The main environmental concern in the textile industry is about the amount of water discharged and the chemical load it carries. The total quantity of chemicals used in textile mills varies from 10% to over 100% of the weight of the cloth produced. Many chemicals currently used in the textile industry affect the amount and the type of waste produced and their influence the aquatic life of the receiving stream. One of the critical steps in pollution prevention studies is auditing the use of chemicals and making the necessary chemical substitutions. Chemical substitution simply means the replacement and/or reduction of hazardous chemicals in products and processes with less- or non-hazardous ones. This study was conducted on one of the major textile factories in Turkey with a capacity of 20,000 tons of denim fabric per year. During this study, chemical consumption level, recipes applied, environmentally problematic and alternative chemicals were examined. Integrated Pollution Prevention and Control (IPPC) Reference Document on Best Available Techniques (BAT) for the Textile Industry was accepted as main reference document and also related case studies were examined. According to the study, over 60% reduction in sulphide, which is very toxic to aquatic life, was achieved by replacing sulphur dyestuff with low sulphide content. By replacing an alternative complexing agent, the mill not only prevented the 3100 kg/month COD load to the WWTP, but also obtained more biodegradable wastewater generated during production. On the other hand, some of the chemical substitution options were on progress or dropped.

The main objective of this study was to conduct a systematic investigation of the disinfection by-products (DBPs) formation in low-bromide and low- specific ultraviolet absorbance (SUVA) waters and the control of DBP precursors by nanofiltration (NF) and ultrafiltration (UF) processes in such waters. To this end, firstly, the effect of bromide ion on the formation and speciation of DBPs was investigated. In fractionated Alibeyk&ouml / y source water, increasing bromide concentrations in NOM fractions increased concentrations of trihalomethanes (THMs), haloacetic acids (HAAs) and adsorbable organic halides (AOX) and resulted in a shift toward the formation of brominated species. Secondly, the impacts of SUVA and differential UV spectroscopy (&amp / #916 / UV), which has been shown to correlate well with DBP formation has been elucidated in terms of DBP formation and speciation. Alibeyk&ouml / y and Karaca&ouml / ren waters were fractionated employing various separation methods and it has been shown that SUVA did not correlate well with the formation and speciation of THMs and HAAs in tested low-SUVA waters. Similarly, no correlations were found among THMs/HAAs formations and &amp / #916 / UV. Finally, the NOM rejection performances of NF and UF membranes were investigated. NF and UF membranes (&lt / 2000 dalton) was found to be suitable for the removal NOM from surface waters having low SUVA and low bromide contents. While higher molecular weight (HMW) fraction was successfully rejected (&gt / 90%) by all membrane types, lower molecular weight (LMW) fraction could be removed with ranging efficiencies from 1.5 to 30%. NF membranes provided DOC, UV254 absorbance, THM, and HAA reductions up to 90%.

Cocoon cooking wastewaters (CW) and silk degumming wastewaters (SDW) of silk processing industry were treated by membrane processes for sericin recovery. CW contains only sericin while SDW contains both sericin and soap. Sericin in CW had four molecular weight (MW) fractions / 175-200 kDa (Sericin-1), 70-90 kDa (Sericin-2), 30-40 kDa (Sericin-3) and 10-25 kDa (Sericin-4). Two alternative process trains were developed for CW / 1. centrifugation + microfiltration + nanofiltration + precipitation, 2. centrifugation + microfiltration + nanofiltration + dialysis + precipitation. In the first process, a sericin/silkworm protein mixture was obtained with a sericin content of 39-46%. In the second one, however, a pure sericin product was obtained. The sericin recovery efficiency of the developed process train was found as 76%. Severe flux declines of 70-75% were observed in NF stage in both process trains. However, cleaning with 0.5 M NaOH and 190-200 mg/L free chlorine restored the fluxes by 83-127%. The MW of sericin in SDW was 110-120 kDa. The soap and sericin were separated in the pre-treatment stage consisting of centrifugation (pH 3.5, 10 min) and gravity settling (4 oC, 24 h). The ultrafiltration membrane with molecular weight cut-off of 5 kDa achieved 59% sericin recovery at pH 3.5, accompanied by severe flux decline of 88%. Furthermore, clean water flux was restored by only 31% via chemical cleaning.

