Performance and evaluation of a coupled prognostic model TAPM over a mountainous complex terrain industrial area

Abstract

Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO 2 concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3year period (2009-2011) and one configuration applied to predict SO 2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO 2 concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model's performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model's performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO 2 concentrations were assessed by the model with IOA varied from 0.37 to 0.57, mostly dependent on the grid/monitoring station of the simulated domain. The present study can be used, with relevant adaptations, as a user guideline for future conducting simulations in mountainous complex terrain.

Study loading capacties of air pollutant emissions for developing countries: a case of Ho Chi Minh City, Vietnam

“…TAPM also simulated well the surface wind speed in the study area, with the mean values of both predicted and observed wind speed were approximate 2 m/s during the study period. However, it slightly overestimated the wind speed comparing with observed value and with 0.4 and 0.7 m/s difference in February and June, respectively, which were the same ranges as those in Matthaios's study 23 . Figure 3 and Table 3 present the performance of SO 2 , NO 2 , and O 3 from 12 th to 17 th June 172017.…”

“…However, TAPM slightly underestimated the temperatures during the dry season but a little overestimated during the rainy season. This result also agreeded with the findings in Matthaios's study in 2018 about the evaluation of TAPMP model over a mountainous complex terrain industrial area 23 .…”

Positive matrix factorization on elemental concentrations of PM10 samples collected in areas within, proximal and far from mining and power station operations in Greece.

“…During the summer months these temperature values are 23.7, 29.4 and 17.9 °C, respectively. For the same period, the prevailing winds in the industrial basin are weak to mediocre and mainly blow with NW/SE direction ( Figure 2) due to channeling of the synoptic wind (Matthaios et al, 2018). Strong ground base nocturnal inversions are observed in the basin where the main industrial activity take place during the whole period of the year.…”

Genetics and epigenetics of Pinus nigra populations with differential exposure to air pollution

“…TAPM is an operational, threedimensional (3D) terrain following sigma-coordinate, coupled prognostic meteorological and pollutant dispersion model, that uses the fundamental equations of atmospheric flow, thermodynamics, moisture conservation, turbulence, and dispersion (Hurley et al, 2001;Hurley et al, 2003a;Hurley et al, 2003b). The model was configured, ran and validated in the area of interest by Triantafyllou et al (2011); Aidoui et al (2015) and Matthaios et al (2017). Based on the model results tailored to this study, out of the eight locations studied, the two areas -receptors of natural P. nigra populations, associated with the extreme concentrations (maximum and minimum), were selected, following a methodology adapted from Rajput and Agrawal (2005).…”