Introduction
This study has been part of the multidisciplinary research programs entitled
“The Influence of Air Pollution, Pollen and Ambient Temperature on
Asthma and Allergies in Changing Climate, APTA”
(http://www.oulu.fi/apta-en/, last access: 1 June 2018) and
“Understanding the link between Air pollution and Distribution of related
Health Impacts and Welfare in the Nordic countries, NordicWelfAir”
(http://projects.au.dk/nordicwelfair/, last access: 1 June 2018). The
overall objective of the APTA project was to assess the influence of
environmental exposures related to changing climate on asthma and allergies,
and on cause-specific mortality. One of the aims of the NordicWelfAir project
was to further understand the links among air pollution levels, the chemical
composition of pollution and the related health effects.
A special focus of the present study was on health effects of air pollution,
allergenic pollen and extreme climatic factors, especially ambient
temperatures. Asthma is globally the most common chronic disease in children
(WHO, 2011) and the prevalence of asthma in adult populations varies from 5
to 25 %. Allergies are even more common, with prevalence of 30–40 %.
Accurate information on the long-term concentrations of air pollutants and
pollen species on a high spatial and temporal resolution was required for
conducting lifetime health impact assessments.
Only a few studies have estimated the past levels of air pollution over decades.
However, some studies have been conducted, e.g. for the Northern Hemisphere
(Xing et al., 2015) and for specific continents, regions or countries
(Guerreiro et al., 2014; Colette et al., 2011; Cho et al., 2011; Astitha et
al., 2017). All of these studies have addressed a relatively short time
period, commonly from a few years up to 2 decades. The evaluations of air
quality for urban areas over longer periods have been especially rare (Gupta
and Christopher, 2008; Wise and Comrie, 2005). Previous investigations have
not addressed the modelling of spatially and temporally high-resolution
concentrations in cities or urban agglomerations over a period of several
decades.
Xing et al. (2015) evaluated air quality trends during a 21-year period,
1990–2010, in the Northern Hemisphere, using both observations and
modelling. They used the Community Multiscale Air Quality (CMAQ) model driven
by meteorology from Weather Research and Forecasting (WRF) simulations and
the emission inventories from EDGAR. They concluded that the model
successfully reproduced the observed decreasing trends in SO2,
NO2, 8 h O3 maxima, SO42- and elemental
carbon (EC) in the US and Europe. However, the model also failed to reproduce
part of the trends, such as that of NO3- in the US, and some
trends for China. Due to the coarse spatial resolution, the modelling
underestimated most concentrations in urban networks.
Guerreiro et al. (2014) presented an analysis of air quality in Europe from
2002 to 2011. The evaluation was based on ambient air measurements and data
on anthropogenic emissions. They reported that the emissions of the main air
pollutants in Europe declined in the considered period, resulting in improved
air quality across the region, for some of the pollutants. The trends of
PM10 were on average decreasing across Europe. Most of the
registered significant trends of the measured PM10 concentrations
were decreasing in monitoring stations that were situated mostly in France
and Germany. However, the data at most of the stations did not show
significant trends. In the case of the PM2.5 concentrations, most of the stations did
not register significant trends in the period 2006–2011. Colette et
al. (2011) analysed air quality trends in Europe during a decade, from 1998
to 2007, using both measurements and six regional and global
chemistry-transport models. They presented the trends of NO2,
O3 and PM10, based on both the measured data and modelled
results. However, this article was focused on the evaluation of the
performance of the selected models.
Astitha et al. (2017) evaluated the trends of ozone concentrations over the
contiguous United States from 1990 to 2010, using the coupled WRF–CMAQ
system. They addressed various O3 measures, such as, for example, the
daily maximum 8 h ozone concentrations, during the warm seasons. During the
earlier decade (from 1990 to 2000), the simulated and observed trends were
not statistically significant. During the more recent decade, all observed
trends were decreasing statistically significantly. Wise and Comrie (2005)
analysed meteorologically adjusted ozone and PM data from 1990 to 2003 for
five major metropolitan areas in the southwestern US. Long-term ozone trends
indicated increasing concentrations over the considered decade; the long-term
PM concentrations did not show any significant trends.
Gupta and Christopher (2008) analysed particulate matter concentrations in
Birmingham, US, during a 7-year period, 2002–2006, based on both
surface and satellite measurements. They used the aerosol optical thickness
(AOT) values determined by the Moderate Resolution Imaging Spectroradiometer
(MODIS), and ground measurements of particulate matter mass. They concluded
that the PM2.5 mass concentrations over northern Birmingham were
decreased by about 23 %. However, the MODIS-Terra AOT data were only available
about 50 % of the time, due to cloud cover and unfavourable surface
conditions.
Cho et al. (2011) reviewed studies that addressed the concentrations and size
distributions of ambient airborne particulate matter containing lead (Pb), from 1980
to 2008. The maximum 3-month-average airborne Pb concentrations in total
suspended particulate matter decreased by 92 % at 19 national sites in the
US during the considered period. However, the relevant size distribution
data were very scarce.
The first systematic review of urban air quality in Finland was presented by
Kukkonen et al. (1999). They considered the data from 42 Finnish cities and
towns, in 1990–1993. The results showed that the national air quality
guidelines were fairly often exceeded in the early 1990s in urban areas,
most commonly for particulate matter. Some exceedances also occurred for
NO2 and CO, at sites with high traffic densities. However, the
European Union air quality limit values were only exceeded at one monitoring
station for all of the years considered. More recently, Anttila and
Tuovinen (2010) evaluated the trends in the atmospheric concentrations of the
main gaseous and particulate pollutants in Finland for the period of
1994–2007, based on measured data at both regional background and urban
stations. They found that the concentrations of SO2, CO and
NOx declined considerably during the considered period, whereas the
concentrations of O3 increased in the urban data. For
PM10, 5 of the 12 studied trends were decreasing at the urban
locations.
The main empirical framework of the present study addressed the Helsinki
Metropolitan Area (HMA), from 1980 to 2014. The Espoo Cohort Study (ECS),
which is a major prospective, population-based epidemiologic study, formed
the basis for investigating the health impacts, in addition to registry-based
hospitalisation and mortality data. The ECS includes detailed information on
the lifetime history of the residences and work places of the cohort members,
as well as extensive information on health, environmental exposures and
behavioural factors collected both prospectively and by linkage to available
health and environmental information registries (Jaakkola et al., 2010; Paaso
et al., 2014). However, for the assessment of lifetime environmental
exposure, a database would first be required on the concentrations of the key
pollutants, allergenic pollen species and meteorological factors over a time
period of 35 years.
The first aim of this study was to provide an accurate and reliable
high-resolution database on the urban-scale concentrations of PM2.5, to
be used in the subsequent health impact assessments. This article presents
the emission and dispersion modelling in the Helsinki Metropolitan Area, for
3.5 decades. A new, comprehensive emission inventory has been
presented for PM2.5 originating from small-scale combustion in this
area. We have also extended the urban emission inventories regarding the
exhaust and non-exhaust emissions of vehicular traffic, and those for
small-scale combustion, for a multi-decadal period. The second aim was to
evaluate the reliability and accuracy of the modelled concentrations, by
comparing those with the corresponding available measured concentrations.