In this study, aerobic biological treatment of opium alkaloid wastewater and the effect of gamma preirradiation and fenton&amp / #8217 / s oxidation were investigated. First, the biodegradability of alkaloid wastewater was investigated by batch reactors and wastewater was found to be highly biodegradable providing 83 &amp / #8211 / 90 % COD degradation. In order to evaluate the effect of irradiation, original wastewater and irradiated wastewaters (40 &amp / 140 kGy) were compared by means of BOD5/COD values and through aerobic batch experiments. Results indicated that irradiation imparted no further enhancement in biodegradability. Sequencing batch reactor (SBR) studies revealed that the treatment operation was not possible due to sludge settleability problem observed beyond an influent COD value of 2 g/L. Possible reasons for problem were investigated, and the high molecular weight, larger size and aromatic structure of the organic matters present in wastewater was thought to contribute to poor settleability characteristics. Some operational modifications including phosphate buffer addition cured the settleability problem. Influent COD was then increased to 5,000 mg/L. Significant COD removal efficiencies (&gt / 70 %) were obtained in SBRs fed with both original and irradiated wastewaters. Preirradiated wastewater provided a better settling sludge in comparison to original wastewater. Degradation of the complex structure was followed by GC/MS analyses, particle size measurements and enhancement in filterability. Pre-irradiation enhanced the filterability of wastewater more than Fenton&amp / #8217 / s treatment and degradation by irradiation was proved by GC/MS analyses.

Ozonation is an efficient method to degrade refractory organics in textile wastewaters. In recent years, catalytic ozonation is applied to reduce ozone consumption and to increase chemical oxygen demand and total organic carbon (TOC) removal efficiencies. The ozonation of two industrial dyes, namely Acid Red-151 (AR-151) and Remazol Brilliant Blue R (RBBR) was examined separately both in a semi-batch reactor and also in a fluidized bed reactor (FBR) by conventional and catalytic ozonation with alumina and perfluorooctyl alumina (PFOA) catalysts. The conventional and catalytic ozonation reactions followed a pseudo-first order kinetics with respect to the dye concentration. The highest COD reductions were obtained in the presence of the catalysts at pH=13, with alumina for AR-151 and with PFOA for RBBR. Residence time distribution experiments were performed to understand the degree of liquid mixing in the reactor. The behaviour of the FBR was almost equivalent to the behaviour of one or two completely stirred tank reactors in series in the presence of the solid catalyst particles. The volumetric ozone-water mass transfer coefficients (kLa) were found at various gas and liquid flow rates and catalyst dosages in the FBR. A model was developed to find kLa in the reactor by comparing the dissolved O3 concentrations in the experiments with the model results. kLa increased significantly by the increase of gas flow rate. Higher catalysts dosages at the fluidization conditions yielded higher kLa values indicating higher rates of mass transfer. Dye ozonation experiments without catalyst and with alumina or PFOA catalyst were conducted at different conditions of the inlet dye concentration, gas and liquid flow rates, inlet ozone concentration in the gas, catalyst dosage, particle size and pH. The dye and TOC removal percentages were increased with the increase of gas flow rate and with the decrease of both the liquid flow rate and inlet dye concentration. The addition of the catalyst was beneficial to enhance the TOC degradation. The ozone consumed per liter of wastewater was much lower when the catalyst was present in the reactor. In terms of TOC removal and O3 consumption, the most efficienct catalyst was PFOA. According to the organic analysis, the intermediate by-products were oxalic, acetic, formic and glyoxalic acids in RBBR and AR-151 ozonation. The dye and dissolved ozone concentration profiles were predicted from a developed model and the model results were compared with the experimental results to obtain the enhanced kLa values. The presence of the chemical reaction and the catalysts in the FBR, enhanced the kLa values significantly. The enhancement factor (E) was found as between 0.97 and 1.93 for the non-catalytic ozonation and 0.96 and 1.53 for the catalytic ozonation at pH = 2.5. The dimensionless number of Hatta values were calculated between 0.04-0.103 for the sole ozonation of RBBR and AR-151 solutions. According to the calculated Ha values, the reaction occurred in the bulk liquid and in the film being called as the intermediate regime in the literature.

Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo / Matlab R 2007b&rdquo / software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

In this study, it is aimed to develop a database on Seyhan River Basin / and an environmental pollution index for the middle section of Seyhan Basin by combining different pollution indices such as water, and air. Water and air pollution indices are developed for the selected quality monitoring stations and the selected years. Water pollution index is calculated by using the method suggested by National Sanitation Foundation of United States, and air pollution index is developed according to the modified Environmental Protection Agency&rsquo / s air pollution index. As aggregation method, weighted arithmetic mean function is used for development of indices. After development of separate water and air pollution indices, environmental pollution index is developed for the year 2008 by two aggregation methods, one based on linguistic interpretation and the other again on weighted arithmetic mean function. The water pollution index for the middle section of the basin is determined as &ldquo / good&rdquo / , and air pollution index as &ldquo / low pollution&rdquo / . According to calculated water and air pollution indices, environmental pollution index is calculated with two methods and obtained the index equal to 4, which is classified as &ldquo / good&rdquo / . Although, both of the methods give same result in development of environmental pollution index, the second method based on weighted arithmetic mean function concluded to be more user friendly.

