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 PM₁₀ 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 PM₁₀ trends ranged between −0.09 and −1.16 µg m⁻³ yr⁻¹ with urban traffic sites experiencing the greatest mean decrease in PM₁₀ concentrations at −0.77 µg m⁻³ yr⁻¹. Similar magnitudes have been reported for normalised PM₁₀ trends for earlier time periods in Switzerland which indicates PM₁₀ 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 PM₁₀ concentrations. Notably, two regimes were suggested by the models which cause elevated PM₁₀ 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.

Interventions used to improve air quality are often difficult to detect in air quality time series due to the complex nature of the atmosphere. Meteorological normalisation is a technique which controls for meteorology/weather over time in an air quality time series so intervention exploration (and trend analysis) can be assessed in a robust way. A meteorological normalisation technique, based on the random forest machine learning algorithm was applied to routinely collected observations from two locations where known interventions were imposed on transportation activities which were expected to change ambient pollutant concentrations. The application of progressively stringent limits on the content of sulfur in marine fuels was very clearly represented in ambient sulfur dioxide (SO2) monitoring data in Dover, a port city in the South East of England. When the technique was applied to the oxides of nitrogen (NOx and NO2) time series at London Marylebone Road (a Central London monitoring site located in a complex urban environment), the normalised time series highlighted clear changes in NO2 and NOx which were linked to changes in primary (directly emitted) NO2 emissions at the location. The clear features in the time series were illuminated by the meteorological normalisation procedure and were not observable in the raw concentration data alone. The lack of a need for specialised inputs, and the efficient handling of collinearity and interaction effects makes the technique flexible and suitable for a range of potential applications for air quality intervention exploration.

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 (O_x) 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.

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.

Diesel-powered road vehicles are important sources for nitrogen oxide (NO_x) emissions and the European passenger fleet is highly dieselised, which has resulted in many European roadside environments being non-compliant with legal air quality standards for nitrogen dioxide (NO₂). Based on vehicle emission remote sensing data for 300 000 light-duty vehicles across the United Kingdom, light-duty diesel NO_x emissions were found to be highly dependent on ambient temperature with low temperatures resulting in higher NO_x emissions, i.e., a "low temperature NO_x emission penalty" was identified. This feature was not observed for gasoline-powered vehicles. Older Euro 3 to 5 diesel vehicles emitted NO_x similarly, but vehicles compliant with the latest Euro 6 emission standard, emitted less NO_x than older vehicles and demonstrated less of an ambient temperature dependence. This ambient temperature dependence is overlooked in current emission inventories, but is of importance from an air quality perspective. Owing to Europe's climate, a predicted average of 38 % more NO_x emissions have burdened Europe when compared to temperatures encountered in laboratory test cycles. However, owing to the progressive elimination of vehicles demonstrating the most severe low temperature NO_x penalty, light-duty diesel NO_x emissions are likely to decrease more rapidly throughout Europe than currently thought.

We present a full-year online global simulation of tropospheric chemistry (158 coupled species) at cubed-sphere c720 ( ∼ 12.5×12.5 km²) resolution in the NASA Goddard Earth Observing System Model version 5 Earth system model (GEOS-5 ESM) with GEOS-Chem as a chemical module (G5NR-chem). The GEOS-Chem module within GEOS uses the exact same code as the offline GEOS-Chem chemical transport model (CTM) developed by a large atmospheric chemistry research community. In this way, continual updates to the GEOS-Chem CTM by that community can be seamlessly passed on to the GEOS chemical module, which remains state of the science and referenceable to the latest version of GEOS-Chem. The 1-year G5NR-chem simulation was conducted to serve as the Nature Run for observing system simulation experiments (OSSEs) in support of the future geostationary satellite constellation for tropospheric chemistry. It required 31 wall-time days on 4707 compute cores with only 24 % of the time spent on the GEOS-Chem chemical module. Results from the GEOS-5 Nature Run with GEOS-Chem chemistry were shown to be consistent to the offline GEOS-Chem CTM and were further compared to global and regional observations. The simulation shows no significant global bias for tropospheric ozone relative to the Ozone Monitoring Instrument (OMI) satellite and is highly correlated with observations spatially and seasonally. It successfully captures the ozone vertical distributions measured by ozonesondes over different regions of the world, as well as observations for ozone and its precursors from the August–September 2013 Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC⁴RS) aircraft campaign over the southeast US. It systematically overestimates surface ozone concentrations by 10 ppbv at sites in the US and Europe, a problem currently being addressed by the GEOS-Chem CTM community and from which the GEOS ESM will benefit through the seamless update of the online code.