Abstract
The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.
Highlights
The haze problem has intensified in recent years
Since Klang Valley has been experiencing bad haze conditions for the past many years, the PM10 data for Klang Valley was used as case study
Hamid et al considered two seasons, i.e., wet season and dry season and developed seasonal autoregressive integrated moving average model to predict the PM10 concentration in Negeri Sembilan [12]. They reported that Seasonal ARIMA (SARIMA) was a suitable model in predicting the PM10 concentration levels
Summary
The haze problem has intensified in recent years. For instance, Malaysia has been facing increasing bad haze problems since the 1990s, which typically occur during the southwest monsoon season from July till September. ISSN: 2302-9285 namely, sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), Ozone (O3) and suspended particulate matter (PM) [4] Of these pollutants, the suspended particulate matter of less than 10 microns in size (PM10) is the chief cause of the cardio-respiratory mortality and morbidity among children and elderly [5]. Since PM10 is the dominant air pollutant during haze episodes, the study sets to develop an intelligent, web-based Haze Alarm Visual Map application called HazeViz to forecast the PM10 value and indicate whether the haze condition is alarming or not, as well as to visualize the pattern of PM10 in a region. Since Klang Valley has been experiencing bad haze conditions for the past many years, the PM10 data for Klang Valley was used as case study.
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