Spatial and Temporal Analysis of Air Pollution in Makkah, the

Kingdom of Saudi Arabia

Turki M. Habeebullah 1, Said Munir

1, Essam A. Morsy

1 and Atef M.F. Mohammed

1,2

1 The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University,

Makkah, Saudi Arabia 2 Air Pollution Department, National Research Center, Cairo, Egypt

Abstract. Spatial – temporal analysis of air pollutants is fundamental to the process of air pollutants related

risk and damage assessment. This paper analyses spatiotemporal variability of air pollutants in Makkah,

using data from 4 monitoring sites during the Hajj (Pilgrimage) 1433 (October, 2012). The analysis is based

on graphical presentations, correlation analysis and analysis of variance. The analysis of variance showed

significant difference (p-value < 0.05) between various monitoring sites and dates for all pollutants. Both

diurnal and weekly cycles of the air pollutants demonstrated considerable variations at different sites.

Correlation coefficients (R) between PM10 concentrations measured at different monitoring sites were mostly

positive and ranged from 0.01 to 0.45. Correlation analysis showed mostly negative and much weaker

association between SO2 measured at different monitoring sites (R = -0.02 to -0.21). O3 demonstrated the

strongest positive correlation between different monitoring sites and ranged from 0.55 to 0.86. The

correlation coefficients of CO monitored at different monitoring sites ranged from 0.21 to 0.63, whereas

those of NO and NO2 ranged from 0.39 to 0.71 and 0.20 to 0.60, respectively. The strongest spatial

correlation of O3 is probably due to the fact that O3 is a regional pollutant and is more related to the regional

emissions of precursors and meteorological parameters. This is the first attempt to analyse the spatial

variability of air pollutants in Makkah, however the study is based over a shorter period of time, therefore

further work is required to analyse these trends over a greater range of time and space.

Keywords: Spatial-temporal analysis, air pollution, Makkah, NOx, PM10.

1. Introduction

Air pollution is one of the most pressing environmental problems and is among the critical challenges

facing the modern societies. Air pollution is responsible for significant harmful effects on human health and

environment. Furthermore, air pollution is responsible for climate change due to greenhouse effect, acid rain,

and the depletion of the ozone layer [1]-[3]. In Makkah, most of the combustion related-air pollutants (e.g.,

sulphur dioxide (SO2), nitrogen oxides (NOx) – the sum of nitric oxide (NO) and nitrogen dioxide (NO2),

carbon monoxide (CO), and Hydrocarbons (HC)) are predominantly emitted by road traffic [4]. Particulate

matters in Makkah are emitted by awide range of sources, which include road traffic, construction work,

resuspension of dust particles, and windblown sand and dust particles [5], [6]. In addition to emission

sources, air pollutant concentrations are affected by meteorological parameters (e.g., wind, temperature,

rainfall) [7]-[9], which can influence the transport, dispersion and chemical reactions of air pollutants.

Several authors, including [5], [7], [10]-[13] have studied air pollutant levels in Makkah and in the

surrounding areas. Most of them have focused mainly on particulate matter and have reported high levels of

particles in Makkah, particularly during the Hajj period. However, some other air pollutants (e.g., NO2, NO,

O3, HC, and non-methane hydrocarbon (NMHC)) have also been investigated [12]. Makkah is a densely

populated city and during the Hajj season and the month of Ramadhan several million people visit the city

from all over the world to perform Hajj and Umrah. Therefore it is important to assess the levels of various

pollutants and investigate their spatial and temporal variability in Makkah during the Hajj season.

2014 5th International Conference on Environmental Science and Technology

IPCBEE vol.69 (2014) © (2014) IACSIT Press, Singapore

DOI: 10.7763/IPCBEE. 2014. V69. 14

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It is important to characterise the spatial and temporal variations in order to locate the hotspots where air

quality standards are exceeded and determine the factors responsible. Recently [5] quantified long-term

temporal trends in air pollutant levels in Makkah using data from one monitoring stations, where several

pollutants demonstrated positive trends. In this paper the aim is to analyse spatial-temporal variability of air

pollutants in Makkah. Limited data are available for spatial analysis, which were collected during the Hajj

(Pilgrimage) season 2012, when over 3 millions pilgrims visited Makkah. The high number of people within

a limited area further highlights the importance of clean air and of investigation into air quality related issues

in Makkah.

