CPP Explains the Pitfalls in AERMOD’S Building Downwash

Since the mid-90s, the US EPA has recommended the AERMOD atmospheric dispersion modeling system for estimating the impact of new or existing sources of pollution on ambient air quality. But AERMOD is far from perfect, and recent CPP studies have revealed key shortcomings, documented in the August issue of the Journal of Air and Waste Management Association.

In summary, downwash effects in AERMOD can overestimate pollutant concentrations for short, wide and long buildings, and structures that are both wide and narrow. The inflated numbers also occur with lattice structures, and streamlined objects such as tanks and hyperbolic cooling towers.

CPP has recently conducted field and wind tunnel studies revealing that AERMOD can over-predict concentrations by factors of 2 to 8 for these building configurations. The culprit: notable flaws in AERMOD’s current downwash formulation. You’ll find a detailed review in the article, downloadable for free:

CPP is hard at work to address the most critical issues in the current downwash theory. We’re grateful for funding from the American Petroleum Institute, the Corn Refiners Association, the American Forest & Paper Association, and the Electric Power Research Institute.

In the next few months, the updated theory will be published in a peer-reviewed journal article. Our goal is to submit by the end of 2017 a fully operational version of AERMOD with improved building downwash algorithms. EPA would then review and evaluate these changes before implementing them in the model.

Until that time, a wind tunnel study can minimize some of the downwash over-predictions in the current theory. Wind-tunnel-derived Equivalent Building Dimensions (EBDs) offer a more accurate, but still conservative, methodology to improve the inputs to AERMOD. This is preferred over AERMOD’s questionable inputs developed with the building preprocessor (i.e., BPIP). Learn more about the EBD method:

The EBD method has helped facilities mitigate downwash over-predictions and achieve compliance. Learn more from these examples:

Please contact CPP today to learn more.