Personal exposure of children to air pollution

Personal exposure of children to air pollution

Atmospheric Environment 43 (2009) 128–141 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locat...

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Atmospheric Environment 43 (2009) 128–141

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Personal exposure of children to air pollution M.R. Ashmore a, *, C. Dimitroulopoulou b,1 a b

Environment Department, University of York, York YO10 5DD, UK BRE Environment, Building Research Establishment Ltd, UK

a b s t r a c t Keywords: Personal exposure Air pollution Children Indoor air quality

Changes over recent decades in outdoor concentrations of air pollutants are well documented. However, the impacts of air pollution on an individual’s health actually relate not to these outdoor concentrations but to their personal exposure in the different locations in which they spend time. Assessing how personal exposures differ from outdoor concentrations, and how they have changed over recent decades, is challenging. This review focuses on the exposure of children, since they are a particularly sensitive group. Much of children’s time is spent indoors, and childhood exposure is closely related to concentrations in the home, at school, and in transport. For this reason, children’s personal exposures to air pollutants differ significantly from both those of adults and from outdoor concentrations. They depend on a range of factors, including urbanisation, energy use, building design, travel patterns, and activity profiles; analysis of these factors can identify a wider range of policy measures to reduce children’s exposure than direct emission control. There is a very large variation in personal exposure between individual children, caused by differences in building design, indoor and outdoor sources, and activity patterns. Identifying groups of children with high personal exposure, and their underlying causes, is particularly important in regions of the world where emissions are increasing, but there are limited resources for environmental and health protection. Although the science of personal exposure assessment, with the associated measurement and modelling techniques, has developed to maturity in North America and western Europe over the last 50 years, there is an urgent need to apply this science in other parts of the world where the effects of air pollution are now much more serious. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Human exposure was defined by Ott (1982) as ‘the event when a person comes into contact with a pollutant of a certain concentration during a certain period of time’. This means that exposure requires both the pollutant and the person to be present. Most of the epidemiological evidence of the health effects of relatively low concentrations of air pollutants, and in particular of fine particles, uses outdoor concentrations as a surrogate for human exposure. However, most people in North America and Europe spend 80–90% of their time indoors, where exposure to major air pollutants is quite different from that outdoors. Therefore, understanding how indoor and personal exposure relates outdoor concentrations is critical to assessment of policy interventions to reduce adverse health effects.

* Corresponding author. Tel.: þ44 1904 434070. E-mail address: [email protected] (M.R. Ashmore). 1 Present address: The National Centre for Environment and Sustainable Development, Athens, Greece. 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.09.024

Our aims in this paper are to review how knowledge of personal exposure has developed over the last 50 years and to consider key issues for the future. Since the methods and models to assess personal exposure did not exist over much of this period, and studies remain limited even now, we rely primarily on indirect evidence to evaluate changing patterns of personal exposure. In order to provide a clear focus, we consider one population group – children – that is recognised krt:trun 0 as particularly sensitive to air pollution (Schwarz, 2004; Salvi, 2007), since their lung structure and immune system is not fully developed. This allows us to focus on particular microenvironments, specifically home, school, and transport. We exclude specific problems associated with indoor exposure, such as environmental tobacco smoke, and focus on pollutants for which exposure occurs both outdoors and indoors. We maintain primarily a European and North American perspective, but also refer to key issues in other parts of the world, especially in considering future changes in exposure. An analysis of children’s exposure to air pollution requires a focus on indoor locations where they spend most time, in terms of both indoor sources and building design and ventilation, which affect the ingress of outdoor pollution. The lifestyle of children, in particular the time they spend in different locations, is also

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important. Given these factors, we consider first the development of understanding and methods to assess personal exposure, and the findings for major pollutants. We then consider factors influencing exposure in key microenvironments – home, school and transport. Finally, we consider how this information could be used to identify groups, locations, and factors associated with high personal exposure, to provide a focus for assessment of policy measures to improve the health of children. 2. Development of personal exposure concepts and methods Given the high proportion of the day that people spend indoors, indoor air quality is closely linked to personal exposure. The need for adequate indoor air quality was first observed by the ancient Egyptians, who noticed that stone carvers working indoors had a higher incidence of respiratory distress than those working outdoors. They attributed this to higher levels of dust indoors (Woods, 1988). Contemporary recognition of the importance of air pollution in the built environment came when indoor measurements were made in 1960 and 1970s and their contribution to personal exposure was recognised. At the same time, occupants of residential, commercial and institutional buildings reported health problems associated with their buildings (e.g. headaches, eye and respiratory irritation, breathing difficulties or asthma) (Kreis, 1989). It was only after 1970 that the concept was developed of using personal exposure to integrate exposure in different indoor and outdoor microenvironments. Our knowledge of the movement of people between different microenvironments can be traced back to this time (Szalai, 1972; Chapin, 1974), when social scientists reported that most Americans spend most of their time indoors. The problems of using fixed monitoring stations to represent the personal exposure of individuals had been recognised by the mid1970s. This information, in conjunction with the development of the first reliable personal exposure monitors in the 1970s, led to the design of the first major exposure studies in the 1980s. The basic concepts of personal exposure assessment had become established by the mid-1980s. In the following sections, we discuss in more detail how the concepts and methods of indoor and personal exposure assessment developed over the last 50 years, especially in the context of childhood exposure. 2.1. Development of microenvironmental and exposure measurements 2.1.1. Sulphur dioxide In the London smog episode of 1952, the mortality rate for newborn infants doubled and that of infants 1–12 months more than doubled (Brodine, 1971). Whether this reflected a greater sensitivity of these groups or a difference in their exposure is unknown. Analysis of smog episodes in the 1950s and 1960s focussed on outdoor concentrations; there was only intermittent interest in the relationships between indoor and outdoor concentrations, and the factors determining them. Most of these studies were snapshots of a small number of locations, but Biersteker et al. (1965) reported a larger study that measured indoor and outdoor concentrations in 60 homes in Rotterdam. While mean indoor smoke concentrations were about 80% of those outdoors, mean indoor SO2 concentrations were about 20% of those outdoors. However, a small number of homes had indoor concentrations much higher than outdoors, and the authors speculated on their contribution to elevated mortality during smog episodes. It is likely that higher concentrations would have been found in homes with open coal fires, as was common in the UK during the 1950s, than in this Dutch sample of homes primarily using gas for heating. The reasons for the much lower indoor concentrations of SO2 were considered by Spedding (1974), who

