Source apportionment of human personal exposure to volatile organic compounds in homes, offices and outdoors by chemical mass balance and genetic algorithm receptor models

SC IE N CE OF T H E TOT AL E N V I RO N ME N T 4 0 7 ( 2 00 8 ) 1 2 2–1 38

a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / s c i t o t e n v

Source apportionment of human personal exposure to volatile organic compounds in homes, offices and outdoors by chemical mass balance and genetic algorithm receptor models Sharad Gokhalea,⁎, Tibor Kohajdab , Uwe Schlinkb a

Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India Helmholtz Centre for Environment Research (UFZ), Department of Exposure and Epidemiology, Permoserstr. 15, Leipzig 04318, Germany

b

AR TIC LE D ATA

ABSTR ACT

Article history:

A number of past studies have shown the prevalence of a considerable amount of volatile

Received 24 April 2008

organic compounds (VOCs) in workplace, home and outdoor microenvironments. The

Received in revised form

quantification of an individual's personal exposure to VOCs in each of these

6 August 2008

microenvironments is an essential task to recognize the health risks. In this paper, such a

Accepted 7 August 2008

study of source apportionment of the human exposure to VOCs in homes, offices, and

Available online 25 September 2008

outdoors has been presented. Air samples, analysed for 25 organic compounds and sampled during one week in homes, offices, outdoors and close to persons, at seven locations in the

Keywords:

city of Leipzig, have been utilized to recognize the concentration pattern of VOCs using the

Indoor air quality (IAQ)

chemical mass balance (CMB) receptor model.

Human personal exposure

In result, the largest contribution of VOCs to the personal exposure is from homes in the

Health effect

range of 42 to 73%, followed by outdoors, 18 to 34%, and the offices, 2 to 38% with the

Source apportionment

corresponding concentration ranges of 35 to 80 μg m− 3, 10 to 45 μg m− 3 and 1 to 30 μg m− 3

Volatile organic compound (VOC)

respectively. The species such as benzene, dodecane, decane, methyl-cyclopentane,

Genetic algorithm

triethyltoluene and trichloroethylene dominate outdoors; methyl-cyclohexane,

Chemical mass balance (CMB)

triethyltoluene, nonane, octane, tetraethyltoluene, undecane are highest in the offices;

receptor model

while, from the terpenoid group like 3-carane, limonene, a-pinene, b-pinene and the aromatics toluene and styrene most influence the homes. A genetic algorithm (GA) model has also been applied to carry out the source apportionment. Its results are comparable with that of CMB. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Human-health risks associated with the exposure of volatile organic compounds (VOCs) have been well documented in the literature (Molhave, 1991; Wallace, 2001; Adgate et al., 2004; Sexton et al., 2003; Dodson et al., 2007). There are array of sources in indoor and outdoor environments, which contribute to the VOCs significantly. VOCs in urban outdoor environment largely originate from traffic and therefore are ubiquitous and associated with increased long-term health risks (Woodruff et al., 1998; Pratt

et al., 2000; Edwards et al., 2001) while in the indoor environment, the major sources are heating, cooking, paints, room fresheners and carpets (Wallace et al., 1985; Diez et al., 2000; Wallace, 2001; Rehwagen et al., 2001, 2003). VOCs are the major indoor air quality issue because daily routine of humans includes spending a significant time mainly at work and home (Franck et al., 2006) for as much as 90% (Klepeis et al., 2001). Since considerable amount of VOCs are prevalent in these microenvironments, it is apparent that people moving through them during daily routine activities are exposed to different levels. Their concentration

⁎ Corresponding author. Tel.: +91 361 258 2419; fax: +91 361 258 2440. E-mail address: [email protected] (S. Gokhale). 0048-9697/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2008.08.025

