Dry deposition calculations for the clean air status and trends network

Clarke, J. F.; Edgerton, Eric S.; Martin, Blair

doi:10.1016/s1352-2310(97)00141-6

Cited by 155 publications

(118 citation statements)

References 9 publications

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“…15,23,24 The differences in the deposition velocities of SO 4 and NO 3 precursors could also affect the spatial differences in SO 4 and NO 3 variability; the dry deposition velocity of the precursor HNO 3 is approximately an order of magnitude larger than that of the precursor SO 2 . 25 However, it is unlikely that the dry deposition rates of SO 4 and NO 3 aerosols themselves play a role, because the dry deposition velocities of both particulate species are predominantly controlled by aerodynamic resistance and are not expected to differ significantly. 25,26 Based on county-level NO x and SO 2 emission estimates ( Figure 5) from the 1996 National Emissions Trends (NET) inventory, 27 it is less likely that the distribution of emissions are a large influence on the spatial homogeneity of SO 4 versus NO 3 , because a large emissions gradient is evident for both species between the urban and rural monitors.…”

Section: Resultsmentioning

confidence: 99%

“…25 However, it is unlikely that the dry deposition rates of SO 4 and NO 3 aerosols themselves play a role, because the dry deposition velocities of both particulate species are predominantly controlled by aerodynamic resistance and are not expected to differ significantly. 25,26 Based on county-level NO x and SO 2 emission estimates ( Figure 5) from the 1996 National Emissions Trends (NET) inventory, 27 it is less likely that the distribution of emissions are a large influence on the spatial homogeneity of SO 4 versus NO 3 , because a large emissions gradient is evident for both species between the urban and rural monitors. If the emissions are summed for a 40-mi radius (to span the 80-mi distance between the two closest monitors) around each monitor (Table 3), the NO x and SO 2 emissions are significantly higher at the Washington area when compared with the three rural monitors, with the one exception being similar SO 2 emissions estimates for the Dolly Sods and Washington areas.…”

Section: Resultsmentioning

confidence: 99%

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Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

Walsh

Gilliland

2001

Journal of the Air & Waste Management Association

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

Walsh

Gilliland

2001

Journal of the Air & Waste Management Association

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“…as the input into the optimized Equation (5). Recall that such optimized equations exist for each grid-cell i, for which an interpolation is to be done.…”

Section: Mobile Measurementsmentioning

confidence: 99%

“…Temperature was processed in analogous way as PM 2.5 -concentrations [28] to obtain observed temperature at the resolution of the interpolation grid. These observed temperatures then were included in developing Equation (5). The inclusion of any of the meteorological quantities means a reduction of the CMAQ-database to only those concentrations that were determined for the respective meteorological conditions.…”

Section: Sensitivity Studiesmentioning

confidence: 99%

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A Tool for Public PM<sub>2.5</sub>-Concentration Advisory Based on Mobile Measurements

Tran¹,

Leelasakultum²,

Mölders³

2012

JEP

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A tool was developed that interpolates mobile measurements of PM 2.5 -concentrations into unmonitored areas of the Fairbanks nonattainment area for public air-quality advisory. The tool uses simulations with the Alaska adapted version of the Weather Research and Forecasting (WRF) and the Community Modeling and Analysis Quality (CMAQ) modeling system as a database. The tool uses the GPS-data of the vehicle's route, and the database to determine linear regression equations for the relationships between the PM 2.5 -concentrations at the locations on the route and those outside the route. Once the interpolation equations are determined, the tool uses the mobile measurements as input into these equations that interpolate the measurements into the unmonitored neighborhoods. An episode of winter 2009/10 served as database for the tool's interpolation algorithm. An independent episode of winter 2010/11 served to demonstrate and evaluate the performance of the tool. The evaluation showed that the tool well reproduced the spatial distribution of the observed as well as simulated concentrations. It is demonstrated that the tool does not require a database that contains data of the episode for which the interpolation is to be made. Potential challenges in applying this tools and its transferability are discussed critically.

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Estimation of regional trends in sulfur dioxide over the eastern United States

Holland

Cox

et al. 2000

Environmetrics

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Emission reductions were mandated in the Clean Air Act Amendments of 1990 with the expectation of concomitant reductions in ambient concentrations of atmospherically-transported pollutants. To evaluate the effectiveness of the legislated emission reductions using monitoring data, this paper proposes a two-stage approach for the estimation of regional trends and their standard errors. In the first stage, a generalized additive model (GAM) is fitted to airborne sulfur dioxide (SO 2 ) data at each of 35 sites in the eastern United States to estimate the form and magnitude of the site-specific trend (defined as percent total change) from 1989 to 1995. This analysis is designed to adjust the SO 2 data for the influences of meteorology and season. In the second stage, the estimated trends are treated as samples with site-dependent measurement error from a Gaussian random field with a stationary covariance function. Kriging methodology is adapted to construct spatially-smoothed estimates of the true trend for three large regions in the eastern U.S. Finally, a Bayesian analysis with Markov Chain Monte Carlo (MCMC) methods is used to obtain regional trend estimates and their standard errors, which take account of the estimation of the unknown covariance parameters as well as the stochastic variation of the random fields. Both spatial estimation techniques produced similar results in terms of regional trend and standard error.

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Dry deposition calculations for the clean air status and trends network

Cited by 155 publications

References 9 publications

Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

A Tool for Public PM<sub>2.5</sub>-Concentration Advisory Based on Mobile Measurements

Estimation of regional trends in sulfur dioxide over the eastern United States

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Dry deposition calculations for the clean air status and trends network

Cited by 155 publications

References 9 publications

Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

Regional Variations in Sulfate and Nitrate on Annual, Seasonal, and Synoptic Time Scales

A Tool for Public PM&lt;sub&gt;2.5&lt;/sub&gt;-Concentration Advisory Based on Mobile Measurements

Estimation of regional trends in sulfur dioxide over the eastern United States

Contact Info

Product

Resources

About

A Tool for Public PM<sub>2.5</sub>-Concentration Advisory Based on Mobile Measurements