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Evaluation of Data Replacement Strategies for CASTNET Dry Deposition Modeling
Citation:
Rogers, C. M., T. F. Lavery, K. P. Mishoe, AND R. E. BAUMGARDNER. Evaluation of Data Replacement Strategies for CASTNET Dry Deposition Modeling. ENVIRONMENTAL SCIENCE AND TECHNOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, B(1):789-799, (2012).
Impact/Purpose:
The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) to provide data for determining relationships between changes in emissions and any subsequent changes in air quality, atmospheric deposition, and ecological effects. The monitoring network with sites located in rural areas was mandated by the 1990 Clean Air Act Amendments (CAAA) to assess the effectiveness of reduced emissions of sulfur dioxide (SO2) and oxides of nitrogen (NOx) as Congress recognized the need to track real-world environmental results as the Acid Rain Program (ARP) provisions of the CAAA were implemented. CASTNET has its origins with the National Dry Deposition Network (NDDN), which was established in 1986 and began operation in 1987. Many of the original NDDN sites are still operational after 20 years and provide useful information on trends in air quality.
Description:
The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) and its predecessor, the National Dry Deposition Network (NDDN), as national air quality and meteorological monitoring networks. The purpose of CASTNET is to track the progress of the 1990 Clean Air Act Amendments (CAAA) Acid Rain Program (ARP) in terms of reductions in sulfur and nitrogen deposition, improved air quality, and changes to affected ecosystems that result from reductions in emissions of sulfur dioxide (SO2) and oxides of nitrogen (NOx). Both CASTNET and NDDN were designed to measure concentrations of sulfur and nitrogen gases and particles and to estimate dry deposition using an inferential model. The design was based on the concept that atmospheric dry deposition flux could be estimated as Flux = C*Vd,) where C represents a measured air pollutant concentration and Vd) represents a modeled deposition velocity. In other words, the flux is directly proportional to the deposition velocity and concentration. Consequently, an uncertainty in the two parameters produces an uncertainty in the flux estimate. The multi-layer model (MLM), the computer model used to simulate dry deposition, requires information on meteorological conditions and vegetative cover as model input. Specifically, the MLM requires hourly averages of wind speed, standard deviation of wind direction (sigma theta), temperature, relative humidity, and surface wetness. Also as input, the MLM uses plant speciation data specific to each monitoring site. Speciation data include minimum and maximum leaf area index (LAI) values and data on the temporal evolution of vegetation leaf-out characterizing the surroundings of the site within a radius of 1 kilometer (km). The MLM calculates hourly deposition velocities for each pollutant. Any missing meteorological data point for an hour renders Vd) missing for that hour. The MLM post-processor aggregates the hourly deposition velocity and deposition rates into weekly, monthly, quarterly, seasonal, and annual values. Each aggregation step requires a percent completeness of 69 percent for the relevant underlying values. As a result of the percent completeness requirements, annual deposition estimates for some sites are not always available primarily because of invalid meteorological input data. This work investigates the ability to substitute historical values of deposition velocity or meteorological measurements from the site being modeled or from nearby sites for missing on-site data or measurements in order to improve the completeness of the dry deposition flux estimates while not significantly increasing their uncertainty.