Introduction

Europe was identified as an epicentre of the COVID-19 pandemic in early March, 2020.[1] In response to widespread community spread and to urgently reduce infection rates of SARS-CoV-2, the virus which causes COVID-19 in the population, many European governments imposed strict “lockdown” measures in early or mid-March, 2020. These lockdown measures were unprecedented and compelled (or forced) most of the population to stay in their homes, work from home if possible, and businesses such as restaurants, shopping malls, schools, universities, playgrounds, etc. were closed. Essential services remained open and generally included commercial activities around food, medication, transportation, and utilities.

The Swiss Federal Council announced a country-wide lockdown on March 16, 2020 which would begin on March 17 and provisionally end on April 20 (but was subsequently extended to April 27).[2] The Swiss lockdown was somewhat more lenient than those seen in other European countries where leisure and recreational activities were more strictly controlled. Due to Switzerland’s federalist structure, there were also some differences among the different cantons. For example, the southern canton of Ticino boarding Italy introduced many measures earlier than the rest of the country. This was because the area experienced an earlier increase in infections due to the proximity to the northern Italian outbreak.

The lockdowns experienced in Europe effectively acted as a switch where economic activity all but stopped within a very short amount of time. The financial and economic consequences of such measures will likely be dire.[3] A small positive which comes from such actions is that air quality should have improved rather quickly due to emissions of atmospheric pollutants being dramatically reduced across almost all of Europe.[4] There have been many news articles showing this effect, and the satellite imagery over China and Europe have been particularly striking.[5–10] CO2 emissions have also reduced.[11] Here, we will explore the air quality situation in Switzerland with ambient monitoring data from the National Air Pollution Monitoring Network (NABEL).

The importance of weather

A discussion which has been lacking in most of the news articles, and is very important to consider, is the influence of weather when investigating changes in air pollutant concentrations.[12] It is obvious that emissions of pollutants would have reduced throughout Europe due the the COVID-19 lockdown measures from curtailed economic activities.[13] This would have resulted in lower concentrations and better air quality in many locations. However, the same effect of decreasing concentrations would be observed if, for example, wind speed and atmospheric dispersion had increased. This is a classic issue and question in air quality interpretation: “Are the changes observed due to emissions or weather?” Here, we take into account changes in weather so we can more robustly determine what effect the lockdown had on air quality. A similar analysis has been conducted in the United Kingdom by David Carslaw, a previous supervisor and a colleague.[14, 15]

All data and analyses are preliminary at this stage and research is ongoing.

Analysis framework

Hourly nitrogen dioxide (NO2), oxides of nitrogen (NOx), and ozone (O3) data from many NABEL ambient monitoring sites across Switzerland were used.[16] Basic details of the sites can be found in Table 1 and the locations of the sites are shown in Figure 1. Not all NABEL sites were included in the analysis. Sites were selected based on what pollutants are monitored and their site type (classification of what type of environment the sites are located in, for example, roadside or rural). The sites’ observations were used to train random forest machine learning models[17] to predict pollutant concentrations based on meteorological/weather measurements. The random forest models also used time variables as inputs because pollutants vary over different time scales, for example, by season and throughout the day. Complete and thorough details of the method can be found elsewhere.[18, 19] The approach has been used in Switzerland before, but for a different application.[20]

Table 1: Information about the NABEL Swiss air quality monitoring sites.
Site Site (NABEL code) Site name Lat. Long. Elevation (m) Site type Site area
ch0001g JUN Jungfraujoch 46.548 7.985 3578 High alpine Rural
ch0002r PAY Payerne 46.813 6.944 489 Rural background Rural
ch0003r TAE Tänikon 47.480 8.905 538 Rural background Rural
ch0004r CHA Chaumont 47.050 6.979 1136 Rural elevated Rural
ch0005a DUE Dübendorf-Empa 47.403 8.613 432 Suburban Suburban
ch0005r RIG Rigi-Seebodenalp 47.067 8.463 1031 Rural elevated Rural
ch0008a BAS Basel-Binningen 47.541 7.583 316 Suburban Suburban
ch0010a ZUE Zürich-Kaserne 47.378 8.530 409 Urban background Urban
ch0011a LUG Lugano-Università 46.011 8.957 280 Urban background Urban
ch0028a LAU Lausanne-César-Roux 46.522 6.640 530 Urban roadside Urban
ch0031a BER Bern-Bollwerk 46.951 7.441 536 Urban roadside Urban
ch0032a HAE Härkingen-A1 47.312 7.821 431 Rural motorway Rural
ch0033a MAG Magadino-Cadenazzo 46.160 8.934 203 Rural background Rural
ch0052a SIO Sion-Aéroport-A9 46.220 7.342 483 Rural motorway Rural
ch2000e BER Beromünster 47.190 8.175 797 Rural background Rural
ch2001e DAV Davos-Seehornwald 46.802 9.839 1637 Rural elevated Rural

Figure 1: Locations of Switzerland’s NABEL air quality monitoring sites.

