Difference between revisions of "Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland"

Revision as of 13:37, 27 April 2012

This page (including the files available for download at the bottom of this page) contains a draft version of a manuscript, whose final version is published and is available in the Atmospheric Environment 44 (2010). If referring to this text in scientific or other official papers, please refer to the published final version as: M. Tainio, J.T. Tuomisto, J. Pekkanen, N. Karvosenoja, K. Kupiainen, P. Porvari, M. Sofiev, A. Karppinen, L. Kangas, J. Kukkonen: Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland. Atmospheric Environment 44 (2010) 2125e2132 doi:10.1016/j.atmosenv.2010.02.036 .

Title

Editing Uncertainty in health risks due to anthropogenic primary fine particulate matter from different source types in Finland

Authors and contact information

M. Tainio, correspondence author

(Marko.Tainio@thl.fi)

(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)

(Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland)

J.T. Tuomisto

(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)

(Academy of Finland, Helsinki, Finland)

J. Pekkanen

(Department of Environmental Health, National Institute for Health and Welfare (THL), Kuopio, Finland)

(University of Eastern Finland, Kuopio, Finland)

N. Karvosenoja

(Finnish Environment Institute (SYKE), Helsinki, Finland)

K. Kupiainen

(Finnish Environment Institute (SYKE), Helsinki, Finland)

P. Porvari

(Finnish Environment Institute (SYKE), Helsinki, Finland)

M. Sofiev

(Finnish Meteorological Institute (FMI), Helsinki, Finland)

A. Karppinen

(Finnish Meteorological Institute (FMI), Helsinki, Finland)

L. Kangas

(Finnish Meteorological Institute (FMI), Helsinki, Finland)

J. Kukkonen

(Finnish Meteorological Institute (FMI), Helsinki, Finland)

Article info

Article history:

Received 4 June 2009

Received in revised form 18 January 2010

Accepted 24 February 2010

Abstract

The emission-exposure and exposure-response (toxicity) relationships are different for different emission source categories of anthropogenic primary fine particulate matter (PM2.5). These variations have a potentially crucial importance in the integrated assessment, when determining cost-effective abatement strategies.We studied the importance of these variations by conducting a sensitivity analysis for an integrated assessment model. The model was developed to estimate the adverse health effects to the Finnish population attributable to primary PM2.5 emissions from the whole of Europe. The primary PM2.5 emissions in the whole of Europe and in more detail in Finland were evaluated using the inventory of the European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario model (FRES), respectively. The emission-exposure relationships for different primary PM2.5 emission source categories in Finland have been previously evaluated and these values incorporated as intake fractions into the integrated assessment model. The primary PM2.5 exposure-response functions and toxicity differences for the pollution originating from different source categories were estimated in an expert elicitation study performed by six European experts on air pollution health effects. The primary PM2.5 emissions from Finnish and other European sources were estimated for the population of Finland in 2000 to be responsible for 209 (mean, 95% confidence interval 6e739) and 357 (mean, 95% CI 8e1482) premature deaths, respectively. The inclusion of emission-exposure and toxicity variation into the model increased the predicted relative importance of traffic related primary PM2.5 emissions and correspondingly, decreased the predicted relative importance of other emission source categories.We conclude that the variations of emission-exposure relationship and toxicity between various source categories had significant impacts for the assessment on premature deaths caused by primary PM2.5.

Abbreviations

CAFE, Clean Air For Europe-program; CI, Confidence interval; EDM, Equal-weight decision maker; EMEP, European Monitoring and Evaluation Programme; EPA, U.S. Environmental Protection Agency; ER, Exposure-response; ExternE, Externalities of Energy; FRES, Finnish Regional Emission Scenario model; ICD, International Classification of Disease; iF, Intake fraction; PDM, Performancebased decision maker; PM, Particulate matter; PM2.5, Fine particulate matter; RAINS, Regional Air Pollution Information and Simulation e model; SILAM, Air Quality and Emergency Modeling System; WHO, World Health Organization.

Keywords

Fine particulate matter, Intake fraction, Exposure-response, Integrated assessment, Sensitivity analysis

Introduction

Integrated assessment models can describe quantitative dependences between emission sources and the adverse health effects. This is essential to understand these dependences if one wishes to devise effective emission mitigation strategies.

Fine particulate matter (PM2.5) air pollution has been associated with several adverse health effects (e.g. Pope and Dockery, 2006). The exposure to PM2.5 originating from different sources or source categories can be estimated with atmospheric dispersion models or with source apportionment of measured PM mass (Hopke et al., 2006). Most of the integrated assessment studies for PM are based on dispersion modeling. For example, Levy and Spengler (2002) demonstrated a modeling framework to estimate the adverse health effects due to secondary PM2.5 formed from precursor gas emissions from two power plants in Massachusetts, U.S. Künzli et al. (2000) estimated the exposure for traffic related PM in Austria, France and Switzerland. Both of the above mentioned studies have focused on one or a few emission sources, or a single emission source category.

