Difference between revisions of "Exposure of Finnish subpopulations to fine particles due domestic wood combustion"

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Poster abstract for [http://www.ises09.org/ ISES 2009] will be written here. Please feel free to comment.
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{{variable|moderator=Pauliina}}
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[[Category:Contains R code]]
  
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Abstract Requirements:
 
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*Abstracts should not exceed 300 words (2,000 characters).
 
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*Abstracts reporting on research or investigations must include results. Statements such as “findings will be reported” are not sufficient.
 
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*Abstracts must be submitted electronically at [http://www.ises09.org/ www.ises09.org] by '''May 1, 2009'''.
 
  
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*Analytica calculations can be found [[:Image:IF exposure subpopulations.ANA|here]].
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*ArcMap based data of concentrations and age subpopulation can be found from N:\YTOS\Projects\BIOHER\Mallit\model of population groups\data\asuk05_koul04_resi00 .
  
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==Introduction==
 
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This abstract is exploring the method and results of GIS (Geographical Information System) based exposure assessment of Finnish population. Exposure is evaluated for fine particles of domestic wood combustion emissions in Finland. Concentration of PM2.5 (fine particles with aerodynamic diameter ≤ 2.5 µm) is used in intake fraction (iF) based exposure evaluation. In previous studies GIS has been used in evaluation of PM2.5 dispersion and assessment of exposure for PM2.5. Also mortality and hospital admissions as health effects of fine particles have been found in short-term studies. Previous studies have concluded that Finnish population average exposure for primary fine particle emissions is 0.54 µg/m3. Intake fraction for Finnish population has evaluated to be 3.31 per million for fine particles due to domestic wood combustion emissions. In this assessment four different education and age groups in Finland are taken account to intake fraction and exposure evaluations for fine particles of domestic wood combustion. Differences between men and women exposure to fine particles are also studied.
 
  
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==Material and Methods==
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==Question==
  
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The population data for Finland was obtained from the Statistics Finland Grid Database [http://www.stat.fi/tup/ruututietokanta/index_en.html]. The dataset contained population numbers for Finland on a resolution of 250 x 250 m2 for 2004 of different age groups and 2005 of different education groups, respectively. PM2.5 emissions of domestic wood combustion in residential buildings were calculated with the Finnish Regional Emission Scenario (FRES) model. The dispersion model applied in this study was the urban dispersion modelling system. It includes a multiple source Gaussian plume model and a meteorological pre-processor MPP-FMI. Data of PM2.5 contained concentrations (ng/m3) with 1 km grid resolution. Data of dispersed concentrations were joined into population data with ArcGIS. using nearest concentration points of each population grid. Product of population and concentration of each grid and sum of this product over Finland was calculated with ArcGIS (eq. 1). Rest of iF calculation and exposure were implemented using Analytica ™ version 4.1 (eq. 2).
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What is the exposure of Finnish subpopulations to PM<sub>2.5</sub> due domestic wood combustion or traffic?
  
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Intake fraction illustrates the fraction of pollution that is taken in via inhalation, ingestion and dermal by exposure for individual or population. In this study iF has been defined as fraction of fine particles of domestic wood combustion in residential buildings in Finland to Finnish population as following equation:
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==Result==
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More results and talk about the preresults is going on in Talk page{{disclink|Talk:Exposure of Finnish subpopulations to fine particles due domestic wood combustion}}.
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[[Image:Exposure.PNG|thumb|center|600px|Figure 1. Exposure (µg/m<sup>3</sup>) of Finnish subpopulations to fine particles due domestic wood combustion]]
  
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eq. 1 iF = Σi(Ci * Popi) * Br / Q,
 
  
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where Pop is the number of population in grid cell i (persons), i = 1,2, ..., n, n is the total number of grid cells in the study area, C is the concentration increase of PM2.5 in the grid cell i due to a specified emission source category or area of emissions (g/m3), Br is the breathing rate (m3/s/person), and Q is the emission rate (g/s). A nominal breathing rate of 20 m3/day/person (~0.0002 m3/s/person) was adopted in this study as in many previous ones.
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[[Image:Population exposure.PNG|thumb|center|700px|Figure 2. Population weighted exposure (ng/m<sup>3</sup>) of different age groups in Finland to fine particles due domestic wood combustion]]
  
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eq. 2 C = (E x iF) / (Pop x Br)
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==Definition==
  
