Air Quality, Atmosphere and Health Concentrations, sources and exposure risks associated with particulate matter in the Middle East Area - a review --Manuscript Draft-Manuscript Number:
AIRQ-D-14-00042R3
Full Title:
Concentrations, sources and exposure risks associated with particulate matter in the Middle East Area - a review
Article Type:
Review Article
Keywords:
Particulate matter; Middle East; Air pollution exposure; air quality management; Fine particles
Corresponding Author:
Konstantinos E. Kakosimos, Ph.D. Texas A&M University at Qatar Doha, QATAR
Corresponding Author Secondary Information: Corresponding Author's Institution:
Texas A&M University at Qatar
Corresponding Author's Secondary Institution: First Author:
Vasiliki Tsiouri, Ph.D.
First Author Secondary Information: Order of Authors:
Vasiliki Tsiouri, Ph.D. Konstantinos E. Kakosimos, Ph.D. Prashant Kumar, Ph.D., Senior Lecturer
Order of Authors Secondary Information: Abstract:
Rapid economic expansion, industrialization, urbanization and construction in the Middle East Area (MEA) have led to an increase in the levels of air pollution showing serious effects on human health. For the first time, this article provides a comprehensive synthesis of the currently available published information, which deals with atmospheric particulate matter (PM) in MEA. The focus of the article remains on the PM sources, monitoring, health impacts, and source apportionment. The key objectives are to identify the levels of PM pollution and the associated exposure risks, to highlight the research gaps, and to discuss the future research directions. The limited number of monitoring studies available for MEA indicates that dust storms augmented by the rapid increase in urban population are the key reasons for the high PM concentration levels. The findings of reviewed monitoring studies suggest that the levels of both annual mean PM10 (20 μg/m3) and PM2.5 (10 μg/m3) concentrations exceed the World Health Organization (WHO) guidelines during most of the non-dust storm episodes, and as expected, the PM pollution levels become even higher during dust storm episodes. For example, 24-h mean PM10 concentrations of over 1000 μg/m3 were noted during a severe dust storm episode in Kuwait. The findings of the epidemiological and toxicological studies in MEA have shown that dust storm events have a significant impact on respiratory admissions, and the adverse health effects of PM are particularly in the form of asthma, respiratory and cardiovascular diseases. It is concluded that PM pollution in MEA is a significant problem and quantification of PM emissions and the design of control measures to abate their impacts on public health are of primary importance. Besides, there is a need for more systematic PM data collection for source apportionment and assessment of PM levels that would enable air pollution related health impact assessments of MEA. Furthermore, this review highlights that the release of airborne PM from major building activities such as building construction is largely unknown and emission inventories for different situations are needed.
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Manuscript Click here to download Manuscript: rev3_Manuscript_AirQ.doc Click here to view linked References 1 2 3 4 5 6 7 8 9 Vasiliki Tsiouria, Konstantinos E. Kakosimosa,*, Prashant Kumar b, c 10 a 11 Department of Chemical Engineering & Mary Kay O’Connor Process Safety Center, Texas A&M University at Qatar, 23874 Doha, Qatar. 12 13 b Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, 14 Guildford GU2 7XH, United Kingdom 15 16 c Environmental Flow (EnFlo) Research Centre, FEPS, University of Surrey, Guildford GU2 7XH, United Kingdom 17 18 19 Abstract 20 21 Rapid economic expansion, industrialization, urbanization and construction in the Middle East 22 Area (MEA) have led to an increase in the levels of air pollution showing serious effects on 23 human health. For the first time, this article provides a comprehensive synthesis of the currently 24 25 available published information, which deals with atmospheric particulate matter (PM) in MEA. 26 The focus of the article remains on the PM sources, monitoring, health impacts, and source 27 28 apportionment. The key objectives are to identify the levels of PM pollution and the associated 29 exposure risks, to highlight the research gaps, and to discuss the future research directions. The 30 limited number of monitoring studies available for MEA indicates that dust storms augmented by 31 32 the rapid increase in urban population are the key reasons for the high PM concentration levels. 33 The findings of reviewed monitoring studies suggest that the levels of both annual mean PM10 34 35 (20 μg/m3) and PM2.5 (10 μg/m3) concentrations exceed the World Health Organization (WHO) 36 guidelines during most of the non-dust storm episodes, and as expected, the PM pollution levels 37 become even higher during dust storm episodes. For example, 24-h mean PM10 concentrations of 38 39 over 1000 μg/m3 were noted during a severe dust storm episode in Kuwait. The findings of the 40 epidemiological and toxicological studies in MEA have shown that dust storm events have a 41 42 significant impact on respiratory admissions, and the adverse health effects of PM are 43 particularly in the form of asthma, respiratory and cardiovascular diseases. It is concluded that 44 PM pollution in MEA is a significant problem and quantification of PM emissions and the design 45 46 of control measures to abate their impacts on public health are of primary importance. Besides, 47 there is a need for more systematic PM data collection for source apportionment and assessment 48 49 of PM levels that would enable air pollution related health impact assessments of MEA. 50 Furthermore, this review highlights that the release of airborne PM from major building activities 51 such as building construction is largely unknown and emission inventories for different situations 52 53 are needed. 54 55 56 57 58 *Corresponding Author: Texas A&M Engineering Building, Education City PO Box 23874, Doha, Qatar; tel: +974 44230678, 59 mob: +974 55069244, email:
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Concentrations, sources and exposure risks associated with particulate matter in the Middle East Area – a review
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Keywords: Particulate matter; Middle East; Air pollution exposure; Air quality management; Fine particles 1.
Introduction
The rapid rate of urbanization and unprecedented scale of construction in Middle East Area (MEA) has given rise to health concerns related to the exposure to ambient particulate matter (PM) pollution. MEA has many significant sources of PM, including energy and industry, transportation, and building construction (Hansen et al. 2008; Kumar et al. 2012a; Kumar et al. 2012b). Another important factor contributing to airborne PM pollution in MEA is Aeolian processes. Severe dust storms occur frequently in the arid, semi-arid or desert Middle East region (Akbari 2011; Saliba and Massoud 2011; Wang et al. 2005) as a result of pressure gradients. Low vegetation cover and high winds whip-up loose soil from a large area and transport it across the land up to great distances. When the wind speed reaches a threshold value, the dust and the sand surface particles begin to vibrate and consequently ejected into air to cause ‘saltation’ process (Jayaratne et al. 2011; Kurosaki and Mikami 2003). In such events large scale transport of pathogens can be produced affecting downwind populations and ecosystems (Bu-Olayan 2011; Kellogg and Griffin 2006). During any dust storm episode, 24-h mean PM10 concentrations of over 1000 mg/m3 are recorded (Engelbrecht et al. 2008; Engelbrecht et al. 2009), exceeding the World Health Organization (WHO) guidelines. According to the findings of Khamdan et al. (2009), PM10 and PM2.5 originated from dust storms from the local desert and the trans-boundary airborne effects of southern Iraq desert impacts Bahrain, Kuwait, eastern province of Saudi Arabia and perhaps extends further to Arabian Gulf. The consequence exposure to PM pollution has shown to adversely impact the human health (Draxler et al. 2001). During the last decade, a large number of studies have reported an association between cardiovascular symptoms and exposure to high level of ambient PM in various size ranges (Brook et al. 2010; Heal et al. 2012; Zanobetti and Schwartz 2007). In many epidemiological studies evidences of association between short- or long-term exposure to various airborne PM with human mortality and morbidity are clear (Chen and Lippmann 2009; Dockery and Pope 1994; Pope et al. 2002; Vedal et al. 2003). Mortality estimates for Delhi show about 1,900 deaths per million due to traffic related particle exposure in 2010 (Kumar et al. 2011a). The association between long-term exposure to PM concentrations and human mortality was also pointed out by a research study for six US cities (Laden et al. 2000). The 2012 Yearbook of United Nations Environmental Programme (UNEP) Global Environmental Outlook (GEO) suggests that in many cities in MEA, such as Damascus, Baghdad and Manama, air pollution levels exceed the WHO guidelines for PM10 and PM2.5 (WHO 2006; WHO 2011). According to the Egyptian Environment Affairs Agency, air pollution in Cairo is responsible for 3,400 deaths, 15,000 cases of bronchitis, and 329,000 cases of respiratory infection each year. Gibson et al. (2013), found that outdoor air pollution was the leading contributor to mortality in UAE with 651 attributable deaths, which are ~7.3% of total deaths in 2008. Page 2 of 25
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All the above evidences call for a comprehensive evaluation of PM pollution in MEA. This article is therefore critically synthesising the published literature on PM in this region, including the available studies on monitoring, health assessment and source apportionment. This work aims to identify the PM sources and the current PM pollution levels, analyse its deleterious effects on air quality and human health, highlight the research gaps and discuss the future research directions.