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levels also vary with the seasons. According to one of the studies on seasonal cycle in the apartments, the indoor burden of VOCs was found more in winters than in summers (Rehwagen et al., 2003). A risk from exposure of VOCs in indoors although not fully recognized, the inhalation of indoor air has been considered a major determinant of environmental exposures (Brown et al., 1994). A number of studies in the recent past have addressed the concerns over population exposure to the broad spectrum of VOCs. The associated problems of it are equally severe in children as reported by a few recent studies (Sexton et al., 2004). Numerous studies were therefore carried out in schools and kindergartens (He et al., 1993; Rehwagen et al., 1999; Adgate et al., 2004). These studies show that children whose whereabouts generally limited to a kindergarten and its surroundings, the prevalence of bronchitis is in association with the increasing pollution and is typically influenced by both domestic heating and traffic emissions. In the recent past, a number of studies have been conducted on VOC measurements and exposures. A few relevant of them have been discussed here briefly. Baek et al. (1997) established an indoor–outdoor air quality relationship for the respirable suspended particulate matter, carbon monoxide, nitrogen oxides, and VOCs. The study revealed that indoor air quality is derived by both the indoor and outdoor sources, mostly the vehicles. It is further influenced by heating and cooling practices coupled with the poor ventilations. Even at smoking sites, environmental tobacco smoke was found to be the minor contributor to the VOCs, therefore, showing that there is a strong relationship between the vehicles related pollutants in indoors and outdoors. Jo and Moon (1999) analysed the homemaker's exposure to the VOCs relative to the proximity of roadside service stations. The study determined the share of VOCs exposure to which the neighbourhood people are expected to be exposed from the nearby service stations and major roads. It was found that the indoor levels are higher than the outdoor levels and a significant relationship exists between the indoor and the exposure levels of the homemakers in the residential zones. Later, Edwards et al. (2001) demonstrated the need for human exposure sampling by showing that exposure is not represented by any particular microenvironment, given the times spent by persons in different microenvironments. This was done by identifying the 30 target VOCs in the residential indoors, the residential outdoors, the workplace and the human exposure samples. It was revealed that the VOCs in residential outdoors are dominated by long-range transport followed by vehicles and trees, in residential indoors by indoor cleaning products followed by vehicle emissions and in the workplaces by product emissions, long-range transport and air fresheners. These studies show that indoor air quality is also affected by outdoor sources. Lai et al. (2004) carried out the simultaneous measurements of VOCs on the human exposure in the microenvironments of home indoor, home outdoor and the work indoor over a period of 48 h, which showed that people spend about 90% of the time indoors than in other microenvironments and the concentration for most species in home indoors exceeds by about 20% of outdoor environments. This study thus asserts that other microenvironments are insignificant compared with home and workplace indoors. The recent study of Loh et al. (2006), however, reported that individuals spend about 25% of their

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time in non-residential indoor environments such as stores and restaurants. The study found that the VOC concentrations in stores and restaurants are potentially important sources of exposure for individuals working in these microenvironments. These studies thus show that health outcomes of individuals depend upon the effect of VOCs, which varies with the exposure time elapsed in these microenvironments and its intensity to which people are exposed. It is for these reasons that the identification of typical sources along with the relative amount of contribution (source apportionment) to the person's exposure levels is important and essentially a need of the hour. Furthermore, such a source apportionment of organic species is itself important because many species are mutagenic, carcinogenic, or otherwise toxic (Gordon, 1988). Studies carried out in the recent past reveal that the relative concentrations of VOCs measured indoor, outdoor, and on human exposure are functions of relative indoor and outdoor source contributions, and depend upon the time-activity patterns. The study in this paper presents an apportionment of VOCs observed in personal exposure into three sources: homes, offices, and outdoors in the urban area of the city of Leipzig, Germany. This is based on VOCs measured simultaneously in different microenvironments and the persons' exposures when they spent actual time in each of them. While many studies into the health effects of air pollution consider just the outdoor exposure, the present work estimates the contribution from each microenvironment to the total human exposure and can provide valuable inputs for a comprehensive and realistic exposure assessment in future health studies. Other microenvironments such as stores, markets and restaurants have not been considered in this study assuming that working people do not visit these microenvironments regularly, although may be important for individuals working there as reported by Loh et al. (2006). Indoor environments such as homes and offices are the major contributor of VOCs to the personal exposure of working people. Seven locations covering most urbanized region of the city were selected. Seven volunteer participants for the simultaneous measurements of VOCs in the environments of outdoors, homes, and offices were selected. Three volunteer participants were from three different kindergartens, one from the public health department, two from different educational institutes and one from the residential area. Air samples were collected by passive samplers for one week simultaneously in home, office and outdoor and at one volunteer for each of the respective locations. The concentration pattern for 28 species was evaluated using a chemical mass balance (CMB) approach for each individual's exposure sample from three source categories of homes, offices and outdoors as well as by Genetic Algorithm (GA). An application of this approach is rather new in the realm of the source apportionment studies and therefore included in this work for two reasons, first to check its applicability to such problems and second to inter-compare with the CMB.

2.

Study methods

2.1.

Target volatile organic compounds

The 28 constituents of VOCs were selected for the source apportionment study. These species mostly belonged to the

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Table 1 – Description of work places Sr. no.

Site

Location

1

PB1 (public building)

Outskirts, light traffic

2

PB2 (public building)

3

PB3 (public building)

4 5

HO (hospital) KG1 (kindergarten)

Near PB1; four-lane road with medium traffic Downtown; low traffic; surrounded by green space Outskirts; light traffic Very light traffic

6

KG2 (kindergarten)

Light traffic

7

KG3 (kindergarten)

Very calm and light traffic area

Peculiarities III floor; office equipped with wall-to-wall carpet Built in 2004; office equipped with wall-to-wall carpet Flooring with PVC material Flooring with PVC material Classroom equipped with wall-to-wall carpet Building was painted two years ago; linoleum flooring. Linoleum flooring

non-methane hydrocarbon groups, viz. alkanes, cycloalkanes, aromatics, halogenated hydrocarbons, and terpenes groups. Species utilized with their abbreviations for modelling purposes were: hexane (Hexa), heptane (Hept), octane (Octa), nonane (Nona), decane (Deca), undecane (Unde), dodecane (Dode), tridecane (Trde), methyl-cyclopentane (Mycp), cyclohexane (Cyhe), methyl-cyclohexane (Mych), benzene (Benz), toluene (Tolu), ethylbenzene (Eytb), m,p-xylene (mpXy), styrene (Styr), o-xylene (oXyl), 4-ethyltoluene (4Eyt), 3-ethyltoluene (3Eyt), 2-ethyltoluene (2Eyt), tricholorethylene (3Cle), apinene (aPin), b-pinene (bPin), 3-carene (3Car), limonene (Limo), naphthalene (Napt), methylbenzene (Mytb) and tetrachloroethylene (4Cle). These target compounds were assumed to account for the total VOCs of the ambient and different microenvironments and thus approximately represent the atmospheric VOCs (NoMiracle, 2007a,b).

