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

Пономарев, Н. А.; Yushkov, V. P.; Elansky, N. F.

doi:10.3390/atmos12030374

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Bernardino

Mevi²,

Iannarelli³

et al. 2023

Atmosphere

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To assess the best measures for the improvement of air quality, it is crucial to investigate in situ and columnar pollution levels. In this study, ground-based measurements of nitrogen dioxide (NO2) and ozone (O3) collected in Rome (Italy) between 2017 and 2022 are analyzed. Pandora sun-spectrometers provided the time series of the NO2 vertical column density (VC-NO2), tropospheric column density (TC-NO2), near-surface concentration (SC-NO2), and the O3 vertical column density (VC-O3). In situ concentrations of NO2 and O3 are provided by an urban background air quality station. The results show a clear reduction of NO2 over the years, thanks to the recent ecological transition policies, with marked seasonal variability, observable both by columnar and in situ data. Otherwise, O3 does not show inter-annual variations, although a clear seasonal cycle is detectable. The results suggest that the variation of in situ O3 is mainly imputable to photochemical reactions while, in the VC-O3, it is triggered by the predominant contribution of stratospheric O3. The outcomes highlight the importance of co-located in situ and columnar measurements in urban environments to investigate physical and chemical processes driving air pollution and to design tailored climate change adaptation strategies.

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Section: Introductionmentioning

confidence: 99%

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

Bernardino

Mevi²,

Iannarelli³

et al. 2023

Atmosphere

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“…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 ].…”

Section: Methodsmentioning

confidence: 99%

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

Skorokhod

Rakitin

Kirillova

2022

Russ. Meteorol. Hydrol.

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Changes in the atmospheric composition during different periods of 2020 in Moscow which were associated with the COVID-19 pandemic preventing measures as well as corresponding pollutant emission reduction, are investigated. Surface concentrations of nitrogen dioxide (NO 2 ), carbon monoxide (CO), ozone (O 3 ), aerosol fraction (PM 10 ), and meteorological parameters during different periods of 2020 were compared with similar data for the previous five years. The analysis of ground-based measurements, as well as of high-resolution satellite distributions of CO and NO 2 indicated that the concentration of major pollutants and its spatial distribution in the Moscow region were significantly affected by both restrictive measures and abnormal meteorological conditions in 2020.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Penenko

Tsvetova

et al. 2021

Atmosphere

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Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

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

Cited by 6 publications

References 30 publications

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

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

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

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

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