AIR POLLUTION – MONITORING MODELLING AND HEALTH

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338 Air Pollution – Monitoring, Modelling and Health in different parts of the world, perhaps because of the difference in spatial and temporal variability of air pollutants sources and composition between different regions (Kim et al. 2005; Kim et al. 2008; Levy et al. 2010 ), and also, it still remains unclear whether the association of the pollutants with increased asthma prevalence in children is independent of each other or is attributable to other toxic air pollutants, such as combustion-related organic compounds that are not routinely monitored (Delfino et al. 2006). The purpose of this chapter is to review/summarize the evidence regarding the link between air pollution and children’s asthma and asthma related symptoms. We would like to discuss gender difference for air pollution effects, as well as some other health effect susceptibilities, such as age, genetic predisposition, and disease susceptibility. Finally, the potential health effects from preventive strategies for air pollution would be also proposed. 2. Air pollution and asthma in children 2.1 Particulate Matter (PM) Numerous studies worldwide have reported the associations between particulate matter (PM) and asthma outcomes (Berhane et al. 2011; McConnell et al. 2010; Özkaynak et al. 1987; Pope 1991; Samoli et al. 2011; Spira-Cohen et al. 2011), and most of those studies focused on fine particulate matter < 2.5 μm in average aerodynamic diameter (PM 2.5 ) and thoracic particulate matter < 10 μm in average aerodynamic diameter (PM 10 ) that dominate concentrations of fine particles in most urban areas. The health effects of the different particle sizes may differ between various areas as a result of variation in particle origin, chemical composition and biogenic content. The distinction of health effects between the fine and coarse fractions is important to define because these particles may arise from different sources (Kim et al. 2005; Kim et al. 2008; Levy et al. 2010). The coarse fraction of relatively large inhalable particles, ranging from 2.5 to 10 µm in average aerodynamic diameter (PM 10-2.5 ), generally consists of particles which are mainly derived from reentrained road dust (containing soil particles, engine oil, metals, tire particles, sulfates and nitrates), construction and wind-blown dusts (mostly soil particles), and mechanical wear processes. The chemical signatures of PM 2.5 contains a mixture of particles including carbonaceous material like soot (with possibly adsorbed reactive metals and organic compounds), and secondary aerosols like acid condensates, and sulphate and nitrate particles, and is derived direct or indirect primarily from combustion of fossil fuels used in power generation, and industry, and automobile, and ultrafine particles produced by traffic, coal combustion, and metal, oil, and chemical manufacturing. This fine-mode faction has the largest surface area and contributes also to the total particle mass concentration (Bree and Cassee 2000; Özkaynak et al. 1987; Pope 1991). Based on the previous epidemiologic studies, it was believed that PM 2.5 fraction of PM 10 had stronger effects than the PM 10-2.5 fraction (Dreher 1996; Schwartz and Neas 2000; Tzivian 2011). However, recent analyses are somewhat more ambiguous, and inconsistent results have been reported on the association between the coarse particle fraction and asthma outcomes. Several studies compared the effects of PM 10–2.5 , PM 2.5 , and PM 10 on hospitalizations of children for asthma (Dreher et al. 1996; Lin et al. 2002; Schwartz and Neas 2000; Strickland et al. 2010). A study conducted in 41 Metropolitan Atlanta hospitals indicated that the emergency department visits for asthma or wheeze among children was significantly associated only with PM 2.5 in cold season, however, only with PM 10 and PM 10-2.5 in warm season (Strickland
Air Pollution and Health Effects in Children 339 et al. 2010). Lin et al. (2002) found an effect for PM 10–2.5 but not for PM 2.5 and PM 10 after adjustment for daily weather conditions. The study in Toronto also found the PM 10–2.5 fraction to be a better predictor of asthma admissions than the PM 2.5 and PM 10 fractions in subjects of all ages. The estimated relative risks for PM 10–2.5 , PM 2.5 , and PM 10 corresponding to an increase of 10 μg/m 3 with a 3-day averaging were 1.04, 1.01, and 1.01, respectively (Burnett et al. 1999). Whereas, Tecer et al. (2008) found that PM 2.5 had larger effects than did PM 10–2.5 , despite that both were significantly associated with hospitalizations for asthma after adjustment for daily weather conditions. Also, a recent study found strong adverse effects of exposure to PM 10 on emergency hospital admissions for asthma-related diagnosis among children, although analyses for PM 10–2.5 and PM 2.5 were not evaluated (Samoli et al. 2011). Besides the emergency hospital admissions for asthma mentioned above, the health effects of the different particle sizes on other asthma outcomes are also inconsistent. In the FACES cohort study of asthmatic children conducted in California of USA, wheeze was associated significantly with PM 10-2.5 but not with PM 2.5 (Mann et al. 2010). However, the results from France study conflict in this regard, no association between wheeze episodes or prevalence and PM
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AIR POLLUTION - MONITORING, MODELLI
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Contents Preface IX Chapter 1 Urban
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Preface This book provides a compre
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AIR POLLUTION – MONITORING MODELLING AND HEALTH

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