2.
Middle East Area and associated PM issues
MEA is politically and economically heterogeneous and its geographic boundaries vary in the international literature. In this review, MEA is referred to the area that is somehow enclosed by Eastern Mediterranean and Suez Canal over Arabic peninsula and around Arabian Gulf (Figure 1a). Most of this region, except some parts as it can be seen in Figure 1b, has an arid or desert land that occupies the heart area of world dry land, containing nearly 58% of the world’s known oil reserves, and nearly 29% of natural gas reserves (Tolba 2008). Thus, the MEA countries share similar environmental issues and goals to control the PM pollution. 2.1
Current state of regulations
The usually responsible for developing the air quality standards and executing solutions for reduction of the air pollution for each MEA country is the Ministry of the Environment (MoE). In order to attain specific PM levels and to protect the public health a thorough knowledge of local dose-effect relationships is very important. Consequently, the air quality standards are important for the national risk management and the environmental policies. However, in some MEA countries such as Oman and Kuwait, local air quality standards for PM are presently not available. Just a few countries in MEA such as Saudi Arabia and Jordan have their own set of regulations as it can be seen in Table 1. In the countries where there are no local PM standards, tentative air quality standards are adopted from well-established legislations elsewhere in the world. For example, the Ministry of Environment and Climate Affairs (MECA) in Oman recommends the use of US Environmental Protection Agency (USEPA) standards for ambient air quality (HMR 2010). Likewise, air quality standards in Kuwait are based on US Clean Air Act for PM10 and WHO guidelines for PM2.5. These limits are subject to be reviewed every two years to ensure they conform to conditions that satisfy the public health and welfare. However, sometimes such an adoption can be subjected to a weakness, because these standards are often set in conformity with the effective standards applied in developed industrialised countries and could be unreflective of the specific environmental conditions. For this reason, regulatory authorities should carefully consider their local circumstances before adopting the guidelines directly.
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2.2
Climate
The dispersion of the PM emissions is greatly influenced by the climate and the meteorological conditions including temperature, humidity, wind speed, wind direction, atmospheric pressure and height of the mixing layer (Holmes and Morawska 2006; Kumar et al. 2011b). Intensity of the dispersion problem increases in the case of temperature inversion. Under normal conditions temperature decreases with height, but in the case of temperature inversion with low wind speeds PM are trapped in the mixing layer and move horizontally since dispersion is blocked vertically. However this is not the case for MEA where both extremely stable and unstable atmospheric conditions are frequent. MEA experiences a dry climate with high temperatures, dust storms, and shortage of rainfall. With the exception of the humid and rainy territories in the coastal highlands of Lebanon and in the Iraqi highlands, MEA is a part of the arid belt that extends across Africa towards north of the equator to West Asia. This region comprises the southern sector of the Mediterranean basin (a territory of winter rainfall), and extends southwards to territories of summer rainfall (tropical) in Sudan, Yemen and Oman. On the whole, aridity prevails and the climate is characterized by long summers (extending from April to October and sometimes even longer) and daily average temperatures are generally higher than 35 ºC. The average annual rainfall through most of the region varies considerably from less than 5 cm to 50 cm in limited areas. In elevated and mountainous areas, the annual rainfall might reach or exceed 100 cm (El-Bagouri 2001). MEA is mainly affected by four weather systems: (i) the Siberian anticyclone in winter over central Asia, (ii) the polar anticyclone in summer over east of Europe and Mediterranean sea, (iii) the monsoon cyclones in summer over the India Sub-continent, south and south-east of Iran and southeast of Arabian peninsula, and (iv) the depressions travelling from north of Africa and south and east of Mediterranean sea across the middle-east and south-west of Asia during spring and winter. These low rainfall rates (Babu et al. 2011) coupled with weak advections and frequent recirculation episodes of air masses result in frequent regional pollution episodes that increase aerosols residence time in the region and give rise to high PM levels recorded in summer. A brief summary of the climate of MEA countries is presented in Table 2.
3.
