Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia

The declining of air quality mostly affects the elderly, children, people with asthma, as well as a restriction on outdoor activities. Therefore, there is an importance to provide a statistical modelling to forecast the future values of surface layer ozone (O3) concentration. The objectives of this study are to obtain the best multivariate time series (MTS) model and develop an online air quality forecasting system for O3 concentration in Malaysia. The implementations of MTS model improve the recent statistical model on air quality for short-term prediction. Ten air quality monitoring stations situated at four (4) different types of location were selected in this study. The first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban located in Banting, Kangar, and Tanjung Malim, also the only background station at Jerantut. The hourly record data from 2010 to 2017 were used to assess the characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH) were used to examine the best MTS models. Three methods of MTS namely vector autoregressive (VAR), vector moving average (VMA), and vector autoregressive moving average (VARMA), has been applied in this study. Based on the performance error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained the VMA(2) as the best model. The lowest root mean square error (RMSE) and normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system for O3 was successfully developed based on the best MTS models to represent each monitoring station

The behavior of Particulate Matter (PM10) Concentrations at Industrial Sites in Malaysia

Particulate Matter (PM10) is one of the atmospheric pollutants that can cause significant effect to human health. Meteorological factors such as wind speed (WS), relative humidity (RH) and temperature (T), and gaseous pollutants namely surface layer ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are reported as some of the main factors that influence the concentration of PM10. Therefore, the aim of this study is to investigate the pattern and behaviour of PM10 concentration at three industrial sites which were Pasir Gudang in Johor, Perai in Penang and Nilai in Negeri Sembilan. In the current study, the descriptive statistics, correlation analysis and multiple linear regressions were used to analyse the hourly average data from 2010 to 2014. The maximum values of PM10 concentration recorded at Pasir Gudang, Nilai and Perai stations were 995 μg/m³, 711 μg/m³ and 232 μg/m³, respectively. Positive correlation was found between PM10 concentration and all gaseous pollutants. While for meteorological parameters, only wind speed had negative relations at all monitoring stations. The values of R2 for Pasir Gudang, Perai and Nilai were 0.539, 0.628 and 0.634, respectively. Overall, this study proved that most of the selected meteorological parameters and gaseous pollutants positively influenced the concentration of PM10

A review on short-term prediction of air pollutant concentrations

In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality. The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems. As such, the air pollution modelling appears to be an important tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future. Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series. Besides, this paper also presents comprehensive literature on univariate time series modelling. Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term prediction of air pollutant concentrations

Time series analysis of PM10 concentration in Parit Raja residential area

Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10. Since PM10 can give the adverse effect to human health such as asthma, cardiovascular disease and lung problem, appropriate action mainly involve short-term prediction maybe required as a precaution. This research was conducted to predict the PM10 concentration using the best time series model in Parit Raja, Batu Pahat, Johor. Primary data was obtained using E-Sampler at three monitoring stations; Sekolah Menengah Kebangsaan (SMK) Tun Ismail, Kolej Kediaman Melewar and Sekolah Rendah Kebangsaan Pintas Raya. ARIMA time series model was used to predict the PM10 concentration and the most suitable model is identify using by Akaike Information Criterion (AIC). Prediction of PM10 concentration for for the next 48 hours at all monitoring locations was verified using three error measures which are mean absolute error (MAE), normalized absolute error (NAE) and root mean square error (RMSE). After comparing the time series model, the short term prediction model for station 1 is AR(1), station 2 is ARMA(1,1) and station 3 is ARMA(2,1) based on the smallest AIC value and the best time series model that used for prediction at Parit Raja residential area is AR(1). Since the best model was identified for Parit Raja residential area, PM10 concentration can be predicted using AR(1) model to identify the value of PM10 concentration in the next day

A study on bus operation performance and demand sustainability (case study : Kota Kinabalu, Sabah)

Public Transport is one of the facilities that facilitate the movement and connecting communities from a place to another place. A good public transport system need to comply with world Bank Standard and other standard or requirement that can prove it their service is can be providing best service to consumer. In Malaysia, The public transport system has always been the government attention to be improved from time to time until 2011 government carried out the plan to improve the urban public transport in Malaysia. This Study On Bus Operating Performance and Demand Sustainability was conduct at Kota Kinabalu City and involved urban public transport City Bus as the main factor to Analysis. The analysis was conduct by observation on site or terminal and also on board observation. The main observation for this study is headway, Travel Distance, Load Factor, Availability and number of passenger. This observation shown that the headway range 26-28minute,Travel Distance Per bus Per day in range 86.40 to 178.70 KM, Availability range 75% to 83.33%. Comparing between the observation result and the standard show that the service by City Bus Kota Kinabalu need to improve their service since many of that indicator or standard observed is far from word Bank standard provided

Fitting statistical distribution on air pollution: an overview

High event of air pollution would give adverse effect to human health and cause of instability towards environment. In order to overcome these issues, the statistical air pollution modelling is an important tool to predict the return period of high event on air pollution in future. This tool also will be useful to help the related government agencies for providing a better air quality management and it can provide significantly when air quality data been analyze appropriately. In fitting air pollutant data, statistical distribution of gamma, lognormal and Weibull distribution is widely used compared to others distributions model. In addition, the aims of this overview study are to identify which distributions is the most used for predicting the air pollution concentration thus, the accuracy for prediction future air quality is the important aspect to give the best prediction. The comprehensive study need to be conducted in statistical distribution of air pollution for fitting pollutant data. By using others statistical distributions model as main suggested in this paper