Bottom–Up Inventory of Residential Combustion Emissions in Poland for National Air Quality Modelling: Current Status and Perspectives

For many years, the Polish air quality modelling system was decentralized, which significantly hampered the appropriate development of methodologies, evaluations, and comparisons of modelling results. The major contributor to air pollution in Poland is the residential combustion sector. This paper demonstrates a novel methodology for residential emission estimation utilized for national air quality modelling and assessment. Our data were compared with EMEP and CAMS inventories, and despite some inequalities in country totals, spatial patterns were similar. We discuss the shortcomings of the presented method and draw conclusions for future improvements.

CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling

This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for a 18-year time series (2000–2017) at a 0.1° × 0.05° grid, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5 and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses where possible the officially reported emission data by European countries to the UN Framework Convention on Climate Change and the Convention on Long-Range Transboundary Air Pollution as the basis. Where deemed necessary because of errors, incompleteness of inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector-fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector-fuel combinations, pollutants and years. Point source emissions are largely based on reported facility level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from the a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as to alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting PM and NMVOC into individual component are provided, as well as information on the height profile by sector and temporal disaggregation down to hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (Kuenen et al., 2021).

The Characteristics of PM2.5 Pollution and Policy Implications in Chungcheong Region

Objectives : This study aims to discuss air quality policy improvement that reflect regional characteristics through analyzing recent PM2.5 concentration, air pollutant emission sources and those contributions to annual PM2.5 concentration in Chungcheong region (Daejeon Metropolitan City, Sejong Metropolitan Autonomous City, the Province of Chungcheongnam-do, and Chungcheongbuk-do) in South Korea. In addition, we identified the characteristics of the PM2.5 pollution at the level of fundamental local government, and demonstrated the number of vulnerable population exposed to high level of PM2.5 concentration in order to propose policy implications in Chungcheong region.Methods : Based on the national emissions estimates (CAPSS: Clean Air Policy Support System) and air quality modelling system, major sectors/sources of air pollutants emission and national contributions of PM2.5 concentrations in Chungcheong region were analyzed. Furthermore, the study identified the number of people exposed to the higher PM2.5 concentrations (>25 µg/m3) by the measurement data and demographics available in 2019.Results and Discussion : The national air pollutants emissions in Chungcheong region were emitted from Chungnam (about 59% of NOx emission volume, 89% of SOx, 70% of NH3, 54% of VOCs, 79% of PM2.5, and 68% of TSP respectively), mainly from industry, domestic, energy, and road sector. According to the results of the air quality modelling, Chungcheong region also had the largest contribution on the average annual PM2.5 concentration in South Korea (27%). Chungnam emitted the largest emission volume of air pollutants, mainly from industry and power generation sectors (especially in Dangjin, Seosan, and Boryeong), while Asan, Yesan, Hongseong, and Cheongyang were classified as the areas with higher PM2.5 concentrations (>25 µg/m3), showing a gap between the areas with large emission volume and high concentration. Chungbuk and Sejong had higher annual PM2.5 concentration due to the influence of external sources and their geographical characteristics. The largest vulnerable population (over 65 years old and under 18 years old) exposed to high PM2.5 concentrations annually lived in Cheongju. Chungbuk had about 40% more air pollutant emission volume than Chungnam, but about 17% more vulnerable population.Conclusions : At the current stage of “master plan” in Chungcheong region, it is important to mitigate air pollutants emissions on the basis of the local emissions characteristic at the level of fundamental local government (such as industry sector in Dangjin, Seosan, and Danyang/ Domestic buring in Cheongju, Cheonan, and Daejeon/power generation in Boryeong, Taean and Dangjin/ road in Daejeon, Cheongju, and Cheoan). In addition, Chungbuk requires management of the areas with higher PM2.5 concentration such as Goesan, Boeun, Okcheon, and Yeongdong located outside “air control zone”. To reduce high level of PM2.5 concentration in Chungcheong region, cooperation with neighboring local governments such as Gyeonggi Province is crucial, and policy solutions are needed between the stakeholders to resolve the disparity issues between areas with larger emission volume and higher PM2.5 concentration.

Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.