The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO
, O
, and PM
concentrations arising from these emission changes in 11 ...cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO
concentrations and increases in O
were observed in almost all cities. However, the decline in NO
concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O
increased by 2 to 30% (except for London), the total gaseous oxidant (O
= NO
+ O
) showed limited change, and PM
concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.
London, like many major cities, has a noted air pollution problem, and a better understanding of the sources of airborne particles in the different size fractions will facilitate the implementation ...and effectiveness of control strategies to reduce air pollution. Thus, the trace elemental composition of the fine and coarse fraction were analysed at hourly time resolution at urban background (North Kensington, NK) and roadside (Marylebone Road, MR) sites within central London. Unlike previous work, the current study focuses on measurements during the summer providing a snapshot of contributing sources, utilising the high time resolution to improve source identification. Roadside enrichment was observed for a large number of elements associated with traffic emissions (Al, S, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Rb and Zr), while those elements that are typically from more regional sources (e.g. Na, Cl, S and K) were not found to have an appreciable increment. Positive Matrix Factorization (PMF) was applied for the source apportionment of the particle mass at both sites with similar sources being identified, including sea salt, airborne soil, traffic emissions, secondary inorganic aerosols and a Zn-Pb source. In the fine fraction, traffic emissions was the largest contributing source at MR (31.9%), whereas it was incorporated within an “urban background” source at NK, which had contributions from wood smoke, vehicle emissions and secondary particles. Regional sources were the major contributors to the coarse fraction at both sites. Secondary inorganic aerosols (which contained influences from shipping emissions and coal combustion) source factors accounted for around 33% of the PM10 at NK and were found to have the highest contributions from regional sources, including from the European mainland. Exhaust and non-exhaust sources both contribute appreciably to PM10 levels at the MR site, highlighting the continuing importance of vehicle-related air pollutants at roadside.
•Hourly elemental composition of fine and coarse fraction measured during summer.•Improved source apportionment by PMF compared to previous work in London.•Sources included marine, soil, traffic, secondary inorganic, construction, Zn-Pb.•Regional sources were major contributors to the coarse fraction at both sites.•Shipping and industrial emissions have significant impact upon secondary aerosol.
Trace element composition of airborne particles in London reveals major contributions from regional (including shipping and industry) and local sources (road traffic and construction).
Reduced visibility is an indicator of poor air quality. Moreover, degradation in visibility can be hazardous to human safety; for example, low visibility can lead to road, rail, sea and air ...accidents. In this paper, we explore the combined influence of atmospheric aerosol particle and gas characteristics, and meteorology, on long-term visibility. We use visibility data from eight meteorological stations, situated in the UK, which have been running since the 1950s. The site locations include urban, rural and marine environments. Most stations show a long-term trend of increasing visibility, which is indicative of reductions in air pollution, especially in urban areas. Additionally, the visibility at all sites shows a very clear dependence on relative humidity, indicating the importance of aerosol hygroscopicity on the ability of aerosol particles to scatter radiation. The dependence of visibility on other meteorological parameters, such as wind speed and wind direction, is also investigated. Most stations show long-term increases in temperature which can be ascribed to climate change, land-use changes (e.g. urban heat island effects) or a combination of both; the observed effect is greatest in urban areas. The impact of this temperature change upon local relative humidity is discussed. To explain the long-term visibility trends and their dependence on meteorological conditions, the measured data were fitted to a newly developed light-extinction model to generate predictions of historic aerosol and gas scattering and absorbing properties. In general, an excellent fit was achieved between measured and modelled visibility for all eight sites. The model incorporates parameterizations of aerosol hygroscopicity, particle concentration, particle scattering, and particle and gas absorption. This new model should be applicable and is easily transferrable to other data sets worldwide. Hence, historical visibility data can be used to assess trends in aerosol particle properties. This approach may help constrain global model simulations which attempt to generate aerosol fields for time periods when observational data are scarce or non-existent. Both the measured visibility and the modelled aerosol properties reported in this paper highlight the success of the UK's Clean Air Act, which was passed in 1956, in cleaning the atmosphere of visibility-reducing pollutants.
China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is ...important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O3 showed significant declines from 2013 to 2020, with PM2.5 annual decline rates of 6–9% in most cities. In contrast, O3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O3 to NO2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.
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•We used a random forest method to remove meteorological effect for air quality data.•The deweathered results show smoothed but similar variation in annual trends.•The meteorology can play positive or negative role depending on specific conditions.•Beijing shows positive meteorology in reducing pollutants for two clean air actions.