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summarised studies showing a large variation in the capacity of indoor materials to absorb SO2; he used measured deposition velocities and surface areas in a typical UK house to identify emulsion paint as the most important sink for SO2. Very little attention has been paid to SO2 in personal exposure studies, either of adults or children, conducted since the mid-1970s, possibly reflecting the fall in its urban concentrations. In view of the rising outdoor concentrations of SO2 in regions such as South and East Asia, with large use of coal for heating and power generation, this gap in knowledge needs to be addressed. 2.1.2. Carbon monoxide A small number of measurements of indoor/outdoor CO ratios can be traced back to the 1960s and 1970s in the US and Russia (e.g. Yocom et al., 1969; Lambert, 1959; Berdyev et al., 1967; cited by Benson et al., 1972). More systematic measurements of residential CO levels started in the US and Europe in the 1990s (e.g. Colome et al., 1994; Coward et al., 2001). The US studies found that a small percentage of homes could still have indoor CO problems even if outdoor CO levels complied with federal standards. In the UK study of Coward et al. (2001), CO levels were very low most of the time in the majority of homes, and factors influencing long-term CO levels were gas cooking, unflued heaters, smoking and outdoor air. In the 1960s and 1970s, studies of CO in the US introduced the field of exposure analysis. Much of this early work focussed on CO because of the advent of light real-time monitors with a sensitivity that was adequate for the high concentrations then experienced in urban areas. Ott et al. (2007) provide a comprehensive review of these studies, which showed that personal CO exposures were both higher than outdoor concentrations and poorly correlated with them. Many early studies focussed on commuting; for example, Cortese and Spengler (1976) found that personal exposures to CO of commuters in the Boston area were consistently underestimated by fixed site monitoring stations. Personal exposures were related to mode of transport (car commuters had higher exposures than rail commuters) and travel route, suggesting that traffic management and vehicle emission control policies would have a direct effect in reducing personal exposures. More recent studies in Europe have combined personal exposure measurements with microenvironmental measurements. In the EXPOLIS study (Jantunen et al., 1998), a European multi-centre exposure study of adults, residential indoor concentrations of CO were typically lower in northern than central Europe, and even lower in southern Europe (Georgoulis et al., 2002). Alm et al. (1994) found that the CO exposures of preschool children in Helsinki were shifted upwards by gas stoves, parental smoking at home, low parental socio-economic status, and commuting to day care centres by car. The INDEX project (Kotzias et al., 2005), based on a detailed literature review, concluded that CO sources in EU residences now contribute mainly to short-term, rather than long-term, exposures. Personal exposures, averaged over 1 h, were considered to be only of moderate concern even for the most susceptible subgroups, including children. 2.1.3. VOCs In the 1970s, studies of indoor concentrations of volatile organic compounds (VOCs) in Scandinavian countries (e.g. Johansson, 1978; Berglund et al., 1982) identified the importance of VOC emissions from building materials. These emissions and their effects on personal exposure are still a matter of great concern (e.g. European BUMA project; Bartzis et al., 2008). Payne-Sturges et al. (2004) estimated that median lifetime cancer risks from VOC exposure in a US city based on modelled personal exposures were three times higher than those based on modelled outdoor concentrations, due to the importance of indoor emissions. Major indoor VOC sources, ¨ zkaynak et al., 1987), and identified in subsequent US studies (e.g. O

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in large-scale studies in the Netherlands, Germany and the UK (e.g. Lebret et al., 1986; Hoffmann et al., 2000; Seifert et al., 2000; Ullrich et al., 2002; Coward et al., 2001, 2002), were consumer products (cosmetics, air fresheners), building materials and smoking. Some UK studies focussed on children’s homes (Berry et al., 1996; Brown et al., 1996), where a strong relationship was found between VOC levels and painting activity, decorating and installation of new furniture, as well as smoking activity. Major indoor and personal VOC exposure studies (e.g. TEAM studies in the US, Wallace et al., 1991a,b) and the EXPOLIS study in Europe (Saarela et al., 2003; Edwards et al., 2005) identified the role of indoor sources and traffic to personal exposure. The importance of indoor sources for personal exposure to VOCs has opened discussion about the need to better regulate indoor VOC concentrations. The INDEX project (Kotzias et al., 2005) identified high priority compounds to be regulated in indoor environments, based on health impact criteria, including benzene, naphthalene, formaldehyde, toluene, xylenes, styrene, limonene and alpha-pinene. Given the high proportion of time spent by children in the home, indoor exposure to VOCs is significant, and needs further quantification. 2.1.4. Nitrogen dioxide Many studies measuring indoor NO2 levels have been carried out since the late 1970s in Europe, US and Canada (e.g. Melia et al., 1978; Boleij et al., 1986; Englert et al., 1987; Lebret et al., 1987; Levy et al., 1998; Coward et al., 2001, 2002; Dimitroulopoulou et al., 2005). ECA (1989) and Kotzias et al. (2005) independently summarised these results, and agree that the most important indoor sources of NO2 are gas appliances and unflued kerosene heaters. The determinants of NO2 concentrations in UK homes include time of year, outdoor levels, cooking fuel, dwelling type, age of dwelling, presence of extractor fans, smoking and window opening (Berry et al., 1996). In the absence of indoor sources, NO2 concentrations in winter tend to be lower than in summer, due to lower ventilation rates (e.g. Alm, 1999; Dimitroulopoulou et al., 2005). Mean indoor concentrations of NO2 in the EXPOLIS study ranged from 13 mg m3 to 43 mg m3 in different cities (Kousa et al., 2001). Typical daily mean indoor air concentrations in homes with gas cooking vary between 25 and 200 mg m3 (WHO, 2006). However, maximum indoor 1-h peak concentrations are in the range 180–2500 mg m3. Exposure at these levels could affect the pulmonary function of asthmatics, as the lower end of the range is close to the WHO guideline (200 mg m3, 1-h average), established for the protection of asthmatic individuals (Kotzias et al., 2005). Studies measuring personal exposure to NO2 were initially carried out in the US in the 1980s (e.g. Dockery et al., 1981; Quackenboss et al., 1982, 1986). Sexton and Ryan (1988), reviewing these studies, concluded that outdoor monitors overestimated exposures of people not exposed to indoor sources but underestimated exposures in homes with un-vented combustion appliances, a finding which has been replicated in Europe (e.g. BraunFahrlander et al., 1992). Personal exposure studies of children have identified determinants of personal NO2 exposure that are related to home conditions. For instance, the most important factors in increased exposures of preschool children in Finland (Alm, 1999) were gas stoves and parental smoking at home, and preschools situated in the city centre. The personal exposures of newborn children in Helsinki (Oie et al., 1993) were identical to indoor home concentrations; time–activity profiles showed that these children spent 50% of time in their bedrooms and 83% in their own homes. Factors influencing the personal exposure of children to NO2 are thus clearly identified in North America and western Europe, but exposure patterns may be influenced by different factors elsewhere in the world.