2.2.

Field studies

The measurements of VOCs were carried out for a period of one week starting from April 11, 05 to April 18, 05. Seven persons (2 male scientists, 3 female kindergartners, and 2 female practitioners) aged between 35 and 65 years volunteered in the measurement campaign. Each of them carried a VOC passive collector monitoring the personal exposure during the period of sampling. For each of these seven human receptors, the three most important sources were considered, viz (i) the outdoor pollution, (ii) the exposure of the work place (i.e. the office), and (iii) the exposure at home. It was however ensured during the sampling that these persons would stay at other places (microenvironments) for not more than 1 h in a day. In contrast to indoor exposure, which can differ strongly between flats and offices, outdoor pollution can be regarded as spatially auto-correlated and, therefore, is representative for larger urban regions. Therefore, outdoor VOCs were measured just outside the workplaces of the volunteers. The workplaces are located in semi-urban and urban regions of Leipzig city, including three public buildings (PB1-3), one hospital (HO) and three kindergartens (KG1-3). The description of work places is given in Table 1. The flats of the volunteers are situated in different parts of the Leipzig city. Exposure at home was measured with passive samplers in parallel to the outdoor, work place and personal measurements.

Remarks – – Office users complained of strong smell of floor-cleaning products in mornings – Smell of carpet was perceivable, even though the window was open during most time. – –

The general aim is to apportion the personal exposure to the concentrations observed in the three microenvironments based on 28 VOCs.

3.

Sampling methodology

For passive sampling of VOCs, 3M monitors of the type OVM 3500 (3M Deutschland GmbH, 1986) were used. The sampling was carried out continuously for one week because it describes the human exposure in integrative manner than a short term measurement. Beagerow et al. (1999) and Schields and Weschler (1987) have demonstrated the usefulness of passive sampling for the determination of VOCs in indoor and outdoor air for several weeks. During the 1-week period in this study, the monitors were kept in the rooms where the volunteers spent most of their time such as the office and or classroom and the living room or bedroom at the home of the volunteers. The monitors were placed in the middle of the rooms between 1.5 to 2 m height with a distance of at least 50 cm to the next furniture. The outdoor air measurements were also carried out in parallel with the indoor air measurements. The outdoor monitors were placed at a rain-protected position directly on the outer side of one of the windows (Schlink et al., 2004). This is because the workplaces selected in the study were neither renovated nor reconstructed recently and therefore the outdoor measurements were assumed to be least affected due to the building emissions. Additionally, every volunteer carried one sampler at the shoulder. All the measurements are referred to the normal conditions of 25 °C and 760 mm Hg.

4. Results of measurements in microenvironments Fig. 1 shows the source (for three microenvironments, i.e. home, office and outdoor) profiles of VOCs (1 week average) along with the personal exposure measurements in relative mass fraction for 25 constituents. The three constituents namely 4-chloroethylene, naphthalene, and methylbenzene were removed from the source and exposure profiles to stabilize the CMB modelling and to avoid possible negative source contribution, discussed later. The uncertainty associated with species concentrations in the source as well as human exposure profiles was assumed to be 20%

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Fig. 1 – Comparison of VOCs source profile for persons and three microenvironments.

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(Watson et al., 2004). This study incorporates all the major constituents in the source apportionment that represent the most classes of sources of VOCs in the ambient as well as indoors of offices and homes. It was further observed that the source profiles exhibited similar patterns with respect to several important constituents observed at workplaces, outdoors and homes, except the places PB2 and KG2, where, the ethylene group showed relatively steep rise in concentrations. The terpenes group depicted relatively higher concentrations in most source profiles except for PB1 and KG1. Limonene was particularly highest in all the source profiles. These results have been comparable with the results of past studies (Table 2) which were carried out in the Germany (Rehwagen et al., 2003; Schlink et al., 2004). Since somewhat similar environment exists in typical urban indoors and outdoors of the Europe (Table 3), these source profiles may be used as the potential characteristic profiles for designing such studies in future.

4.1.

Characterization of microenvironments

Source profiles represent the distribution of chemical abundances in indoor and outdoor environments.

4.1.1.

