Health impacts of PM2.5 originating from residential wood combustion in four nordic cities.

BACKGROUND: Residential wood combustion (RWC) is one of the largest sources of fine particles (PM2.5) in the Nordic cities. The current study aims to calculate the related health effects in four studied city areas in Sweden, Finland, Norway, and Denmark. METHODS: Health impact assessment (HIA) was employed as the methodology to quantify the health burden. Firstly, the RWC induced annual average PM2.5 concentrations from local sources were estimated with air pollution dispersion modelling. Secondly, the baseline mortality rates were retrieved from the national health registers. Thirdly, the concentration-response function from a previous epidemiological study was applied. For the health impact calculations, the WHO-developed tool AirQ + was used. RESULTS: Amongst the studied city areas, the local RWC induced PM2.5 concentration was lowest in the Helsinki Metropolitan Area (population-weighted annual average concentration 0.46 µg m- 3) and highest in Oslo (2.77 µg m- 3). Each year, particulate matter attributed to RWC caused around 19 premature deaths in Umeå (95% CI: 8-29), 85 in the Helsinki Metropolitan Area (95% CI: 35-129), 78 in Copenhagen (95% CI: 33-118), and 232 premature deaths in Oslo (95% CI: 97-346). The average loss of life years per premature death case was approximately ten years; however, in the whole population, this reflects on average a decrease in life expectancy by 0.25 (0.10-0.36) years. In terms of the relative contributions in cities, life expectancy will be decreased by 0.10 (95% CI: 0.05-0.16), 0.18 (95% CI: 0.07-0.28), 0.22 (95% CI: 0.09-0.33) and 0.63 (95% CI: 0.26-0.96) years in the Helsinki Metropolitan Area, Umeå, Copenhagen and Oslo respectively. The number of years of life lost was lowest in Umeå (172, 95% CI: 71-260) and highest in Oslo (2458, 95% CI: 1033-3669). CONCLUSIONS: All four Nordic city areas have a substantial amount of domestic heating, and RWC is one of the most significant sources of PM2.5. This implicates a substantial predicted impact on public health in terms of premature mortality. Thus, several public health measures are needed to reduce the RWC emissions.

Costs and benefits of implementing an Environmental Speed Limit in a Nordic city.

We present a comprehensive study on the impacts and associated changes in costs resulting from the implementation of Environmental Speed Limits (ESLs), as a measure to reduce PM10 and associated health effects. We present detailed modelled emissions (i.e., CO2, NOx, PM2.5 and PM10), concentration levels (i.e., PM2.5 and PM10) and population exposure to PM2.5 and PM10 under three scenarios of ESL implementation for the Metropolitan Area of Oslo. We find that whilst emissions of NOx and CO2 do not seem to show significant changes with ESL implementation, PM10 emissions are reduced by 6-12% and annual concentration levels are reduced up to 8%, with a subsequent reduction in population exposure. The modelled data is used to carry out a detailed analysis to quantify the changes in private and social costs for the roads in Oslo where ESL are implemented today. This involves assessments related to human health, climate, fuel consumption, time losses and the incidence of traffic accidents. For a scenario using actual speed data from ESL implementation, our study shows a net benefit associated with the implementation of ESLs, whilst for a theoretical scenario with strict speed limit compliance we find a net increase in costs. This is largely due to variation in costs due to time losses between the scenarios, although uncertainties are high.

Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions.

In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old stoves and new stoves, respectively. Emission factors are heavily dependent on technology and this information is therefore essential for determining accurate emissions. The collected data are compared with existing information from the statistical register at county and national level in Norway. The comparison shows good agreement for the proportion of residential heating systems between the webcrawled data and the official statistics. The high resolution and level of detail of the extracted data show the value of open data to improve emission inventories. With the increased amount and availability of data, the techniques presented here add significant value to emission accuracy and potential applications should also be considered across all emission sectors.