The start of 2020 has been characterized by emission reductions in various countries across the globe following the implementation of different lock-down measures to control the transmission of the SARS-CoV-2 (COVID-19). Consequently, these reductions influenced the air quality globally. In this study, we focus on daily nitrogen dioxide (NO2) as well as ozone (O3) concentrations measured across the Maltese Islands between January and mid-October 2020. Changes in air quality are generally difficult to detect due to the complex composition and interactions occurring within the atmosphere. To quantify changes in NO2 and O3 concentrations during the COVID-19 period, we use a random forest machine learning algorithm to determine a business as usual counterfactual scenario. Results highlight a decrease in monthly mean NO2 concentrations by up to 54% in the traffic site of Msida (~21 μg m−3). In contrast, the monthly mean O3 concentrations during the COVID-19 months are up to 61% higher compared to a business as usual scenario in Msida (~28 μg m−3). In this study, we also estimate the differences in attributable fraction (AF) associated with short-term exposure to NO2 and O3 concentrations. In Msida, the AF is up to 0.9% lower and 0.8% higher for measured NO2 and O3 concentrations, respectively. Our results highlight the favorable effects of decreasing traffic-related emissions on NO2 concentrations however, we also note increases in other pollutants for example O3 concentrations which especially in the short-term can lead to various adverse health effects.

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.

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