The exposure assessment studies have rarely taken into account PM emissions from several source categories. One such study is the European Clean Air For Europe (CAFE) -program that evaluated the adverse health effects due to PM2.5 in Europe, taking into account all anthropogenic PM2.5 emission sources in Europe (Watkiss et al., 2005). However, the CAFE program did not report any source category specific results. Another study performed in the U.S. (Fann et al., 2009), evaluated the effectiveness of emission reduction of different air pollutants in 9 urban areas and for 5 emission source categories. They concluded that reduction of carbonaceous PM would generate the largest benefit in all locations, in comparison to secondary PM.

The assessment studies for PM2.5 usually assume equal toxicity for all PM2.5, regardless of the pollution source or chemical composition. Toxicity is here defined to refer to the ability of air pollution to cause adverse health effects to a human population. The chemical and physical properties of PM are known to be substantially different for various emission sources, and these properties could modify the relative toxicity of PM2.5. For example, several epidemiological studies have reported in both U.S. and Europe that one obtains higher relative risk estimates for PM from combustion sources (e.g., Laden et al., 2000; Lanki et al., 2006). However, there is limited understanding of the differences of toxicity in terms of PM properties. A World Health Organization (WHO) workshop concluded in 2007 that the current scientific knowledge is not sufficient to differentiate the toxicity of different PM sources (WHO, 2007). Despite the lack of information, the workshop acknowledged the need to perform sensitivity analyses for toxicity differences in integrated assessments.

In this study, we describe an integrated assessment model to estimate the adverse health effects due to anthropogenic primary PM2.5 from six different emission source categories, taking into account also the differences in emission-exposure relationships, and the variations of toxicity between PM from different source categories. In this study we did not take into account the formation of secondary PM2.5 from anthropogenic or natural gaseous emissions. We also did not include all the source categories of primary PM2.5 from natural emissions (for instance, wild-land fires and sea salt aerosols); however, fugitive dust was included.

The aims of this study were (i) to develop an integrated assessment model that allows an estimation of the adverse health effects caused by primary PM2.5, (ii) to estimate how emissionexposure and toxicity differences for various primary PM2.5 source categories influence the results of the assessments, (iii) to evaluate the uncertainties in emissions, dispersion, and health effects, and (iv) to estimate the primary PM2.5 induced premature deaths and the change in life-expectancy in Finland in 2000. The modeling of life-expectancy, the uncertainties of emissions, and emissionexposure relationships have been previously published in Tainio et al. (2007), Karvosenoja et al. (2008), and Tainio et al. (2009), respectively.

Materials and methods

The schematic flowchart of the integrated assessment model is presented in Fig. 1. Different modules that are part of the integrated assessment model are described in the following chapters.

Emissions

The emissions of anthropogenic primary PM2.5 from Finland and from other European countries in 2000 were estimated using two different emission datasets. The emissions of primary PM2.5 from Finland were estimated with the Finnish Regional Emission Scenario (FRES) model (Karvosenoja, 2008). The FRES model has calculated primary PM2.5 emissions with detailed chemical and size-segregation splits. The emissions are derived from 205 point sources with detailed plant and stack characteristics, and area sources with aggregation into 112 source categories and 15 fuels. In this study, the emissions of anthropogenic primary PM2.5 in Finland were divided into 6 emission source categories, which were further divided into 13 sub-categories. These source categories and subcategories are described in the supplementary material (Table S1).

The primary PM2.5 emission strength uncertainties for Finnish emissions were estimated for all 13 sub-categories. The primary PM2.5 emission strength uncertainties for traffic and domestic wood combustion have been evaluated by Karvosenoja et al. (2008). They concluded that the uncertainties of small-scale domestic combustion emissions were responsible for most of the emission uncertainties regarding primary PM2.5 from these two source categories. The emission model used by Karvosenoja et al. (2008) was used also in this study.

The primary PM2.5 emission strength uncertainties for other emission source categories were estimated separately for activities and emission factors, based on Karvosenoja (2008), Karvosenoja et al. (2008) and Kupiainen et al. (2006). Both the uncertainties of activities and emission factors were highest for emission source categories ‘small power plants’ and ‘other sources’. The emission strengths of annual average primary PM2.5 emissions from Finland in 2000 and their respective uncertainties are presented in the supplementary material (Table S1). Spatial uncertainty (i.e., the location of emissions, which is not known with accuracy) was not included in these uncertainties.

