AN INVESTIGATION OF THE EFFECTS OF METEOROLOGY ON AIR POLLUTION In Makkah التحقق من تأثير الأرصاد الجوية على تلوث الهواء في مکة المکرمة | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Assiut University Bulletin for Environmental Researches | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Article 4, Volume 16.1, Issue 16.1, March 2013, Page 63-85 PDF (1.9 MB) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| DOI: 10.21608/auber.2013.148462 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| ABSTRACT: Air pollutant concentrations are not only affected by emission sources but also by meteorological variables. Meteorological variables play an important role in the dispersion, transport, photochemical reactions and formation of secondary air pollutants. In this study, the effect of meteorological variables on different air pollutant concentrations has been analyzed using correlation analysis and graphical presentation in Makkah, Saudi Arabia during the month of Ramadan (20 July to 18 August, 2012), which is the busiest month of the year after the Hajj season. PM10, had relatively weaker correlation with other air pollutants, most probably suggesting different sources of emission. Among meteorological variables, as expected temperature showed strong positive correlation with ozone (0.74), and negative correlation with NOx, CO, SO2, and PM10, whose concentrations are rather dependent on the emission sources. Wind speed disperses local pollutants, which probably explains why it was negative correlation with NOx, SO2 and CO, however it was positive correlation with ozone and PM10, probably because higher wind speed encourages sand storms and resuspension of particles from roadsides and bared deserts and transport of ozone from the surrounding rural areas. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. The effect of rainfall was negligible because no rain occurred during the study period. The effects of meteorological variables have also been analysed using polar plots and pollution roses, which provide further insight into the association between air pollutants and meteorology. Factors responsible for the high concentrations during the PM10 episode from 26 to 28 July 2012 were analyzed. Unexpectedly, atmospheric pressure and relative humidity seemed to be responsible for the episode, and not the sources of emissions, which are higher during the last 10 days of Ramadan (08 to 18 August). الملخص العربي : لا يتأثر ترکيز ملوثات الهواء فقط بمصادر التلوث، وإنما تتأثر کثيراً بمتغيرات عناصر الأرصاد الجوية والتي تلعب دوراً هاماً في تشتت وإنتقال المرکبات والتفاعلات الکيميائية للملوثات الثانوية في الغلاف الجوي. تم في هذه الدراسة تحليل أثر تغير عناصر الأرصاد الجوية على تراکيز مختلفة من الملوثات الهوائية بإستخدام معادلات تحليل الإرتباط والعروض الرسومية لمکة المکرمة خلال شهر رمضان 1433هـ (20/7 – 18/8/2012)، والذي يعد أزحم شهور السنة بعد شهر ذي الحجة. تبين من خلال التحليلات بأن الأتربة الصدرية أقل إرتباطاً بالملوثات الأخرى بسبب تغير مصادر التلوث. بينما هناک علاقة وثيقة بتغير درجة الحرارة مع الأوزون والتي وصل فيها معامل الإرتباط إلى (0.74)، في حين إنخفض معامل الإرتباط مع الملوثات الأخرى وهي أکاسيد النيتروجين وأول أکسيد الکربون وثاني أکسيد الکبريت والأتربة الصدرية، وهذه الملوثات مرتبطة إرتباطاً وثيقاً بمصادر التلوث. سرعة الرياح أيضاً ساعدت في تشتت الملوثات الهوائية، وهذا يفسر أن معامل الارتباط مع أکاسيد النيتروجين وثاني أکسيد الکبريت وأول أکسيد الکربون سالباً، بينما کان معامل الإرتباط موجباً مع الأوزون والأتربة الصدرية بسبب زيادة سرعة الرياح والذي ساعد على عدم ثبات العواصف الترابية في الشوارع والمناطق الصحراوية، وأيضاً إنتقال الأوزون من المناطق البعيدة عن مدينة مکة المکرمة. الرطوبة النسبية هي بالتالي سجلت علاقة قوية وموجبة مع الأتربة الصدرية وسالبة مع بقية ملوثات الهواء. کما أن معامل الإرتباط لتأثير سقوط الأمطار کان ضئيلاً بسبب عدم وجود تساقط للأمطار فترة إجراء الدراسة. کذلک تم تحليل تأثير عناصر الأرصاد الجوية بواسطة الرسمة القطبية ووردة الرياح. کما تم تحليل العوامل المسئولة في زيادة تراکيز الأتربة الصدرية عن الحدود المسموح بها خلال الفترة من 26-28 يوليو 2012. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Highlights | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
An investigation into the effects of meteorology on air pollution in Makkah Turki M. Habeebullah Assistant Professor of Environmental Pollution The Custodian of the Two Holy Mosques Institute for Hajj Research, Umm Al QuraUniversity, Makkah, Kingdom of Saudi Arabia
Abstract Air pollutant concentrations are not only affected by emission sources but also by meteorological variables. Meteorological variables play an important role in the dispersion, transport, photochemical reactions and formation of secondary air pollutants. In this study, the effect of meteorological variables on different air pollutant concentrations has been analysed using correlation analysis and graphical presentation in Makkah, Saudi Arabia during the month of Ramadhan (20 July to 18 August, 2012), which is the busiest month of the year after the Hajj season. PM10 had relatively weaker correlation with other air pollutants, most probably suggesting different source of emission. Among meteorological variables, as expected temperature showed strong positive correlation with ozone (0.74), and negative correlation with NOx, CO, SO2, and PM10, whose concentrations are rather dependent on the emission sources. Wind speed helps disperse local pollutants, which probably explains why it had negative correlation with NOx, SO2 and CO, however it had positive correlation with ozone and PM10, probably because higher wind speed encourages sand storms and resuspension of particles from roadsides and bared deserts and transport of ozone from the surrounding rural areas. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. The effect of rainfall was negligible because no rain occurred during the study period. The effects of meteorological variables have also been analysed using polar plots and pollution roses, which provide further insight into the association between air pollutants and meteorology. Factors responsible for the high concentrations during the PM10 episode from 26 to 28 July 2012 were analysed. Unexpectedly, atmospheric pressure and relative humidity seemed to be responsible for the episode, and not the sources of emissions, which are higher during the last 10 days of Ramadhan (08 to 18 August). Keywords: Air pollution, Meteorology, Polar plots, Pollution roses, Makkah
Air pollution in urban areas in both developing and developing countries adversely affects human health, urban ecosystem, building materials and visibility (e.g., Harrison, 2001; WHO, 2008; Bell and Treshow, 2008; Air pollution in the UK, 2011). In this paper we consider five of the most common air pollutants, which are sulphur dioxide (SO2), carbon monoxide (CO), nitrogen oxides (NOx): the sum of nitric oxide (NO) and nitrogen dioxide (NO2), ozone (O3) and particulate matter with aerodynamic diameter of 10 um or less (PM10). Individually and in combination with other air pollutants, these pollutants can cause different health problems. For example, SO2 is a respiratory irritant and can cause constriction of the airways of the lung, particularly in people suffering from asthma and chronic lung disease. NO2 acts as an irritant, causing inflammation of the airways and increasing susceptibility to respiratory infections. Fine particulate matter can penetrate deep into the airways, carrying surface-absorbed harmful compounds into the lungs, increasing the risk of health effects, including cancer. Ozone is an oxidising agent and acts as an irritant, causing inflammation of the respiratory tract and irritating the eyes, nose, and throat, causing coughing and discomfort whilst breathing (Harrison, 2001; WHO, 2008; Air pollution in the UK, 2011; AQEG, 2005; AQEG, 2009).
