- Email: [email protected]
Assessment of population exposure to air pollution by benzene
ARTICLE IN PRESS
Int. J. Hyg. Environ.-Health 210 (2007) 407–410 www.elsevier.de/ijheh
Assessment of population exposure to air pollution by benzene Oxana Tchepel, Ana Penedo, Madalena Gomes Department of Environmental Engineering, School of Technology and Management, Polytechnic Institute of Leiria, Morro do Lena, Alto do Vieiro, Apt. 4163, 2401-951 Leiria, Portugal
Abstract Biomonitoring is one of the methods that allow to identify population groups that have significantly higher exposures to a particular chemical than the general population. However, use of biomonitoring is particularly useful when applied in combination with other methods of pollution exposure assessment. The current study is focused on the developing of the modelling approach to estimate population exposure to benzene through inhalation. The model is based on a microenvironment approach and is adapted to be applied in urban areas where the pattern of exposure is complex. The results provided by the model may be used in combination with human biomonitoring in order to select who and where should monitoring be done, as well as for interpretation and extrapolation of biomonitoring results. r 2007 Elsevier GmbH. All rights reserved. Keywords: Benzene; Population exposure; Exposure modelling; Air quality modelling; Biomonitoring
Introduction Numerous studies indicate that air pollution cause severe health effects and, therefore, an assessment of the population’s exposure to air pollution is crucial for a proper determination of possible links between the pollution levels and health effects. There are different approaches that could be applied to assess exposure to air pollution (Hertel et al., 2001):
biomonitoring, personal exposure monitoring, analysis of air pollution data from monitoring network, and air pollution exposure models.
Biomonitoring and personal exposure monitoring are the techniques that can provide a direct measure of the Corresponding author. Tel.: +351 244 820 300; fax: +351 244 820 310. E-mail address: [email protected] (O. Tchepel).
1438-4639/$ - see front matter r 2007 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijheh.2007.01.008
exposure to air pollutants while other approaches are indirect tools that determine the exposure by combining information about pollutant concentrations at locations with information about the time spent in specific environments. Use of biomonitoring is particularly useful when applied in combination with other methods (Hertel et al., 2001) of air pollution exposure assessment (Fig. 1). The current study is focused on developing a modelling approach to estimate population exposure to benzene adapted to applications in urban areas where the pattern of exposure is complex.
Methodology The modelling approach developed to estimate population exposure involves several steps: (1) population data processing; (2) air pollutants concentration data processing; and (3) population exposure quantification.
ARTICLE IN PRESS 408
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
Air pollution monitoring
Human Biomonitoring
Air pollution modelling
Human Exposure Models
Population time-activity pattern
Personal Exposure Monitoring
Population Exposure Assessment
Fig. 1. Combination of methods for air pollution exposure assessment.
Population data processing
Inputs: Total population in the study area
The main objective of this task is to obtain temporal and spatial distributions of inhabitants within the study domain based on data for different population age groups, mobility data and employment information focused on the description of the proportion of time spent in the various microenvironments. Additionally to numerical information, digital maps are considered in order to provide spatial distribution of the required parameters.
Temporal population distribution for microenvironment Spatial distribution of microenvironments Outdoor pollutant concentration field
Air pollutants concentration data processing The pollutant concentration data is one of the main inputs for exposure quantification. It is distinguished between the ambient air (outdoor) concentrations provided by the ADMS air quality dispersion models, and indoor concentrations calculated through empirical indoor/outdoor penetration factors. Additionally to indoor/outdoor pollutant penetration, the methodology considers an additive factor related with emission sources within a microenvironment.
Population exposure quantification The exposure calculation module is constructed to assess and to predict population exposure distribution from modelled air quality concentrations in different microenvironments of an urban area and from modelling of the number of persons present in the same environment (Fig. 2). This modelling approach has been previously applied for quantification of population exposure to PM10 (Borrego et al., 2006). In the current work the methodology is adapted to quantification of population exposure to benzene.
