5.2.1. Water activity exposure 83
4.5.2.2. Raw seafood diet exposure 89
4.5.3. Dose–response assessment 93
4.5.4. Risk characterization 97
4.6. Uncertainty analysis 101
CHARPTER 5. RESULTS 102
5.1. Model development 102
5.1.1. MPs mass concentration estimation 102
5.1.2. Surface area–particle size relationships 107
5.1.3. Salinity on adsorption efficiency 109
5.1.4. Initial adhering population estimation 112
5.2. MPs surface CIP estimates 114
5.3. Population dynamics simulation 119
5.4. Population development outcomes 123
5.5. Correlation performances 127
5.6. Degree of exposure 131
5.6.1. Water activity 131
5.6.2. Raw seafood diet 142
5.7. Infection scale 148
5.7.1. Global water activity 148
5.7.2. Raw oyster diet 164
5.8. Acceptable exposure limitation 167
5.9. Determination of infection risk 172
5.9.1. Global water activity 172
5.9.2. Raw oyster diet 180
5.10. Distribution of infection risk 183
5.10.1. Global water activity 183
5.10.1.1. Exceedance risk 183
5.10.1.2. Relative risk 191
5.10.2. Raw oyster diet 193
5.10.2.1. Exceedance risk 193
5.10.2.2. Suggestions on oyster consumption 196
CHARPTER 6. DISCUSSION 200
6.1. MPs polymer-specific CIP interactions 200
6.2. P. aeruginosa population on waterborne MPs surface 203
6.3. CIP-resistant/susceptible P. aeruginosa development 205
6.4. P. aeruginosa exposure and infection risks in water activities 207
6.5. MPs accumulation and P. aeruginosa contamination in oysters 211
6.6. Foodborne P. aeruginosa exposure and infection risks 213
6.7. Infection reducing measures 215
6.8. Limitations and implications 219
CHARPTER 7. CONCLUSIONS 224
CHARPTER 8. SUGGESTIONS FOR FUTURE RESEARCHES 227
BIBLIOGRAPHY 230
CURRICULUM VITAE 279
Appendix A: Estimated surface CIP concentrations on each types of MPs among study regions 281
Appendix B: Developed resistant/susceptible proportions contributed from each types of MPs among study regions 291
Appendix C: Developed total cell counts (TCCs) contributed from each types of MPs among study regions 293
Appendix D: Developed cell density on each types of MPs among study regions 295
Appendix E: Estimated exposure levels among water activities 297
Appendix F: Estimated infection probability in single exposure event among water activities 300
Appendix G: Annual infection risks of water activities 307
Appendix H: Hazard quotient (HQ)-determined infection risks for water activities 313
Appendix I: Exceedance risks (ER) of infection scale among water activities 329
-
dc.language.isoen-
dc.subject環丙沙星zh_TW
dc.subject綠膿桿菌zh_TW
dc.subject抗藥性zh_TW
dc.subject等溫吸附模式zh_TW
dc.subject藥理動力/動態模式zh_TW
dc.subject族群動態模式zh_TW
dc.subject水域環境zh_TW
dc.subject定量微生物風險評估zh_TW
dc.subject微型塑膠zh_TW
dc.subjectQuantitative microbial risk assessmenten
dc.subjectMicroplasticsen
dc.subjectCiprofloxacinen
dc.subjectPseudomonas aeruginosaen
dc.subjectAntibiotic resistanceen
dc.subjectAdsorption isotherm modelen
dc.subjectPharmacokinetic/pharmacodynamic modelen
dc.subjectPopulation dynamic modelen
dc.subjectAquatic environmenten
dc.title水域系統中因微型塑膠吸附性引起抗環丙沙星綠膿桿菌之發生:動態行為與人類感染風險評估zh_TW
dc.titleEmergence of ciprofloxacin-resistant Pseudomonas aeruginosa from microplastics adsorbability through aquatic systems: Dynamics and human infection risk assessmenten
dc.typeThesis-
dc.date.schoolyear113-1-
dc.description.degree博士-
dc.contributor.oralexamcommittee劉振宇;廖秀娟;藍忠昱;陳韋妤zh_TW
dc.contributor.oralexamcommitteeChen-Wuing Liu;Hsiu-Chuan Vivian Liao;Chung-Yu Lan;Wei-Yu Chenen
dc.subject.keyword微型塑膠,環丙沙星,綠膿桿菌,抗藥性,等溫吸附模式,藥理動力/動態模式,族群動態模式,水域環境,定量微生物風險評估,zh_TW
dc.subject.keywordMicroplastics,Ciprofloxacin,Pseudomonas aeruginosa,Antibiotic resistance,Adsorption isotherm model,Pharmacokinetic/pharmacodynamic model,Population dynamic model,Aquatic environment,Quantitative microbial risk assessment,en
dc.relation.page343-
dc.identifier.doi10.6342/NTU202500356-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2025-02-04-
dc.contributor.author-college生物資源暨農學院-
dc.contributor.author-dept生物環境系統工程學系-
dc.date.embargo-lift2030-02-03-
顯示於系所單位:生物環境系統工程學系

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