Ozone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health

TitleOzone modelling and mapping for risk assessment: An overview of different approaches for human and ecosystems health
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2022
AuthorsDe Marco, Alessandra, García-Gómez H., Collalti A., Khaniabadi Y.O., Feng Z., Proietti C., Sicard P., Vitale M., Anav A., and Paoletti E.
JournalEnvironmental Research
Keywordsconcentration (composition), ecosystem health, mapping method, Modeling, Oceania Ecozone, Ozone, public health, Risk assessment, spatiotemporal analysis, troposphere

Tropospheric ozone (O3) is one of the most concernedair pollutants dueto its widespread impacts on land vegetated ecosystems and human health. Ozone is also the third greenhouse gas for radiative forcing. Consequently, it should be carefully and continuously monitored to estimate its potential adverse impacts especially inthose regions where concentrations are high. Continuous large-scale O3 concentrations measurement is crucial but may be unfeasible because of economic and practical limitations; therefore, quantifying the real impact of O3over large areas is currently an open challenge. Thus, one of the final objectives of O3 modelling is to reproduce maps of continuous concentrations (both spatially and temporally) and risk assessment for human and ecosystem health. We here reviewedthe most relevant approaches used for O3 modelling and mapping starting from the simplest geo-statistical approaches andincreasing in complexity up to simulations embedded into the global/regional circulation models and pro and cons of each mode are highlighted. The analysis showed that a simpler approach (mostly statistical models) is suitable for mappingO3concentrationsat the local scale, where enough O3concentration data are available. The associated error in mapping can be reduced by using more complex methodologies, based on co-variables. The models available at the regional or global level are used depending on the needed resolution and the domain where they are applied to. Increasing the resolution corresponds to an increase in the prediction but only up to a certain limit. However, with any approach, the ensemble models should be preferred. © 2022 Elsevier Inc.


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