The objectives of this white paper are to describe the state of the science with respect to total Nr deposition budgets in the United States and the research needed to improve these budgets from both measurement and modeling perspectives. The document is intended to serve as a plan for TDep research activities but also, more broadly, to provide program managers, natural resource managers, policy makers and scientists with an understanding of (1) the need for complete and accurate Nr deposition budgets to protect ecosystem health and human welfare, and (2) the linkages between the underlying policy-relevant science questions and the specific knowledge and data gaps needed to improve Nr deposition budgets.

Atmospheric deposition, both wet and dry, can be an important contributor to nitrogen and sulfur loading to ecosystems. Excessive deposition can cause acidification and eutrophication which can lead to harmful effects such as algal blooms, decreases in forest growth, loss of species richness, and shifts in species distribution. In more moderate amounts, atmospheric deposition can be a source of nutrients reducing the need for fertilization of agricultural areas. Quantifying the amount of atmospheric deposition is therefore a critical activity for both the US and Canada.

Determination of the amount of reactive nitrogen (Nr) deposition in excess of the ecosystem critical load (CL) requires an estimate of total deposition. Because the CL exceedance is used to inform policy decisions, uncertainty in both the CL and the exceedance itself must be understood. In this paper we review the state of the science with respect to the sources of uncertainty in total Nr deposition budgets used for CL assessments in North America and put forth recommendations for research and monitoring to improve deposition measurements and models. In the absence of methods to rigorously quantify uncertainty in total Nr deposition, a simple weighted deposition uncertainty metric (WDUM) is introduced as a tool for scientists and decision makers to use in assessing CL exceedances. Maps of the WDUM applied to National Atmospheric Deposition Program (NADP) Total Deposition (TDep) estimates show greater uncertainty in areas of the U.S. where dry deposition makes a larger contribution to the deposition budget, particularly ammonia (NH3) in agricultural areas and oxidized nitrogen (NOx) in urban areas. Organic N deposition is an important source of uncertainty over much of the U.S. Our analysis illustrates how the WDUM can be used to assess spatial patterns of deposition uncertainty and inform actions to improve deposition budgets for CL assessments at the local scale.

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of 'in-situ' hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of 'a posteriori' data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

The Clean Air Status and Trends Network (CASTNET) is a nationwide air quality monitoring network that began operation in 1991 under Title IX of the 1990 Clean Air Act Amendments (CAAA). Under that directive, CASTNET has collected air pollutant concentration data at rural sites across the nation to determine the effectiveness of national and regional emission control programs by evaluating air quality, atmospheric deposition, and ecological effects. CASTNET provides estimates of dry deposition fluxes across the nation using the Multi-Layer Model (MLM). Current and future work is focused on improving the measurement and modeling of deposition fluxes with the implementation of advanced sampling and modeling techniques.

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Network (NTN) to develop values of total deposition of sulfur and nitrogen. Data developed using this method are made available via the CASTNET website.

Environmental models are frequently used within regulatory and policy frameworks to estimate environmental metrics that are difficult or impossible to physically measure. As important decision tools, the uncertainty associated with the model outputs should impact their use in informing regulatory decisions and scientific inferences. In this paper, we present a case study illustrating a process for dealing with a key issue in the use and application of air quality models, the additional error in annual mean aggregations resulting from imputation of missing data from model data sets. The case study is based on the US Environmental Protection Agency’s Multi-layer Model, which estimates the hourly dry deposition velocity of air pollutants based on hourly measurements of meteorology and site characteristics. A simulation was implemented to evaluate the effect of substituting historical hour-specific average values for missing model deposition velocity predictions on annual mean aggregations. Sensitivity studies were performed to test the effects of different missing data patterns and evaluate the relative impact of the substitution procedure on annual mean SO2 deposition velocity estimates. The substitution procedure was shown to result generally in long-term unbiased estimates of the annual mean and contributed less than 20% additional error to the estimate even when all data were missing. Consequently, it may be possible to use the historical record of deposition velocities to provide reasonably accurate and unbiased annual estimates of deposition velocities for years without meteorological measurements.

To assess long-term trends in atmospheric deposition, the U.S. operates the Clean Air Status and Trends Network (CASTNET) and Canada operates the Canadian Air and Precipitation Monitoring Network (CAPMoN). Both networks use modeled dry deposition velocities and measured atmospheric concentrations to compute estimates of dry deposition. While concentration measurements from the two networks are comparable, flux estimates can be significantly different due to differences in the model-estimated dry deposition velocities. This study intercompares the dry deposition velocity models used by the networks to identify those model inputs and model algorithms that are responsible for the differences in the dry deposition velocity predictions of the gaseous trace species ozone (O3), sulfur dioxide (SO2), and nitric acid (HNO3). The Big-Leaf Model (BLM) used for CAPMoN was inserted into the CASTNET modeling framework so that the on-site meteorological data obtained at the CASTNET sites could be used as input to both models. The models were run for four CASTNET sites that spanned different land use types and climatologies. The models were incrementally modified to assess the impacts of algorithmic differences on the predicted deposition velocities. While differences in aerodynamic resistance between the models contributed strongly to differences in predicted dry deposition velocities for HNO3, it is the non-stomatal (ground and cuticle) resistance parameterizations that cause the largest differences for other chemical species. The study points to the need for further consideration of these resistances. Additionally, comparisons of both models against recent independent flux data are needed to assess the accuracy of the models.