IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 201745

Comparative Analysis of different Statistical Methods for
Prediction of PM2.5 and PM10 Concentrations in Advance for
Several Hours
Sharjil Saeed1, Lal Hussain1, 2*, Imtiaz Ahmed Awan1, Adnan Idris3
1 Department of Computer Science & IT, The University of Azad Jammu and Kashmir, City Campus, 13100,
Muzaffarabad,*2 Correspondence Author, Quality Enhancement Cell, The University of Azad Jammu and Kashmir, City Campus, 13100,
Muzaffarabad, Pakistan
3

Pakistan
Department of Computer Science & IT, University of Poonch Rawalakot, Rawalakot, Azad Kashmir, Pakistan

Summary
Atmospheric particulate matter (APM) is harmful for living
being due to their small size which is ranging from ultra-fineparticles up to particles with aerodynamic diameter up to 10
micrometers and hence because of their ability to penetrate
deeper into human respiratory system. Particulates less than 2.5
micrometer (PM 2.5 ) are more hazardous as compared to coarse
particles of size 10 micrometer (PM 10 ). The damage due to APM
can be minimized through appropriate preventive measures. In
order to gage the sway of air on the health and welfare of every
living being it is necessary to perform an analysis of air quality
for accurate decisions about preventive measures. Different
machine learning methods including Support Vector Machines,
Decision Trees, Neural Networks and Linear Discriminant
analysis have been proposed for robust forecasting and prediction.
This work is aimed at analyzing and benchmarking different
methods for the prediction of average PM 2.5 concentrations. For
this purpose, data was acquired during the hours of the day fromthe ambient air and indoor environment in the suburb of
Muzaffarabad (Azad Jammu and Kashmir, Pakistan). Linear andRadial Support Vector Regressors and RF algorithm were used
for model generation and prediction. Results from these methods
are then compared using root mean squared error (RMSE) for
accurate predictions. The finding indicated that RF method andRadial Support Vector Regressor provided better prediction with
RMSE as compared to Linear Support Vector Regressor.

Key words:
Atmospheric particulate matter, forecasting, PM2.5, Machine
Learning, Support Vector Regressor

1. Introduction
The atmospheric aerosol or particulate matter (PM) is oneof the major factors that distort the urban air quality andaffect human and ecosystem wellbeing across the globe. It
is highlighted topic of research to know the effects ofparticulates that are present in the indoor environment
especially those which are located near the roadways. It
has been found by many studies that indoor particulates
were associated with many human diseases like asthma,
Manuscript received November 5, 2017
Manuscript revised November 20, 2017rise in blood pressure, respiratory and cardiovasculardiseases [1]; [2]. The lifetime of any pollutant from its
origination to removal is the critical component of theforecasting. Transportation, dispersion or concentration oforiginated pollutant depends on many factors including its
physics,sources,composition
topography
andmeteorology of the region [3]. The airborne cycle ends
when a pollutant deposit on surfaces, is washed out fromthe atmosphere or escape into the space [3]. The lifetimeof PM2.5 varies from a few seconds to weeks.
The activities that we carry out in indoor environment
reasoned deposited particles on the surface of the room to
become re-suspended in indoor air [4]. APM consists ofnumerous particles of different sizes, ranging from ultrafine particles up to particles with an aerodynamic diameter
up to 10μm (PM10 also known as coarse particulates) orlarger. It has been reported that particulates less than
2.5μm (PM2.5) are more hazardous due to their ability topenetrate deeper into human lungs and blood and hence
become a major cause of increase in respiratory andcardiovascular morbidity compared to coarse particulates.
Increased mortality from long-term air pollution exposure
is caused by cardiopulmonary diseases or from lung cancer[5]. Short-term exposure generally corresponds withhigher concentrations of air pollutants and this exposure
has been linked to several health problems. Correlations
between asthma and interim exposure to air pollution,
unrelieved bronchitis, and ischemic heart diseases havealso been established [6] ; [7]. Time series analyses(compares short-term changes in particulate matter anddeath counts on daily basis) have established a link
between exposure and increased mortality. Exposure toparticulate matter has also been connected to increased
rates of hospitalization. The impact of particle size onhuman health is of increasing interest [8]. Small particles
with diameters less than 10μm can bypass mechanisms
within the nasal passage that prevent unwanted material
from entering the body and depositing in the lungs. The