Sources of PM in ambient air
PM consists of organic and inorganic compounds that are released directly from a source (primary) or are formed by chemical reactions in the atmosphere (secondary). In MEA, PM 10 (coarse particles) originate from primary sources, such as Aeolian soil dust and traffic induced dust re-suspension, while PM2.5 (fine particles) and PM1 are mainly the results of secondary sources including reaction of gases in the atmosphere (Heal et al. 2012). As a result, PM are made of a range of chemical compounds which can aid the identification of their sources (Pant and Harrison 2012). The PM pollution comes from a variety of sources including both Page 4 of 25
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anthropogenic activities and natural phenomena. In order to determine the reduction needed in the anthropogenic emissions for health considerations, it is important to differentiate between the natural and anthropogenic contributions. A usual way to determine the natural contribution is via chemical analysis that provides quantitative evidence of the contribution of the natural emissions. Exceedances that are attributed to natural sources can be excluded from the assessment according to Waste Framework Directive (2008/98/EC) on ambient air quality and cleaner air for Europe. Both natural and anthropogenic sources of PM emissions are identified and discussed in the subsequent sections. 3.1
Anthropogenic sources
The anthropogenic PM sources can be broadly divided into: (i) transportation related sources (including road transport such as motorcycles, cars, trucks, air transport, and sea transport), (ii) energy and industry related sources, and (iii) construction and earth work activities. 3.1.1 Road transport Emissions due to road transport are known as a major contributor to PM pollution in many MEA countries due to the increase in the traffic volume during the past years (ESCWA 2010). The traffic related sources of air pollution are drawing increasing concerns for the MEA countries that are highly reliant on personal transport, a fact highlighted by the soaring car ownership rates. For example, the numbers of vehicles per 1000 inhabitants were 434 in Lebanon, 378 in Qatar, 357 in Kuwait, 336 in Saudi Arabia, and 322 in Bahrain. PM emissions from road vehicles include exhaust from tailpipe which contributes predominantly to PM 2.5 and emissions due to wear and tear of vehicle parts such as brake and tire wear, and re-suspension of dust (non-exhaust emissions) that contribute mainly to PM10 (Kam et al. 2012). Local emission inventories are sparse for MEA and there is clearly a need for such detailed emission inventories in order to quantify the accurate contributions of the numerous sources and design mitigation measures to reduce the negative effect of emissions on human health and the environment. A study by Waked and Afif (2012) presented a first temporally and spatially-resolved PM10 and PM2.5 emission inventory for Lebanon and Beirut. The inventory was compiled for the base year 2010 and uses a bottom-up approach according to the European Environment Agency/ European Monitoring and Evaluation Program (EEA/EMEP) guidelines (EMEP/EEA 2013). Road tire and surface wear were found to be responsible for 55% of PM10 emissions. The emissions calculated for Beirut were compared to those obtained for Mediterranean basin cities such as Barcelona and Athens in order to assess patterns between summer and winter for those touristic regions in Western Europe compared to a touristic place in the Middle East region. Comparison of emissions among these cities shows that PM10 are highest in Barcelona. While traffic emissions continue to contribute to primary PM emissions in urban areas, quantitative knowledge of the contribution of non-exhaust emissions remains inadequate in MEA (Waked and Afif 2012) and elsewhere (Pant and Harrison 2013). The key reasons for understanding non-exhaust emissions Page 5 of 25
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include their inherent toxicity and tendency to act as carriers of heavy metals and carcinogenic components (Amato et al. 2011). Therefore, more detailed studies are needed to apportion the exhaust and non-exhaust contributions. Furthermore, improvement in engine technology can make road transport more emission-efficient and encouragement of hybrid vehicles and cleaner fuels, along with developing and promoting public transportation system such as metro and trams, can help reduce exhaust emissions of PM from road vehicles in MEA countries. Despite the fact that MEA is strategically located on the most important global trade route between Western markets and the mushrooming economies of East and South-East Asia, it is hard to locate published studies for the emissions from sea and air transport systems for this area. Countries in MEA – Saudi Arabia, United Arab Emirates, Kuwait, Qatar, Bahrain, and Oman – have become key hubs for global East-West trade. The most important oil traffic lane is Suez Canal, through which 90% of total oil tanker traffic passes. They serve enormous volume of trade in and out of Arabian Gulf free-trade zones, as well as notable amounts of oil, chemicals, and other industrial exports coming out of the large oil-production and industrial areas on East Coast of the Arabian Peninsula. Consequently, the understanding of characteristics of the emissions from all sea and air transport sources is equally important for conducting health effects studies. 3.1.2 Energy and industry A significant amount of PM in MEA is generated from power plants and various industrial processes. Energy intensive economic activities such as desalination, aluminium smelting, cement production, and the high demand for air-conditioning needed in the harsh meteorological conditions result in high-energy consumption. The electric power generation is dominated by thermal power plants, which account for more than 93% of total installed capacity. Some of the largest oil and gas reserves in the world are in this region holding nearly 58% of the world’s oil reserves (OAPEC 2009). Proven reserves in Saudi Arabia are estimated to be 264.6 billion barrels, ranking Saudi Arabia at the first place in the world and they constitute 38.71% of Arab reserves and 22.5% of world’s aggregate reserves. MEA holds nearly 29% of the world’s gas reserves (OAPEC 2010) having the largest gas reserves in the state of Qatar mounting to 25.4 trillion cubic meters, which represents 46.6% of MEA and 13.6% of world reserves, respectively (OAPEC 2010). Furthermore, Qatar is the fourth largest exporter of natural gas in the world and the largest exporter of liquefied natural gas (LNG). A first temporally and spatially-resolved PM10 and PM2.5 emission inventory for Lebanon and Beirut was recently published (Waked et al. 2012). In Lebanon, the large capacity power and industrial plants in the region of Zouk Mikael, Jieh, Chekka and Selaata are some of the major contributors to PM10 and PM2.5 emissions, 62% of PM10 emissions and 59% of PM2.5 emissions are calculated to originate from power plants and industrial sources (Waked et al. 2012). PM pollution from the industrial sector is also recorded in the vicinity of major cement factories along the Lebanese coast (Tolba 2008). Consequently, these findings pose the challenge of Page 6 of 25
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addressing the air quality impacts associated with intensive oil and gas exploitation, processing, reformulation as well as those from expanding energy-intensive industries such as power generation, steel, aluminium, and cement factories. Focused efforts are needed to promote cleaner production in energy and industrial sectors and harmonized environmental standards among the countries in MEA. 3.1.3 Construction and earth works activities Construction and earth work activities are an important factor to PM pollution in developing countries. Many countries in MEA – such as Qatar and UAE – are constantly under construction. In Qatar, for example, US$160 billion will be spent on infrastructure in the next ten years (Pope 2011). Construction activities have been found to increase particle mass concentrations in the close vicinity of building sites. This increase is likely to be for short periods (Dorevitch et al. 2006), but may adversely affect respiratory health of nearby residents. Both coarse and fine PM are released during the construction activities and can escape to the ambient environment during the use of crushers, screeners, cutting and drilling activities at the construction plants due to re-suspension and mechanical attrition between the building materials. The diesel fuel combustion in construction machinery is another source of PM10, PM2.5, PM1 and PM0.1 at construction sites (Kumar et al. 2013), besides the soil excavation which is a major contributor to PM10. Although construction sector is one of the major contributor to PM pollution, there are currently no regulations in place to control PM at building sites (Kumar et al. 2012b). Therefore studies focused on PM pollution from construction activities are needed. 3.2
PM contribution from natural sources
The contribution of anthropogenic sources to air pollution levels have been a key issue in decades for policy and regulation development worldwide. In addition, natural sources also play an important role in determining ambient levels of PM. In the majority of the PM studies in arid areas a far greater contribution from natural sources such as dust storms and forest fires is estimated compared with the anthropogenic emissions (Guenther et al. 1995). In MEA this source type is regarded as the major contributor due to dust storms and its climate (see paragraph 2.2). 3.2.1 Dust storms The arid areas of Kuwait and Iraq are among the largest sources of airborne dust on Earth (Washington et al. 2003). Large scale atmospheric instability, high surface winds, and dry rich dust sources create giant dust storms that increase PM10 concentrations to notable levels. Residence time of the dust particles in the atmosphere range from hours to about 10 days – these are removed from the atmospheric environment by sedimentation and dry or wet deposition (Papayannis et al. 2008). In Saudi Arabia and Kuwait, PM10 concentrations are recorded up to 2500 μg/m3 and 3000 μg/m3, respectively (Draxler et al. 2001; Léon and Legrand 2003). Seasonal variation of dust events in MEA is complex and differs for each country. The Page 7 of 25
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occurrence of dust events increases in March and April, peaks in June and July, and weakens in September (Shao 2008). These dust events can deliver significant amounts of PM via inhalation through mouth, nasal pharynx, and lungs in significant amounts (Gunasekar et al. 2011) and can cause destructive health effects, including asthma, respiratory and cardiovascular diseases. Dust storms are the most frequent phenomenon in MEA (Figure 2a). The airborne dust have found to reduce the visibility to below 11 km in Saudi Arabia and neighbouring countries for more than 30% of the summer months (Kutiel and Furman 2003; Washington et al. 2003). One of the most severe dust storms to be recorded in MEA was on 10–11 March 2009. This storm affected several cities in north-eastern, eastern, and central parts of Saudi Arabia and most of Kuwait, covering a distance of 1500 km and an area of ~300,000 km2. This dust storm was associated with enormous increase in respiratory hospital admissions in the city of Riyadh (Alharbi et al. 2013). Despite the frequent dust storms that Saudi Arabia and some other MEA countries experience, there is not much information available on the associated assessment of health impacts in this region. 3.2.2 Forest fires The forest fires are caused by natural drought, accumulation of dead and highly flammable wood, or by arson producing large smoke plumes charged with fine particles rich in carbon and potassium that spread across land and have severe immediate implications for the health of local population. The wildfires emit hydrocarbon compounds producing secondary particles (Jimenez et al. 2009). Dempsey (2013), confirmed with a high degree of confidence that smoke made a contribution to the PM measured pollutants. The study pointed wildfires in central or western Canada as the sources of the smoke causing increased concentrations of PM 2.5 that were detected by air quality monitoring networks, such as in Ontario. In MEA, the forests have high susceptibility for forest fires, because of the dry climate and the high temperature conditions. As suggested in Swaine (1997), during a warm period a combination of reduced canopy cover leading to a drier and more flammable forest can be the reason for the frequent and devastating forest fires. Together with heat and lack of moisture, wind is another factor causing forest fires. In MEA, summer winds are characterised by high speeds and strong desiccating power. For example, the “Khamsin” in Lebanon and Syria, and the “Sharav” in Israel, cause atmospheric humidity to fall below 30% and contribute to the spread of fires by carrying sparks over great distances. In MEA, approximately 10% of the land is forests and woodlands. Productive lands represent 30% of the total area of Lebanon, and to a minimum of 0.5% in Saudi Arabia and Oman. Lebanon is one of the high risk countries in MEA – there were 705 recorded fires in the country between 2008 and 2009, devastating ~45 km2 of forests which is equivalent to 1.8% of forest cover of Lebanon (MoE-Lebanon 2010).
