Abstract. Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM 10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM 10 trends ranged between −0.09 and −1.16 µg m −3 yr −1 with urban traffic sites experiencing the greatest mean decrease in PM 10 concentrations at −0.77 µg m −3 yr −1 . Similar magnitudes have been reported for normalised PM 10 trends for earlier time periods in Switzerland which indicates PM 10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM 10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM 10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

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Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

Abstract. In March 2020, non-pharmaceutical intervention measures in the form of lockdowns were applied across Europe to urgently reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus which causes the COVID-19 disease. The aggressive curtailing of the European economy had widespread impacts on the atmospheric composition, particularly for nitrogen dioxide (NO2) and ozone (O3). To investigate these changes, we analyse data from 246 ambient air pollution monitoring sites in 102 urban areas and 34 countries in Europe between February and July 2020. Counterfactual, business-as-usual air quality time series are created using machine-learning models to account for natural weather variability. Across Europe, we estimate that NO2 concentrations were 34 % and 32 % lower than expected for respective traffic and urban background locations, whereas O3 was 30 % and 21 % higher (in the same respective environments) at the point of maximum restriction on mobility. To put the 2020 changes into context, average NO2 trends since 2010 were calculated, and the changes experienced across European urban areas in 2020 was equivalent to 7.6 years of average NO2 reduction (or concentrations which might be anticipated in 2028). Despite NO2 concentrations decreasing by approximately a third, total oxidant (Ox) changed little, suggesting that the reductions in NO2 were substituted by increases in O3. The lockdown period demonstrated that the expected future reductions in NO2 in European urban areas are likely to lead to widespread increases in urban O3 pollution unless additional mitigation measures are introduced.