2.1.5. Particles Although the first measurements of indoor particles started in the 1950s to 1960s in Japan, Russia and the US (e.g. Ishido, 1959; Lambert et al., 1959; Goldwater et al., 1961; Jacobs et al., 1962; Yocom et al., 1971), as reviewed in Benson et al. (1972), the concept of personal exposure to particles was introduced in the 1970s with the first personal exposure measurements in the US (e.g. Binder, 1976; Dockery and Spengler, 1981). Sexton and Ryan (1988), reviewing these early exposure studies, concluded that individual exposures to respirable particles were higher than outdoor concentrations and that exposure to tobacco smoke was the major determinant. The major PTEAM exposure study in the US (Clayton ¨ zkaynak et al., 1996) discovered the existence of et al., 1993; O a ‘‘personal cloud’’, since personal PM10 exposures were higher than both indoor and outdoor levels, and, together with two largescale US studies in the 1980s and 1990s (Koutrakis et al., 1992), identified cooking and smoking as important indoor contributors to personal exposure to particles. 2.1.6. Interpretation of epidemiological studies Current estimates of substantial health impacts of outdoor particulates are based on spatial and temporal associations between mortality, morbidity and outdoor levels. The landmark Harvard Six-Cities study, whose analysis continues to the present day, included detailed measurements of particle levels in homes (Dockery and Spengler, 1981), and found that these were associated with a decrease in lung function. Further analysis (Dockery et al., 1993) found that mortality across the cities increased with outdoor pollution, providing evidence of chronic health effects from longtem exposure to particles, while a recent re-analysis of this study (Laden et al., 2000) concluded that concentrations of combustion particles, but not soil particles, were related to increased mortality. However, few subsequent epidemiological studies have assessed personal and indoor exposure in such detail. The fact that timeseries studies within a city consistently find positive associations between outdoor concentrations and health effects may be due to the high correlation between mean population exposures and outdoor concentrations over time (e.g. for children, Janssen et al., 1999; Yip et al., 2004). However, correlations between individual personal exposures and their residential outdoor concentration are often weaker, and this may explain the weak associations found in some epidemiological studies. For example, the PEACE study of acute effects of particles, black smoke and other inorganic pollutants on the respiratory health of children with chronic respiratory symptoms, in 14 cities in Europe, found no consistent association between outdoor air pollution and respiratory health (Roemer, 1998). The importance of differences in housing characteristics between cities is illustrated by the findings of Janssen et al. (2002), who showed that the association between ambient air PM10 and respiratory and cardiovascular mortality was much weaker in cities with a high proportion of air conditioned homes compared with cities where homes are mostly naturally ventilated. Studies in the US and Canada (e.g. Pellizzari et al., 1999) indicate that outdoor PM concentrations are often poor predictors of personal exposures to particles, while the European EXPOLIS study showed that correlations between personal PM2.5 exposures and indoor concentrations were stronger than those with outdoor residential concentrations (Kousa et al., 2002b). Furthermore different determinants of indoor pollutant concentrations were found in different European cities in the EXPOLIS study (Lai et al., 2006, 2007). Interpretation of the effects of pollutant mixtures represents a further challenge. Several studies show significant relationships between outdoor urban NO2 levels and health effects, including respiratory symptoms, episodes of respiratory illness, lung function, and even mortality (WHO, 2006). However, since NO2 shares

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sources with other pollutants, it is difficult to be confident that reported health effects are caused by NO2 (Kotzias et al., 2005). Indoor epidemiological studies on effects of NO2 do not provide consistent evidence for adverse effects (Samet and Utell, 1990; Samet and Basu, 1992), which may be because of the possible confounding effects of fine and ultra-fine particles emitted from gas burning (WHO, 2006). Assessment based on personal exposure, rather than indoor or outdoor concentrations, may offer additional power in interpreting such studies, especially as outdoor concentrations act as surrogates for personal exposure for some pollutants but not others (Sarnet et al., 2001). Calculations based on measurements of exposure suggest that particulates generated by outdoor sources contribute less than half of population exposure in the US (Yeh and Small, 2002). In order to interpret epidemiological evidence and identify effective policy interventions, it is useful to consider three components of exposure (Wilson et al., 2000): outdoor exposure to outdoor-generated pollutants, indoor exposure to indoor generated pollutants, and indoor exposure to outdoor-generated pollutants. An important recent development is the modelling of personal exposure of children to outdoor-derived particulates, using factor analysis or chemical component markers such as sulphate and iron, which are almost entirely of outdoor origin. This approach may provide improved estimates of the health benefits of control of outdoor sources, especially as the toxicity of indoor and outdoor-generated particles may differ. This would allow better transferability between locations, seasons and subject behaviour than relationships based on outdoor concentrations only. Strand et al. (2007) showed health effect estimates for asthmatic children based on total exposure to outdoor-generated PM2.5 that were quite different than those based on outdoor concentrations of PM2.5. However, does assessing personal exposure provide an improved basis for interpreting the effects of air pollution on children’s health when comparisons are made between personal and outdoor exposures? Studies comparing daily health outcomes to daily personal exposure and ambient concentrations of PM2.5, elemental and organic carbon, and NO2 show complex patterns. While personal exposures were better predictors than ambient concentrations for PM2.5, significant relationships with both personal and ambient levels were found for elemental carbon and NO2 (Delfino et al., 2004; Delfino et al., 2006). The complexity of these results may derive from the fact that outdoor concentrations, but not personal exposures, of ozone and NO2 can act as surrogates for personal exposures to PM (Sarnet et al., 2001). Van Roosbroeck et al. (2008) recently reported that effect estimates for the impact of soot and NO2 on the prevalence of symptoms of asthma in children in the Netherlands were 2– 3 times higher when based on estimated personal exposure than when based on concentrations outside their schools. This emphasises the need to assess more fully whether existing estimates of the impacts of air pollution on childhood disease, which are based on outdoor concentrations, systematically underestimate effects of children’s exposure to these pollutants. 2.2. Exposure modelling Modelling human exposure to air pollution provides an indirect assessment of exposure, in contrast to direct assessment using personal monitors. Models are useful when measurements are not available, and can also be used to explore the implications of measures to reduce exposure. Exposure models can be categorised into three major groups. Firstly, in many population-based epidemiological studies, the estimated exposure is mainly based on outdoor air concentrations only, ignoring the different outdoor and indoor locations and the different activities of individuals (e.g. Speizer et al., 1980). A second method, also frequently used in epidemiological studies, is the development of empirical regression

