Chapter 31
A High-Resolution National Emission
Inventory and Dispersion Modelling—Is
Population Density a Sufficient Proxy
Variable?
Niko Karvosenoja, Ville-Veikko Paunu, Mikko Savolahti, Kaarle Kupiainen,
Ari Karppinen, Jaakko Kukkonen and Otto Hänninen
Abstract Air quality modeling at high spatial resolution over large domains enables
comprehensive health impact assessment. Spatially finely resolved emission inventories are a crucial component for reliable modeling. Spatialization of emissions from
disperse emission sources (e.g. road transport) is performed using GIS-based spatial
information, i.e. spatial proxies (e.g. road network and traffic volume data). For some
important emission source sectors, however, it is challenging to define proxies that
adequately represent the spatial distribution of emissions, and, for the lack of more
representative information, population density is often used as a proxy. However,
that is rarely a realistic representation and might distort the resulting assessments
of their population exposure and health impacts. This study presents the impacts of
the spatial allocation process and its improvements for machinery sector, by using
an emission model at 250 m grid resolution in Finland. The corresponding influence
on the modeled population exposure to PM 2.5 is also presented. The improvements
in the gridding procedures had a substantial impact on the modeled concentrations,
especially in areas with denser population. For example, the emissions in Helsinki
area from the machinery sector decreased by 41% due to the improvements. We conclude that it is necessary to use more realistic spatial proxies instead of the population
density for evaluating the emissions originated from various emission categories.
N. Karvosenoja (B) · V.-V. Paunu · M. Savolahti · K. Kupiainen
Finnish Environment Institute (SYKE), POB 140, 00251 Helsinki, Finland
e-mail: niko.karvosenoja@ymparisto.fi
A. Karppinen · J. Kukkonen
Finnish Meteorological Institute (FMI), Helsinki, Finland
O. Hänninen
National Institute for Health and Welfare (THL), Helsinki, Finland
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_31
199
A High-Resolution National Emission
Inventory and Dispersion Modelling—Is
Population Density a Sufficient Proxy
Variable?
Niko Karvosenoja, Ville-Veikko Paunu, Mikko Savolahti, Kaarle Kupiainen,
Ari Karppinen, Jaakko Kukkonen and Otto Hänninen
Abstract Air quality modeling at high spatial resolution over large domains enables
comprehensive health impact assessment. Spatially finely resolved emission inventories are a crucial component for reliable modeling. Spatialization of emissions from
disperse emission sources (e.g. road transport) is performed using GIS-based spatial
information, i.e. spatial proxies (e.g. road network and traffic volume data). For some
important emission source sectors, however, it is challenging to define proxies that
adequately represent the spatial distribution of emissions, and, for the lack of more
representative information, population density is often used as a proxy. However,
that is rarely a realistic representation and might distort the resulting assessments
of their population exposure and health impacts. This study presents the impacts of
the spatial allocation process and its improvements for machinery sector, by using
an emission model at 250 m grid resolution in Finland. The corresponding influence
on the modeled population exposure to PM 2.5 is also presented. The improvements
in the gridding procedures had a substantial impact on the modeled concentrations,
especially in areas with denser population. For example, the emissions in Helsinki
area from the machinery sector decreased by 41% due to the improvements. We conclude that it is necessary to use more realistic spatial proxies instead of the population
density for evaluating the emissions originated from various emission categories.
N. Karvosenoja (B) · V.-V. Paunu · M. Savolahti · K. Kupiainen
Finnish Environment Institute (SYKE), POB 140, 00251 Helsinki, Finland
e-mail: niko.karvosenoja@ymparisto.fi
A. Karppinen · J. Kukkonen
Finnish Meteorological Institute (FMI), Helsinki, Finland
O. Hänninen
National Institute for Health and Welfare (THL), Helsinki, Finland
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_31
199