August 2012
| Monitoring & Testing Events Update August 2012 |
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| Upcoming Conferences & Trade Shows - Mark Your Calendars! |
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Singapore - Singapore Sep. 6, 2012 - Sep. 7, 2012 |
2-Day Practical Air Quality Modeling with AERMOD/CALPUFF (Singapore)
This 2-day course will focus on the fundamentals and applications of AERMOD, including the comparison of AERMOD with ISCST3, and provide hands-on practices on AERMOD modeling. A half-day overview of the CALPUFF modeling theories and applications is also covered. The attendees will gain in-depth understanding of applying AERMOD and CALPUFF for air quality modeling. The new generation dispersion models, including AERMOD and CALPUFF, have been adopted in the U.S. as regulatory air quality models and widely used by many other countries. These models stand for the state-of-the-science in air quality modeling and provide powerful features to simulate various modeling situations and considerations. AERMOD replaced the outdated ISCST3 as the preferred near-field dispersion model with many new and improved features. CALPUFF is the non-steady-state puff model suitable for regional air quality modeling with gridded 3-dimensional meteorological data to the fate and transport of air pollutants. | ||
Denver - Colorado (USA) Sep. 19, 2012 - Sep. 20, 2012 |
Environmental Sampling and Data Quality™ - September 19-20, 2012
Understanding and improving sampling methods and the planning approach to sampling provides one of the best opportunities to improve sampling and analytical results. Every site is different, and the dynamic process of sample collection and analysis poses greater risk of error than laboratory results. Proper planning and use of statistics throughout the process can result in more accurate results and prevent wasted time and money. The Environmental Sampling and Data Quality™ course is designed for those responsible for developing sampling plans, evaluating sampling results, assuring data quality, or performing field sampling. You will learn to select an appropriate sampling method and gain an understanding of the associated variables and common mistakes, develop a scientifically and legally defensible sampling plan, and apply practical statistics throughout the planning and analysis process. | ||
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