Air pollutant concentrations are not only affected by the sources of emission but also by meteorological variables (e.g., Elminir, 2005; Ordonez et al., 2005; Cheng et al., 2007; Beaver and Palazoglu, 2009; Pearce et al., 2011). Meteorological variables play an important role in the dispersion, transport, photochemical reactions and secondary pollutants formation, including ozone, NO2 and particulate (e.g., sulphate and nitrate ions), however in spite of the presence of a vast body of literature, many aspects of the association between air pollutants and meteorology are still not clear (Pearce et al., 2011). This is due to the interaction between various meteorological variables, for example the dependency of boundary layer height on surface temperature, the link between surface temperature and radiation or the association between relative humidity and temperature, which make separating the effects of individual parameter a highly complex task. Meteorological variables can affect the concentrations of air pollutant directly (e.g., affecting photochemical ozone formation or dispersing locally emitted pollutants) or indirectly by affecting other meteorological parameters or affecting some pollutants which in turn affect other pollutants (Ordonez et al., 2005; Jacob and Winner, 2009). Furthermore, the effects of meteorological variables on air pollutants vary both temporally and spatially and with the concentration of the pollutants (Baur et al., 2004). See Schlink et al. (2006), Camalier et al. (2007), Thompson et al. (2001), Baur et al. (2004) and Pearce et al., 2011 for various approaches used to investigate the association of meteorological variables on air pollutant concentrations.
National and international policies, demanding for clean air have resulted in great interest in air pollution in Saudi Arabia. Numerous studies have been conducted in Saudi Arabia to report the levels of different air pollutants in many regions, especially in Jeddah, Makkah and Madinah (Kadi et al., 2009; Aburas et al., 2011; Al-Zahrani, 2010; Othman et al., 2010). Research into identifying various sources of air pollutants and quantifying their contribution to the observed levels of air pollutants has also been carried out (Khodeir et al., 2011). Most of these studies are related to particulate matters (PM10, PM2.5, and heavy metals), probably because the concentrations of particulate matters observed in Saudi Arabia are generally high (Seroji, 2011; Othman et al., 2010) and exceed the air quality limits set for the protection of human health. It has been reported that in Saudi Arabia, being an arid region a significant amount of particles are generated by natural sources, including windblown dust and sand and resuspension of particles (e.g., Aburas et al., 2011 and Khodeir et al., 2011). However, no published work was found intending to investigate the effect of meteorological variables on the concentrations of air pollutants.
In this study the aim is to analyse the effect of meteorological variables (wind speed, wind direction, temperature, relative humidity, atmospheric pressure and rain fall) on the concentrations of five major pollutants (SO2, NOx, CO, ozone and PM10) in Makkah using exploratory data analysis techniques. The study was conducted during the month of Ramadhan, 1433 H (18 July to 20 August, 2012), when millions of people come to Makkah to perform Umrah. This is the second busiest month of the year after Zulhijjah (the month of Pilgrimage - Hajj), which further signifies the need for clear air.
In this study air pollutants and meteorology data have been analysed. The data were collected at the Presidency of Meteorology and Environment (PME) air quality monitoring station (AQMS 112) situated near Al-Haram (the Holy Mosque) in Makkah, the Kingdom of Saudi Arabia. Figure 1 shows the location of PME (AQMS 112) and other air quality monitoring sites in Makkah. The data considered here are for the month of Ramadhan 1433 H (20th July to 18th August, 2012), when millions of people come to Makkah to perform Umrah. This is the second busiest time of the year after Hajj. In this study the following parameters were considered: Sulphur Dioxide (SO2 µg/m3), Carbon Monoxide (CO mg/m3), Nitrogen Oxides (NOx µg/m3), Nitric Oxide (NO µg/m3), Nitrogen Dioxide (NO2 µg/m3), Particulate Matter with aerodynamic diameter of 10 um or less (PM10 µg/m3), Ozone (O3 µg/m3), Wind Speed (WS m/s), Wind Direction(WD Degrees from the north), Relative Humidity (RH %), Temperature (T oC), Rain Fall (RF mm), and atmospheric Pressure (P hPa), where hectopascal (hPa) is the same as kilopascal (kPa) and is equivalent to the older unit millibar (mbar). Statistical data analysis was carried out in R - programming language (R-development team, 2012) and one of its package open air (Carslaw, 2012). Correlation analysis and graphical presentations (scatter plots, polar plots, and time series plots) were used to investigate the association of various air pollutants with each other and with meteorological variables. A summary of the parameters is presented in Table 1. Table 1.A summary of the parameters for the month of Ramadhan 1433 H (20th July to 18th August, 2012), number of observations for each parameters were 715.
1NA represents missing data and DC represents data capture.
Figure 1. Map of the air quality and meteorological monitoring sites in Makkah.
3.1 Correlation analysis Knowing the association of different variables is important and can be helpful in identifying the emission sources of air pollutants. In this paper correlation matrix plot (Carslaw and Ropkins, 2012) is used, which provides correlation between all pairs of the data. Correlation plot shows the correlation coded in three ways: by shape (ellipses), colour and the numeric value. The ellipses are similar to scatter plot. A perfect positive correlation is represented by a line at 45 degrees, whereas no correlation is shown by a circle of points. Furthermore, hierarchical clustering is applied to the correlation matrices to group variables that are most similar to one another. The numerical values are shown from -100 to 100, where zero shows no correlation and 100 shows perfect positive and -100 shows perfect negative correlation. Figure 2 shows correlation matrix plot of various air pollutants and meteorological variables. Several clusters can be clearly observed. For example NOx and CO show very strong positive correlation, whereas NOx and ozone show strong negative correlation, which is expected as NOx and CO have the same sources of emissions in Makkah, predominantly road traffic; and the negative correlation of ozone and NOx is due to the chemical coupling between these species (Jenkin et al., 2004). SO2 is positive correlated with CO and NOx, however the strength is weaker, indicating SO2 has different sources of emissions (e.g., burning of crude oil and diesel vehicles) (Habeebullah et al., 2012). PM10 has relatively weak correlation with other air pollutants, most probably because most of the PM10 in Saudi Arabia, being an arid regionis generated by non-combustion sources, such as construction work and windblown dust and sand. Among meteorological variables, temperature show strong positive correlation with ozone, which is due to the fact that ozone is a secondary air pollutant and is formed in the atmosphere by photochemical reaction of hydrocarbons and NOx in the presence of sunlight. Generally high temperature accelerates photochemical formation of ozone molecules, due to this reason ozone level are higher in summer than in winters seasons (AQEG, 2009). In contrast temperature has negative correlation with NOx, CO, SO2, and PM10, whose concentration is more dependent on the emission sources. However, the negative correlation indicates that probably high temperature results in greater dispersion and dilution of the air pollutants, probably linked with vertical and horizontal turbulence (EPA, 2010). The effect is negligible on PM10. It is important to highlight that in the case of PM10 greater turbulence can generate more dust particles in a region like Makkah, which may offset the effect of pollutants dispersion. Wind speed help disperse local pollutants, which probably explains why it has negative correlation with NOx and CO, however it has positive correlation with ozone and PM10, most probably due to raising particles from bared surfaces and road sides and transport of ozone from the surrounding rural areas. The effect of wind speed is generally related to its direction, which is further elaborated in later sections with the help of polar plots. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. Duenas et al. (2002) has reported that relative humidity plays an important role in air quality, as relative humidity may play a role in the overall reactivity of the atmospheric system, either by affecting chain termination reactions or in the production of wet aerosols, which in turn affect the flux of ultraviolet radiation. Furthermore, relative humidity is also considered to be a limiting factor in the disposition of NO2 because high percentages of humidity favour the reaction of the NO2 with particles of sodium chloride salt (Duenas et al., 2002). Relative Humidity can also act on air pollutants to create secondary aerosols, such as sulphate and nitrate ions, which contribute positively to PM10 concentrations. Rain washout most of the dust from the atmosphere and may encourage wet deposition of some of the gaseous pollutants, however in this analysis rain fall has shown weak association with all air pollutants, because Makkah being part of an arid region receives very limited rain throughout the year. Atmospheric pressure is the weight of the atmosphere at a given point. The height and temperature of a column of air determines the atmospheric weight. Because cold air weights more than warm air, a high pressure mass of air is made up of cold and heavy air. Conversely, a low pressure mass of air is made up of warmer and lighter air. Differences in pressure cause air to move from high pressure areas to low pressure areas, resulting in wind. Wind speed can greatly affect the pollutant concentration in a local area (as described above). Furthermore, high-pressure systems often combine with stable atmospheric conditions and low wind speeds, which can lead to episodes of severe air pollution (EPA, 2010).