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, where C is population average primary fine particle concentration (unit g/m3), iF is intake fraction, Pop is number of people, and Br is breathing rate. Constant 20 dm3/day breathing rate was used in the analyses.
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This assessment is exploring the method and results of GIS (Geographical Information System) based exposure assessment of Finnish population. Exposure is evaluated for fine particles of domestic wood combustion emissions in Finland region. Concentration of PM<sub>2.5</sub> (fine particles with aerodynamic diameter ≤ 2.5 µm) is used in exposure evaluation. In previous studies GIS has been used in evaluation of PM<sub>2.5</sub> dispersion and assessment of exposure for PM<sub>2.5</sub> (e.g. Tian et al 2004, Heinrich et al 2005, Brauer et al 2003).
 +
Intake fraction for Finnish population has evaluated to be 3.31 per million for fine particles due to domestic wood combustion emissions. 
 +
In this assessment four different education and age groups in Finland are taken account to exposure evaluations for fine particles of domestic wood combustion. Differences between men and women exposure to fine particles are also studied.    
  
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==Results==
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===Boundaries===
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*Exposure for fine particles PM<sub>2.5</sub>
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*Population of Finland
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**Male/female
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**Age
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**Education
  
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===Intake fraction===
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===Data===
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The intake fraction didn’t vary a lot between men and woman (Table 1). Children had highest intake fraction and juvenile lowest. There were more variation between iF of educational  subpopulation groups. People who had vocational education had highest iF and upper secondary school passed population had lowest.
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* Modeled dispersed concentrations of PM<sub>2.5</sub> due domestic wood burning
 +
PM<sub>2.5</sub> emissions of domestic wood combustion in residential buildings were calculated with the Finnish Regional Emission Scenario (FRES) model of Finnish environment institute (Karvosenoja 2008)
 +
The dispersion model applied in this study was the urban dispersion modelling system developed at the Finnish Meteorological Institute (UDM-FMI). It includes a multiple source Gaussian plume model and a meteorological pre-processor MPP-FMI (Karppinen et al., 1997, 1998). Data of PM2.5 contained concentrations (ng/m<sup>3</sup>) with 1 km grid resolution.  
  
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Table 1. The intake fraction of fine particles of domestic wood combustion in residential buildings to female and males and different age and educational groups in Finnish population.
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* Data of Finnish population:
 +
The population data for Finland was obtained from the Statistics Finland Grid Database (http://www.stat.fi/tup/ruututietokanta/index_en.html). The dataset contained population numbers for Finland on a resolution of 250 x 250 m<sup>2</sup> for 2004 of different age groups and 2005 of different education groups, respectively (Table 1).
 +
 
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Table 1. Population of different education and age groups. Total population of Finland in year 2004.
 
{| {{prettytable}}
 
{| {{prettytable}}
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|
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| Population group
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| iF (per million)
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| Total population
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|----
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| Comprehensive school
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| 1398225
 
|----
 
|----
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| Female
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| Upper secondary school
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| 1.7
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| 325831
 
|----
 
|----
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| Male
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| Vocational education
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| 1.6
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| 1793804
 
|----
 
|----
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| Children (0-17)
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| Higher education
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| 0.7
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| 570267
 
|----
 
|----
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| Juvenile (18-24)
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| Pensioner (65–)
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| 0.3
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| 836977
 
|----
 
|----
 
| Middle age (45–54)
 
| Middle age (45–54)
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| 0.5
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| 763460
 
|----
 
|----
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| Pensioner (65–)
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| Juvenile (18-24)
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| 0.5
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| 453075
 
|----
 
|----
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| Higher education
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| Children (0-17)
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| 0.4
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| 1094751
 
|----
 
|----
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| Vocational education
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| Male
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| 1.1
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| 2539492
 
|----
 
|----
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| Upper secondary school
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| Female
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| 0.2
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| 2664334
 
|----
 
|----
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| Comprehensive school
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| Finnish total population
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| 0.8
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| 5203826
 
