IER Photochemical Smog Evaluation and Forecasting of Short-Term Ozone Pollution Levels with Artificial Neural Networks

• Published in: Process safety and environmental protection 2001-03, Vol.79 (2), p.117-128
• Main Author: Abdul-Wahab, S.A.
• Format: Article
• Language: English
• Subjects: IER mechanism; Kuwait; neural network; neural networks; ozone
• ISSN: 0957-5820; 1744-3598
• DOI: 10.1205/09575820151095201

The experimental work of this paper has been conducted over a period of one year, starting in January 1997, for measurement of air pollutants and meteorological parameters in the urban atmosphere of the Khaldiya residential area in Kuwait. The measurements were carried out simultaneously every 5 minutes by using the Kuwait University mobile air pollution monitoring laboratory (Chemical Engineering Department). The main emphasis of the paper has been placed on the problem of ozone for those days that are characterized by events of photochemical smog. The first objective of this paper deals specifically with the use of the Integrated Empirical Rate (IER) photochemical kinetic mechanism that has been developed at the Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia as a screening tool for photochemical smog assessment. The IER has been used to determine whether the local photochemistry of ozone events is light-limited (VOC-limited) or NO x -limited. Such information is necessary in developing an effective emission control plan and enables the decision as to whether NO x or NMHC emission needs to be controlled. On the other hand, the available models to predict the concentrations of ozone are complex and require a number of input data that are not easily acquired by environmental protection agencies or local industries. Thus, the second objective concerns the short-term forecasting of ozone concentration based on a neural network method.