Air Pollution in Moscow Megacity: Data Fusion of the Chemical Transport Model and Observational Network

Abstract

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.

Temporal Variation of NO2 and O3 in Rome (Italy) from Pandora and In Situ Measurements

“…Several studies have demonstrated that atmospheric pollution affects medium and small-sized cities [3,4], even if the problem is more evident in large urban areas and metropolises [5,6], where the uneven distribution of emissive sources, the complex arrangement of buildings, and the composite photochemical reactions seriously complicate the assessment of the air quality level [7]. Moreover, the dense geometry of buildings and streets reduces the ventilation at the pedestrian level, limiting the exchange of momentum and mass between the canyons and the free atmosphere, further worsening the air quality and, consequently, the urban livability and human comfort [8,9].…”

Impact of COVID-19 Pandemic Preventing Measures and Meteorological Conditions on the Atmospheric Air Composition in Moscow in 2020

“…1 ). Measurements at the stations are conducted using modern gas analyzers, which are regularly calibrated according to international standards and instructions of the World Meteorological Organization (WMO) developed for the Global Atmospheric Watch (WMO GAW) observation network [ 18 ].…”

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

“…The methods of utilizing heterogeneous measurement data are referred to as "data fusion" [36][37][38]. Data of different sources and types can complement and cross-validate each other.…”