Essential variables for air quality estimation

ABSTRACT

Within this survey we describe the conceptual architecture of the infrastructure to measure PM2.5/PM10 concentration in the atmosphere over the Kyiv city using modern monitoring instruments. We define the requirements for information tools and network for informing Kyiv city community on the state of PM pollutions that will be created. This infrastructure will provide long-term PM2.5/PM10 observations that could be included in the AirBase network. The comprehensive review of in-situ and satellite measurements of PM2.5/PM10 is provided as well as the description current state-of-the-art for Air Quality monitoring with intelligent sensors and systems in Ukraine as-awhole and in Kyiv in particular. It is proposed to apply the concept of essential variables (EVs) used in Earth Observation to identify the variables that should be measured in priority when designing, deploying and maintaining observation systems. In this study we use and validate the global air quality products from Copernicus Atmosphere Monitoring Service obtained from modeling by GEOS-Chem model and other sources. The influence of PM and aerosols on a human health is estimated in terms of possible diseases and dangerous concentrations.

KEYWORDS:

• Essential variable
• sustainable development goal
• air quality
• remote sensing

Acknowledgement

The authors are grateful to Tatyana Maremukha from Laboratory of Air Quality, Marzeiev Institute for Public Health, National Academy of Medical Science of Ukraine, Kyiv, Ukraine for provision results of PM data analysis in Table 2.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Andrii Shelestov http://orcid.org/0000-0001-9256-4097

Andrii Kolotii http://orcid.org/0000-0002-6972-4483

Additional information

Funding

The authors would like to acknowledge the European Commission ‘Horizon 2020 Program’ that funded ERA-PLANET/SMURBS, ERA-PLANET/GEOEssential, ERA-PLANET/IGOSP (Grant Agreement no. 689443) and ‘Intelligent technologies for satellite monitoring of environment based on deep learning and cloud computing’ InTeLLeCT (STCU project no. 6386).