Figure 2. Correlation Matrix plot of various parameters from 20th July to 18th August, 2012.
3.2 Polar plot The bivariate polar plot is a useful diagnostic tool for quickly gaining an idea of potential sources (Carslaw and Ropkins, 2012). The plots are constructed by averaging pollutant concentration by wind speed categories (0–1 m/s, 1–2 m/s, etc.) as well as wind direction (0–10, 10–20, etc.). The principal aim of Figure 3 shows the polar plots of various air pollutants for the study period (20 July to 18 August, 2012) at PME monitoring site, Near Al-Haram, Makkah. In Figure 3, polar plot for CO (top-left), NO (top-right), SO2 (middle-left) and NO2 (middle-right) with slight variation show high concentrations at low wind speed, however high concentrations of NO2 are also linked with high wind speed from the southeast direction. High concentrations at low wind speed suggest local sources of these air pollutants, which may disperse at high wind speed. In contrast, high levels of ozone and PM10 concentrations are kinked with high wind speed from northwest and southeast, respectively and at low wind speed their levels are low, which may suggest these air pollutants are transported from the surrounding areas. Ozone is inversely proportion to NO and NO2 and hence the polar plots show the opposite pattern of these pollutants. There is a construction site towards west and northwest of Al-Haram, however the PM10 polar plot does not shows significant contribution from it, which is further investigated in later sections. There are some local roads, bus stations, and parking place in the surrounding areas, which probably contribute to the emissions of traffic related air pollutants, however the levels of these air pollutants have not exceeded the air quality standards during the study period. PM10 was the only pollutants which exceeded 24 hr PME air quality guideline on 26 – 28 July. This is further discussed in section 3.3.
Figure 3. Polar plots of various air pollutants for the study period at PME site near Al-Haram, Makkah.
3.3 PM10 episode (26 to 28 July, 2012) Statistical analysis shows that the concentrations of the air pollutants during the study period were below the air quality standards set by World Health Organisation (WHO) and the Presidency of Meteorology and Environment (PME) of the Saudi Arabia. The only exception was PM10 concentrations, which exceeded the 24 hour average air quality limit of 340 µg/m3 set by PME for the protection of human health. On 26 to 28 July 2012, the 24 hour average concentrations of PM10 were 518, 790 and 389 340 µg/m3, respectively, which are shown in Table 2 along with some other air pollutant concentrations. In this section, these three days have been further investigated to determine the causes of high PM10 concentrations. Table 2.Daily average concentrations of various air pollutants during 26 to 28 July 2012.
Pollution roses (Figure 4) are used to show the effect of wind on PM10 concentrations. Pollution rose is a variant of wind rose and is useful for considering pollutant concentrations by wind direction, or more specifically the percentage time the concentration is in a particular range. These plots are very useful for understanding which wind directions control the overall mean concentrations (Carslaw and Ropkins, 2012). It is worthwhile that polar plot shows pollutant concentrations by wind speed and wind direction, in contrast pollution rose depicts pollutant concentrations by wind frequency (the number of hours wind is blowing from a certain direction) and wind direction. Figure 4 (top-left) shows that during the study period, wind is predominantly blowing from the northwest direction, however high PM10 concentrations (shown by the colour and width of the paddles) are linked with westerly and south-easterly winds. Figure 3 has also shown high PM10 concentrations linked with south-easterly wind, where wind speed reach up to 4 m/s. Figure 4 (top-right) shows PM10 concentrations during the three days (26 to 28 July, 2012), when the PME air quality standards were violated. In this panel high PM10 concentrations are linked with south, south-easterly and south-westerly wind. When the data was divided into two subsets: (a) PM10 concentration > 500 (µg/m3); and (b) PM10 concentrations < 500 (µg/m3), dataset (a) clearly linked high concentrations with south-easterly wind. High concentration of PM10 from the south-easterly direction either could be due to the high wind speed, as shown in Figure 3 or there might be an emission source in this direction, or both. It is a fact that Makkah being part of an arid region receives low precipitations and has large barren sandy land, therefore when wind blows it can generate considerable amount of atmospheric dust. The large heavy particles quickly deposit under the action of gravity, however smaller particle can stay in the atmosphere and travel large distances. The contributions from road traffic in the surrounding areas might add a significant amount, however on this occasion it was not considered as the main source, otherwise highest PM10 concentrations would have been observed during the last 10 days of the study period (08 to 18 August, 2012), when the number of visitors to the Makkah and hence traffic flow reach the peak level.
Time plots of the various air pollutants and meteorological variables (24 hour average) are plotted for the period of study (20 July to 18 August, 2012) (Figure 5). It can be observed in Figure 5 (top panel) that pollutant concentrations show considerable variations in their levels during the study period, however the pattern in PM10 concentrations is significantly different than that of other pollutants, which suggest that the effect of different factors (emission sources and meteorological variables), controlling their concentrations varies on each pollutant. When PM10 concentration is highest (26 to 28 July), ozone concentration is lowest and vice versa. During these three days, the concentrations of other pollutants (SO2, NO2 and CO) are pretty low as well. Figure 5 (bottom panel) shows the levels of and variations in meteorological variables and it can be observed in the Figure 5 that atmospheric pressure is low and relative humidity is high during the 3 days period. Other meteorological variation do not show any distinct characteristics, except wind direction which seems to be blowing at about 200o (southern direction), however it does not correlate well with Figure 4 (top-left), where the wind direction during the three days vary considerably. The dissimilarities are due to different averaging time and the circular nature of wind direction. Therefore, the wind direction in Figure 4 is considered here, which associates high PM10 concentrations with the southeast directions. Hence we conclude that low pressure and high relative humidity, are probably the main reasons for the high PM10 concentrations, where the former might have encouraged the moving-in of the particles from the surrounding area as wind blow from high to low pressure areas (EPA, 2010), whereas the latter might have encouraged secondary aerosols formation by the process of coagulation and condensation (Harrison, 2001).
Figure 4. Pollution Rose, colour coded by the levels of mean hourly PM10 concentrations (µg/m3): Top-left panel shows the whole month data (20 July to 18 August, 2012); Top-right panel shows three days data (26 to 28 July, 2012); Bottom-left shows when PM10 concentrations > 500 µg/m3; and Bottom-right shows when PM10 concentrations < 500 µg/m3.
Figure 5. Time plots of various air pollutants (top-panel) and meteorological variables (bottom-panel), showing 24 hour average at the PME monitoring site, from 20th July to 18 August, 2012.