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models, based on exposure measurements (e.g. Spengler et al., 1985), to predict the exposure of individuals or a wider population. A major issue in the use of such models is their transferability to other locations and other periods in time. The third approach is mechanistic, rather than empirical, modelling, based on the concept of the microenvironment, a generic exposure location such as schools, cars, or bars, which was first introduced by Fugas (1975), Duan (1982) and Ott (1982, 1984). The mean exposure of an individual is then represented as a linear combination of concentrations in various microenvironments (MEs), weighted by the time spent in each. The inputs to such models in the form of activity patterns and microenvironment concentrations can be measured or modelled. The first microenvironment model to simulate the personal exposure of children (Letz et al., 1984), used data for five microenvironments (inside and outside at home and school, and transport) to calculate 24 h mean exposure to respiratory particulates in US cities. The results highlighted the importance of passive smoking in influencing the upper percentiles of the exposure distributions. A similar approach was recently reported to provide reliable predictions of individual children’s exposure to PM2.5 in southern California (Wu et al., 2005). The results of modelling studies highlight the importance of the difference between mean population exposure and that of individuals. For risk assessment purposes, it is the frequency distribution of exposures within a population that is of concern, rather than mean or individual exposures. This requires the development of probabilistic models, which express concentrations in each microenvironment, or the parameters controlling them, as frequency distributions, and consider the variation in activity patterns within the population. Fig. 1 illustrates one example of the application of such a probabilistic model of indoor concentrations (Dimitroulopoulou et al., 2006) linked to a time–activity model to children in Leicester, UK. Results are shown both with and without home cooking and smoking sources. In both cases, the modelled 95& and 99& personal exposures were well above the outdoor urban background concentrations, while mean personal exposures were quite similar; this effect is particularly marked in periods when home sources are in operation. Such model predictions allow the factors associated with the highest children’s exposures to be identified. Klepeis (2006) summarised the characteristics of different exposure models and divided them into two categories: the ‘‘exploratory’’, which are designed to develop methods and approaches and establish and test mechanisms of exposure, and the ‘‘regulatory’’, which are designed for use in the development of government regulations or risk assessments. Models such as SHAPE (Ott, 1984), CONTAM (Walton and Dols, 2006), and RISK (Sparks et al., 1993) fall in the first category, whereas models such as the NAAQS exposure model (NEM) (e.g. Johnson and Paul, 1983), SHEDS-PM (Burke et al., 2001), EXPOLIS (e.g. Kruize et al., 2003; Ha¨nninen et al., 2003) and INDAIR/EXPAIR framework (Ashmore et al., 2005) may be considered as regulatory. Geographical Information Systems may also provide valuable tools for exposure assessment by combining spatial traffic, pollution and population data (e.g. Jensen et al., 2001; Kousa et al., 2002a; Briggs, 2005; Gulliver and Briggs, 2005), including home characteristics (Baxter et al., 2007). All these models are applicable to estimation of children’s exposure, but depend on the availability of reliable information on the time–activity patterns and locations of the children of concern. 3. Factors influencing personal exposure of children The personal exposure of children is dominated by air pollution concentrations in three microenvironments – home, school, and transport. We now consider these locations in more detail.

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M.R. Ashmore, C. Dimitroulopoulou / Atmospheric Environment 43 (2009) 128–141

a 250

UB

100

80 70

geomean 95%ile

150

b

NO2, ppb

NO2, ppb

200

baseline scenario, home sources

99%ile arith mean

baseline scenario, no home sources UB geomean

60

95%ile

50

99%ile arith mean

40 30 20

50

10 0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00

0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00

time (h)

time (h)

Fig. 1. Simulated exposure of the population of school age children in Leicester (a) with and (b) without home cooking and smoking sources. The graphs show the arithmetic mean exposure of the modelled population exposure, alongside concentrations at an urban background site (UB), as well as the 95& and 99& of the modelled population exposure.

3.1. Homes 3.1.1. Airtightness Indoor air quality is determined by infiltration (uncontrolled ingress of outdoor air through the building fabric) and ventilation (intentional transport of outdoor air via natural or mechanical ventilation). High infiltration can increase energy use to maintain comfortable temperatures, whereas low infiltration without adequate ventilation can increase indoor levels of air pollutants. Keeping the right balance between infiltration, ventilation and indoor air quality is a challenge. Today, the rule to achieve satisfactory indoor air quality is ‘‘build tight and ventilate right’’. Improving building airtightness allows better control of air infiltration. Measurements of airtightness (air leakage rates) have

been carried out over the last 25 years, mainly in the US (Sherman and Dickerhoff, 1998; Chan et al., 2005) and the UK (Etheridge et al., 1987; Stephen, 1998). Table 1 provides a summary of air leakage rates of dwellings in various countries (Orme, 1994), together with more recent data. In almost all cases, the range of values is very broad; for instance, air leakage rates of UK houses vary by over a factor of 10 (Stephen, 1998). Analysis of the same UK database (BRE, 471 houses) shows that factors that affect airtightness are the year built, type of wall construction and ground floor type. Chan et al. (2005), analysing more than 70,000 air leakage measurements in US houses, found that the year built and floor area are the two most significant predictors; older and smaller houses tend to have higher air leakage rates. Other building criteria that affect leakage are the thermal distribution system and retrofitting (Sherman and Dickerhoff, 1998).

Table 1 Comparison of whole houses air leakage rates. Meana (sd) (ACH at 50 Pa)

Normalised air leakage rates (dimensionless)

Comments – sample size

AIVC database (Orme, 1994) Belgium Canada France Netherlands New Zealand Norway Sweden Switzerland UK USA

8.2 5.3 3.6 10.1 9.5 4.9 5.1 3.2 13.6 11.2

– – – – – – – – – –

57 474 66 303 83 40 144 37 385 435

Recent data UK (Stephen, 1998)

13.1

0.8b

BRE database (471 houses)

UK (Dimitroulopoulou et al., 2005)

12.9 (3.7)

0.7b

Homes built since 1995 (37 homes)

US (Sherman and Dickerhoff, 1998)

29.7

1.7

12,900 houses

US (Sherman and Matson, 2002)

5.3b

0.3 0.6 (0.6) 0.3 (0.1) 0.2 (0.1)

LBNL new home database (8300 new houses) 1200 conventional 2100 energy efficient 7500 AKWarm

0.9 (1.9) 0.5 (1.9) 0.5 (2.0)

LBNL home database (70,000 houses) Low income Conventional Whole US

Country

(7.2) (3.3) (2.0) (6.7) (4.9) (1.8) (3.8) (1.5) (5.7) (6.2)

10.5 5.3b 3.5b

b

15.8 8.8b 8.8b

b

US (Chan et al., 2005)

a b

Arithmetic mean (asd). Derived by the average ratio between ACH at 50 Pa (ACH50) and normalised leakage (NL), NL ¼ ACH50/17.5.

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The oldest UK homes tend to be more airtight, with air leakage rates rising in the 1920s, when sash windows were used less but cavity walls were introduced (Stephen, 1998). Homes built since about 1980 are more airtight on average than those built since the 1930s, but the range of values is still very wide. Similar trends appear in UK homes built since 1995 (Dimitroulopoulou et al., 2005) (Table 1). In the US, the breaking point is also around 1980 (Chan et al., 2005), when the energy efficiency of new houses improved through a variety of regulatory and voluntary measures (Sherman and Dickerhoff, 1998; Sherman and Matson, 2002). There is little information to explain the trends of airtightness with date built but possible factors include refurbishment of older properties, sealing of unwanted chimneys, changes in types of heating appliance, better building techniques, improved materials, and lesser degrees of age-induced deterioration (Stephen, 1998; Chan et al., 2005). Finally, the type of structure affects airtightness; apartment blocks in the Netherlands tend to be tighter than single-family houses (Orme, 1994), although multi-story houses in the US are leakier than single-story houses (Sherman and Dickerhoff, 1998). 3.1.2. Building design, geographical regions and exposure The effect of variation in home air exchange rates on indoor and personal exposure depends on the relative importance of outdoor and indoor sources. A reduced infiltration rate would reduce the contribution of outdoor sources to indoor exposure, reduce correlations between personal exposures and outdoor concentrations, and increase indoor/outdoor (I/O) ratios in the presence of indoor sources. This is also true for ventilation systems. Hanninen et al. (2005) showed that penetration of ambient air PM2.5 into buildings built after 1990, when balanced mechanical ventilation systems became mandatory in Finland, was significantly lower than in older buildings, and that the exposure of the occupants to PM2.5 of ambient origin was also significantly lower in the post-1990 buildings. Differences in indoor/outdoor (I/O) ratios in different European cities may be partly explained by tighter and perhaps underventilated buildings in northern cities designed for colder climates. The EXPOLIS study (Saarela et al., 2003; Edwards et al., 2005) found that home VOC levels were higher than outdoors, with higher concentrations in Milan and Athens than in Helsinki and Basel. Noullett et al. (2006), in a wintertime study in British Columbia, found relatively weak temporal correlations between children’s personal exposure to PM2.5 and ambient concentrations compared to other studies, which they attributed to the relatively low air exchange rate in this location. These factors mean that personal exposure studies in a single city may not be readily transferred to other cities where building design and climatic conditions may be quite different. For example, results from recent studies in homes in European cities (e.g. Lai et al., 2006; Ballesta et al., 2006; Lai et al., 2007) show positive correlations between indoor and outdoor concentrations within some cities but not others; only cigarette smoking was found to have a consistent effect in elevating indoor concentrations. The significance of smoking for childhood exposure is illustrated by the study of Janssen et al. (1997), who measured winter personal and outdoor PM10 levels of 45 children (aged 10– 12) in the Netherlands for 24-h and reported a personal-to-outdoor ratio of 2.1 and 3.2 for non-smoking and smoking homes, respectively. 3.1.3. Adult and children exposure at home It is important to assess if the indoor and personal exposures of children are comparable to those of adults, on whom many more personal exposure studies have been conducted. However, few such comparisons have been made. Within four French cities, Gonzales-Flesca et al. (2007) reported 48 h mean benzene personal exposures that were very similar in non-smoking,

