Personal exposure models estimate the exposure of an individual from different MEs, an approach also referred to as the microenvironmental approach (Chan et al. 1999; Jensen 1999). These models summarise exposure using the sum of visited MEs during a day, multiplied by the time spent in each one of them. The times in each ME are then time-weighted to get exposure estimates for example for different hours of a day. This exposure model (E) has been written as follows by Kruize et al. (2003):
46
Equation 1: Microenvironmental Approach
Where Ci is the concentration in microenvironment i, fi the fractional time spent in microenvironment i, and N the number of MEs. This microenvironmental approach has been applied in many studies and has also been referred to as ‘integrated modelling approach’ by some studies (Baklanov et al. 2007; Steinle et al. 2011; Briggs et al. 2005), as it combines ambient concentrations with time-activity data and different ME models.
In the following paragraphs several studies are reviewed, which developed and in some cases applied integrated personal exposure models for particle pollution. A focus on European studies is taken, as lifestyle, built environment, building design, population density, and city layout are relatively similar within Europe, but different from other parts of the world. For example common use of air conditioning for buildings and vehicles is especially important in Northern America (Gupta & Cheong 2007; Thornburg et al. 2001; Croxford et al. 2000). Use of air conditioning however substantially alters indoor particle concentrations (Hänninen 2005; Ho et al. 2004; Gupta & Cheong 2007; Horemans et al. 2008; Nı ́ Riain et al. 2003). One US-American model is however of major importance in terms of its general approach: The SHEDS (Stochastic Human Exposure and Dose Simulation) model, developed by the US EPA is a temporally and spatially transferable modelling framework that has been applied by several studies. It provides parameters for indoor modelling and estimates for other MEs, which can be combined with modelled or monitored ambient concentration studies (Burke et al. 2001; Özkaynak et al. 2013; Georgopoulos et al. 2005). No equivalent transferable model has been developed yet for the European context. A study by Zidek et al. (2003) developed the pCNEM (clear definition for acronym not given) model following principles of the SHEDS model and applied it in London. MEs used for the pCNEM model were however calculated predominantly with data collected in North America and the application for London was not validated by monitoring data.
47
Study Pollutant Study Location Ambient Model Spatial Precision Transferability
Individual/Population
Level Validation
AB2C (Dons, Van Poppel,
Kochan, et al. 2014) BC Flanders, Belgium LUR, adjusted to hourly
2386 (sub)zones
of Flanders Within Flanders
Population (individual possible) Yes
Borrego et al. (2006) PM10 Lisbon, Portugal Hybrid model
2km population
grid No Population No
Borrego et al. (2009) PM10 (O3) Portugal
air quality - exposure integrated forecasting system (MM5-CHIMERE)
10km
population grid No Population No
EXPAH (Gariazzo et al.
2014)
PM2.5,
(PAH) Rome, Italy Chemical transport model
Residential
areas Yes
Individual (scaled to city level) Yes
EXPAND (Baklanov et al.
2007; Hänninen 2005b; Kousa, Kukkonen, et al. 2002)
PM2.5
(NO2)
Helsinki, Finland Dispersion 100m
population grid No Population No
EXPOLIS (Kruize et al.
2003; Hänninen et al. 2003)
Not specified
Athens (Greece), Basel (Switzerland), Helsinki (Finland), and Prague (Czech Republic)
None None
Yes, in areas where monitored data of study areas is representative Population (individual possible) Comparison with other monitoring results Gerharz et al. (2009) PM2.5, PM10
Munster, Germany Dispersion with temporal adjustments
Individual level time-space location
Yes Individual No
STEMS (Briggs et al. 2005;
Gulliver & Briggs 2005) PM10 Northampton, UK Dispersion
Individual level time-space location
Yes Individual Yes, for model components
48 A number of European studies have developed personal exposure models including MEs, these are presented in Table 4. The largest of these studies was the EXPOLIS (Air Pollution Exposure Distributions of Adult Urban Populations in Europe) study. It used a probabilistic exposure modelling framework based on collected information from 1427 people in 7 European cities. It was aimed to compare exposure of different scenarios or population groups. In order to estimate concentrations for different MEs, concentration distributions were sampled for each ME, either based on monitored or modelled concentrations. The modelling framework has been used in several applications (Kruize et al. 2003; Hänninen et al. 2003). Only crude estimates for transport MEs were included in the framework (sampled from concentration distributions at roadside stations, or using home indoor estimates). The model also did not include geographical information, apart from distinctions of inter- country or urban-rural differences (Kruize et al. 2003; Hänninen et al. 2003; Hänninen et al. 2004; Schweizer et al. 2007; Kousa, Oglesby, et al. 2002).