In this study the effects of meteorological variables on the concentrations of various air pollutants, including SO2, CO, NOx, PM10 and ozone have been investigated during the month of Ramadhan (20 July to 18 August, 2012) in Makkah near Al-Haram. Correlation analysis has been used to investigate the association of air pollutants with each other and with meteorological variables. PM10 has relatively weaker correlation with other air pollutants, most probably because most of the PM10 in Saudi Arabia, being an arid region is generated by non-combustion sources, such as construction work and windblown dust and sand, whereas the other pollutants like SO2, CO and NOx are mainly emitted by combustion sources, including road traffic. Among meteorological variables, temperature show strong positive correlation with ozone (0.74), which is probably due to the fact that ozone is a secondary air pollutant and is formed in the atmosphere by photochemical reaction of hydrocarbons and NOx in the presence of sunlight. In contrast temperature has negative correlation with NOx, CO, SO2, and PM10, whose concentration is more dependent on the emission sources. However, the negative correlation indicates that probably high temperature results in greater dispersion and dilution of the air pollutants, probably linked with vertical and horizontal turbulence (EPA, 2010). Wind speed help disperse local pollutants, which probably explains why it has negative correlation with NOx and CO, however it has positive correlation with ozone and PM10, most probably due to raising particles from bared surfaces and road sides and transport of ozone from the surrounding rural areas. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. The effect of rainfall was negligible most probably due to the fact that no rain occurred during the study period. The effects of meteorological variables have also been analysed using polar plots and pollution roses, which provide further insight into the association between air pollutants and meteorology. Factors responsible for the high concentrations of pollutants, particularly during the PM10 episode from 26 to 28 July 2012 are analysed. Unexpectedly, atmospheric pressure and relative humidity seem to be responsible for the episode, and not the sources of emissions, which are higher during the last 10 days of Ramadhan (08 to 18 August).
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التحقق من تأثير الأرصاد الجوية على تلوث الهواء في مکة المکرمة ترکي محمد حبيب الله أستاذ التلوث البيئي المساعد معهد خادم الحرمين الشريفين لأبحاث الحج والعمرة – جامعة أم القرى – المملکة العربية السعودية
لا يتأثر ترکيز ملوثات الهواء فقط بمصادر التلوث، وإنما تتأثر کثيراً بمتغيرات عناصر الأرصاد الجوية والتي تلعب دوراً هاماً في تشتت وإنتقال المرکبات والتفاعلات الکيميائية للملوثات الثانوية في الغلاف الجوي. تم في هذه الدراسة تحليل أثر تغير عناصر الأرصاد الجوية على تراکيز مختلفة من الملوثات الهوائية بإستخدام معادلات تحليل الإرتباط والعروض الرسومية لمکة المکرمة خلال شهر رمضان 1433هـ (20/7 – 18/8/2012)، والذي يعد أزحم شهور السنة بعد شهر ذي الحجة. تبين من خلال التحليلات بأن الأتربة الصدرية أقل إرتباطاً بالملوثات الأخرى بسبب تغير مصادر التلوث. بينما هناک علاقة وثيقة بتغير درجة الحرارة مع الأوزون والتي وصل فيها معامل الإرتباط إلى (0.74)، في حين إنخفض معامل الإرتباط مع الملوثات الأخرى وهي أکاسيد النيتروجين وأول أکسيد الکربون وثاني أکسيد الکبريت والأتربة الصدرية، وهذه الملوثات مرتبطة إرتباطاً وثيقاً بمصادر التلوث. سرعة الرياح أيضاً ساعدت في تشتت الملوثات الهوائية، وهذا يفسر أن معامل الارتباط مع أکاسيد النيتروجين وثاني أکسيد الکبريت وأول أکسيد الکربون سالباً، بينما کان معامل الإرتباط موجباً مع الأوزون والأتربة الصدرية بسبب زيادة سرعة الرياح والذي ساعد على عدم ثبات العواصف الترابية في الشوارع والمناطق الصحراوية، وأيضاً إنتقال الأوزون من المناطق البعيدة عن مدينة مکة المکرمة. الرطوبة النسبية هي بالتالي سجلت علاقة قوية وموجبة مع الأتربة الصدرية وسالبة مع بقية ملوثات الهواء. کما أن معامل الإرتباط لتأثير سقوط الأمطار کان ضئيلاً بسبب عدم وجود تساقط للأمطار فترة إجراء الدراسة. کذلک تم تحليل تأثير عناصر الأرصاد الجوية بواسطة الرسمة القطبية ووردة الرياح. کما تم تحليل العوامل المسئولة في زيادة تراکيز الأتربة الصدرية عن الحدود المسموح بها خلال الفترة من 26-28 يوليو 2012. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Keywords: Air pollution; Meteorology; Polar plots; Pollution roses; Makkah | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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An investigation into the effects of meteorology on air pollution in Makkah Turki M. Habeebullah Assistant Professor of Environmental Pollution The Custodian of the Two Holy Mosques Institute for Hajj Research, Umm Al QuraUniversity, Makkah, Kingdom of Saudi Arabia
Abstract Air pollutant concentrations are not only affected by emission sources but also by meteorological variables. Meteorological variables play an important role in the dispersion, transport, photochemical reactions and formation of secondary air pollutants. In this study, the effect of meteorological variables on different air pollutant concentrations has been analysed using correlation analysis and graphical presentation in Makkah, Saudi Arabia during the month of Ramadhan (20 July to 18 August, 2012), which is the busiest month of the year after the Hajj season. PM10 had relatively weaker correlation with other air pollutants, most probably suggesting different source of emission. Among meteorological variables, as expected temperature showed strong positive correlation with ozone (0.74), and negative correlation with NOx, CO, SO2, and PM10, whose concentrations are rather dependent on the emission sources. Wind speed helps disperse local pollutants, which probably explains why it had negative correlation with NOx, SO2 and CO, however it had positive correlation with ozone and PM10, probably because higher wind speed encourages sand storms and resuspension of particles from roadsides and bared deserts and transport of ozone from the surrounding rural areas. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. The effect of rainfall was negligible because no rain occurred during the study period. The effects of meteorological variables have also been analysed using polar plots and pollution roses, which provide further insight into the association between air pollutants and meteorology. Factors responsible for the high concentrations during the PM10 episode from 26 to 28 July 2012 were analysed. Unexpectedly, atmospheric pressure and relative humidity seemed to be responsible for the episode, and not the sources of emissions, which are higher during the last 10 days of Ramadhan (08 to 18 August). Keywords: Air pollution, Meteorology, Polar plots, Pollution roses, Makkah
Air pollution in urban areas in both developing and developing countries adversely affects human health, urban ecosystem, building materials and visibility (e.g., Harrison, 2001; WHO, 2008; Bell and Treshow, 2008; Air pollution in the UK, 2011). In this paper we consider five of the most common air pollutants, which are sulphur dioxide (SO2), carbon monoxide (CO), nitrogen oxides (NOx): the sum of nitric oxide (NO) and nitrogen dioxide (NO2), ozone (O3) and particulate matter with aerodynamic diameter of 10 um or less (PM10). Individually and in combination with other air pollutants, these pollutants can cause different health problems. For example, SO2 is a respiratory irritant and can cause constriction of the airways of the lung, particularly in people suffering from asthma and chronic lung disease. NO2 acts as an irritant, causing inflammation of the airways and increasing susceptibility to respiratory infections. Fine particulate matter can penetrate deep into the airways, carrying surface-absorbed harmful compounds into the lungs, increasing the risk of health effects, including cancer. Ozone is an oxidising agent and acts as an irritant, causing inflammation of the respiratory tract and irritating the eyes, nose, and throat, causing coughing and discomfort whilst breathing (Harrison, 2001; WHO, 2008; Air pollution in the UK, 2011; AQEG, 2005; AQEG, 2009).
