Mikko Savolahti

Particle emissions in Belarus and in the Nordic countries

The overall goal of the project is to stimulate decision-makers in Belarus to prioritize abatement measures aimed at black carbon in their efforts to reduce emissions of PM2.5, as encouraged in the ...

Health Impacts of Ambient Air Pollution in Finland

Air pollution has been estimated to be one of the leading environmental health risks in Finland. National health impact estimates existing to date have focused on particles (PM) and ozone (O3). In this work, we quantify the impacts of particles, ozone, and nitrogen dioxide (NO2) in 2015, and analyze the related uncertainties. The exposures were estimated with a high spatial resolution chemical transport model, and adjusted to observed concentrations. We calculated the health impacts according to Word Health Organization (WHO) working group recommendations. According to our results, ambient air pollution caused a burden of 34,800 disability-adjusted life years (DALY). Fine particles were the main contributor (74%) to the disease burden, which is in line with the earlier studies. The attributable burden was dominated by mortality (32,900 years of life lost (YLL); 95%). Impacts differed between population age groups. The burden was clearly higher in the adult population over 30 years (98%), due to the dominant role of mortality impacts. Uncertainties due to the concentration–response functions were larger than those related to exposures.

Nearly Zero-Energy Buildings in Finland: Legislation Alternatives for Residential Wood Combustion and the Impact on Population Exposure to Fine Particles

Wood combustion is being promoted as an environmentally friendly energy source in the residential sector, although it’s fine particle emissions and consequential detrimental effects on human health has been clearly shown in recent scientific literature. In Finland, supplementary wood heating is common, and the popularity of masonry heaters in new detached buildings has been on the rise. Finnish legislation concerning EU’s requirements on nearly zero-energy buildings is in preparation, and possibly includes a component that may have an increasing effect on the need of supplementary wood heating. This study demonstrates that the potential increase would cause notable fine particle emissions in the future. We studied several wood consumption scenarios and the resulting PM2.5 concentrations in 2050. In the scenario with the biggest increase in wood consumption, the masonry heaters in new detached buildings would cause an additional 10% rise in the current background concentrations in some suburban areas. Increasing the share of wood heating would also be somewhat counterproductive to the purpose of the Energy Performance of Buildings directive, since the legislation won’t improve the actual energy efficiency of these houses.

Validation of PM2.5 Concentrations Based on Finnish Emission—Source-Receptor Scenario Model

Atmospheric fine particulate matter (PM2.5) is a major health risk in both developing and developed countries. Health impact assessments utilize often air quality models, consisting of emission and atmospheric dispersion and meteorological models. For policy purposes, there is often a need to assess the air quality impact of large number of alternative emission reduction measures. For such assessments at high spatial resolution for regional scale domains, e.g. the area of a whole country, simplified linear source-receptor relationships can be used to substitute more laborious atmospheric models. In this study we compared PM2.5 concentrations calculated with our policy analysis emission model with available measurement data. The PM2.5 concentrations were modelled using the Finnish Regional Emission Scenario (FRES) model coupled with source-receptor matrices at various resolutions. The measurement data for comparisons were taken from several monitoring stations across Finland, and represented different site types i.e. rural and urban background and traffic dominated environments. In general the model overestimated the PM2.5 concentrations in urban locations and underestimated in rural stations. One possible reason for the overestimation is that emissions from some sectors may have inaccurate spatial disaggregation. Especially the use of population density as a spatial proxy for the distribution of emissions often poorly represents the polluting activity and results in too high modelled concentrations in densely populated areas. In rural regions the omission of sea traffic emissions and natural sources might explain some of the underestimation. The results highlight the importance of the quality of the emission data used as input in dispersion modelling and the need for reliable spatial representation of emissions in the model.