(i) USA

The US EPA collates a comprehensive database of outdoor air pollutant measurements conducted at about 3000 locations by state and local air quality monitoring agencies following Federal Reference Methods for the criteria air pollutants (ozone, NO_x/NO₂, SO₂, PM_2.5/PM₁₀, CO, and lead). This network (EPA, 2011a) was initiated in 1978, although monitoring approaches and specific pollutants that have been included have changed over time. At a subset of about 250 of these sites, several air toxics such as VOCs, metals in PM₁₀, and mercury are monitored (EPA, 2012a). This network is complemented by about 180 chemical speciation network monitoring sites (EPA, 2013e), where specific components of PM_2.5 are measured. Several smaller routine monitoring networks are also operated with specific objectives, for example the IMPROVE network to assess the impacts of air pollution on visibility in protected environments (IMPROVE, 2012). In addition, the National Air Toxics Trends Station (NATTS) Network (EPA, 2012b) provides long-term monitoring data for air toxics at 27 sites (20 urban, 7 rural) across the country.

Regular status and trends reports provide information on concentrations of criteria and toxic air pollutants (EPA, 2012c). Summary information for 2010 is presented in Fig. 1.4, Fig. 1.5, Fig. 1.6, Fig. 1.7, and Fig. 1.8 and shows substantial variability in outdoor pollutant concentrations across the USA. The highest concentrations of ozone were observed in California as well as the Ohio River valley, the New England states, Texas, and several south-eastern states. Ozone levels are more heterogeneous over space, with most sites reporting levels below the National Ambient Air Quality Standards (NAAQS) of 75 ppb (annual fourth-highest daily maximum 8-hour concentration) (Fig. 1.4). Concentrations (annual average) of PM_2.5 were highest in California, Indiana, Pennsylvania, and Hawaii. Current levels of PM_2.5 (annual average) are below 15 µg/m³ at all but 6 (of > 700) reporting monitoring sites (Fig. 1.5). During winter periods, high concentrations of PM_2.5 were measured in regions where wood burning is prevalent, such as the Pacific North-western and Alaska (EPA, 2012c). High PM₁₀ concentrations were observed in California as well as Utah, Colorado, and New Mexico, especially in arid regions or industrial areas with multiple coarse particle sources (Fig. 1.6). NO₂ concentrations generally correspond to population density, with the highest concentrations observed in California, the Midwest, and the East Coast (Fig. 1.7). Annual average NO₂ levels have a range of 1–28 ppb, with a mean across 142 Metropolitan Statistical Areas of 10 ppb, well below the NAAQS of 53 ppb. To further describe spatial patterns at high resolution (30 m), Novotny et al. (2011) used a combination of satellite-based estimates and land-use characteristics to model NO₂ across the USA. Using this approach, a population-weighted mean annual average concentration of 10.7 ppb was estimated. SO₂ concentrations are highest in the Upper Midwest and portions of the North-eastern, where coal-fired power generation and industrial sources are common (Fig. 1.8). The 1-hour maximum SO₂ levels ranged from 0.1 ppb to 10.5 ppb, with a mean across 178 Metropolitan Statistical Areas of 2.4 ppb, well below the annual mean NAAQS of 30 ppb. Lead concentrations are much higher near stationary sources such as metals processing, battery manufacturing, and mining (~8 times the concentrations at sites not located near stationary sources) (Fig. 1.9). Levels of airborne lead (maximum 3-month average) are mostly below 0.07 µg/m³ (about half the level of the current NAAQS of 0.15 µg/m³), but levels as high as 1.4 µg/m³ have been measured at a subset of sites (EPA, 2012c).

Fig. 1.4

Annual fourth-highest daily maximum 8-hour ozone concentrations in 2010 in the USA (applicable NAAQS is 0.075 ppm)

Fig. 1.5

Annual average (98th percentile of 24-hour concentrations) PM_2.5 concentrations in 2010 in the USA

Fig. 1.6

Annual average (2nd highest maximum of 24-hour concentrations) PM₁₀ concentrations in 2010 in the USA

Fig. 1.7

NO₂ (98th percentile of 1-hour daily maximum) concentrations in 2010 in the USA

Fig. 1.8

SO₂ (99th percentile of daily 1-hour maximum) concentrations in 2010 in the USA

Fig. 1.9

Lead (maximum 3-month average) concentrations in 2010 in the USA

For all of the criteria pollutants, concentrations have decreased over the past 10 years, after even larger decreases in earlier periods. PM_2.5 and PM₁₀ concentrations show steady reductions that coincide with emissions reduction programmes (EPA, 2012c). Nationally, between 2001 and 2010, 24-hour PM_2.5 and PM₁₀ concentrations declined by 28% and 29%, respectively.

The US EPA operates several networks (the Urban Air Toxics Monitoring Program [UATMP], the National Air Toxics Trends Station [NATTS] Network, and the Community-Scale Air Toxics Ambient Monitoring [CSATAM] Program) that collect information on outdoor concentrations of HAPs. The 2010 report includes data from samples collected at 52 monitoring sites that collected 24-hour air samples, typically on a 1-in-6 day or 1-in-12 day schedule. Of these, 24 sites sampled for 61 VOCs, 30 sites sampled for 14 carbonyl compounds, 26 sites sampled for 22 PAHs, 14 sites sampled for 11 metals, and 23 sites sampled for hexavalent chromium (EPA, 2012d). The report provides detailed summary (and individual site) statistics on all of the measured pollutants, and a risk-based screening approach is applied to identify pollutants of highest priority based on the proportion of measurements exceeding risk-based screening levels. These “pollutants of interest” and 2010 summary concentrations are presented in Table 1.5. Information on trends is provided for individual sites, most of which indicate small decreases over the past about 8 years of monitoring, but data are not systematically analysed for temporal trends at the national level (EPA, 2012d).

Table 1.5

Summary concentrations of air toxics “pollutants of interest” in the USA for 2010.

The US EPA also produces the National-Scale Air Toxics Assessment (NATA) as a screening risk assessment tool that is used to identify pollutants and locations of specific concern in relation to potential cancer risk from air pollution and to assess trends. The most recent NATA, for 2005, was published in 2011 (EPA, 2012e) and includes information on 177 HAPs identified in the Clean Air Act as well as diesel PM.

In addition to government reporting, several research projects have reported outdoor concentrations of HAPs at the national scale. The Health Effects Institute (HEI) summarized outdoor concentrations of seven priority mobile-source air toxics (acetaldehyde, acrolein, benzene, 1,3-butadiene, formaldehyde, naphthalene, and polycyclic organic matter) because it was determined that mobile sources were a sizeable source of human exposure and existing data suggested potential for adverse health effects at outdoor concentrations. The report provides summaries of outdoor concentrations for each of these pollutants, except naphthalene (for which outdoor concentrations are reported as being < 1 µg/m³) (HEI, 2007).

(ii) Canada

In Canada, the National Air Pollution Surveillance (NAPS) network was initiated in 1969 and currently includes about 300 sites in more than 200 communities located in every province and territory of the country. Monitoring is focused on SO₂, NO₂, ozone, CO, PM₁₀, and PM_2.5, and a suite of 50 elements (including metals such as arsenic, lead, and mercury), 14 inorganic and organic anions, and 11 inorganic cations are measured in PM samples. Additional measurements of trace contaminants, including VOCs, PAHs, polychlorinated biphenyls (PCBs), and dioxins, are made at a subset of about 40 locations. Results are summarized in a series of annual and summary reports (Environment Canada, 2010). Concentrations of major air pollutants have declined dramatically over the past about 40 years of measurement, as seen in Fig. 1.10, Fig. 1.11, Fig. 1.12, and Fig. 1.13.