We present 2 years of NOx observations from the Cape Verde Atmospheric Observatory located in the tropical Atlantic boundary layer. We find that NOx mixing ratios peak around solar noon (at ...20–30 pptV depending on season), which is counter to box model simulations that show a midday minimum due to OH conversion of NO2 to HNO3. Production of NOx via decomposition of organic nitrogen species and the photolysis of HNO3 appear insufficient to provide the observed noontime maximum. A rapid photolysis of nitrate aerosol to produce HONO and NO2, however, is able to simulate the observed diurnal cycle. This would make it the dominant source of NOx at this remote marine boundary layer site, overturning the previous paradigm according to which the transport of organic nitrogen species, such as PAN, is the dominant source. We show that observed mixing ratios (November–December 2015) of HONO at Cape Verde (∼ 3.5 pptV peak at solar noon) are consistent with this route for NOx production. Reactions between the nitrate radical and halogen hydroxides which have been postulated in the literature appear to improve the box model simulation of NOx. This rapid conversion of aerosol phase nitrate to NOx changes our perspective of the NOx cycling chemistry in the tropical marine boundary layer, suggesting a more chemically complex environment than previously thought.
Measurements of OH, HO2, complex RO2 (alkene- and aromatic-related RO2) and total RO2 radicals taken during the integrated Study of AIR Pollution PROcesses in Beijing (AIRPRO) campaign in central ...Beijing in the summer of 2017, alongside observations of OH reactivity, are presented. The concentrations of radicals were elevated, with OH reaching up to 2.8×107moleculecm-3, HO2 peaking at 1×109moleculecm-3 and the total RO2 concentration reaching 5.5×109moleculecm-3. OH reactivity (k(OH)) peaked at 89 s-1 during the night, with a minimum during the afternoon of ≈22s-1 on average. An experimental budget analysis, in which the rates of production and destruction of the radicals are compared, highlighted that although the sources and sinks of OH were balanced under high NO concentrations, the OH sinks exceeded the known sources (by 15 ppbvh-1) under the very low NO conditions (<0.5ppbv) experienced in the afternoons, demonstrating a missing OH source consistent with previous studies under high volatile organic compound (VOC) emissions and low NO loadings. Under the highest NO mixing ratios (104 ppbv), the HO2 production rate exceeded the rate of destruction by ≈50ppbvh-1, whilst the rate of destruction of total RO2 exceeded the production by the same rate, indicating that the net propagation rate of RO2 to HO2 may be substantially slower than assumed. If just 10 % of the RO2 radicals propagate to HO2 upon reaction with NO, the HO2 and RO2 budgets could be closed at high NO, but at low NO this lower RO2 to HO2 propagation rate revealed a missing RO2 sink that was similar in magnitude to the missing OH source. A detailed box model that incorporated the latest Master Chemical Mechanism (MCM3.3.1) reproduced the observed OH concentrations well but over-predicted the observed HO2 under low concentrations of NO (<1ppbv) and under-predicted RO2 (both the complex RO2 fraction and other RO2 types which we classify as simple RO2) most significantly at the highest NO concentrations. The model also under-predicted the observed k(OH) consistently by ≈10s-1 across all NOx levels, highlighting that the good agreement for OH was fortuitous due to a cancellation of missing OH source and sink terms in its budget. Including heterogeneous loss of HO2 to aerosol surfaces did reduce the modelled HO2 concentrations in line with the observations but only at NO mixing ratios <0.3ppbv. The inclusion of Cl atoms, formed from the photolysis of nitryl chloride, enhanced the modelled RO2 concentration on several mornings when the Cl atom concentration was calculated to exceed 1×104atomscm-3 and could reconcile the modelled and measured RO2 concentrations at these times. However, on other mornings, when the Cl atom concentration was lower, large under-predictions in total RO2 remained. Furthermore, the inclusion of Cl atom chemistry did not enhance the modelled RO2 beyond the first few hours after sunrise and so was unable to resolve the modelled under-prediction in RO2 observed at other times of the day. Model scenarios, in which missing VOC reactivity was included as an additional reaction that converted OH to RO2, highlighted that the modelled OH, HO2 and RO2 concentrations were sensitive to the choice of RO2 product. The level of modelled to measured agreement for HO2 and RO2 (both complex and simple) could be improved if the missing OH reactivity formed a larger RO2 species that was able to undergo reaction with NO, followed by isomerisation reactions reforming other RO2 species, before eventually generating HO2. In this work an α-pinene-derived RO2 species was used as an example. In this simulation, consistent with the experimental budget analysis, the model underestimated the observed OH, indicating a missing OH source. The model uncertainty, with regards to the types of RO2 species present and the radicals they form upon reaction with NO (HO2 directly or another RO2 species), leads to over an order of magnitude less O3 production calculated from the predicted peroxy radicals than calculated from the observed peroxy radicals at the highest NO concentrations. This demonstrates the rate at which the larger RO2 species propagate to HO2, to another RO2 or indeed to OH needs to be understood to accurately simulate the rate of ozone production in environments such as Beijing, where large multifunctional VOCs are likely present.
