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Integrating in situ measurements and city scale modelling to assess the COVID–19 lockdown effects on emissions and air quality in Athens, Greece
Georgios Grivas, Eleni Athanasopoulou, Anastasia Kakouri, Jennifer Bailey, Eleni Liakakou, Iasonas Stavroulas, Panayiotis Kalkavouras, Aikaterini Bougiatioti, Dimitris G. Kaskaoutis, Michel Ramonet, 2020, izvirni znanstveni članek

Opis: The lockdown measures implemented worldwide to slow the spread of the COVID–19 pandemic have allowed for a unique real-world experiment, regarding the impacts of drastic emission cutbacks on urban air quality. In this study we assess the effects of a 7-week (23 March–10 May 2020) lockdown in the Greater Area of Athens, coupling in situ observations with estimations from a meteorology-atmospheric chemistry model. Measurements in central Athens during the lockdown were compared with levels during the pre- and post-lockdown 3-week periods and with respective levels in the four previous years. We examined regulatory pollutants as well as CO2, black carbon (BC) and source-specific BC components. Models were run for pre-lockdown and lockdown periods, under baseline and reduced-emissions scenarios. The in-situ results indicate mean concentration reductions of 30–35% for traffic-related pollutants in Athens (NO2, CO, BC from fossil fuel combustion), compared to the pre-lockdown period. A large reduction (53%) was observed also for the urban CO2 enhancement while the reduction for PM2.5 was subtler (18%). Significant reductions were also observed when comparing the 2020 lockdown period with past years. However, levels rebounded immediately following the lift of the general lockdown. The decrease in measured NO2 concentrations was reproduced by the implementation of the city scale model, under a realistic reduced-emissions scenario for the lockdown period, anchored at a 46% decline of road transport activity. The model permitted the assessment of air quality improvements on a spatial scale, indicating that NO2 mean concentration reductions in areas of the Athens basin reached up to 50%. The findings suggest a potential for local traffic management strategies to reduce ambient exposure and to minimize exceedances of air quality standards for primary pollutants.
Ključne besede: pandemic, urban air pollution, traffic, chemical transport model, TAPM, mapping
Objavljeno v RUNG: 10.05.2024; Ogledov: 62; Prenosov: 0
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2.
Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Rajan Gupta, Gaurav Pandey, Saibal K. Pal, 2021, izvirni znanstveni članek

Opis: Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
Ključne besede: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Objavljeno v RUNG: 15.07.2021; Ogledov: 2310; Prenosov: 33
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