Results The methodology developed in this work was applied for a selected area in Lisbon. The study area is about
Estimations: Number of inhabitants per grid cell per hour Indoor pollutant concentration Human exposure per grid cell Outputs: Average population exposure Number of inhabitants exposed above the Limit Value
Fig. 2. Schematic representation of the exposure model.
2 km2 (1.5 km 1.3 km) located in the western part of the city and involves one of the main Lisbon entrances of road traffic with approximately 100,000 vehicles per day. The population spatial and temporal distribution required for the exposure modelling were obtained
ARTICLE IN PRESS
1.5-3.0 3.0-5.0 5.0- 6.5 6.5 - 8.0
average exposure (g/m3)
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
409
6 5 4 3 2 1 0 Residence School
Office
Outdoor In-vehicle
>8.0
Fig. 3. Spatial distribution of outdoor benzene concentration (mg/m3) at 10 h.
based on census statistical data, land use map and time–activity patterns that provide hourly profiles for different microenvironments. The following microenvironments are distinguished: (1) (2) (3) (4) (5)
residence, school, office, outdoor, in-vehicle.
A spatial distribution of outdoor pollution is provided by the air pollution dispersion model. For this step, the detailed data on pollutant emission from road transport were considered taking into account the number of vehicles for each road segment, average vehicle speed and fleet composition (Tchepel, 2003). Also, dispersion conditions for the atmospheric pollutants are considered, including wind speed and direction. The output information from the air pollution dispersion model is hourly pollutant concentration for the study area with a 100 m2 resolution (Fig. 3).An average population exposure is quantified for each microenvironment taking into account benzene concentration and time spent in each microenvironment for various subpopulation groups. The results for the average hourly benzene exposure are presented in Fig. 4. The modelling approach may be applied for different temporal (from 1 h to 1 year) and spatial scales (from one street to the entire city).
Discussion Recent research in the field of human exposure to airborne benzene has focused on environmental exposure of the general population. Urban areas with nonhomogeneous exposure pattern are under major con-
Fig. 4. Average hourly exposure calculated for different microenvironments.
sideration due to the contribution of vehicle emissions to the total atmospheric benzene. Data from biomonitoring can provide information on the amount of a chemical absorbed into the human body. However, the relationship between environmental benzene concentrations and biological monitoring data is a challenging issue. For interpreting biomonitoring results several techniques, such as environmental sampling and behavioural data are needed to assess where the exposure came from (BEST, 2006). One of the ways to obtain benzene concentrations in the atmosphere is through air quality monitoring stations. Although it should be noted that such monitoring data have temporal variation but very limited spatial representativeness, specifically in urban areas with complex dispersion conditions. Several studies report very weak correlation between personal exposure monitoring data and environmental concentration measurements (Crebelli et al., 2001; Gulliver and Briggs, 2004). Also, these measurements characterize only outdoor concentrations while concentrations of indoor benzene often exceed the outdoor levels (Choi et al., 2000; Duarte-Davidson et al., 2001). In this context, modelling tools can provide valuable data on benzene concentration with higher spatial and temporal resolution. Additionally, they allow establishing source–receptor relations and in this way to evaluate the contribution of different emission sources to environmental benzene concentration and exposure levels. The current work presents the methodology to quantify population exposure to air pollution by benzene in complex urban areas, combining information on modelled air pollutant concentrations at different microenvironments and population time–activity pattern data. An example of application of the methodology to a selected area in Lisbon provides preliminary results on population exposure to benzene for different microenvironments. The methodology proposed in the work could provide information on the location of city ‘‘hot spots’’ and
ARTICLE IN PRESS 410
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
exposure duration for different socio-economic population groups. These data could be used in combination with human biomonitoring in order to select who should be monitored, as well as when and where. Also, the applied methodology could be used for interpretating biomonitoring data providing relevant information on exposure levels within different microenvironments. The results on external exposure patterns could be useful for extrapolation of biomonitoring results from individuals to population subgroups. It is expected that the combination of different approaches, such as biomonitoring, modelling tools and monitoring data allows to reduce the uncertainty inherent within exposure assessment.