The third aim was to present selected results regarding the long-term
temporal evolution of the local-scale emissions and concentrations of
PM2.5.
Materials and methods
Overview of the domain and the set-up of the computations
The Helsinki Metropolitan Area and its surrounding regions are situated
on a fairly flat coastal area by the Baltic Sea. The climate is relatively
milder, compared with most other areas in the same latitudes, caused by the
warming effect of the Gulf Stream and the prevailing global atmospheric
circulation. The HMA comprises four cities; Helsinki, Espoo, Vantaa and
Kauniainen. The total population in the HMA is approximately 1.1 million in
2018, while the population of Helsinki is about 0.63 million inhabitants.
The larger, so-called greater Helsinki region includes 1.5 million
inhabitants. The cities in this area and the selected measurement stations
have been presented in Fig. 1.
The cities in the Helsinki Metropolitan Area (Helsinki, Espoo,
Vantaa and Kauniainen) and the selected four measurement stations. The
geographical extent of the area is approximately 35 × 30 km.
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The previous studies in this area have found that the most important local
source categories for both the concentrations of the PM mass fractions and
the PM number concentrations have been vehicular traffic and small-scale
combustion, with smaller contributions from shipping and harbour operations,
industrial sources, and aviation (Soares et al., 2014; Kukkonen et al., 2016;
Aarnio et al., 2016). The aim of this study was to evaluate the
concentrations of PM2.5 for a multi-decade period at a high
temporal and spatial resolution. It was therefore necessary to simplify the
computations, compared to corresponding assessments for shorter periods.
In this study we have therefore explicitly addressed only long-range
transport, local vehicular traffic and small-scale combustion. The
information on the temporal evolution of shipping, the technical
specifications of ships, and the engines and emissions of ships is very
scarce for the earlier parts of the target period.
Soares et al. (2014) evaluated the emissions from shipping and major
stationary sources in this area for 2008. The emissions from ship traffic
were modelled using the Ship Traffic Emissions Assessment Model (STEAM)
presented by Jalkanen et al. (2016) and Johansson (2017). The geographical
domain was selected to include all the major harbours in Helsinki. However,
the secondary particulate matter from shipping was not included. They also
could not include the influence of small-scale combustion. They found that
over all receptor grid locations, long range transport, vehicular traffic and
shipping contributed 86, 11 and 3 % to the PM2.5
concentrations, respectively. However, considering in detail the
high-resolution spatial concentrations distributions, Soares et al. (2014)
found that the contribution of shipping to the total PM2.5
concentrations was on the average higher than 20 % within a distance of
1 km of the major harbours.
We have used a roadside dispersion model and a multiple-source Gaussian model
for evaluating the atmospheric dispersion on an urban scale; however, we have
not modelled the dispersion in street canyons in detail. The computations
were performed on a high temporal resolution, 1 h, and on a high spatial
resolution, ranging from 10 to 200 m within the domain.
Meteorological pre-processing
Measured meteorological data were analysed using the meteorological
pre-processing model MPP-FMI that has been adapted for an urban environment
(Karppinen et al., 2000a). The MPP-FMI model is based on the energy budget
method of van Ulden and Holtslag (1985). The model utilises meteorological
synoptic and sounding observations. The output of the model contains an
hourly time series of the meteorological data that is needed for the local
dispersion modelling, including temperature, wind speed, wind direction,
Monin–Obukhov length, friction velocity and boundary layer height. The
pre-processed meteorological parameters were evaluated for all hours during
the target period. However, the predicted meteorological dataset is not
spatially variable; i.e. the same meteorological parameters were used for
the whole of the Helsinki Metropolitan Area in the dispersion calculations.
We used the synoptic weather observations at the station of Helsinki-Vantaa
airport (18 km north of Helsinki city centre) and at marine stations south
of Helsinki (Katajaluoto, Isosaari and Harmaja), radiation observations at
Helsinki-Vantaa, and sounding observations at the station of Jokioinen
(90 km north-west of Helsinki) for the whole target period. We had to use a
compilation of the wind data measured at several stations located south of
Helsinki in marine environments, as the time series of data at any of these
stations was not complete for the whole of the target period. All three
marine stations were located on islands in the Gulf of Finland.
The synoptic data used in the model were based primarily on observations at
Helsinki-Vantaa. At any specific time, the predicted wind values were,
however, obtained as a combination of observational data at two stations.
This procedure yields a better spatial representativity of the wind values
for the considered domain, compared with using only the data at one station.
The sounding data included observed vertical temperature and pressure
profiles.
However, after the procedure described above, the time series of all the
relevant observations was still not totally complete as input for the
meteorological pre-processor. Single (hourly) missing observations or short
intervals of observations were therefore completed, by interpolating, or
replacing the missing values by the corresponding values at each previous
measured time instant. There was one longer period with missing data at the
station of Helsinki-Vantaa (the month of April in 1986) that was replaced by
(i) the corresponding synoptic data at a station in the city Turku and (ii)
the radiation data at the station of Jokioinen.
Evaluation of the emissions in the Helsinki Metropolitan Area
Evaluation of the traffic flows and vehicular emissions
The emission inventory included exhaust and suspension emissions that originated
from vehicular traffic for the network of roads and streets in the HMA.
Evaluation of traffic flows and vehicular exhaust
emissions in 2012
We have used the detailed geographical information on the line source network
in the Helsinki Metropolitan Area that was provided by Helsinki Region
Transport. We also conducted a thorough check of the accuracy of this
data, based on independent information on the location of streets and roads
reported by (i) Google Earth, (ii) National Land Survey of Finland and (iii)
the Helsinki Region Environmental Services Authority. The locations of the
reported line sources were in a number of cases found to be substantially
inaccurate and were therefore revised. The final number of line sources in
the revised inventory was 26 536.
The traffic volumes and average travel speeds at each traffic link were
computed using the EMME/2 transportation planning system for 2008 (Helsinki
Region Transport, 2011). The traffic flow data included geographical
information and vehicle mileage for the above-mentioned line sources within
the Helsinki Metropolitan Area, for three selected hours of each day. The
traffic flow data also included average driving speeds for personal cars.
The hourly traffic volumes were computed using a set of regression-based
factors for the diurnal variation of hourly traffic flows, separately for
weekdays, Saturdays and Sundays. These factors were estimated by the
Helsinki Region Transport.
We also used the most recent data of the national calculation system for
traffic exhaust emissions and energy consumption in Finland, called LIPASTO
(Mäkelä and Auvinen, 2009;
http://lipasto.vtt.fi/, last access: 1 June 2018). The
system comprises two separate units, the emission inventory part and the unit
emission database part. The LIPASTO system also includes data on mileages.
