Search

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; 10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement. [ABSTRACT FROM AUTHOR]

The mixing ratios of highly volatile organic compounds (VOCs) were studied on Utö Island in the Baltic Sea. Measurements of non-methane hydrocarbons (NMHCs) and methanethiol (unexpectedly found during the experiment) were conducted using an in situ thermal desorption–gas chromatography–flame ionization detector/mass spectrometer (TD-GC-FID/MS) from March 2018 until March 2019. The mean mixing ratios of NMHCs (alkanes, alkenes, alkynes, and aromatic hydrocarbons) were at the typical levels for rural/remote sites in Europe, and, as expected, the highest mixing ratios were measured in winter, while in the summertime, the mixing ratios remained close to or below detection limits for most of the studied compounds. Sources of NMHCs during wintertime were studied using positive matrix factorization (PMF) together with wind direction analyses and source area estimates. Shipping was found to be a major local anthropogenic source of NMHCs with a 21 % contribution. It especially contributed to ethene, propene, and ethyne mixing ratios. Other identified sources were petrol fuel (15 %), traffic exhaust (14 %), local solvents (6 %), and long-range-transported background (42 %). Contrary to NMHCs, high mixing ratios of methanethiol were detected in summertime (July mean of 1000 pptv). The mixing ratios followed the variations in seawater temperatures and sea level height and were highest during the daytime. Biogenic phytoplankton or macroalgae emissions were expected to be the main source of methanethiol. [ABSTRACT FROM AUTHOR]

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 on 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 years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at https://doi.org/10.5281/ZENODO.8070155 (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 consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for machinery and off-road 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 the CAMS-REGv4.2 was able to produce similar spatial patterns as our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than the CAMS-REGv4.2, which had 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 is 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. [ABSTRACT FROM AUTHOR]