Air pollutant concentrations are not only affected by the sources of emission but also by meteorological variables (e.g., Elminir, 2005; Ordonez et al., 2005; Cheng et al., 2007; Beaver and Palazoglu, 2009; Pearce et al., 2011). Meteorological variables play an important role in the dispersion, transport, photochemical reactions and secondary pollutants formation, including ozone, NO2 and particulate (e.g., sulphate and nitrate ions), however in spite of the presence of a vast body of literature, many aspects of the association between air pollutants and meteorology are still not clear (Pearce et al., 2011). This is due to the interaction between various meteorological variables, for example the dependency of boundary layer height on surface temperature, the link between surface temperature and radiation or the association between relative humidity and temperature, which make separating the effects of individual parameter a highly complex task. Meteorological variables can affect the concentrations of air pollutant directly (e.g., affecting photochemical ozone formation or dispersing locally emitted pollutants) or indirectly by affecting other meteorological parameters or affecting some pollutants which in turn affect other pollutants (Ordonez et al., 2005; Jacob and Winner, 2009). Furthermore, the effects of meteorological variables on air pollutants vary both temporally and spatially and with the concentration of the pollutants (Baur et al., 2004). See Schlink et al. (2006), Camalier et al. (2007), Thompson et al. (2001), Baur et al. (2004) and Pearce et al., 2011 for various approaches used to investigate the association of meteorological variables on air pollutant concentrations.
National and international policies, demanding for clean air have resulted in great interest in air pollution in Saudi Arabia. Numerous studies have been conducted in Saudi Arabia to report the levels of different air pollutants in many regions, especially in Jeddah, Makkah and Madinah (Kadi et al., 2009; Aburas et al., 2011; Al-Zahrani, 2010; Othman et al., 2010). Research into identifying various sources of air pollutants and quantifying their contribution to the observed levels of air pollutants has also been carried out (Khodeir et al., 2011). Most of these studies are related to particulate matters (PM10, PM2.5, and heavy metals), probably because the concentrations of particulate matters observed in Saudi Arabia are generally high (Seroji, 2011; Othman et al., 2010) and exceed the air quality limits set for the protection of human health. It has been reported that in Saudi Arabia, being an arid region a significant amount of particles are generated by natural sources, including windblown dust and sand and resuspension of particles (e.g., Aburas et al., 2011 and Khodeir et al., 2011). However, no published work was found intending to investigate the effect of meteorological variables on the concentrations of air pollutants.
In this study the aim is to analyse the effect of meteorological variables (wind speed, wind direction, temperature, relative humidity, atmospheric pressure and rain fall) on the concentrations of five major pollutants (SO2, NOx, CO, ozone and PM10) in Makkah using exploratory data analysis techniques. The study was conducted during the month of Ramadhan, 1433 H (18 July to 20 August, 2012), when millions of people come to Makkah to perform Umrah. This is the second busiest month of the year after Zulhijjah (the month of Pilgrimage - Hajj), which further signifies the need for clear air.
In this study air pollutants and meteorology data have been analysed. The data were collected at the Presidency of Meteorology and Environment (PME) air quality monitoring station (AQMS 112) situated near Al-Haram (the Holy Mosque) in Makkah, the Kingdom of Saudi Arabia. Figure 1 shows the location of PME (AQMS 112) and other air quality monitoring sites in Makkah. The data considered here are for the month of Ramadhan 1433 H (20th July to 18th August, 2012), when millions of people come to Makkah to perform Umrah. This is the second busiest time of the year after Hajj. In this study the following parameters were considered: Sulphur Dioxide (SO2 µg/m3), Carbon Monoxide (CO mg/m3), Nitrogen Oxides (NOx µg/m3), Nitric Oxide (NO µg/m3), Nitrogen Dioxide (NO2 µg/m3), Particulate Matter with aerodynamic diameter of 10 um or less (PM10 µg/m3), Ozone (O3 µg/m3), Wind Speed (WS m/s), Wind Direction(WD Degrees from the north), Relative Humidity (RH %), Temperature (T oC), Rain Fall (RF mm), and atmospheric Pressure (P hPa), where hectopascal (hPa) is the same as kilopascal (kPa) and is equivalent to the older unit millibar (mbar). Statistical data analysis was carried out in R - programming language (R-development team, 2012) and one of its package open air (Carslaw, 2012). Correlation analysis and graphical presentations (scatter plots, polar plots, and time series plots) were used to investigate the association of various air pollutants with each other and with meteorological variables. A summary of the parameters is presented in Table 1. Table 1.A summary of the parameters for the month of Ramadhan 1433 H (20th July to 18th August, 2012), number of observations for each parameters were 715.
1NA represents missing data and DC represents data capture.
Figure 1. Map of the air quality and meteorological monitoring sites in Makkah.
3.1 Correlation analysis Knowing the association of different variables is important and can be helpful in identifying the emission sources of air pollutants. In this paper correlation matrix plot (Carslaw and Ropkins, 2012) is used, which provides correlation between all pairs of the data. Correlation plot shows the correlation coded in three ways: by shape (ellipses), colour and the numeric value. The ellipses are similar to scatter plot. A perfect positive correlation is represented by a line at 45 degrees, whereas no correlation is shown by a circle of points. Furthermore, hierarchical clustering is applied to the correlation matrices to group variables that are most similar to one another. The numerical values are shown from -100 to 100, where zero shows no correlation and 100 shows perfect positive and -100 shows perfect negative correlation. Figure 2 shows correlation matrix plot of various air pollutants and meteorological variables. Several clusters can be clearly observed. For example NOx and CO show very strong positive correlation, whereas NOx and ozone show strong negative correlation, which is expected as NOx and CO have the same sources of emissions in Makkah, predominantly road traffic; and the negative correlation of ozone and NOx is due to the chemical coupling between these species (Jenkin et al., 2004). SO2 is positive correlated with CO and NOx, however the strength is weaker, indicating SO2 has different sources of emissions (e.g., burning of crude oil and diesel vehicles) (Habeebullah et al., 2012). PM10 has relatively weak correlation with other air pollutants, most probably because most of the PM10 in Saudi Arabia, being an arid regionis generated by non-combustion sources, such as construction work and windblown dust and sand. Among meteorological variables, temperature show strong positive correlation with ozone, which is due to the fact that ozone is a secondary air pollutant and is formed in the atmosphere by photochemical reaction of hydrocarbons and NOx in the presence of sunlight. Generally high temperature accelerates photochemical formation of ozone molecules, due to this reason ozone level are higher in summer than in winters seasons (AQEG, 2009). In contrast temperature has negative correlation with NOx, CO, SO2, and PM10, whose concentration is more dependent on the emission sources. However, the negative correlation indicates that probably high temperature results in greater dispersion and dilution of the air pollutants, probably linked with vertical and horizontal turbulence (EPA, 2010). The effect is negligible on PM10. It is important to highlight that in the case of PM10 greater turbulence can generate more dust particles in a region like Makkah, which may offset the effect of pollutants dispersion. Wind speed help disperse local pollutants, which probably explains why it has negative correlation with NOx and CO, however it has positive correlation with ozone and PM10, most probably due to raising particles from bared surfaces and road sides and transport of ozone from the surrounding rural areas. The effect of wind speed is generally related to its direction, which is further elaborated in later sections with the help of polar plots. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. Duenas et al. (2002) has reported that relative humidity plays an important role in air quality, as relative humidity may play a role in the overall reactivity of the atmospheric system, either by affecting chain termination reactions or in the production of wet aerosols, which in turn affect the flux of ultraviolet radiation. Furthermore, relative humidity is also considered to be a limiting factor in the disposition of NO2 because high percentages of humidity favour the reaction of the NO2 with particles of sodium chloride salt (Duenas et al., 2002). Relative Humidity can also act on air pollutants to create secondary aerosols, such as sulphate and nitrate ions, which contribute positively to PM10 concentrations. Rain washout most of the dust from the atmosphere and may encourage wet deposition of some of the gaseous pollutants, however in this analysis rain fall has shown weak association with all air pollutants, because Makkah being part of an arid region receives very limited rain throughout the year. Atmospheric pressure is the weight of the atmosphere at a given point. The height and temperature of a column of air determines the atmospheric weight. Because cold air weights more than warm air, a high pressure mass of air is made up of cold and heavy air. Conversely, a low pressure mass of air is made up of warmer and lighter air. Differences in pressure cause air to move from high pressure areas to low pressure areas, resulting in wind. Wind speed can greatly affect the pollutant concentration in a local area (as described above). Furthermore, high-pressure systems often combine with stable atmospheric conditions and low wind speeds, which can lead to episodes of severe air pollution (EPA, 2010).