Fig. 1.10

Trends (1970–2008) in annual mean particle (TSP, PM₁₀, PM_2.5) concentrations measured at National Air Pollution Surveillance (NAPS) sites in Canada

Fig. 1.11

Trends (1970–2008) in annual mean SO₂ concentrations at National Air Pollution Surveillance (NAPS) sites in Canada

Fig. 1.12

Trends (1970–2008) in annual mean particulate lead concentrations at National Air Pollution Surveillance (NAPS) sites in Canada

Fig. 1.13

Trends (1990–2007) in annual mean total volatile organic compounds (VOCs) at National Air Pollution Surveillance (NAPS) sites in Canada

In addition, the Canadian Air and Precipitation Monitoring Network operates 29 locations where PM_2.5 speciation is measured. Hystad et al. (2011) used a combination of satellite-based estimates and land-use characteristics to model the concentrations of PM_2.5 and NO₂ across Canada. National models for benzene, ethylbenzene, and 1,3-butadiene were also developed based on land use and source proximity characteristics (Hystad et al., 2011).

Setton et al. (2013) used data from the NAPS network (for 2006) and measurements reported in the literature or government reports since 2000 along with deterministic concentration gradients based on proximity to major roads and industrial sources to estimate exposure to several IARC Group 1 carcinogens in outdoor air in Canada. Table 1.6 presents estimated exposures to selected IARC Group 1, Group 2A, and Group 2B carcinogens in outdoor air in Canada for 2010, based on data from the CAREX database (CAREX Canada, 2013).

Table 1.6

Estimated exposures (in 2010) to selected IARC Group 1, Group 2A, and Group 2B carcinogens in outdoor air in Canada.

(iii) Mexico

The National Information System for Air Quality (SINAICA) operates about 50 sites in Mexico where ozone, NO_x, CO, SO₂, PM₁₀, TSP, and VOCs are measured (SINAICA, 2011). An additional network of about 60 sites is operated in Mexico City for the same general suite of pollutants (Secretaría del Medio Ambiente, 2013). Data from the air quality monitoring networks are centralized by the National Institute of Ecology, with detailed reports provided for specific airsheds. Parrish et al. (2011) provided summaries of trends of annual average concentrations in Mexico City over a 20-year period in which air quality has improved substantially (Fig. 1.14).

Fig. 1.14

Trends in outdoor concentrations of lead, SO₂, NO₂, CO, and particles (TSP, PM₁₀, PM_2.5) in Mexico City

Annual average particulate PAH concentrations (in PM₁₀) collected at a site in Mexico City over a period of several years are summarized in Table 1.7. For several of the PAHs, concentrations increased during this 4-year period, even as PM₁₀ concentrations decreased (Amador-Muňoz et al., 2013).

Table 1.7

Annual medians of mass polycyclic aromatic hydrocarbons (PAHs) concentrations in PM₁₀ (10th–99th percentile) (pg/m³) from the sampling days of 1999–2002 at a site in south-west Mexico City.

Mexico, Canada, and the USA operate a collaborative network for measurement of dioxins and furans, including nine stations in Mexico (five rural, two semi-urban, and two urban sites) (Cardenas et al., 2011). The mean concentrations for the background (rural) and semi-urban sites were 1.59 fg/m³ and 18.6 fg/m³, respectively, which are of the same order of magnitude as those reported by the outdoor monitoring networks in the USA and Canada. However, the mean concentration for the urban sites was 282 fg/m³, which is significantly higher than concentrations measured at similar sites in the USA and Canada.

(i) PM₁₀ and PM_2.5

The PM₁₀ and PM_2.5 maps (Fig. 1.15 and Fig. 1.16) show that concentrations are lower in northern Europe than in southern and eastern Europe. The PM₁₀ map is based on a substantially larger number of sites than the PM_2.5 map, because PM_2.5 monitoring has not been fully developed within Europe because of later adoption of the air quality guideline for PM_2.5. Several research projects have broadly confirmed the general patterns across Europe (Hazenkamp-von Arx et al., 2004; Putaud et al., 2004, 2010; Van Dingenen et al., 2004; Eeftens et al., 2012). In the ESCAPE study (Eeftens et al., 2012), based on standardized gravimetric measurements using the Harvard impactor in 20 study areas, average PM_2.5 concentrations below 10 μg/m³ were found in northern Europe (Fig. 1.17). In southern European cities, for example Athens (Greece) and Turin (Italy), annual average PM_2.5 concentrations above 20 μg/m³ were measured. Relatively high concentrations were also found in the two central European cities Györ (Hungary) and Kaunas (Lithuania). Fig 1.17 further illustrates significant intra-urban spatial variation, particularly for coarse particles (calculated as PM₁₀ − PM_2.5) and PM_2.5 absorbance. A regression analysis of PM_2.5 on PM₁₀ concentrations for 60 sites across Europe found site-specific slopes varying between 0.44 and 0.90 (Putaud et al., 2010). Fig 1.18 illustrates the spatial variation of PM_2.5 and PM₁₀ concentrations across European cities. A large range of PM₁₀ concentrations (5–54 μg/m³ annual average) is observed across the network. Urban background PM₁₀ annual mean and median values are significantly larger in southern Europe (median, 36 μg/m³) than in north-western Europe (median, 24 μg/m³) and central Europe (median, 26 μg/m³). The range of PM_2.5 concentrations observed across the network (3–35 μg/m³ annual average) is similar to that of PM₁₀. In north-western and southern Europe, an increasing gradient in PM_2.5 is generally observed from rural to urban sites. In central Europe, PM_2.5 can be as large at rural sites as at urban sites (Putaud et al., 2010). The chemical composition of PM differs widely across Europe, with generally more carbonaceous matter in central Europe, more nitrate in north-western Europe, and more mineral dust in southern Europe (Putaud et al., 2010; Tørseth et al., 2012). Table 1.8 presents the average contributions of major components to PM concentrations. The elemental composition of eight elements representing major sources across Europe has recently been published based on the ESCAPE study (de Hoogh et al., 2013). Significant variability of concentrations both within and between study areas across Europe was found.

Fig. 1.17

Spatial variation of 2009–2010 annual average particulate matter (PM) concentrations across Europe

Fig. 1.18

Spatial variation of 1996–2007 annual average particulate matter (PM) concentrations across Europe

Table 1.8

Major constituent contributions to PM₁₀, PM_2.5, and PM_coarse in Europe.

There is a lack of comprehensive monitoring for the heavy metals lead, arsenic, cadmium, and nickel across Europe. In general, low concentration levels are measured (often below the lower assessment threshold), with the exception of sites located next to specific industries (EEA, 2012).

There are no routine measurements of ultrafine particles available across Europe, as in other parts of the world. In individual cities, including Amsterdam (the Netherlands), Athens (Greece), Birmingham (United Kingdom), and Helsinki (Finland), total particle number counts are available. Research projects have included snapshots of spatial patterns across Europe (e.g. Puustinen et al., 2007). Urban background levels were about 10 000–20 000 particles/cm³ in four large cities, with substantially higher concentrations measured near major roads (Puustinen et al., 2007).