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non-occupationally exposed adults and children from the same home, with both about 3 times ambient concentrations. This implies that indoor sources in homes were the dominant exposure route for both children and adults. However, the same personal exposure will lead to higher uptake per unit body weight and greater health effects in children. A further factor is whether the particular location of young children within the home may influence their exposure. However, Jones et al. (2007) reported that PM exposures at a child’s cot did not differ significantly from those in other parts of the home, and there was no evidence of a significantly higher concentration close to the floor, where infants spend most of their time. These results imply that a close agreement between personal exposure of children and adults can be expected if personal exposure is mainly due to sources in the home, but when exposure in other locations is more important, significant differences in exposure might be expected. However, this conclusion applies to adults and children occupying the same home, and it is also important to consider that the presence of children in the home may modify personal activities in ways that affect both adult and child exposure. Edwards et al. (2006) reported that homes in EXPOLIS cities with children had lower adult exposure to traffic-related VOCs, but higher exposure to terpenes related to greater use of cleaning products; emissions of terpenes may lead to chemical formation of high concentrations of secondary compounds indoors (Carslaw, 2007) and as a personal reactive cloud (Corsi et al., 2007), and hence higher exposure of children as well as adults. 3.2. Schools 3.2.1. Building design There is considerable qualitative information on health complaints and indoor air quality problems in schools in the US and Europe (especially Scandinavia), but few detailed studies. Persuasive evidence links high indoor NO2 levels to reduced school attendance whereas suggestive evidence links low ventilation rates to reduced school performance (Mendell and Heath, 2005). Dampness and inadequate ventilation are common problems in schools. A recent EU study highlights the link between wheezing and classroom PM concentrations, and the benefits of the investment in improved school ventilation in Scandinavia in the 1990s, which has not occurred elsewhere in western Europe (WHO, 2007). The first study of ventilation in school buildings was carried out in 1910 by a commission appointed by the Chicago Department of Health. They concluded that carbon dioxide was not a harmful agent and that high relative humidity was an important outcome of poor ventilation. The minimum ventilation rate was then defined as 14 l s1 per occupant of outdoor air (Chicago DoH, 1914; quoted in Janssen, 1999). Another study of 216 classrooms in New York, which lasted for 10 years from 1913, provided guidance on ventilation to schools throughout the US; the minimum ventilation requirements (4.7 l s1 per person), based on odour, formed the basis for most regulations until in the 1970s. However, despite the low ventilation standards in the 1970s, reflecting the concern over energy consumption, a rate of 8 l s1 per person was suggested as desirable (Janssen, 1999). A comprehensive review of ventilation standards worldwide (mostly in Europe, USA and Canada) showed specified amounts of fresh air varying from 3 to 8 l s1 per person (White and Mohle, 2001). However, in many schools in the US, ventilation rates fall below the standard of 8 l s1 per person (Daisey et al., 2003). A small UK sample suggests that schools are under-ventilated but leaky (ODPM, 2006). Analysis of airtightness for a limited number of non-residential buildings, including schools, from the USA, Canada and Belgium, showed that they are often not airtight enough (Sherman and Chan, 2004). No correlation between airtightness and building age or wall construction was observed,

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and, as for homes, there was a wide range of values, even among newly constructed schools.

sources, will all influence the personal exposure of individual children (Barrero-Moreno, 2008).

3.2.2. Exposure Given these concerns over inadequate ventilation of schools, it is important to consider the empirical evidence that schools make a significant contribution to personal exposure of children. This evidence is strong for particulate matter; exposure at school is important because I/O ratios are typically higher than at home and hence schools contribute significantly to children’s personal 24 h mean exposure to PM2.5 (e.g. Wu et al., 2005). Ligman et al. (1999), in a national survey of US schools, reported indoor concentrations of PM10 in classrooms that were over twice those outdoors, and significantly higher than in typical office buildings, and partly ascribed this to poor housekeeping and deterioration of building structures. Crist et al. (2008) reported greater variation in indoor and personal exposure to PM2.5 than outdoor concentrations in three US schools, with a strong seasonal effect due to ventilation and children’s activities. Harrison et al. (2002) found that personal exposures to PM10 but not CO were higher for children than for adults; the differential exposure to PM10 was only found on weekdays, suggesting a significant contribution from exposure at school. Studies that have focussed on particular fractions of PM suggest possible causes. Patterson and Eatough (2000), in a study in a US primary school, found correlations between indoor and outdoor concentrations for sulphate and nitrate aerosol, soot and particle number, but not for metal concentrations or PM2.5 levels, which were strongly influenced by indoor activity. However, Molna´r et al. (2007) reported I/O ratios for trace elements of PM2.5 to be rather similar between homes and schools in Stockholm. In schools in Athens, Chaloulakou and Mavroidis (2002) reported that indoor and outdoor concentrations of CO were tightly coupled with a ratio just below 1, but I/O ratios for both PM10 and PM2.5 were significantly greater than 1. Continuous monitoring showed that periods of increased indoor concentrations were associated with human activity indoors (Diapouli et al., 2007). Zhao et al. (2007) used elemental composition to partition sources of personal and indoor exposure to PM2.5 for children attending a school for asthmatics situated on a road with high traffic flow in Denver, Colorado. Cooking in the school and outdoor motor vehicle exhaust were both estimated to contribute about 30% of school concentrations, and the combined effect of cooking at home and school contributed an estimated 50% of personal exposure. The importance of outdoor concentrations at schools was shown by Van Roosbroeck et al. (2007), who found that personal exposure to soot and PM2.5 of children attending a school near a busy motorway was significantly increased compared to children attending a school in an urban background location in the same Dutch town. The same effect was not found for NO2, or at a second school further from a major road. These results are consistent with significantly elevated indoor PM concentrations in schools close to major roads (e.g. Janssen et al., 2001). The importance of pollutants other than PM is illustrated by the AIRMEX project that recently investigated schools and kindergartens in southern and central European cities, to assess the relationship between indoor air concentrations and long-term exposure to selected VOCs (aromatics, carbonyls and terpenes). Current results indicate that personal exposures are at least twice as high as indoor concentrations and are significantly higher than outdoor levels. Carbonyl and formaldehyde indoor concentrations were several times higher than outside, suggesting important indoor sources. Hence, elevated air pollutant concentrations in schools may be related to children’s activities, outdoor sources, and indoor combustion sources. The hours during which children attend the school, its specific location and the importance of home

