Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models

Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 , namely PM10. Statistical modellings are required to predict future PM10 concentrations. The aims of this study are to develop and predict future PM10 concentration for next day (D+1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010. Three main models for predicting PM10 concentration i.e. multiple linear regression, artificial neural network and hybrid models were used. The methods which were used in multiple linear regression were multiple linear regression (MLR), robust regression (RR) and quantile regression (QR), while feedforward backpropagation (FFBP) and general regression neural network (GRNN) were used in artificial neural network. Hybrid models are combination of principal component analysis (PCA) with all five prediction methods i.e. PCA-MLR, PCA-QR, PCA-RR, PCA-FFBP and PCA-GRNN. Results from the regression models show that RR and QR are better than the MLR method and they can act as an alternative method when assumption for MLR is not satisfied. The models for artificial neural network show that FFBP is better than the GRNN. Hybrid models gave better results compared to the single models in term of accuracy and error. Lastly, a new predictive tool for future PM10 concentration was developed using ten models for each site with average accuracy for D+1(0.7930), D+2 (0.6926) and D+3 (0.6410). This application will help local authority to take proper action to reduce PM10 concentration and as early warning system

PM10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models

Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 11m, namely PMlO. Statistical modellings are required to predict future PMlO concentrations. The aims of this study are to develop and predict future PMlO concentration for next day (D+ 1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010

Reduction of Response Variable Influential Outliers Using M-Estimation in the Next Day Prediction of Ground-Level Ozone Concentration

Ground-level ozone concentration (O3) is a second significant air pollutant in Malaysia after particulate matter 10 micrometres or less in diameter (PM10) concentration. It is a secondary pollutant that created by photochemical reaction of primary pollutant such as volatile organic compound (VOCs) and nitrogen oxides (NOx) under the influence of solar radiation (UVB). O3 photochemical reactions used solar radiation with certain wavelength as the catalyst. In statistical analysis of prediction, the concentration level of O3 contains the influential outliers due to several factors such as offense in data recording and sampling, the error in data acquisition or data management and the damage of monitoring instrument in data recording that can lead to misleading result or information. The objective of this study is to predict the level of O3 concentration for next day (D+1) by using predictors of wind speed (WS), temperature (T), relative humidity (RH), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) for selected urban area of Shah Alam by the method of minimizing influential outliers from response variable using M-estimation. The influential outliers from response variable is minimized using tuning constant approached at 95% level of efficiency. The improvement has been proved when Fair method has minimized 5.34% influential outliers from response variable and the average accuracy of the model is 0.513

Identification of Source Contributions to Air Pollution in Penang Using Factor Analysis

Penang is one of the rapidly developed states in Malaysia with large numbers of population industrial activities, motor vehicles density and development projects.  The concentrations of air pollution parameters in Penang were investigated and analyzed together with meteorological parameters in order to determine their characteristics and contributions to air pollution in Penang using factor analysis (FA).  The air pollution parameters include ground level ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matters of less than 10 microns in size (PM10) while the meteorological parameters include relative humidity, wind speed and temperature.  The data was obtained from the Department of Environment (DOE) for the Universiti Sains Malaysia (USM) monitoring station for the period of 10 years from 2004 to 2013.  In this study, concentrations of PM10 was found to be the highest among the air pollutants and the concentrations was at its highest between the months of June to September for almost all years of observation due to the southwest monsoon.  As for the source contributions of air pollutions, O3 and meteorological parameters were found to be the largest contributor to air pollutions in Penang, followed by the traffic emissions and industrial activities

Pedestrians perspectives on environmental problems, awareness and willingness in changing current mode to walking in a possible way to reduce exposure of o3 concentrations to school children

Increasing ground level ozone has become an important issue because of its adverse effects on health and the environment. Increasing numbers of vehicles is known to be one of the sources of its precursors where gas emissions from vehicle exhausts lead to the production of ground level ozone. Active transports, mainly walking have been found to be the most effective way to reduce the use of private vehicles especially for short-distance travel. In this study, pedestrians’ perspectives on the existence of environmental problems and awareness regarding negative effects of these issues and their perceptions towards changing the current mode to active mode were evaluated. According to the surveys conducted at the four selected schools, by referring to the gender, as compared to male respondents, female respondents mostly testified that there were local environmental problems occurred at their area and are aware of the adverse effects of air pollutants exposed to human. As for types of respondents, teachers were much concern with the environmental problems as they spent more time in schools compared than other types of respondents. In terms of race, Indian and Malay respondents were more aware of the negative effects of air pollutants and most willingly to change from current mode to walking. From the analysis of one-way ANOVA and independent t-test, respondents’ level of agreement with environmental problems, awareness and potential in changing the current mode to walking were related to the gender, types of respondents and race. Nevertheless, factor of travel distance did not influence the given level of agreement by respondents