Figure 2. Correlation Matrix plot of various parameters from 20th July to 18th August, 2012.
3.2 Polar plot The bivariate polar plot is a useful diagnostic tool for quickly gaining an idea of potential sources (Carslaw and Ropkins, 2012). The plots are constructed by averaging pollutant concentration by wind speed categories (0–1 m/s, 1–2 m/s, etc.) as well as wind direction (0–10, 10–20, etc.). The principal aim of Figure 3 shows the polar plots of various air pollutants for the study period (20 July to 18 August, 2012) at PME monitoring site, Near Al-Haram, Makkah. In Figure 3, polar plot for CO (top-left), NO (top-right), SO2 (middle-left) and NO2 (middle-right) with slight variation show high concentrations at low wind speed, however high concentrations of NO2 are also linked with high wind speed from the southeast direction. High concentrations at low wind speed suggest local sources of these air pollutants, which may disperse at high wind speed. In contrast, high levels of ozone and PM10 concentrations are kinked with high wind speed from northwest and southeast, respectively and at low wind speed their levels are low, which may suggest these air pollutants are transported from the surrounding areas. Ozone is inversely proportion to NO and NO2 and hence the polar plots show the opposite pattern of these pollutants. There is a construction site towards west and northwest of Al-Haram, however the PM10 polar plot does not shows significant contribution from it, which is further investigated in later sections. There are some local roads, bus stations, and parking place in the surrounding areas, which probably contribute to the emissions of traffic related air pollutants, however the levels of these air pollutants have not exceeded the air quality standards during the study period. PM10 was the only pollutants which exceeded 24 hr PME air quality guideline on 26 – 28 July. This is further discussed in section 3.3.
Figure 3. Polar plots of various air pollutants for the study period at PME site near Al-Haram, Makkah.
3.3 PM10 episode (26 to 28 July, 2012) Statistical analysis shows that the concentrations of the air pollutants during the study period were below the air quality standards set by World Health Organisation (WHO) and the Presidency of Meteorology and Environment (PME) of the Saudi Arabia. The only exception was PM10 concentrations, which exceeded the 24 hour average air quality limit of 340 µg/m3 set by PME for the protection of human health. On 26 to 28 July 2012, the 24 hour average concentrations of PM10 were 518, 790 and 389 340 µg/m3, respectively, which are shown in Table 2 along with some other air pollutant concentrations. In this section, these three days have been further investigated to determine the causes of high PM10 concentrations. Table 2.Daily average concentrations of various air pollutants during 26 to 28 July 2012.
Pollution roses (Figure 4) are used to show the effect of wind on PM10 concentrations. Pollution rose is a variant of wind rose and is useful for considering pollutant concentrations by wind direction, or more specifically the percentage time the concentration is in a particular range. These plots are very useful for understanding which wind directions control the overall mean concentrations (Carslaw and Ropkins, 2012). It is worthwhile that polar plot shows pollutant concentrations by wind speed and wind direction, in contrast pollution rose depicts pollutant concentrations by wind frequency (the number of hours wind is blowing from a certain direction) and wind direction. Figure 4 (top-left) shows that during the study period, wind is predominantly blowing from the northwest direction, however high PM10 concentrations (shown by the colour and width of the paddles) are linked with westerly and south-easterly winds. Figure 3 has also shown high PM10 concentrations linked with south-easterly wind, where wind speed reach up to 4 m/s. Figure 4 (top-right) shows PM10 concentrations during the three days (26 to 28 July, 2012), when the PME air quality standards were violated. In this panel high PM10 concentrations are linked with south, south-easterly and south-westerly wind. When the data was divided into two subsets: (a) PM10 concentration > 500 (µg/m3); and (b) PM10 concentrations < 500 (µg/m3), dataset (a) clearly linked high concentrations with south-easterly wind. High concentration of PM10 from the south-easterly direction either could be due to the high wind speed, as shown in Figure 3 or there might be an emission source in this direction, or both. It is a fact that Makkah being part of an arid region receives low precipitations and has large barren sandy land, therefore when wind blows it can generate considerable amount of atmospheric dust. The large heavy particles quickly deposit under the action of gravity, however smaller particle can stay in the atmosphere and travel large distances. The contributions from road traffic in the surrounding areas might add a significant amount, however on this occasion it was not considered as the main source, otherwise highest PM10 concentrations would have been observed during the last 10 days of the study period (08 to 18 August, 2012), when the number of visitors to the Makkah and hence traffic flow reach the peak level.
Time plots of the various air pollutants and meteorological variables (24 hour average) are plotted for the period of study (20 July to 18 August, 2012) (Figure 5). It can be observed in Figure 5 (top panel) that pollutant concentrations show considerable variations in their levels during the study period, however the pattern in PM10 concentrations is significantly different than that of other pollutants, which suggest that the effect of different factors (emission sources and meteorological variables), controlling their concentrations varies on each pollutant. When PM10 concentration is highest (26 to 28 July), ozone concentration is lowest and vice versa. During these three days, the concentrations of other pollutants (SO2, NO2 and CO) are pretty low as well. Figure 5 (bottom panel) shows the levels of and variations in meteorological variables and it can be observed in the Figure 5 that atmospheric pressure is low and relative humidity is high during the 3 days period. Other meteorological variation do not show any distinct characteristics, except wind direction which seems to be blowing at about 200o (southern direction), however it does not correlate well with Figure 4 (top-left), where the wind direction during the three days vary considerably. The dissimilarities are due to different averaging time and the circular nature of wind direction. Therefore, the wind direction in Figure 4 is considered here, which associates high PM10 concentrations with the southeast directions. Hence we conclude that low pressure and high relative humidity, are probably the main reasons for the high PM10 concentrations, where the former might have encouraged the moving-in of the particles from the surrounding area as wind blow from high to low pressure areas (EPA, 2010), whereas the latter might have encouraged secondary aerosols formation by the process of coagulation and condensation (Harrison, 2001).
Figure 4. Pollution Rose, colour coded by the levels of mean hourly PM10 concentrations (µg/m3): Top-left panel shows the whole month data (20 July to 18 August, 2012); Top-right panel shows three days data (26 to 28 July, 2012); Bottom-left shows when PM10 concentrations > 500 µg/m3; and Bottom-right shows when PM10 concentrations < 500 µg/m3.
Figure 5. Time plots of various air pollutants (top-panel) and meteorological variables (bottom-panel), showing 24 hour average at the PME monitoring site, from 20th July to 18 August, 2012.
