9 research outputs found
Reviews and syntheses : Arctic fire regimes and emissions in the 21st century
In recent years, the pan-Arctic region has experienced increasingly extreme fire seasons. Fires in the northern high latitudes are driven by current and future climate change, lightning, fuel conditions, and human activity. In this context, conceptualizing and parameterizing current and future Arctic fire regimes will be important for fire and land management as well as understanding current and predicting future fire emissions. The objectives of this review were driven by policy questions identified by the Arctic Monitoring and Assessment Programme (AMAP) Working Group and posed to its Expert Group on Short-Lived Climate Forcers. This review synthesizes current understanding of the changing Arctic and boreal fire regimes, particularly as fire activity and its response to future climate change in the pan-Arctic have consequences for Arctic Council states aiming to mitigate and adapt to climate change in the north. The conclusions from our synthesis are the following. (1) Current and future Arctic fires, and the adjacent boreal region, are driven by natural (i.e. lightning) and human-caused ignition sources, including fires caused by timber and energy extraction, prescribed burning for landscape management, and tourism activities. Little is published in the scientific literature about cultural burning by Indigenous populations across the pan-Arctic, and questions remain on the source of ignitions above 70 degrees N in Arctic Russia. (2) Climate change is expected to make Arctic fires more likely by increasing the likelihood of extreme fire weather, increased lightning activity, and drier vegetative and ground fuel conditions. (3) To some extent, shifting agricultural land use and forest transitions from forest-steppe to steppe, tundra to taiga, and coniferous to deciduous in a warmer climate may increase and decrease open biomass burning, depending on land use in addition to climate-driven biome shifts. However, at the country and landscape scales, these relationships are not well established. (4) Current black carbon and PM2.5 emissions from wildfires above 50 and 65 degrees N are larger than emissions from the anthropogenic sectors of residential combustion, transportation, and flaring. Wildfire emissions have increased from 2010 to 2020, particularly above 60 degrees N, with 56% of black carbon emissions above 65 degrees N in 2020 attributed to open biomass burning - indicating how extreme the 2020 wildfire season was and how severe future Arctic wildfire seasons can potentially be. (5) What works in the boreal zones to prevent and fight wildfires may not work in the Arctic. Fire management will need to adapt to a changing climate, economic development, the Indigenous and local communities, and fragile northern ecosystems, including permafrost and peatlands. (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes. This review highlights that much research is needed in order to understand the local and regional impacts of the changing Arctic fire regime on emissions and the global climate, ecosystems, and pan-Arctic communities.peerReviewe
Air pollution emission inventory using national high-resolution spatial parameters for the Nordic countries and analysis of PM<sub>2.5</sub> spatial distribution for road transport and machinery and off-road sectors
Air pollution is an important cause of adverse health effects, even in the Nordic countries, which have relatively good air quality. Modelling-based air quality assessment of the health impacts relies on reliable model estimates of ambient air pollution concentrations, which furthermore rely on good-quality spatially resolved emission data. While quantitative emission estimates are the cornerstone of good emission data, description of the spatial distribution of the emissions is especially important for local air quality modelling at high resolution. In this paper we present a new air pollution emission inventory for the Nordic countries with high-resolution spatial allocation (1 km × 1 km) covering the years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at https://doi.org/10.5281/zenodo.10571094 (Paunu et al., 2023). To study the impact of applying national data and methods to the spatial distribution of the emissions, we compared road transport and machinery and off-road sectors to CAMS-REGv4.2, which used a consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for the machinery and off-road sector, the choice of proxies is not as straightforward as it includes a variety of different type of vehicles and machines operating in various environments. We found that CAMS-REGv4.2 was able to produce similar spatial patterns to our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than CAMS-REGv4.2, which had a resolution of 0.1° × 0.05° (longitude–latitude, roughly 5.5 km × 3.5–6.5 km in the Nordic countries). The EMEP/EEA Guidebook chapter on spatial mapping of emissions has recommendations for the sectoral proxies. Based on our analysis we argue that the guidebook should have separate recommendations for proxies for several sub-categories of the machinery and off-road sectors, instead of including them within broader sectors. We suggest that land use data are the best starting point for proxies for many of the subsectors, and they can be combined with other suitable data to enhance the spatial distribution. For road transport, measured traffic flow data should be utilized where possible, to support modelled data in the proxies.</p
Clean air policies are key for successfully mitigating Arctic warming
A tighter integration of modeling frameworks for climate and air quality is urgently needed to assess the impacts of clean air policies on future Arctic and global climate. We combined a new model emulator and comprehensive emissions scenarios for air pollutants and greenhouse gases to assess climate and human health co-benefits of emissions reductions. Fossil fuel use is projected to rapidly decline in an increasingly sustainable world, resulting in far-reaching air quality benefits. Despite human health benefits, reductions in sulfur emissions in a more sustainable world could enhance Arctic warming by 0.8 °C in 2050 relative to the 1995–2014, thereby offsetting climate benefits of greenhouse gas reductions. Targeted and technically feasible emissions reduction opportunities exist for achieving simultaneous climate and human health co-benefits. It would be particularly beneficial to unlock a newly identified mitigation potential for carbon particulate matter, yielding Arctic climate benefits equivalent to those from carbon dioxide reductions by 2050
Risk of Air Pollution in Relation to Cancer in the Nordic Countries
Seventeen pollutants (particles, heavy metals, inorganic gasses and organic compounds) are for the first time analyzed in a screening of the carcinogenic risk at very high resolution and large scale in ambient air in the Nordic countries. Modelled 2010 annual mean air concentrations show no exceedances of the EU air quality values. The only exceedance of US-EPA 1:100,000 cancer risk concentrations occurs for the PAH BaP in Denmark. However, the EU target value threshold for BaP is not exceeded. No emission data for BaP are available for the other countries and important uncertainties are still related to the Danish emissions. Long-range transport is significant except for BaP that originates mostly from residential wood combustion. It is recommended to monitor the influence from residential wood combustion more extensively, and to analyze longer time trends for long-term human exposure
Spatial distribution of residential wood combustion emissions in the Nordic countries : How well national inventories represent local emissions?
Highlights
• New high-resolution emission inventory for Nordic residential wood combustion.
• Country level methods can produce similar spatial distributions as local level.
• Difference between urban and rural RWC is important for the spatial distribution.
• National characteristics are essential for spatial representation of RWC emissions.Residential wood combustion (RWC) is a major source of air pollutants in the Nordic and many other countries. The emissions of the pollutants have been estimated with inventories on several scopes, e.g. local and national. An important aspect of the inventories is the spatial distribution of the emissions, as it has an effect on health impact assessments. In this study, we present a novel residential wood combustion emission inventory for the Nordic countries based on national inventories and new gridding of the emissions. We compare the emissions of the Nordic inventory, and especially their spatial distribution, to local assessments and European level TNO-newRWC-inventory to assess the spatial proxies used. Common proxies used in the national inventories in the Nordic countries were building data on locations and primary heating methods and questionnaire-based wood use estimates for appliances or primary heating methods. Chimney sweeper register data was identified as good proxy data, but such data may not be available in an applicable format. Comparisons of national inventories to local assessments showed the possibility to achieve similar spatial distributions through nation-wide methods as local ones. However, this won't guarantee that the emissions are similar. Comparison to the TNO-newRWC-inventory revealed the importance of how differences between urban and rural residential wood combustion are handled. The comparison also highlighted the importance of local characteristics of residential wood combustion in the spatial distribution of emissions