(ii) NO₂

The most striking feature of the NO₂ map is the higher concentrations in major cities (Fig. 1.19). European research studies have also shown a general north-to-south increasing gradient in NO₂ concentrations (Hazenkamp-von Arx et al., 2004; Cyrys et al., 2012). Fig 1.20 further illustrates significant intra-urban spatial variation, which exceeded between-area variability. In virtually all study areas, there was at least one site in which the current EU annual average standard of 40 μg/m³ was exceeded.

Fig. 1.20

Spatial variation of 2008–2011 annual average nitrogen dioxide (NO₂) and nitrogen oxides (NO_x) concentrations across Europe

(iii) SO₂

Current average SO₂ concentrations in Europe are low, typically well below 10 μg/m³ in large parts of Europe (Fig. 1.21). The highest concentrations occur in eastern Europe, related to industrial activities and the remaining coal burning (EEA, 2012). Currently, emissions are predominantly from power generation (Tørseth et al., 2012). International shipping emissions have become a significant source because shipping emissions have been much less affected by policies than industrial emissions have (Tørseth et al., 2012).

(iv) CO

CO concentrations are typically low, due to the significant reduction in traffic emissions by catalytic converters. Still, the highest concentrations occur in urban areas, especially at traffic sites and occasionally at industrial locations (EEA, 2012). There is not a clear pattern across Europe.

(v) Ozone

Fig. 1.22 shows the map of maximum 8-hour average ozone concentrations. The 26th highest value is shown because of the formulation of the EU standard (120 µg/m³ as an 8-hour maximum not to be exceeded on > 25 days). The highest concentrations occur in southern Europe and in Austria and Switzerland, related especially to higher temperatures and altitude (EEA, 2012). Ozone concentrations are generally higher at rural stations than at urban background stations. Concentrations at traffic sites are even lower, related to scavenging of ozone by NO (EEA, 2012).

(vi) Benzene

Current average benzene concentrations in Europe are low, typically below 5 μg/m³ in large parts of Europe. The highest concentrations occur at traffic sites and at industrial locations (EEA, 2012).

(vii) Benzo[a]pyrene

Current average B[a]P concentrations in Europe are low, typically below 1 ng/m³ in large parts of Europe. There is no clear north-to-south gradient. The highest concentrations occur in areas with domestic coal or wood burning and industrial areas, particularly in eastern Europe (EEA, 2012).

(viii) Pollution trends

Fig. 1.23, Fig. 1.24, Fig. 1.25, and Fig. 1.26 show the trends in annual average concentrations of PM and major gaseous components based on the European AirBase database (EEA, 2012).

Fig. 1.23

Trends in annual average concentrations of PM₁₀ (2001–2010) and PM_2.5 (2005–2010) by station type across Europe

Fig. 1.24

Trends in NO₂ and NO_x annual mean concentrations (2001–2010) by station type across Europe

Fig. 1.25

Trend in annual average SO₂ concentrations (2001–2010) by station type across Europe

Fig. 1.26

Trend in annual average mean benzene concentrations (2001–2010) by station type across Europe

Annual average concentrations of PM₁₀ and PM_2.5 have not decreased much since 2000 despite assumed decreases in emissions of precursors (Fig. 1.23). Between 1990 and 2004, a clear decrease (∼44%) in total PM emissions occurred (EEA, 2007; Harrison et al., 2008). At the EMEP regional background sites, concentrations of PM₁₀ and PM_2.5 decreased by 18% and 27%, respectively, between 2000 and 2009 (Tørseth et al., 2012). Longer-term trends are difficult to quantify from monitoring networks because PM₁₀ and especially PM_2.5 were often not measured until the 1990s. High annual average concentrations of PM₁₀ and PM_2.5 were measured in research projects in central and eastern Europe in the 1990s (Hoek et al., 1997; Houthuijs et al., 2001). A series of studies in eastern Germany reported a significant decline in particle mass concentration, accompanied by an increase in concentrations of ultrafine particles (Kreyling et al., 2003).

Longer trends are available for sulfate, although sampling artefacts complicate the assessment (Tørseth et al., 2012). Consistent with the large reduction in SO₂ emissions, sulfate concentrations decreased by 70% between 1980 and 2009, mostly between 1990 and 2009 (56% reduction). Nitrate concentrations decreased by much less than sulfates (8% between 1990 and 2009), reflecting the smaller reduction in precursor emissions and a shift in the equilibrium with ammonia and nitric acid towards particulate nitrate (Tørseth et al., 2012).

Annual average concentrations of NO₂ have remained fairly stable since 2000, whereas NO_x concentrations did decrease substantially at traffic sites (Fig. 1.24). At the EMEP regional background sites, NO₂ concentrations decreased by 23% between 1990 and 2009 (Tørseth et al., 2012). The decrease in NO_x concentrations is explained by lower emissions from motorized traffic, since NO_x emissions in Europe had increased, especially until about 1990, because of emissions from road transportation (Vestreng et al., 2009). The reduced fuel consumption and early technological changes in western Europe between 1980 and 1990 were not sufficiently effective to reduce emissions (Vestreng et al., 2009). After 1990, emissions decreased because of new technologies in western Europe and the economic recession in eastern Europe, while increasing car ownership in eastern Europe resulted in increased road traffic emissions from that region (Vestreng et al., 2009).

The limited decrease in NO₂ concentrations is due to an increase in primary NO₂ in road traffic emissions. Primary NO₂ emissions have gained importance compared with the ozone/NO_x equilibrium (Keuken et al., 2009; Mavroidis & Chaloulakou, 2011). The increase in primary NO₂ emissions has been attributed to increased use of diesel-powered vehicles, which emit a higher fraction of NO₂ compared with gasoline-powered vehicles (Grice et al., 2009; Anttila et al., 2011; Carslaw et al., 2011). In addition, the aftertreatment devices (such as oxidation catalysts) implemented for reducing PM emissions by diesel vehicles contribute to the increasing fraction of primary NO₂ in NO_x (Mavroidis & Chaloulakou, 2011; Williams & Carslaw, 2011). For diesel-fuelled vehicles equipped with catalytic diesel particulate filters, primary NO₂ fractions of about 40–50% are reported (Carslaw et al., 2007). A consequence of this trend is that the value of NO₂ as a marker of the mixture of traffic-related pollutants may have changed.

Concentrations of SO₂ have continued to decrease significantly in Europe at traffic sites, urban background sites, and regional background sites (Fig. 1.25). On average, concentrations were halved between 2000 and 2010 (EEA, 2012). Compared with the early 1990s, concentrations have decreased several-fold, due to significant reductions in the use of coal for power generation and other sources such as domestic heating, lower sulfur content in fuel, and substantial technological developments such as desulfurization at power plants (Tørseth et al., 2012). At the EMEP regional background sites, SO₂ concentrations decreased by 92% between 1980 and 2009, mostly between 1990 and 2009 (75% reduction) (Tørseth et al., 2012). Modest reductions in emissions between 1980 and 1989 occurred largely in western Europe, whereas large reductions between 1990 and 1999 occurred mainly in central and eastern Europe (Vestreng et al., 2007). Important factors were the drop in industrial activity in eastern Europe after the political changes in 1989 and a switch from solid fuel to oil and natural gas containing lower amounts of sulfur (Vestreng et al., 2007).

Concentrations of benzene have decreased substantially in the past decade, especially at traffic sites (Fig. 1.26). The main explanation for this trend is the lower benzene content of gasoline.

