DOI: 10.1016/j.atmosenv.2016.11.030
Scopus记录号: 2-s2.0-85006165941
论文题名: Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality
作者: Taylan O
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 150 起始页码: 356
结束页码: 365
语种: 英语
英文关键词: Air quality
; ANFIS
; Environmental factors
; Modelling
; Ozone level
Scopus关键词: Air pollution
; Air quality
; Artificial intelligence
; Atmospheric humidity
; Atmospheric pressure
; Environmental Protection Agency
; Fuzzy inference
; Greenhouse gases
; Models
; Nitrogen oxides
; Ozone
; Pollution
; Public health
; Quality control
; Surge protection
; Volatile organic compounds
; Adaptive neuro-fuzzy inference
; ANFIS
; Artificial intelligent techniques
; Environment protection
; Environmental factors
; Kingdom of Saudi Arabia
; Monitoring and controlling
; Ozone levels
; Air quality standards
; nitrogen oxide
; ozone
; air quality
; artificial intelligence
; artificial neural network
; atmospheric pollution
; concentration (composition)
; emission
; environmental assessment
; environmental factor
; environmental monitoring
; environmental protection
; fuzzy mathematics
; greenhouse gas
; nitrogen oxides
; ozone
; performance assessment
; public health
; urban atmosphere
; adaptive neuro fuzzy inference approach
; air pollution
; air quality
; air quality standard
; Article
; artificial intelligence
; atmosphere
; atmospheric pressure
; clinical assessment
; clinical evaluation
; concentration process
; environmental factor
; environmental monitoring
; environmental temperature
; fuzzy logic
; fuzzy system
; humidity
; nonhuman
; outcome assessment
; priority journal
; Jeddah
; Makkah [Saudi Arabia]
; Saudi Arabia
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes. © 2016 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82234
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia
Recommended Citation:
Taylan O. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality[J]. Atmospheric Environment,2017-01-01,150