3.3. Transport Although children spend much less time in transport than at home or school, the limited available data suggest this could be an important source of exposure. The PEOPLE project (Ballesta et al., 2006) showed that school children generally have a higher benzene exposure than control subjects who primarily remain at home or walk locally. This was primarily associated with exposure during travel to and from school, as concentrations in schools were comparable to those in the home, and the personal exposure of children was comparable to that of adult commuters. Indoor exposure to ultra-fine particles has been highlighted as a potential concern for childhood asthma (Weichenthal et al., 2007). Diapouli et al. (2007) reported that mean indoor number concentrations of ultra-fine particles in Athens were generally lower than those outdoors at homes, except during specific activities, such as cleaning; however, much higher concentrations were found in car journeys, implying that exposure during transport to school could be a significant contributor to daily mean exposure for many children. Studies in the US suggest that long journeys in school buses may significantly increase personal exposure of children. In a recent study in Los Angeles, elevated pollutant concentrations in school buses were linked to the type of vehicle, the degree of self-pollution, window opening, and following other buses; for groups who travel across the city rather than to neighbourhood schools, bus commuting was estimated to contribute 30% of daily personal exposure to black carbon and diesel particulate, 15% of PM2.5, and 10% of NO2 (Behrentz et al., 2005; Sabin et al., 2005). The choice of school and travel mode may thus be an important factor in personal exposure. Policies that allow greater parental choice rather than assigning children to neighbourhood schools have implications for CO2 emissions and air pollutants because of the increased travel, and may also influence the personal exposure of children (Wilson et al., 2007). Current policy often encourages children to walk or cycle to school; besides the safety issues, the implications for personal exposure need to be considered. The potential for high exposure of cyclists, who are exercising vigorously, to traffic-generated pollutants such as CO was demonstrated in the 1970s. Kleiner and Spengler (1976) not only measured the CO exposures of Boston cyclists but also identified measures for provision of cycling routes and facilities that would minimise their personal exposure. Similarly, Mudu et al. (2006) demonstrated that in Leicester, UK, exposure to traffic-generated air pollution was 4– 10 times higher for children walking to school than for those being driven by a car if they followed the same route. This has important implications for policies encouraging walking and highlights the need for low pollution walking routes. 3.4. Comparison of childhood, adult and outdoor exposure The results summarised above for the three specific microenvironments which most influence children’s personal exposure, together with the data for individual pollutants that were discussed in Section 2, can be used to assess what is, and is not, known about the personal exposure of children. Table 2 summarises this information, for each major pollutant, in terms of a comparison of children with adults and with outdoor concentrations. In many cases, especially with respect to comparisons of children and adults, the conclusions in Table 2 are based on inference rather than on measurements, and brief explanations are therefore included to explain the basis of the conclusions. We emphasise that these are broad conclusions and will not apply to every group of children in

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Table 2 Comparison of the exposure of children with adults and outdoor air, based on information in North America and western Europe. Exposure of school children vs. working adults

Exposure of school children vs. outdoor air

SO2

Insufficient data

Lower: in the absence of indoor sources, residential indoor levels are about 5 times lower than outdoors

CO

Insufficient data from studies Lower than adults with exposure in non-residential MEs (e.g. bars, restaurants, commuting)

Similar if no indoor sources and no commuting Higher with indoor sources and commuting

NOx

Insufficient data

Higher with indoor sources at home or school Lower without indoor sources at home or school

Benzene

Similar to non-smoking, non-occupationally exposed adults living in the same home

Higher: residential concentrations exceed outdoor concentrations by factor of 1.5–4.0

VOCs

Similar to non-occupationally exposed adults living in the same home Higher to VOCs from cleaning products Lower to traffic-related VOCs

Higher: personal exposures may exceed median outdoor levels by a factor of 2–5

PM10

Higher on school days due to exposure in school

Higher: school concentrations and personal exposures exceed outdoor concentrations by a factor of 2–3

PM2.5

Insufficient data, but also may be higher on school days

Higher: school concentrations exceed outdoor concentrations

every location; as emphasised above, wide variation in individual children’s exposure has been shown in measurement and modelling studies, and can be inferred from the wide variation in building design and activity patterns. Table 2 emphasises the importance of sources in the home, and school in elevating personal exposure above outdoor concentrations for many pollutants. It also indicates a lack of information on whether children experience higher personal exposures than adults. It is important to highlight that the broad conclusions summarised in Table 2 are likely to be inappropriate in regions outside North America and western Europe, where indoor and outdoor sources building design, and children’s lifestyles, may be very different. 4. Global perspectives and future challenges A key conclusion from the analysis of the physical features of major microenvironments, and from measurement and modelling exercises, described in Sections 2 and 3, is that very large variations in personal exposure occur within populations of children. It is those children with the highest exposure who have the greatest risk of adverse health effects, and who can experience the greatest benefit of measures to reduce exposure. Here, we consider the implications of this large variation in children’s exposure in a broader global context, and identify links to causal factors and hence to effective policy interventions. 4.1. Relationship to social deprivation Within western countries, there has been increasing interest in the concept of environmental justice, primarily in relation to exposure to industrial pollution. The greater prevalence of persistent cough in children from lower socio-economic groups in the UK, which was more marked in cities with higher levels of air pollution (Brodine, 1971), and the differential morbidity of the poor and black in the US, had been identified as an issue by the early 1970s. However, few studies have specifically compared personal exposure in different socio-economic groups, although a recent study suggested that exposure of children to NO2 in Sweden was higher in lower socio-economic groups (Chaix et al., 2006). Low-income houses in US tend to have greater air leakage, regardless of year built and floor area (Chan et al., 2005), and hence greater penetration of outdoor pollutants. Wallace et al. (2003) found high pollutant concentrations in inner-city homes of asthmatic children in seven US cities, and