2. Materials and Methods

This study was conducted at the Hajj Research Institute (HRI), Umm Al-Qura University in the Holy

City of Makkah during the Hajj season 1433 (October 2012). The air pollutants considered in this project

were CO mg/m3, SO2 µg/m

3, O3 µg/m

3, NO µg/m

3, NO2 µg/m

3 and PM10 µg/m

3 (Particulate Matter with

aerodynamic diameter of 10 µm or less). The air quality data used in this paper were collected at four

monitoring stations in Makkah (Fig. 1). The monitoring stations were: (a) Presidency of Meteorology and

Environment (PME) near the Holy Mosque (Al-Haram); (b) Masfalah; (c) Rabwaa in Mina and (d) Near

Medical Centre in Mina. The PME and Masfalah are continuous monitoring stations, however the other two

stations in Mina were installed temporarily during the Hajj days from 20th to 31st October 2012. For more

details regarding air quality monitoring network in Makkah see [4], [5], [7]. Mean, maximum and standard

deviation (given in parentheses) of the air pollutants at various monitoring stations are shown in Table 1.

Statistical data analysis was carried out in the statistical software R programming language [14] and its

packages openair [15]. In addition to graphical presentations, correlation analysis and analysis of variance

are used to investigate the similarities and dissimilarities between various monitoring sites.

Fig. 1: The four monitoring stations in Makkah.

Table 1: Mean, maximum, and standard deviations (given in parentheses) of hourly concentrations of air pollutants at

the 4 monitoring stations.

Pollutant/

Site

Rabwaa Medical PME Masfalah

Mean Max Mean Max Mean Max Mean Max

CO mg/m3 1.8 (±0.9) 5.8 0.8 (±0.4) 3.1 1.2 (±0.6) 4.3 1.4 (±0.8) 8.1

O3 µg/m3 51.9 (±39.2) 276.0 39.9 (±33.7) 149.5 49.2 (±34.1) 133.5 49.4 (±34.9) 139.6

NO2 µg/m3 62.6 (±24.6) 199.8 58.3 (±34.7) 179.0 47.1 (±26.6) 218.5 37.6 (±14.1) 94.9

NO µg/m3 33.5 (±30.9) 271.0 39.1 (±61.6) 314.8 12.0 (±15.2) 93.8 38.4 (±38.8) 255.0

SO2 µg/m3 10.9 (±10.6) 106.0 25.3 (±34.3) 172.4 4.8 (±2.2) 12.5 17.8 (±13.6) 84.5

PM10 µg/m3 194.5 (±108.8) 743.8 113.7 (±99.9) 502.3 162.4(±96.7) 615.2 143.4(±49.9) 489.0

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3. Results and Discussions

The analysis of variance showed significant difference (p-value < 0.05) between various monitoring sites,

and also between various dates. The highest average PM10 concentration was observed at the Rabwaa site

and lowest at the Medical centre in Mina (see Table 1). The highest average O3, CO and NO2 concentrations

were also observed at Rabwaa site. Average of maximum concentrations for all air pollutants was also

highest at the Rabwaa monitoring site, followed by the Medical Centre. Both of these sites are situated in

Mina, where pilgrims stay for several days during Hajj.

Fig. 2 shows the weekly cycles of various air pollutants at the four monitoring sites. The plots show

considerable variations during the weekly cycles in both space and time. Lowest NO2 concentration was

observed at Masfalah site, particularly on Thursday (25th of October, the day of Arafah) and highest at

Rabwaa and Medical centre. In case of NO, lowest concentration was observed at the PME site on Thursday

and highest at the Medical Centre on Friday. Medical Centre also experienced the highest concentration of

SO2 on Thursday. Highest concentrations of CO, O3 and PM10 were observed at Rabwaa. On Thursday (the

day of Arafah) in the morning all pilgrims move from Mina (or from their houses in Makkah) to Arafat. On

this morning Mina is very busy in terms of traffic, whereas the Masfalah and the Holy Mosques areas are

quieter, which probably explains why Masfalah demonstrated the lowest concentration of traffic related air

pollutants, whereas Medical centre and Rabwaa demonstrated higher concentrations on this day.

The diurnal cycles (Fig. 3) demonstrate considerable variations from site to site. Apart from some minor

variations, generally on the basis of the pattern in their diurnal cycles, NO, NO2 and CO can be put in one

group, whereas other pollutants exhibit different diurnal cycles from each other. NO, NO2 and CO are

combustion-related air pollutants and are predominantly emitted by road traffic. These pollutants show

highest concentrations in the morning traffic-peak hours (8:00 – 9:00 hours) and lowest during the afternoon.