Data between January 1, 2018 and February 29, 2020 were used for training of the random forest models. Data from March 1, 2020 onwards were not used for model training, however, the meteorological data were used to predict pollutant concentrations. These predictions after March 1 can be thought of as a “business as usual” scenario and gives an estimate on what pollutant concentrations would have been without the hypothesised decrease in emissions resulting from reduced economic activity due to forced reductions in mobility. Predictions can be compared with the observed values to see how the measurements differ. The models used are regression models so they generalise and are unable to capture minima and maxima of concentrations of some pollutants particularly well. However, if we look at errors over time, and if the business as usual scenario is true, the errors will bounce around zero. In contrast, if the system changes, the errors will diverge from zero. This is the basis of the analysis.

Results and discussion

First checks

Time series of pollutant concentrations at selected monitoring sites for between March 1 and July 31, 2020 are shown in Figure 2. Generally, it seems that concentrations of NO2, NOx, and O3 did not drastically change after the lockdown was implemented. There are however examples, such as NO2 and NOx at Lugano-Università, where concentrations have visibly decreased after the lockdown, indicating that concentrations and lockdown measures are connected. Therefore, additional, more in-depth exploration is needed to help explain the patterns seen in Figure 2.

Time series of rolling weekly means of daily NO~2~, NO~x~, and O~3~ between March 1 and July 31, 2020 for selected of monitoring sites in Switzerland.

Figure 2: Time series of rolling weekly means of daily NO2, NOx, and O3 between March 1 and July 31, 2020 for selected of monitoring sites in Switzerland.

When starting an analysis such as this, it is important to consider what the general state-of-play of weather conditions have been for the analysis period. The weather in the first five months of 2020 was mild in most of Switzerland.

In Figure 3, it can been seen that average air temperatures in the first seven months of 2020 have been higher in many locations plotted compared to the last few years. In some locations, such as Basel and Zürich, wind speed has also been unusually high. The warmer temperatures and higher wind speeds have likely led to a less stable planetary boundary layer (the lowest portion of the atmosphere). Although this may not have been universal across all of Switzerland, the dispersion characteristics of the atmosphere have likely been enhanced in the January–July 2020 period compared to previous years in many locations. These observations suggest that emissions of pollutants may have been lower than the last previous years, and in some locations there would have been enhanced dispersion.

Means of meteorological variables for a number of locations across Switzerland between January and July for 2017, 2018, 2019, and 2020.

Figure 3: Means of meteorological variables for a number of locations across Switzerland between January and July for 2017, 2018, 2019, and 2020.

Mean pollutant concentrations for the same periods (between January and July for 2017, 2018, 2019, and 2020) are displayed in Figure 4. Here, we can see for NO2 and NOx, that concentrations are generally lower in 2020 than in previous years. In some locations such as Bern-Bollwerk, Härkingen-A1, and Payerne the mean NO2 and NOx concentrations in 2020 are much lower than in previous years. However, for O3, this is not the case.

Pollutant concentration means for selected monitoring sites in Switzerland between January and June for 2017, 2018, 2019, and 2020.

Figure 4: Pollutant concentration means for selected monitoring sites in Switzerland between January and June for 2017, 2018, 2019, and 2020.

The aggregations in 2020 shown in Figure 4 include a few months of lockdown measures, so this question can be asked: "Are the lockdown measures responsible for the observed decreases?’’ Based on the meteorological data shown in Figure 3, it is expected that concentrations may have been lower due to a mild winter and increased atmospheric dispersion in many locations in Switzerland. Therefore, further work is required to disentangle the influence of the lockdown measures and the potentially confounding meteorological factors.

Modelling pollutant concentrations between March 1 and July 31, 2020

After the random forest models were trained, checked for adequate skill, and used to predict concentrations of NO2, NOx, and O3 between March 1 and July 31, 2020, the observed concentrations were compared to those which were predicted. Figure 5 shows daily time series of observed and predicted concentrations for between March and June for the sites included in the analysis. For some sites, such as Bern-Bollwerk and Lugano-Università, a clear divergence is seen between the NO2 observed and predicted values after the March 17 lockdown date. The observed concentrations were lower than the predicted values suggesting that actual NO2 concentrations were lower than those based on a business as usual scenario. The differences (delta) between the observed and predicted values are also shown in Figure 6.