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IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 2017

deposited particles then pass through the alveolar
membrane of the lungs into the blood stream. Studies havefound that PM2.5 can be associated with increase of stroke,
MI (cardiac infarction), arrhythmia and heart failure [8].
Ultrafine particles and gaseous co-pollutants (e.g. ozone
and nitrogen oxides) have also been implicated in theeffects of particulate matter on the human body. However,few studies have analyzed the effects of ultrafine particles
because of the challenges involved in conducting the
research [8]. To issue warning about air pollution episodes
in a region, the air quality predictions is of main concern.
Recent technological developments and research haveshown that finer particles (particles produced by chemical
reactions in the atmosphere are mostly PM2.5) have theadverse impact on human’s health. The major sources of
PM2.5 in the urban areas are vehicle exhaust emissions
and suspended surface dusts containing carbon,
ammonium sulphate and nitrate [9]. Generally, theconcentrations of APM remain high in ambientenvironment due to direct exposure to the sources of APM
as compared to indoor environment where individuals areless prone to the dangers of APM. In countries like
Pakistan, sources of air pollution are diverse and also thegeographical pattern varies from city to city. Particularly
in some areas meteorological and geographical conditions
do not allow good air circulation and there exists large
population not in well-designed cities. City Muzaffarabad,
Azad Jammu & Kashmir, Pakistan is one of such cities. In
such conditions, episodes of high atmospheric pollution
demand to take some extreme actions to limit the potential
damages of atmospheric pollution. If such episodes are
predicted one or two days in advance, more efficientactions could be taken and advisory to take necessary
preventive measures may be issued in order to protect thecitizens.
It is widely accepted that prediction based on single scalartime series assuming that, all the information regarding theexternal factors is contained in that single time series ofthe system, is possible [10]. Moreover, in literature
different algorithms have been proposed to build
predictive models using single time series data including
Support Vector Regressors.
In present study, the mass concentrations of atmosphericparticulates are predicted using linear and radial Support
Vector Regressors and data collected from ambient air andindoor environment in the suburb of the city Muzaffarabad(Azad Jammu and Kashmir, Pakistan). The data ofconsecutive ten days was used to build the predictivemodel, which was later on used to predict massconcentration of six consecutive hours of next day.

2. Related Work
Prediction of atmospheric particulate matter is of great
importance and researchers have investigated the methods
for early prediction of the APM concentrations. [11]perform experiments and compare SVM and RBF (Radialbasis function) and demonstrated that SVM is the bestclassifier for air quality prediction than RBF network
having better generalization performance when comparedto RBF.[12] chooses Aviles urban area (Spain) at local
scale and generates a model of regression for air quality
prediction using the support vector machine (SVM)
technique. [13] performed experiments using ELM(Extended Machine Learning) and SVM for the predictionof Minority Class for suspended particulate matters. [14]used Principal Components Analysis to identify the air
pollution and predicted the air quality using ensemble
learning and using a tree based classifier. [15] collected
and analyzed the sensor information in smart environments
using tree base classifier and also found the association
between air quality and resident actions.
For PM10 forecasting various techniques have been
employed such as ARMA (Auto Regressive Integrated
Moving Average) and ARIMA (Auto Regressive
Integrated Moving Average) [16]. On the other hand,because for a representation of a system that is non-linear
and we represent it by some linear representation, and notable to incarcerate intense concentrations [17]. Besides,
these methods require continuous historical data [18].
ARIMA and ARMA models have the ability to take inexternal explanatory variables and in this case, they are
named ARIMAX and ARMAX, correspondingly. In somestudies, hybrid models are developed by coupling the
ARMA or ARIMA with other methods (ANNs forexample). [19] evaluated the performance of ARMAX,
ANNs, MLR and hybrid ARIMAX-ANN for theforecasting of daily maximum PM10 moving averagevalues in one station in Temuco, Chile. The foundation of
Support Vector Machines (SVM) is a machine learningtechnique that was initially developed for classiﬁcation
problems by Cortes and Vapnik [20]. A regression
technique based upon SVM was then developed [21].
SVM is able to perform linear and non-linear regressions.
The advantages of SVM, compared with MLP, are its
better generalization ability and its capability for learning,
using a small number of training data and huge number of
input variables [12]. Researchers [12] employed SVM for
simulation of PM10 in Avilés, Spain and they showed that
SVM with different Kernel function performs better than
MLP. [22] compared SVM and ANN for temporal
Prediction of PM10 in a station in Goteborg, Sweden, andthey found that the SVM produced better hourly PM10
forecasting results than an ANN. [23] demonstrated that
CART technique outperforms than linear regression
techniques.

IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 2017

In this work, linear and radial Support Vector Regressors
and RF algorithm are used for the prediction of
atmospheric particulate matter concentration on the basisof the data collected from the said study area.

3. Study Area
Muzaffarabad is the capital city of Pakistani administered
Kashmir commonly known as the state of Azad Jammu &Kashmir (AJ&K). It is a beautiful cup shaped valley; theland of velvet green plateaus, charming lakes andwaterfalls. At the conflux of Jhelum river and Neelum
river (old names were Hydaspes and Kishan-Ganga
respectively), the city is located at 73.47˚E (Longitude)
and 34.37˚N (Latitude). Arrogant mountains bordered thecity and weather conditions are almost same as Islamabad,the capital city of Pakistan. Muzaffarabad district isadjacent to the Khyber Pakhtunkhwa province of Pakistan
in the west, Indian held Kashmir in the east and Chinesearea in the North [24]. The infrastructure of Muzaffarabad
was badly affected in 08th Oct. 2005 horrible earthquakewith intensity of 7.6 on Richter scale [25]. Almost fifty
percent of buildings were destroyed in the said event.
Rehabilitation process started soon after the earthquake
happening. Government of Pakistan, in addition with
International aid, started to build destroyed roads,
buildings. Re-construction process severely deteriorated
environmental conditions in the area. During reconstruction process, small roads were flooded with huge
number of vehicles. The atmosphere was affected withdifferent type of pollutants. The concentrations ofParticulate Matters increased drastically due to closed (cupshaped) environment of Muzaffarabad City. Therefore,
there is a need to study the environmental pollutants after
the devastating event of earthquake. APM concentrations
depend on direct and indirect exposure to its sources.
Therefore, particulate matter time series concentrations
were monitored from six sites near the road sides using
portable air sampling instrument, EPAM-5000, both forambient and indoor settings. The sites were selected on thebasis of traffic flow, number of vehicles crossings, weather
conditions, and number of people around the selected site.
The monitor is installed for the period of continuously six
hours at each site. PM (1.0), PM (2.5) and PM (10) filters
were used to get the particulates at each site. Justificationsof the monitoring sites are available in the following table1 below.

47

Table 1: Justification of particulate sites in examination

Site
Index

Site

Land Use

Road
Grade

A

Bank
Road

Residential/Commercial

Flat

B

Bus Stand Residential/Commercial

Flat

C

ChattarResidential/Commercial
Chowk

Flat

D

ChehlaBridge

E
F

CMH
Chowk
Old
Secretariat

Commercial

TypeHigh traffic
densityHigh traffic
densityHigh traffic
density

Flat with High traffic
potholes density

Commercial

Flat

Commercial/
Government offices

FlatHigh traffic
densityHigh traffic
density

3. Predictions of Indoor Particulates
Predictions on the basis of historical data is important inseveral fields of life and it becomes utmost importantwhen human health related matters are involved. Increased
mass concentration affects respiratory track and lungs andhence causes serious health problems.
Therefore,prediction of mass concentration may be advantageous totake precautionary measures in order to void or minimize
the effects of increased concentrations. In this section
simulation settings will be presented first and thenobtained results will be discussed.

