In the beginning, even before the desert skyline was punctuated by stacks from the first LNG plants in the northeast corner of Qatar, air quality models were being run to estimate potential air quality impacts (AQIA) due to the proposed developments. AQIAs were required as part of the Environmental Impact Assessment (EIA) obligation to SCENR (now Ministry of Environment). With time, Ras Laffan (RLC) has grown into one of the premier energy hubs in the world with contemporaneous improvement in the pedigree of air modelling. This paper provides a genesis of air quality modelling in Qatar with initial screening estimates graduating to Industrial Source Complex (ISCST3) and now AERMOD as the mainstay model. The RLC coastline with its dense mass of industrial growth presents a daunting challenge in terms of predictions of air quality impacts due to a mix of uncertainties in emission inventories, coastal phenomena etc., which cannot be accounted for by the existing raft of air dispersion models. This paper makes a case for moving to an established scalable puff model (CALPUFF) operated in near real-time with dynamic Weather Research and Forecasting (WRF) data as the regulatory model of choice. The proposed platform will vastly improve RLC's Air Quality Management Plan (AQMP) by improving allowable increment planning, handle complex coastline conditions local to RLC, addressing the lacunae of plume models and photochemistry.

Applications:

  • Assess and mitigate air quality impacts from large gas and industrial projects through air dispersion modelling

  • AQMP to assess future industrial growth using real-time AQIA

Results and Conclusions:

  • Results from preliminary one-year RLC WRF-CALPUFF compared against other platforms

  • Innovative technological approach by leveraging dynamic computation platforms to improve air quality impact predictions

Technical Contributions:

  • Innovative air quality dispersion modelling using WRF-CALPUFF for assessing near real-time air quality impacts

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