James G. Wilkinson

Temporal and Spatial Distributions of Ozone in Atlanta: Regulatory and Epidemiologic Implications

Relationships between ambient levels of selected air pollutants and pediatric asthma exacerbation in Atlanta were studied retrospectively. As a part of this study, temporal and spatial distributions of ambient ozone concentrations in the 20-county. Atlanta metropolitan area during the summers of 1993, 1994, and 1995 were assessed. A universal kriging procedure was used for spatial interpolation of aerometric monitoring station data. In this paper, the temporal and spatial distributions of ozone are described, and regulatory and epidemiologic implications are discussed. For the study period, the Atlanta ozone nonattainment area based on the 1-h, exceedance-based standard of 0.12 ppm is estimated to expand--from 56% of the Atlanta MSA by area and 71% by population to 88% by area and 96% by population--under the new 8-h, concentration-based standard of 0.08 ppm. Regarding asthma exacerbation, a 4% increase in pediatric asthma rate per 20-ppb increase in ambient ozone concentration was observed (p-value = 0.001), with ambient ozone level representing a general indicator of air quality due to its correlations with other pollutants. The use of spatial ozone estimates in the epidemiologic analysis demonstrates the need for control of demographic covariates in spatiotemporal assessments of associations of ambient air pollutant concentrations with health outcome.

Development of a comprehensive, multiscale ''one-atmosphere'' modeling system: application to the Southern Appalachian Mountains

A comprehensive three-dimensional Eulerian photochemical model (URM-1ATM) was developed that simulates urban and regional gas and size-resolved aerosol concentrations of pollutants in the atmosphere and both wet and dry deposition. In this study, RAMS and EMS-95 are used to generate meteorological and emission input files, respectively. The modeling system is then applied to simulate the evolution, transport, and removal of atmospheric pollutants over the Eastern US for the 11–19 July 1995 episode. Performance statistics are calculated for ozone, speciated fine particles, and acid deposition mass fluxes. r 2002 Elsevier Science Ltd. All rights reserved.

Integrated assessment modeling of atmospheric pollutants in the Southern Appalachian Mountains: Part II. Fine particulate matter and visibility.

Abstract As part of the Southern Appalachian Mountains Initiative, a comprehensive air quality modeling system was developed to evaluate potential emission control strategies to reduce atmospheric pollutant levels at the Class I areas located in the Southern Appalachian Mountains. Six multiday episodes between 1991 and 1995 were simulated, and the skill of the modeling system was evaluated. Two papers comprise various parts of this study. Part I details the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. This paper (part II) addresses issues involved with modeling particulate matter (PM) and its relationship to visibility. For most of the episodes, the fractional error was ∼50% or less for the major constituents of the fine PM (i.e., sulfate [SO4] and organics) in the region. The mean normalized errors and fractional errors are generally larger for the NO3 and soil components, but these components are relatively small. Variations in modeling bias with pollutant levels were also examined. The model showed a systematic overestimation for low levels and an underestimation for high levels for most PM species. For ammonium, the model showed better performance at lower SO4 concentrations when the measured SO4 was assumed to be completely neutralized (ammonium sulfate) and better performance at higher SO4 concentrations when the partially neutralized (ammonium bisulfate) assumption was made. The contributions of various components of PM to reductions in visibility were also calculated; SO4 was found to be the major contributor.

Integrated Assessment Modeling of Atmospheric Pollutants in the Southern Appalachian Mountains. Part I: Hourly and Seasonal Ozone

Abstract Recently, a comprehensive air quality modeling system was developed as part of the Southern Appalachians Mountains Initiative (SAMI) with the ability to simulate meteorology, emissions, ozone, size- and composition-resolved particulate matter, and pollutant deposition fluxes. As part of SAMI, the RAMS/EMS-95/URM-1ATM modeling system was used to evaluate potential emission control strategies to reduce atmospheric pollutant levels at Class I areas located in the Southern Appalachians Mountains. This article discusses the details of the ozone model performance and the methodology that was used to scale discrete episodic pollutant levels to seasonal and annual averages. The daily mean normalized bias and error for 1-hr and 8-hr ozone were within U.S. Environment Protection Agency guidance criteria for urban-scale modeling. The model typically showed a systematic overestimation for low ozone levels and an underestimation for high levels. Because SAMI was primarily interested in simulating the growing season ozone levels in Class I areas, daily and seasonal cumulative ozone exposure, as characterized by the W126 index, were also evaluated. The daily ozone W126 performance was not as good as the hourly ozone performance; however, the seasonal ozone W126 scaled up from daily values was within 17% of the observations at two typical Class I areas of the SAMI region. The overall ozone performance of the model was deemed acceptable for the purposes of SAMI’s assessment.

