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Atmospheric ozone (O 3 ) is a pollutant produced through chemical chain reactions where volatile organic compounds (VOCs), carbon monoxide and methane are oxidized in the presence of oxides of nitrogen (NO x ). For decades, the controlling chain termination step has been used to separate regions into either ‘NO x limited’ (peroxyl-radical self-reactions dominate) or ‘VOC limited’ (hydroxyl radical (OH) + nitrogen dioxide (NO 2 ) reaction dominates). The controlling regime would then guide policies for reducing emissions and so O 3 concentrations. Using a chemical transport model, we show that a third ‘aerosol inhibited’ regime exists, where reactive uptake of hydroperoxyl radicals (HO 2 ) onto aerosol particles dominates. In 1970, 2% of the Northern Hemisphere population lived in an aerosol-inhibited regime, but by 2014 this had increased to 21%; 60% more than lived in a VOC-limited regime. Aerosol-inhibited chemistry suppressed surface O 3 concentrations in North America and Europe in the 1970s and is currently suppressing surface O 3 over Asia. This third photochemical O 3 regime leads to potential trade-off tensions between reducing particle pollution in Asia (a key current health policy and priority) and increasing surface O 3 , should O 3 precursors emissions not be reduced in tandem. Global chemical transport simulations reveal an ozone photochemistry regime where the uptake of hydroperoxyl radicals onto aerosol particles dominates ozone production.

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.

Many European countries do not meet legal air quality standards for ambient nitrogen dioxide (NO 2 ) near roads; a problem that has been forecasted to persist to 2030. Although European air quality standards regulate NO 2 concentrations, emissions standards for new vehicles instead set limits for NO x —the combination of nitric oxide (NO) and NO 2 . From around 1990 onwards, the total emissions of NO x declined significantly in Europe, but roadside concentrations of NO 2 —a regulated species—declined much less than expected. This discrepancy has been attributed largely to the increasing usage of diesel vehicles in Europe and more directly emitted tailpipe NO 2 . Here we apply a data-filtering technique to 130 million hourly measurements of NO x , NO 2 and ozone (O 3 ) from roadside monitoring stations across 61 urban areas in Europe over the period 1990–2015 to estimate the continent-wide trends of directly emitted NO 2 . We find that the ratio of NO 2 to NO x emissions increased from 1995 to around 2010 but has since stabilized at a level that is substantially lower than is assumed in some key emissions inventories. The proportion of NO x now being emitted directly from road transport as NO 2 is up to a factor of two smaller than the estimates used in policy projections. We therefore conclude that there may be a faster attainment of roadside NO 2 air quality standards across Europe than is currently expected. The fraction of NO 2 in NO x emitted from European road transport is up to a factor of two smaller than used in policy projections, suggests an analysis of 130 million roadside observations. Roadside air quality standards may thus be obtained faster.

Volatile organic compounds in U.S. urban air increasingly derive from consumer products The atmospheric chemistry that leads to photochemical smog and climate-active aerosols requires the presence of volatile organic compounds (VOCs) ( 1 , 2 ). The VOCs in urban air typically derive from the prevailing energy and transport technologies as well as the use of petrochemical-derived products. On page 760 of this issue, McDonald et al. ( 3 ) report that a notable change in emissions may be underway in U.S. cities, with effects on secondary pollutants such as organic aerosols. Shifting from an urban atmosphere dominated by transport-related VOCs to one dominated by VOCs from coatings, adhesives, and consumer products would alter predictions of urban air quality and challenge the existing policy framework for emissions control.

Atmospheric non-methane hydrocarbon concentrations began declining in the 1970s. Surface and column measurements show that Northern Hemisphere ethane concentrations are now rising, probably due to North American oil and natural gas emissions. Non-methane hydrocarbons such as ethane are important precursors to tropospheric ozone and aerosols. Using data from a global surface network and atmospheric column observations we show that the steady decline in the ethane mole fraction that began in the 1970s 1 , 2 , 3 halted between 2005 and 2010 in most of the Northern Hemisphere and has since reversed. We calculate a yearly increase in ethane emissions in the Northern Hemisphere of 0.42 (±0.19) Tg yr −1 between mid-2009 and mid-2014. The largest increases in ethane and the shorter-lived propane are seen over the central and eastern USA, with a spatial distribution that suggests North American oil and natural gas development as the primary source of increasing emissions. By including other co-emitted oil and natural gas non-methane hydrocarbons, we estimate a Northern Hemisphere total non-methane hydrocarbon yearly emission increase of 1.2 (±0.8) Tg yr −1 . Atmospheric chemical transport modelling suggests that these emissions could augment summertime mean surface ozone by several nanomoles per mole near oil and natural gas production regions. Methane/ethane oil and natural gas emission ratios could suggest a significant increase in associated methane emissions; however, this increase is inconsistent with observed leak rates in production regions and changes in methane’s global isotopic ratio.

Near-continuous measurements of hydroxyl radical (OH) reactivity in the urban background atmosphere of central London during the summer of 2012 are presented. OH reactivity behaviour is seen to be broadly dependent on air mass origin, with the highest reactivity and the most pronounced diurnal profile observed when air had passed over central London to the east, prior to measurement. Averaged over the entire observation period of 26 days, OH reactivity peaked at  ∼  27 s−1 in the morning, with a minimum of  ∼  15 s−1 during the afternoon. A maximum OH reactivity of 116 s−1 was recorded on one day during morning rush hour. A detailed box model using the Master Chemical Mechanism was used to calculate OH reactivity, and was constrained with an extended measurement data set of volatile organic compounds (VOCs) derived from a gas chromatography flame ionisation detector (GC-FID) and a two-dimensional GC instrument which included heavier molecular weight (up to C12) aliphatic VOCs, oxygenated VOCs and the biogenic VOCs α-pinene and limonene. Comparison was made between observed OH reactivity and modelled OH reactivity using (i) a standard suite of VOC measurements (C2–C8 hydrocarbons and a small selection of oxygenated VOCs) and (ii) a more comprehensive inventory including species up to C12. Modelled reactivities were lower than those measured (by 33 %) when only the reactivity of the standard VOC suite was considered. The difference between measured and modelled reactivity was improved, to within 15 %, if the reactivity of the higher VOCs (⩾ C9) was also considered, with the reactivity of the biogenic compounds of α-pinene and limonene and their oxidation products almost entirely responsible for this improvement. Further improvements in the model's ability to reproduce OH reactivity (to within 6 %) could be achieved if the reactivity and degradation mechanism of unassigned two-dimensional GC peaks were estimated. Neglecting the contribution of the higher VOCs (⩾ C9) (particularly α-pinene and limonene) and model-generated intermediates increases the modelled OH concentrations by 41 %, and the magnitude of in situ ozone production calculated from the production of RO2 was significantly lower (60 %). This work highlights that any future ozone abatement strategies should consider the role that biogenic emissions play alongside anthropogenic emissions in influencing London's air quality.

Dirty outdoor air might grab the headlines, but learning how pollutants inside buildings form, accumulate and affect our health is equally crucial. Dirty outdoor air might grab the headlines, but learning how pollutants inside buildings form, accumulate and affect our health is equally crucial.

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.

We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.