Graz Lagrangian Model (GRAL) for Pollutants Tracking and Estimating Sources Partial Contributions to Atmospheric Pollution in Highly Urbanized Areas

Abstract

Computational modeling allows studying the air quality problems in depth and provides the best solution reducing the population risks. This research demonstrates the Graz Lagrangian model effectiveness for assessing emission sources contributions to the air pollution: particles tracking and accumulation estimate. The article describes model setting up parameters and datasets preparation for the analysis. The experiment simulated the dispersion from the main groups of emission sources for real weather conditions during 96 h of December 2018, when significant excess of NO2, CO, SO2, PM10, and benzo(a)pyrene concentrations were observed in the Krasnoyarsk surface atmospheric layer. The computational domain was a parallelepiped of 40 × 30 × 2.5 km, which was located deep inside the Eurasian continent on a heterogeneous landscape exaggerated by high-rise buildings, with various pollutions sources and the ice-free Yenisei River. The results demonstrated an excellent applicability of the Lagrange model for hourly tracking of particle trajectories, taking into account the urban landscape. For values <1 MPC (maximum permissible concentration) of peak pollutants concentrations, the coincidences were 93 cases, and for values < 0.1 shares of MPC, there were 36 cases out of the total number of 97. The same was found for the average daily concentration for values <1 MPC—31, and for values <0.1 MPC—5 matches out of 44. Wind speeds COR—65.3%, wind directions COR—68.6%. The Graz Lagrangian model showed the ability to simulate air quality problems in the Krasnoyarsk greater area conditions.

Influence of Selected Air Pollutants on Mortality and Pneumonia Burden in Three Polish Cities over the Years 2011–2018

“…An important advantage of a general linear model is its ability to describe non-linear relationships between variables due to the application of appropriate transformations of predictive factors and the use of substitution of z-score standardized variable methods. These types of models have previously been used to identify cause–effect relationships between air pollutant concentrations and selected phenomena (including health effects) [ 30 , 31 , 32 , 33 , 34 ]. In our study, the curvilinear relationships between variables were also described through the use of appropriate transformations of the predictors as well as the application of substitution methods using a standardized variable and a series of selected transformations of linearizing variables and through a non-linear linking function.…”

Monitoring and assessment of the state of the atmosphere over the Yenisei riverbed using drone

“…Model calculations show that ascending wind flows form over the central part of the Yenisei riverbed in winter [4,7]. As a consequence of this process, it is assumed that there are gradients of temperature and relative humidity over the water area of the river [4].…”

Real-world wintertime CO, N<sub>2</sub>O and CO<sub>2</sub> emissions of a Central European village

“…For the estimation of the influence of the emission from the village on the concentration measurements at the monitoring tower, the Graz Lagrangian Model (GRAL v14.8 -Oettl et al 2002;2015a;2015b;Romanov et al, 2020) has been used. This 3D particle dispersion model was originally developed for the dispersion of pollutants from a road tunnel portal but is suitable to describe the 3-dimensional concentration distribution of area sources.…”