3.1 Concentration mapping The measured CH 4 concentrations were plotted for each round on a map of the Oktoberfest premises to show that there is a clear correlation between the wind directions and the enhancements. As the variations in the boundary layer height should not be taken into account, these plots do not show the absolute concentration values but just the enhancements above the determined background concentrations (see Sect. 2.2.2). Two such plots for two different wind directions are shown in Fig. 7. In addition to the concentration enhancements and the wind direction, the 16 emission sources are shown as black dots on top of each tent. The Gaussian plumes themselves are also represented. These two plots reveal that the highest concentration enhancements can be observed downwind of the Oktoberfest premises.

3.2 Emission number The average emission of the Oktoberfest 2018 E Okt,avg is determined by averaging the emission numbers of the N plume signals E Okt,i during the complete Oktoberfest time period (including the weekdays and weekends), accordingly: (6) E Okt , avg = 1 N ∑ i = 1 N E Okt , i . To make the final emission number more robust and to determine an uncertainty, the basic approach of Eq. (6) was improved. Instead of just using the actual measured data, an uncertainty range was applied to the four main input parameters, each using Gaussian distributions (see Sect. 2.2.6). For every plume signal i, 1000 samples of randomly chosen input datasets from the aforementioned normal distributions of the input parameters were used to determine 1000 slightly different emission numbers E Okt , i , k . Using Eq. (6), an average emission number for each realization E Okt , avg , k was calculated: (7) E Okt , avg , k = 1 N ∑ i = 1 N E Okt , i , k . The average emission number including an uncertainty assessment can be obtained by determining the mean μ Okt and standard deviation σ Okt of those 1000 realizations: (8) μ Okt = 1 1000 ∑ k = 1 1000 E Okt , avg , k , (9) σ Okt = ∑ k = 1 1000 ( E Okt , avg , k - μ Okt ) 2 999 . The result for the total emission number of Oktoberfest 2018 is shown in Fig. 8 and has a value of (10) E Okt , total = μ Okt ± σ Okt = ( 6.7 ± 0.6 ) µ g ( m 2 s ) - 1 . To verify whether those emissions were caused by Oktoberfest, Fig. 8 also shows the emissions determined for the time after Oktoberfest (from 8 October through 25 October). This number (1.1±0.3) µg (m2 s)−1 is significantly smaller than the one during Oktoberfest but still not zero. It indicates that the emissions are caused by Oktoberfest, and the disassembling of all the facilities, which takes several weeks, still produces emissions after Oktoberfest. After grouping the emission numbers into the two categories, weekday (in total 47 valid plumes) and weekend (27 valid plumes), two separated distributions are visible in Fig. 9. The average emission for the weekend (8.5±0.7) µg (m2 s)−1 is higher than the averaged emission for the weekdays (4.6±0.9) µg (m2 s)−1, almost by a factor of 2. To interpret this result, the visitor trend of Oktoberfest was investigated. This trend is based on the officially estimated numbers of visitors (muenchen.de, 2018) and was linearly interpolated (see Fig. 10). Besides the daily trend, it also shows the mean values of the weekdays and weekend days (dotted lines). As the number of visitors at Oktoberfest was also significantly higher on a weekend day than on a weekday (approximately a factor of 2; see Fig. 10), a higher number of visitors results in higher emissions, which indicates the CH 4 emissions are anthropogenic. Download Download Download

3.3 Daily emission cycle To assess the daily cycle of the CH 4 emissions, the emission numbers of the plume signals E Okt , i , k are grouped into hourly bins. Then, for each bin an average emission E Okt , hour , k is calculated. Afterwards, these numbers are averaged for the 1000 realizations to obtain robust results including an uncertainty estimate: (11) μ Okt , hour = 1 1000 ∑ k = 1 1000 E Okt , hour , k , (12) σ Okt , hour = ∑ k = 1 1000 ( E Okt , hour , k - μ Okt , hour ) 2 999 . In Fig. 11, the variation in the hourly emission mean μ Okt,hour is shown as a blue line. The grey shaded area shows the uncertainty σ Okt,hour of the emission numbers within that hour. The daily emission cycle shows an oscillating behavior overlaid on an increasing trend towards the evening. The linear increasing trend is in agreement with Fig. 10, which shows a linearly increasing visitor amount throughout the day, confirming the anthropogenic nature of the emissions. The oscillating behavior indicates that the emissions are related to time-dependent events, such as cooking, heating, and cleaning, which tend to show peaks in the morning, noon, and evening. Download

3.4 Biogenic human CH 4 emissions To address the question of whether the people themselves caused the emissions or whether the emissions were caused by processes related to the number of visitors, such as cooking, heating, sewage, etc., we took a closer look at human biogenic emissions. Most of the previous studies define a methane producer as a person that has a breath CH 4 mixing ratio at least 1 ppm above ambient air (Polag and Keppler, 2019). Keppler et al. (2016), however, used laser absorption spectroscopy to confirm that all humans exhale CH 4 . In that study, the mean of the breath CH 4 enhancements above the background from 112 test persons between 1 and 80 years of age is 2316 ppb and the values vary from 26 ppb to 40.9 ppm. In addition, we have considered the values reported in Polag and Keppler (2019). The authors provided a summary of various studies of human CH 4 emissions in Table 1 and Sect. 3.2, and we used these results to calculate average human CH 4 emissions, which are 2.3 mmol d−1 via breath and 7 mmol d−1 via flatus. We multiplied these values with the 300 000 persons that visit the Oktoberfest premises ( ≈ 3.45 × 10 5 m 2 ) every day. This represents an upper limit of people who are at the Oktoberfest at the same time, as most visitors do not stay all day long. Please note the average emission numbers are not factor weighted by ethnicity, age, and sex, because we do not have those statistics for Oktoberfest. The expected CH 4 emission from the human breath and flatulence in total was calculated as (13) E human = ( 2.3 mmol d - 1 + 7 mmol d - 1 ) ⋅ 3 × 10 5 ⋅ 16 g mol - 1 24 ⋅ 3600 s d - 1 ⋅ 3.45 × 10 5 m 2 = 1.5 µ g ( m 2 s ) - 1 . Although we assumed the maximum possible number of visitors, the resulting biogenic component is 22 % of the emissions we determined for Oktoberfest. Therefore, the emissions are not solely produced by the humans themselves, but by processes that are related to the number of visitors.

3.5 Emissions from sewage Besides the direct biogenic human emissions, CH 4 emissions from sewer systems are also possible sources. These emissions are a product of bacterial metabolism within waste water, whose emission strength depends in particular on the hydraulic retention time (Liu et al., 2015; Guisasola et al., 2008) which represents the time the waste water stays in the system. This time decreases with a higher amount of waste water, as the flow increases in such a case. At Oktoberfest, the amount of waste water is very high as the 107 million L of water and the 8 million L of beer consumed have to flow into the sewer system at some time (München, 2018a). Therefore, the retention time in the sewer system underneath the Theresienwiese is quite low, which makes high CH 4 emissions from sewage unlikely. Furthermore, the waste water consists primarily of dirty water and urine but not feces, which contain many carbon compounds necessary to produce CH 4 .