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4.
Overview of PM studies
PM studies, based on their primary objectives can be classified into the three following distinct classes: field campaigns (experimental), health-assessment, and source apportionment. In this section, a detailed overview of the PM studies carried out in MEA is presented. 4.1
Field campaigns
Monitoring of airborne PM has been an important research area worldwide, mostly because of the acute and chronic respiratory effects on human health (Pope et al. 2002). Monitoring studies are needed to assess population exposure and to assist local authorities in establishing plans for improving air quality. The most commonly used instrument for measuring PM in ambient air is the high-volume sampler, which consists of a pump and a filter. The highvolume sampler is usually operated in a standard shelter to collect samples on filters on a 24-h averaging basis. The samples are weighed to determine concentrations and these are analysed further for determining their chemical composition. The two most commonly used types of filter material to collect suspended particles are fiberglass and Teflon (membrane) filters. Around 10 different field studies took place in MEA since 2004. Most of them are limited in terms of location, duration or sampling points. However, all of them demonstrated high PM values and indicate dust storms augmented by the rapid increase in urban population and transportation to be the key reasons for the high PM concentration levels. Figure 2b shows a map of the monitoring studies carried out in MEA and a summary of their findings is provided in Table 3. These studies also seem to follow the pattern of the modern military conflicts in the region. The first three studies have been conducted just after the first phase of Iraq war (from 2004 to 2005). Four more studies followed during the civil war in Iraq (from 2006 to 2008). Finally, the more recent ones (from 2010 to 2011) took place during the gradual withdrawn of the foreign military forces from Iraq. While some of these studies seem directly related with the war, probably the rest just followed the pattern of conflicts. A more detailed description of these studies is provided in both chronological and relevance order in the subsequent sub-section. The first field campaign identified was conducted in Lebanon, a relatively small developing country which has a high density of residential and commercial premises and a very high population of road traffic; most of the PM studies were carried out for the city of Beirut (Kouyoumdjian and Saliba 2006). Mass and chemical composition of PM10 and PM2.5 between February 2004 and January 2005 was studied as a part of this work. PM10 and PM2.5 samples were collected. Annual mean PM10 and PM2.5 concentrations were noted as 84±27 µg/m3 and 31 ±9 μg/m3, respectively, which is much higher than the corresponding WHO and USEPA standards (Table 1). In another study by (Massoud et al. 2011), ambient PM10 and PM2.5 levels were sampled at three different sites in Beirut between May 2009 and April 2010. 24-h PM10 and PM2.5 samples were collected at each site, on Teflon laminated filters which had a pore size of 0.22 μm. Variations among PM10 concentrations were found to be much higher than those among Page 9 of 25
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PM2.5 values. This was attributed to coarse particles containing re-suspended particles during dry storm periods. Consequently annual average PM 10 and PM2.5 concentrations exceeded WHO standards by ~273 and 100%, respectively (Table 1). PM10 and PM2.5 samplings were also conducted for over a year (2004-2005) at three different sites in Kuwait, a desert country located on the Persian Gulf, with a large petroleum industry and associated industrial and urban land uses (Brown et al. 2008). One of the sites was located at Kuwait University (Khaldiyah Campus) in the middle of Kuwait City. The remaining two sites were located at Kuwait Environmental Public Authority monitoring stations at Um-Al-Aish in northern Kuwait and Um-Al-Haiman in southern Kuwait. PM10 and PM2.5 sample mass on Teflon filters was collected. Particle mass concentrations were determined using gravimetric analysis. The annual mean concentrations for PM 10 were found to range between 66 µg/m3 and 93 µg/m3 across the three sites, exceeding WHO air quality guidelines for PM 10 (Table 1). The annual mean PM2.5 concentrations were found to be 31, 37 and 38 µg/m3 at northern, southern and central sites, respectively. All sites had annual mean PM2.5 concentrations more than twice the USEPA standards, high enough to cause substantial health impacts. Later, replicate samples (10 each) of PM1 from six Governorate areas in Kuwait were collected during the years 20072010 (Bu-Olayan 2011). They chose PM1 for their study, because of their much larger health effects on the respiratory system of residents in Kuwait compared with their larger counterparts. Furthermore, PM1 is often neglected as a result of inadequate infrastructural facilities and protracted time required for air pollution studies in Kuwait. PM 1 was impacted through a 47Ømm membrane filter of EPA-FRM (Federal Reference Method) style. The particles collected on the filter were weighed and analyzed by gravimetric analysis. The study found high trace metals levels in PM1 dust in summer than in winter, due to prevalent dust storms and high atmospheric temperature. One major field campaign was carried out in 2004-2005 that involved 20 sites in Tehran city, Iran (Nabi and Halek 2007) – a city with a high rate of population growth and rural-urban migration combined with a strong tradition of centralization in the capital. PM was collected on a 47 mm fiberglass filter that had pore sizes below 0.45μm and PM concentrations were determined by gravimetric analysis. The levels of total suspended particulate matter (TSP) concentrations across the 20 sites varied from a minimum of 62 μg/m3 at Haram-Emam site (which is a Tehran suburban area, not heavily inhabited and impacted by traffic) to a maximum of 1915 μg/m3 and 1127 μg/m3 at Shoosh site (crowded district in the south of Tehran) and Shahre-Rey site, respectively. Average 24-h PM10 concentrations over the period of study at three sites in the Tehran city were found to be ~147 μg/m3 (Bazar site), ~73 μg/m3 (Fatemi site) and ~70 μg/m3 (Aghdesieh site). Annual average PM10 at each site exceeded USEPA Ambient Air Quality Standard of 50 μg/m3, while even the lowest value (~104 μg/m3) was ~2-fold higher than the standard. Other two sampling campaigns were conducted some years later in Ahvaz, south-western Iran – a city that is subjected to severe dust storms due to its proximity to the Page 10 of 25
