Browse
S-Space
College of Agriculture and Life Sciences (농업생명과학대학)
Dept. of Agricultural Biotechnology (농생명공학부)
Theses (Ph.D. / Sc.D._농생명공학부)
Exposure, Risk Assessment and Predictive Exposure Model Development for Agricultural Operator in Representative Crop Fields
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김정한 | - |
dc.contributor.author | 김은혜 | - |
dc.date.accessioned | 2017-07-13T08:26:07Z | - |
dc.date.available | 2017-07-13T08:26:07Z | - |
dc.date.issued | 2017-02 | - |
dc.identifier.other | 000000142217 | - |
dc.identifier.uri | https://hdl.handle.net/10371/119546 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 농생명공학부, 2017. 2. 김정한. | - |
dc.description.abstract | Korea predictive model for the estimation of agricultural operator exposure has been developed on the basis of new exposure data to improve the current agricultural operator exposure and risk assessment in the Korea. The new operator exposure model represents current application techniques (speed sprayer and power sprayer) and practices in representative crop fields (apple orchard and rice field). 30 replicate exposure studies conducted between 2010 and 2012 were evaluated for the new model. Exposure and risk assessment were conducted for agricultural applicators during preparation of spray suspension and application with speed sprayer and power sprayer on crop fields. Several exposure matrices, including patches, cotton gloves, socks, masks and XAD-2 resin were used to measure the potential exposure for workers. The analytical methods were fully validated to guarantee the precision and accuracy of analysis. As a major factor contributing to the exposure of operators, the amount of active ingredient used per day was identified. Other parameters such as formulation type, density of the canopy were selected as factors for sub-scenarios. Accordingly, 75 percentile of exposure dose was calculated for mixing / loading and application according to scenario, and it was derived as exposure factor of Korean model. In vitro metabolism of kresoxim-methyl was conducted with human liver microsome. Two metabolites were identified. The screening test for identifying which recombinant CYP involved with metabolism of kresoxim-methyl was conducted with 10 human cDNA-expressed CYP isoforms. Eight rCYPs (except 2A6, 2E1) contributed to metabolism of kresoxim-methyl. | - |
dc.description.tableofcontents | Chapter I: Exposure of Operators, Risk Assessment, and Model Development 1
Introduction 1 Occupational exposure study 1 Methodology of agricultural worker exposure to pesticides 5 Dermal exposure 5 Risk assessment 6 Predictive model 8 Korea predictive operator exposure model 10 Part 1 : Probabilistic Exposure Assessment for Applicators during Treatment of the Fungicide Kresoxim-methyl on Apple Orchard by Speed Sprayer 13 Introduction 15 Materials and Methods 17 Reagents and materials 17 Dermal exposure matrices 17 Inhalation exposure matrices 17 Experimental sites and field trial 18 Exposure matrices sampling 20 Extraction of kresoxim-methyl from exposure matrices 20 Instrumental conditions 20 Method validation 21 Exposure assessment 22 Exposure estimation using Monte Carlo simulation for kresoxim-methyl 23 Risk assessment 24 Results and Discussion 25 Selection of crops and pesticide 25 Method validation 25 Determination of the number of iterations 26 Dermal exposure assessment 33 Inhalation exposure assessment 34 Exposure database for predictive model 34 Risk assessment 39 Part 2 : Exposure and Risk Assessment of Operators to Insecticide Acetamiprid during Treatment on Apple Orchard 45 Introduction 47 Materials and Methods 49 Reagents and materials 49 Exposure matrices 49 Experimental sites 50 Chromatographic condition 50 Limit of detection (LOD), limit of quantitation (LOQ), reproducibility and linearity of calibration curve 52 Trapping efficiency and breakthrough tests 52 Recovery (Matrix extraction efficiency) test 52 Extraction of acetamiprid from exposure matrices 53 Field trials and sampling procedure 53 Calculation of potential dermal and inhalation exposure 54 Risk assessment 54 Results and Discussion 56 Method validation 56 PDE and PIE 60 MOS and Risk Assessment 67 Database for model 67 Part 3 : Comparative Exposure of Operators to Fenthion during Treatment in Paddy Field 73 Introduction 75 Materials and Methods 77 Reagents and materials 77 Sampling methodology 77 Calculation of dermal and inhalation exposure 77 Analytical condition 77 Method validation 78 Sampling and field experiment 78 Results and Discussion 80 Method validation 80 Potential dermal exposure and inhalation exposure 80 Risk Assessment 87 Database for model 87 Part 4 : Operator Exposure to Indoxacarb Wettable Powder and Water Dispersible Granule during Mixing/loading and Risk Assessment 93 Materials and Methods 95 Experimental materials 95 Exposure study samples and analytical conditions 95 Extraction of exposure matrices 95 LOD, LOQ, and reproducibility 96 Recovery (Matrix extraction efficiency) test 96 Trapping efficiency and breakthrough tests 96 Field study, calculation of exposure, and risk assessment 97 Results and Discussion 98 Method Validation 98 Hand exposure, inhalation exposure and MOS 99 Part 5 : Hand Exposure of Operator to Chlorpyrifos during Mixing/loading and Risk Assessment 107 Materials and Methods 109 Reagents and materials 109 Analytical method validation 109 Measurement of hand exposure and risk assessment 109 Results and Discussion 111 Method validation 111 Hand exposure and risk assessment 111 Chapter II: In vitro metabolism of kresoxim-methyl by human liver microsomes 117 Introduction 119 In vitro human metabolism studies of pesticides 119 Human liver microsomal CYP450 122 Enzyme kinetics in metabolism 125 Materials and Methods 128 Chemicals and reagents 128 Analytical instruments and conditions 128 Metabolism of kresoxim-methyl in HLMs (Phase I reaction) 130 Metabolite identification 130 Optimization of metabolic conditions and kinetic studies 130 Metabolism of kresoxim-methyl by cDNA-expressed CYP450 isoforms 131 Determination of crystal structure 131 Results and discussion 132 Formation of the kresoxim-methyl metabolite by HLMs 132 Optimization of metabolic conditions and kinetic studies 132 Metabolism of kresoxim-methyl in cDNA-expressed CYP450 isoforms 133 Determination of crystal structure for kresoxim-methyl 142 Supplementary Materials 146 References 169 Abstract in Korean 179 | - |
dc.format | application/pdf | - |
dc.format.extent | 1979655 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | dermal exposure | - |
dc.subject | exposure model | - |
dc.subject | human liver microsome | - |
dc.subject | inhalation exposure | - |
dc.subject | metabolism | - |
dc.subject | risk assessment | - |
dc.subject.ddc | 630 | - |
dc.title | Exposure, Risk Assessment and Predictive Exposure Model Development for Agricultural Operator in Representative Crop Fields | - |
dc.type | Thesis | - |
dc.description.degree | Doctor | - |
dc.citation.pages | 180 | - |
dc.contributor.affiliation | 농업생명과학대학 농생명공학부 | - |
dc.date.awarded | 2017-02 | - |
- Appears in Collections:
- College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Agricultural Biotechnology (농생명공학부)Theses (Ph.D. / Sc.D._농생명공학부)
- Files in This Item:
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.