4. Simulation Setting
In machine learning literature, different supervised
learning algorithms have been proposed for building
predictive models [41]. Each algorithm has its own method
for learning different parameters from provided data.
Performance of a learned predictive model depends onlearning algorithm also on data used for building
predictive model [42]. In this work, mass concentrations
for both indoor and outdoor environments have been
predicted using linear, radial SVRs and RF methods. For
this purpose, data collected for consecutive ten days has
been used for training purposes and the mass concentration
for six hours of next day have been predicted. For
evaluation of prediction models Root Mean Squared Error(RMSE) was computed for each of the Predictive Models.
This procedure is illustrated in figure 1.

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IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 2017

Data

Train set

Test set

Predictive
Model

Learnin

Prediction

RMSE

Figure 1: Simulation plan for indoor and outdoor mass concentration
predictions

5. Prediction of Indoor Mass Concentrations
Results of indoor mass concentration predictions
computed by using simulation plan presented in figure 2
and indoor data are illustrated in figure 1. Data collected
for consecutive ten days has been used for training
purposes while test set is further divided into six different
test sets on the basis of time (hour-wise data) to represent
the predicted results in more visible manner. In the figure
3 actual mass concentrations from test set are plotted in
red color lines, predicted mass concentrations by linear
SVRs in green color, radial SVRs in blue color and RF inbrown color. Presented results show that predictions basedon RF based model are closest to actual massconcentrations for all six hours data of test set whichshows that RF method is more efficient and accurate forindoor mass concentration predictions compared to resultsof other two predictions models used in this work.

Figure 3: Mass concentrations predictions in indoor environment using
SVR.L, SVR.R and RF based prediction models

6. Comparison of Predictions Models For
Indoor Mass Concentrations
In this section, test set obtained from indoor data is divided
into six different test sets on the basis of time (hour-wisedata). In the figure 4 RSME valued computed for linear
SVR (SVR.L), radial SVR (SVR.R) and RF are presented
using bar charts. It is clear from the figure that prediction
model based on RF is the robust model with least RMSE
values for all six hours of test set. Figures 3 & 4 show thatfor time period from 11:00 to 12:00Noon and from 01:00
PM to 2:52PM, RMSE values of SVR.L and RVR.R are
almost the same. During this period of time most of massconcentrations values lie between 0.04 and 0.08 with few
exceptions. For rest of the time periods the RMSE valuesof SVR.L are much lower than SVR.R. Showing that
SVR.L is the second robust predictor for indoor data usedin this work

IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 201749

in red colour lines, predicted mass concentrations by linear
SVRs in green color, radial SVRs in blue color and RF
algorithm by black color. It is evident from this figure thatpredictions based on RF are closest to actual massconcentrations for all six hours data of test set representingthat RF method is more efficient and accurate for outdoor
mass concentration predictions compared to other two
methods of predictions used in this work.

Figure 4: RMSE values of SVR.L, SVR.R and RF based prediction
models in indoor environment.

Results presented in this section shat that RF based
predictions (compared to other two prediction models) areclosest to actual mass concentration values obtained during
data collection. Moreover, RMSE values for RF based
prediction models are the least. All this reflects that RF is
more suitable learning algorithm for mass concentration
predictions compared to SVR.L and SVR.R. Analysis ofresults presented in Figures 4.15 through 4.18 also showsthat mass concentration predictions in indoor environment
are more accurate than those of outdoor environment. Thisdifference may be due to the reason that in indoor
environment impurities/noise is less in data than outdoor
environment data which may have more contaminations.

7. Comparison of Predictions Models For
Indoor Mass Concentrations
As in case of indoor data, test set obtained from outdoor
data is divided into six different test sets on the basis oftime (hour-wise data) for clear representation of predicted
results. In the figure 5 actual values of test set are plotted

Figure 5: Mass concentrations predictions in Outdoor environment using
SVR.L, SVR.R and RF based prediction models.