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major Middle East dust storms areas of Iraq, Kuwait, and Saudi Arabia. In the first study, TSP samples were collected on fiberglass filters using a high volume air sampler over the period from April 2010 to March 2011 (Sowlat et al. 2012). TSP concentrations were calculated by gravimetric method and the overall mean value was found to be about 1290 μg/m3. In the second study, PM10, PM2.5 and PM1 concentrations were measured using a Grimm model 1.177 aerosol spectrometer during the dust events in Ahvaz from April to September 2010. Overall, average 24-h values of ~320 µg/m3, ~70 µg/m3, and ~37 µg/m3 were obtained for PM10, PM2.5 and PM1, respectively, with corresponding maximum values of 5338 µg/m3, 910 µg/m3 and 495 μg/m3 (Shahsavani et al. 2012). The temporal behavior of airborne pollutants from natural and anthropogenic sources was studied in the work of Al-Katheeri et al. (2012). They studied the data collected from the ambient air quality station located in the vicinity of Al-Mirfa power plant in Abu Dhabi during 20072009. The aim of their work was to determine the levels of PM10 and other pollutants and assessing their levels against the permissible limits given by UAE Federal Environment Agency (FEA). 24-h mean PM10 concentrations violated UAE FEA standard limit on 235 occasions. These exceedances were mainly attributed to: (i) natural phenomenon that originated as a result of dust storms due to the typical nature of the local desert, and (ii) trans-boundary transport from southern Iraq desert that has shown to affect PM levels in almost entire Arabian Gulf. Control strategies to reduce PM2.5 mass, and other specific components such as organic carbon (OC) and elemental carbon (EC) was discussed In the work of Sarnat et al. (2010). PM2.5 samples were collected on 47 mm filters (2 Teflon, 1 quartz fiber) using a multi-channel air sampler (URG-3000ABC) at eleven sites in Israeli, Jordanian and Palestinian cities between January and December 2007. Annual mean PM2.5 concentrations ranged from 20 to 35 μg/m3 in Haifa and Amman respectively, and exceeded both WHO and USEPA standards (Table 1). Mean PM2.5 concentrations in Tel Aviv and Amman were found to be the largest among the locations examined, with up to 3-fold higher than both WHO and USEPA standards (Table 1). The broadest study in MEA focused on the chemical and physical properties of collected PM over a period of approximately one year (2006-2007) in Qatar, UAE, Iraq (Balad, Baghdad, Tallil, Tikrit, Taji, Al Asad), and Kuwait (northern, central, coastal, and southern regions) (Engelbrecht et al. 2009). Under the Enhanced Particulate Matter Surveillance Program (EPMSP), three sizes of dust samples (TSP, PM10, and PM2.5) were collected using co-located, low-volume (5 L/min; Airmetrics MiniVol) particulate samplers on Teflon filters. According to Engelbrecht et al. (2008), the largest annual mean concentrations of TSP (605 μg/m3) and PM2.5 (114 μg/m3) were found at Tikrit site, and the largest annual mean PM10 (303 μg/m3) was found at Tallil site – all in Iraq (Figure 3). Annual mean PM10 and PM2.5 values at all sites exceeded WHO guidelines (Table 1). Furthermore, 24-h USEPA standard for PM10 (150 μg/m3) was exceeded at 10 out of 15 sites and for PM2.5 (35 μg/m3) at all sites. While compositions of PM Page 11 of 25
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from different regions vary, this study showed that PM concentrations from MEA are as much as 10-times greater than those at typical urban and rural south-eastern U.S air monitoring sites. According to the review of the Department of Defense Enhanced Particulate Matter Surveillance Program Report (NAS 2010), the data presented by Engelbrecht et al. (2008) constitute compelling evidence that a large-scale assessment of the air-pollution exposures of military personnel and associated health risks is feasible and needed. The data that were collected are not adequate for performing a reliable health-effects research study, but is an effort to a preliminary understanding of the composition of particles in the setting in question, which can help to guide the design of future surveillance and research programs. To assess health responses, it will be necessary to collect more data to increase statistical power, to account for effects of other exposures, and to improve data quality. The latest field study in MEA is reported by Khodeir et al. (2012) for the periods between June and September 2011. The sampling sites were selected according to traffic densities and human activities. Three of the examined sites were situated in urban areas, one in a sub-urban area, two in residential areas, and the remaining one in a newly developed residential area in Saudi Arabia. Automated Cartridge Collector Unit samplers for PM10 and PM2.5 were installed at these seven different sites throughout Jeddah. On a daily basis, 24-h average samples were collected on 37 mm Teflon filters that have 0.2 μm pore–size. 24-h mean PM10 and PM2.5 concentration was found as ~87 μg/m3 and ~28 μg/m3, respectively. Both these PM types exceeded 24-h average WHO standards, and ~87 and 100% of PM10 and PM2.5 samples, respectively, exceeded 24-h average EU limits (Table 1). When compared with 24-h average USEPA standards, these exceedances reduced to only 10 and 18% of PM 10 and PM2.5 samples, respectively (Table 1). These results clearly indicate that the levels of airborne PM concentrations have been generally high in Jeddah and the numbers of exceedances can vary depending on with which standards are compared. 4.2
Health assessment
Much stronger adverse health effects are assigned to PM compared with other air pollutants (Dominici et al. 2006; Kappos et al. 2004; Pope et al. 2002; Vedal et al. 2003). PM10 and PM2.5 include inhalable particles that are small enough to penetrate the thoracic region of the respiratory system. A recent study estimates that in 2010, ambient air pollution, as annual PM2.5, accounted for 3.1 million deaths and around 3.1% of global disability-adjusted life years (Lim et al. 2012). The evidences of the adverse health effects from the exposure to ambient PM are derived principally from toxicology and epidemiology studies. There are numerous studies that have linked PM2.5 exposure with adverse health effects (Dockery et al. 1993; Pope and Dockery 2006). In 2000, the average loss of life expectancy due to PM2.5 in Europe was estimated at 8.6 months, varying from around 3 months in Finland to 12-36 months in Benelux, Silesia and the Po Valley (WHO 2006). A recent review by Kumar et al. (2014) discussed the concentration and exposure levels of ultrafine particles (those below 100 nm in diameter) across several cities Page 12 of 25
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worldwide. They highlighted the hypotheses suggesting that ultrafine particles have a greater potential for health impacts than PM with larger sizes, and the fact that studies of airborne ultrafine particles in MEA are nearly non-existent. Despite the high PM concentration levels recorded in MEA only a limited number of epidemiological and toxicological studies are available. More studies are needed to cover this region to accurate estimate mortality and morbidity and to design appropriate mitigation measures. In this section a review of the epidemiological and toxicological studies associated with PM in MEA is provided and the need of a greater number of studies can be identified. Two studies (Abal et al. 2010; Al-Dawood 2000) reported that many countries in MEA that impacted by desert dust storm such as Kuwait and Saudi Arabia have a higher prevalence of asthma, particularly in pediatric population, compared with European countries. Further, a total of 8157 and 1131 morbidity and mortality cases were attributed to high PM 10 and PM2.5 concentrations, respectively, during dust events in Ahvaz (Iran) indicating once more the importance of dust events and their adverse health impacts (Shahsavani et al. 2012). In a recent study by Thalib and Al-Taiar (2012) the impact of dust storms on hospital admissions, due to asthma and all respiratory diseases was investigated and a total of 856,107 emergency hospital admissions occurred over the five-year study period. Out of this total, respiratory and asthma emergency admissions were observed during 10.3% (85, 6107 days) and 1.8% (15,256 days), respectively. In the work of Braunstein and Goren (2000) the possible effects of PM10 on mortality in Tel Aviv, Israel was studied. A significant response of PM10, attributing 70 out of a total of 3,697 deaths per year from cardiovascular disease in Tel Aviv was found. The association of PM10 with health effects was also studied by Sun et al. (2012). In this work the global gene expression changes in human cells exposed to PM10 was investigated and the pathways that may contribute to PM10 related adverse health effects in terms of respiratory and cardiovascular disease were intensified. Human bronchial epithelial cells were exposed to samples of PM10 collected from Jeddah, Saudi Arabia. This study is the first to report that PM 10 exposure can modulate genes related to cholesterol and lipid metabolism, providing a new insight into the mechanisms underlying PM induced cardiovascular disease. It is worth noting that studies on PM1 are rarely available, despite their much larger potential to adversely affect human health compared with PM10 and PM2.5 (Colls and Tiwary 2010; Heal et al. 2012), and the case for ultrafine particles is even worse (Kumar et al. 2010; Kumar et al. 2011c). 4.3