On the other hand, SO2 although is mostly emitted by combustion process, its diurnal cycle is different to

that of NOx and CO. Sulphur content of gasoline has significantly reduced since 2005 [15] due to strict

government regulation to improve air quality in Saudi Arabia. However, crude oil and coal burning might be

contributing to the observed concentration of SO2 in Makkah. Except PME, the rest of the 3 sites show peak

SO2 concentrations during late afternoon (16:00 to 17:00 hours). All four sites show different diurnal cycles

of SO2 concentrations. O3 concentrations are higher during the afternoon (about 12:00 to 18:00 hours) and

lower during night and early morning hours. During nighttimes due to the absence of solar radiation

photochemical O3 formation stops and O3 levels are further reduced due to dry deposition (loss of O3

molecules to ground surfaces, plant leaves and other materials) and NOx scavenging effects (NO + O3

NO2 + O). Diurnal cycles of PM10 also vary at different monitoring sites, however generally bimodal diurnal

cycles can be observed at most of the sites: a peak in the morning and a peak in the afternoon, as explained

by [4]. It is worth mentioning that particulate matters in Makkah, which is situated in an arid region have

various emission sources [4], [5], the most dominant sources are the re-suspended and windblown dust and

sand particles. Therefore particulate concentration is more related to wind speed and wind direction rather

than to traffic flow.

Correlation analysis was performed to investigate the association between various air pollutants

monitored at different monitoring sites. Correlation coefficients (R) between PM10 concentrations measured

at different monitoring sites are mostly positive but not very strong (R < 0.5). Strongest correlation was

observed between Rabwaa and PME (R = 0.45) and weakest between Masfalah and Medical Centre (R =

0.01). Negative correlation was observed between PME and Medical centre (R= -0.20) and Rabwaa and

Medical centre (R=-0.27). Correlation analysis showed mostly negative and much weaker association

between SO2 measured at different monitoring sites. SO2 is probably emitted by crude oils burning which

takes place locally in some areas and probably this is the reason why SO2 has very weak spatial correlation

between different monitoring sites. Strongest correlation was observed between Rabwaa and PME (R = -

0.21). O3 demonstrated the strongest positive correlation between different monitoring sites and ranged from

0.55 to 0.86, where the weakest correlation was observed between Rabwaa and Medical centre and strongest

between Masfalah and PME sites. The strongest correlation of O3 is probably due to the fact that O3 is a

regional pollutant and is more related to the regional emissions of precursors and meteorological parameters

(e.g., solar radiation and temperature). Although O3 concentration is affected by local NO, which is a local 67

sink for O3, however the regional component seems to play the dominant role here. The correlation

coefficients of CO monitored at different monitoring sites ranged from 0.21 to 0.63, whereas those of NO

and NO2 ranged from 0.39 to 0.71 and 0.20 to 0.60, respectively. This study provides an insight into the

spatial variations of various air pollutants during the Hajj period, however to understand the long term spatial

variation of these pollutants further investigation is required over a longer period of time and greater spatial

coverage.

Fig. 2: Weekly cycles of various air pollutants at the four monitoring stations. Mas stands for Masfalah, rab for Rabwaa,

med for Medical, and pme for the Presidency of Meteorology and Environment monitoring station. The data shown are

from 20th

to 31st of October 2012.

Fig. 3: Diurnal cycles of various air pollutants at the 4 monitoring sites. Mas stands for Masfalah, rab for Rabwaa, med

for Medical, and pme for the Presidency of Meteorology and Environment monitoring station. The data shown are from

20th

to 31st of October 2012.

4. Conclusion

In this paper the aim was to analyse the spatial and temporal trends of air pollutants in Makkah during

the Hajj season 1433 (October 2012). Hourly data from 4 monitoring stations situated within Makkah are

used. Graphical presentations, correlation analysis, and analysis of variance demonstrated that air pollutant

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concentrations varied significantly within Makkah during the study period at different monitoring sites. Most

of the pollutants, understandably, showed positive spatial correlation, except SO2, where the association

between different sites was negative. O3 showed the strongest correlation between different monitoring sites,

probably because O3 is a secondary pollutant and its concentrations are more affected by meteorological

conditions and regional precursor emissions, rather than by local sinks and sources. Being the first of its kind,

this study provides a good insight into the spatial and temporal variability of air pollutants in Makkah,

however the limited number of monitoring sites (4) and shorter study period (20th to 31

st October, 2012) may

limit its applicability. Therefore, further research over extended period of time and greater number of

monitoring stations, is recommended, which is part of the ongoing research project.

