As part of the EU-funded SAVIAH project, a regression-based methodology for mapping tra c-related air pollution was developed within a GIS environment. Mapping was carried out for NO2 in Amsterdam, Hudders® eld and Prague. In each centre, surveys of NO2 , as a marker for tra c-related pollution, were conducted using passive di usion tubes, exposed for four 2-week periods. A GIS was also established, containing data on monitored air pollution levels, road network, tra c volume, land cover, altitude and other, locally determined, features. Data from 80 of the monitoring sites were then used to construct a regression equation, on the basis of predictor environmental variables, and the resulting equation used to map air pollution across the study area. The accuracy of the map was then assessed by comparing predicted pollution levels with monitored levels at a range of independent reference sites. Results showed that the map produced extremely good predictions of monitored pollution levels, both for individual surveys and for the mean annual concentration, with r 2~0´7 9± 0´87 across 8± 10 reference points, though the accuracy of predictions for individual survey periods was more variable. In Hudders® eld and Amsterdam, further monitoring also showed that the pollution map provided reliable estimates of NO2 concentrations in the following year (r 2~0´5 9± 0´86 for n=20).
The COVID-19 pandemic forced governments around the world to impose restrictions on daily life to prevent the spread of the virus. This resulted in unprecedented reductions in anthropogenic activity, and reduced emissions of certain air pollutants, namely oxides of nitrogen. The UK ‘lockdown’ was enforced on 23/03/2020, which led to restrictions on movement, social interaction, and ‘non-essential’ businesses and services. This study employed an ensemble of measurement and modelling techniques to investigate changes in air quality, atmospheric composition and boundary layer reactivity in the South East of the UK post-lockdown. The techniques employed included
in-situ
gas- and particle-phase monitoring within central and local authority air quality monitoring networks, remote sensing by long path Differential Optical Absorption Spectroscopy and Sentinel-5P's TROPOMI, and detailed 0-D chemical box modelling. Findings showed that de-trended NO
2
concentrations decreased by an average of 14–38% when compared to the mean of the same period over the preceding 5-years. We found that de-trended particulate matter concentrations had been influenced by interregional pollution episodes, and de-trended ozone concentrations had increased across most sites, by up to 15%, such that total O
x
levels were roughly preserved. 0-D chemical box model simulations showed the observed increases in ozone concentrations during lockdown under the hydrocarbon-limited ozone production regime, where total NO
x
decreased proportionally greater than total non-methane hydrocarbons, which led to an increase in total hydroxyl, peroxy and organic peroxy radicals. These findings suggest a more complex scenario in terms of changes in air quality owing to the COVID-19 lockdown than originally reported and provide a window into the future to illustrate potential outcomes of policy interventions seeking large-scale NO
x
emissions reductions without due consideration of other reactive trace species.
22To gain a better understanding on the spatiotemporal variation of ultrafine particles (UFPs) in urban 23 environments, this study reports on the first results of a long-term UFP monitoring network, set up in were still obtained in terms of particle numbers (20-38% for total particle numbers and up to 49% for 38 size-resolved particle numbers), confirming the importance of local source contributions and the need
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