|----
 
|----
 
|}
 
|}
  
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===Exposure===
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===Methodological issues===
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Exposure for fine particles due to wood combustion in household buildings of Finland population varied between educational groups (Fig 1). Upper secondary school and higher education groups had higher exposure level (0.58 and 0.60 µg/m3) than comprehensive school and vocational education groups (0.50 and 0.54 µg/m3). In age groups juveniles exposed most (0.59 µg/m3) and pensioner least (0.50 µg/m3) to fine particles of domestic wood combustion. Children and middle age groups exposed to fine particles as 0.55 and 0.54 µg/m3. As the same as in iF estimation exposure was higher for females than for males (0.55 and 0.54 µg/m3).
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Data of dispersed concentrations were joined into population data with ArcMap version 9.2. [http://www.esri.com/] using nearest concentration points of each population grid. Product of population and concentration of each grid and sum of this product over Finland was calculated with ArcMap (see equation 1). Rest of calculations were implemented using Analytica ™ version 4.1. (Lumina Decision Systems, Inc., CA).
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where Pop is the number of population in grid cell i (persons), i = 1,2, ..., n, n is the total number of grid cells in the study area, C<sub>i</sub> is the concentration increase of PM<sub>2.5</sub> in the grid cell i due to a specified emission source category or area of emissions (g/m<sup>3</sup>)and Pop<sub>tot</sub> is total population of each subpopulations in Finland.
 +
 
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Fine particle concentrations was divided to 15 different divisions with ArcMap and total population in each concentration subdivision was counted with statistical computation and graphic system R version 2.7.0 (The R Foundation for Statistical Computing, http://www.r-project.org/).
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Script for R. This is an example for under 100 ng/m3 concentration calculations.
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I hope that someone knows how to script this in shorter version. Otherwise I have to calculate all 11 concentration limitations separately. 
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Do someone knows how to make R to write table with column names and with several different vectors in the same table?
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===Formula===
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<rcode graphics="1" variables="
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name:groups|description:Mitkä ryhmät haluat nähdä eriteltynä?|type:checkbox|options:1;Sex;2;Education;3;Age|default:1|
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name:exclude|description:Hylkää nämä ryhmät|type:checkbox|options:1;NAISET;2;MIEHET;3;IKA_0_2;4;IKA_3_6;5;IKA_7_12;6;IKA_13_17;7;IKA_18_24;8;IKA_25_34;9;IKA_35_44
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    ;10;IKA_45_54;11;IKA_55_64;12;IKA_65_74;13;IKA_75_;14;KO_PERUS;15;KO_YLIOP;16;KO_AMMAT;17;KO_KORK|default:">
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library(OpasnetBaseUtils)
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library(ggplot2)
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library(xtable)
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groups
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exclude
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exclude <- c("NAISET","MIEHET","IKA_0_2","IKA_3_6","IKA_7_12","IKA_13_17","IKA_18_24","IKA_25_34","IKA_35_44","IKA_45_54",
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    "IKA_55_64","IKA_65_74","IKA_75_","KO_PERUS","KO_YLIOP","KO_AMMAT","KO_KORK")[exclude]
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data <- op_baseGetData("opasnet_base", "Op_en2961")[, -c(1,2)]
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avg <- data[data$Border == "-999",]
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n <- as.data.frame(as.table(tapply(data$Result, list(data$Age, data$Sex, data$Education, data$Emission), max)))
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colnames(n) <- c("Age", "Sex", "Education", "Emission", "n")
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avg <- merge(avg, n)[ !(avg$Age %in% exclude | avg$Sex %in% exclude | avg$Education %in% exclude), ]
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head(avg)
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condition <- as.list(avg[, groups])
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head(condition)
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avg2 <- as.data.frame(as.table(tapply(avg$Result, condition, sum)))
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n2 <- as.data.frame(as.table(tapply(avg$n, condition, sum)))
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avg2 <- merge(avg2,n2)
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avg2[avg2$n != 0, "Result"] <- avg[avg2$n != 0, "Result"] / n2[avg2$n != 0, "n"]
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avg2
  
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[[Image:Exposure2000.PNG|thumb|center|500px|Figure 1. Exposure (µg/m3) of different population groups in Finland for fine particles of domestic wood combustion emissions.]]
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</rcode>
  
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==See also==
  
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==Conclusions==
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*[[ISES2009 Poster Abstract: Exposure of Finnish subpopulations to fine particles due domestic wood combustion]]
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[[Category:Fine particles]]

Latest revision as of 09:19, 26 August 2013


  • Analytica calculations can be found here.
  • ArcMap based data of concentrations and age subpopulation can be found from N:\YTOS\Projects\BIOHER\Mallit\model of population groups\data\asuk05_koul04_resi00 .


Question

What is the exposure of Finnish subpopulations to PM2.5 due domestic wood combustion or traffic?

