# Exposure to PM2.5 in Finland

## Question

How to estimate exposure to fine particles (PM2.5) in the Finnish population? Only outdoor sources are considered here.

Use source-receptor matrix (e.g. the so-called PILTTI matrix) to estimate exposure levels and population sizes around a given source point. Georeference and source strength must be given. The concentrations per unit emission around the source and population density across Finland are already known in the system.

## Rationale

### Dispersion modelling

 ```library(OpasnetUtils) library(OpasnetUtilsExt) library(ggplot2) library(rgdal) library(maptools) library(RColorBrewer) library(classInt) library(RgoogleMaps) objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]] # GIS points for emissions. districts <- tidy(opbase.data("Op_en3435.kuopio_city_districts"), widecol = "Location") # [[Exposure to PM2.5 in Finland]] districts <- Ovariable("districts", data = data.frame(districts, Result = 1)) cat("PM2.5 intake fractions are being calculated for these locations.\n") oprint(districts) dis <- ova2spat(EvalOutput(districts), coord = c("E", "N"), proj4string = "+init=epsg:3067") # Long-distance iF of PM2.5 for exposures beyond 10 km. objects.latest("Op_en5813", code_name="initiate") # Long-distance iF for PM2.5 [[Intake fractions of PM]] iF.PM2.5@data <- iF.PM2.5@data[iF.PM2.5@data\$Subcategory == "Large power plants" , ] iF.PM2.5@data <- iF.PM2.5@data[!colnames(iF.PM2.5@data) %in% c("Obs", "Geographical area", "Year", "PM type", "Source category", "Subcategory")] # Calculate exposure concentration * population for a unit emission and all emission points. out <- Ovariable() for(i in 1:length(dis\$City.area)) { print(paste(i, "\n")) temp <- GIS.Exposure(GIS.Concentration.matrix( 1, LA = coordinates(dis)[i, 2], LO = coordinates(dis)[i, 1], N = 1 )) out@output <- rbind(out@output, data.frame(City.area = dis\$City.area[i], temp@output)) } out@output <- out@output[out@output\$HAVAINTO == "VAESTO" , ] out@marginal <- !grepl("Result\$", colnames(out@output)) out <- unkeep(out, cols = c("KUNTA", "ID_NRO", "XKOORD", "YKOORD", "HAVAINTO", "dx", "dy"), sources = TRUE) # Large matrix with detailed exposures in grids. PILTTI.matrix <- out # This produces an intake fraction if you give PM2.5 emissions as ton /a. GIS.Concentration.matrix takes ton /a and gives ug /m3. # iF = intake (g /s) per emission (g /s) = concentration (ug /m3) * population (#) * breathing rate (m3 /s) / emission (g /s). iF <- oapply(out, cols = c("LAbin", "LObin"), FUN = "sum", na.rm = TRUE) iF <- iF * 20 / (24 * 3600) * 1E-6 # Divide by breathing rate 20 m3 /d and scale from ug to g to get intake fraction. iF@output <- data.frame(Emissionheight = "Low", iF@output) iF@output <- orbind(iF, data.frame(Emissionheight = "High", Result = 0)) iF@marginal <- c(TRUE, iF@marginal) iF@output <- fillna(iF@output, marginals = colnames(iF@output)[iF@marginal]) iF <- iF + iF.PM2.5 * 1E-6 # Scale iF.PM2.5 from ppm to fractions. colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data)) objects.store(PILTTI.matrix, iF) cat("Objects PILTTI.matrix, emissionLocations and iF saved.\n") ```

### Data

Based on Piltti source-receptor matrix.

#### Emission and city area locations

A previous table about emission locations in Kuopio was moved to Kuopio energy production and that about Kuopio city district was moved to Building stock in Kuopio.

#### Intake fractions of PM

A previous table by Humbert et al 2011 was moved to Intake fractions of PM.