The database contained city-level data for the years 2012–2014, and the trends
of emissions and mileages since 1980. The emission factors of vehicles are
based on the guidelines of the Intergovernmental Panel on Climate Change for
national greenhouse gas inventories in 2006, the Emission Inventory Guidebook
of the European Monitoring and Evaluation Programme and the European
Environment Agency, and on national laboratory measurements
(http://lipasto.vtt.fi/liisa/liisa_menetelma.pdf, last access: 1 June 2018). The LIPASTO system is similar, for example, to the
corresponding system that is used in the UK (AEA, 2009). The mileage trend
data were used to scale the 2008 traffic volume data to evaluate traffic flows
in 2012.
Separate emission factors were used in this study for various classes of
vehicle types, including personal vehicles, vans, buses, and a combination
of lorries and trucks. However, the emissions from motorcycles and mopeds
were not included. In the case of personal vehicles, vans and lorries, we also
allowed for differing emissions for (i) the streets within the city centres
and other highly populated areas and (ii) the roads in suburban areas and
in the outskirts of the most populated urban areas.
Evaluation of traffic flows and vehicular exhaust
emissions for the whole target period
We have used the inventory of traffic flows and traffic exhaust emissions in
2012 in the Helsinki Metropolitan Area as a starting point for evaluating
the traffic flows and emissions for the whole target period. This inventory
also contains the detailed spatial distribution of the roads and streets,
their traffic flows, and the evaluated emissions from all the main roads and
streets in this area.
Evaluations of the annual values of the total vehicular emissions within the
Helsinki Metropolitan Area are contained in the LIPASTO system, since the
early 1980s. These values are based on detailed computations for the
changing properties of the vehicle fleet, including factors such as the
composition of the gasoline and diesel fuels, the various vehicle
categories, the changing emission standards for vehicles, the modes of
driving (urban and highway conditions), occupancy of vehicles (numbers of
persons), etc. We have applied the trends of the total traffic flows and
vehicular exhaust emissions based on the values in the LIPASTO system. The
emissions for the road and street network in the area were scaled for the
other years using these annually and spatially averaged (for the whole of
the Helsinki Metropolitan Area) values.
The emission inventory for 2012 was scaled for the whole target period, by
scaling the emission in each road link. The scaling was done by multiplying
the emissions in 2012 by the ratio of the total exhaust emission values in
the Helsinki Metropolitan Area in any selected year to that in 2012. The
total exhaust emissions in the area were extracted from the LIPASTO system.
However, when completing this procedure, one needs to assume that the
spatial distribution of the traffic exhaust emissions does not substantially
change from year to year.
Evaluation of the emissions of suspended dust for the
target period
It was not possible to use detailed suspension emission models, such as the
NORTRIP or FORE models (Kauhaniemi et al., 2014), for such a long period. The
input values for such models, regarding road sanding and the use
of studded tyres especially, were not available for the whole target period. We have
therefore used a simpler semi-empirical modelling approach for evaluating
the emissions of suspended dust.
First, we evaluated the fractions of suspended particle emissions based on
model computations for a couple of recent years, for which the detailed input
information was available. Second, we evaluated the evolution of these
fractions for the whole period. We computed both the PM2.5
concentrations that originated from traffic exhausts, using the CAR-FMI model, and
the suspended PM2.5 concentrations, using the FORE model, for the
whole of the Helsinki Metropolitan Area in 2008 and 2010. We have
subsequently computed the monthly ratios of the concentrations,
Csusp/Cexh, in which Csusp is the suspended
PM2.5 concentration and Cexh is the corresponding
concentration that originated from the traffic exhausts. These ratios were
computed at four stations. The monthly average ratios were then used to
evaluate the traffic suspension emissions. The annual average values of the
ratio Csusp/Cexh for PM2.5 were 0.33 and 0.36
in 2008 and in 2010, respectively.
We evaluated the deviations of the above-described simpler semi-empirical
method, in comparison with the computations by the FORE model for 3 years, 2012–2014. The predicted annual average suspended PM2.5
concentrations at four measurement stations (Mannerheimintie, Kallio2,
Leppävaara4 and Tikkurila3) computed with the semi-empirical method
ranged from 67 to 98 % of the predictions by the FORE model. The monthly
averaged variation of the suspended concentrations was slightly more
moderate, using the simpler model.
The computations using the FORE model allow for the suspended dust that originated
from sanding and the wear of the road surfaces, including the wear
caused by the studded tyres especially. However, the emissions of tyre, brake and clutch
wear are not explicitly included in the FORE model. Kupiainen et al. (2015)
have estimated that the particulate matter emissions for tyre, brake and
pavement wear in the Helsinki Metropolitan Area were less than one-third of the
total vehicular non-exhaust emissions. The model computations of the present
study include the rest of the non-exhaust emissions; these contain
approximately two-thirds of the whole, and in addition a substantial part of
the remaining third of the non-exhaust emissions (i.e. the wear of road
surfaces due to studded tyres). The omitted fraction of the predicted
vehicular non-exhaust emissions can therefore be estimated to be clearly
smaller than one-third.
The estimation of suspended dust emissions for the other years was done
using the emissions and traffic volumes obtained from the LIPASTO system.
The ratio of vehicular suspension and exhaust emissions during year y is by
definition
Ry=esusp(y)eexh(y),
where esusp and eexh are the suspension and exhaust
emissions, respectively.
For simplicity, we assume that the total emission from vehicular suspension
sources is directly proportional to the total traffic volume, both evaluated
within the considered domain for each year. The annual vehicular suspension
emission in the domain is also dependent on other factors, especially on the
fraction of studded tyres in cars, characteristic traffic speeds and the
types of street surfaces used. However, sufficiently detailed information on
these factors for the whole of the target period was not available.
Equation (1) can therefore be written as
Ry=VyVTesusp(T)eexh(y),
where V(y) and V(T) are the traffic volumes during the years y and any
other year T. T can be selected to be the target year, 2012.
Using Eq. (1) to rewrite esusp(T) yields
Ry=VyVTRTeexhTeexhy.
In Eq. (3), the ratios of the traffic volumes V(y)/V(2012) and those of the
exhaust emissions esusp(2012)/eexh(y) can be computed.
The ratio of vehicular suspension and exhaust emissions during the target
year R(2012) is also known, based on the above-mentioned evaluation.
The vehicular suspension emissions can therefore be computed for any
historical year (y) based on Eq. (3), if the exhaust emissions and the
total traffic volume during that year are known. We have used this approach
for evaluating the suspension emissions for all the other years, except for
2012.
Evaluation of the emissions from small-scale combustion
A novel emission inventory for the small-scale
combustion of wood in 2014
A novel emission inventory was compiled for 2014 for the following
pollutants: particles (PM1, PM2.5 and PM10),
nitrogen oxides (NOx), non-methane volatile organic compounds
(NMVOCs), carbon monoxide (CO), black carbon (BC) and benzo(a)pyrene (BaP).
