Person: EKER ŞANLI, GİZEM
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EKER ŞANLI
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GİZEM
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Publication Effects of hydrogen peroxide and temperature on removal of polycyclic aromatic hydrocarbons (Pahs) from soil during photodegradation applications(Taylor, 2021-01-07) Sengül, Burcu; Eker Sanlı, Gizem; Sakin, Ahmet Egemen; ŞENGÜL, BURCU; EKER ŞANLI, GİZEM; SAKIN, AHMET EGEMEN; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü; 0000-0003-0877-402X; 0000-0002-0513-0520; AAZ-1166-2020; AAH-3216-2021; AAH-3216-2021This study investigated the removal of polycyclic aromatic hydrocarbons (PAHs) from soils around a cement factory in Bursa-Turkey. UVA and UVA-H2O2 applications were carried out with the particular apparatus. The evaporated PAHs were collected with the polyurethane foam (PUF) column in this apparatus. UVA applications were carried out simultaneously at two temperatures (18 degrees C and 35 degrees C) to determine the effect of temperature on PAHs' removal. Evaporated PAH amounts and PAH removal efficiencies were calculated. In UVA applications (without using H2O2), n-ary sumation (12) PAH removal efficiency was calculated as 90%, and this value increased to 95% with the use of H2O2. The highest PAH removal ratio (95%) was obtained with UVA-H2O2 (1% H2O2) application. An increase in temperature did not contribute significantly to the removal of PAHs (except for 20% H2O2). During the experiments, the amount of evaporated PAHs from soil to air increased depending on the temperature. It was observed that 80% of evaporated PAHs were formed 3 ring compounds (Phe, Ant) at 35 degrees C. It was concluded that only low H2O2 dose (1%) was effective on the removal of PAHs from soils at 18 degrees C and 35 degrees C.Publication Determination of polychlorinated biphenyl (PCB) concentrations of olive groves in spring season(Gazi Üniversitesi, 2020-01-01) Erkul, Seyma Nur; Şanlı, Gizem Eker; Erkul, Seyma Nur; EKER ŞANLI, GİZEM; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü; 0000-0002-3686-9950; 0000-0002-7175-2942; AAH-3216-2021; EVB-2364-2022Soil pollution is an important problem in the world. Various organic micro pollutants accumulate in the soil structure. In regions where olive farming activities are concentrated, it is very important to determine the concentrations of pollutants in the soil such as polychlorinated biphenyl (PCB) in the lipophilic property. In the present study, it was aimed to determine concentrations and 3-month homologue distribution of PCBs which are important in human health and environment in olive fields for spring in Bursa. Soil samples were extracted by ultrasonic method and PCB concentrations were measured by gas chromatography- micro-electron capture detector. As a result of the measurements, PCB pollution in olive groves in industrial areas was relatively higher than in rural areas. The PCB homologue group was identified as the most dominant group in 3 months. Then soil temperature has no significant effect on the total PCB concentration in soil. The data obtained in the present study reveals that PCB pollution level is within the acceptable limits in the sampling areas. This research, suggests that the concentration of PCBs in olive leaves and olives should also be investigated and the risks should be determined in the olive groves in Bursa province where olive cultivation is intense.Publication Performance evaluation of diethylamine to the removal of polycyclic aromatic hydrocarbons (PAHs) from polluted soils with sunlight(Taylor, 2021-02-07) Eker, Gizem; Şengül, Burcu; Cindoruk, Sabahattin Sıddık; EKER ŞANLI, GİZEM; ŞENGÜL, BURCU; CİNDORUK, SABAHATTİN SIDDIK; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü; 0000-0003-0877-402X; 0000-0001-7536-0332; AAZ-1166-2020; CNL-8702-2022; JRB-1424-2023; GBB-7012-2022In the present study, the removal of polycyclic aromatic hydrocarbon (PAH) compounds from contaminated soil with solar radiation was investigated. In this context, the effect of diethylamine (DEA) as a photo-sensitizer on the photodegradation of PAH was examined, and the ring distributions of the PAHs were determined. DEA was added to samples at the doses of 1%, 10%, and 20% of dry weight of soil, and samples were kept in ambient air for 24 hours. Concentrations of sigma(12)PAH in soil samples were detected with Gas Chromatography-Mass Spectrophotometer (GC-MS) as 4382 ng/g