Düşük SUVA Değerlikli Sular için Trihalometan Tahmin Modelinin Geliştirilmesi ve Analitik Hiyerarşi Yöntemi ile En İyi Tahmin Modelinin Belirlenmesi

Yıl 2023, , 25 - 45, 28.03.2023

Cihan Özgür , Gökhan Civelekoğlu , Şehnaz Şule Kaplan Bekaroğlu

https://doi.org/10.35234/fumbd.1143444

Öz

Bu çalışmanın temel amacı genotoksik, sitotoksik ve kanserojenik olduğu bilinen dezenfeksiyon yan ürünlerinden trihalometanların miktarının belirlenmesi için çoklu lineer regresyon analizi kullanılarak tahmin modelinin oluşturulması ve literatürde mevcut modelleme çalışmaları ile karşılaştırılarak en iyi tahmin modelinin belirlenmesidir. Tahmin modelinde kullanılan bağımsız değişkenler Isparta şebekesinde ölçülen serbest klor konsantrasyonu, UV254 absorbansı, sıcaklık ve pH, bağımlı değişken ise trihalometan konsantrasyonudur. Çoklu lineer regresyon analizi neticesinde ölçülen trihalometan değerleri ile hesaplanan trihalometan değerleri arasındaki R2: 0,51; kök-ortalama-kare hata: 0,16 μg/L; ortalama mutlak yüzde hata: %3; hassasiyet analizi ise %51 oranla sıcaklık olarak bulunmuştur. Çalışma sonucu elde edilen tahmin modeli sonucu ile literatürdeki 10 farklı tahmin modelinin karşılaştırılması analitik hiyerarşi süreci aracılığıyla yapılmıştır. Analitik hiyerarşi sürecinde kullanılan kriterler: “Kısa Analiz Süresi”, “Düşük Maliyet”, “Az Analitik Cihaz Gereksinimi”, “Az Parametre Sayısı” ve “Yüksek R2” şeklinde belirlenmiştir. Analitik hiyerarşi sürecine göre en önemli kriter “Kısa Analiz Süresi (0.40/1)” olarak tespit edilmiştir. Analitik hiyerarşi prosesinin alternatifleri ise literatürden seçilmiş 10 model çalışması ve bu makaleden elde edilen model çalışmasının sonuçlarıdır. Bu çalışmanın sonuçlarına göre alternatif modeller arasından A4 (0.20/1) numaralı model kısa zamanda, az analitik cihaz kullanarak, en yüksek verim elde edilebilecek model olarak tercih edilebilir.

Anahtar Kelimeler

Analitik hiyerarşi prosesi, çoklu lineer regresyon, super decision, tahmin modeli, trihalometan

Destekleyen Kurum

TÜBİTAK

Proje Numarası

113Y416

Teşekkür

"İçme Suyu Kaynakları ve Şebekelerinde Azot Bazlı Dezenfeksiyon Yan Ürünleri ve Öncülerinin Mevsimsel Değişimlerinin İncelenmesi" başlıklı TÜBİTAK projesi (Proje No: 113Y416), bu çalışmanın modelleme kısmı için finansman sağlamıştır.

