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

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 SO2 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 3-year period (2009–2011) and one configuration applied to predict SO2 concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO2 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 SO2 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.

[1]  H. H. Lettau,et al.  Physical and meteorological basis for mathematical models of urban diffusion processes. , 1970 .

[2]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[3]  T. Oke.  The urban energy balance , 1988 .

[4]  Steven R. Hanna,et al.  Confidence limits for air quality model evaluations, as estimated by bootstrap and jackknife resampling methods , 1989 .

[5]  William M. Cox,et al.  A statistical procedure for determining the best performing air quality simulation model , 1990 .

[6]  Peter Hurley.  PARTPUFF - a Lagrangian particle-puff approach for plume dispersion modeling applications , 1994 .

[7]  David R. Stauffer,et al.  A Multiscale Four-Dimensional Data Assimilation System Applied in the San Joaquin Valley during SARMAP. Part I: Modeling Design and Basic Performance Characteristics , 1995 .

[8]  Boundary layer evolution over a large and broad mountain basin , 1995 .

[9]  Nelson L. Seaman,et al.  Meteorological modeling for air-quality assessments , 2000 .

[10]  A. Triantafyllou,et al.  Patterns and Concentrations of PM10 in a Mountainous Basin Region , 2000, Journal of the Air & Waste Management Association.

[11]  Robin L. Dennis,et al.  NARSTO critical review of photochemical models and modeling , 2000 .

[12]  P. Hurley,et al.  Verification of a prognostic meteorological and air pollution model for year-long predictions in the Kwinana industrial region of Western Australia , 2001 .

[13]  A. Triantafyllou.  PM10 pollution episodes as a function of synoptic climatology in a mountainous industrial area. , 2001, Environmental pollution.

[14]  Aspects of atmospheric flow and dispersion of air pollutants in a mountainous basin. , 2002, The Science of the total environment.

[15]  P. Hurley,et al.  Evaluation of TAPM, a prognostic meteorological and air pollution model, using urban and rural point-source data , 2003 .

[16]  A. Triantafyllou.  Levels and Trend of Suspended Particles around Large Lignite Power Stations , 2003, Environmental monitoring and assessment.

[17]  P. Manins,et al.  Year-long, high-resolution, urban airshed modelling: verification of TAPM predictions of smog and particles in Melbourne, Australia , 2003 .

[18]  Peter J. Hurley,et al.  Application of a prognostic model TAPM to sea-breeze flows, surface concentrations, and fumigating plumes , 2004, Environ. Model. Softw..

[19]  S. Hanna,et al.  Air quality model performance evaluation , 2004 .

[20]  Nicolas Moussiopoulos,et al.  A modelling method for estimating transboundary air pollution in southeastern Europe , 2004, Environ. Model. Softw..

[21]  Simon Kingham,et al.  Evaluation of a year-long dispersion modelling of PM10 using the mesoscale model TAPM for Christchurch, New Zealand. , 2005, The Science of the total environment.

[22]  Peter J. Hurley,et al.  TAPM: a practical approach to prognostic meteorological and air pollution modelling , 2005, Environ. Model. Softw..

[23]  Paul James,et al.  A modelling study of an extraordinary night time ozone episode over Madrid domain , 2005, Environ. Model. Softw..

[24]  A. Sturman,et al.  DISPERSION MODELLING OF PM 10 FOR CHRISTCHURCH , NEW ZEALAND : AN INTERCOMPARISON BETWEEN MM 5 AND TAPM , 2005 .

[25]  Constantini Samara,et al.  Chemical mass balance source apportionment of TSP in a lignite-burning area of Western Macedonia, Greece , 2005 .

[26]  Nicolas Moussiopoulos,et al.  Wind flow and photochemical air pollution in Thessaloniki, Greece. Part II: Statistical evaluation of European Zooming Model's simulation results , 2006, Environ. Model. Softw..

[27]  Roberto San José García,et al.  Prediction of ozone levels in London using the MM5-CMAQ modelling system , 2006, Environ. Model. Softw..

[28]  P. J. Hurley,et al.  Grid sensitivity analysis for the calibration of a prognostic meteorological model in complex terrain by a screening experiment , 2007, Environ. Model. Softw..

[29]  Srinath Krishnan,et al.  Modeling atmospheric transport and fate of ammonia in North Carolina—Part I: Evaluation of meteorological and chemical predictions , 2008 .

[30]  Ashok K. Luhar,et al.  TAPM V4. Part 2 - summary of some verification studies , 2008 .

[31]  Philippe Thunis,et al.  The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations , 2009 .

[32]  David Hargreaves,et al.  A computational study of particulate emissions from an open pit quarry under neutral atmospheric conditions , 2009 .

[33]  Alexis Zubrow,et al.  Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models , 2011, Environ. Model. Softw..

[34]  Shu-hui Cheng,et al.  Application of MM5 in China: Model evaluation, seasonal variations, and sensitivity to horizontal grid resolutions , 2011 .

[35]  C. Srinivas,et al.  Evaluation of the operational atmospheric model used in emergency response system at Kalpakkam on the east coast of India , 2011 .

[36]  Steven R. Hanna,et al.  Acceptance criteria for urban dispersion model evaluation , 2012, Meteorology and Atmospheric Physics.

[37]  Application of a coupled prognostic model to turbulence and dispersion in light-wind stable conditions, with an analytical correction to vertically resolve concentrations near the surface , 2012 .

[38]  Yoshihide Tominaga,et al.  CFD Simulation of Near-Field Pollutant Dispersion in the Urban Environment: A Review of Current Modeling Techniques , 2013 .

[39]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[40]  Lakhdar Aidaoui,et al.  Elevated stacks’ pollutants’ dispersion and its contributions to photochemical smog formation in a heavily industrialized area , 2014, Air Quality, Atmosphere & Health.

[41]  A. G. Triantafyllou,et al.  Interactions between complicated flow-dispersion patterns and boundary layer evolution in a mountainous complex terrain during elevated SO2 concentrations , 2017, Meteorology and Atmospheric Physics.