PROJECT PROFILE FUNDED BY WNI WxBunka Foundation
VOTE NUMBER GENERATED AT UNIVERSITI TEKNOLOGI MALAYSIA: R.J130000.7352.4B406
Estimating and Forecasting PM2.5 Concentrations in Malaysia

Research Area: Basic Science
Name of Project Leader: Prof Dr Kasturi Devi Kanniah
Research Alliance : Sustainability
Universiti Teknologi Malaysia
E-mail: kasturi@utm.my
ABSTRACT (120 words)
Compared to PM10, PM2.5 can penetrate deeper into the lungs, and can cause aggravation ofcardiovascular and systemic inflammation. Recently, only 65 stations are available over whole
Malaysia territory to measure PM2.5. These Limited stations provide insufficient data to describe
the spatial variations in PM2.5 and to forecast its concentrations. Therefore, remotely sensed
satellite data were used in this study to estimate PM2.5 on a daily basis over a large spatial domain
i.e. entire Malaysia territory. Mapping highly polluted locations with PM2.5 will help the publichealth organizations and environmental protection agencies to estimate mortality rate attributed
to ambient air pollution and the future PM2.5 concentration prediction can help local authorities toperform preventative measures.
1. INTRODUCTION
In Malaysia, Particulate Matter <10 micron (PM10) were measured as a main parameter for Air
Pollutant index (API) from 1995-2017. Previously, Malaysian API became an issue when people
notice the stark difference in air pollution reading between Malaysia (specifically in Sothern
Peninsular) and Singapore even though the haze was evidently bad (New Straits Times., 2015).
Thus, starting mid-August 2018, DOE improved the calculation of API by using Particulate Matter<2.5 micron (PM2.5). However, only 65 operational stations are still insufficient to cover the whole
Malaysian territory with an area of 330,290 km2. Establishing more air quality monitoring stations isvery costly and a certain station is only capable to satisfactorily represent the pollutant
concentrations within a radius of about 15 km (Ibrahim, Ismail, & Yong, 2012). Alternatively,
remote sensing data encourages more studies to be conducted on atmospheric particulates and air
quality, since satellite-based technology provides aerosol optical depth (AOD) as a key predictor of
PM over a large area (Van Donkelaar et al., 2010). During the last 20 years, several studies on PM
estimation using AOD products from various satellite sensors have been conducted in polluted
regions such as India, China, SEA and elsewhere (Dey et al., 2012; Gupta et al., 2006; Sinha et al.,2015; Wang & Christopher, 2003). In order to extend the spatial coverage to the whole country(both Peninsular and Island Malaysia) and for improving the accuracy of PM2.5 estimates, thepresent study integrates hourly AOD products from Himawari-8 satellite sensor, along withmeteorological parameters and other gaseous pollutants using machine learning techniques i.e.,
random forest (RF) and Support Vector Regression (SVR).
2. RESEARCH METHODOLOGY
Data
The prime data that was used to obtain PM2.5 over a large spatial domain is the Aerosols Optical
Depth (AOD) data. AOD corresponds to the total columnar aerosol, whereas PM refers to particle
concentration at the surface. These two parameters are strongly correlated and the correlation
depends on the vertical distribution of aerosols and several other factors (described below). AOD

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data used in this study is provided by the Himawari-8 satellites. Daily data with 5km spatial resolution
covering years 2018 to 2019 were used. PM2.5, meteorological and gases data obtained from the
Department of Environment (DOE) Malaysia. Hourly PM2.5 concentrations collected from 65 stations
around Malaysia were used for an empirical model development. In order to develop a robust
prediction model, it is necessary to consider potential factors that affect the PM2.5 concentrations asmuch as possible. Atmospheric gases data such as Carbon monoxide, Nitrogen dioxide, Ozone andSulphur dioxide also will be used as the combination of these precursor gases are able to form PM2.5,
while meteorological parameters such as wind speed, wind direction, ambient temperature, andrelative humidity can affect the concentration and distribution of PM2.5.
Methods
PM2.5 will be estimated using an empirical model relating PM2.5 measured in the field with AOD(from Himawari-8 satellite data) coupled with meteorological and chemical parameters. A machinelearning techniques called Random Forest (RF) and Support Vector Regression (SVR) were used as it
can provide insight into the spatial-temporal distribution of PM2.5.
Once PM2.5 is estimated for entire Malaysia, forecasting of PM2.5 for the next day was performed.
In the forecasting model, the average daily PM2.5 is considered as dependent variable and windspeed, wind direction, ambient temperature, relative humidity, rainfall CO, NO2, O3 and SO2 as
independent variables. All the parameter for the year 2018-2019 were used in order to gain a better
understanding of PM2.5 temporal variability. The results of the study (PM2.5 estimation) was validated
using cross validation (CV) technique in “e1071” and “Random Forest” in the R program. The
developed model were validated by using 10 fold cross-validation techniques. During the validationof the second model (forecast model), the input parameters of the days before were used and thepredicted PM2.5 was compared with actual PM2.5 value. The accuracy of the models will be evaluatedthrough performance indicators i.e. Root Mean Square Error, Mean Bias Error (MBE) and NashSutcliffe efficiency (NSE).
3. LITERATURE REVIEW
Significant progress has been made in developing and establishing various techniques for estimating
PM concentrations at local, regional and global scales. In early 2000s, most researchers predicted PM
using only AOD, by means of simple linear regression techniques (J. A. Engel-Cox, Holloman, Coutant,& Hoff, 2004; Wang & Christopher, 2003). Later, more advanced techniques were developed toincorporate AOD and other important parameters that may influence PM distribution spatially andtemporarily, starting from multiple linear regressions (Benas, Beloconi, & Chrysoulakis, 2013;
Chitranshi, Sharma, & Dey, 2015; Gupta & Christopher, 2009a; Schaap, Apituley, Timmermans,
Koelemeijer, & Leeuw, 2009; Zaman, Kanniah, & Kaskaoutis, 2017), chemical transport models (CTM)(Crouse et al., 2016; J. Engel-Cox, Kim Oanh, van Donkelaar, Martin, & Zell, 2013; Liu et al., 2004; Van
Donkelaar, Martin, & Park, 2006), mixed effect models (MEM) (Beloconi, Kamarianakis, &Chrysoulakis, 2016; Kloog, Koutrakis, Coull, Lee, & Schwartz, 2011; Lee, Liu, Coull, Schwartz, &Koutrakis, 2011; Xie et al., 2015), artificial neural networks (ANN) (Di et al., 2016; Grivas &Chaloulakou, 2006; Gupta & Christopher, 2009b; Wu, Guo, Zhang, & Li, 2011; Zaman et al., 2017),
geographic weighted regression (GWR) (Bai et al., 2016; Z. Hu, 2009; Ma, Hu, Huang, Bi, & Liu, 2014;
You et al., 2016) and generalized additive models (GAM) (Liu, Paciorek, & Koutrakis, 2009; Paciorek,
Liu, Moreno-Macias, & Kondragunta, 2008; Song et al., 2015; Zou, Chen, Zhai, Fang, & Zheng, 2016).
These techniques are limited to capture the non-linear relationships that exist between the variables.
Consequently, complex techniques have been developed by combining two or more statisticaltechniques; for instance, merging MEM and GWR (X. Hu et al., 2014) or incorporating MEM into
GAM (Ma et al., 2016) etc. Machine learning techniques such as deep neural network (DNN), support
vector regression (SVR) and random forest (RF) are also able to capture the complex relationships
between parameters and have greater performance in estimating PM2.5 (Danesh Yazdi et al., 2020; Liet al., 2018). Nowadays, machine learning techniques are increasingly used in air quality studies(Gholami, Mohamadifar, Sorooshian, & Jansen, 2020; Gholami, Mohammadifar, Bui, & Collins, 2020;
Kleine Deters, Zalakeviciute, Gonzalez, & Rybarczyk, 2017; Shin et al., 2020).

