DEVELOPMENT OF A POPULATION EXPOSURE MODEL, USING ATMOSPHERIC DISPERSION MODELLING TOGETHER WITH MEASURED CONCENTRATIONS AND PERSONAL EXPOSURES - EXPAND

Project leader: Jaakko Kukkonen, Finnish Meteorological Institute, Sahaajankatu 20 E,
FIN-00810 Helsinki, Finland, tel. +358-9-1929 5450, e-mail: Jaakko.Kukkonen@fmi.fi
 
 

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TIIVISTELMÄ SUOMEKSI

Researchers:
Ari Karppinen, Finnish Meteorological Institute, tel. +358-9-1929 5453, e-mail: Ari.Karppinen@fmi.fi
Päivi Aarnio, Helsinki Metropolitan Area Council, tel. +358-9-156 1222, e-mail: Paivi.Aarnio@ytv.fi
Anu Kousa, Helsinki Metropolitan Area Council, tel. +358-9-1561 398, e-mail: Anu.Kousa@ytv.fi
Matti Jantunen, National Public Health Institute, tel. +358-17-201 340, e-mail: Matti.Jantunen@ktl.fi
Kimmo Koistinen, National Public Health Institute, tel. +358-17-201 172, e-mail: Kimmo.Koistinen@ktl.fi
Mia Pohjola, Finnish Meteorological Institute, tel. +358-9-1929 5457, e-mail: Mia.Pohjola@fmi.fi
Jari Härkönen, Finnish Meteorological Institute, tel. +358-9-1929 5452, e-mail: Jari.Harkonen@fmi.fi

Consortium: Urban air particles and environmental health
Financing SYTTY organisation:  The Academy of Finland
Funding from SYTTY / Total funding of project (€): 81907 / 233230
Person-months of work funded by SYTTY / Total person-months of work: 22,5 / 54

KEY WORDS: exposure, population exposure model, urban air quality, particulate matter, oxides of nitrogen
 

EXTENDED ABSTRACT

1 Introduction

In this project, we have developed a model for evaluating the human exposure to ambient air pollution in an urban area, in a co-operation of Helsinki Metropolitan Area Council (YTV), Finnish Meteorological Institute (FMI) and National Public Health Institute (KTL). The main objective has been to evaluate the exposure of population with a reasonable accuracy, instead of the personal exposures of specific individuals. The model has been applied in the modelling of population exposure to NO2 in the Helsinki Metropolitan Area in 1996 and 1997. Various models have also been developed in order to evaluate urban particle matter concentrations.

2 Methods

2.1. Emissions and meteorological data

We have used an updated emission inventory of NOx in the Helsinki Metropolitan Area (Karppinen et al., 2000) for the years 1996 and 1997. The inventory includes the emissions from various mobile sources (road traffic, harbours and marine traffic, and aviation) and stationary sources (power plants, other point sources and residential heating). The traffic flows and average travel speeds were computed using the EMME/2 transportation planning system (INRO, 1994); vehicular emissions were evaluated using the EMME/2 system and emission factors that have been evaluated for this area (Helsinki Metropolitan Area Council, 1997). The model allows for the diurnal and weekly variations both in traffic volumes and speeds, as well as in traffic emissions. Stationary sources are considered as point or area sources. The computations included approximately 5000 line sources, 169 point sources, area sources and the regional hourly background concentrations.

We have also used the meteorological database of the Finnish Meteorological Institute, which contains weather and sounding observations. A combination of data from the stations at Helsinki-Vantaa airport (about 15 km north of Helsinki town center) and Helsinki-Isosaari (an island about 20 km south of Helsinki) were employed. The relevant meteorological parameters were evaluated using a meteorological pre-processing model, adapted specifically for urban environment (Karppinen et al., 1998, 2000c). The model utilises meteorological synoptic and sounding observations, and its output consists of hourly time series of relevant atmospheric turbulence parameters (the Monin-Obukhov length scale, the friction velocity and the convective velocity scale) and the boundary layer height.

2.2. Atmospheric dispersion modeling

The dispersion modelling is based on a combined application of the road network dispersion model CAR-FMI (Härkönen et al., 1995 and 1996), applied for evaluating the dispersion of pollution originating from vehicular traffic, and the Urban Dispersion Modelling system UDM-FMI (Karppinen et al., 1998), for evaluating the dispersion from stationary sources. The predictions of the CAR-FMI model have previously been compared with the results of two measurement campaigns conducted near major roads (i) in a suburban area (Härkönen et al., 1997, Walden et al., 1995) and (ii) in a rural area (Kukkonen et al., 2001a, Öttl et al., 2001).