5. Acknowledgements

Authores are thankful to the Custodian of the Two Holy Mosques Institiute for Hajj and Ummrah

research for supporting this project. Many thanks are extended to the Presedency of Meteorology and

Environmnet for providing air quality data.

6. References

[1] Committee on the Medical Effects of Air Pollutants (COMEAP). Review of the UK Air Quality Index. ISBN 978-

0-85951-699-0, 2011.

[2] World Health Organization (WHO). Health Risks of Ozone from Long-range Transboundary Air Pollution. A

Report Prepared by WHO Regional Office for Europe,

(http://www.euro.who.int/__data/assets/pdf_file/0005/78647/E91843.pdf), 2008.

[3] Air Quality Strategy of England, Scotland, Wales and Northern Ireland (AQS), vol. 1.Available

online:http://archive.defra.gov.uk/environment/quality/air/airquality/strategy/documents/air-qualitystrategy-

vol1.pdf, 2007.

[4] T.M. Habeebullah. An Analysis of Air Pollution in Makkah - a View Point of Source Identification.

EnvironmentAsia, 2013. 6 (2): 11-17.

[5] S. Munir, T.M. Habeebullah, A.R. Seroji, S.S. Gabr, A.M.F. Mohammed, and E.A. Morsy. Quantifying temporal

trends of atmospheric pollutants in Makkah (1997 –2012). Atmospheric Environment, doi:

10.1016/j.atmosenv.2013.05.075, 2013.

[6] M. Khodeir, M. Shamy, M. Alghamdi, M. Zhong, H. Sun, M. Costa, L.C. Chen, and P.M. Maciejcczyk. Source

apportionment and elemental composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia. Atmospheric Pollution

Research. 2012, 3: 331-340.

[7] S. Munir, T.M. Habeebullah, A.R. Seroji, E.A. Morsy, A.M.F. Mohammed, W.A. Saud, A. Esawee, A.H. Awad.

Modelling Particulate Matter Concentrations in Makkah, Applying a Statistical Modelling Approach. Aerosols Air

Quality Research. 2013, 13: 901-910.

[8] N. Liu, Y. Yu, J.B. Chen, J.J. He, and S.P. Zhao. Identifcation of potential sources and transport pathways of

atmospheric PM10 using HYSPLIT and hybrid receptor modelling in Lanzhou, China. In: Brebbia, C.A., Longhurst,

J.W.S., Popov, V. (Eds.), Air Pollution XIX. Wessex Institute of Technology, UK, 2011.

[9] C. Andersson, J. Langner, and R. Bergstrom. Interannual variation and trends in air pollution over Europe due to

climate variability during 1958–2001 simulated with a regional CTM coupled to the ERA40 reanalysis. Tellus.

2006, 59: 77–98.

[10] M.M. Nasrallah, A.R. Seroji. Primary pollutants and potential photochemical smog formation in Makkah, Saudi

Arabia. Arab Gulf Journal of Scientific Research. 2007, 25: 153 – 161.

[11] M.M. Nasrallah, and A.R. Seroji. Particulates in the atmosphere of Makkah and Mina valley during Ramadhan and

Hajj season of 1424 and 1425 H (2004 – 2005). Arab Gulf Journal of Scientific Research. 2008, 26: 199 – 206.

[12] H.A. Al-Jeelani. Evaluation of Air Quality in the Holy Makkah during Hajj Season 1425 H. Journal of Applied

Sciences Research. 2009, 5: 115-121.

[13] H.A. Al-Jeelani. Air quality assessment at Al-Taneem area in the Holy Makkah City, Saudi Arabia. Environmental

Monitoring Assessment. 2009, 156: 211-22. 69

[14] R Development Core Team. R: A language and environment for statistical computing.R Foundation for Statistical

Computing, Vienna, Austria. ISBN 3-900051-07-0, URL (http://www.R-project.org/). R version 2.14, 2012.

[15] N. Othman, M.Z. Mat-Jafri, and L.H. San. Estimating Particulate Matter Concentration over Arid Region Using

Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia. Modern Applied Science. 2010, 4: 11–20.

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