Observed and predicted NO~2~, NO~x~, and O~3~ concentrations (as weekly rolling means) between March 1 and July 31, 2020 for selected Switzerland's monitoring sites.

Figure 5: Observed and predicted NO2, NOx, and O3 concentrations (as weekly rolling means) between March 1 and July 31, 2020 for selected Switzerland’s monitoring sites.

Delta of observed and predicted NO~2~, NO~x~, and O~3~ concentrations between March 1 and July 31, 2020 for the selected Switzerland's monitoring sites

Figure 6: Delta of observed and predicted NO2, NOx, and O3 concentrations between March 1 and July 31, 2020 for the selected Switzerland’s monitoring sites

To more clearly expose the differences between the observed and predicted values, cumulative sums (cumsum) were calculated for the selected sites’ pollutants. This technique simply aggregates the deltas/differences over time and allows for identification of both the direction and time when the divergence occurred. Cumulative sums of NO2, NOx, and O3 for the selected sites are displayed in Figure 7.

Cumulative sum for observed and predicted NO~2~, NO~x~, and O~3~ values between March 1 and July 31, 2020 for selected Switzerland's monitoring sites.

Figure 7: Cumulative sum for observed and predicted NO2, NOx, and O3 values between March 1 and July 31, 2020 for selected Switzerland’s monitoring sites.

Figure 7 is a key figure in many ways because the changes shown are relative to the business as usual scenario. First, it shows that NO2 and NOx have decreased across most of Switzerland, but for the rural sites (Beromünster and Payerne), this decrease has been much less dramatic. For Bern-Bollwerk, Lausanne-César-Roux, and Härkingen-A1 (all traffic sites), the divergence from the business as usual scenario occurs immediately at the start of March which suggests that concentrations of NO2 and NOx were lower than what the model predicts two weeks before the lockdown was announced. This could be explained by NOx emissions already being lower than normal three weeks before the lockdown, or limited skill of the model from using the surface meteorological variables for training – or perhaps a combination of both. These features in Figure 7 link back to the interpretation of the simple aggregations shown in Figure 3 and Figure 4. Because these three sites are classed as traffic sites (Table 1), the decreases can be interpreted as lower emissions of these pollutants in close proximity to the monitoring sites.

When the sites were aggregated to their site classifications, all site types showed lower NO2 and NOx concentrations than expected three weeks before the lockdown measures were implemented (Figure 8). Figure 8 gives further evidence that economic slowdown and subsequent reduction of emissions was occurring three weeks before the national lockdown on March 17.

Mean weekly predicted-observed deltas for the three site types for Switzerland's selected monitoring sites between February and June, 2020.

Figure 8: Mean weekly predicted-observed deltas for the three site types for Switzerland’s selected monitoring sites between February and June, 2020.

Figure 7 and Figure 8 suggests that O3 concentrations have been higher between March and June, 2020 than the business as usual scenario. O3 is not a primary (directly emitted) pollutant and is generated in the atmosphere. O3 and NOx are linked by a transformation cycle where a component of NOx is transformed to O3 in the presence of ultra-violet light. Because of this relationship, if NOx concentrations decrease, O3 concentrations increase which is what is observed in Figure 7 and Figure 8.

Looking at the differences after March 17

For the period after March 17, 2020, the differences between the observed and predicted values can be aggregated and transformed into percentage change. Table 2 shows these calculations and a plot of the observed and predicted means are displayed in Figure 9. Table 2 demonstrates that NO2 and NOx concentrations have decreased by up to 44 and 58 % respectively (at Lugano-Università) based on the scenario modelling technique. Care is needed with interpreting these results however because the uncertainty calculations have not been conducted yet. Additionally, Figure 7 demonstrates that some models showed immediate departure from the observed time series when used to predict at the start of March, 2020. This may led to an overestimation of the changes seen in Table 2.

Inversely, O3 concentrations have increased in all locations across Switzerland in response to lower NOx which is to be expected due to the chemical relationship between NOx and O3 within the troposphere (lower atmosphere). A figure visualising the percentage changes is available in Figure 10. Please note that the percentage change calculations are relative to one another and need to be interpreted alongside the concentrations (these data are in Table 2 and Figure 9).

