Removal efficiency of pharmaceutical and personal care products (PPCPs) by NF/RO membranes : factors, mechanisms and modeling

다국어 초록 (Multilingual Abstract)

Pharmaceutical and personal care products (PPCPs) are important substances for the survival of humans and animals. As the demand and production of PPCPs increase, trace amounts of PPCPs substances that are not properly removed in sewage treatment plants and water purification processes are being consistently detected in the aquatic ecosystem. PPCPs remaining in water cause disruption of the endocrine system of organisms in the aquatic ecosystem, and when exposed to humans, they have the potential to affect the health of sensitive groups such as children, the elderly, and pregnant women. Currently, in countries around the world, including Korea, PPCPs are consistently detected at trace concentrations in rivers and surface water, so they are selected as substances subject to management and intensive management and monitoring are being carried out. Additionally, process introduction and research to remove PPCPs are continuing. This study confirmed the PPCPs removal rate according to the pH of raw water using membrane technology for eight types of PPCPs. Through the PPCPs removal rate results, we sought to determine the correlation between material properties such as MW, pKa, and log Kow, and the pH of raw water and removal efficiency using a membrane. In addition, through the removal prediction model equation created through linear regression analysis, it was statistically confirmed whether the characteristics of the material and the pH of the raw water were significant factors in the removal prediction model equation.

목차 (Table of Contents)

• Chapter 1. Introduction 1
• 1.1 Research background and purpose 1
• Chapter 2. Literature review 4
• 2.1 Environmental hazards of PPCPs 4
• 2.1.1 Status of detection of residual PPCPs domestically and internationally 7
• 2.2 Removal of residual PPCPs using advanced water treatment process 10
• 2.2.1 Activated Carbon Adsorption 10
• 2.2.2 Advanced Oxidation Process (AOP) 11
• 2.2.3 Membrane filtration 11
• 2.3 Membrane 12
• 2.3.1 Membrane type 13
• 2.3.2 PA (Polyamide) membrane 14
• 2.3.3 PPCPs removal rate calculation 15
• 2.3.4 Economic feasibility study according to membrane type 15
• 2.4 Characteristics of PPCPs 17
• 2.4.1 Molecular Weight (MW) 17
• 2.4.2 pKa 18
• 2.4.3 log Kow 19
• Chapter 3. Materials and Methods 20
• 3.1 Selection of pharmaceutical Compounds 21
• 3.2 pH test using NF/RO Membranes 24
• 3.2.1 NF/RO Membranes 24
• 3.2.2 SEPA cell NF/RO removal efficiency test 25
• 3.3 Instrumental chemistry 28
• 3.3.1 Preprocessing method (Solid Phase Extraction, SPE) 28
• 3.3.2 LC-MS/MS analysis conditions 30
• 3.4 PPCPs removal predictive modeling 36
• 3.4.1 Linear Regression Analysis 36
• Chapter 4. Results and Discussions 38
• 4.1 External standard calibration curve and correlation coefficient (R2) 38
• 4.2 Confirmation of removal rate in pH 7.5, 4, 10 experiments using NF/RO membrane 43
• 4.2.1 NF pH 4 Comparison of removal rate over time 43
• 4.2.2 NF pH 7.5 Comparison of removal rate over time 45
• 4.2.3 NF pH 10 Comparison of removal rate over time 47
• 4.2.4 RO pH 4 Comparison of removal rate over time 49
• 4.2.5 RO pH 7.5 Comparison of removal rate over time 51
• 4.2.6 RO pH 10 Comparison of removal rate over time 53
• 4.3 Comparison of removal rates according to PPCPs 55
• 4.3.1 Molecular weight, MW 55
• 4.3.1.1 MW 100 ~ 200 g/mol 56
• 4.3.1.2 MW 200 ~ 300 g/mol 57
• 4.3.1.3 MW 300 ~ 800 g/mol 58
• 4.3.1.4 Effect of MW on removal efficiency 59
• 4.3.2 pKa 60
• 4.3.2.1 pKa 3 ~ 6 61
• 4.3.2.2 pKa 7.5 ~ 7.6 62
• 4.3.2.3 pKa 8.99 ~ 10 63
• 4.3.2.4 Effect of pKa on removal efficiency 64
• 4.3.3 Effect of log Kow on removal efficiency 66
• 4.3.3.1 log Kow 2 or less (hydrophilic material) 67
• 4.3.3.2 log Kow 2 or more (hydrophobic material) 68
• 4.3.3.3 Effect of log Kow on removal efficiency 69
• 4.4 Identifying removal factors and mechanisms through modeling 71
• 4.4.1 NF removal prediction modeling using linear regression 71
• 4.4.2 RO removal prediction modeling using linear regression 76
• Chapter 5. Conclusions 79