Exposure to PM2.5 in Finland

This page is a knowledge crystal of subtype variable. The page identifier is Op_en3435
Moderator:Virpi Kollanus (see all)

Upload data {{#opasnet_base_link:Op_en3435}}

Question

What is the exposure to fine particles (PM2.5) in the Finnish population? Only outdoor sources are considered here. Exposures are calculated for defined locations (listed in a table in Rationale).

----#: . This could be implemented (as a method) for any locations in Finland, but the calculations are so slow that it is better to calculate actual answers for specified locations (as a variable) before applying it in a model. --Jouni (talk) 16:58, 5 March 2014 (EET) (type: truth; paradigms: science: comment)

Answer

Example run

Rationale

Dispersion modelling

+ Show code - Hide code

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.

emissionLocations <- Ovariable("emissionLocations", # [[Exposure to PM2.5 in Finland]]
	ddata = 'Op_en3435',
	subset = 'Emission locations'
)
emissionLocations@data$emissionLocationsResult <- 1
colnames(emissionLocations@data) <- gsub("[ \\.]", "_", colnames(emissionLocations@data))

objects.store(PILTTI.matrix, iF, emissionLocations)
cat("Objects PILTTI.matrix, emissionLocations and iF saved.\n")

Data

Emission locations

Where and how do the emissions of heating take place?

Emission locations(-)
Obs	Heating	Emission site	Emission height
1	District	Haapaniemi	High
2	Electricity	Haapaniemi	High
3	Geothermal	Haapaniemi	High
4	Oil	At site of consumption	Ground
5	Wood	At site of consumption	Ground
6	Gas	At site of consumption	Ground

Intake fractions of PM

These data come from ^[1]

Pollutants:

PM10-2.5: Primary PM10 - primary PM2.5
PM2.5: Primary PM2.5
SO2: Secondary PM2.5 derived from SO2 (in practice, SO_4)
NOx: Secondary PM2.5 derived from NOx (in practice, NO_3)
NH3: Secondary PM2.5 derived from NH3 (in practice, NH4)

Intake fractions of PM(ppm)
Obs	Pollutant	Emission height	Urban	Rural	Remote	Average
1	PM10-2.5	High	8.8	0.7	0.04	5.0
2	PM10-2.5	Low	13	1.1	0.04	7.5
3	PM10-2.5	Ground	40	3.7	0.04	23
4	PM10-2.5	Average	37	3.4	0.04	21
5	PM2.5	High	11	1.6	0.1	6.8
6	PM2.5	Low	15	2.0	0.1	6.8
7	PM2.5	Ground	44	3.8	0.1	25
8	PM2.5	Average	26	2.6	0.1	15
9	SO2		0.99	0.79	0.05	0.89
10	NOx		0.2	0.17	0.01	0.18
11	NH3		1.7	1.7	0.1	1.7

Kuopio city districts

The exposures are calculated for the points listed below. The coordinates were visually checked from http://www.karttapaikka.fi

Kuopio city districts(ETRS-TM35FIN)
Obs	City area	N	E
1	Itkonniemi	6974469	536853
2	Männistö	6974607	535746
3	Linnanpelto	6975014	535490
4	Itkonniemi-Männistö-Linnanpelto	6974697	536030
5	Niirala	6973403	532903
6	Puijonlaakso	6975016	532625
7	Rahusenkangas	6976558	534851
8	Rahusenkangas-Kuivinniemi	6976558	534851
9	Haapaniemi	6972486	534457
10	Levänen	6970609	532094
11	Saaristokaupunki	6968569	534906
12	Jynkkä	6969424	533150
13	Kettulanlahti	6977547	534958
14	Petonen	6967759	532822
15	Neulamäki	6973454	530655
16	Kelloniemi	6976486	535799
17	Särkiniemi	6971873	532398
18	Särkilahti	6971406	531919
19	Särkiniemi-Särkilahti	6971640	532159
20	Saarijärvi	6975883	535098
21	City center	6973853	535023
22	Inkilänmäki	6976028	534434
23	Inkilänmäki-Peipposenrinne	6975755	534453
24	Pitkälahti	6963542	531123
25	Julkula	6977523	532059
26	Peipposenrinne	6975481	534472
27	Päiväranta	6978479	533542
28	Länsi-Puijo	6976667	532399

Dependencies

**Urgenche research project 2011 - 2014: city-level climate change mitigation**
Urgenche pages Urgenche main page · Category:Urgenche · Urgenche project page (password-protected) Relevant data Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings Relevant methods Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎ City Kuopio Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing City Basel Buildings in Basel (password-protected) Energy balances Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou

References

↑ Sebastien Humbert, Julian D. Marshall, Shanna Shaked, Joseph V. Spadaro, Yurika Nishioka, Philipp Preiss, Thomas E. McKone, Arpad Horvath, and Olivier Jolliet. Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment (2011). Environmental Science and Technology, 45, 4808-4816.

[1] Sebastien Humbert, Julian D. Marshall, Shanna Shaked, Joseph V. Spadaro, Yurika Nishioka, Philipp Preiss, Thomas E. McKone, Arpad Horvath, and Olivier Jolliet. Intake Fraction for Particulate Matter: Recommendations for Life Cycle Impact Assessment (2011). Environmental Science and Technology, 45, 4808-4816.

[1]