Yüzeysel su kalitesi modellerinde kullanılan model katsayılarının değer dağılımlarının türetilmesi
Derivation of value distributions of model coefficients used in surface water quality modelling
Yazar: GİZEM ÇORUH
Danışman: DOÇ. DR. ALPASLAN EKDAL
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Çevre Mühendisliği Ana Bilim Dalı / Çevre Bilimleri, Mühendisliği ve Yönetimi Bilim Dalı
Derece: Yüksek Lisans, 2019
Konu: Çevre Mühendisliği = Environmental Engineering

Özet:
Su kalitesini korumak ve su kirleticilerin akıbetini tahmin etmek, mevcut çevre sorunlarının çözümünde önemli noktalardan biridir. Farklı kirlilik senaryolarını tahmin etmenin en iyi aracı, çevre yönetimi için temel ve teknik destek sağlayabilecek matematiksel modellerin simülasyonudur.
Çalışmanın amacı, su kalitesi modellerinin belirsizlik analizi aşamasına bir altyapı oluşturmak amacıyla, modellerde ihtiyaç duyulan stokiyometrik ve kinetik katsayılar için değer dağılımlarının oluşturulmasıdır. Bu dağılımlar, model parametrelerinin kalibrasyonunun daha dar bir aralığa indirgenmesi ile kullanıcılara belirsizlik analizi aşamada kolaylık sağlar.
Su kalitesi modellerinde; fiziksel, kimyasal ve biyolojik süreçler için stokiyometrik ve kinetik katsayılar kullanılmaktadır. Bu katsayıların modellenen su kütlesini en iyi şekilde temsil etmesi model parametrelerinin kalibrasyonu ve modelin doğrulanması açısından önemlidir.
Bu çalışmada, farklı su kütleleri için farklı model türleri kullanılarak geliştirilen modellerde kullanılan stokiyometrik ve kinetik katsayılar detaylı bir literatür araştırması ile bir veritabanında derlenmiştir. Bu veritabanı, Çilek (2005) tarafından hazırlanmış olup, 2005 yılı sonrası veriler eklenerek güncellenmiştir. Veritabanı, kullanıcıların üzerinde çalıştıkları su kütlesine ve kullandıkları model türüne benzer nitelikteki çalışmalarda kullanılmış katsayılara ulaşma imkanı sağlar. Bu durumda kullanıcılar daha dar bir aralıkta kalibrasyon işlemini daha hızlı bir şekilde gerçekleştirebilirler. Buna ek olarak kullanıcılar, veritabanında sorgulama yaparak seçim aralıklarını daraltıp daha gerçekçi sonuçlara ulaşabilirler.
Veritabanında sistematik bir şekilde gruplandırılan verilerin, belirsizlik analizine bir altyapı oluşturması amacıyla, değer dağılımları türetilmiş ve olasılık dağılım fonksiyonları belirlenmiştir. Materyal metot bölümünde bu işlemlerin nasıl yapıldığı anlatılmış ve sonuçlar bölümünde kullanılan verileri içeren tablolar, belirlenen değer aralıkları ve hesaplanan olasılıkları, bu sonuçlardan elde edilen değer dağılım grafikleri ve olasılık dağılım fonksiyonları ve kümülatif olasılık dağılım fonksiyonları verilmiştir.

Abstract:
Fresh water resources are vital for life on earth and play a fundamental role in supporting the environment, society and economy. Population growth, urbanization and industrialization are the main factors affecting water pollution. Freshwater ecosystems, such as wetlands, rivers, aquifers and lakes, are indispensable for life on our planet and are vital to deliver a range of benefits and services, including drinking water, water required for agricultural and industrial activities, cleaning purposes and environmental needs.
Decreasing water quality is a global problem that is increasing day by day in the world. Water pollution is classified as point-based wastewater treatment plants and non-point pollution such as agricultural areas. Some of the pollutants that prevent or restrict the use of water by degrading the quality of water as a result of human activities are; microbial pathogens, nutrients, oxygen-consuming organic and inorganic substances, heavy metals, persistent organic substances, pesticides, oil and petroleum products.