The Helsinki based EXPAND (Exposure to air pollution, especially to nitrogen dioxide and particulate matter) model combines a GIS-based PM dispersion model (based on the principle that PM disperses away from sources) and an ME model which takes into account population-level time-activity data and trips. The ME component of the model however only included one ratio for indoor to outdoor differences, and the approach does not follow an individual’s time-activity. It can therefore not be used to describe variability between personal exposures (Baklanov et al. 2007; Hänninen 2005b; Kousa, Kukkonen, et al. 2002). The EXPAND model has later been applied as part of the European FUMAPEX (Integrated systems for forecasting urban meteorology, air pollution and population exposure) study in Oslo, Copenhagen, and Helsinki.
Borrego et al. (2006) developed and applied an integrated model using data from Lisbon. Their approach to ambient pollution uses a hybrid model (linking a meteorological model, a dispersion model, and a fluid dynamic model). Results were displayed in a GIS-based grid (grid accuracy: 2km); population exposure was then modelled per grid cell per time (inhabitants per grid cell per hour). Adjustments for different microenvironments were then applied to the grid cell estimates (Borrego et al. 2006). Borrego et al. (2009) developed a similar exposure modelling approach for Portugal. The model was based on predictions for ambient concentrations from a hybrid modelling system (combining several models) and predicts exposure for 10km grid cells. Simple ratios were applied to adjust for indoor MEs. Both studies (Borrego et al. 2006; Borrego et al. 2009) are specifically aimed at predicting exposure for the population in the study area. Direct transferability of the model to other areas is not possible, as data underlying the developed grid cells, such as number of inhabitants is location specific.
49 In a study by Gariazzo et al. (2014) the EXPAH (Population Exposure to PAH) model was developed for Rome, Italy. The model uses a chemical transport model (designed to calculate the lifecycle of chemical components in air pollution) to estimate ambient PAH concentrations in a regular grid of 60x60m. Exposure for the elderly and children was assumed to take place mainly in their neighbourhood. All-day exposure was therefore estimated by residential area, modified by ratios for different MEs, such as home or school indoors. Exposure in transport was considered equal to ambient concentrations.
A further modelling system was developed by Gulliver & Briggs (2005) with the integrated STEMS (Space-Time Exposure Modelling) model. Ambient PM was calculated using a dispersion model and combined with microenvironmental model components, as well as time-activity data. Random sampling was used to extract time-activities for virtual individuals, and a GIS based time-space- activity model was developed to simulate these individual’s transport routes. Modifications for each ME were applied by inside-to-outside ratios (Gulliver & Briggs 2005); (Briggs et al. 2005).
Dons, Van Poppel, Kochan, et al. (2014) developed the AB2C model combining temporally and spatially refined ambient predictions (using time-adjusted land-use regression, described below) and fixed ratios for adjustment of indoor MEs. Time-activity was calculated using FEATHERS (Forecasting evolutionary activity-travel of households and their environmental repercussions), which also determined destination and duration of trips between subzones (2386 areas in Flanders, Belgium). Adjustments for different MEs were made using ratios. The model was validated using concentrations data collected by 54 volunteers during one week. The Pearson’s correlation between modelled and monitored personal exposure for this model was 0.452.
A pilot study by Gerharz et al. (2009) used time-adjusted results from a dispersion model to predict geographic locations of modelled individuals. MEs were adjusted using comparably refined methods, using ratios for different traffic modes and a mass-balance model for indoor MEs. Mass-balance models are based on physical principles underlying outdoor to indoor pollution differences, and are explained in detail below.
In summary, studies have developed a range of different approaches to calculate exposure using integrated personal exposure models. The approaches used however vary significantly. One main difference for the development and application of an approach is whether it is aimed at predictions at individual or population level. Population level predictions cannot and are not aimed at predicting exposure variability between individuals, but can be used for example to compare regional-level air pollution scenarios (Borrego et al. 2009; Hänninen 2005b). Individual level exposure predictions are usually limited to a relatively small number of subjects, due to the model complexity and computer
50 processing times. Individual level models can however be used to account for and interpret differences in variability between people. Improvements in computer technology and growing availability of individual-level data makes these models increasingly a possibility for larger population size applications (Borrego et al. 2009; Dons, Van Poppel, Kochan, et al. 2014).
Some of the studies are additionally developed specifically for one study area, while other studies focus on building a transferable model. The approaches also differ in the importance they apply to different modelling components, such as precision of geographical locations, importance and treatment of ambient concentrations for the model, number of different MEs, or type of adjustment applied for different MEs (e.g. using a simple ratio or a mass-balance model for indoor MEs).