Outdoor sources

In the outdoor environment, major sources of VOCs such as vehicle related emissions including exhaust, evaporated fuels,

and liquid fuels ubiquitous in urban areas are responsible mainly for alkane and aromatic groups (Fujita et al., 1995; Tucker, 2001). The constituents of these groups have been included in this study, which ensured the abundance of most possible species of the VOCs in the outdoor environment. Table 3 shows the summarized statistics on the variance, the arithmetic mean, the coefficient of variation and the ratio of variance to mean for all the outdoor and home measurements. The coefficient of variation for the outdoor was found to be marginally varying within the range of 1.3 to 1.6 indicating that the outdoor pollution can be regarded as spatially auto-correlated.

4.1.2.

Office sources

Terpenes are usually not quantified in the ambient (outdoor) environment due to measurement difficulties of these reactive substances, which therefore occur in very low concentration, often below the detection limit. These constituents are, however, abundant in indoors (Guenther 1997; Lindfors et al., 2000; Hakola et al., 2003). Nonane is also observed significantly abundant in offices since it is emitted from the printing inks and office equipments (Wolkoff et al., 1993). Indoors, particularly inside the laboratories, the traces of VOCs from walls, carpets and the emissions of which are generally comparable with other solvents (Little et al., 1994). In the source profiles of the present study, the cleaners and fragranced products might be the sources of the hexane and liquid floor wax of the dodecane.

Table 2 – Comparison of results with the past studies Arithmetic mean value in μg m− 3

VOC Outdoor a

Hexane Heptane Octane Nonane Decane Undecane Dodecane Tridecane Methyl-cyclopentane Cyclohexane Methyl-cyclohexane Benzene Toluene Ethylbenzene m+p-Xylene Styrene o-Xylene 4-Ethyltoluene 3-Ethyltoluene 2-Ethyltoluene Trichloroethane Tetracholorethane a-Pinene b-Pinene 3-Carene Limonene a

Home a

Rehwagen et al. (2003)

Present study

Rehwagen et al. (2003)

Schlink et al. (2004)b

Present study

2.13 0.82 0.21 0.31 0.53 0.55 0.42 0.23 0.92 0.79 0.46 1.72 4.28 0.71 2.01 0.09 0.59 0.56 0.27 0.23 0.19 0.24 1.40 0.16 0.37 0.57

3.12 1.2 0.2 0.26 1.14 0.57 5.41 0.21 0.56 0.30 0.26 1.59 3.74 0.66 1.85 0.09 0.53 0.54 0.24 0.15 0.12 0.10 0.2 0.03 0.04 0.05

6.95 8.40 2.92 4.28 10.01 10.19 7.76 3.29 1.81 5.48 5.27 3.09 31.81 3.59 9.64 1.47 2.84 2.36 1.98 1.60 1.24 4.30 24.77 3.01 8.91 36.69

7.30 7.60 2.80 4.00 9.00 8.90 6.50 3.10 2.00 5.50 5.30 3.20 29.5 3.60 9.80 1.40 2.70 2.40 1.70 1.60 1.30 5.30 23.3 2.70 7.30 32.90

4.69 3.35 0.62 1.45 2.20 2.36 6.27 1.05 1.40 0.75 0.94 2.40 21.73 2.55 5.46 1.61 1.78 1.47 0.66 0.42 0.16 0.33 6.4 0.99 4.88 14.58

This study was carried out for the city Leipzig from November 1994 to October 2001 for indoor, N = 1499 and from the year 2000 to 2001 for outdoor, N = 222. b This study included measurements of two more cities, München (from the year 1998 to 2000, indoor, N = 266) and Köln (from the year 2000 to 2001, indoor, N = 327) to the Leipzig (indoor, N = 1510) data (Rehwagen et al., 2003). The total measurements were = 2103.

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Table 3 – Statistics on outdoors and homes' measurements Microenvironments Outdoor environment Workplaces PB1 PB2 KG1 HO PB3 KG2 KG3 Indoor (home) environment Homes of volunteers PB1 PB2 KG1 HO PB3 KG2 KG3

4.1.3.