Source apportionment
Source apportionment techniques assist the identification of the major sources and can be used in the development of best possible control practices in order to ensure sufficient reduction of pollution from individual sources. There is a wide range of published literature on source apportionment studies using dispersion models and monitoring data (Colvile 2003; Laupsa H. 2009; Pant and Harrison 2012). A review of source apportionment studies and techniques used in MEA to quantify the source contributions is provided in this section and the need of a greater number of in-depth studies to fully understand the source contributions throughout MEA which Page 13 of 25
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could assist in identifying and targeting specific emission sources to cut emissions of a particular PM type can be identified. Most of the following MEA studies have been conducted using receptor models. Receptor models form a subset of source apportionment techniques and apportion pollutant concentration based on measured ambient air data and knowledge about composition of contributing sources (Henry et al. 1984). The two broad categories of receptor models are: (i) the microscopic methods, and (ii) the chemical methods. Microscopic methods, which include optical, scanning electron microscopy (SEM) and automated SEM analyses, are based on analysis of morphological features of many individual particles in ambient air (Cooper and Watson 1980). The chemical methods include: Chemical Mass Balance (CMB) model, statistical models such as the Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF), multi linear engine (ME), enrichment factor analysis, time series analysis, species series analysis, and UNMIX model (Cooper and Watson 1980; Henry et al. 1984). These methods use trace elements, elemental/organic carbon, and organic molecular markers for identification of the sources. Source apportionment analysis using PMF was conducted by Massoud et al. (2011). PM10 and PM2.5 samples were collected from three different sites in the urban city of Beirut, in Lebanon, between May 2009 and April 2010. The results showed similar factors for all three sites; only their contributions to total PM mass varied on each site. In case of PM 10, sources like (i) markers of tires and brakes, (ii) dust re-suspension, and (iii) biogenic crustal emissions were found to be dominating contributors at all three sites. In case of PM2.5, secondary aerosols, determined by high concentration of nitrated and sulphated compounds, dominated other sources. The PMF receptor model was also used in a recent study by Engelbrecht and Jayanty (2013), to model the chemical signatures for sources of (TSP, PM10, PM2.5). Five source types were modelled; four of which were assigned to components of geological dust, and the fifth to gasoline vehicle emissions and battery smelting operations. PMF modelled factor contributions are similar for TSP and PM10, but both vary from PM2.5. Average mass ratios for TSP:PM10:PM2.5 at each of the six monitoring sites (Tikrit, Balad, Taji, Baghdad, Tallil and Al Asad) in Iraq over the 2006 sampling year were found to be 1.55:1:0.34, showing a clear evidence of high proportions of coarse fugitive dust in the samples. Likewise, in a study by Sowlat et al. (2012) the PMF receptor model was used to apportion the possible source of TSP and the relative contributions in Ahvaz (south-western Iran) after applying to the data collected between April 2010 and March 2010. Seven factors were resolved by the model: crustal dust (56%), road dust (7%), motor vehicles (8%), marine aerosol (9%), secondary aerosol (7%), metallurgical plants (4.5%), and finally petrochemical plants and fossil fuel combustion (8.5%). In a study of Sowlat et al. (2013) the potential sources of PM10 in an arid area of Ahvaz, located in southwest of Iran, were identified using PMF analysis: crystal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosols (8%), secondary aerosol (9.5%), metallurgical plants (6%), petrochemical industries and fossil fuel combustion (13%), and vegetative burning (5%). The results of this study suggest that Page 14 of 25
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natural sources contribute most to PM10 particles in the area, closely followed by anthropogenic sources. A most recent study by Alolayan et al. (2013) investigated the major sources of PM2.5 in the atmosphere of Kuwait, based on a sampling program conducted between February and October 2005. The following three source identification techniques were used in this study: PMF, backward trajectory profiles, and concentration rose plots. Five major sources of PM2.5 and their contributions were estimated: (i) sand dust from storms (54%), (ii) oil combustion in power plants (18%), (iii) petrochemical industry such as fertilizer, nylon and catalyst regeneration facilities (12%), (iv) road traffic emissions and road dust re-suspension (11%), and (v) transported emissions from outside Kuwait, such as those coming from automobiles, road dust or smelters (5%). The relatively high levels of PM2.5 were contributed by anthropogenic local sources (e.g., oil combustion, petrochemical industry emissions and traffic indicated), indicating an opportunity for Kuwait policy makers to target them for reducing PM 2.5 emissions. A CMB model using measurements of PM2.5 that were taken at a total of 11 sites in Israel, Jordan and Palestine was applied by von Schneidemesser et al. (2010). Biomass burning and vehicular emissions were found to be the most important sources of carbonaceous PM2.5 in this region. In another study by Khodeir et al. (2012) chemical composition data were modelled using factor analysis with Varimax orthogonal rotation in a multi-week, multiple sites sampling campaign that was conducted in Jeddah between June and September 2011. This was the first comprehensive investigation of PM10 and PM2.5 sources in Saudi Arabia with the aim to determine significant particle source categories. The key sources identified for both PM2.5 and PM10 were: (i) heavy oil combustion, (ii) re-suspended soil, and (iii) a mixed industrial source. The two other sources for PM2.5 were: (iv) road traffic, and (v) other industrial source mixture. To estimate mass contributions of each individual source category, PM mass concentration was regressed against factor scores. Re-suspended soil and oil combustion contributed ~77% and 82% of total PM2.5 and PM10 mass, respectively. Findings of above studied allowed to conclude that oil combustion, re-suspended soil, traffic, crustal dust and marine aerosol are key contributing factors for PM10 whilst oil combustion in power plants, re-suspended soil, sand dust, and road traffic for PM2.5 in MEA.
5.
