Regulators are requesting more frequently that request applicants utilize CALPUFF to perform Class I area impact analyses, long range transport of pollutants, visibility studies for BART, and impact evaluation of source close to the ocean. These studies involve visibility analysis, shoreline fumigation, and calculation of the deposition of acidic species. However, the execution of CALPUFF requires considerable expertise and resources. Modelers performing these analyses may require external support on several fronts, from preparation of the input data to the execution of the model.

As a case in point, we present our experience with a reputable client in Kuwait. The project is a typical air quality modeling exercise for a thermal power plant facility. The client needs to use refined CALPUFF modeling in a manner similar to Class I area analysis. This assessment begins with first evaluating the impacts from the existing sources. Subsequently, one must estimate the increase in pollution levels due to the new sources. Furthermore, various emission rate scenarios are inspected to assess air quality as the new power plant expands its output in subsequent stages. Each calculation must be performed over a 5-year period of meteorological data.

Preparation of meteorological data is often the first hurdle for modelers. Many international users often face particular difficulty in obtaining conventional data for their home countries. We needed to employ meteorological data generated by MM5, a refined meteorological model. The resources required for executing MM5 model is beyond the scope of this paper. However, it suffices to say that it 10 to 100 times more expertise and computer resources than common regulatory models such as AERMOD and CALPUFF. For the Kuwait project we executed the prognostic model over the entire Kuwait country at a resolution of 12 km. To complete the preparation of model input, we also included satellite base maps, global terrain data, and land use data from us with resolution of 1 km.

The execution of the CALPUFF model can be intolerantly slow because it has to trace numerous puffs in the computational domain. A single straight 5-year CALPUFF simulation of the client’s project required 80 days of CPU time on a single high-end PC. The whole project, with various emissions scenarios, would take 336 days for completion over a bank of 14 high-end Xeon workstations in our company. To avoid any project delay in the project, we developed an effective computing solution.

Lakes Environmental offers a supercomputing service based on grid computing. The system distributes many small jobs running in the background of every computer and workstation available in the company. The straight CALPUFF execution was sliced into a series of short monthly calculations for each single source. These small jobs were conveniently fed into the PC cluster. This resulted in a quick turnaround and less disturbance to coworkers, who had contributed their desktops for the computing pool. The partial impacts from each source were later summed to yield the total impact.

The above method works because puffs are non-interacting in the CALPUFF model. Moreover, the incremental impacts of the new sources were obtained right away. There was no need to repeat the calculations for the old sources. Further simplification was obtained by grouping identical sources to reduce the total number of sources. In addition, scaling of source strength was employed to estimate impact changes with respect to the fuel sulfur concentration. The latter is an approximation as the chemical transformation is nonlinear in nature. Nevertheless, it turned out to be an excellent approximation for the problem concerned. We completed all the necessary calculations in one month, instead of one year.

---

Phone: (519) 746-5995
Fax: (519) 746-0793
E-mail: Jesse.The@weblakes.com
Web: www.weblakes.com

Dr Ka-Hing Yau is a Senior Scientist at Lakes Environmental
Prof. Jesse L. Thé, is Lakes Environmental CEO.