Chlorophyll-a is a parameter which can be used to understand the trophic state of water bodies. Therefore, monitoring of this parameter is required. Yet, distribution of chlorophyll-a in water bodies is not homogeneous and exhibits both spatial and temporal variations. Therefore, frequent sampling and high sample sizes are needed for the determination of chlorophyll-a quantities. This would in return increase the sampling costs and labor requirement, especially if the topography makes the location hard to reach. Remote sensing is a technology that can aid in handling of these difficulties and obtain a continuous distribution of chlorophyll-a concentrations in a water body. In this method, reflectance from water bodies in different wavelengths is used to quantify the chlorophyll-a concentrations. In previous studies in literature, empirical regression models that use the reflectance values in different bands in different combinations have been derived. Yet, prediction performances of these models decline especially in shallow lakes. In this study, the spatial distribution of chlorophyll-a in shallow Lake Eymir is determined using both regression models and artificial neural network models that use hybrid inputs. Unlike the models generated before, field measured parameters which can influence the reflectance values in remotely sensed images have been used in addition to the reflectance values. The parameters that are considered other than reflectance values are photosynthetically active radiation (PAR), secchi depth (SD), water column depth, turbidity, dissolved oxygen concentration (DO), pH, total suspended solids (TSS), total dissolved organic matter (TDOM), water and air temperatures, wind data and humidity. Reflectance values are obtained from QuickBird and World View 2 satellite images. Effect of using hybrid input in mapping the reflectance values to chlorophyll-a concentrations are studied. In the context of this study, three different high-resolution satellite images are analyzed for the spatial distribution of chlorophyll-a concentration in Lake Eymir. Field and laboratory studies are conducted for the measurement of parameters other than the reflectance values. Principle component analysis is applied on the collected data to decrease the number of model input parameters. Then, linear and non-linear regression and artificial neural network (ANN) models are derived to model the chlorophyll-a concentrations in Lake Eymir. Results indicate that ANN model shows better predictability compared to regression models. The predictability of ANN model increases with increasing variation in the dataset. Finally, it is seen that in determination of chlorophyll-a concentrations using remotely sensed data, models with hybrid inputs are superior compared to ones that use only remotely sensed reflectance values.

Thesis (Ph. D.)--Joint Program in Oceanography (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), 2002. / Includes bibliographical references.

Skin and blubber tissue samples were collected from two hundred and six freeranging Heaviside’s dolphins (Cephalorhyncus heavisidii) during January through June of 2008, 2009, and 2010, along the coast of South Western Africa, to be analyzed for heavy metal concentration levels. Inductively coupled mass spectrometry (ICP-MS) was used to determine sample solution concentrations of all metals (aluminum, arsenic, cadmium, chromium, copper, iron, lead, manganese, mercury, nickel, selenium and zinc). Data provided by this thesis is the first reference of its kind for this species (Cephalorhyncus heavisidii), which is one of the least-known cetacean species in the world. In addition to baseline heavy metal concentration information for the Heaviside’s dolphin, meaningful population structural and ecological information was obtained. A level of site fidelity or specific movement pattern across certain sites was indicated by the presence of different concentration levels across individuals sampled in different areas, especially adjacent areas. With an investigation into the potential sources of the marine heavy metal contamination, it was deduced that these sources contributed to each site’s unique and characteristic toxicological and hydrological profile as reflected by the bioaccumulation patterns seen in this study. It was confirmed by this study that bioaccumulation levels are a meaningful and novel measure of population structure. Potential evidence of differences in bioaccumulation patterns between sexes was observed in this study, as well. Although not definitive, a difference in metabolism, heavy metal elimination, or ranging pattern between males and females was suggested by the results. Variation in the results suggests a deepened level of complexity in the physiological and ecological aspects of trace elements accumulation and detoxification in the tissue of an apex predator like the Heaviside’s dolphin. Finally, a general assessment of health implications for the species was established, and it was found that Heaviside’s dolphins have higher than average mammalian trace element concentrations, and higher than average concentrations as specifically compared with other odontocetes. Further research using a larger sample size and using internal organs will provide more insight and a deeper understanding of the novel information presented here. / published_or_final_version / Biological Sciences / Master / Master of Philosophy