In this study the effects of meteorological variables on the concentrations of various air pollutants, including SO2, CO, NOx, PM10 and ozone have been investigated during the month of Ramadhan (20 July to 18 August, 2012) in Makkah near Al-Haram. Correlation analysis has been used to investigate the association of air pollutants with each other and with meteorological variables. PM10 has relatively weaker correlation with other air pollutants, most probably because most of the PM10 in Saudi Arabia, being an arid region is generated by non-combustion sources, such as construction work and windblown dust and sand, whereas the other pollutants like SO2, CO and NOx are mainly emitted by combustion sources, including road traffic. Among meteorological variables, temperature show strong positive correlation with ozone (0.74), which is probably due to the fact that ozone is a secondary air pollutant and is formed in the atmosphere by photochemical reaction of hydrocarbons and NOx in the presence of sunlight. In contrast temperature has negative correlation with NOx, CO, SO2, and PM10, whose concentration is more dependent on the emission sources. However, the negative correlation indicates that probably high temperature results in greater dispersion and dilution of the air pollutants, probably linked with vertical and horizontal turbulence (EPA, 2010). Wind speed help disperse local pollutants, which probably explains why it has negative correlation with NOx and CO, however it has positive correlation with ozone and PM10, most probably due to raising particles from bared surfaces and road sides and transport of ozone from the surrounding rural areas. Relative humidity is positively correlated with PM10 and negatively correlated with the rest of the air pollutants. The effect of rainfall was negligible most probably due to the fact that no rain occurred during the study period. The effects of meteorological variables have also been analysed using polar plots and pollution roses, which provide further insight into the association between air pollutants and meteorology. Factors responsible for the high concentrations of pollutants, particularly during the PM10 episode from 26 to 28 July 2012 are analysed. Unexpectedly, atmospheric pressure and relative humidity seem to be responsible for the episode, and not the sources of emissions, which are higher during the last 10 days of Ramadhan (08 to 18 August).
5. References Aburas, H. M., Zytoon, M. A., Abdulsalam, M. I. 2011. Atmospheric Lead in PM2.5 after Leaded Gasoline Phase-out in Jeddah City, Saudi Arabia, CLEAN – Soil, Air, Water, Volume 39, Issue 8, pages 711–719. Air Pollution in the UK, 2011. Published by the Department for Environment, Food and RuralAffairs, September 2012.http://uk-air.defra.gov.uk/library/annualreport/viewonline? year=2011 issue_2&jump=tp (accessed 30/11/2012). Al-Zahrani, 2010.The Road to Saudi Arabian Clean Fuels, Downstream Process Engineering Division, Saudi Aramco (http://www.hartfuel.com/0908/f.saudicleanfuels.html). AQEG, 2005. Particulate matter in the United Kingdom, the second report produced by the Air Quality Expert Group, Prepared for the Department for Environment, Food and Rural Affairs. DEFRA Publication London. 2005AQEG. AQEG, 2009.Ozone in the UK, the fifth report produced by air quality expert group.Published by the Department for the Environment, Food and Rural Affairs.DEFRA publication London. 2009AQEG. Baur, D., Saisana, M. and Schulze, N., 2004.Modelling the effects of meteorological variables on ozone concentration-a quantile regression approach. Atmospheric Environment 38 (28), 4689 – 4699. Beaver, S., Palazoglu, A., 2009. Influence of synoptic and mesoscale meteorology onozone pollution potential for San Joaquin Valley of California. AtmosphericEnvironment 43 (10), 1779-1788. Bell, J.N. and Treshow, M., 2008. Air pollution and plant life, 2nd ed. London: John Wiley and Sons, LTD, 2008. Camalier, L., Cox, W., and Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment Volume 41, Issue 33, October 2007, Pages 7127-7137. Carslaw, D., and Ropkins, K., 2012. Openair - an R package for air quality data analysis. Environmental Modelling & Software 27-28, 52-61. Cheng, C.S.Q., Campbell, M., et al., 2007.A synoptic climatological approach to assess climatic impact on air quality in South-central Canada. Part I: historical analysis. Water Air and Soil Pollution 182 (1e4), 131-148. Duenas, C., Fernandez, M. C., Canete, S., Carretero, J. and Liger, E., 2002. Assessment of ozone variations and meteorological effects in an urban area in the Mediterranean Coast, The Science of The Total Environment, Volume 299, Issues 1-3, 1 November 2002, Pages 97-113.Elminir, H.K., 2005. Dependence of urban air pollutants on meteorology. Science of the Total Environment 350 (1-3), 225-237. EPA, 2010. US Environmental Protection Agency, Air Pollution Control Orientation Course: Control Emmissions Technologies - Transport & Dispersion of Air Pollutants http://www.epa.gov/apti/course422/ce1.html (Accessed 25/11/2012). Habeebullah, T.M, Munir, S., Morsy, E.A., 2012. An Analysis of Air Pollution in Makkah: A View Point of Source Identification. A Report submitted to the Department of Environment and Health Research, the Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, UmmAl-QuraUniversity, Makkah, Kingdom of Saudi Arabia. Harrison, R.M., 2001. Air Pollutant: sources, concentrations and measurements.InHarrison, R.M. (ed), 2001. Pollution, caused, effects and control, Fourth edition, Royal Society of Chemistry, ISBN 0-85404-621-6. Jacob, D.J., Winner, D.A., 2009. Effect of climate change on air quality. Atmospheric Environment 43 (1), 51-63. Jenkin, M.E., 2004. Analysis of sources and partitioning of oxidant in the UK. Part 1: The NOX-dependence of annual mean concentrations of nitrogen dioxide and ozone. Atmospheric Environment38, 5117-5129. Kadi, M.W., 2009. Soil Pollution Hazardous to Environment: A Case Study on the Chemical Composition and Correlation to Automobile Traffic of the Roadside Soil of Jeddah City, Saudi Arabia, J. Hazard. Matter.2009, 168 (2–3), 1280. Khodeir, M., Shamy, M., Alghamdi, M., Zhong, M., Sun, H., Costa, M., Chen, L.C., Maciejcczyk, P.m 2012. Source apportionment and elemental composition of PM2.5 and PM10 in JeddahCity, Saudi Arabia.Atmospheric Pollution Research 3 (2012) 331‐340. Ordonez, C., Mathis, H., et al., 2005.Changes of daily surface ozone maxima in Switzerland in all seasons from 1992 to 2002 and discussion of summer 2003. Atmospheric Chemistry and Physics 5, 1187-1203. Othman, Mat-Jafri, M.Z., and San, L.H., 2010.Estimating Particulate Matter Concentration over Arid Region Using Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia, Modern Applied Science Vol. 4, No. 11. Pearce, J.L., Beringer, J., Nicholls, N., Hyndman, R.J., Tapper, N.J., 2011. Quantifying the influence of local meteorology on air quality using generalized additive models, Atmospheric Environment 45 (2011) 1328-1336. Schlink, U., Herbarth, O., Richter, M., Dorling, S., Nunnari, G., Cawley, G., and Pelikan,.E., 2006. Statistical models to assess the health effects and to forecast ground-level ozone. Environmental Modelling & Software 21 (2006) 547–558. Thompson, M.L., Reynolds, J., Cox, L.H., Guttorp, P., Sampson, P.D., 2001. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35 (3), 617–630. Westmoreland, E.M., Carslaw, N., Carslaw, D.C., Gillah, A., and Bates, E., 2007.Analysis of air quality within a street canyon using statistical and dispersion modelling techniques. Atmospheric Environment 41, 9195–9205. WHO, 2008. World Health Organisation, Health risks of ozone from long-range transboundary air pollution. A report prepared by WHO Regional Office for Europe, 2008 (http://www.euro.who.int/Document/E91843.pdf). Wood, S.N., 2006. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.