Globally, the most common water quality problem is eutrophication, which is the result of high nutrient loads (especially phosphorus and nitrogen) reaching water resources, which significantly reduces the beneficial uses of water. Eutrophication is a process that occurs due to the acceleration of the growth of certain algae species in the aquatic environment where the nutrient load is high, especially phosphorus.
The quantity of high quality fresh water is limited in the world, so comprehensive water management is needed. Policies and strategies necessary for the effective management of water quality should be identified and applicable mechanisms and tools for water pollution control should be developed.
Contamination of surface waters damages ecological balance and adversely affects human life. Control of environmental pollution by monitoring water quality in river basins is of utmost importance. It is necessary to predict the effects of point or diffuse pollution loads along the river, to take precautionary measures, to set-up water quality models and to simulate them in a computer environment in order to determine the effects of possible implementations on the environment. For the reliability of these models, values of the model parameters must be well defined. Most of the current model software does not have this feature and trial and error methods are used for parameter setting. 
In water quality models; stoichiometric and kinetic coefficients are used for physical, chemical and biological processes. It is important that these coefficients represent the modeled water body in the best way in terms of calibration of model parameters and verification of the model.
The purpose of this study to derive the value distributions in order to provide support to the uncertainty analysis with the values used for the calibration of the model parameters.
In this study, the model coefficients used in eutrophication-focused modeling studies, which were conducted in the surface water areas such as lakes, rivers and lagoons, were collected with a comprehensive literature review. In order to convert the compiled model coefficients into information easily by the user, a data processing platform has been developed which has the ability to store data and to evaluate these data statistically. 
The database which has been prepared by Çilek (2005) is updated by adding data after 2005. In the process of determining the model coefficients, the design of the computer program to be used in the database was also considered. When the researches were completed, coefficients of the designed database were processed, the data were analyzed and the distribution of the coefficients and the values taken by the variables were determined. With this database; it will be possible to examine the values taken for similar aquatic environments and calibrate the model coefficients with a narrower range. In order to provide a basis for the uncertainty analysis, value distributions of the data are derived, which are systematically in the database.
Data are searched from the 'sciencedirect' database using the keywords 'eutrophication, modeling, nutrients, surface water'. Data from 150 articles were first compiled in Microsoft Excel. In these tables, the name of the water body, the country where it is located, the type of ecosystem, the type of model, the unit and the source of the data are indicated. A separate column is opened for information that may be given as an explanation where it is necessary. Water bodies and model types that are not included in the database are added to the database with their identification numbers. Then, the minimum, maximum and average values are added into the relevant tables in the database with model and aquatic ecosystem identification numbers and references. New tables in the same format is created for some parameters that are not included in the database.
The data obtained as a result of literature review are, as main headings; light-related coefficients, nitrification rate constant, denitrification rate constant, temperature correction coefficients, stoichiometric and kinetic coefficients for phytoplankton and zooplankton, kinetic coefficients in organic nitrogen, phosphorus and carbon cycles, coefficients used for changes in dissolved oxygen and sediment.
Water quality model coefficients compiled in the database were transferred from Microsoft Access to Microsoft Excel and value distributions were derived. Value distributions are derived for coefficients that have 30 or more data.. While 2 of these coefficients are stoichiometric coefficients, 21 are kinetic coefficients. The data are sorted from small to large by taking all minimum, maximum and average values into a single column.
In most parameters, some data are excluded from the distribution, because some of the minimum and maximum values are selected from wide ranges to stay on the safe side. This determination process was made by looking at the proximity of the R2 value of the trend line to 1. Therefore, the value ranges for most parameters could not be selected equally. This is mainly due to agglomeration of small value ranges. These sequencing and screening results were determined by trial and error in different ranges of values. The number of data in these ranges is calculated with the "countifs"  command. The averages of the starting and ending values of the intervals are calculated. The probability of data in the range was found by dividing the number of data in the range by the total number of data. The data distribution graphs are created by using these mean values and probabilities. Polynomial trend lines were added to the distribution graphs to determine the probabilty distribution functions. By integrating these functions, cumulative probability distribution functions are obtained. In the results section, probability distribution graphs and cumulative probability distribution functions for these coefficients are given.