There are insufficient data from networks to specify a Europe-wide trend for B[a]P concentrations (EEA, 2012), although there are studies from selected locations. A study in Munich showed a decrease in concentrations by an order of magnitude between 1981 and 2001, with most of the change occurring before 1993 (Schauer et al., 2003). Large decreases in PAH concentrations have also been reported for the United Kingdom (Brown et al., 2013). Comparison of data from different sites in London showed a decrease in B[a]P concentrations from 10–100 ng/m³ in the 1950s to less than 0.1 ng/m³ currently. Median B[a]P concentrations of all sites in the current PAH network have decreased from about 1.4 ng/m³ to 0.2 ng/m³ (Brown et al., 2013). The decline in the past two decades was attributed to dramatically reduced emissions from industrial metal processing and a ban on burning agricultural stubble (Brown et al., 2013).

Overall, air quality has generally improved in Europe, and the mixture has clearly changed in composition.

(i) India

Outdoor air quality information in India is collected primarily by the National Air Quality Monitoring Programme (NAMP). Administered by the Central Pollution Control Board (CPCB), Ministry of Environment and Forests, Government of India, the NAMP network was initiated in 1984 with seven stations in the cities of Agra and Anpara (situated close to the National Capital Region). This network has steadily grown to include nearly 503 outdoor air quality monitoring stations across 209 cities in 26 states and 5 union territories in 2011. Criteria air pollutants listed under the earlier 1994 NAAQS and monitored under the NAMP include PM_10, SO₂, and NO₂. Integrated 8-hour and 24-hour measurements are performed twice a week, resulting in about 104 observations from each station annually. In addition, CO, NH₃, lead, and ozone are monitored at selected locations. The NAAQS were recently revised (CPCB, 2009b). PM_2.5 and air toxics such B[a]P, arsenic, and nickel are now included in the revised NAAQS and are slowly being added to the routine monitoring performed under the NAMP. The CPCB collates the data received by the entire network in the central Environmental Data Bank. After completion of quality assurance/quality control, these data are made available in the public domain. This section summarizes pollutant-specific information available from the CPCB, with additional details from relevant published studies where available.

Analyses of CPCB data from 402 stations on criteria air pollutants for the 10-year period 2000–2010 indicate a decline in the national annual average SO₂ concentration; NO₂ levels remained largely unchanged, and PM₁₀ levels showed a modest increase (Fig. 1.27). Although monitoring locations do not cover all cities across India, they do provide coverage across all states, indicating the extent of exposures that urban populations are likely to experience (CPCB, 2012).

Fig. 1.27

National mean concentrations derived from data across National Air Quality Monitoring Programme (NAMP) stations together with the 10th and 90th percentile for SO₂, NO₂, and PM₁₀ in India

The CPCB classifies the air quality at NAMP locations into four broad categories – low (acceptable), moderate, high, and critical levels of pollution – based on the exceedance factor (the ratio of annual mean concentration of a pollutant to that of the respective standard), as shown in Table 1.9.

Table 1.9

India Central Pollution Control Board (CPCB) criteria for classification of pollution levels.

By these criteria, the levels of SO₂ at most locations have not only declined but are mostly low across the locations monitored, whereas NO₂ and PM₁₀ levels have remained moderately to critically high across many locations over the years (Fig. 1.28). Maximum levels in the most polluted states/cities often exceed the NAAQS by 2–5-fold, as may be seen in Table 1.10.

Fig. 1.28

Trends in pollution levels for 2000−2010 in India in relation to Central Pollution Control Board (CPCB) criteria given in Table 1.9

Table 1.10

Profile of the 10 most polluted Indian cities in 2010^a.

Limited information is currently available on chemical speciation of PM fractions or the differential distribution of PM and gaseous pollutants in relation to land use. In a recent national source apportionment study performed across six cities (CPCB, 2011), levels of PM₁₀ and PM_2.5 in the outdoor air were consistently in excess of the NAAQS across background, kerbside, industrial, commercial, and residential sites, and winter and post-monsoon season levels were much higher than summer levels (CPCB, 2011). NO₂ levels were of concern at several locations, whereas SO₂, ozone, and CO levels were generally within the prescribed standards. Results from analyses of PM components indicate that EC and OC accounted for 20–45% of PM₁₀ and 25–75% of PM_2.5 in cities. SO₄²⁻ and NO₃⁻ accounted for 10–30% of PM₁₀ in cities. Vehicle exhaust, secondary particulates, construction activities, oil burning (e.g. diesel or heavy oil), biomass burning, coal combustion, kerosene combustion, and industrial emissions have been identified to be the dominant sources for criteria air pollutants in these cities (CPCB, 2011).

In addition, several industrial hotspots have been identified by the CPCB using the new risk assessment criteria of the Comprehensive Environmental Pollution Index (CEPI) (CPCB, 2009a). The CEPI weights the toxicity of the agents, the volume of emissions, the scale of the population exposed, and the exposure pathways involved. Of special relevance to carcinogenicity is the fact that unlike criteria air pollutant data provided by the NAMP, the CEPI includes weighted contributions from a range of compounds including probable carcinogens (US EPA Class 2 and 3 or substances with some systemic toxicity, such as VOCs, PAHs, and PCBs) as well as known carcinogens or chemicals with significant systemic or organ system toxicity (such as vinyl chloride, benzene, lead, radionuclides, hexavalent chromium, cadmium, and organophosphates) (CPCB, 2009a).

Data from the NAMP network of the CPCB provide the most comprehensive description of the status of air quality across Indian cities as far as criteria air pollutants are concerned. Air toxics are seldom monitored routinely, and hence information on air toxics is mostly contained in individual studies conducted by academic and/or research organizations.

(ii) China

As a result of the unprecedented rapid development in industrialization and urbanization in the past decades, many Chinese cities have air pollution levels well above health-based standards (HEI, 2010b; Gao et al., 2011), and air pollution associated with health impacts has become a growing concern (Zhang et al., 2010a). In this section, information on outdoor concentration levels, spatial variation, and time trends in major outdoor air pollutants is summarized. Data are extracted primarily from publications on air pollution and epidemiological research conducted in China, as well as from government routine monitoring networks.

In the last century, air pollution from coal combustion was the dominant type of air pollution in most cities in China, and the air pollution was severe. Various pollution control measures and devices have been gradually put in place for the industrial and residential sectors.

Coal will remain the major energy source in China for the near future. However, in recent years, outdoor air pollution in most Chinese cities has become a mixture of emissions from coal combustion, vehicles, and biomass burning, as well as from sandstorms in the north-western region (HEI, 2010b). The annual average levels of PM₁₀, SO₂, and NO₂ in 31 provincial capital cities in China are summarized in Fig. 1.29. The concentrations of PM₁₀, SO₂, and NO₂ in most large urban areas in China have generally stabilized or are decreasing (albeit with some notable exceptions). However, along with the reductions in concentrations of PM₁₀, SO₂, and NO₂ in China, the pollution episodes of PM_2.5 and ozone in some city cluster areas suggest the degradation of regional air quality. As total air pollution sources and overall emissions increase in China, yet become more dispersed, regional and transboundary air quality issues are likely to become increasingly important. The mean concentrations of PM₁₀, PM_2.5, SO₂, NO₂, and ozone reported in time-series studies conducted in China from 1990 to 2012 are presented in Table 1.11.

Fig. 1.29

Annual average levels of PM₁₀, SO₂, and NO₂ in 31 provincial capital cities in China, 2003–2010

Table 1.11

Mean concentrations of PM₁₀, PM_2.5, SO₂, NO₂, and O₃ reported in time-series studies conducted in China (1990–2012).