For a more detailed description of this inventory, the reader is referred to
Hellen et al. (2017) and Kaski et al. (2016).
The procedures and habits of combustion and the amount of combusted wood
have been estimated by a questionnaire (Kaski et al., 2016). Wood combustion has been commonly used both as a supplementary
heating method and as a fuel in the stoves in saunas. In total, wood
combustion has been used as either a primary or secondary heating method in
approximately 90 % of the detached and semidetached houses in the area.
However, wood combustion has seldom been used as a primary heating method,
only in approximately 2 % of the houses.
The small-scale combustion that is not using oil or gas in this domain uses
almost solely wood as a fuel. The share of pellet use as a fuel has been
negligible. The average amount of wood burned per house was annually 1.52
solid cubic metres. Most of the wood was used in heat-storing masonry heaters
(0.72 solid-m3 house-1) and sauna stoves
(0.31 solid-m3 house-1); only a minor amount was burned in
heating boilers (0.09 solid-m3 house-1) and other devices.
The emission factors and total emissions from
small-scale wood combustion
The emission factors of PM2.5 were evaluated for a unit of energy
contained in the wood fuels (mg MJ-1). The evaluations of emission
factors were mainly based on nationally conducted laboratory measurements
(Kaski et al., 2016).
The emission factors were first separately analysed for 12 considered heating
units for the year 2014. The applied values were (all in units of
milligrams per megajoule, mg MJ-1) masonry heater 125, convection fireplace 49, open fireplace
with convection 125, open fireplace without fire doors 638, a combination of
fireplace and baking oven 125, a combination of stove and baking oven 53,
wood-burning stove 53, baking oven 48, wood-burning oven 93, wood-burning
sauna heater 470, heating boiler 248, and other fireplaces (on average) 470.
Considering also the frequencies of the use of these units within the whole
of the Helsinki Metropolitan Area in 2014, it was computed that most of the
emissions originated from wood combustion were caused by wood-burning sauna
heaters (77 t) and masonry heaters (48 t). According to these
computations, these two heating categories were responsible for 71 % of
the total emissions of PM2.5 from wood burning in the area (175 t) in 2014.
The temporal evolution of the emissions from small-scale
wood combustion
There are numerous factors that affect the long-term (decadal) temporal
evolution of the emissions from small-scale wood combustion. For estimating
the amount of wood combustion, it is essential to know (i) the numbers of
detached and semidetached houses, (ii) the amounts of firewood used, (iii)
the shares of primary heating sources, (iv) the numbers of boilers and sauna
stoves, and (v) the changes of technology and fuels during the considered
period. We have allowed for the influence of the above-mentioned factors
(i)–(iv) for the whole of the considered period. Regarding item (iv), we
have also allowed for the temporal changes of the usage of the main
categories of technologies and heating devices.
However, there are also other factors, which we evaluated to be relatively
less influential; these were not taken into account in detail. These include
(i) the detailed composition of wood fuels, including their humidity and the
fractions of various tree species, and (ii) the changes of the habits and
procedures of combustion. The former factor refers to the ways by which the
wood fuels are collected, pre-processed and stored, and the latter how these
are ignited and combusted.
We used the wood combustion emissions evaluated for 2014 as a starting
point. The total emission values for the other years were estimated by using
previous available emission surveys and various statistical data. In
addition to the above-mentioned survey for 2014, the Helsinki Region
Environmental Services Authority has estimated the amount of wood combustion
in the area for 2002. This survey was conducted using the results of the
previous corresponding studies in this domain, and various statistical data.
In addition, the emissions were evaluated for 2007 by a questionnaire.
The numbers of detached and semidetached houses were extracted from the
Helsinki Region Trends statistical database, and from the statistical data
reported by the urban municipalities in this area. The estimate on the amount
of firewood used is based both on the above-mentioned questionnaire survey
for the year 2014, and on a survey by the Forest Research Institute in
Finland for the years 1992, 1993 and 2000–2001. The shares of primary
heating sources are based on statistical data and the above-mentioned surveys
by the Helsinki Region Environmental Services Authority.
The historical fractions of sauna stoves in detached and semidetached houses
during previous years were evaluated partly based on Luoma (1997). The
estimates in this inventory are based on a limited amount of data, regarding
300 detached and semidetached houses. According to Luoma (1997), there was a
sauna stove in approximately 50 % of the detached and semi-detached
houses in this area in 1980. According to more recent statistics by the
Helsinki Region Environmental Services Authority, this share has decreased to
28 % in 2013. The amounts of firewood used for sauna heating were
evaluated based on the survey by the Helsinki Region Environmental Services
Authority. The amount of heating boilers was analysed based on Luoma (1997)
and the surveys by the Helsinki Region Environmental Services Authority. For
boilers, the number of heating days per year was also taken into account.
We have evaluated separately the temporal trends of the frequencies of three
fireplace categories, i.e. sauna stoves, boilers and all other devices.
Regarding sauna stoves, the technologies have changed very slowly. Only a low
fraction of sauna stoves are currently based on more modern technologies with
relatively lower emissions (Savolahti et al., 2016). There is also
information on more modern, so-called low-emission masonry heaters. These
devices have been available since 2000, and their number has been assumed to
have increased linearly since that time. Their share was 7.1 % of all
masonry heaters in 2010 (Kaski et al., 2016). However, there is not enough
information to take into account the changes of technological developments
for the various heating devices during the 20th century.
Atmospheric dispersion modelling
Urban-scale dispersion modelling
The urban-scale dispersion of vehicular emissions was evaluated with the
CAR-FMI model (Contaminants in the Air from a Road – Finnish Meteorological
Institute; e.g. Kukkonen et al., 2001). The model computes an hourly
time series of the pollutant dispersion from a network of line sources. The
dispersion equation is based on a semi-analytical solution of the Gaussian
diffusion equation for a finite line source. The dispersion parameters are
modelled as a function of the Monin–Obukhov length, the friction velocity
and the mixing height. Traffic-originated turbulence is modelled with a
semi-empirical treatment.
The modelling system containing the CAR-FMI model has been evaluated against
the measured data of urban measurement networks for gaseous pollutants (e.g.
Karppinen et al., 2000c; Kousa et al., 2001) and for PM2.5,
PM10 and particle number concentrations in the Helsinki
Metropolitan Area (Kauhaniemi et al., 2008; Aarnio et al., 2016; Kukkonen et
al., 2016). The model has also been evaluated against both gaseous and
particulate pollutant measurements in London (Sokhi et al., 2008; Singh et
al., 2014; Srimath et al., 2017) and in Birmingham, UK
(Srimath et al., 2017). The performance of the CAR-FMI model has also been
evaluated for gaseous pollutants against the results of field measurement
campaigns and inter-compared with other models (Kukkonen et al., 2001;
Öttl et al., 2001; Levitin et al., 2005; Srimath et al., 2017).
