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Monitoring urban–industrial emissions is often challengingbecause observations are scarce and regional atmospheric transport models aretoo coarse to represent the high spatiotemporal variability in the resultingconcentrations. In this paper we apply a new combination of an Eulerian model(Weather Research and Forecast, WRF, with chemistry) and a Gaussian plume model (Operational PrioritySubstances – OPS). The modelledmixing ratios are compared to observed CO2 and CO mole fractions atfour sites along a transect from an urban–industrial complex (Rotterdam, theNetherlands) towards rural conditions for October–December 2014. Urbanplumes are well-mixed at our semi-urban location, making this location suitedfor an integrated emission estimate over the whole study area. The signals atour urban measurement site (with average enhancements of 11 ppm CO2and 40 ppb CO over the baseline) are highly variable due to the presence ofdistinct source areas dominated by road traffic/residential heating emissionsor industrial activities. This causes different emission signatures that aretranslated into a large variability in observedΔCO : ΔCO2 ratios, which can be used to identifydominant source types. We find that WRF-Chem is able to represent synopticvariability in CO2 and CO (e.g. the median CO2 mixing ratiois 9.7 ppm, observed, against 8.8 ppm, modelled), but itfails to reproduce the hourly variability of daytime urban plumes at theurban site (R2 up to 0.05). For the urban site, adding a plume model tothe model framework is beneficial to adequately represent plume transportespecially from stack emissions. The explained variance in hourly, daytimeCO2 enhancements from point source emissions increases from 30 %with WRF-Chem to 52 % with WRF-Chem in combination with the most detailedOPS simulation. The simulated variability inΔCO :  ΔCO2 ratios decreases drastically from 1.5 to0.6 ppb ppm−1, which agrees better with the observed standarddeviation of 0.4 ppb ppm−1. This is partly due to improved windfields (increase in R2 of 0.10) but also due to improved point sourcerepresentation (increase in R2 of 0.05) and dilution (increase in R2 of0.07). Based on our analysis we conclude that a plume model with detailed andaccurate dispersion parameters adds substantially to top–down monitoring ofgreenhouse gas emissions in urban environments with large point sourcecontributions within a  ∼  10 km radius from the observation sites

A multi-model approach to monitor emissions of CO&lt;sub&gt;2&lt;/sub&gt; and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex