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Air Quality Analysis by Using Fuzzy Inference System and Fuzzy C-mean Clustering in Tehran, Iran from 2009–2013

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      Since the industrial revolution, the rate of industrialization and urbanization has increased dramatically. Regarding this issue, specific regions mostly located in developing countries have been confronted with serious problems, particularly environmental problems among which air pollution is of high importance.


      Eleven parameters, including CO, SO 2, PM 10, PM 2.5, O 3, NO 2, benzene, toluene, ethyl-benzene, xylene, and 1,3-butadiene, have been accounted over a period of two years (2011–2012) from five monitoring stations located at Tehran, Iran, were assessed by using fuzzy inference system and fuzzy c-mean clustering.


      These tools showed that the quality of criteria pollutants between the year 2011 and 2012 did not as much effect the public health as the other pollutants did.


      Using the air EPA AQI, the quality of air, and also the managerial plans required to improve the quality can be misled.

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      Most cited references 5

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      Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects.

      It is very useful for the authorities and the people to have daily easy understandable information about the levels of air pollution and the proper measures to be taken for the protection of human health. In this paper we develop an aggregate Air Quality Index (AQI) based on the combined effects of five criteria pollutants (CO, SO2, NO2, O3 and PM10) taking into account the European standards. We evaluate it for each monitoring station and for the whole area of Athens, Greece, an area with serious air pollution problems. A comparison was made with a modified version of Environmental Protection Agency/USA (USEPA) maximum value AQI model adjusted for European conditions. Hourly data of air pollutants from 4 monitoring stations, available during 1983-1999, were analysed for the development of the proposed index. The analysis reveals the Athenian population exposure reaches high levels and during last years a gradual increase of days with unhealthy conditions was detected. The proposed aggregate model estimates more effectively the exposure of citizens comparing with the modified USEPA maximum value model, because counts the impact of all the pollutants measured. Towards the informing and protection of the citizens in an urban agglomeration this model advantages as a political and administrative tool for the design of abatement strategies and effective measures of intervention.
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        A review on integration of artificial intelligence into water quality modelling.

        With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.
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          Comparison of the Revised Air Quality Index with the PSI and AQI indices.

          Air pollution indices are commonly used to indicate the level of severity of air pollution to the public. The Pollution Standards Index (PSI) was initially established in response to a dramatic increase in the number of people suffering respiratory irritation due to the deteriorating air quality. The PSI was subsequently revised and implemented by the USEPA in 1999, and became known as the Air Quality Index (AQI) that includes data relating to particle suspension, PM2.5, and a selective options of either 8-hour or 1-hour ozone concentration during increased O3 periods. Yet, the costs of launching a network of PM2.5 monitoring stations are prohibitively high for many countries to implement the AQI from the PSI system in the foreseeable future. Therefore, the purpose of this research is to discuss the optimal method of assessing air quality using the latest developed Revised AQI (RAQI), a system that serves as an alternative to the PSI and AQI systems. The feasibility, effectiveness, and the differences between RAQI, AQI, and PSI in their applications to several air pollution conditions are also studied in this research. The results show that southern Taiwan's suspended particulates have significantly greater impact on PM2.5/PM10 ratios than in central and northern metropolitan areas, and that the ratios are higher in Taiwan as a whole compared to many other countries. We also found that the RAQI shows more significant results compared to the PSI and AQI as it has a wider coverage of the range of pollutant concentration levels.

            Author and article information

            [1. ]Dept. of Environmental Health Engineering, School of Public Health,International Branch Shahid Beheshti University of Medical Sciences, Tehran, Iran
            [2. ]School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran
            [3. ]Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran
            Author notes
            [* ] Corresponding Author: Email: monireh_majlessi@
            Iran J Public Health
            Iran. J. Public Health
            Iranian Journal of Public Health
            Tehran University of Medical Sciences
            July 2016
            : 45
            : 7
            : 917-925
            27516999 4980347 ijph-45-917
            Copyright© Iranian Public Health Association & Tehran University of Medical Sciences

            This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.

            Original Article

            Public health

            fuzzy c-mean clustering, air quality, fuzzy inference system, iran


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