References BEST (Board on Environmental Studies and Toxicology), 2006. Human Biomonitoring for Environmental Chemicals. The National Academies Press, Washington, DC. Borrego, C., Tchepel, O., Costa, A.M., Martins, H., Ferreira, J., Miranda, A.I., 2006. Traffic-related particulate air
pollution exposure in urban areas. Atmos. Environ. 40, 7205–7214. Choi, Y., Shin, D., Park, S., Chung, Y., Kim, M., 2000. Biological monitoring of benzene in residents living near petrochemical industrial areas in Korea. J. Occup. Health 42, 31–37. Crebelli, R., Tomei, F., Zijno, A., Ghittori, S., Imbriani, M., Gamberale, D., Martini, A., Carere, A., 2001. Exposure to benzene in urban workers: environmental and biological monitoring of traffic police in Rome. Occup. Environ. Med. 58, 165–171. Duarte-Davidson, R., Courage, C., Rushton, L., Levy, L., 2001. Benzene in the environment: an assessment of the potential risks to the health of the population. Occup. Environ. Med. 58, 2–13. Gulliver, J., Briggs, D.J., 2004. Personal exposure to particulate air pollution in transport microenvironments. Issue Series Title: Atmos. Environ. 38, 1–8. Hertel, O., Leeuw, F., Raaschou-Nielsen, O., Jensen, S., Gee, D., Herbarth, O., Pryor, S., Palmgren, F., Olsen, E., 2001. Human exposure to outdoor air pollution. Pure Appl. Chem. 73, 933–958. Tchepel, O., 2003. Emission modelling as a decision support tool for air quality management. Ph.D. Thesis, University of Aveiro, Portugal, 213pp.
Int. J. Hyg. Environ.-Health 210 (2007) 407–410 www.elsevier.de/ijheh
Assessment of population exposure to air pollution by benzene Oxana Tchepel, Ana Penedo, Madalena Gomes Department of Environmental Engineering, School of Technology and Management, Polytechnic Institute of Leiria, Morro do Lena, Alto do Vieiro, Apt. 4163, 2401-951 Leiria, Portugal
Abstract Biomonitoring is one of the methods that allow to identify population groups that have significantly higher exposures to a particular chemical than the general population. However, use of biomonitoring is particularly useful when applied in combination with other methods of pollution exposure assessment. The current study is focused on the developing of the modelling approach to estimate population exposure to benzene through inhalation. The model is based on a microenvironment approach and is adapted to be applied in urban areas where the pattern of exposure is complex. The results provided by the model may be used in combination with human biomonitoring in order to select who and where should monitoring be done, as well as for interpretation and extrapolation of biomonitoring results. r 2007 Elsevier GmbH. All rights reserved. Keywords: Benzene; Population exposure; Exposure modelling; Air quality modelling; Biomonitoring
Introduction Numerous studies indicate that air pollution cause severe health effects and, therefore, an assessment of the population’s exposure to air pollution is crucial for a proper determination of possible links between the pollution levels and health effects. There are different approaches that could be applied to assess exposure to air pollution (Hertel et al., 2001):
biomonitoring, personal exposure monitoring, analysis of air pollution data from monitoring network, and air pollution exposure models.
Biomonitoring and personal exposure monitoring are the techniques that can provide a direct measure of the Corresponding author. Tel.: +351 244 820 300; fax: +351 244 820 310. E-mail address: [email protected] (O. Tchepel).