Kaynakça

• Cortes C, Marcos R. Genotoxicity of disinfection byproducts and disinfected waters: A review of recent literature. Mutat Res Gen Tox En 2018; 831: 1–12.
• Hu J, Chu W, Sui M, Xu B, Gao N, Ding S. Comparison of drinking water treatment processes combinations for the minimization of subsequent disinfection by-products formation during chlorination and chloramination. Chemical Engineering Journal 2018; 335: 352–361.
• Roth D, Cornwell DA. DBP Impacts from Increased Chlorine Residual Requirements. Journal AWWA 2018; 110: 2.
• Alexandrou L, Meehan BJ, Jones OAH. Regulated and emerging disinfection by-products in recycled waters. Science of the Total Environment 2018; 637–638: 1607–1616.
• Chhipi-Shrestha G, Rodriguez M, Sadiq R. Unregulated disinfection Byproducts in drinking water in Quebec: A meta analysis. Journal of Environmental Management 2018; 223: 984–1000.
• Ersan MS, Liu C, Amy G, Karanfil T. The interplay between natural organic matter and bromide on bromine substitution. Science of the Total Environment 2019; 646: 1172–1181.
• Lin J, Chen X, Zhu A, Hong H, Liang Y, Sun H, Lin H, Chen J. Regression models evaluating THMs, HAAs and HANs formation upon chloramination of source water collected from Yangtze River Delta Region, China. Ecotoxicology and Environmental Safety 2018; 160: 249–256.
• Avsar E, Toröz İ, Hanedar A, Yılmaz M. Chemical Characterization of Natural Organic Matter and Determination of Disinfection By-Product Formation Potentials in Surface Waters of Istanbul (Omerli and Buyukcekmece Water Dam), Turkey. Fresenius Environmental Bulletin 2014; 23(2a): 494-502.
• U.S. Environmental Protection Agency (USEPA) 1998. National Primary Drinking Water Regulations: Disinfectants and Disinfection Byproducts Notice of Data Availability, Proposed Rule. Federal Register 61, 62:15677.
• U.S. Environmental Protection Agency (USEPA) 2006. National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection Byproducts Rule, Final Rule, Federal Register 71:2.
• Avsar E, Toröz İ. Seasonal Determination and Investigation of Disinfection By Product Formation Potentials (DBPFPS) of Surface Waters, Istanbul Omerli and Buyukcekmece Case Study. Anadolu University Journal of Science and Technology B- Theoritical Sciences 2018; 6(1): 22-35.
• World Health Organization (WHO) 2004. Guidelines for Drinking Water Quality, 3rd ed., http://www.who.int/water_sanitation_- health/dwq/gdwq3rev/en.
• Bond T, Kamal NHM, Bonnisseau T, Templeton MR. Disinfection by-product formation from the chlorination and chloramination of amines. Journal of Hazardous Materials 2014; 278: Pages 288-296.
• Garcia-Villanova RJ, Garcia C, Gomez JA, Garcia MP, Ardanuy R. Formation, Evolution and Modeling of Trihalomethanes in The Drinking Water of A Town: II. In The Distribution System. Wat. Res. 1997; Vol. 31: pp. 1405-1413.
• Golfinopoulos SK, Xilourgidis NK, Kostopoulou MN, Lekkas TD. Use of A Multiple Regression Model for Predicting Trihalomethane Formation. Wat. Res; 1998: Vol. 32, No. 9, pp. 2821-2829.
• Golfinopoulos SK, Arhonditsis GB. Multiple regression models: A methodology for evaluating trihalomethane concentrations in drinking water from raw water characteristics. Chemosphere 2002; 47: 1007–1018.
• Civelekoğlu G, Yiğit NO, Diamadopoulos E, Kitiş M. Prediction of Bromate Formation Using Multi-Linear Regression and Artificial Neural Networks. Ozone: Science and Engineering 2007; 29: 353–362.
• Uyak V, Ozdemir K, Toroz I. Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs. Science of the Total Environment 2007; 378: 269–280.
• Chowdhury S. Champagne P. An Investigation on Parameters for Modeling THMs Formation. Global NEST Journal 2008; Vol 10: No 1, pp 80-91.
• Mishra BK, Priya T, Gupta SK, Sinha A. Modeling and Characterization of Natural Organic Matter and Its Relationship with The THMs Formation. Global NEST Journal 2016; Vol 18: No 4, pp 803-816.
• Triantaphyllou E. Multi-Criteria Decision Making Methods: A Comparative Study. Springer New York, NY: Kluwer Academic Publishers, 2000.
