Zeynep Cansu Ayturan

Bio: Zeynep Cansu Ayturan is an academic researcher from Selçuk University. The author has contributed to research in topics: Air pollution & Particulates. The author has an hindex of 3, co-authored 9 publications receiving 25 citations.

Air Pollution Modelling with Deep Learning: A Review

Abstract: Air pollution is one of the fundamental environmental problems of the industrialized world due to its adverse effects on all organisms. Several institutions warn that there exist serious air pollution in many regions of the world. When all devastating effects of air pollutants considered, it is crucial to create valid models to predict air pollution levels in order to determine future concentrations or to locate pollutant sources. These models may provide policy implications for governments and central authorities in order to prevent the excessive pollution levels. Though there are a number of attempts to model pollution levels in the literature, recent advances in deep learning techniques are promising more accurate prediction results along with integration of more data. In this study, a detailed research about modelling with deep learning architectures on real air pollution data is given. With the help of this research we attempt to develop air pollution architectures with deep learning in future and enhance the results further with insights from recent advances of deep learning research such as Generative Adversarial Networks (GANs), where two competing networks are working against each other, one for creating a more realistic data and the other one to predict the state.

Metal bioaccumulation/toxicity test for metal industry wastewaters

Abstract: Metal industry wastewaters include different types of heavy metals with respect to the metal production processes and products. There are several methods used for metal production industry such as refining and smelting operations. Both may produce air emissions like SO2 and particulate matter, wastewater originating from floatation and leachate, and other wastes like sludge and slag. Heavy metals of metal industry wastewaters are nickel, brass, chrome, gold, cadmium, copper, brass, and silver. Most of them may give severe damage to human and environment. For example, chrome ion leads to lung cancer, stomach ulcer, kidney and liver function disorders and death on human. Thus, heavy metal containing wastewaters could be very dangerous. Besides, plant species which have capability of accumulate heavy metals can be an option to bioaccumulate metal industry wastewaters while plant species which are sensitive to heavy metals can be used as a plant for phytotoxicity tests. In this study metal industry wastewaters were analysed in order to determine plant species whether they are sensitive or tolerant to heavy metals. During analysis phytotoxicity tests were conducted with different plant species.

Usage of photocatalytic oxidation for the removal of air pollutants

Abstract: International Journal of Ecosystems and Ecology Science (IJEES) Volume 8, issue 4, 2018 https://doi.org/10.31407/ijees https://doi.org/10.31407/ijees84 _____________________________________________________________________________________________ 9 Vol. 8 (4): 711-716 (2018) USAGE OF PHOTOCATALYTIC OXIDATION FOR THE REMOVAL OF AIR POLLUTANTS Zeynep Cansu Ayturan*, Sukru Dursun *Environmental Engineering Department, Engineering Faculty, Selcuk University, Konya, Turkey; Corresponding author: Zeynep Cansu Ayturan, email: zcozturk@selcuk.edu.tr; sdursun@selcuk.edu.tr; Received May, 2018; Accepted June, 2018; Published July, 2018; DOI: https://doi.org/10.31407/ijees8409 UOI license: http://u-o-i.org/1.01/ijees/38796960

Modelling of PM10 Pollution in Karatay District of Konya with Artificial Neural Networks

Abstract: Air pollution is one of the most significant issues of human being faced nowadays because it can create adverse effects on both health of human and other livings. There are several air pollutants which are considered as dangerous such as sulphur dioxide (SO 2 ), nitrous oxide (NO x ), carbon monoxide (CO), volatile organic compounds (VOC) and particulate matter (PM). Particulate matter is one the most significant air pollutants because it may create respiratory, cardiological and pulmonary problems by inhalation by nose on humans. Also, heavy metals and hydrocarbons may be adsorbed on PM surface, so it is considered as carcinogenic by World Health Organization (WHO). When all these negative effects of PM are taken into consideration, it is important that PM future concentration should be determined for taking precautions. PM is classified according to the diameter of the particles and PM 10 is described as particulates which has diameter smaller than 10 micrometres. In this study, PM 10 pollution was predicted with artificial neural network (ANN) for Karatay district of Konya. ANN includes interconnected structures that can make parallel computations. Several meteorological factors and air pollutant concentrations was provided by database of Ministry of Environment and Urbanisation belonging to autumn period of 2016 such as SO 2 concentration, NO concentration, NO x concentration, NO 2 concentration, CO concentration, O 3 concentration, wind speed, temperature, relative humidity, air pressure, wind direction and previous day’s PM 10 concentration. These parameters were used in the model as input parameters and PM 10 concentration for one day later was used as an output parameter. Prediction performance of the obtained model was very promising when the similar studies are examined.

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Abstract: Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

Synthesis of Magnetic Biochar for Efficient Removal of Cr(III) Cations from the Aqueous Medium

Abstract: Porous biochars obtained from coniferous woods, and magnetic biochars based on them, which showed high sorption properties when extracting Cr(III) from aqueous solutions (from 0.005 to 0.0125 mol/L), were studied. The adsorption properties of the magnetic biochar are compared with the initial biochar. It has been established that the preparation of materials by the method of pyrolysis and subsequent treatment in a plasma reactor makes it possible to bring the samples under study into a number of promising adsorbents for the extraction of chromium from aqueous solutions.