1438-4639/$ - see front matter r 2007 Elsevier GmbH. All rights reserved. doi:10.1016/j.ijheh.2007.01.008
exposure to air pollutants while other approaches are indirect tools that determine the exposure by combining information about pollutant concentrations at locations with information about the time spent in specific environments. Use of biomonitoring is particularly useful when applied in combination with other methods (Hertel et al., 2001) of air pollution exposure assessment (Fig. 1). The current study is focused on developing a modelling approach to estimate population exposure to benzene adapted to applications in urban areas where the pattern of exposure is complex.
Methodology The modelling approach developed to estimate population exposure involves several steps: (1) population data processing; (2) air pollutants concentration data processing; and (3) population exposure quantification.
ARTICLE IN PRESS 408
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
Air pollution monitoring
Human Biomonitoring
Air pollution modelling
Human Exposure Models
Population time-activity pattern
Personal Exposure Monitoring
Population Exposure Assessment
Fig. 1. Combination of methods for air pollution exposure assessment.
Population data processing
Inputs: Total population in the study area
The main objective of this task is to obtain temporal and spatial distributions of inhabitants within the study domain based on data for different population age groups, mobility data and employment information focused on the description of the proportion of time spent in the various microenvironments. Additionally to numerical information, digital maps are considered in order to provide spatial distribution of the required parameters.
Temporal population distribution for microenvironment Spatial distribution of microenvironments Outdoor pollutant concentration field
Air pollutants concentration data processing The pollutant concentration data is one of the main inputs for exposure quantification. It is distinguished between the ambient air (outdoor) concentrations provided by the ADMS air quality dispersion models, and indoor concentrations calculated through empirical indoor/outdoor penetration factors. Additionally to indoor/outdoor pollutant penetration, the methodology considers an additive factor related with emission sources within a microenvironment.
Population exposure quantification The exposure calculation module is constructed to assess and to predict population exposure distribution from modelled air quality concentrations in different microenvironments of an urban area and from modelling of the number of persons present in the same environment (Fig. 2). This modelling approach has been previously applied for quantification of population exposure to PM10 (Borrego et al., 2006). In the current work the methodology is adapted to quantification of population exposure to benzene.
Results The methodology developed in this work was applied for a selected area in Lisbon. The study area is about
Estimations: Number of inhabitants per grid cell per hour Indoor pollutant concentration Human exposure per grid cell Outputs: Average population exposure Number of inhabitants exposed above the Limit Value
Fig. 2. Schematic representation of the exposure model.
2 km2 (1.5 km 1.3 km) located in the western part of the city and involves one of the main Lisbon entrances of road traffic with approximately 100,000 vehicles per day. The population spatial and temporal distribution required for the exposure modelling were obtained
ARTICLE IN PRESS
1.5-3.0 3.0-5.0 5.0- 6.5 6.5 - 8.0
average exposure (g/m3)
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
409
6 5 4 3 2 1 0 Residence School
Office
Outdoor In-vehicle
>8.0
Fig. 3. Spatial distribution of outdoor benzene concentration (mg/m3) at 10 h.
based on census statistical data, land use map and time–activity patterns that provide hourly profiles for different microenvironments. The following microenvironments are distinguished: (1) (2) (3) (4) (5)
residence, school, office, outdoor, in-vehicle.
A spatial distribution of outdoor pollution is provided by the air pollution dispersion model. For this step, the detailed data on pollutant emission from road transport were considered taking into account the number of vehicles for each road segment, average vehicle speed and fleet composition (Tchepel, 2003). Also, dispersion conditions for the atmospheric pollutants are considered, including wind speed and direction. The output information from the air pollution dispersion model is hourly pollutant concentration for the study area with a 100 m2 resolution (Fig. 3).An average population exposure is quantified for each microenvironment taking into account benzene concentration and time spent in each microenvironment for various subpopulation groups. The results for the average hourly benzene exposure are presented in Fig. 4. The modelling approach may be applied for different temporal (from 1 h to 1 year) and spatial scales (from one street to the entire city).