Variance

Standard deviation

Mean

Coefficient of variation

Variance to mean ratio

5.25 1.34 2.17 1.19 1.69 1.11 1.24

2.30 1.20 1.50 1.10 1.30 1.10 1.10

1.45 0.87 0.90 0.74 0.97 0.75 0.79

1.60 1.30 1.60 1.50 1.30 1.40 1.40

3.62 1.55 2.40 1.61 1.75 1.47 1.58

61.89 13.71 4.46 43.74 84.79 16.97 20.32

7.90 3.70 2.10 6.60 9.20 4.10 4.50

5.47 2.45 1.52 4.11 5.94 3.22 2.54

1.40 1.50 1.40 1.60 1.60 1.30 1.80

11.32 5.60 2.93 10.64 14.27 5.27 8.01

Home sources

Smoking, cooking especially meat cooking and other activities have large effects on the total VOCs inside homes and responsible for a large number of varied constituents. As far as aromatics group concern, their presence in indoor environments results from an entry into the buildings from the outdoor environment or their generation within the buildings from household products and construction materials (Levin, 1989; Sack et al., 1992; Tucker, 2001). The relative importance of these indoor and outdoor origins differs for different pollutants and may vary over the time. The major reasons are the poorly vented combustion products and smoking of tobacco products in homes (NoMiracle, 2007a,b). Gas cooking stoves and ovens also release these products directly into the indoor air. In the present study, a significant amount of benzene was observed in the homes and offices of the kindergartens, which may have been attributed to the significant air exchanges in the kindergartens than in common houses. Further indoor to outdoor ratios for some marker constituents were very high for decane, styrene, a-pinene and limonene indicating their dominance in the indoors. Toluene was yet another constituent generally originates from the outdoor like vehicles, gasoline, and other fuels as well as indoors like household solvents, paint remover, and latex paints. Indoor to outdoor ratio for toluene was 2.3, indicating the dominance of indoors over the outdoors particularly of growing vehicle emissions. The summarized statistics for the home microenvironment (Table 3) indicate that the degree of variation in the dispersion of measurements around mean is minimal. Some of the VOCs constituents showed significant differences in concentrations of offices and outdoors compared to homes, such as hexane, toluene, m,p-xylene, 4-ethyltoluene, dodecane, styrene, a-pinene, limonene and 3-carane, nonane, undecane, methyl-cyclohexane, and ethylbenzene except of the PB2 and KG2, where, the significant difference was found between 3-ethyltoluene and 2-ethyltoluene. This pattern underlined the presence of distinct sources within indoors, i.e. at offices and homes, affecting the VOCs concentrations

greatly such as printing inks, photocopier, office equipments at offices, while, cooking, furniture, cleaning agents, detergents, paints, etc. at homes. The higher concentrations of some constituents like m,p-xylene, ethylbenzene, styrene and benzene further indicated the presence of environmental tobacco smoke or diffusion of vehicular emissions into the indoors or both.

4.2.

Personal exposure

Volunteers who participated in the VOCs exposure measurement campaign were exposed to significantly elevated concentrations of hexane, heptane, dodecane, toluene, m,pxylene, a-pinene, 3-carane and limonene compared to other constituents. The patterns of exposure of 3 female kindergartners were similar except for the one of KG1, who exposed relatively more to 4-ethyltoluene and styrene. Klepeis et al. (2001) in the national human activity pattern survey concluded that humans spend on average nearly 90% of their time indoors. Franck et al. (2006) recently studied the relationship of sub micrometer particles between indoors and outdoors, which showed that the time budgets for individuals in outdoor, office and home environments are approximately 20%, 30% and 50%, respectively. This implies that an individual typically spends about 80 to 90% of the time indoors. The results of the measurements in this study are in conformity with the similar trends for the volunteers in different microenvironments.

5.

Receptor modelling approaches

The CMB is a widely used receptor model (Gordon, 1980, 1988; Watson, 1984; Watson et al., 1990, 1991) for the source apportionment of the particulates of different sizes and of the VOCs (Watson et al., 2004; Coulter, 2004). The fundamental idea of this model construction is that the composition patterns of emissions from various classes of sources are different enough to identify their contributions by measuring

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concentrations of many species in samples collected at receptors (i.e. human exposure samples in this study) (Hopke, 1991). A genetic algorithm (GA) is another approach that is additionally explored in this study for estimating the source contributions. Though GAs are strong in solving optimization problems (Reis et al., 2005), they have not been broadly applied in the field of air pollution modelling yet. They use an evolutionary process driven by natural processes like fitness for survival, recombination and mutation to find optimal solutions for optimization problems. The application of these models explains the personal exposure in terms of the concentrations observed in the microenvironments. The personal exposure therefore is an addition of weighted concentrations of microenvironments. The models assess these weights, which represents the time an individual spends in each of the microenvironment.

1992). The MPIN matrix basically recognizes marker species which influences the SCE, that is, identifies which fitting species have the largest influence on the SCE from each profile. This matrix therefore indicates the degree of influence each species concentration has on the contribution and standard error of the corresponding source category. It is normalized such that it takes on values from − 1 to 1. Species with MPIN absolute values of 0.5 to 1.0 are considered influential species. MPIN is calculated by Eq. (3):

5.1.

GA is a general description for a class of algorithms used to find approximate solutions to difficult-to-solve problems, inspired by and named after biological processes of mutation, natural selection, and the genetic crossover that occurs when parents mate to produce offspring (Pal and Wang, 1996). Genetic algorithms are a particular class of evolutionary algorithms. The problem to be solved is represented by a list of parameters called chromosome. This chromosome is used to obtain an evaluation procedure. The starting chromosome is randomly initialized. It is then evaluated and a value of fitness is returned. After sorting by fitness, a pair of chromosomes is randomly chosen by roulette wheel selection. The chosen chromosomes undergo crossovers or mutations and, afterwards, they are added to the next generation chromosome pool. When the genome has the same size like the last generation, the whole procedure starts again by evaluating the fitness of the new generation. The cycle is terminated when a solution is found that satisfies minimum criteria, a fixed number of generations or any other termination criterion is reached. The GA used for this apportionment has a fixed number of generations for termination and uses elitism, which means the best solution is automatically transferred to the next generation. The chromosome is held as small as possible and consists only of one factor for each source profile. The fitness function compares the source contribution results with the real target pattern and returns the fitness value (Haupt et al., 2006). The fitness value is calculated by the weighted function given by Eq. (4):