PM₁₀

As a result of energy restructuring, the annual average levels of PM₁₀ in 31 provincial capital cities in China decreased by about 25% from 2003 to 2010 (Fig. 1.29); however, the levels are still high compared with elsewhere in the world. In 2010, the annual concentrations of PM₁₀ were 121 μg/m³ in Beijing, 79 μg/m³ in Shanghai, 69 μg/m³ in Guangzhou, and 126 μg/m³ in Xi’an (National Bureau of Statistics of China, 2011). The spatial variations of PM₁₀ in major Chinese cities suggest more serious particulate pollution in northern regions in China, due to the longer heating season in winter as well as the local topography and the impact of sandstorms. The nationwide distribution of air pollution levels is likely to be related to the spatial distribution of emission sources across the country (National Bureau of Statistics of China, 2012).

SO₂

Trends in air quality in 31 provincial capital cities in China from 2003 to 2010 suggest a significant decrease of about 30% in annual average SO₂ concentrations in urban areas, with the exception of an average increase in SO₂ concentration during 2008 (Fig. 1.29). The reductions in SO₂ have resulted from the use of low-sulfur fuels and the relocation of major coal-fired power plants and industrial facilities from urban areas to outside cities. In more recent years, annual levels of SO₂ were below 60 μg/m³ in most cities, and PM₁₀ concentration levels continued to decrease (National Bureau of Statistics of China, 2012).

NO₂

Due to tightened motor vehicle emission standards in place from the early 2000s, annual average NO₂ levels remained stable at 40 μg/m³, with some variations (Fig. 1.29). However, with the increasing numbers of motor vehicles in most Chinese cities, NO₂ levels in the more developed cities tend to be higher, at more than 45 μg/m³ (National Bureau of Statistics of China, 2012; Table 1.11).

PM_2.5 and ozone

At present, very limited data are available on the annual levels of PM_2.5 and ozone, which were newly included in the revised Chinese AAQS released in March 2012 (MEPPRC, 2012).

To assess exposure, time-series studies have been conducted (Shang et al., 2013). The reported average concentrations of PM_2.5 and 8-hour ozone in these studies were in the ranges of 55–177 μg/m³ and 34–86 μg/m³, respectively (Table 1.11). In these studies, the reported PM_2.5 levels in Beijing, Shanghai, Guangzhou, and Xi’an were all well above the Chinese national standards and international air quality standards (Fig. 1.30). Brauer et al. (2012) estimated that the population-weighted annual average levels of PM_2.5 in East Asia had increased from 43 μg/m³ to 55 μg/m³ between 1990 and 2005, whereas they reported the highest measurement of annual average PM_2.5 concentration (in 2005) of 58 μg/m³ in Beijing and the highest derived PM_2.5 concentration (calculated from PM₁₀ measurements) of 121 μg/m³ in Datong, a coal-mining centre in Shanxi Province (Brauer et al., 2012). In northern China, estimated PM_2.5 levels in 2010 were above 80 μg/m³ (Fig. 1.31).

Fig. 1.30

Comparisons of reported annual PM_2.5 levels (μg/m³) in Beijing, Shanghai, Guangzhou, and Xi’an with the Chinese national standards and international air quality standards

Fig. 1.31

Estimated levels of PM_2.5 (μg/m³) in 2010 in China

Geological materials, organic materials, EC, and secondary aerosols (such as SO₄²⁻, NO₃⁻, and NH₄⁺) are the primary components of PM_2.5 in China; however, due to source variations, the concentrations of primary PM_2.5 components vary significantly across locations and seasons (Niu et al., 2006; Cao et al., 2012). On average, SO₄²⁻, NO₃⁻, NH₄⁺, organic materials, and EC account for more than 70% of the PM_2.5 mass in summer, whereas the percentage is even higher in winter (Cao et al., 2012).

Lead levels are still high in Chinese cities, reaching an average of 1.68 µg/m³ in Xi’an during winter. High correlations of lead with arsenic and SO₄²⁻ concentrations indicate that much of the lead derives from coal combustion rather than from leaded fuels, which were phased out by 2000 in China. Although limited fugitive dust markers were available, scaling of iron by its ratios in source profiles showed that in most of the cities, 20% of PM_2.5 derives from fugitive dust (Cao et al., 2012).

Photochemical smog, in the presence of solar radiation, is commonplace in city cluster areas of China with greatly increased numbers of vehicles (e.g. the Beijing–Tianjin–Hebei area and the Pearl River Delta region). Studies in these areas reported high concentrations of PM induced by photochemical smog. For example, in Shenzhen in 2004, the 24-hour average PM_2.5 and PM₁₀ concentrations in summer were 35 μg/m³ and 57 μg/m³, respectively, and in winter were 99 μg/m³ and 137 μg/m³, respectively (Niu et al., 2006). In Guangzhou, the summer 24-hour average PM_2.5 concentration was 97.5 μg/m³ (Wang et al., 2006).

Polycyclic aromatic hydrocarbons

Daily and hourly average or snapshot concentrations of outdoor PAHs in urban and industrial areas in China are high compared with elsewhere in the world (usually 10–20 ng/m³). Mean concentrations of 16 outdoor PAHs of up to 1400 µg/m³ were observed in Taiyuan, a coal-polluted city in central China, in December 2006 (Fu et al., 2010). Concentrations of PAHs in the gas phase were also reported at high levels, in particular in megacities and large cities (e.g. Beijing, Shanghai, and Hangzhou) (Liu et al., 2001, 2007; Wang et al., 2002; Zhang et al., 2009b; Zhu et al., 2009; Wei et al., 2011).

Volatile organic compounds

High daily and hourly average or snapshot concentrations of outdoor benzene, toluene, and xylene have been reported in Chinese megacities (e.g. Beijing, Shanghai, and Guangzhou) compared with the levels observed in the USA (Zou et al., 2003; Zhang et al., 2006a; Wei et al., 2007; Lu et al., 2008; Wang et al., 2010; Zhou et al., 2011).

(iii) Japan

As one of the most developed countries in Asia, Japan experienced serious pollution from industrial and automobile emissions in the 1950s and 1960s, and the main energy source shifted from coal to oil, making SO₂ a major air pollutant (Committee on Japan’s Experience in the Battle Against Air Pollution, 1997). Air pollution levels declined after the introduction of pollution control measures in the 1970s. As an example, the nationwide annual average concentrations of SO₂ decreased to 0.015 ppm [42.3 µg/m³] in the 1970s and further to 0.006 ppm [16.9 µg/m³] in 1990 (Ministry of the Environment of Japan, 2011).

In contrast to the rapid decline in the concentrations of SO₂, pollution from mobile sources increased during the 1970s. The annual concentrations of NO₂ in 1970 were 0.035 ppm [70.9 µg/m³] at general sites and 0.042 ppm [85.1 µg/m³] at roadside sites; those of suspended PM (PM < 7 μm in diameter [SPM]) in 1975 were 50 μg/m³ at general sites and 84 μg/m³ at roadside sites (Ministry of the Environment of Japan, 2011). After the tightened mobile-source emission control regulations and measures were put in place, the concentrations of NO₂ and SPM declined gradually.