Summary, conclusions and future directions
Development in MEA over the past years has led to an increase in PM pollution strongly associated with adverse health effects. The reduction of PM pollution is not a simple undertaking because a number of different natural and anthropogenic sources such as dust storms, transportation, energy, industry, and construction make their contributions to the atmospheric levels of PM. Most of the anthropogenic sources are the aftermath effect of rapidly growing urban population and concentrated industrial sites. In Middle East, the most significant Page 15 of 25
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contributor to PM pollution is dust storms, increasing the PM levels to several times higher than the specified guidelines. Their effects can be seen in terms of suspended dust in the ambient environment for many days after their passing an individual location. The findings of the reviewed monitoring studies indicate that 24-h, annual and peak PM concentrations vary significantly throughout the countries. However, a common observation is that the levels of PM concentrations across MEA violate the limits prescribed by their location regulatory authorities, or international bodies such as USEPA or WHO during the majority of non-episode days. Countries with the highest levels of PM pollution are Kuwait and Iran because these countries are subjected to most frequent dust storms, followed by the rapid rates of urbanization and building construction activities. As expected, severe dust storms are the most probable cause to enhance the levels of PM10 concentration. In addition, the epidemiological and toxicological studies have shown that dust storms events have a great impact on respiratory admissions and that the adverse health effects are generally in form of asthma, respiratory and cardiovascular diseases. The reviewed source apportionment studies indicate that the key contributing factors for PM10 pollution in MEA are: oil combustion, re-suspended soil, road traffic, crustal dust, and marine aerosol. Significant sources of PM2.5 are identified as oil combustion in power plants, re-suspended soil, sand dust, and road traffic. A need for greater number of in-depth studies to fully understand source contributions throughout MEA was recognised. In conclusion, this review highlights a need for further attention to assess PM pollution in MEA and suggests a number of grey areas to be filled by future research. There are still a handful of studies on PM10 and PM2.5 for MEA and the case for PM1 is even less encouraging with no studies currently available to discuss their atmospheric levels. Further, it was clearly noticed from this review that release of airborne PM from major building activities such as construction, recycling and refurbishment is largely unknown. Consequently, there is a need to determine emission rates of dust particles from a range of building activities in order to develop emission inventories for different situations. Detailed emissions inventories would be valuable for managing PM emission load in MEA. Further, more epidemiological and toxicological studies are needed in order to develop localised exposure-response functions for MEA to assess mortality and morbidity effects. A more systematic PM data collection would enable air pollution related health impact assessments. Future research should emphasize the use of standard measuring methods in order to overcome technical hurdles in terms of comparison and generalisation of data collected. Finally, building an air quality information system for publishing and sharing of monitoring data across governmental, academic and other institutions would greatly facilitate the characterization of PM problem, as well as planning and implementation of future studies.
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Acknowledgements This publication was made possible by a NPRP award [NPRP 7 - 649 - 2 - 241] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. References Abal AT, Ayed A, Nair PC, Mosawi M, Behbehani N (2010) Factors responsible for asthma and rhinitis among Kuwaiti schoolchildren. Med Princ Pract 19:295-298 Abdel-Moati MA (2008) Efforts to protect Qatar Environment, Supreme Council for the Environment and Natural Reserves. Paper presented at the Human Health & Environemtnal Challenges, Doha, Akbari S (2011) Dust storms, sources in the Middle East and economic model for survey it s impacts. Aust J Basic Appl Sci 5:227-233 Al-Dawood K (2000) Epidemiology of bronchial asthma among schoolboys in Al-Khobar city, Saudi Arabia: cross-sectional study. Croat Med J 41:437-441 Al-Katheeri E, Al-Jallad F, Al-Omar M (2012) Assessment of Gaseous and Particulate Pollutants in the Ambient Air in Al Mirfa City, United Arab Emirates. J Environ Prot 3 (7):640-647 doi:10.4236/jep.2012.37077 Al-Zubi A (2011) Evaluation of the Jordanian Environmental Legislations. World Applied Sciences Journal 14:1438-1444 Alharbi BH, Maghrabi A, Tapper N (2013) The March 2009 Dust Event in Saudi Arabia: Precursor and Supportive Environment. Bull Am Meteorol Soc 94:515-528 doi:10.1175/bams-d-11-00118.1 Alolayan MA, Brown KW, Evans JS, Bouhamra WS, Koutrakis P (2013) Source apportionment of fine particles in Kuwait City. Sci Total Environ 448:14-25 Amato F et al. (2011) Sources and variability of inhalable road dust particles in three European cities. Atmos Environ 45:6777-6787 doi:10.1016/j.atmosenv.2011.06.003 Babu CA, Samah AA, H. V (2011) Rainfall climatology over Middle East region and its variability. International Journal of Water Resources & Arid Environments 1:180-192 Braunstein R, Goren A (2000) Changes With Time in Air Pollution Levels in Tel-Aviv and Their Effects on Mortality. Epidemiology 11:S126 Brook RD et al. (2010) Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the american heart association. Circulation 121:2331-2378 Brown KW, Bouhamra W, Lamoureux DP, Evans JS, Koutrakis P (2008) Characterization of particulate matter for three sites in Kuwait. J Air Waste Manag Assoc 58:994-1003 Bu-Olayan AH, Thomas, B.V (2011) Monitoring the dust deposition rate and trace metals levels in PM 1.0 from industrial areas of Kuwait. Arch Environ Sci 5:11-16 Chen LC, Lippmann M (2009) Effects of metals within ambient air particulate matter (PM) on human health. Inhalation Toxicol 21:1-31 Colls J, Tiwary A (2010) Air pollution Measurement, Modelling and Mitigation. CRC Press; 3 edition (19 Aug 2009), Colvile RN, Gómez-Perales, J. E., Nieuwenhuijsen, M. J. (2003) Use of dispersion modelling to assess road-user exposure to PM2.5 and its source apportionment. Atmos Environ 37:2773-2782 doi:10.1016/S1352-2310(03)00217-6 Cooper JA, Watson JG (1980) Receptor Oriented Methods of Air Particulate Source Apportionment. J Air Pollut Control Assoc 30:1116-1125 doi:10.1080/00022470.1980.10465157 Dempsey F (2013) Forest Fire Effects on Air Quality in Ontario: Evaluation of Several Recent Examples. Bull Am Meteorol Soc 94:1059-1064 doi:10.1175/bams-d-11-00202.1 Page 17 of 25
