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ÖZENGİN, NİHAN

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ÖZENGİN

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NİHAN

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    Publication
    Air quality level, emission sources and control strategies in Bursa/Turkey
    (Turkish Natl Committee Air Pollution Res & Control-tuncap, 2020-12-01) Çalışkan, Burak; ÇALIŞKAN, BURAK; Özengin, Nihan; ÖZENGİN, NİHAN; Cindoruk, S. Sıddık; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/ Çevre Mühendisliği Bölümü.; 0000-0002-8729-9441; 0000-0001-7536-0332; AAH-1475-2021; AAT-6526-2020
    In parallel with rapidly increasing population and number of motor vehicles, irregular urbanization, and unplanned industrialization, air pollution has reached dangerous levels in developing cities. Various industries such as textile, automotive, chemical, rubber and plastic industries are located in Bursa. In addition, the region receives a lot of migration and there is an intensive air pollution problem due to dense urbanization. The air quality monitoring station results showed that the PM10 and NOx are the main pollutants reducing air quality in the city. Despite the much efforts and regulations, air quality level has been getting worst year by year. Stakeholders were brought together to explore the true causes of non-blocking emissions, identify resource loads and priorities, and develop solutions. The current level and variation of air pollutant concentrations depending on years were presented to stakeholders. A survey and discussion were performed within the workshop, and consequently; industry, transportation, heating and uncontrolled combustion activities came front. Especially the fact that the industry is located in the city and the transportation network of the city is inadequate has emerged as the main source of air pollution problem. In order to develop effective solution, it was emphasized that effective supervision should come to the forefront and new industrial facilities should not be established in the regions in or near the city.
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    Evaluation of phytoplankton composition of the pelagic region of do? Anci dam reservoir (Bursa, Turkey) by artificial neural network (ANN) and clustering technique
    (Aloki Applied Ecological Research and Forensic Inst Ltd, 2022-12-07) Özengin, Nihan; ÖZENGİN, NİHAN; Uludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü; AAH-1475-2021
    This study was carried out in four stations in the Doganci Dam Reservoir in the northwestern part of the Anatolian Region of Turkey. Within this study, phytoplankton composition were evaluated by using artificial neural network and clustering technique. For this purpose, phytoplankton algal flora and some physico-chemical parameters were investigated in water samples taken from four different stations in Doganci Dam Reservoir. A total of 75 taxa belonging to the divisions of Bacillariophyceae (45), Chlorophyceae (12), Cyanophyceae (12), Dinophyceae (2), Chrysophyceae (2) and Euglenophyceae (2) were detected in the algal flora of the pelagic region. In terms of species diversity in the phytoplankton, Bacillariophyceae members were dominant, followed by Chlorophyceae and Cyanophyceae members. As a result of the research, the type list determined is the first report on the phytoplankton composition of the dam reservoir and it is thought to be beneficial in terms of future water quality and water pollution research. Cluster analysis is a classification method that is used to arrange a set of form into clusters. The aim of this method is to classify a set of clusters such that cases within a cluster are more similar to each other and to submit summary information of the data to researchers. For predicting phytoplankton biomass, in this study, ANN was combined with a clustering technique. This case study demonstrated the good performance of ANN models in describing phytoplankton dynamics, and the potential of coupling ANN with a clustering technique to describe the spatial heterogeneity of natural ecosystems.