The emission strengths of anthropogenic primary PM2.5 for other European countries were derived from the European Monitoring and Evaluation Programme (EMEP) database (www.emep. int). The emission strengths were estimated for all anthropogenic sources combined in 2000 and separately for each of the 42 countries (Table S2, Supplementary material). Emission strength uncertainties for these emissions have not been quantitatively reported.

Dispersion and exposure

The population exposure for primary PM2.5 in Finland due to primary PM2.5 emissions from Finland and elsewhere in Europe was evaluated, and presented using the concept of intake fraction (Bennett et al., 2002). The intake fraction is defined as “integrated incremental intake of a pollutant released from a source category and summed over all exposed individuals” (Bennett et al., 2002). In this study, exposure estimates are based on outdoor concentration of primary PM2.5. The exposures in this study are based on values reported by Tainio et al. (2009); they calculated the emissionexposure relationships for various primary PM2.5 emission source categories in Europe using the iF methodology.

The atmospheric dispersion of primary PM2.5 was evaluated using the dual-core Lagrangian-Eulerian dispersion model SILAM (http://silam.fmi.fi), for the primary PM2.5 emissions in 2000. The dispersion was computed for two different study domains. In the European domain, the emissions of primary PM2.5 were based on EMEP data and the concentrations were estimated with approximately with a horizontal resolution of 30 km over the whole of Europe. In the Northern European domain (that included most of Scandinavia and some surrounding regions), the primary PM2.5 emission strengths for Finnish sources were based on the values provided by the FRES model and the concentrations were estimated approximately with a horizontal resolution of 5 km. In the Northern European domain, the PM2.5 concentrations were estimated separately for six different emission source categories. A constant breathing rate of 20 dm3 day�1 (w0.0002m3 s�1)was used in the iF calculations.

The iF values were evaluated in Tainio et al. (2009) using:

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): iF=\sum(Ci*Pop_i)*Br/Q ,

where Pop is the number of population (persons), C is the concentration increase of pollutant due to a specified emission source category or area of emissions (g m^-3), Br is the breathing rate (m³/s/person), and Q is the emission rate (g s^-1). In this study, we estimated the population-weighted average exposure for primary PM2.5 matter in Finland from: Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): C=(Q\times iF)/(Pop\times Br)

The primary PM2.5 emission strengths (Q) for Finnish emissions were updated, in comparison to Tainio et al. (2009). For the primary PM2.5 emission strengths for other European sources, for the population (Pop) data and for the breathing rate (Br), we used the same values as reported in Tainio et al. (2009). The iF data for primary PM2.5 emissions originated from Finland and elsewhere in Europe are summarized in the supplementary material (Tables S1eS2), respectively, with other exposure uncertainties provided in Table S3.

Exposure-response functions

The exposure-response function describes the change in the background health effect attributable to the change in the exposure level. We used the term exposure-response function to emphasize that although the exposure was estimated based on outdoor concentrations of PM, the concentration is used as a proxy for exposure.

The exposure-response functions for primary PM2.5 mortality impacts were extracted from a formal elicitation of expert judgment performed for six European air pollution experts (Cooke et al., 2007; Tuomisto et al., 2008). In the expert elicitation study, experts provided quantitative estimates of mortality impacts of hypothetical short- and long-term changes in PM2.5 concentrations in US and Europe, and for several other variables. The expert answers were combined with two methods: with equal-weight between the experts (equal-weight decision maker, EDM) and with assessing weights based on seed questions on which answers were known (performance-based decision maker, PDM). The experts also gave their estimates for the least and most toxic element of the PM mixture and defined those elements. All the experts assumed that combustion PM were more toxic than the average PM2.5 mass (Cooke et al., 2007). Two experts assumed that traffic related combustion PM (referred as diesel or traffic PM) would be more toxic than the average PM2.5 mass. Four experts claimed that secondary PM (sulfate, nitrate or both) and two experts speculated that PM2.5 from crustal sources would be less toxic than the average PM2.5 mass. The uncertainties were recognized as being high.

For this study, we adopted exposure-response functions for primary PM2.5 induced non-accidental mortality, caused by longterm (chronic) exposure to PM, from the expert-elicitation study (Table S3). Two additional assumptions on toxicity were made. First, we assumed that all the primary PM2.5 possess equal toxicity, regardless of source. For this, we used an average exposureresponse function based on the expert elicitation study. Second, we assumed from the experts’ estimates that the most toxic substances originated from traffic tailpipe emissions (both for on-road and offroad vehicles and machinery) and the least toxic substances are from traffic non-tailpipe and agricultural emissions. For all the other emission source sub-categories (Table S1, Supplementary material) and for primary PM2.5 emissions from other European countries, we used the average exposure-response function from the expert elicitation study. The sensitivity of the model in the