dry matter (DM). The total PAH content of the soil decreased by 45% in the presence of sunlight without using DEA. sigma 12PAH removal efficiency was at the maximum level of 76% with the addition of 1% DEA to the soil. The increase in the dose of DEA adversely affected the PAH elimination process, and minimum removal ratio (32%) was obtained in the sample containing 20% DEA. The use of DEA has contributed to the elimination of most 3-ring PAH species. Heavy species, 5-6 ring compounds, have not been removed in the presence of sunlight.Publication Regional-temporal trends and risk assessment of polychlorinated biphenyls (PCBS) in olive lands in Bursa, Turkey(Taylor, 2021-11-28) Erkul, Şeyma Nur; Sanlı, Gizem Eker; Erkul, Şeyma Nur; EKER ŞANLI, GİZEM; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü; EVB-2364-2022; FVM-6329-2022In this study, it was aimed to determine the spatial and temporal variations of polychlorinated biphenyl (PCB) levels in olive lands (0-5 cm) and to identify dominant PCB homologous groups and cancer risk values. In this context, surface soil samples were taken from seven olive lands in Bursa-Turkey for 9 months. Sampling points were categorized as rural, semi-rural, residential, and industrial areas. Regional-temporal trends of 43 PCB compounds were reported. The total average ( n-ary sumation (43)) PCB concentrations ranged between 8.3 +/- 3.31 ng/g DM and 15.4 +/- 3.3 ng/g DM. The lowest n-ary sumation (43) PCB concentration was measured at the rural area (Mudanya Yorukali-MY) and the highest value was measured at Orhangazi Asilzade (OA), which is an industrial area. The dominant homologous group in all seasons was found to be 5-CBs (31%) followed by 4-CBs (16%) and 6-CBs (15%). Cancer risks were calculated and risk trend was determined as ingestion > dermal > inhalation. The values ranged between 5.26 x 10(-6) and 1.28x10(-5), 2.62 x 10(-5) and 1.08x10(-5), 1.68 x 10(-1) and 6.9 x 10(-12) for dermal, ingestion and inhalation, respectively. Also, the correlation between the levels of certain soil characteristics (dry matter, pH, and temperature) was also investigated. Statistical results showed that for PCB#89/101, PCB#135/144, PCB#126, PCB#156/171/202, PCB#172 PCB#170/190 it was determined that there was a significant relationship.Publication Prediction of polycyclic aromatic hydrocarbons (PAHs) removal from wastewater treatment sludge using machine learning methods(Springer, 2021-02-10) Cağlar Gencosman, Burcu; Eker Şanlı, Gizem; ÇAĞLAR GENÇOSMAN, BURCU; EKER ŞANLI, GİZEM; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.; 0000-0003-0159-8529; AAG-8600-2021; FVM-6329-2022Removal of polycyclic aromatic hydrocarbons (PAHs) from wastewater treatment sludge with appropriate technologies is of great importance for nature and public health. UV technology is one of the most frequently used methods for the removal of PAHs. While various photodegradation applications with UV-C (ultraviolet-C) light and photocatalysts can be performed to remove these compounds, a large number of tests should be implemented to determine optimum removal conditions, which increase time and cost. It is possible to make predictions for the removal efficiency of PAHs by using data mining classification and reveal the hidden knowledge from data. This study aims to determine appropriate machine learning (ML) methods for the prediction of the PAH removal efficiency from wastewater treatment sludges regarding the initial PAH levels. The samples have multi-class imbalanced outputs; thus, random over-sampling and Synthetic Minority Over-sampling TEchniques (SMOTE) are used to improve the prediction results. Well-known data mining classification/machine learning methods, artificial neural network (multi-layer perceptron-MLP), k-means (k-NN), support vector machine (SVM), decision tree (C4.5), random forest (RF), and Bagging, are proposed for the prediction of removal efficiencies. Different evaluation metrics, Accuracy, multi-class AUC (MAUC-multi-class area under ROC curve), F-measure, Precision, Recall, and Specificity are used for the performance comparisons. RF and k-NN perform better with 92.35% and 92.36% average prediction accuracies, respectively. Besides, RF outperforms other methods with 0.97 MAUC value. RF and k-NN can be used for the removal efficiency prediction on the multi-class imbalanced datasets successfully, and removal efficiencies can be highly predicted considering input components with less cost and effort.