Measurements of atmospheric boundary layer nitrous acid (HONO) and nitrogen oxides (NOx) were performed in summer 2016 inside a city centre road tunnel in Birmingham, United Kingdom. HONO and NOx ...mixing ratios were strongly correlated with traffic density, with peak levels observed during the early evening rush hour as a result of traffic congestion in the tunnel. A day-time ΔHONO∕ΔNOx ratio of 0.85 % (0.72 % to 1.01 %, 95 % confidence interval) was calculated using reduced major axis regression for the overall fleet average (comprising 59 % diesel-fuelled vehicles). A comparison with previous tunnel studies and analysis on the composition of the fleet suggest that goods vehicles have a large impact on the overall HONO vehicle emissions; however, new technologies aimed at reducing exhaust emissions, particularly for diesel vehicles, may have reduced the overall direct HONO emission in the UK. This result suggests that in order to accurately represent urban atmospheric emissions and the OH radical budget, fleet-weighted HONO∕NOx ratios may better quantify HONO vehicle emissions in models, compared with the use of a single emissions ratio for all vehicles. The contribution of the direct vehicular source of HONO to total ambient HONO concentrations is also investigated and results show that, in areas with high traffic density, vehicle exhaust emissions are likely to be the dominant HONO source to the boundary layer.
Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to ...on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.
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•In-vehicle and on-road NO2 and PM2.5 concentrations were assessed under different ventilation settings & cabin air filters.•Stepwise GAMM identified significant environmental, vehicle and driving-related factors influencing in-vehicle exposures.•On-road air pollution was the largest contributor accounting for 22.3 & 30% of in-vehicle NO2 and PM2.5 levels.•Vehicle-based factors: age, cabin size, odometer reading, air filter type, window setting, ventilation explained 48.7 & 61.3% of NO2 and PM2.5.•Driving-based factors: driving speed, traffic conditions, traffic lights, roundabouts, high-emitters explained 22 & 7.4% of NO2 and PM2.5.
Air quality networks in cities can be costly and inconsistent and typically monitor a few pollutants. Space-based instruments provide global coverage spanning more than a decade to determine trends ...in air quality, augmenting surface networks. Here we target cities in the UK (London and Birmingham) and India (Delhi and Kanpur) and use observations of nitrogen dioxide (NO2) from the Ozone Monitoring Instrument (OMI), ammonia (NH3) from the Infrared Atmospheric Sounding Interferometer (IASI), formaldehyde (HCHO) from OMI as a proxy for non-methane volatile organic compounds (NMVOCs), and aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for PM2.5. We assess the skill of these products at reproducing monthly variability in surface concentrations of air pollutants where available. We find temporal consistency between column and surface NO2 in cities in the UK and India (R = 0.5–0.7) and NH3 at two of three rural sites in the UK (R = 0.5–0.7) but not between AOD and surface PM2.5 (R < 0.4). MODIS AOD is consistent with AERONET at sites in the UK and India (R ≥ 0.8) and reproduces a significant decline in surface PM2.5 in London (2.7 % a−1) and Birmingham (3.7 % a−1) since 2009. We derive long-term trends in the four cities for 2005–2018 from OMI and MODIS and for 2008–2018 from IASI. Trends of all pollutants are positive in Delhi, suggesting no air quality improvements there, despite the roll-out of controls on industrial and transport sectors. Kanpur, identified by the WHO as the most polluted city in the world in 2018, experiences a significant and substantial (3.1 % a−1) increase in PM2.5. The decline of NO2, NH3, and PM2.5 in London and Birmingham is likely due in large part to emissions controls on vehicles. Trends are significant only for NO2 and PM2.5. Reactive NMVOCs decline in Birmingham, but the trend is not significant. There is a recent (2012–2018) steep (> 9 % a−1) increase in reactive NMVOCs in London. The cause for this rapid increase is uncertain but may reflect the increased contribution of oxygenated volatile organic compounds (VOCs) from household products, the food and beverage industry, and domestic wood burning, with implications for the formation of ozone in a VOC-limited city.
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