The dispersion from small-scale combustion sources was evaluated using the
UDM-FMI model (Urban Dispersion Model – Finnish Meteorological Institute;
Karppinen et al., 2000b). The model is based on multiple-source Gaussian
plume equations for various stationary source categories (point, area and
volume sources). The modelling system including the UDM-FMI and CAR-FMI
models has been evaluated against the measured data of urban measurement
networks for gaseous pollutants (e.g. Karppinen et al., 2000b; Kousa et al.,
2001) and for PM2.5 (Kauhaniemi et al., 2008).
In this study, the emissions from domestic wood combustion were uniformly
distributed in area sources of size 100 m × 100 m. The altitude of
the releases for domestic wood combustion was assumed to be equal to 7.5 m,
including the initial plume rise. This altitude value is based on the average
heights of the detached and semidetached houses and their chimneys within the
study domain, and an estimated plume rise. The dispersion was evaluated
separately for three different emission source categories: sauna stoves,
boilers and other fireplaces. The diurnal, weekly and monthly variations
within the emission inventory were applied for each source category.
Both for the dispersion of vehicular and small-scale combustion pollutants,
PM2.5 was treated as inert, i.e. no chemical or physical
transformation was assumed to take place within the urban timescales.
Regional-scale dispersion modelling
The air quality measurements that are representative for the regional
background of this area have been conducted at the station of Luukki.
However, these measurements were started only after more than a half of the
target period had passed, in 1999 and 2004 for PM10 and
PM2.5, respectively. It was therefore not feasible to use the
measured regional background concentrations for the whole period. Instead, we
used modelled regional background concentrations, which constituted a
self-consistent time series for the whole target period.
SILAM is a global-to-meso-scale dispersion model developed for evaluating
atmospheric composition and air quality, and for emergency decision support
applications, as well as for solving inverse dispersion problems (e.g.
Sofiev et al., 2006). The model incorporates both Eulerian and Lagrangian
transport routines. There are eight chemico-physical transformation modules
(basic acid chemistry and secondary aerosol formation, ozone formation in the
troposphere and the stratosphere, radioactive decay, aerosol dynamics in the
air, and transformations of pollen) and modules for three- and
four-dimensional variational data assimilation. The source-term descriptions
of the SILAM model include point- and area-source inventories, sea salt,
wind-blown dust, natural pollen, natural volatile organic compounds, nuclear
explosion, and interfaces to the ship emission system STEAM and the
fire information system IS4FIRES (http://silam.fmi.fi/, last access: 1 June 2018).
In the present study, we have conducted reanalyses of the atmospheric
composition and air quality for the period 1980–2014 on global, European
and northern European scales, using the SILAM model (version 5.5). The
simulations were conducted in three spatial domains, i.e. global (the
troposphere and the stratosphere), European (troposphere) and northern
European (troposphere) domains. However, as a combination of meteorological
inputs was used for computing the concentrations in the northern European
domain, these computations did not provide a completely homogeneous time
series. We therefore used the corresponding results for the European domain
as boundary conditions for the urban-scale computations.
An overview of the global and regional computations is presented in the
following. For a more detailed description, the reader is referred to Sofiev
et al. (2018).
The global reanalysis was done using a 1.44∘ × 1.44∘
longitude–latitude resolution, for both the troposphere and the stratosphere,
for the period 1980–2014. The meteorological data were the ERA-Interim
archive taken at full resolution, 0.72∘ × 0.72∘, and
the vertical coverage included 61 hybrid levels (Dee et al., 2011; Simmons et
al., 2010). For the global and European reanalysis, we have used the
following emission data: MACCity and EDGAR (EDGAR, 2014) for the
anthropogenic PM emissions (Janssens-Maenhout et al., 2017), ACCMIP for
wild-land fires (Lamarque et al., 2010; Granier et al., 2011), MEGAN for
biogenic emissions (Guenther et al., 2006), and GEIA for the emissions from
lightning (Price et al., 1997) and aircraft. The emissions of sea salt
(Sofiev et al., 2011), wind-blown dust and biogenic VOCs (for the European
and northern European domains; Poupkou et al., 2010) were evaluated using the
embedded modules in SILAM.
The model output on global scale contains hourly surface and
column-integrated concentration, and column aerosol optical depth (AOD), as
3-hourly values at 18 model levels. There were 50 chemical species in
the model output.
The European reanalysis was done using a longitude–latitude resolution of
0.5∘ × 0.5∘, for the troposphere, for the period
1980–2014. The meteorological input data for the dispersion modelling were
the ERA-Interim for the global and European domains (Dee et al., 2011;
Simmons et al., 2010). The zoomed re-analysis for northern Europe at a
resolution of 0.1∘ × 0.1∘ used the BaltAn meteorological
reanalysis by the Estonian Meteorological Institute. As the values included
in the BaltAn reanalysis extend only up to the year 2005, this dataset was
expanded by using the data from the ECMWF operational archives for
2006–2014.
The model output contained hourly three-dimensional surface and
column-integrated concentration and column AOD, at 13 modelled vertical
levels. The model output included 40 chemical species.
In order to obtain the time series of background concentrations, we first
selected four grid points of the SILAM computations that were closest to the
HMA, but outside the urban domain. We then computed an hourly average of the
concentration values at these four locations, and used that value as the
regional background for all the chemical components of particulate matter,
except for mineral dust. This method is less sensitive to potential
occasional local influence of the metropolitan area on the regional
background concentrations, compared with using the predicted value only at
one specific grid point. In the case of mineral dust, we used the lowest hourly
value within the four selected points. The latter procedure was adopted to
avoid the potential double counting of occasional releases of dust
originating from the considered urban area.
Results and discussion
First, the predicted concentrations are evaluated against the measured
values. Second, selected predicted results are presented, especially on the
temporal evolution of the local emissions and urban concentrations. Third,
the concentrations are analysed in terms of the contributions that originated
from the various pollution source categories.
We also analysed the contributions of shipping and harbour operations on the
concentrations of PM2.5. These results have been presented in
Appendix A. The contribution of shipping and harbours to the total
PM2.5 concentrations varies from 10 to 20 % in the close vicinity
of the three major harbours to a negligible contribution in the northern and
western parts of the area.
Evaluation of the predicted results against the measured
concentrations
Statistical parameters for the comparison of predictions and
measurements
Four statistical parameters were computed: the index of agreement (IA), the
factor-of–two (F2), the coefficient of determination (R2) and the
fractional bias (FB). The parameters IA, R2 and F2 are measures of the
correlation of the predicted and observed time series of concentrations,
whereas FB is a measure of the agreement of the predicted and observed mean
concentrations.
The original form of the index of agreement is calculated as (Willmott,
1981)
IA=1-∑i=1nxi-yi2∑i=1nxi1+yi12,
where n is the number of data points, and x and y refer to the
predicted and measured pollutant concentrations, respectively. The symbols
with the superscript “1” are defined as follows:
xi1=xi-y‾,andyi1=yi-y‾,
in which the overbar refers to an average value. Other forms of IA have
also been presented in the literature.
