36 Air Monitoring CO2
EMISSION INVENTORY VERIFICATION THROUGH ASSIMILATION OF NETWORK DATA
Introduction Atmospheric dispersion models are typically used in ‘forward mode’, meaning that source emission rates are specifi ed and then the dispersion model is used to determine the concentration of pollutants in the air depending on the prevailing meteorology. An alternative ‘inverse mode’ assimilates measured concentration data enabling optimisation of emission rates and subsequent improved estimate of pollution concentrations. While emissions inventories take a long time to compile, using sensor data with this inverse approach can improve emissions estimates and hence modelled concentrations in the short term, since our knowledge can be continuously updated and the data can capture events that are not captured by inventories, such as the onset of the Covid-19 lockdown, or fugitive emissions of methane from landfi ll sites. The method can also be used in the longer term, for instance, to optimise or verify annual reporting of emissions of both toxic pollutants and greenhouse gases.
CERC have developed an inverse model, specifi cally a Bayesian- based method which combines hourly modelled pollutant concentrations from the very high resolution ADMS-Urban model (e.g. Hood et al, 2018) with hourly sensor measurements (Carruthers et al, 2019). Critical to the approach is that it can allow for the uncertainty associated with the initial (a priori) estimates of the rate of pollutant emissions from each of the emissions sources and the uncertainty of each of the sensors. It can also allow for correlations in the uncertainty between the emissions rates for different sources (for example, arising for road sources using the same emissions factors) and also between different sensors, if any. Estimating the uncertainties and correlations of these input parameters is a key part of the model set-up as the inverse model output can be highly sensitive to their values. Model output comprises a revised set of hourly concentrations at each sensor location and hourly emission rates for each source.
IET SEPTEMBER / OCTOBER 2023
Figure 1 Map of Glasgow area showing the location of the devices measuring CO2
instruments (GLA5 and GLA7). The AQMesh pods were co-located with the Scottish Air Quality Network (SAQN) reference monitors for NOx
The inverse model has previously been applied to NOx NO2
and concentrations in London during the Covid-19 lockdown
(Stidworthy et al, 2021). In this note we describe the application of the system to emissions of CO2
in Greater Glasgow. The
measurement period coincided with the COP26 conference in Glasgow, highlighting the critical importance of such methods on the path to Net Zero.
Method CO2
CO2
concentrations: AQMesh pods (C01-C15) and the LI-COR reference grade , NO2
, PM10 traffi c emissions were estimated for main roads from DfT
data was collected from 15 AQMesh sensors located at a height of 2 metres across the Glasgow area, at roadside, urban background and rural sites (Figure 1), and collocated with existing regulatory network monitors for air quality. The sensors recorded CO2
concentrations at 1-minute intervals, which were used to calculate hourly averages. LI-COR reference monitors measuring CO2
were also collocated at two of the sites: GLA5 (urban background) and GLA7 (rural).
vehicle fl ow rates and vehicle splits, and COPERT 5 emissions factors using road geometry obtained from Open Roads. Road elevations were all assumed to be zero. Road widths were estimated from the road classifi cation and refi ned near monitoring sites. The impacts of street canyons on dispersion were modelled using the ADMS-Urban advanced street canyon tool; street canyon geometry was determined from GeoFabrix building outlines. Emissions of other sources were represented by 1 km x 1 km gridded emissions from the National Atmospheric Emissions Inventory (Tsagatakis et al, 2020).
Hourly background CO2 concentrations were estimated from
baselines extracted from the 1-minute AQMesh data from the ‘C03’ gold pod sensor collocated with the LI-COR instrument on the site of the GLA5 regulatory monitor (Figure 1). Meteorological data was obtained from Bishopton weather station located approximately 20 km north west of the centre of Glasgow.
For the inversion scheme the measurement uncertainties were
, PM2.5
.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60
