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

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

Comparison of Traffic Speed Before, During and After “Banci Lalu Lintas” at Federal Road ft005

Traffic engineering uses engineering methods and techniques to achieve the safe and time efficient movement of people and goods on roadways and it depends on traffic flow. The three main parameters of a traffic flow are volume, speed and density. Speed is an important transportation consideration because it relates to safety, time, comfort, convenience, and economics. This study is to show the difference of traffic speed for before, during and after a primary traffic survey called “Banci Lalu Lintas”. This study also is conducted at Federal Road FT005 with collaboration of Jabatan Kerja Raya (JKR). In achieving the goal for this study, traffic speed is recorded by using two methods which are manual method and Automatic Traffic Count (ATC). For before and after the survey, manual method is used and the data is collected for 15 minutes, while during the survey, ATC is used in collecting data for 24 hours per day in a week. The data obtained where the mean speed is recorded and is compared as well as analyzed between three categories which are before, during and after “Banci Lalu Lintas” and using statistical analysis. In result, the speed of vehicles for during the survey is the lowest compared to before and after survey where the differential percentage are 6.68% and 23.64% for before – during and during – after “Banci Lalu Lintas”. The study concluded that drivers tend to decrease their vehicles speed when there is an event or unexpected conditions on the road. The result is important for future development and safety of road in Malaysia

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu

Motivational Factors on Adopting Modular Coordination Concept in Industrialized Building System (IBS)

Modular coordination (MC) is recognized as a tool towards rationalization and industrialization. The implementation of MC concept in the design stage may improve the constructability and construction time. However, the implementation of MC in Industrialized Building System (IBS) implementation is still low compare to other developed countries such as the United Kingdom, Sweden and Japan. This paper examined the interrelationship between motivational factors of stakeholders in adopting MC concept using Interpretative Structural Modeling (ISM). Questionnaire survey was engaged in this study to identify significant motivational factors. Then, semi-structure interviews were used to collect qualitative data. ISM was adopted to build relationship between factors and develop an initial model to promote the adoption of MC in IBS construction. Seven (7) significant motivational factors were identified in this research namely 1) ‘stakeholder’s commitment’, 2) ‘reduce site disruption’, 3) ‘increase productivity’, 4) ‘high skilled workers’, 5) ‘site sustainability (environment, economy and social benefits)’ 6) ‘standardization’ and 7) ‘enabling ‘open building’ concept’. The result using Matrice d’Impacts Croises Multiplication Applique an Clasment (MICMAC) shows that there are three factors can be categorized as Independent / Driving Factors namely ‘stakeholder’s commitment’, ‘standardization’ and ‘enabling “open building” concept’. These factors should be explored in details to enhance the adoption of IBS in Malaysia. The findings provide a very good platform for a further research in formulating an efficient solution to promote MC concept adoption among the stakeholders. This scenario will improve the deliverables of IBS construction and eliminate negative perception in its implementation

Motivational Factors on Adopting Modular Coordination Concept in Industrialized Building System (IBS)

Modular coordination (MC) is recognized as a tool towards rationalization and industrialization. The implementation of MC concept in the design stage may improve the constructability and construction time. However, the implementation of MC in Industrialized Building System (IBS) implementation is still low compare to other developed countries such as the United Kingdom, Sweden and Japan. This paper examined the interrelationship between motivational factors of stakeholders in adopting MC concept using Interpretative Structural Modeling (ISM). Questionnaire survey was engaged in this study to identify significant motivational factors. Then, semi-structure interviews were used to collect qualitative data. ISM was adopted to build relationship between factors and develop an initial model to promote the adoption of MC in IBS construction. Seven (7) significant motivational factors were identified in this research namely 1) ‘stakeholder’s commitment’, 2) ‘reduce site disruption’, 3) ‘increase productivity’, 4) ‘high skilled workers’, 5) ‘site sustainability (environment, economy and social benefits)’ 6) ‘standardization’ and 7) ‘enabling ‘open building’ concept’. The result using Matrice d’Impacts Croises Multiplication Applique an Clasment (MICMAC) shows that there are three factors can be categorized as Independent / Driving Factors namely ‘stakeholder’s commitment’, ‘standardization’ and ‘enabling “open building” concept’. These factors should be explored in details to enhance the adoption of IBS in Malaysia. The findings provide a very good platform for a further research in formulating an efficient solution to promote MC concept adoption among the stakeholders. This scenario will improve the deliverables of IBS construction and eliminate negative perception in its implementation

Short-term Predictions of PM

One of the air pollutants that poses the greatest threat to human health is PM10. The objectives of this study are to develop a prediction model for PM10. The Multiple Linear Regression (MLR) and Bayesian Regression (BRM) models were constructed to forecast the following day’s (Day 1) and next two days’ (Day 2) PM10 concentration. To choose the optimal model, the performance metrics (NAE, RMSE, PA, IA, and R2) are applied to each model. Jerantut, Nilai, Shah Alam, and Klang were chosen as monitoring sites. Data from the Department of Environment Malaysia (DOE) was utilised as a case study for five years, with seven parameters (PM10, temperature, relative humidity, NO2, SO2, CO, and O3) chosen. According to the findings, the key factors responsible for the unhealthy levels of air quality at the Klang station include carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) from industrial and maritime activities, which are thought to influence PM10 concentrations in the area. When compared to MLR models, the results demonstrate that BRM are the best model for predicting the next day and next two days PM10 concentration at all locations