The IA, as defined above, ranges from 0.0 (theoretical minimum) to 1.0
(perfect agreement). However, even for a random predicted distribution, the
IA value will be larger than the theoretical minimum. Karppinen et
al. (2000b) found that for a random predicted distribution varying from zero
to twice the measured average concentration, the IA value was approximately
0.40.
F2 is a measure of how many predictions are within a factor of 2 compared
with the observations. Fractional bias ranges from -2.0 to +2.0 for
extreme under- and over-prediction, respectively. The FB values equal to
-0.67 and +0.67 are equivalent to under- and over-prediction by a factor
of 2, respectively.
Statistical analysis of the agreement of the predicted and measured
concentrations
We have considered all the measurement data that have been available for the
five measurement stations considered. For the stations of Kallio2, Luukki,
Mannerheimintie, Tikkurila3 and Leppävaara4, the measured concentrations
of PM2.5 were available since 1999, 2004, 2005, 2009 and 2010,
respectively.
Regional background concentrations at one station
We first compared the predictions of the SILAM model for the regional
background at the station of Luukki with the available measurements. The
above-mentioned statistical parameters were computed for both the hourly and
daily concentrations of PM2.5, separately for each year. The
results for the daily concentrations are presented in Table 1. The
corresponding scatter plots of the predicted and measured concentrations have
been presented in Fig. 2.
The scatter plots of measured and predicted daily average
concentrations of PM2.5 at the station of Luukki, for the period
2004–2014. The factor-of-2 values and the numbers of measured days have also
been presented in the panels.
![]()
The IA values ranged from 0.60 to 0.73, and the F2 values range from 54 to
66 %. These IA values indicate that the temporal variation of the
predicted daily PM2.5 concentrations agrees reasonably well with
the observed data. There are also no evident temporal trends in the
statistical parameters during this period, i.e. the performance of modelling
does not deteriorate for the earlier years. The fractional bias values in
Table 1 indicate a slight under-prediction in most cases, the bias values
ranging from -14 to +11 %.
Selected statistical parameters associated with the agreement of the
measured and predicted daily averaged concentrations of PM2.5, at
the regional background station of Luukki, from 2004 to 2014. The average
values of the measurements and predictions and the numbers of data points
have also been presented.
Luukki
2004
2006
2008
2009
2010
2011
2012
2013
2014
IA
Index of agreement
0.67
0.65
0.73
0.72
0.62
0.65
0.68
0.60
0.66
F2
Factor of 2
61
59
54
66
61
55
64
58
61
FB
Fractional bias
-0.06
-0.03
-0.06
-0.01
-0.14
-0.11
-0.04
0.03
0.11
AvgCp
Predicted
7.6
8.1
7.0
7.1
7.4
6.9
6.7
6.5
8.2
AvgCo
Observed
8.1
8.3
7.4
7.2
8.5
7.7
7.0
6.3
7.4
N
Number of data points
365
360
364
365
360
358
348
365
364
The regional background was represented by the computations of the SILAM
model on a European scale, with a spatial resolution of 0.5∘. Such a
resolution may not be sufficient for achieving a high short-term (daily or
hourly) temporal correlation of the measured and predicted time series. The
coastal meteorological effects could also cause inaccuracies on the shorter
term correlations. In this study, the daily correlations were reasonable;
however, the agreement of the long-term mean (e.g. monthly or annual) values
of measured and predicted data was good.
Urban concentrations at four stations
We have selected the data of four urban measurement stations for the
evaluation of the predicted results. These stations and their
classifications are as follows: Kallio2, urban background; Mannerheimintie,
urban traffic; Leppävaara4, suburban traffic; and Tikkurila3, suburban
traffic. The stations of Kallio2 and Mannerheimintie are situated in central
Helsinki, the station of Leppävaara4 is located in the city of Espoo and
the station of Tikkurila3 in the city of Vantaa. The stations of
Leppävaara and Tikkurila are also influenced by small-scale combustion
from the surrounding residential areas. For simplicity, these stations are
referred to in the following as KAL, MAN, LEP and TIK.
The above-mentioned statistical parameters were computed for both the hourly
and daily concentrations of PM2.5 at the four measurement stations,
separately for each year. The results for the daily concentrations are
presented in Table 2. The corresponding scatter plots of the predicted and
measured concentrations for one of the stations (KAL) have been presented in
Fig. 3. The time series of the measured and predicted annual average
concentrations have also been presented, in Appendix B.
The scatter plots of measured and predicted daily average
concentrations of PM2.5 at the station of Kallio2, for the period
1999–2014. The factor-of-2 values and the numbers of measured days have
also been presented in the panels.
![]()
Three selected statistical parameters associated with the agreement
of the measured and predicted daily averaged concentrations of
PM2.5, at four urban measurement stations, from 1999 to 2014. The
average values of the measurements and predictions and the numbers of data
points have also been presented.
Kallio2
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
IA
Index of agreement
0.75
0.70
0.72
0.71
0.71
0.66
0.81
0.69
0.75
0.77
0.75
0.65
0.75
0.69
0.64
0.71
F2
Factor of 2
80
81
78
72
71
70
73
64
62
56
73
67
63
66
66
65
FB
Fractional bias
-0.01
0.12
0.01
0.02
0.14
0.06
0.07
-0.12
-0.12
-0.11
-0.07
-0.08
-0.03
-0.06
0.01
0.07
AvgCp
Average of predicted data
10.2
9.2
8.5
9.2
10.6
8.6
9.6
9.0
8.0
7.7
7.9
8.3
7.5
7.0
7.1
8.7
AvgCo
Average of observed data
10.3
8.2
8.4
9.0
9.2
8.1
8.9
10.0
9.0
8.6
8.4
9.0
7.8
7.4
7.0
8.1
N
Number of data points
347
366
365
365
365
366
360
365
365
361
365
363
358
363
364
365
Mannerheimintie
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
IA
Index of agreement
0.84
0.66
0.73
0.77
0.75
0.63
0.73
0.71
0.61
0.72
F2
Factor of 2
74
67
68
64
71
61
60
69
65
63
FB
Fractional bias
-0.07
-0.15
-0.12
-0.14
-0.18
-0.24
-0.22
-0.14
-0.15
-0.12
AvgCp
Average of predicted data
10.1
9.5
8.6
8.2
8.3
8.7
7.9
7.5
7.4
9.0
AvgCo
Average of observed data
10.8
11.1
9.7
9.4
10.0
11.0
9.9
8.6
8.6
10.1
N
Number of data points
360
365
365
365
365
365
363
356
363
364
Leppävaara4
2010
2011
2012
2013
2014
IA
Index of agreement
0.75
0.77
0.70
0.70
0.71
F2
Factor of 2
77
72
74
75
68
FB
Fractional bias
0.03
-0.04
0.02
0.08
0.15
AvgCp
Average of predicted data
9.0
8.2
7.7
7.7
9.3
AvgCo
Average of observed data
8.8
8.6
7.6
7.1
8.0
N
Number of data points
355
364
366
363
363
Tikkurila3
2009
2010
2011
2012
2013
2014
IA
Index of agreement
0.71
0.81
0.83
0.77
0.75
0.74
F2
Factor of 2
78
77
75
75
75
69
FB
Fractional bias
0.03
-0.09
-0.02
0.05
0.03
0.07
AvgCp
Average of predicted data
8.2
8.6
7.9
7.5
7.4
9.0
AvgCo
Average of observed data
7.9
9.4
8.0
7.1
7.2
8.4
N
Number of data points
365
365
365
366
363
365
The daily IA values range from 0.64 to 0.81, from 0.61 to 0.84, from 0.70 to
0.77 and from 0.71 to 0.83 at the stations of KAL, MAN, LEP and TIK,
respectively. These fairly high or high IA values indicate that the temporal
variation of the predicted daily PM2.5 concentrations agrees well
or fairly well with the observed data. The other considered statistical
values can also be considered to indicate a good or fairly good agreement. In
particular, there is no deterioration of the agreement of predictions and
data for earlier years, compared with the corresponding results for more
recent years. The scatter plots presented in Fig. 3 do not show any
systematic under- or over-prediction of pollutant concentrations for the
majority of the predicted concentrations.