Table 2: Observed and predicted concentration means, deltas, and percentage change for the period after March 17, 2020 for Switzerland’s selected monitoring sites.
Site name Variable Observed Predicted Observed-predicted delta Predicted-observed percentage change
Lugano-Università NO2 12.6 22.4 -9.8 -43.8
Magadino NO2 8.3 13.2 -5.0 -37.5
Bern-Bollwerk NO2 21.8 31.8 -10.1 -31.7
Zürich-Kaserne NO2 14.6 20.9 -6.2 -29.9
Härkingen-A1 NO2 20.1 27.9 -7.8 -27.8
Lausanne-César NO2 22.7 31.2 -8.6 -27.4
Basel-Binningen NO2 10.2 13.4 -3.3 -24.3
Dübendorf-Empa NO2 14.6 18.7 -4.0 -21.7
Payerne NO2 7.3 9.2 -2.0 -21.2
Beromünster NO2 5.5 5.9 -0.4 -7.2
Lugano-Università NOx 13.9 33.1 -19.2 -58.0
Magadino NOx 10.2 19.7 -9.5 -48.4
Bern-Bollwerk NOx 35.2 61.2 -26.0 -42.5
Zürich-Kaserne NOx 16.9 28.1 -11.2 -39.7
Dübendorf-Empa NOx 17.6 28.8 -11.1 -38.7
Lausanne-César NOx 33.6 52.2 -18.6 -35.6
Härkingen-A1 NOx 34.8 53.1 -18.2 -34.4
Basel-Binningen NOx 11.8 16.4 -4.5 -27.7
Payerne NOx 8.2 11.3 -3.1 -27.4
Beromünster NOx 5.9 6.5 -0.6 -8.7
Payerne O3 67.7 67.5 0.2 0.2
Dübendorf-Empa O3 64.0 63.4 0.6 1.0
Basel-Binningen O3 70.8 68.9 1.8 2.6
Zürich-Kaserne O3 70.2 66.8 3.4 5.2
Härkingen-A1 O3 58.8 55.5 3.3 5.9
Beromünster O3 84.8 79.4 5.5 6.9
Magadino O3 69.1 63.5 5.7 8.9
Lausanne-César O3 65.5 60.1 5.4 9.1
Bern-Bollwerk O3 58.6 51.2 7.4 14.4
Lugano-Università O3 80.6 66.5 14.0 21.1
Observed and predicted concentration means for the period after March 17, 2020 for Switzerland's selected monitoring sites.

Figure 9: Observed and predicted concentration means for the period after March 17, 2020 for Switzerland’s selected monitoring sites.

Predicted and observed percentage change for the period after March 17, 2020 for Switzerland's selected monitoring sites. Sites are ordered by percentage change. Note that the percentage change is a relative change and care is needed in the interpretation.

Figure 10: Predicted and observed percentage change for the period after March 17, 2020 for Switzerland’s selected monitoring sites. Sites are ordered by percentage change. Note that the percentage change is a relative change and care is needed in the interpretation.

Final notes

A classic question in air quality data analysis is if changes in pollutant concentrations are caused by reduction of emissions or changes in weather. To robustly investigate the effect of the COVID-19 lockdown measures in Switzerland on air quality, a scenario modelling approach was applied using machine learning models. This approach allows for an estimate of what pollutant concentrations would have been if lockdown measures were not applied in March, 2020. These estimates can be compared to what was observed and the differences explored.

The results indicated that NO2 and NOx concentrations have decreased in most locations in Switzerland by up to 44 and 58 % respectively due to the lockdown measures. Care is needed with interpreting these results because of the relative nature of these calculations and the lack of uncertainty calculations at this stage. O3 on the other hand has generally increased across Switzerland due to the inverse relationship this pollutant has with NOx. These results outline the complexity of the atmospheric pollutant climate with different sources and the links among different species. This is something which has been stated in some news articles but has rarely been quantified.[12]

Please note that the observations used and the data analysis are preliminary at this stage and research is on going. The results will be enhanced and revised in the near future.

References

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16. Federal Office for the Environment. (2017). National air pollution monitoring network (NABEL). Retrieved from https://www.bafu.admin.ch/bafu/en/home/topics/air/state/data/national-air-pollution-monitoring-network--nabel-.html

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18. Grange, S. K., & Carslaw, D. C. (2019). Using meteorological normalisation to detect interventions in air quality time series. Science of the Total Environment, 653, 578–588. Retrieved from http://www.sciencedirect.com/science/article/pii/S004896971834244X

19. Grange, S. K. (2018). rmweather: Tools to Conduct Meteorological Normalisation on Air Quality Data. Retrieved from https://CRAN.R-project.org/package=rmweather

20. Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., & Hueglin, C. (2018). Random forest meteorological normalisation models for Swiss PM\(_{10}\) trend analysis. Atmospheric Chemistry and Physics, 18(9), 6223–6239. doi:https://doi.org/10.5194/acp-18-6223-2018