Fate and Characteristics of Dissolved Organic Nitrogen through Wastewater Treatment Systems

Dissolved organic nitrogen (DON) represents a significant portion (25-80%) of total dissolved nitrogen in the final effluent of wastewater treatment plants (WWTPs). DON in treated wastewater, once degraded, causes oxygen depletion and/or eutrophication in receiving waters and should be reduced prior to discharge. Biodegradability, bioavailability, and photodegradability are important characteristics of wastewater derived DON and are subjects of research in this dissertation. Four research tasks were performed. In the first task, laboratory-scale chemostat experiments were conducted to examine whether solids retention time (SRT) could be used to control DON and biodegradable DON (BDON) in treated wastewater. Nine different SRTs from 0.3 to 13 were studied. There was no correlation between effluent DON and SRTs. However, BDONs at SRTs of 0.3 to 4 days were comparable and had a decreasing trend with SRTs after that. These results indicate the benefit of high SRTs in term of producing effluent with less BDON. The second task was a comprehensive year-round data collection to study the fate of DON and BDON through the treatment train of a trickling filter (TF) WWTP. The plant removed substantial amounts of DON (62%) and BDON (76%) mainly through the biological process. However, the discharged concentrations in the effluent were still high enough to be critical for a stringent total nitrogen discharge limit (below 5 mg-N/L). Evolution of bioavailable DON (ABDON) along the treatment trains of activated sludge (AS) and TF WWTPs and relationship between ABDON and BDON were examined in the third task. ABDON exerted from a combination of bacteria and algae inocula was higher than algae inoculated ABDON and bacteria inoculated BDON suggesting the use of algae as a treatment organism along with bacteria to minimize effluent DON. The TF and AS WWTPs removed 88% and 64% of ABDON, respectively. In the last task, photodegradable DON (PDON) in primary wastewater and final effluent from TF and AS WWTPs was studied. PDON and BDON fractions of DON data in the final effluent of TF and AS WWTP samples elucidate that photodegradation is as critically important as biodegradation when mineralization of effluent DON is a concern in receiving waters

Mathematical modeling of wastewater-derived biodegradable dissolved organic nitrogen

<p>Wastewater-derived dissolved organic nitrogen (DON) typically constitutes the majority of total dissolved nitrogen (TDN) discharged to surface waters from advanced wastewater treatment plants (WWTPs). When considering the stringent regulations on nitrogen discharge limits in sensitive receiving waters, DON becomes problematic and needs to be reduced. Biodegradable DON (BDON) is a portion of DON that is biologically degradable by bacteria when the optimum environmental conditions are met. BDON in a two-stage trickling filter WWTP was estimated using artificial intelligence techniques, such as adaptive neuro-fuzzy inference systems, multilayer perceptron, radial basis neural networks (RBNN), and generalized regression neural networks. Nitrite, nitrate, ammonium, TDN, and DON data were used as input neurons. Wastewater samples were collected from four different locations in the plant. Model performances were evaluated using root mean square error, mean absolute error, mean bias error, and coefficient of determination statistics. Modeling results showed that the <i>R</i>² values were higher than 0.85 in all four models for all wastewater samples, except only <i>R</i>² in the final effluent sample for RBNN modeling was low (0.52). Overall, it was found that all four computing techniques could be employed successfully to predict BDON.</p

Growth regime and environmental remediation of microalgae

Microalgal bioremediation of CO2, nutrients, endocrine disruptors, hydrocarbons, pesticides, and cyanide compounds have evaluated comprehensively. Microalgal mitigation of nutrients originated from municipal wastewaters, surface waters, and livestock wastewaters has shown great applicability. Algal utilization on secondary and tertiary treatment processes might provide unique and elegant solution on the removing of substances originated from various sources. Microalgae have displayed 3 growth regimes (autotrophic, heterotrophic, and mixotrophic) through which different organic and inorganic substances are being utilized for growth and production of different metabolites. There are still some technology challenges requiring innovative solutions. Strain selection investigation should be directed towards identification of algal that are extremophiles. Understanding and manipulation of metabolic pathways of algae will possible unfold solution to utilization of algae for mitigation of dissolve organic nitrogen in wastewaters

A Cross-Sectional Study Investigating Association Of Liver Diseases In Moderate To Severe Psoriasis Patients

Background and Design: Non-alcoholic fatty liver disease (NAFLD), which is a systemic comorbidity of psoriasis, is the most common liver disease in population with risk of cirrhosis progression. The aim of this study was to investigate the frequency and risk factors of NAFLD in moderate:-severe psoriasis patients. Materials and Methods: Patients aged 18 years and older, who were followed up with a diagnosis of moderate to severe psoriasis at Hacettepe University Faculty of Medicine, Department of Dermatology and Venereology between 2015 and 2016, were included. Demographic and disease-related data (age, sex, age at psoriasis onset, alcohol use, family history of psoriasis, psoriatic arthritis history), and associating systemic diseases were reviewed. Liver function tests (LFT) were evaluated during routine examinations. Evaluation of gastroenterology consultations was reviewed with indications (positive hepatitis markers, elevated LFT, liver disease history). Abdominal, liver and biliary system ultrasonography results were assessed. Descriptive stastistics were evaluated by cross-table and chi-square test. The difference between the two means was evaluated by t-tests. P value less than 0.05 were accepted as statistically significant. Results: Two hundred and sixty-six patients with moderate-severe psoriasis were included. 12% of the patients (n=31) had elevated LFT. Abdominal ultrasonography was performed in 77% (n=24) of patients who were evaluated by gastroenterology department for LFT elevation. NAFLD was found in 65% (n=20) of patients with high LFT. The incidence of coronary artery disease, hypertension and hyperlipidemia was significantly higher in patients with high LFT compared to patients with normal LFT (p=0.003, p=0.011 and p=0.001, respectively). Examination and laboratory values were compared according to presence of elevation in LFT; uric acid levels were statistically higher in psoriatic patients with high LFT (p=0.002). The mean waist circumference in patient group with elevated LFT and in group with normal LFT was found to be 108.3 +/- 9.6 cm and 98.2 +/- 15.4 cm, respectively. The difference was statistically significant (p=0.005). Conclusion: NAFLD should be kept in mind as a frequent and important cause of elevated LFT observed in psoriasis patients. The presence of comorbidities such as cardiovascular diseases, hypertension and hyperlipidemia, which are frequently observed in psoriasis patients diagnosed with NAFLD, should be investigated. We recommend measurement of waist circumference and blood pressure and parameters including fasting blood glucose, lipid profile and uric acid in terms of metabolic syndrome.WoSScopu

Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling

Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET0). The proposed models are developed and tested using daily ET0 data collected over 11 years (2010–2020) in the Samsun province of Türkiye. The results show that the ARIMA–GRNN model reduces the root mean square error by 48.38%, the ARIMA–ANFIS model by 8.56%, and the ARIMA–ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET0, which can be beneficial for many branches such as agriculture and water management that require dependable ET0 estimations