Abstract
This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.
Highlights
IntroductionSudden occurrences of high concentration of vehicular and industrial exhaust emissions are the episodes of air pollution in the urban areas [1]
Nowadays, air pollution becomes a major environmental issue throughout the world
A combination of Principal Component Analysis (PCA) and Artificial Neural Network (ANN) method was used to predict air pollutant index (API) based on 12 historical air quality parameters
Summary
Sudden occurrences of high concentration of vehicular and industrial exhaust emissions are the episodes of air pollution in the urban areas [1]. It can unavoidably cause damages to buildings, monuments and statues. The rapid industrial development and urbanization in the southern region of Peninsular Malaysia have contributed to high levels of atmospheric pollutants to the environment. Stationary and transboundary sources are the major sources of air pollution in Malaysia [3,4]. Mobile sources include motor vehicle, are the main sources of air pollutants in Malaysia [4,5]. The stationary sources within the study area are coming from the emissions of urban construction works, quarries, petrochemical and power plants [6]. The uncontrolled wildfires, earthquake and volcanic eruption from neighbouring countries are the examples of trans-boundary sources within the study area [4,7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