8. Comparison of Predictions
Prediction models built using outdoor mass concentration
data are compared on the basis of RMSE values computedby using simulation plan presented in figure 2 and outdoor
data (divided into six different test sets on the basis of time(hour-wise data)) discussed before. In figure 6, RSME
values computed for linear SVR (SVR.L), radial SVR(SVR.R) and RF are presented using bar charts. It is clear
from the figure that RF based prediction model is therobust model with least RMSE values for all six hours oftest set. From 10:00AM onwards the RMSE values of
SVR.R remain almost half of the RMSE values of SVR.L.
Making SVR.R the second robust predictor for data usedin this work.

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IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.11, November 2017

Figure 6: RMSE values of SVR.L, SVR.R and RF based prediction
models in Outdoor environment

9. Discussion and Conclusion
The mass concentrations of indoor and ambientparticulates were predicted using linear and radial SVRs
and RF approaches. The ten days data was used fortraining purposes and the mass concentration for six hoursof the eleventh day were predicted. The Root Mean
Squared Error (RMSE) was computed to find thedifference between values of the predicted model and theactual values of mass concentrations. Test data wasdivided into six different sets on the basis of time (hourwise data) to represent the predicted results in more visible
manner. The results demonstrated that predictions basedon RF are closest to actual mass concentrations for theboth ambient and indoor mass concentrations ofparticulates with least RMSE.
The particulate matters comprised of highly complex andnonlinear dynamics due variety of factors polluting theenvironment. To predict and detect the behavior of highly
complex nature of data, it requires most robust machinelearning classification and regression methods based on setof selected features. Hussain et al. recently [26-29] usedcomplexity measures to investigate the dynamics ofdifferent physiological systems with various pathologies

and observed the complex dynamics degrade due to loss ofstructural functions.
The coupling dynamics andinteractions among different brain regions during restingstate are investigated by [30-32]. Rathore et al. [33-35]
extracted combination of geometric and hybrid features todetect the colon cancer. Likewise, the nonlinear dynamics
have been investigated using [36-37] to investigate theheart rate variability, emotion recognition [38] and load
forecasting [39] using machine learning and neuralnetwork approaches.
The results reveal that mass concentration predictions inindoor environment are more accurate than those ofoutdoor environment. This difference may be due to thefact that in indoor data was collected in the absence of
external influencing factors, whereas the massconcentration time series was prone to external
perturbations such as, wind direction, wind speed,
humidity, temperature, traffic congestion and road dust[40] show that air quality becomes depreciated due tonoise which limits the accuracy of predictions particularlyin the case of ambient environment. The prediction error
increases in such situations because the noise part of the
future measurement cannot be predicted. Moreover, thedata on the basis of which the predictions are made is itself
noisy and hence the estimates are affected by noise. Thus,
the presence of noise has effects on the prediction results.
Hence, our results are in accordance with the results ofprevious studies [40]. These results can be considered as
the example of successful predictions for theconcentrations of atmospheric particulate matters.

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Biography of Correspondence Author
Dr. Lal Hussain is a Programmer at
Quality Enhancement Cell, University of
Azad Jammu and Kashmir, Muzaffarabad
Pakistan. He obtained his MS in
Communication and Networks from Iqra
University, Islamabad, Pakistan in 2012
with Gold medal. He received Ph.D. from
Department of Computer Science &Information Technology, University of
Azad Jammu and Kashmir, Muzaffarabad, Pakistan in February
2016. He worked as visiting PhD researcher at Lancaster
University UK for six months under HEC International Research
Initiative Program and worked under the supervision of Dr.
Aneta Stefanovska, Professor of Biomedical Physics, Physics
Department, Lancaster University, UK. He is author of more than
10 publications of highly reputed peer reviewed and Impact Fact
Journals. He presented various talks at Pakistan, UK and USA.
His research interest includes Biomedical Signal Processing withconcentration in complexity measures, Time-Frequency
representation methods, Cross Frequency Coupling to analyze
the dynamics of neurophysiological and physiological signals.
His research interest also includes Neural Networks and Machine
Learning classification, regression and prediction techniques for
detection of cancer, epilepsy, Brain Dynamics and Diseases (i.e.
autismspectrum
disorder(ASD)
andattentiondeficit/hyperactivity disorder (ADHD), Alzheimer's Disease,
Brain Tumor), image processing and segmentation etc.