• Peyrelasse C, Jacob M, Lallement A. Multicriteria Comparison of Ozonation, Membrane Filtration, and Activated Carbon for the Treatment of Recalcitrant Organics in Industrial Effluent: A Conceptual Study. Environmental Processes 2022; 9: 9.
• Ebrahimzadeh S, Wols B, Azzelino A, Martijn BJ. Quantification and modelling of organic micropollutant removal by reverse osmosis (RO) drinking water treatment. Journal of Water Process Engineering 2021; 42: 102164.
• Teodosiu C, Gilca AF, Barjoveanu G, Fiore S. Emerging pollutants removal through advanced drinking water treatment: A review on processes and environmental performances assessment. Journal of Cleaner Production 2018; doi: 10.1016/j.jclepro.2018.06.247.
• APHA (1998) Standard Methods for the Examination of Water and Wastewater. 20th Edition, American Public Health Association, American Water Works Association and Water Environmental Federation, Washington DC.
• U.S. Environmental Protection Agency (USEPA) 1996. Method 8270C. Semivolatile Organic Compounds by Gas Chromatography / Mass Spectrometry (GC/MS). In Test Methods for Evaluating Solid Waste: Physical/Chemical Methods, SW- 846, 3rd edition. United States Environmental Protection Agency, Washington.
• Avsar E, Toroz I, Hanedar A. Physical Characterisation of Natural Organic Matter and Determination of Disinfection By-Product Formation Potentials in Istanbul Surface Waters. Fresenius Environmental Bulletin 2015; 24(9): 2763-2770.
• Civelekoğlu G. Arıtma proseslerinin yapay zeka ve çoklu istatistiksel yöntemler ile modellenmesi. Doktora Tezi, Süleyman Demirel Üniversitesi, Isparta, 2006.
• Ömürbek N, Şimşek A. Analitik Hiyararşi Süreci ve Analitik Ağ Süreci Yöntemleri ile Online Alışveriş Site Seçimi. Yönetim ve Ekonomi Araştırmaları Dergisi 2014; 22: 306-327.
• İmren E, Karayılmazlar S, Kurt R, Çabuk Y. Yatırım Kararı Almada AHS Yönteminin Kullanımı: Bartın İli Örneği. Bartın Orman Fakültesi Dergisi 2017; 19(2): 107-114.
• Ozgur C. Farklı Su Kaynaklarında ve Şebekelerde Karbonlu ve Azotlu Dezenfeksiyon Yan Ürünlerinin Oluşumu. Doktora Tezi, Süleyman Demirel Üniversitesi, Isparta, 2019.
• Sadiq R, Rodriguez M. Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review. Sci Total Environ 2004; 321(1-3): 21-46.
• Godo-Pla L, Emiliano P, Poch M, Valero F, Monclus H. Benchmarking empirical models for THMs formation in drinking Water systems: An application for decision support in Barcelona, Spain. Science of the Total Environment, 2021; 763: 144197.
• Albanakis C, Tsanana E, Fragkaki AG. Modeling and prediction of trihalomethanes in the drinking water treatment plant of Thessaloniki, Greece. Journal of Water Process Engineering 2021; 43: 102252.
• Kumari M, Gupta SK. Modeling of trihalomethanes (THMs) in drinking water supplies: a case study of eastern part of India. Environ Sci Pollut Res 2015; 22:12615–12623.
• Islam N, Sadiq R, Rodriguez MJ, Legay C. Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index. Environ Monit Assess 2016; 188: 304.
• Semerjian L, Dennis J, Ayoub G. Modeling the formation of trihalomethanes in drinking waters of Lebanon. Environ Monit Assess 2009; 149: 429–436.
• Mcbean E, Zhu Z, Zeng W. Systems analysis models for disinfection byproduct formation in chlorinated drinking water in Ontario. Civil Engineering and Environmental Systems 2009; 25(2): 127-138.
• Uyak V, Toroz İ. Modeling The Formation Of Chlorination By-Products During Enhanced Coagulation. Environmental Monitoring and Assessment 2006; 121: 503–517.
• Al-Omari A, Fayyad M, Qader AA. Modeling trihalomethane formation for Jabal Amman water supply in Jordan. Environmental Modeling and Assessment 2004; 9: 245–252.
• Feungpean M, Panyapinyopol B, Elefsiniotis P, Fongsatitkul P. Development of statistical models for trihalomethane (THM) occurrence in a Water distribution network in Central Thailand. Urban Water Journal 2015; Vol. 12, No. 4: 275–282.
• Mahato JK, Gupta SK. Modification of Bael fruit shell and its application towards natural organic matter removal with special reference to predictive modeling and control of THMs in drinking Water Supplies. Environmental Technology & Innovation 2020; 18: 100666.