التحقق من تأثير الأرصاد الجوية على تلوث الهواء في مکة المکرمة ترکي محمد حبيب الله أستاذ التلوث البيئي المساعد معهد خادم الحرمين الشريفين لأبحاث الحج والعمرة – جامعة أم القرى – المملکة العربية السعودية
لا يتأثر ترکيز ملوثات الهواء فقط بمصادر التلوث، وإنما تتأثر کثيراً بمتغيرات عناصر الأرصاد الجوية والتي تلعب دوراً هاماً في تشتت وإنتقال المرکبات والتفاعلات الکيميائية للملوثات الثانوية في الغلاف الجوي. تم في هذه الدراسة تحليل أثر تغير عناصر الأرصاد الجوية على تراکيز مختلفة من الملوثات الهوائية بإستخدام معادلات تحليل الإرتباط والعروض الرسومية لمکة المکرمة خلال شهر رمضان 1433هـ (20/7 – 18/8/2012)، والذي يعد أزحم شهور السنة بعد شهر ذي الحجة. تبين من خلال التحليلات بأن الأتربة الصدرية أقل إرتباطاً بالملوثات الأخرى بسبب تغير مصادر التلوث. بينما هناک علاقة وثيقة بتغير درجة الحرارة مع الأوزون والتي وصل فيها معامل الإرتباط إلى (0.74)، في حين إنخفض معامل الإرتباط مع الملوثات الأخرى وهي أکاسيد النيتروجين وأول أکسيد الکربون وثاني أکسيد الکبريت والأتربة الصدرية، وهذه الملوثات مرتبطة إرتباطاً وثيقاً بمصادر التلوث. سرعة الرياح أيضاً ساعدت في تشتت الملوثات الهوائية، وهذا يفسر أن معامل الارتباط مع أکاسيد النيتروجين وثاني أکسيد الکبريت وأول أکسيد الکربون سالباً، بينما کان معامل الإرتباط موجباً مع الأوزون والأتربة الصدرية بسبب زيادة سرعة الرياح والذي ساعد على عدم ثبات العواصف الترابية في الشوارع والمناطق الصحراوية، وأيضاً إنتقال الأوزون من المناطق البعيدة عن مدينة مکة المکرمة. الرطوبة النسبية هي بالتالي سجلت علاقة قوية وموجبة مع الأتربة الصدرية وسالبة مع بقية ملوثات الهواء. کما أن معامل الإرتباط لتأثير سقوط الأمطار کان ضئيلاً بسبب عدم وجود تساقط للأمطار فترة إجراء الدراسة. کذلک تم تحليل تأثير عناصر الأرصاد الجوية بواسطة الرسمة القطبية ووردة الرياح. کما تم تحليل العوامل المسئولة في زيادة تراکيز الأتربة الصدرية عن الحدود المسموح بها خلال الفترة من 26-28 يوليو 2012. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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5. REFERENCES:
Aburas, H. M., Zytoon, M. A., Abdulsalam, M. I. 2011. Atmospheric Lead in PM2.5 after Leaded Gasoline Phase-out in Jeddah City, Saudi Arabia, CLEAN – Soil, Air, Water, Volume 39, Issue 8, pages 711–719.
Air Pollution in the UK, 2011. Published by the Department for Environment, Food and Rural Affairs, September2012. http://uk-air. defra. gov. uk/library/ annualreport/viewonline? year=2011issue_2&jump=tp (accessed 30/11/2012).
Al-Zahrani S.A., 2010.The Road to Saudi Arabian Clean Fuels, Downstream Process Engineering ivision, Saudi Aramco http://www.hartfuel.com/0908/f.saudicleanfuels.html).
AQEG, 2005. Particulate matter in the United Kingdom, the second report produced by the Air Quality Expert Group, Prepared for the Department for Environment, Food and Rural Affairs. DEFRA Publication London. 2005AQEG.
AQEG, 2009.Ozone in the UK, the fifth report produced by air quality expert group.Published by the Department for the Environment, Food and Rural Affairs. DEFRA publication London. 2009AQEG.
Baur, D., Saisana, M. and Schulze, N., 2004.Modelling the effects of meteorological variables on ozone concentration-a quantile regression approach. Atmospheric Environment 38 (28), 4689 – 4699.
Beaver, S., Palazoglu, A., 2009. Influence of synoptic and mesoscale meteorology onozone pollution potential for San Joaquin Valley of California. AtmosphericEnvironment 43 (10), 1779-1788.
Bell, J.N. and Treshow, M., 2008. Air pollution and plant life, 2nd ed. London: John Wiley and Sons, LTD, 2008.
Camalier, L., Cox, W., and Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmospheric Environment Volume 41, Issue 33, October 2007, Pages 7127-7137.
Carslaw, D., and Ropkins, K., 2012. Openair - an R package for air quality data analysis. Environmental Modelling & Software 27-28, 52-61.
Cheng, C.S.Q., Campbell, M., et al., 2007. A synoptic climatological approach to assess climatic impact on air quality in South-central Canada. Part I: historical analysis. Water Air and Soil Pollution 182 (1e4), 131-148.
Duenas, C., Fernandez, M. C., Canete, S., Carretero, J. and Liger, E., 2002. Assessment of ozone variations and meteorological effects in an urban area in the Mediterranean Coast, The Science of The Total Environment, Volume 299, Issues 1-3, 1 November 2002, Pages 97-113.Elminir, H.K., 2005. Dependence of urban air pollutants on meteorology. Science of the Total Environment 350 (1-3), 225-237.
EPA, 2010. US Environmental Protection Agency, Air Pollution Control Orientation Course: Control Emmissions Technologies - Transport & Dispersion of Air Pollutants http://www.epa.gov/apti/course422/ce1.html (Accessed 25/11/2012).
Habeebullah, T.M, Munir, S., Morsy, E.A., 2012. An Analysis of Air Pollution in Makkah: A View Point of Source Identification. A Report submitted to the Department of Environment and Health Research, the Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Makkah, Kingdom of Saudi Arabia.
Harrison, R.M., 2001. Air Pollutant: sources, concentrations and measurements.In Harrison, R.M. (ed), 2001. Pollution, caused, effects and control, Fourth edition, Royal Society of Chemistry, ISBN 0-85404-621-6.
Jacob, D.J., Winner, D.A., 2009. Effect of climate change on air quality. Atmospheric Environment 43 (1), 51-63.
Jenkin, M.E., 2004. Analysis of sources and partitioning of oxidant in the UK. Part 1: The NOX-dependence of annual mean concentrations of nitrogen dioxide and ozone. Atmospheric Environment 38, 5117-5129.
Kadi, M.W., 2009. Soil Pollution Hazardous to Environment: A Case Study on the Chemical Composition and Correlation to Automobile Traffic of the Roadside Soil of Jeddah City, Saudi Arabia, J. Hazard. Matter.2009, 168 (2–3), 1280.
Khodeir, M., Shamy, M., Alghamdi, M., Zhong, M., Sun, H., Costa, M., Chen, L.C., Maciejcczyk, P. 2012. Source apportionment and elemental composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia. Atmospheric Pollution Research 3 (2012) 331340.
Ordonez, C., Mathis, H., et al., 2005.Changes of daily surface ozone maxima in Switzerland in all seasons from 1992 to 2002 and discussion of summer 2003. Atmospheric Chemistry and Physics 5, 1187-1203.
Othman, Mat-Jafri, M.Z., and San, L.H., 2010.Estimating Particulate Matter Concentration over Arid Region Using Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia, Modern Applied Science Vol. 4, No. 11.
Pearce, J.L., Beringer, J., Nicholls, N., Hyndman, R.J., Tapper, N.J., 2011. Quantifying the influence of local meteorology on air quality using generalized additive models, Atmospheric Environment 45 (2011) 1328-1336.
Schlink, U., Herbarth, O., Richter, M., Dorling, S., Nunnari, G., Cawley, G., and Pelikan,.E., 2006. Statistical models to assess the health effects and to forecast ground-level ozone. Environmental Modelling & Software 21 (2006) 547–558.
Thompson, M.L., Reynolds, J., Cox, L.H., Guttorp, P., Sampson, P.D., 2001. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment 35 (3), 617–630.
Westmoreland, E.M., Carslaw, N., Carslaw, D.C., Gillah, A., and Bates, E., 2007. Analysis of air quality within a street canyon using statistical and dispersion modelling techniques. Atmospheric Environment 41, 9195–9205.
WHO, 2008. World Health Organisation, Health risks of ozone from long-range transboundary air pollution. A report prepared by WHO Regional Office for Europe, 2008 (http://www.euro.who.int/Document/E91843.pdf).
Wood, S.N., 2006. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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