The scatter plot of the predicted and measured annual averages is illustrated
in Fig. 4. The results show that the differences of the modelled and measured
annual average concentrations are less than 20 % in all the cases, except
for two (Fig. 4, MAN 2010 and 2011). For three stations (KAL, LEP and TIK),
there are no notable biases; however, for the station of MAN, there is a
systematic under-prediction. The under-prediction could be caused by the
reduced dilution caused by buildings, as this site is located in the centre
area of the city. The MAN site is also close to a junction of two heavily
trafficked streets, and there is frequently a congestion of traffic. In
particular, a substantial road re-construction work was in progress along
this street in the vicinity of the site for part of the time in 2010–2011;
this could possibly have caused relatively higher measured concentrations.
The scatter plot of measured and predicted annual average
concentrations of PM2.5 at four measurement stations. The lowest
values of both axes have been selected to be 6 µg m-3.
![]()
The indices of agreement and the fractional biases for both hourly and daily
values have been presented graphically in Fig. 5a–b. As expected, the IA
values are lower for the hourly values, compared with the daily values,
caused by the larger scatter of hourly values, compared with the daily ones.
The agreement of the temporal variations of the predictions and measured
data (as measured, e.g. by the index of agreement) is on the average
slightly better for the urban stations, compared with that at the regional
background station of Luukki. This is probably caused by the fact that the
modelling of the temporal variations of the emissions and dispersion from
local sources is more accurate, compared with those on a regional scale.
The model performance values can be compared with corresponding model
evaluation studies in other cities. For instance, Singh et al. (2014)
reported the analysis of modelled and measured fine particulate matter
(PM2.5) concentrations within London for 2008, evaluated using the
OSCAR Air Quality Assessment System. Measured concentration values were used
to model the temporal variation of the regional background concentrations.
The evaluation was conducted with measurements from the London air quality
network. For the predicted and measured hourly time series of concentrations
at 18 sites in London, the index of agreement of all stations was 0.86.
The corresponding IA values for the hourly PM2.5 concentrations at
the above-mentioned four urban stations in the present study ranged from 0.60
to 0.73, from 0.55 to 0.78, from 0.64 to 0.69 and from 0.66 to 0.76 at the
stations of KAL, MAN, LEP and TIK, respectively. The IA values in the present
study were therefore slightly lower, although comparable with those found by
Singh et al. (2014). However, the somewhat better performance can also be
partly caused by the use of measured concentration values in evaluating the
temporal variation of the regional background by Singh et al. (2014).
The annually evaluated indices of agreement and fractional biases,
shown separately at four measurement stations and for each year, for hourly
(a) and daily (b) concentrations of PM2.5. The
stations have been indicated by the various colours, and the years by small
text inside each dot.
![]()
The trends of urban emissions and concentrations
The predicted emissions originated from vehicular traffic and small-scale
combustion in the Helsinki Metropolitan Area are presented in Fig. 6. The
vehicular emissions were clearly higher in the 1980s and early 1990s,
approximately 5-fold, compared with the corresponding values during the
latest considered years. The vehicular emissions were highest from 1986 to
1988. During the early 1980s, there was a continuous increase in road
traffic exhaust emissions both at the Helsinki Metropolitan Area and in
Finland (Mäkelä and Auvinen, 2009).
The decreasing trend in vehicular exhaust emissions since the late 1980s
has been achieved due to an increased use of catalytic converters and
improved engine technology (Mäkelä and Auvinen, 2009). The relative
share of suspension emissions (compared with the total vehicular emissions)
has been clearly higher during the most recent years.
During the considered period, the total mileage (as kilometres travelled per
annum) in this area has grown continuously, except for a slight plateau
during the early 1990s, from approximately 28 000 to 58 000 millions of
kilometres per annum (Mäkelä and Auvinen, 2009). The mileage has also been
projected to continue increasing in the foreseeable future. Although the
mileage has almost doubled, at the same time the vehicular emissions have
decreased approximately to one-fifth of their original value.
The predicted emissions that originated from vehicular sources and
small-scale wood combustion in the Helsinki Metropolitan Area from 1980 to
2014. The vehicular emissions have been presented separately for exhaust and
non-exhaust sources. The values for the vehicular exhaust emissions have been
extracted from the LIPASTO system (Mäkelä and Auvinen, 2009).
![]()
There is a slight increasing trend in the emissions from small-scale
combustion. During recent years, the mass-based emissions from small-scale
combustion have been only slightly smaller than those from vehicular
traffic.
The temporal evolution of the modelled annual average PM2.5
concentrations has been presented in Fig. 7 at four stations in the HMA,
together with the modelled regional background concentrations. According to
these computations, the highest concentrations have occurred in the early and
middle years of the 1980s. In 2014, the concentrations have decreased to
about a half of the highest values during the considered period. This has
been caused both by the decreasing regional background and the decreasing
local contributions.
The regional background is clearly the largest fraction of the total
concentrations. The concentrations were systematically highest at the
stations with the most dense traffic in the vicinity of the station (MAN and
LEP), and relatively lowest at the urban background station of Kallio.
However, during the last decade, there has been only a very slight
decreasing trend.
The measured time series of concentrations of PM2.5 extend from the
present to the years from 1999 to 2010, depending on the station (see
Table 2). However, there are measurements of the concentrations of
PM10 and TSP (total suspended particles) for substantially longer
periods. These concentrations can be used as proxy variables for the measured
concentrations of PM2.5. The accuracy of the modelled trends of the
concentrations of PM2.5 can therefore be indirectly evaluated for
earlier years using such a procedure. Such an analysis has been presented in
Appendix C.
The trends of the concentrations, as evaluated using the proxy values based
on the measured PM10 concentrations, agreed well at four stations,
and fairly well at one station, with the modelled long-term evolution of
concentrations. This adds some confidence that the modelled trends are fairly
accurate.