Chemical mass balance

This approach identifies the likely sources of VOCs and their contribution to the receptors (human personal exposure in this study). It combines the chemical and physical characteristics of species measured at the sources and the receptors to quantify the source contributions to receptors by statistically balancing the both. The important assumption of this method is that all the sources contributing to measured concentrations at the receptors have been identified and measured. The CMB modelling, based on a simple relationship of the concentration of species with the source contribution, is represented by Eqs. (1) and (2) (Henry et al., 1984; Henry, 1992). If a number of sources, p, exists, and if there is no interaction between their gaseous molecules that causes mass removal or formation, the total mass of species measured at the receptor, C, will be a linear sum of the contributions of the individual sources, Sj: C¼

p X

Sj

ð1Þ

j¼1

Similarly, the mass concentration of i species property, Ci, will be: Ci ¼

p X

aij dSj

ð2Þ

j¼1

where, aij is the mass fraction of source contribution j, possessing property i, at the receptor. The Eq. (2) assumes that emissions between sources do not interact to cause mass removal and therefore the total mass measured at the receptor (Ci) is linear sum of the contributions from the individual sources. The solution to this equation is obtained by an effective variance least squares method. The source contribution estimate (SCE), i.e. apportionment is obtained by a linear combination of the mass fraction of source contribution and ambient measurement as described by Eq. (2). This particular estimate in this study demonstrates the amount of concentration of particular specie or total VOCs to which a volunteer is exposed while in home, office and outdoor microenvironments. The modified pseudo-inverse normalized matrix (MPIN) is one of the important outcomes of CMB modelling (Henry,

 1 aT ðVe Þ1=2 MPIN ¼ aT ðVe Þ1 a

ð3Þ

Where, Ve is the diagonal matrix of effective variances; a, an i x j matrix of aij, the source composition matrix.

5.2.

f ¼

Genetic algorithm

n  X 5  ðsi  ci Þ2 ; ci Nsi ðsi  ci Þ2 ; ci Vsi i¼1

ð4Þ

where, n is the number of components in the measured target pattern, s, and the calculated target pattern, c. Because of the random selection character of the GA, every calculation of the SCE was repeated 10 times and the mean value was used for further presentation. The standard deviations of all measurements were lower than 1.5% compared the total SCE.

6.

Results of source apportionment

The CMB and GA modelling techniques were applied for source apportionment of personal exposure to VOCs into the

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Fig. 2 – a: SCE from three microenvironments to personal exposure by CMB, b: SCE from three microenvironments to personal exposure by GA.

three sources: home, office and outdoors. For better comparability with the CMB, the analysis with GA was carried out with the same pre-selected 25 VOCs, although only an insignificant difference could be seen when using all 28 VOCs. Human exposure to VOCs was first investigated in three microenvironments using the CMB model. The study revealed that the average source contribution to human exposure was highest from homes followed by offices and then outdoors. These results are valid within the assumptions that the chemicals do not interact with each other and, therefore, concentrations can be added linearly; the compositions of source emissions are constant over the sampling period and linearly independent of each other; and the measurement uncertainties are random and normally distributed. The source profiles, in the study area, represent the source compositions of the human personal exposure samples. The source contribution results were stabilized by applying the following statistical criteria: the coefficient of determination R2 ≥ 0.8, the chi-square value χ2 ≤ 4.0, the mass of total species ≥ 80% ≤ 120%, and the T-stat ≥ 2.0. Besides, the ratio of residual to uncertainty was maintained between − 2.0 and 2.0. The eligible space dimension of co-linearity was considered at 20% uncertainty of the total measured mass of VOCs. The minimum projection was 0.95 to make all the sources under consideration estimable. The CMB model was further stabilized by removing some species that caused large standard errors in SCE estimates (up to ±19.2) and violated the percent mass criteria (up to 70.8%). Those species were, 4-chloroethylene, naphthalene and methylbenzene, which when removed from the total VOCs, improved the % mass along with other criteria and reduced the standard errors up to ±14.9. The further modelling analysis was, therefore, carried out on 25 VOCs

constituents. Some species violated the criteria of residual to uncertainty ratio, e.g., for a volunteer of workplace PB1, styrene was 2.6 and trichloroethylene, 2.1 indicating that these species at sources contributed more to source profiles than to human exposure profile, while for that of KG1, the toluene was − 2.9, indicating the negligible source contribution to it. The person of workplace PB3, tridecane was 2.2, indicating the low exposure. The person of kindergarten KG2, the styrene exceeded the range, i.e. − 2.4, showing the missing of a major source; whereas for other individuals for workplaces, PB2 and KG3, all the species were within the ratio. The results of the CMB modelling, shown in Fig. 2a, illustrate the source contribution of VOCs to individual's exposure from the outdoor, the office and the home with standard errors. The SCE to the individual's exposure is highest from homes compared to other microenvironments. The highest SCE was observed to the person of workplace PB3 followed by the persons of PB1 and then KG2, while, for other individuals, it was relatively less and similar. The SCE from the offices was less compared with the outdoors and homes. This might be attributed to the better ventilation in the office buildings. The range of SCE of VOCs from home was about 35 to 80 μg m− 3, while, from the office, it was between 1 and 30 μg m− 3, and from the outdoor, it was 10 and 45 μg m− 3. Fig. 2b shows the similar results by GA. Fig. 3 shows the percentage of SCE from home, outdoor and office. The SCE from home was in the range of 42 to 73%, from office 2 to 38% and from outdoor, it was 18 to 34%. It was further observed that people particularly working in kindergartens were exposed least in the office premises but more in the homes and outdoors. Volunteers of the public buildings particularly of educational institutions were exposed more in homes and outdoors than in the office environments.