The modelling system includes a statistical and graphical analysis of the computed time series of concentrations. The modelling system also comprises a method that allows for the chemical interaction of pollutants (including the basic reactions of nitrogen oxides and ozone), originating from a large number of urban sources (Karppinen et al., 2000a).

2.3. Exposure modelling

In the development of the population exposure model, we have considered and tested various methods for combining and processing the predicted concentrations and the locations of the population and the times spent at home, at workplace and at other places of activity. A schematic presentation of the exposure model is presented in Figure 1.

The main objective was to evaluate the exposure of the population with reasonable accuracy, instead of the personal exposures of specific individuals. We have, for the time being, assumed that the residential and workplace indoor concentrations of nitrogen oxides were the same as the corresponding outdoor concentrations. The part of the modelling system that evaluates the exposure of the population to air pollution has been named EXPAND (“EXPosure to Air pollution, especially to Nitrogen Dioxide and particulate matter“). The model utilises as input values (i) data on the spatial location of the population, (ii) time-microenvironment activity data and (iii) computed spatial pollutant concentration distributions.  The computational modules contain a separate computer program for combining and processing the data, and the GIS MapInfo.

The program interpolates the concentration data spatially, and combines it with the population activity and location data. The population exposure model consists of four parts: exposure in traffic,  exposure at work, home and other microenvironments. The exposure in traffic is computed separately for each street section, and the exposure elsewhere is computed in a grid with spatial resolution of 100 m. The actual personal exposure, the transport of pollutants from outdoor to indoor air, as well as the inside-the-vehicle and other indoor concentrations have not been evaluated.

2.4. Particulate matter modelling

For the particulate matter  modelling,  data from the air quality monitoring stations in the Helsinki metropolitan area was used (Pohjola et al. 2000, Karppinen et al., 2002).

The semi-empirical model for evaluation of urban PM10 concentrations utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. It is based on linear relationships between the measured urban PM10 and NOx concentration data in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 1996 - 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter (Kukkonen et al., 2001c).

For the evaluation of the regionally and long-range transported (LRT) contribution to the concentrations of fine particulate matter (PM2.5), values of the following quantities were utilised: (i) SO42- (sulfate), (ii) the sum of NO3- (nitrate) and HNO3 (nitrogen acid), and (iii) the sum of NH4+ (ammonium) and NH3 (ammonia); there are measured daily at the EMEP stations. The sum of these variables can be treated as a proxy variable for the long-range-transported PM. The model is a linear regression equation between measured urban PM2.5 concentrations in central Helsinki and the ion sum values at the three nearest EMEP stations during 1998 -2000 (Karppinen et al., 2002).

A model for predicting the measured concentrations of PM2.5 was also developed. The regionally and long-range transported contribution is evaluated based on the semi-empirical mathematical model described in the previous paragraph. The influence of primary vehicular emissions from the nearest roads is evaluated using a roadside emission and dispersion model CAR-FMI, used in combination with a meteorological pre-processing model MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated to be directly proportional to the concentrations originating from primary vehicular emissions (Tiitta et al., 2002).

We have also applied an aerosol dynamical model MONO32, developed by Pirjola and Kulmala (2000), in cooperation with the University of Helsinki. The model takes into account gas-phase chemistry and aerosol dynamics. The particles are classified into four different size modes which are monodisperse. We have  also compiled vehicular exhaust scenarios in selected urban environments. The model input data includes vehicular exhaust particulate matter size distribution and chemical composition, and urban particulate matter and gaseous urban background concentrations. The short-scale dilution (the time-scale of 25 s) of the vehicular exhaust plume is included in the model.

3 Results and discussion

3.1. Exposure modelling

An example of population number distributions in the different microenvironments is presented in Figure 2.

Figure 2. The number of population versus the NO2 concentration that the persons are exposed to, in March 1996, during working day afternoons (Kousa et al., 2002).

The population exposure model developed here has been applied in the modeling of population exposure to NO2 in the Helsinki Metropolitan Area in 1996 and 1997 (Kousa et al., 2002). A detailed description of the model EXPAND has been presented by Kousa et al. (2001b and 2002).

3.2. Dispersion computations for nitrogen oxides and their comparison with measured data

We have compared the NOx and NO2 concentrations predicted using the modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently-located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996 – 1997 (at a total of ten urban and suburban measurement locations).

In comparison with corresponding results previously presented in the literature, the agreement between the measured and predicted datasets is good. An example result of this work is presented in Figure 3. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions (Kousa et al., 2001a).