Simons et al. (2007) reported higher concentrations of PM10, PM2.5, and NO2 in inner-city homes in Baltimore than in suburban homes, as well as different allergen exposures. While the greater prevalence of smoking partly explained this effect, PM concentrations in non-smoking households were still twice as high in inner-city homes, due partly to proximity to road traffic and a greater prevalence of gas stoves. However, caution is needed in ascribing increased health effects in lower socio-economic groups, for example the higher prevalence of asthma in African American children, to increased exposure to air pollution. For example, Diette et al. (2007), also in Baltimore, found that, although indoor PM concentrations were 3 times higher than those outside, there was no significant difference in the concentrations of major air pollutants in homes of inner-city children with and without asthma. For some pollutants, such as ozone, exposures may actually be lower in inner-city neighbourhoods. Studies which engage deprived communities in the measurement of the personal exposure and health status of their children (e.g. Keeler et al., 2002) may play an important role in increasing awareness of measures which can reduce the exposure of these children to air pollutants. 4.2. Urbanisation The greatest difference in personal exposure related to socioeconomic conditions is that between, rather than within, countries. While outdoor concentrations have generally fallen in recent decades in the cities of western Europe and North America, there have been increases in urban air pollution concentrations in many cities of Asia, Africa and Latin America. Large differences in personal exposure of children are also found across Europe. WHO (2007) identified a 100-fold variation in rates of neonatal death due to respiratory disease between European countries that is associated with large differences in outdoor air pollution concentrations and indoor exposure to damp, ETS and use of solid fuel. While there has been a trend for movement out of cities and into suburban areas in North America and Europe, recent decades have seen a dramatic urbanisation of the population in many other parts of the world, with nearly half of the world’s population now living in urban areas (Janssen and Mehta, 2005). The movement of people to Chinese cities in the last 20 years has been characterised as the largest mass migration in human history; the numbers of children exposed to urban concentrations will have increased dramatically, although indoor exposure to pollutants from biomass burning may have decreased in these children. A similar urban migration has occurred

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in many Asian countries. Concentrations of particulate matter in large Asian cities are much higher than in European and North American cities, especially in the dry season. Where detailed chemical analysis has been done, this suggests that there are contributions from a range of sources, including transport, secondary aerosols, biomass burning and soil dust (Kim Oanh et al., 2006). We need much more information on how indoor and outdoor sources in these rapidly expanding cities interact with children’s activity patterns to lead to high levels of personal exposure, and possibly increased prevalence of childhood disease (Ye et al., 2007). There are also few studies of exposure in transport in these cities (Han and Naeher, 2006), and few studies of schools outside Europe and North America. However, a recent study in Shanghai showed high classroom concentrations of NO2 that were significantly correlated with asthma prevalence (Mi et al., 2006). The authors suggest that personal exposure in urban Asian schools may be high, because they are naturally ventilated, have large class sizes, and often have open windows. 4.3. Indoor sources in homes The use of biomass fuels within the home is another important source of exposure that is linked to socio-economic conditions. Indoor exposure to smoke from biomass fuels and coal has long been recognised as a major cause of illness in young children, and has been estimated to be the second biggest environmental contributor to poor health worldwide (WHO, 2002). A recent WHO assessment (Rehfuess, 2006) estimated that 52% of the world’s population uses solid fuels, with typical values in sub-Saharan Africa, South East Asia and the western Pacific of 75%. Although many studies report high indoor concentrations and evidence of health effects due to indoor use of solid fuels, few have explicitly modelled personal exposure. Ezzati et al. (2000) measured PM10 concentrations in rural households in Kenya and linked these to time–activity data to estimate personal exposures. The results showed that simple models that ignore spatial variation in concentrations of particles in the home, especially during short episodes of very high concentrations, underestimate actual exposure. This information was subsequently used to develop new exposure–response relationships (Ezzati and Kammen, 2001). Regional personal exposure surveys are needed to provide an objective basis for identifying policy options to reduce exposure. For example, Balakrishnan et al. (2002) identified stove type, location and design of kitchen, and time spent near kitchens during cooking, as important factors, in addition to fuel type, in influencing personal exposure to respirable particles in southern India. Such studies can also help to identify cost-effective measures to reduce the personal exposure of children in countries such as Bangladesh, where acute respiratory infections account for about 25% of deaths of children under 5 (Khalequzzaman et al., 2007). Dasgupta et al. (2006a) used a detailed survey of PM10 exposure in a stratified sample of poor Bangladeshi households, to identify that policies other than improved stove design or fuel switching could decrease exposure from biomass fuels, since some homes using biomass experiencing relatively low concentrations. These included changes in house characteristics (e.g. avoiding mud for construction) and internal configurations and ventilation practices. Dasgupta et al. (2006b) combined time–activity data with measured concentrations in kitchens, living rooms and outside in Bangladeshi cities; the highest exposures were modelled for children under 5. Living room exposure to cooking smoke was comparable to kitchen exposure because of high air exchange rates, but outdoor concentrations were much lower; hence young children would benefit from spending more time outside. An estimated one billion rural Chinese citizens have benefited from a national improved stove programme in the 1980s and 1990s

aimed primarily at improving fuel efficiency. While there is evidence of significant declines in indoor exposure as a result, concentrations in rural households remain high and are influenced by complex patterns of seasonal fuel use, household design, and a mix of cooking and heating sources (Edwards et al., 2007). Heating is a particularly important source in northern China (Fischer and Koshland, 2007). Jin et al. (2005) compared indoor exposures in four poor Chinese provinces with different fuel sources, climates, building design and living patterns. Homes with biomass as the primary fuel had higher RPM concentrations that those using coal, but the latter had very high SO2 concentrations. These studies emphasise the importance of environmental health interventions based on exposure routes, which take account of specific regional features of house design and the use of cooking and heating, as well as simply reducing emissions at source. 4.4. Building design The wide range of building design leads to large variations in infiltration rate and hence indoor and personal exposure. New houses do not necessarily mean airtight houses. In countries where the maximum allowable air leakage for new dwellings is written into building codes (e.g. Sweden, UK, USA), the reason for airtightness improvement over time is obvious. However, in countries where there is no airtightness standard or code on new dwellings, newer dwellings are not necessarily more airtight than older ones. The main reasons for tighter construction are to reduce energy costs and maintain thermal comfort. Energy-efficiency programmes in the US tend to produce tighter houses, whereas conventional houses are significantly leakier, with a lot more variation (Sherman and Matson, 2002). The large increases in the use of air conditioning units in North American homes over the last two decades may have significantly affected indoor air quality (Franklin, 2007), and the exposure and health benefits of air conditioning of schools, homes and vehicles could be considerable (e.g. Janssen et al., 2002; Hanninen et al., 2005; Chan et al., 2002). Much greater air exchange rates may occur in homes outside Europe and North America; for example, Smith (1993) reported values of 7–30 h1 for Indian homes. Conversely, Kovesi et al. (2006) reported that 40% of homes of young Inuit children in northern Canada had air exchange rates below 0.35 h1, which, when combined with very small houses, led to poor indoor air quality. These wide variations highlight that studies comparing relationships between indoor and outdoor concentrations and personal exposures in different types of city are needed for regions outside Europe and North America, where a different set of factors may influence this relationship (Lung et al., 2007). European studies need to be extended to the east, where ambient concentrations are higher. Higher outdoor concentrations and increased ventilation rates increase the contribution of outdoor concentrations to personal exposure. For example, Jedrychowski et al. (2006) reported that high outside concentrations, partly due to proximity to industrial sources, explained 31% of the variation in personal exposure to PM in Krakow. Rojas-Bracho et al. (2002), reporting a study of children’s personal and indoor exposure in Santiago, estimated that 60% of indoor PM2.5 was of outdoor origin, a higher value than in comparable US studies. They reported higher correlations between personal exposures and outdoor concentrations than in US and European studies, which is also consistent with higher exposures to pollutants generated outdoors. 4.5. Activity patterns There is evidence that children in western societies are spending less time outdoors. For example, the activity data used by Letz et al. (1984) to parameterise a microenvironmental model of children’s