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Fig. 3 – The % age SCE of persons in three microenvironments by CMB and GA.

Fig. 4 (a, b, c) shows the SCE of each constituent from (a) the outdoor, (b) the office and from (c) the home environments. This also includes the SCE of total VOCs. It was observed that in the outdoor environment, hexane, dodecane, toluene and ethylbenzene contributed significantly in comparison with other species. In the office environment, dodecane, toluene and for some persons, 4-ethyltolune and some compounds of terpenes such as 3-carene and limonene contributed more. While in the home environment, toluene, a-pinene, 3-carene, m,p-xylene and limonene dominated over other species. It was interesting to know here that in all these microenvironments, dodecane, toluene and hexane were present in high amounts. However, the hexane and dodecane were more in the outdoors, while, toluene in the

homes followed by outdoors and offices. The limonene and a-pinene were found in significant amounts in the home environment. The SCE of human personal exposure have been further analysed. Figs. 5a,b–11a,b show the SCE of human personal exposures in three microenvironments along with the total calculated and measured by CMB (5a–11a) and GA (5b–11b). Figs. 5a–8a show the source apportionments of the persons of workplaces, estimated by CMB with a mass percentage ranging from 87.2 to 98.1%, shows that all the species were balanced with the measured except the toluene, m,p-xylene and a marginal difference in dodecane, which resulted into the significant mismatch between the measured and calculated. The SCE of persons were dominated with the dodecane,

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131

Fig. 4 – Contribution of VOCs by CMB to personal exposure in a) outdoor b) office and c) home microenvironments.

toluene, and m,p-xylene. It was further observed that the persons were exposed to the highest concentrations of toluene in homes followed by the concentrations of limonene, dodecane, 3-carene and a-pinene, dodecane in outdoors and the hexane, 4-ethyltolune and toluene in the offices. For individuals of the public buildings and hospitals, the trends of SCE were more or less similar. The toluene, terpenes and dodecane dominated equally. Two of these workplaces were in the educational institutions. Figs. 9a–11a show the source apportionment of VOCs of the persons of kindergartens, estimated with the mass ranging from 72.9% to 96.9%. A mismatch has been found in the calculated and the measured toluene, while other species were balanced well. The SCE of toluene was significantly high in the home and dodecane in the outdoor. The trend of SCE for volunteers was found to be different. In the kindergartens, a great mismatch between the measured and calculated was observed for the species like limonene, dodecane and toluene. It was further observed that the species from the terpene group like limonene, a-pinene, 3-carene dominated the most and toluene, dodecane and hexane were highest. Benzene was also significant. Groups of VOCs have different pathways to human personal exposure. The alkenes group is generally prevalent in indoors as

well as outdoors. The halogenated alkenes group is more in indoors than in outdoors, while, the aromatic group is more in outdoors than in indoors. Trichloroethylene is present indoors as well as outdoors. Fig. 12 shows the modified pseudo-inverse matrix (MPIN) for outdoor, office and home environments. MPIN matrix helps identifying the marker species, which have significantly influenced the SCE values in each of the microenvironment. For the persons of public buildings and hospital, the SCE was more influenced by dodecane, methyl-cyclopentane and benzene in outdoors, by methyl-cyclohexane, 3ethyltoluene and octane in offices and by 3-carene and limonene in homes. The trend of exposure to the persons of kindergartens was a little different compared with that of public buildings. For individuals of the kindergartens, it was mainly influenced by benzene and decane in outdoors and by 3-carene in homes except in the offices wherein nonane, undecane and 3ethyltoluene were crucial. Table 4 describes the fit statistics with the ratios of residual to uncertainty. Figs. 5b–11b show the calculated total SCE in comparison to the measured human personal exposure with the contribution of three sources. With the GA, the highest SCE was observed to the persons of workplace HO followed by to that of PB1 and then of KG2. This is comparable to the results obtained by CMB. Except for the persons of public buildings of PB2 and PB3,

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Fig. 5 – Source apportionment of VOCs with contribution of three microenvironments to the person of PB1 by a) CMB and b) GA.

Fig. 6 – Source apportionment of VOCs with contribution of three microenvironments to the person of PB2 by a) CMB and b) GA.

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133

Fig. 7 – Source apportionment of VOCs with contribution of three microenvironments to the person of PB3 by a) CMB and b) GA.

Fig. 8 – Source apportionment of VOCs with contribution of three microenvironments to the person of HO by a) CMB and b) GA.