Modeling and Direct Sensitivity Analysis of Biogenic Emissions Impacts on Regional Ozone Formation in the Mexico-U.S. Border Area

ABSTRACT A spatially and temporally resolved biogenic hydrocarbon and nitrogen oxides (NOx) emissions inventory has been developed for a region along the Mexico-U.S. border area. Average daily biogenic non-methane organic gases (NMOG) emissions for the 1700 × 1000 km2 domain were estimated at 23,800 metric tons/day (62% from Mexico and 38% from the United States), and biogenic NOx was estimated at 1230 metric tons/day (54% from Mexico and 46% from the United States) for the July 1820, 1993, ozone episode. The biogenic NMOG represented 74% of the total NMOG emissions, and biogenic NOx was 14% of the total NOx. The CIT photochemical airshed model was used to assess how biogenic emissions impact air quality. Predicted ground-level ozone increased by 510 ppb in most rural areas, 10-20 ppb near urban centers, and 20-30 ppb immediately downwind of the urban centers compared to simulations in which only anthropogenic emissions were used. A sensitivity analysis of predicted ozone concentration to emissions was performed using the decoupled direct method for three dimensional air quality models (DDM-3D). The highest positive sensitivity of ground-level ozone concentration to biogenic volatile organic compound (VOC) emissions (i.e., increasing biogenic VOC emissions results in increasing ozone concentrations) was predicted to be in locations with high NOx levels, (i.e., the urban areas). One urban center—Houston—was predicted to have a slight negative sensitivity to biogenic NO emissions (i.e., increasing biogenic NO emissions results in decreasing local ozone concentrations). The highest sensitivities of ozone concentrations to on-road mobile source VOC emissions, all positive, were mainly in the urban areas. The highest sensitivities of ozone concentrations to on-road mobile source NOx emissions were predicted in both urban (either positive or negative sensitivities) and rural (positive sensitivities) locations.

Ozone Sensitivity and Uncertainty Analysis Using DDM-3D in a Photochemical Air Quality Model

Sensitivity analysis plays an important role in understanding the response of an environmental system to the variation of model inputs or parameters. This information can be further utilized to explore the model uncertainties introduced from these model inputs and parameters. However, sensitivity analysis has not been used as widely as desired in multidimensional models because of its complexity. A fast and formal sensitivity technique (DDM-3D) has been developed and implemented in the CIT (California/Carnegie Institute of Technology) airshed model to evaluate the sensitivity of predicted pollutant levels to the reaction rate constants of gas-phase photochemical mechanism. The study focuses on the chemical rate constants, which have been identified to be influential to the predicted ozone uncertainty in previous studies. The ozone sensitivities to rate parameters are then computed spatially in a multiday ozone episode, August 27–29, 1987, applied to the Los Angeles area, southern California. It was found that only a limited number of rate constants have a significant influence on ozone predictions. Combined with the sensitivity analysis, a first-order uncertainty was conducted and results indicate that uncertainty of reaction rate constants have significant impacts on the ozone levels. The uncertainty (± lσ) in ozone ranged from 10–35% of predicted levels downwind of Los Angeles and 35–50% for the urban core during afternoon high-ozone hours. In addition to the first-order uncertainty analysis, a Monte Carlo simulation incorporated with Latin Hypercube Sampling (LHS) technique was conducted to assess the importance of non-linearities. It was found that the two approaches gave very similar results. The results also suggest that the overall uncertainty of predicted ozone is highly dominated by the uncertainty of rate constant of HNO3 formation.

Integrated Regional Modeling of Ozone, Particulate Matter, and Deposition over the Eastern United States: Comparisons over Wet and Dry Episodes

A “One-Atmosphere” modeling approach has been taken to help assess the impact of control strategies on air quality in the Southern Appalachians. The modeling system, consisting of RAMS, EMS-95 and URM, simulates gaseous and condensed phase pollutants, and uses a sectional approach to provide size distributions of the aerosol. It is also used to simulate dry and wet deposition. In this paper, the model’s results were compared against a suite of observations for ozone and PM concentrations, as well as wet deposition, for four episodes.Two of those episodes were relatively wet, and two drier. While the results tended to be good for both sets of episodes,there was a tendency to overestimate ozone and underestimate aerosols during the wet episodes.

MM5 Simulation of the Meteorological Conditions During a South Coast ozone study (SCOS’97) Episode

A five day MM5 simulation of meteorological conditions during an important SCOS’97 ozone episode showed that the model accurately reproduced most of the observed features associated with a inland moving sea breeze front, i.e., its movement speed and inland penetration. Statistical comparisons between near-surface observed and predicted meteorological values (using MAPS) showed reasonable agreement, with differences in temperature, however, largest on the day with the strongest large scale forcing. This effect was not reproduced in the current simulation, as it did not include (model) analysis nudging.

Fast Sensitivity Analysis of Three-Dimensional Photochemical Models

Photochemical air quality models increasingly are being used to understand the atmospheric dynamics of air pollutants and as the basis of emission control regulations. The response of the these model predictions to system parameters or emission controls provides valuable information for the strategy design to improve air quality. Such information can be pursued via sensitivity analysis, the systematic calculation of sensitivity coefficients, to quantitatively measure these dependencies. However, sensitivity analysis has not seen as wide of use as desired, in part because of the implementation complexity as well as computational limitations. For these reasons, sensitivity analysis has been applied primarily to subsystems of air quality models (e. g. Koda et al., 1974; Rabitz et al., 1983; Milford et al., 1992), or to limited aspects in air quality models (Cho et al., 1987). The “brute-force” method has been the most frequently used to determine model sensitivities, but it rapidly becomes less viable and prohibitively inefficient for a model when a large number of sensitivity coefficients needs to be computed.