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4. FINDINGSFor the overall model, SVR calibration performed slightly better than RF with R2 =0.69 and RMSE=
10.62 μg m-3 against measured PM2.5 concentrations. Whilst, for the spatial models the RF validation
performed slightly better than SVR for both urban/industrial and suburban/rural areas with statisticalindicators of R2 =0.76, RMSE= 11.47 μg m-3 and R2 =0.64, RMSE=10.76 μg m-3, respectively. Therefore,both RF and SVR displayed slightly higher performance for PM2.5 estimations at urban/industrial sites
with higher levels of AOD and air pollutants. Furthermore, the estimation accuracy of SVR and RFmodels was lower in the wet (November-March) and inter-monsoon (April-May) seasons comparedto the dry (June-September) season. Based on the model accuracy and variable importance analysis,
CO was always the most influential predictor variable for PM2.5 estimations in Malaysia, followed by
AOD, O3, NO2, SO2 and meteorological parameters, but with different order depending on the dataset
and model. An important finding was the very weak correlation and contribution of themeteorological variables to PM2.5 estimations, as well as the low correlation between PM2.5 andcolumnar AOD, indicating that surface pollution follows a different temporal pattern than AOD, andthe presence of a significant aerosol layer aloft due to transported smoke plumes from wildfires insoutheast Asia.
5. CONCLUSION
The current results show that the use of machine-learning techniques for PM2.5 estimations over
Malaysia is promising as these models can satisfactorily represent the values and temporal evolutionof PM2.5 concentrations over both urban/industrial and suburban/rural sites. In a next step, we will
include gases pollutants from satellite remote sensing observations in order to estimate PM 2.5
concentrations over large areas aiming to cover the whole Malaysian territory with high spatial
resolution.
6. PROBLEM/ CONSTRAIN
Due to COVID-19, we cannot disseminate knowledge to the public via workshops. Even though, wehave prepare all the paperwork.
7. RESEARCH OUTPUT(i) Kanniah, K. D., Zaman, N. A. F. K., Kaskaoutis, D. G., & Latif, M. T. (2020). COVID-19's impact on theatmospheric environment in the Southeast Asia region. Science of the Total Environment, 139658.
Impact factor: 6.551 (Q1)(ii) Zaman, N. A. F. K., & Kanniah, K. D. 2020, Spatio-temporal assessment of Aerosol Optical Depthfrom Himawari-8 satellite data over Malaysia. In IOP Conference Series: Earth and Environmental
Science (Vol. 540, No. 1, p. 012053). IOP Publishing.
(iii) Kasturi Devi Kanniah and Nurul Amalin Fatihah Kamaruzaman, 2021, Remotely Sensed
Particulate Matter Estimation in Malaysia during the Biomass Burning Season in Southeast Asia
Published by CRC Taylor & Francis (Chapter in Press)(iv) Nurul Amalin Fatihah Kamarul Zaman, Kasturi Devi Kanniah, Dimitris G. Kaskaoutis and Mohd
Talib Latif, 2021, PM2.5 estimations in Malaysia using machine learning techniques, submitted to
Atmospheric Research (Q1 journal)

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8. HUMAN CAPITAL DEVELOPMENT(i) Suman A/L Selvaraju (Masters student)(ii) Nurul Amalin Fatihah binti Kamarul Zaman (PhD student)

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