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Fig. 9 – Source apportionment of VOCs with contribution of three microenvironments to the person of KG1 by a) CMB and b) GA.

Fig. 10 – Source apportionment of VOCs with contribution of three microenvironments to the person of KG2 by a) CMB and b) GA.

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135

Fig. 11 – Source apportionment of VOCs with contribution of three microenvironments to the person of KG3 by a) CMB and b) GA.

the SCE from the offices was less than the outdoors and their homes for persons of other locations. For the volunteers of PB1 and KG1, the SCE for the office was almost close to zero. The effect for the person of PB1 can be explained by a higher amount of toluene at home, so that the home source diminishes the SCE of other sources. This is a similar result as with CMB. For the person of KG1, the result was similar. The higher amount of toluene and dodecane in the SCE of the home source discriminated the SCE of the other two sources. It was seen that most of the SCEs were dominated by toluene, the terpenes and dodecane for the home environment. These observations demonstrate that in home environment, more species affect the SCE compared with the other environments. Amongst, the volunteers working in the kindergartens and educational institutions were exposed to a larger range of species in the home environment.

7.

Inter-comparison of CMB and GA

The GA, an evolutionary computational process, establishes a link between a fraction of the pollution load released at sources and that the load is measured at personal receptors. It uses a fitting function to yield a new solution. The fitting function describes the non-linearity that exists in reality between the aij and the Ci (see Eq. (2)), while CMB assumes it to be linear. The CMB, however, is

good in sense, it uses uncertainty associated with every species observed at source as well as measured at receptors while fitting with effective variance method. The CMB and GA methods have both shown similar results of source apportionment for all the volunteers except the one, for which the GA over estimated total SCE of VOCs by about 5%. However, for all the volunteers, the CMB and GA have underestimated the total SCE of VOCs by about 2 to 20%. The CMB is robust as it accounts for uncertainty. A disadvantage is that it considers linear relationship between the source signatures and receptor concentration data. Conversely, GA does not consider uncertainty but relates both non-linearly and thus exhibits the dynamic nature of the dispersion of pollutants from source to receptors. Further, the CMB is often stalled due to the co-linearity when two or more species are identical forcing investigators to ignore such species. Additionally, some species having negligible contributions do not produce the acceptable fitness, are generally removed from the analysis to improve the fitting criteria. Moreover, the fitness function in CMB is constrained by requirement of an equal or greater number of sources than receptors. The results of both the modelling approaches are widely comparable, however. The performance of CMB model needs to be intervened to improve fitting criteria requiring some species of negligible importance to be removed. This is not a satisfactory way of source apportionments (Cartwright and Harris, 1993). The GA on the other hand does not require such interferences and is therefore potent to produce more accurate

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Fig. 12 – Marker species influencing personal exposure in three microenvironments.

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Table 4 – Fit statistics and criteria Volunteers of workplaces PB1 PB2 KG1 HO PB3 KG2 KG3

Criteria 2

2

T-stat

R

X

% mass

DF

Outdoor

Office

Home

Styl

3Cle

3Car

Tolu

Trde

0.89 0.97 0.93 0.96 0.95 0.95 0.96

2.10 0.65 1.25 0.76 0.98 0.98 0.83

87.2 98.1 72.9 98.1 90.4 88.5 96.9

22 22 22 22 22 22 22

4.74 4.97 3.74 3.27 4.66 2.78 4.65

1.67 7.48 0.57 1.47 3.87 1.47 1.50

10.11 7.35 8.69 6.15 5.50 8.63 6.96

2.6 – – – – −2.4 –

2.1 – – – – – –

2.8 – – – – – –

– – −2.9 – – – –

– – – 2.2 – – –

results than the CMB. In light of these problems, the GA seems to be more robust in such studies.

8.

Residual/uncertainty

Conclusions

Source apportionment by chemical mass balance model shows that the contribution of volatile organic compounds to human personal exposure from homes is in the range of 42 to 73%, from outdoors 18 to 34%, and from offices 2 to 38%. The results of genetic algorithm model have also been quite promising and comparable with the results of the chemical mass balance model. The inter-comparison of the modelling techniques reveals that genetic algorithm is more robust than chemical mass balance for several reasons. This modelling approach can therefore be explored further in such studies with more comprehensive information on source profiles of chemical species. The modelling results further ascertain a fact that percentage contribution of species to individual's exposure coincides with the amount of time people spend in each of the three microenvironments. The analysis also recognizes the pattern of species that noticeably influences the homes, the outdoors and the offices, viz., limonene in homes, dodecane in offices and benzene in outdoors. This study as a result entails that degree of humans' exposure to local sources varies with the spatial and microenvironmental relationships of the locally dominant sources. In this study, we observed that air pollution in homes generally is much more diverse than in workplaces or outdoors. Accordingly, the specification of indicator components for apartments has to involve a large set of chemical substances. Further inclusion of time-activity pattern will improve the estimates.

Acknowledgement Authors are grateful to the reviewers for their valuable suggestions and the DAAD (Deutscher Akademischer Austausch Dienst), New Delhi for funding a modelling part of this study.

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