Result

More results and talk about the preresults is going on in Talk pageD↷.

Error creating thumbnail: Unable to save thumbnail to destination
Figure 1. Exposure (µg/m3) of Finnish subpopulations to fine particles due domestic wood combustion


Error creating thumbnail: Unable to save thumbnail to destination
Figure 2. Population weighted exposure (ng/m3) of different age groups in Finland to fine particles due domestic wood combustion

Definition

This assessment is exploring the method and results of GIS (Geographical Information System) based exposure assessment of Finnish population. Exposure is evaluated for fine particles of domestic wood combustion emissions in Finland region. Concentration of PM2.5 (fine particles with aerodynamic diameter ≤ 2.5 µm) is used in exposure evaluation. In previous studies GIS has been used in evaluation of PM2.5 dispersion and assessment of exposure for PM2.5 (e.g. Tian et al 2004, Heinrich et al 2005, Brauer et al 2003). Intake fraction for Finnish population has evaluated to be 3.31 per million for fine particles due to domestic wood combustion emissions. In this assessment four different education and age groups in Finland are taken account to exposure evaluations for fine particles of domestic wood combustion. Differences between men and women exposure to fine particles are also studied.

Boundaries

  • Exposure for fine particles PM2.5
  • Population of Finland
    • Male/female
    • Age
    • Education

Data

  • Modeled dispersed concentrations of PM2.5 due domestic wood burning

PM2.5 emissions of domestic wood combustion in residential buildings were calculated with the Finnish Regional Emission Scenario (FRES) model of Finnish environment institute (Karvosenoja 2008) The dispersion model applied in this study was the urban dispersion modelling system developed at the Finnish Meteorological Institute (UDM-FMI). It includes a multiple source Gaussian plume model and a meteorological pre-processor MPP-FMI (Karppinen et al., 1997, 1998). Data of PM2.5 contained concentrations (ng/m3) with 1 km grid resolution.

  • Data of Finnish population:

The population data for Finland was obtained from the Statistics Finland Grid Database (http://www.stat.fi/tup/ruututietokanta/index_en.html). The dataset contained population numbers for Finland on a resolution of 250 x 250 m2 for 2004 of different age groups and 2005 of different education groups, respectively (Table 1).

Table 1. Population of different education and age groups. Total population of Finland in year 2004.

Population group Total population
Comprehensive school 1398225
Upper secondary school 325831
Vocational education 1793804
Higher education 570267
Pensioner (65–) 836977
Middle age (45–54) 763460
Juvenile (18-24) 453075
Children (0-17) 1094751
Male 2539492
Female 2664334
Finnish total population 5203826

Methodological issues

Data of dispersed concentrations were joined into population data with ArcMap version 9.2. [1] using nearest concentration points of each population grid. Product of population and concentration of each grid and sum of this product over Finland was calculated with ArcMap (see equation 1). Rest of calculations were implemented using Analytica ™ version 4.1. (Lumina Decision Systems, Inc., CA).


where Pop is the number of population in grid cell i (persons), i = 1,2, ..., n, n is the total number of grid cells in the study area, Ci is the concentration increase of PM2.5 in the grid cell i due to a specified emission source category or area of emissions (g/m3)and Poptot is total population of each subpopulations in Finland.

Fine particle concentrations was divided to 15 different divisions with ArcMap and total population in each concentration subdivision was counted with statistical computation and graphic system R version 2.7.0 (The R Foundation for Statistical Computing, http://www.r-project.org/).

Script for R. This is an example for under 100 ng/m3 concentration calculations. I hope that someone knows how to script this in shorter version. Otherwise I have to calculate all 11 concentration limitations separately.

Do someone knows how to make R to write table with column names and with several different vectors in the same table?

Formula

Mitkä ryhmät haluat nähdä eriteltynä?:
Sex
Education
Age

Hylkää nämä ryhmät:
NAISET
MIEHET
IKA_0_2
IKA_3_6
IKA_7_12
IKA_13_17
IKA_18_24
IKA_25_34
IKA_35_44
IKA_45_54
IKA_55_64
IKA_65_74
IKA_75_
KO_PERUS
KO_YLIOP
KO_AMMAT
KO_KORK

+ Show code

See also

Retrieved from "http://www.opasnet.org/testiwiki/index.php?title=Exposure_of_Finnish_subpopulations_to_fine_particles_due_domestic_wood_combustion&oldid=30592"