Discussion Recent research in the field of human exposure to airborne benzene has focused on environmental exposure of the general population. Urban areas with nonhomogeneous exposure pattern are under major con-
Fig. 4. Average hourly exposure calculated for different microenvironments.
sideration due to the contribution of vehicle emissions to the total atmospheric benzene. Data from biomonitoring can provide information on the amount of a chemical absorbed into the human body. However, the relationship between environmental benzene concentrations and biological monitoring data is a challenging issue. For interpreting biomonitoring results several techniques, such as environmental sampling and behavioural data are needed to assess where the exposure came from (BEST, 2006). One of the ways to obtain benzene concentrations in the atmosphere is through air quality monitoring stations. Although it should be noted that such monitoring data have temporal variation but very limited spatial representativeness, specifically in urban areas with complex dispersion conditions. Several studies report very weak correlation between personal exposure monitoring data and environmental concentration measurements (Crebelli et al., 2001; Gulliver and Briggs, 2004). Also, these measurements characterize only outdoor concentrations while concentrations of indoor benzene often exceed the outdoor levels (Choi et al., 2000; Duarte-Davidson et al., 2001). In this context, modelling tools can provide valuable data on benzene concentration with higher spatial and temporal resolution. Additionally, they allow establishing source–receptor relations and in this way to evaluate the contribution of different emission sources to environmental benzene concentration and exposure levels. The current work presents the methodology to quantify population exposure to air pollution by benzene in complex urban areas, combining information on modelled air pollutant concentrations at different microenvironments and population time–activity pattern data. An example of application of the methodology to a selected area in Lisbon provides preliminary results on population exposure to benzene for different microenvironments. The methodology proposed in the work could provide information on the location of city ‘‘hot spots’’ and
ARTICLE IN PRESS 410
O. Tchepel et al. / Int. J. Hyg. Environ.-Health 210 (2007) 407–410
exposure duration for different socio-economic population groups. These data could be used in combination with human biomonitoring in order to select who should be monitored, as well as when and where. Also, the applied methodology could be used for interpretating biomonitoring data providing relevant information on exposure levels within different microenvironments. The results on external exposure patterns could be useful for extrapolation of biomonitoring results from individuals to population subgroups. It is expected that the combination of different approaches, such as biomonitoring, modelling tools and monitoring data allows to reduce the uncertainty inherent within exposure assessment.
References BEST (Board on Environmental Studies and Toxicology), 2006. Human Biomonitoring for Environmental Chemicals. The National Academies Press, Washington, DC. Borrego, C., Tchepel, O., Costa, A.M., Martins, H., Ferreira, J., Miranda, A.I., 2006. Traffic-related particulate air
pollution exposure in urban areas. Atmos. Environ. 40, 7205–7214. Choi, Y., Shin, D., Park, S., Chung, Y., Kim, M., 2000. Biological monitoring of benzene in residents living near petrochemical industrial areas in Korea. J. Occup. Health 42, 31–37. Crebelli, R., Tomei, F., Zijno, A., Ghittori, S., Imbriani, M., Gamberale, D., Martini, A., Carere, A., 2001. Exposure to benzene in urban workers: environmental and biological monitoring of traffic police in Rome. Occup. Environ. Med. 58, 165–171. Duarte-Davidson, R., Courage, C., Rushton, L., Levy, L., 2001. Benzene in the environment: an assessment of the potential risks to the health of the population. Occup. Environ. Med. 58, 2–13. Gulliver, J., Briggs, D.J., 2004. Personal exposure to particulate air pollution in transport microenvironments. Issue Series Title: Atmos. Environ. 38, 1–8. Hertel, O., Leeuw, F., Raaschou-Nielsen, O., Jensen, S., Gee, D., Herbarth, O., Pryor, S., Palmgren, F., Olsen, E., 2001. Human exposure to outdoor air pollution. Pure Appl. Chem. 73, 933–958. Tchepel, O., 2003. Emission modelling as a decision support tool for air quality management. Ph.D. Thesis, University of Aveiro, Portugal, 213pp.