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exposure to respiratory particles in the US showed that children spent 18% of time annually (29% in summer) outside, whereas a similar recent study in southern California (Wu et al., 2005) used activity data for female primary school children of less than 3% annually outside (5% in summer). Such trends mean that indoor exposure has become more important than outdoor exposure. Reduced time spent outdoors leads to reduced personal exposure to pollutants such as ozone for which indoor concentrations are consistently low (e.g. Lee et al., 2004). Assessments of the effects of activity patterns need to focus more on critical periods for children, such as immediately before and after birth. Preschool children and pregnant mothers in the UK spend over 85% of their time at home, making this a particularly important microenvironment (Farrow et al., 1997). No studies to our knowledge have reported the personal exposure of young babies, and only recently have studies considered the personal exposure of pregnant women, which may be important for the health of newborn children (e.g. Tonne et al., 2004; Jedrychowski et al., 2006; Nethery et al., 2008). 4.6. Integrating personal exposure into policy assessment Much of the analysis of human exposure that has been carried out by organisations such as WHO relies on separate isolated evaluations of the health significance of indoor and outdoor air pollution. In the absence of detailed assessments of personal exposure, such an approach is understandable. However, there are several reasons why such an approach may lead to misleading implications about personal exposure to air pollution:  There is evidence for some pollutants that personal exposure is actually higher than measured concentrations both inside and outside the home;  The same children may experience high concentrations both inside and outside the home, hence producing an added risk which is ignored if the effects of indoor and outdoor sources are considered in isolation;  Exposure in schools and nurseries, and in transport, is largely ignored, despite the fact that it is an important contributor to exposure of children to some pollutants;  While outdoor pollutant concentrations are quantified, exposures indoors are usually only compared in terms of emission sources;  Features of building design, which may either lead to high ingress of outdoor pollutants, or to low ventilation that enhances the effect of indoor sources, are ignored. All of these factors indicate the importance of a more integrated approach based on assessment of personal exposure. There is a particular need to assess the contribution of indoor and outdoor sources in situations where both may reach high concentrations. For example, a model to estimate PM10 exposure in China concluded that rural exposures are higher, with 80–90% due to indoor exposure, compared with 50–60% in the major cities (Mestl et al., 2007); the study also estimated that the contribution of biomass combustion was greater than that of coal. Nevertheless, air quality management and health risk assessment for air pollution remain primarily based on the assumption that outdoor concentrations are the prime driver of adverse health outcomes, despite the fact that in Europe and North America, people typically spend almost 90% of their time indoors. The recent increased emphasis on the role of exposure assessment in air quality management in Europe is based on outdoor residential concentrations, and does not consider indoor and personal exposure. Although in principle the assessment tools recommended for urban air quality management include microenvironmental monitoring and modelling, time–activity tools and databases,

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exposure scenarios and finally exposure monitoring and microenvironmental based modelling (Krzyzanowski et al., 2004), few studies linked to policy assessment extend beyond measurement and modelling of outdoor concentrations. Even if risk assessment based on personal exposure has not been incorporated into policy evaluation, the benefits for exposure of reducing indoor sources of pollution and improving ventilation are well established. However, recognition of the various problems associated with indoor air pollution has not necessarily led to successful policy interventions to reduce indoor and personal exposure of children and other sensitive groups. With some notable exceptions, relatively little progress has been made in reducing indoor exposures to emissions from solid fuel combustion in developing countries. Likewise in developed countries, there is extensive knowledge of how buildings should be designed to improve indoor air quality, but little use of this knowledge in building regulations. Some improvements in indoor air quality result from other factors such as reduced cooking hours and increased use of microwaves, especially in the US (Samet and Spengler, 2003). Some progress has been made in improved ventilation of school buildings in Scandinavia, and California has recently passed a law banning construction of new schools within 200 m of major highways (Green et al., 2004), but more needs to be done to address this important contributor to childhood exposure. 5. Conclusions Since the publication of the first issue of Atmospheric Environment in 1958, there have been remarkable advances in our understanding of personal exposure. This was driven partly by increased concern about indoor air quality, as building design began to reflect the need for energy conservation, partly by dramatic technological advances in the techniques for measurement of personal exposure to a range of pollutants, and partly because of an increased concern about the health effects of urban air pollution. This review allows us to draw some conclusions about the personal exposure of school aged children to specific pollutants in western Europe and North America. For SO2, exposure is expected to be lower than outdoor levels, due to the low measured indoor concentrations. In the presence of indoor sources (e.g. unflued heaters and parental smoking), CO exposure of children at home can be higher than outdoor concentrations, and their exposure may be further increased due to commuting to school by car or walking or cycling along busy roads. The home environment has lower exposures to NO2 than outdoors in the absence of indoor sources but in the presence of sources, such as gas stoves, indoor NO2 levels at home may greatly exceed those outdoors, affecting the exposure of children, who spend a substantial percentage of their time at home. Children’s exposure to VOCs is affected by parental smoking at home and by cleaning products and building materials at home and school and it is consistently higher than outdoor air. Commuting to school can also contribute significantly to their exposure. Finally, in the case of fine particles, exposure may be higher than both outdoor concentrations and adult exposure, especially due to contributions from the school environment. However, these conclusions are based on a limited number of studies of children, and information on the exposure of very young children and pregnant women is even more limited. Comprehensive new programmes of exposure measurement, such as those planned in the National Children’s Study in the US (Gilliland et al., 2005) will add to our knowledge in the future. Effective policies to deal with specific air pollution issues, such as exposure of children to lead and ETS, have been put in place in developed countries, and in the case of lead, are extending to developing countries (WHO, 2002). However, many more

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challenges remain to reduce personal exposure to air pollution and effects on children’s health. The importance of a holistic approach to managing all aspects of the indoor environment, including air pollution, and the importance of multiple exposures in sensitive groups such as children, have been recognised (Mitchell et al., 2007), and need to extend to all aspects of their personal exposure. Although, as we have explained in this review, the basic concepts and techniques of personal exposure assessment have been established through research over the past 30 years, Ott et al. (2007) identified that personal exposure studies should continue in developed countries as an essential tool to identify health risks, set and review air quality standards and evaluate effective policy interventions. These studies need to be targeted on those groups of children with the highest exposure. More importantly, the current trends in many less developed countries, with rapid growth of outdoor emission sources and urban populations, mean that new programmes of personal exposure evaluation, including both simple screening approaches and more ambitious measurement and modelling campaigns, are urgently needed. These are essential to provide a sound exposure-based evaluation of which policy interventions will most cost-effectively reduce the impacts of air pollution on children’s health.

Acknowledgements The authors would like to thank Dr. Dimitrios Kotzias from JRC, Italy and Prof. Paolo Carrer from the University of Milan, Italy for very kindly providing very important material for this review.

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