Figures 3 a-d. The ratio of predicted and observed concentrations of NO2 in terms of the atmospheric stability class, at the stations of Töölö and Vallila in central Helsinki, in 1996 and 1997 (Kousa et al. 2001a).

3.3. Particulate matter modelling

The performance of the semi-empirical model for evaluation of urban PM10 concentrations performance was evaluated against the PM10 data from five measurement stations measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM10 vs. NOx) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM10: the FB values range from – 0.05 to + 0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. (Kukkonen et al., 2001c).

PM2.5  was modelled with the ion sum correlation model (Karppinen et al., 2002), for the EXPOLIS measurement points. The correlation achieved was R2 = 0.65. In addition, we have investigated  the correlations of spatial and temporal variation of particulate matter on meteorological and other factors (Pohjola et.al 2001a,b).

A particle measurement campaign was conducted in suburban environment near a major road in Kuopio, from August 3 to September 9, 1999. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were evaluated with the developed model to contribute on the average 41+ 6, 33 + 6 and 26 + 7 % of the observed PM2.5 concentrations, respectively (Tiitta et al., 2002).

We have applied an aerosol dynamical model MONO32, developed by Pirjola and Kulmala (2000), in cooperation with the University of Helsinki. The first objective was to evaluate quantitatively the influence on aerosol evolution of various chemistry and aerosol processes. We have studied the effects of coagulation, condensation, concentration of the condensable organic vapor, and dilution in the exhaust plume, on the number concentrations, compositions and particle radii in the size modes (Pohjola et al., 2002) .

4 Conclusions

The main objective of this project has been the development of a model for evaluating the human exposure to ambient air pollution in an urban area, in a co-operation of YTV, FMI and KTL. The aim has been to evaluate the exposure of population with a reasonable accuracy, instead of the personal exposures of specific individuals. The computer program EXPAND has been developed for the evaluation of population exposure, and the computed results are processed and visualised using the Geographical Information System (GIS) MapInfo. The model has been applied in the modelling of population exposure to NO2 in the Helsinki Metropolitan Area in 1996 and 1997.

We have compared the NOx and NO2 concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations for the years 1996 - 1997. The agreement between the measured and predicted datasets was good.

We have also investigated the correlations of spatial and temporal variation of PM10 and PM2.5 on meteorological and other factors. A novel mathematical model was also developed for evaluating the various contributions to measured concentrations of PM2,5 originating from local traffic, long-range transport and other sources

We have applied an aerosol dynamical model to evaluate quantitatively the influence on aerosol evolution of various chemistry and aerosol processes. We have studied the effects of coagulation, condensation, concentration of the condensable organic vapour, and dilution in the exhaust plume on the number concentrations, compositions and particle radii.

5 References

Helsinki Metropolitan Area Council, 1997. Liikennejärjestelmän vaikutukset ilmalaatuun (The impacts of transportation system to air quality) Helsinki Metropolitan Area Series B 1997:11 YTV Helsinki Metropolitan Area Council, Helsinki.

Härkönen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Jalkanen, L., Lahtinen, K., 1995. An operational dispersion model for predicting pollution from a road. International Journal of Environment and Pollution, 5 (4-6), 602-610.

Härkönen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Lahtinen, K., Karppinen, A., Jalkanen, L., 1996. A model for the dispersion of pollution from a road network. Finnish Meteorological Institute, Publications on Air Quality 23. Helsinki.

Härkönen J., Walden J., Kukkonen, J., 1997. Comparison of model predictions and measurements near a major road in an urban area. International Journal of Environment and Pollution, 8, (3-6), 761-768.

INRO, 1994. EMME/2 User’s manual. INRO Consultants Inc., Montreal, Canada.

Karppinen, A., Joffre, S., Vaajamaa, P., 1997. Boundary layer parametrization for Finnish regulatory dispersion models. International Journal of Environmental Pollution 8 (3-6), 557-564.

Karppinen, A., Kukkonen, J., Nordlund, G., Rantakrans, E., Valkama, I., 1998. A dispersion modelling system for urban air pollution. Finnish Meteorological Institute, Publications on Air Quality 28, Helsinki.

Walden, J., Härkönen, J., Pohjola, V., Kukkonen, J., Kartastenpää, R., 1995. Vertical concentration profiles in urban conditions – comparison of measurements and model predictions, In: Anttila P., e el. (Eds.) Proceedings of the 10th World Clear Air Congress, Espoo, Finland May 28-June 2, 1995. Vol 2. The Finnish Air Pollution Prevention Society, Helsinki.
 

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