In 2011, the annual concentrations of major air pollutants in Japan were as follows: SO₂, 0.002 ppm [5.64 µg/m³] at general sites and 0.003 ppm [8.46 µg/m³] at roadside sites; NO₂, 0.011 ppm [22.3 µg/m³] at general sites and 0.021 ppm [42.5 µg/m³] at roadside sites; SPM, 20 μg/m³ at general sites and 22 μg/m³ at roadside sites; PM_2.5, 15.4 μg/m³ at general sites and 16.1 μg/m³ at roadside sites; and CO, 0.3 ppm [370 µg/m³] at general sites and 0.5 ppm [617 µg/m³] at roadside sites (Ministry of the Environment of Japan, 2011). In recent years, in addition to making the necessary efforts towards reducing the concentrations of these pollutants, Japan has also faced problems such as relatively high and stable concentrations of ozone in metropolitan areas (e.g. annual concentration of 0.028 ppm [59.2 µg/m³] in Tokyo in 2011) (Bureau of Environment of Tokyo, 2013).

(iv) Other Asian countries

Since the 1990s, most Asian countries have established national routine air quality monitoring networks for the criteria pollutants PM₁₀, SO₂, and NO₂, whereas the air quality data on PM_2.5 and ozone have been very limited. In the cities with routine air quality monitoring systems, some improvements in air quality have been achieved in the past decades; however, the levels of PM₁₀ and SO₂ still exceed the World Health Organization (WHO) air quality guidelines (AQG) (Fig. 1.32; Clean Air Asia, 2010). PM₁₀ has been a major pollutant in Asian cities, with annual average PM₁₀ concentrations well above the WHO AQG. Since 1995, most Asian cities have reported reduced NO₂ levels, with annual average concentrations below the WHO AQG. For SO₂, the annual average levels have decreased remarkably from the 1990s to the 2000s in most Asian cities, due to energy restructuring in the area.

Fig. 1.32

Average of annual average outdoor air quality in selected Asian cities (1993–2008)

PM₁₀

As of 2008, PM₁₀ was still a major pollutant in Asia; annual average PM₁₀ concentrations ranged from 11 μg/m³ to 375 μg/m³ in the 230 Asian cities with the highest levels observed in East and South-East Asia (Fig. 1.33; Clean Air Asia, 2010).

Fig. 1.33

Annual PM₁₀ concentrations (μg/m³) in 230 Asian cities

SO₂

In 2008, the monitoring data for 213 Asian cities showed that SO₂ levels were still high in some cities in East Asia, particularly those near industries. Annual average SO₂ concentrations ranged from 1.3 μg/m³ to 105 µg/m³. The mean of annual average SO₂ concentrations for 213 Asian cities was 18.7 μg/m³ in 2008. See Fig. 1.34 (Clean Air Asia, 2010).

Fig. 1.34

Annual SO₂ concentrations (μg/m³) in 213 Asian cities

NO₂

In 2008, annual average NO₂ concentrations ranged from 1.9 μg/m³ to 77 μg/m³ in 234 Asian cities; the mean of annual average NO₂ concentrations was 30.7 μg/m³. About 73% of the 234 cities had annual average NO₂ concentrations below the WHO AQG of 40 μg/m³. See Fig. 1.35 (Clean Air Asia, 2010).

Fig. 1.35

Annual NO₂ concentrations (μg/m³) in 234 Asian cities

PM_2.5

PM_2.5 levels have increased in medium to large Asian cities. Only a few Asian countries have set PM_2.5 air quality standards, and of those countries, none have standards equivalent to the WHO AQG, but generally the standards are close to the WHO interim target. The population-weighted annual average concentrations of PM_2.5 were estimated to range between 16 μg/m³ and 55 μg/m³, with the highest levels observed in East Asia, followed by South Asia, in 2005 (Brauer et al., 2012).

A multicity study examined the seasonal variations of PM_2.5 mass concentrations and species in mixed urban areas (2001–2004) in six Asian cities: Bandung (Indonesia), Bangkok (Thailand), Beijing (China), Chennai (India), Manila (Philippines), and Hanoi (Viet Nam) (Table 1.12). These cities differed in geographical location, topography, energy use, industry, mix of vehicles, and density. The climate of the region is dominated by monsoons, with two distinct seasons, dry and wet, although each dry and wet season may cover different months of the year in different countries. In these cities, the major components of PM_2.5 and PM₁₀ were found to be organic matter (calculated in this study as 1.7 times the OC content); crustal material, including aluminium, calcium, silicon, titanium, iron, potassium, and their oxides; the secondary aerosols NO₃⁻ and SO₄²⁻; and EC/BC. The “trace metals” group included all the remaining elements except crustal elements, sodium, and sulfur. OC was not analysed in most sites, except for the Bangkok Metropolitan Region and Beijing; hence, comparison of OC levels was not possible. In all these cities, the levels of PM₁₀ and PM_2.5 were found to be high, especially during the dry season, frequently exceeding the US EPA standard for PM₁₀ and PM_2.5, especially at the traffic sites (Kim Oanh et al., 2006).

Table 1.12

City-wise average mass and major components of PM_2.5 (μg/m³) during the dry and wet seasons in six cities in Asia (2001–2004).

PAHs in particles are also important in Asia. Shen et al. (2013) estimated that Asian countries contributed 53.5% of the global total PAH emissions, with the highest emissions from China (106 Gg) and India (67 Gg) in 2007.

(i) Africa

Measurements of air pollution in Africa are limited, and environmental agencies do not exist in all countries. World agencies provide some aggregate information for the continent, which can be complemented by local research, as air quality data in Africa are scarce.

Fig. 1.36 and Fig. 1.37 present the mean concentrations for PM measured with at least 2 months of monitoring coverage in selected African cities. The limited data show that the concentrations range from 7 µg/m³ to more than 100 µg/m³ for PM_2.5 and from 12 µg/m³ to more than 230 µg/m³ for PM₁₀ in the African cities studied. Among the reported air pollution measurement campaigns, Dionisio et al. reported the geometric mean concentrations of PM_2.5 and PM₁₀ along the mobile monitoring path [street-level monitoring] of 21 µg/m³ and 49 μg/m³, respectively, in the neighbourhood with the highest socioeconomic status and 39 µg/m³ and 96 μg/m³, respectively, in the neighbourhood with the lowest socioeconomic status and the highest population density in Accra, Ghana. The factors that had the largest effects on local PM pollution were nearby wood and charcoal stoves, congested and heavy traffic, loose-surface dirt roads, and trash burning (Dionisio et al., 2010a).

Fig. 1.36

PM₁₀ concentrations (μg/m³) in selected African cities

Fig. 1.37

PM_2.5 concentrations (μg/m³) in selected African cities

(ii) South America

Continuous measurements of air pollution are available in more than half of the South American countries. However, the spatial distribution of air monitoring stations in South America is not balanced. For instance, in Brazil monitoring stations are located mostly in large metropolitan regions and do not cover the remaining areas of Brazil; only 8 out of 27 Brazilian states (including the Federal District) have set up air monitoring networks. Fig 1.38 and Fig. 1.39 summarize the most recent data on PM concentrations in South American countries; concentrations ranged from 22 µg/m³ to 70 µg/m³ for PM₁₀ and from 7 µg/m³ to 35 µg/m³ for PM_2.5.

Fig. 1.38

PM₁₀ concentrations (μg/m³) in selected South American cities

Fig. 1.39

PM_2.5 concentrations (μg/m³) in selected South America cities

Besides high PM concentrations measured in South American cities, high concentrations of formaldehyde were reported in some countries, such as Brazil. In downtown Rio de Janeiro, mean formaldehyde concentrations rose 4-fold from 1998 to 2002, to 96 μg/m³ (with peak 2-hour concentrations as high as 138 μg/m³) (Corrêa & Arbilla, 2005). A further 10-fold increase in formaldehyde concentrations was reported in Rio de Janeiro from 2001 to 2004, as a consequence of the introduction of compressed natural gas vehicles in 2000 (Martins et al., 2007).

