Evaluation of local and regional air quality forecasts for London

Stidworthy, Amy; Jackson, Mark; Johnson, Kate; Carruthers, David; Stocker, Jenny

doi:10.1504/ijep.2018.099160

Cited by 12 publications

(5 citation statements)

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“…This is because air quality ground control units in fixed locations are not homogeneously distributed in space, across regions, and specifically, in the areas of our particular interest (national parks), and they can be subject to stopping for maintenance. Among the many research papers using CAMS data for these reasons, see [25][26][27].…”

Section: Data Descriptionmentioning

confidence: 99%

The Green Lung: National Parks and Air Quality in Italian Municipalities

et al. 2023

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In Italy, 25 percent of the 7903 municipalities include protected areas, while 6.4 percent—which we define as park municipalities—are national parks. Using data from the Copernicus programme databases, we investigated the relationship between park municipalities and the air quality, and we found that the air pollution levels in these areas were much lower than in the rest of the municipalities for the period 2017–2020. The gross difference ranged from 25 to 30 percent lower levels of particulate matter (as measured in terms of both PM10 and PM2.5), and three times lower levels of nitrogen dioxide. In our multivariate econometric analysis, we found that part of this difference depends on the lower population density and manufacturing activity in municipalities with national parks. Furthermore, we showed that park municipalities: (i) had progressively reduced levels of particulate matter during the period 2017–2020, and (ii) had a “green lung” function, since in non-park municipalities’ air pollution levels increased with the distance from national parks. Based on empirical evidence on the impact of the main air pollutants on mortality documented in the literature, we calculated that living in park municipalities reduces mortality rates by around 10 percent.

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Section: Data Descriptionmentioning

confidence: 99%

The Green Lung: National Parks and Air Quality in Italian Municipalities

et al. 2023

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“…Allowance is made for the effect of buildings including street canyons on the flow and hence dispersion The dispersion calculations are driven by hourly meteorological profiles of wind speed and direction, among other parameters, which are characterised using Monin-Obukhov length similarity; meteorological input data may be derived from measurements or output from a mesoscale model such as WRF. The ADMS-Urban model has been used to simulate air quality within cities worldwide; applications include testing of emission-reduction scenarios and forecasting (Stidworthy et al, 2017). The module has been validated extensively by comparison with measurements from monitoring networks in Hong Kong and London (Hood et al, 2014.…”

Section: Adms-urbanmentioning

confidence: 99%

Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

Carruthers

Stidworthy

Clarke

et al. 2019

IJEP

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An optimisation scheme has been developed that applies a Bayesian inversion technique to a high resolution (street-level) atmospheric dispersion model to modify pollution emission rates based on sensor data. The scheme minimises a cost function using a non-negative least squares solver. For the required covariance matrices, assumptions are made regarding the magnitude of the uncertainties in source emissions and measurements and the correlation in uncertainties between different source emissions and different measurement sites. The scheme has been tested in an initial case study in Cambridge using monitored data from four reference monitors and twenty AQMesh sensor pods for the period, 30 June 2016 -30 September 2016. Hourly NOx concentrations from road sources modelled using ADMS-Urban and observed concentrations were processed using the optimisation scheme and the adjusted emissions were re-modelled. The optimisation scheme reduced average road emissions on average by 6.5% compared to the original estimates, changed the diurnal profile of emissions and improved model accuracy at four reference sites.

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“…The ADMS-Urban model has been used to simulate air quality within cities worldwide; applications include testing of emission-reduction scenarios and forecasting (Stidworthy et al, 2017). Emissions from all sources within the model domain are included, either explicitly with detailed time-varying profiles, for instance major road and industrial sources, or as grid-averaged emissions, representing diffuse sources such as those from heating and minor roads as a grid of regular volume sources, with simpler time variation.…”

Section: Urban-scale Modelling: Adms-urbanmentioning

confidence: 99%

Revised manuscript with changes marked

Hood¹

2018

Preprint

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A coupled regional-to-local modelling system comprising a regional chemistry-climate model with 5 km horizontal resolution (EMEP4UK) and an urban dispersion and chemistry model with explicit road source emissions (ADMS-Urban) has been used to simulate air quality in 2012 across London. The study makes use of emission factors for NO x and NO 2 and non-exhaust emission rates of PM 10 and PM 2.5 which have been adjusted compared to standard factors to reflect real-world emissions, with increases in total emissions of around 30 % for these species. The performance of the coupled model and each of the two component models is assessed against measurements from background and near-road sites in London using a range of metrics concerning annual averages, high hourly average concentrations and diurnal cycles. The regional model shows good performance compared to measurements for background sites for these metrics, but under-predicts concentrations of all pollutants except O 3 at near-road sites due to the low resolution of input emissions and calculations. The coupled model shows good performance at both background and near-road sites, which is broadly comparable with that of the urban model that uses measured concentrations as regional background, except for PM 2.5 where the under-prediction of the regional model causes the coupled model to also under-predict concentrations. Using the coupled model, it is estimated that 13 % of the area of London exceeded the EU limit value of 40 µg m −3 for annual average NO 2 in 2012, whilst areas of exceedances of the annual average limit values of 40 and 25 µg m −3 for PM 10 and PM 2.5 respectively were negligible.

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Evaluation of local and regional air quality forecasts for London

Cited by 12 publications

References 0 publications

The Green Lung: National Parks and Air Quality in Italian Municipalities

The Green Lung: National Parks and Air Quality in Italian Municipalities

Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

Revised manuscript with changes marked

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