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Dockery DW et al. (1993) An Association between Air Pollution and Mortality in Six U.S. Cities. N Engl J Med 329:1753-1759 doi:10.1056/NEJM199312093292401 Dockery DW, Pope CA, 3rd (1994) Acute respiratory effects of particulate air pollution. Annu Rev Public Health 15:107-132 Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL, Samet JM (2006) FIne particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295:11271134 doi:10.1001/jama.295.10.1127 Dorevitch S, Demirtas H, Perksy VW, Erdal S, Conroy L, Schoonover T, Scheff PA (2006) Demolition of high-rise public housing increases particulate matter air pollution in communities of high-risk asthmatics. J Air Waste Manag Assoc 56:1022-1032 Draxler RR, Gillette DA, Kirkpatrick JS, Heller J (2001) Estimating PM10 air concentrations from dust storms in Iraq, Kuwait and Saudi Arabia. Atmos Environ 35:4315-4330 doi:10.1016/S13522310(01)00159-5 EAD (2014) The National Ambient Air Quality Standards for Environment Agency Abu Dhabi (EAD). Available at: https://www.adairquality.ae/en/home/theme.aspx?ThemeID=bc1b661a-ba6e4ef9-866a-639bb1e5bfde. Accessed May 2014 EC (2010) European Commission, Air Quality Standards. Available at: http://ec.europa.eu/environment/air/quality/standards.htm. Accessed May 2014 El-Bagouri IH Marginal lands of the Arab World - Constraints and Potentials. In: Regional Workshop on Degradation and Rehabilitation of Marginal Lands in the Arab Region, Cairo, Egypt, 2-4 July 2001. ELARD (2009) Environmental and Social Impact Assessment of construction and operation of Syria cement and captive power plant and associated quarrying activities, Syria. Earth Link and Advanced Resources Development S.A.R.L. (ELARD). submitted to Syrian Cement Company. EMEP/EEA (2013) Air Pollution Emission Inventory Guidebook. Technical report No 12/2013. Engelbrecht JP, McDonald EV, Gillies JA, A.W. G (2008) Enhanced Particulate Matter Surveillance Program Desert Research Institute, W9124R-05-C-0135/SUBCLIN 000101-ACRNAB Engelbrecht JP, McDonald EV, Gillies JA, Jayanty RKM, Casuccio G, Gertler AW (2009) Characterizing Mineral Dusts and Other Aerosols from the Middle East—Part 1: Ambient Sampling. Inhalation Toxicol 21:297-326 doi:doi:10.1080/08958370802464273 Engelbrecht JP, Jayanty RKM (2013) Assessing sources of airborne mineral dust and other aerosols, in Iraq. Aeolian Res 9:153-160 EPA (2010) National Ambient Air Quality Standards (NAAQS). Available at: http://www.epa.gov/air/criteria.html. Accessed May 2014 ESCWA (2010) Transport for sustainable development for the Arab Region:Measures, Progress achieved Challence and Policy Framework Report. Gibson J, Thomsen J, Launay F, Harder E, DeFelice N (2013) Deaths and Medical Visits Attributable to Environmental Pollution in the United Arab Emirates. PLoS ONE 8 doi:10.1371/journal.pone.0057536 Government of Israel (1992) Abatement of Nuisances Regulations (Air Quality), 5752. Guenther A et al. (1995) A global model of natural volatile organic compound emissions. Journal of Geophysical Research: Atmospheres 100:8873-8892 doi:10.1029/94jd02950 Gunasekar P, Stanek G, Lindsay W (2011) Advances in exposure and toxicity assessment of particulate matter: An overview of presentations at the 2009 Toxicology and Risk Assessment Conference. Toxicol Appl Pharmacol 254:141-144 doi:10.1016/j.taap.2010.10.020 Hansen D, Blahout B, Benner D, Popp W (2008) Environmental sampling of particulate matter and fungal spores during demolition of a building on a hospital area. J Hosp Infect 70:259-264
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LIST OF FIGURES Figure 1: (a) Schematic of MEA examined in this review and (b) aridity zones all over the world (WRI 2012). Figure 2: (a) A typical example of dust storm in Saudi Arabia (source: http://photo.accuweather.com/, Riyadh; October 2009), and (b) locations of the field campaigns in MEA. Figure 3. Three-dimensional bubble map showing annual mean mass concentrations for TSP, PM10 and PM2.5 in various countries in MEA. Please note that the size of the bubble correspond to the TSP concentrations, which are also marked in parenthesis (in µg/m3) against each city or country.
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LIST OF TABLES: Table 1: Particulate Matter (PM) Standards PM Type and averaging time PM10 (μg/m3) PM2.5 (μg/m3) 24-h annual 24-h annual 150 50 35 15 50 40 25 50 20 25 10
Standards USEPA (EPA 2010) EU (EC 2010) WHO (WHO 2006; WHO 2011) Israel (Government of Israel 1992) Jordan (Al-Zubi 2011) Kuwait (IES 2011) Lebanon (LEDO 2001) Oman (HMR 2010) Qatar (Abdel-Moati 2008) Saudi Arabia (PME 2012) Syria (ELARD 2009) UAE (EAD 2014)
150
60
-
-
120 150 80 150 150 (1-h) 340 100 150
70 90 50 (3 months) 80 -
35 35 35 -
15 15 15 -
Table 2: Climate of MEA countries MEA Countries
Climate
Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria UAE Yemen
Arid; very hot, humid summers; mild winters Hot desert, generally dry climate; hot summers; mild winters (November to April) Mostly arid or semiarid; subtropical along Caspian coast Hot, dry climate; long, hot, dry summers; short, cool winters Temperate; hot and dry in southern and eastern desert areas Mostly arid desert; rainy season in west (November to April) Dry desert; intensely hot summers; short and cool winters Mediterranean; hot, dry summers; mild to cool, wet winters; in mountains heavy winter snows Hot, arid climate; long and very hot summers; warm winters; precipitation is scarce Temperate, Mediterranean climate; rainy season (November to April) Hot desert climate; long summer (May to September); scarce rainfall; warm winters Dry desert with great temperature extremes Mediterranean influenced climate; long, hot and mostly dry summers; mild, wet winters Subtropical dry, hot desert climate; low annual rainfall Subtropical dry, hot desert climate; low annual rainfall; very high temperatures in summer
Table 3: Summary of the field campaigns studies in MEA PM types
PM concentrations (μg/m3)
2004 - 2005
PM10 PM2.5
02/2004 - 01/2005
PM10
Annual mean: PM10: 66-93 PM2.5: 31-38 Annual mean:
Sampling Period
MEA country
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Author (year)
Kuwait
Brown et al. (2008)
Lebanon
Kouyoumdjian and Saliba
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PM2.5 01/2004 - 01/2005
TSP PM10
2006 - 2007
TSP, PM10, PM2.5
01/2007 - 12/2007
PM2.5
2007 - 2009
PM10
PM10: 84±27 PM2.5: 31 ±9 62 to 1915 Average 24-h PM10: 70.16-147.40 Lower annual Average PM10: 104.22 Annual mean Highest levels: TSP at Tikrit : 605; PM10 at Tallil: 303 PM2.5 at Tikrit: 114 Annual mean 19.9 (in Haifa) 34.9 (in Amman) 24-h average: 4-1426
2007 - 2010
PM1
High concentrations
Kuwait
Overall average 24-h: ~320 ±407, max:~5338 ~70 ±83, max:~911 ~37 ±35, max:495 overall mean: ~1290±1440
Iran
Shahsavani et al. (2012)
PM10 PM2.5 PM1 TSP
Iran
Sowlat et al. (2012)
The 24-h overall mean: PM10: 87.3±47.3 PM2.5: 28.4±25.4
Saudi Arabia
Khodeir et al. (2012)
04/2010 - 09/2010
04/2010 - 03/2011 06/2011 - 09/2011
PM10 PM2.5
(2006) Iran
Qatar, UAE, Iraq, Kuwait
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Israel, Jordan, Palestine UAE
Nabi and Halek (2007)
Engelbrecht (2008, 2009, 2013)
Sarnat et al. (2010); von Schneidemesser et al. (2010); www.sviva.gov.il Al-Katheeri et al. (2012) Bu-Olayan (2011)
Figure 1 Click here to download high resolution image
Figure 2a Click here to download high resolution image
Figure 2b Click here to download high resolution image
Figure 3 Click here to download high resolution image