(iii) The Middle East

WHO (2011) depicts air monitoring information for 39% of the Middle East countries. Air pollution monitoring coverage in the Middle East is similar to that in South American countries – 67% of both regions have some type of air quality data available; however, the air pollution monitoring sites are not evenly distributed across Middle East countries. Fig 1.40 and Fig. 1.41 depict PM concentrations across the Middle East; concentrations mostly ranged from 25 µg/m³ to 100 µg/m³ for PM₁₀ and from 50 µg/m³ to 300 µg/m³ for PM_2.5.

Fig. 1.40

PM₁₀ concentrations (μg/m³) in selected Middle East cities

Fig. 1.41

PM_2.5 concentrations (μg/m³) in selected Middle East cities

(iv) Australia

Between 1999 and 2008, there were significant decreases in the levels of air pollution in Australia. Levels of CO, NO₂, SO₂, and lead in urban areas declined to levels significantly below the national air quality standards. However, levels of PM and ozone did not decrease significantly over the time period. Between 1999 and 2008, the median 1-hour and 4-hour ozone levels varied between 0.02 ppm and 0.04 ppm in most Australian cities; the higher levels (~0.04 ppm) were observed in some areas including South East Queensland and Toowoomba. For PM, the median annual levels of PM₁₀ remained at 15–20 μg/m³ and the PM_2.5 levels were 5–10 μg/m³ in most Australian cities in 2008 (Australian Government, 2010).

(i) Concentration in outdoor air

People may be exposed to outdoor air pollutants directly while spending time outdoors. However, a significant fraction of the exposure to outdoor air pollutants occurs while spending time indoors, as people generally spend a large fraction of their time indoors and pollutants penetrate into the indoor environment. Because people spend a significant fraction of their time in or near their own home, exposure in epidemiological studies is often characterized based on the residential address. Residential address information is generally available from ongoing epidemiological studies designed for purposes other than studying air pollution effects. Most often the outdoor concentration at the address is characterized. A large number of studies have evaluated spatial variation of outdoor air pollution (Monn, 2001; HEI, 2010b). Spatial variation can be present at various scales, ranging from global to local (HEI, 2010b). Examples of the various scales of variation are illustrated in Fig. 1.42 and Fig. 1.43, and in Fig. 1.3 in Section 1.4.1a. Contrasts across countries within a continent are discussed further in Section 1.4.1.

Fig. 1.42

Estimated United Kingdom annual average background PM₁₀ concentrations (μg/m³) during 2002

Fig. 1.43

Annual average PM₁₀ concentrations (μg/m³) in London calculated for 2004

As Fig. 1.43 illustrates, within urban areas, significant spatial variation is present related to proximity to major roads. Large gradients with distance to major roads have been identified for traffic-related pollutants, including NO_2, CO, benzene and other VOCs, EC, and ultrafine particles (HEI, 2010b). Gradients are relatively small for PM_2.5 and PM₁₀ compared with, for example, EC (HEI, 2010b; Janssen et al., 2011). A summary of studies measuring both PM_2.5 or PM₁₀ and BC reported an average ratio of 2 for BC and 1.2 for PM concentrations at street sites compared with urban background levels (Janssen et al., 2011). Spatial gradients vary significantly by pollutant and are nonlinear near major roads, with steep decreases in the first 50–100 m and smaller decreases up to about 300–500 m (HEI, 2010b). In compact urban areas, gradients from major roads are much smaller.

A growing number of studies have documented significant exposures to a range of traffic-related air pollutants, including fine and ultrafine particles, EC, and VOCs, while in transit, including walking, cycling, car and bus driving, and underground (Fig. 1.44; Kaur et al., 2007; de Hartog et al., 2010; Zuurbier et al., 2010; de Nazelle et al., 2011). Commuters’ exposures further differ significantly with route, and despite the relatively short time typically spent in traffic, significant contributions to average personal exposure may occur (Marshall et al., 2006; Kaur et al., 2007; Van Roosbroeck et al., 2008; de Nazelle et al., 2013; Dons et al., 2012, 2013). Van Roosbroeck et al. (2008) found that time spent in traffic was a significant predictor of 48-hour personal exposure to soot and PM_2.5 in elderly adults in the Netherlands. A study in 62 volunteers in Belgium reported that 6% of time was spent in traffic, but the contribution to the measured 24-hour average personal exposure to BC was 21%, and to calculated inhaled doses was 30% (Dons et al., 2012). Home-based activities, including sleep, accounted for 65% of time, 52% of exposure, and 36% of inhaled dose (Dons et al., 2012). For volunteers in Barcelona, Spain, in-transit exposures accounted for 6% of time, 11% of NO₂ exposure, and 24% of inhaled dose (de Nazelle et al., 2013). Setton and co-workers documented that ignoring residential mobility in epidemiological studies using individual-level air pollution may (modestly) bias exposure response functions towards the null (Setton et al., 2011).

Fig. 1.44

Concentrations in modes of transportation and at the urban background location on corresponding sampling days

(ii) Time–activity patterns

A range of surveys in developed countries have shown that most people spend a large fraction of their time indoors. An example is shown from the large National Human Activity Pattern Survey (NHAPS) in the USA (Fig. 1.45; Klepeis et al., 2001). On average, subjects spent 87% of their time indoors, of which a large fraction was spent in their own residence. Time spent outdoors accounted for about 2 hours of the day. These broad patterns have been found in other surveys as well (Jenkins et al., 1992; Leech et al., 2002).

Fig. 1.45

Time spent in various microenvironments by subjects in the USA

However, individual time–activity patterns differ substantially, related to factors such as age, employment, and socioeconomic status. A recent survey showed that German children spent on average 15.5 hours per day in their own home (65% of time), 4.75 hours in other indoor locations (for a total of 84% of time spent indoors), and 3.75 hours outdoors (16% of time) (Conrad et al., 2013). The German survey did not distinguish between “outdoors” and “in traffic,” which may be partly responsible for the share of time spent outdoors.

Time–activity patterns vary significantly over the day, as does the air pollution concentration, supporting the use of more dynamic exposure estimates (Beckx et al., 2009). Time–activity patterns may thus differ across population groups (related to age, sex, employment status, socioeconomic position, and other factors), contributing to contrasts in exposure between population groups beyond contrasts in outdoor concentrations. A study in Delhi, India, showed a high proportion of time spent indoors, with significant variability across population groups (Saksena et al., 2007).

The contribution to time-weighted average exposure is a function of the time spent in a microenvironment and the concentration in that microenvironment. Thus, for pollutants that infiltrate poorly indoors, the relatively short time spent outdoors, including in transit, may nevertheless amount to a significant fraction of total exposure.

