Time variations in the concentrations of gas pollutants CO, NO, NO2, SO2, and PM10aerosol in the surface atmospheric layer over Moscow show a weekly cycle, manifested as a decrease in the...

Data of multiyear observations from Mosecomonitoring network stations were used to calculate the CO, NO, NO₂, SO₂, and PM₁₀ emissions from urban sources, their spatial distribution, and time variations. The emission matrix thus obtained was used in the SILAM chemical transport model to estimate the air quality in the Moscow megacity. The comparisons of the calculations with the observations, performed by applying correlation relations and Student’s test, were used to correct the emission matrix. To optimize the spatial distribution of sources and the magnitude of emissions in the Moscow megacity, air pollutant fields for the summer and winter months were calculated applying chemical transport models SILAM and COSMO-ART using emissions both calculated and available from the TNO emission inventory database. Comparison of these calculations made it possible to reduce the uncertainties of estimating the air quality in the Moscow region.

Comparisons of observational data obtained at the Moscow Ecological Monitoring network (MEM) with numerical simulations using a chemical transformation and transport model (SILAM—System for Integrated modeLling of Atmospheric coMposition) showed that the errors in determining the gaseous pollutant concentrations in the urban atmosphere have a more complex structure than those assumed under the conventional algorithms of data assimilation. These errors are statistically nonstationary; they show a pronounced diurnal cycle and a significant lifetime. The statistical features of errors in numerical calculations also depend upon the type of pollutants, i.e., the chemical reactions in which they participate. Our analysis showed that the simulation errors are not small: the ratios of calculated and measured concentrations (even for daily averages at all measuring stations) may vary in a wide range. For the chemically active pollutants, the intradiurnal error variations may reach 100%. The diurnal cycle of such errors was found to vary according to seasons (in our case, summer and winter). The analysis of statistical properties of the errors, including their temporal and spatial variability, allows one to correct and adequately forecast the air pollution in the metropolis area at lead times up to three days in advance.

Estimating biogenic CO ₂ fluxes is essential to quantify urban anthropogenic emissions, yet urban vegetation heterogeneity presents a significant challenge to making accurate estimations. We have developed an hourly temporal, 10‐m spatial resolution biogenic CO ₂ flux estimation framework based on the Vegetation Photosynthesis and Respiration Model (VPRM) and its variants (UrbanVPRM and VPRM‐modified). Unlike lower‐resolution models, our approach captures finer‐scale variability, particularly in fragmented urban green spaces like street trees and lawns. Results show that vegetation in Munich offsets 2.0%–2.8% of annual anthropogenic CO ₂ emissions in the study domain, with tree‐covered areas as primary sinks and grasslands as net sources. During summer, daytime CO ₂ uptake can match or exceed anthropogenic emissions. Evaluations employing city park field measurements and eddy covariance towers confirm strong performance of our models, while highlighting VPRM‐modified's advantage in grasslands and croplands, and UrbanVPRM's improvements in urban areas via impervious surface correction. These findings highlight the value of high‐resolution modeling in improving urban carbon flux assessments.