Abstract. Methane (CH4) emissions from coal production amount to roughly one-third of European anthropogenic CH4 emissions in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish side of the Upper Silesian Coal Basin (USCB) as the main part of it. Emission estimates for CH4 from the USCB for individual coal mine ventilation shafts range between 0.03 and 20 kt a−1, amounting to a basin total of roughly 440 kt a−1 according to the European Pollutant Release and Transfer Register (E-PRTR, http://prtr.ec.europa.eu/, 2014). We mounted a ground-based, portable, sun-viewing FTS (Fourier transform spectrometer) on a truck for sampling coal mine ventilation plumes by driving cross-sectional stop-and-go patterns at 1 to 3 km from the exhaust shafts. Several of these transects allowed for estimation of CH4 emissions based on the observed enhancements of the column-averaged dry-air mole fractions of methane (XCH4) using a mass balance approach. Our resulting emission estimates range from 6±1 kt a−1 for a single shaft up to 109±33 kt a−1 for a subregion of the USCB, which is in broad agreement with the E-PRTR reports. Three wind lidars were deployed in the larger USCB region providing ancillary information about spatial and temporal variability of wind and turbulence in the atmospheric boundary layer. Sensitivity studies show that, despite drawing from the three wind lidars, the uncertainty of the local wind dominates the uncertainty of the emission estimates, by far exceeding errors related to the XCH4 measurements themselves. Wind-related relative errors on the emission estimates typically amount to 20 %.

Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.

Abstract. This study presents the first investigation of the methane (CH4) emissions of a large festival. Munich Oktoberfest, the world's largest folk festival, is a potential source of CH4 as a large amount of natural gas for cooking and heating is used. In 2018 we measured the CH4 emissions of Oktoberfest using in situ measurements combined with a Gaussian plume dispersion model. Measurements were taken while walking and biking around the perimeter of the Oktoberfest premises (Theresienwiese) at different times of the day, during the week and at the weekend. The measurements showed enhancements of up to 100 ppb compared to background values and measurements after Oktoberfest. The average emission flux of Oktoberfest is determined as (6.7±0.6) µg (m2 s)−1. Additional analyses, including the daily emission cycle and comparisons between emissions and the number of visitors, suggest that CH4 emissions of Oktoberfest are not due solely to the human biogenic emissions. Instead, fossil fuel CH4 emissions, such as incomplete combustion or loss in the gas appliances, appear to be the major contributors to Oktoberfest emissions. Our results can help to develop CH4 reduction policies and measures to reduce emissions at festivals and other major events in cities. Furthermore, events with a limited duration have not yet been included in the state-of-the-art emission inventories, such as TNO-MACC, EDGAR or IER. Our investigations show that these emissions are not negligible. Therefore, these events should be included in future emission inventories.

<p><strong>Abstract.</strong> This study presents the first investigation of the methane (CH<sub>4</sub>) emissions of a big festival. In 2018 we measured the CH<sub>4</sub> emissions of Munich Oktoberfest, the world's largest folk festival, using in-situ measurements combined with a Gaussian plume dispersion model. Oktoberfest is a potential source for CH<sub>4</sub> as a high amount of natural gas for cooking and heating is used.</p> <p>Measurements were performed by walking and biking around the perimeter of the Oktoberfest premises (Theresienwiese) at different times of the day, during the week and at the weekend. The measurements show enhancements of up to 100 ppb compared to background values and measurements performed after Oktoberfest. The average emission flux of Oktoberfest is determined as 6.7 ± 0.6 μg/(m<sup>2</sup>s). Additional analyses, including the daily emission cycle and comparisons between emissions and the number of visitors, suggest that CH<sub>4</sub> emissions of Oktoberfest are not only due to the human biogenic emissions; it is likely that fossil fuel CH<sub>4</sub> emissions, such as incomplete combustion or loss in the gas appliances, are the major contributors to Oktoberfest emissions.</p> <p>Our results can help to develop CH<sub>4</sub> reduction policies and measures to reduce emissions at festivals and other major events in cities. Furthermore, events with a limited duration have not yet been included in the state-of-the-art emission inventories, such as TNO-MACC, EDGAR or IER. Our investigations show that these emissions are not negligible. Therefore, these events should be included in future emission inventories.</p>

Abstract. Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources of anthropogenic methane (CH4) emissions in Europe. Here, we report on CH4 emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pairwise upwind–downwind observations of the column-average dry-air mole fractions of CH4 (XCH4) by a network of four portable, ground-based, sun-viewing Fourier transform spectrometers of the type EM27/SUN operated during the CoMet campaign in May–June 2018. The EM27/SUN instruments were deployed in the four cardinal directions around the USCB approximately 50 km from the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips–Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the emissions averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the emissions averaging kernels is essential to detect correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for the southeastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 % and 36 %, dominated by the error contribution from uncertain wind fields.