The predicted annual average concentrations of PM2.5 at
four stations in the Helsinki Metropolitan Area from 1980 to 2014, and the
predicted regional background concentrations (SILAM).
![]()
The contributions originated from various source categories
The modelled urban source contributions (vehicular exhaust, vehicular
suspension and small-scale combustion) have been presented in Fig. 8a–d. For
clarity, the regional background values have not been presented in this
figure. The local contributions range from 0.5 to 4.5 µg m-3.
Vehicular exhaust has commonly been the largest urban contribution. However,
at the stations that are most directly influenced by small-scale combustion
(TIK and LEP), that contribution is also comparable with that of vehicular
sources, during the most recent years.
The predicted annual average concentrations of PM2.5
originated from the three most important local source categories, i.e.
small-scale combustion, vehicular non-exhaust and vehicular exhaust, at four
measurement stations.
![]()
Conclusions
We have modelled the concentrations of fine particulate matter globally, in
Europe and in the Helsinki Metropolitan Area for a period of 35 years, from
1980 to 2014. The present study addresses the results in the case of
PM2.5 for the urban area. Results on urban concentrations have not
previously been presented in the literature on such a high spatial (tens or a
couple of hundreds of metres) and temporal (hourly) resolution for several
decades. Examination of such historical results on the local emissions and
concentrations also allows a wider perspective in view of the future
evolution of air quality and its effects. These long-term results and trends
were also thoroughly evaluated against the measured data at one regional
background station and at four urban stations.
The results were subsequently post-processed to provide for the lifetime
concentrations of PM2.5 for each of the members of the Espoo Cohort
Study. The cohort population included a random sample of all children of the
city of Espoo, born between 1984 and 1989, in total 2568 children (Jaakkola
et al., 2010; Paaso et al., 2014). There is information on the complete
history of the places of residence for the members of the cohort, as well as
on their workplaces in adult age. We have therefore allowed for the changes
of their lifetime exposure, caused by the changes of the residences,
separately for each cohort member. This analysis has enabled us to assess the
health effects of lifetime, time-specific, and both long- and short-term
exposures.
Clearly, there are numerous challenges in modelling accurately the emissions
and atmospheric dispersion for such an extended period. Various factors
affecting the relevant emissions, the emission coefficients and the absolute
amounts of emissions are known less accurately for the earlier period, i.e.
especially for the 1980s and also for the 1990s. This is the case
regarding both the global- and European-scale emission inventories, and the
urban-scale inventories.
We have examined the available quantitative information on the changes of
the street and road network in the considered area, but that information was
not sufficiently detailed to be included in the emission modelling. The
fractions and types of studded tires used in vehicles in the 1980s and
1990s were not known sufficiently accurately to be included in the
modelling. It was also not possible to use more detailed models, such as
those for evaluating the vehicular suspension emissions and those for
street canyon dispersion, due to missing input data (in the former case) and
practical limitations on the amount of computations (in the latter case).
The regional-scale computations rely on the data of several previously
compiled emission inventories; the accuracy of these datasets could
potentially be worse for earlier decades.
A novel, detailed emission inventory for small-scale wood combustion was
compiled in this metropolitan area for 2014 for a wide range of pollutants.
The emission values provided by this inventory were also extended for a
multi-decadal period. There are numerous factors that affect the decadal
temporal evolution of the emissions from small-scale wood combustion. These
include (i) the numbers of detached and semidetached houses; (ii) the amounts
of firewood used; (iii) the shares of heating sources, boilers and stoves;
and (iv) the changes of technology and fuels. We have evaluated the temporal
evolution of the above-mentioned factors (i)–(iii), and modelled their
influence on the emissions from wood combustion, for the considered period.
We have also allowed for the temporal changes of the usage of the main
categories of technologies and heating devices. However, there were also
other factors, which were not taken into account in detail. These include (i)
the possible changes of the detailed composition of wood fuels and (ii) the
changes of the habits and procedures of combustion.
The results of this study include a novel quantification of the emissions
from two main local source categories, i.e. local vehicular traffic and
small-scale wood combustion, for a multi-decadal period. The local vehicular
emissions of PM2.5 were about 5-fold higher in the 1980s, compared
with the emissions during the latest considered years. This substantial
decrease in vehicular emissions has been achieved due to an increased use
of catalytic converters and improved engine technologies. During the same
period, the total mileage (as kilometres driven per annum) in the area has
almost doubled. However, the emissions originated from small-scale
combustion have increased slightly in time.
The modelled concentrations of PM2.5 agreed fairly well with the
measured data at four measurement stations, during 1999–2014. These stations
represented regional background, urban background, urban traffic and suburban
traffic environments. Two of these stations were also influenced by
small-scale combustion from the surrounding residential areas. However, we
did not find any systematic deterioration of the agreement of predictions and
data for earlier years (the 1980s and 1990s), compared with the
corresponding results for more recent years (2000s and early 2010s). This
gives more confidence that the emission inventories used for the earlier
years were not substantially more inaccurate, compared with those for the
more recent years.
The measured concentrations of PM2.5 were slightly under-predicted for
most years at the regional background station. However, these were not
systematically under- or over-predicted for most of the urban stations and
years. There was a systematic under-prediction of concentrations for one
station, viz. Mannerheimintie in central Helsinki. Part of this
under-prediction was probably caused by the reduced dilution caused by
buildings and the increased vehicular emissions resulting from the
frequently congested traffic conditions. The road re-construction works in
the vicinity of this station for part of the time during the 2 years could
also have caused a slight additional contribution to the measured values.
We also evaluated the accuracy of the modelled trends of concentrations of
PM2.5, by using the measured values of the concentrations of
PM10 and TSPs (total suspended particles) as proxy variables. Using
such an analysis, the model results can be evaluated for a substantially
longer period than would be possible using only the PM2.5
measurements. In particular, the trends of the concentrations using
PM10 as a proxy agreed well at four stations, and fairly well at
one station, with the modelled long-term evolution of concentrations. This
adds more confidence that the modelled trends are fairly accurate.
The highest concentrations of PM2.5 have occurred in the early and
middle years of the 1980s. The urban concentrations of PM2.5 have
decreased to about a half of their highest values during the considered
period. However, during the last decade, there has been only a very slightly
decreasing trend. The regional background constitutes clearly the largest
fraction of the total concentrations. Vehicular exhausts have commonly been
the most important local source, but the relative share of small-scale
combustion has continuously increased.
The study has provided high-resolution concentration databases on global,
European and urban scales. The global- or regional-scale data can
be used as boundary conditions for any selected location, for the time
period from 1980 to 2014. Clearly, this could in many cases be much more
cost-effective than starting from scratch to conduct such large-scale
emission and dispersion computations. The urban-scale data have already been
used for the assessment of individual-level lifetime exposure to air
pollution and its effects, especially with respect to the risk of developing
asthma during the first 3 decades of life in the Espoo Cohort Study.