Because of the large fraction of time spent in the home, residential history is an important determinant of long-term average exposure to air pollution. In epidemiological studies, air pollution exposure is often assigned based on the most recent address or the address at recruitment into the (cohort) study. Because a significant number of subjects may change address before inclusion in the study or during follow-up, misclassification of exposure may occur. This is particularly problematic because limited information is available about the critical window of exposure. In a study in California of children with leukaemia, residential mobility differed with age and socioeconomic status, and accounting for residential mobility significantly affected the assigned neighbourhood socioeconomic status and urban/rural status (Urayama et al., 2009). A case–control study in Canada reported that in the 20-year exposure period, 40% of the population lived at the same address (Hystad et al., 2012). The correlation between air pollution exposure estimates with and without residential history was 0.70, 0.76, and 0.72 for PM_2.5, NO₂, and ozone, respectively. About 50% of individuals were classified into a different PM_2.5, NO₂, and ozone exposure quintile when using study-entry postal codes and spatial pollution surfaces, compared with exposures derived from residential histories and spatiotemporal air pollution models (Hystad et al., 2012). Recall bias was reported for self-reported residential history, with lung cancer cases reporting more residential addresses than controls (Hystad et al., 2012). In a Danish cohort study, exposure was characterized as the average concentration of all addresses 20–25 years before enrolment and during follow-up weighted with the time lived at an address (Raaschou-Nielsen et al., 2011). People moved on average 2.4 times before enrolment and 0.3 times during follow-up. Exposure estimates from different periods were highly correlated (Section 1.4.2).

(iii) Infiltration of pollutants indoors

Because people generally spend a large fraction of their time indoors and outdoor air pollution infiltrates indoors, this section examines relationships between outdoor and indoor pollutant levels.

Mass-balance models have been used extensively to describe the concentration in indoor air as a function of outdoor air and indoor sources. The indoor concentration of an air pollutant can be expressed simply as C_ai = F_inf C_a, where C_ai = is the indoor pollutant concentration originating from outdoors, F_inf is defined as the infiltration factor, and C_a is the ambient (outdoor) concentration. The infiltration factor describes the fraction of outdoor pollution that penetrates indoors and remains suspended. Penetration efficiency depends on several factors, including the air velocity, the dimensions of the opening, and the particle size, with ultrafine and especially coarse particles penetrating less efficiently (Liu & Nazaroff, 2001).

Hänninen et al. (2011) evaluated the original data of European studies of indoor–outdoor relationships for PM_2.5. The overall average infiltration factor was 0.55, illustrating significant infiltration of outdoor fine particles. A review including European and North American studies reported infiltration factors of 0.3–0.82 for PM_2.5 (Chen & Zhao, 2011). Since people in Europe and North America spend a large fraction of their time indoors, human exposure to fine particles of outdoor origin occurs mostly indoors. Infiltration factors were consistently higher in the summer than in the winter (Hänninen et al., 2011). A study in seven cities in the USA included in the MESA Air study also reported high infiltration factors in the warm season (Allen et al., 2012). The implication is that for the same outdoor concentration, the actual human exposure is higher in the summer than in the winter. Higher infiltration factors in the summer are explained by higher air exchange rates in the summer than in the winter.

In the four European cities included in the RUPIOH study, infiltration factors for ultrafine particles assessed by total particle number counts were somewhat lower than those for PM_2.5 (Table 1.13; Hoek et al., 2008b) but higher than those for coarse particles. A large study in Windsor, Ontario, Canada, that measured total particle number counts reported infiltration factors of 0.16–0.26, with a large variability for individual homes (Kearney et al., 2011). The lower infiltration of ultrafine particles is consistent with lower penetration and higher decay rates due to diffusion losses compared with accumulation mode particles. Studies in the USA that measured particle size distributions have also found lower infiltration factors, on the order of 0.5 for particles in the ultrafine range and up to 0.7 for PM_2.5 (Abt et al., 2000; Long et al., 2001; Sarnat et al., 2006). A study conducted in a Helsinki, Finland, office found that indoor particle number concentrations tracked outdoor concentrations well but were only 10% of the outdoor concentrations (Koponen et al., 2001). A study in two empty hospital rooms in Erfurt, Germany, reported a high correlation between indoor and outdoor concentrations of PM_2.5, black smoke, and particle number concentration and an indoor–outdoor ratio of 0.42 for total number concentration, compared with 0.79 for PM_2.5 (Cyrys et al., 2004). There is thus a large range in reported infiltration factors, related to differences in air exchange rates and building characteristics, and likely also to differences in measurement methods across studies.

Table 1.13

Infiltration factors estimated as regression slope for the relationships between indoor and outdoor 24-hour average concentrations of different particle metrics from the RUPIOH study.

The composition of particles infiltrated indoors also differs from the outdoor composition. Infiltration factors for EC exceeded those for PM_2.5 significantly (Fig. 1.46). EC is concentrated in submicrometre particles, is non-volatile, and has few indoor sources (Noullett et al., 2010). Although smoking affects EC levels, the impact is less than on PM_2.5 concentrations (Götschi et al., 2002). A detailed analysis of the RIOPA study showed that 92% of the indoor EC concentration was due to outdoor EC, whereas the corresponding contribution for PM_2.5 was 53% (Meng et al., 2009).

Fig. 1.46

Infiltration factors for PM_2.5 and soot (EC, BC) measured in the same study

Sulfates have few indoor sources and high infiltration factors (Noullett et al., 2010). Indoor concentrations of SO₂ in the absence of indoor sources (e.g. unvented kerosene heaters) are typically low, related to large losses to indoor surfaces (Koutrakis et al., 2005).

Nitrates typically show low infiltration factors, ranging from 0.05 to 0.2 in studies in Europe and the USA (Sarnat et al., 2006; Hoek et al., 2008b). Indoor concentrations of NO₂ in the absence of indoor sources (e.g. gas cooking, unvented heaters) are substantially lower than outdoor concentrations (Monn, 2001). In a recent review of studies of personal and outdoor NO₂ exposure, the overall average personal–outdoor regression slope was between 0.14 and 0.40, depending on the study type (Meng et al., 2012a). A study in Spain reported indoor–outdoor slopes of 0.20 and 0.45 for two cities, after adjusting for the large influence of gas cookers and gas heaters (Valero et al., 2009). Personal exposure may be affected by more factors, but studies have shown that the indoor concentration is the dominant factor, with large heterogeneity observed between studies (Monn, 2001; Meng et al., 2012a).

Indoor ozone concentrations are typically low because ozone is a highly reactive component with a high decay rate and no indoor sources in residences (Monn, 2001). Indoor–outdoor ratios of between 0.2 and 0.8 were reported in previous studies, depending on air exchange rates (Monn, 2001). A recent analysis of the DEARS study in Detroit, USA, reported a personal–outdoor regression slope of 0.03 in summer and 0.002 in winter (Meng et al., 2012b), even lower than that for NO₂ and much lower than that for PM_2.5.

In large-scale epidemiological studies, indoor measurements of infiltration factors are not feasible. Hystad and co-workers developed a model for PM_2.5 infiltration based on measurements in 84 North American homes and publicly available predictor variables, including meteorology and housing stock characteristics (Hystad et al., 2009). A model including season, temperature, low building value, and heating with forced air predicted 54% of the variability in measured infiltration factors (Hystad et al., 2009). Low building value increased infiltration factors, increasing exposure contrasts across different socioeconomic groups. Other modelling studies in North America reported similar results, with a substantial fraction of the variability of infiltration factors explained by factors including window opening, air exchange rate, and presence or use of central air conditioning and forced air heating, with indications that predictors differ by season (Clark et al., 2010; Allen et al., 2012).

(iv) Indoor sources of pollutants

Numerous indoor sources, including tobacco smoking, cooking, heating, appliances, consumer products, building construction, and activities such as vacuum cleaning, have been identified to affect indoor concentrations and personal exposure for a wide range of air pollutants (Weschler, 2009).

Indoor sources affect different pollutants to a different degree. As noted above, sulfate and EC are affected more by outdoor air pollution than by indoor sources. Sulfate has therefore been used to evaluate the personal or indoor exposure to particles of outdoor origin (Sarnat et al., 2002).