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.     

Source Apportionment of Phoenix PM2.5 Aerosol with the Unmix Receptor Model

Abstract

The multivariate receptor model Unmix has been used to analyze a 3-yr PM_2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories: gasoline engines (33 ± 4%), diesel engines (16 ± 2%), secondary SO₄ ^2? (19 ± 2%), crustal/soil (22 ± 2%), and vegetative burning (10 ± 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Field studies of ethoprop movement and degradation in two Florida soils

Unsaturated- and saturated-zone field studies were conducted under two different agricultural conditions in Florida, U.S.A., to measure the movement and degradation of ethoprop.When ethoprop was applied to a sand soil in an orange grove located on the Florida central ridge, soil residues declined with a half-life of 13 days to near the limit of detection (0.01 μg g⁻¹ within 2 months. Ethoprop residues were generally contained in the upper 2 m of soil. Although low levels of ethoprop were found in four groundwater samples within the first 3 months after application, the cause of these isolated and transient residues could not be determined. The data show that there was no widespread plume of ethoprop residues beneath or downgradient from the treated area.On a flatwood soil on the west coast of Florida, ethoprop was incorporated into raised beds and protected by a plastic mulch in which tomatoes were grown. When the plastic mulch was present, ethoprop soil residues declined with a half-life of 40 days and were confined to the upper 0.3 m of soil. After the mulch was removed at crop harvest, the remaining ethoprop dissipated with a half-life of 12 days. Groundwater residues in excess of 1 μg L⁻¹ were confined to a single sample collected after the well had been damaged during a rototilling operation. Resides of < 1 μg L⁻¹ were present in three later samples from this well. At the sampling interval where the highest residues were detected in the damaged well, ethoprop residues of < 1 μg L⁻¹ were also found in three other wells but no residues were detected in these wells in later samples.     

Improved estimates of certainty in stable-isotope-based methods for tracking migratory animals.

The use of stable-hydrogen isotopes (deltaD) has become a common tool for estimating geographic patterns of movement in migratory animals. This method relies on broad and relatively predictable geographic patterning in deltaD values of precipitation, but these patterns are not estimated without error. In addition, deltaD measurements are relatively imprecise, particularly for organic tissue. Most models for estimating geographic locations have ignored these sources of error. Common modeling approaches include regression, range-matching, and likelihood-based assignment tests (including discriminant analysis). Here, we show the benefits of a simple stochastic extension to likelihood-based assignment tests that incorporates two estimable sources of error and describe the resulting influence on the certainty of assigning breeding origins for wintering American Redstarts (Setophaga ruticilla), a small Nearctic-Neotropical migratory bird. Through simulation, we incorporated both spatial interpolation error associated with models of deltaD in precipitation and analytical error associated with the measurement of deltaD in tissue samples. In general, assignments that did not include these sources of error fell within the ranges of the stochastic results, but the difference in proportion of birds assigned to any one breeding region varied by as much as 54%. To explore how the distribution of assignments generated from error models influenced the application of these results, we developed a simple model of winter habitat loss. We removed the proportion of Redstarts wintering at a particular site from the global population and then used the isotope-based assignments to predict the resulting population declines for each breeding region. This gave distributions of change in population sizes, some of which included no change or even a population increase. The sources of error we modeled may challenge the degree of certainty in the use of stable-isotope-based data on connectivity to predict population dynamics of migratory animals. We suggest that stronger inference will result from incorporating these sources of error into future studies that use deltaD or other stable isotopes to infer the geographic origin of individuals.     

Effects of dyadic duration on the relationship between leader behavior perceptions and follower outcomes

This research focused on the role of dyadic duration, the amount of time a subordinate has worked for the same supervisor, in leadership dynamics. Specifically, a field study of engineering personnel examined dyadic duration as a moderator of the relationships between supervisory leadership behavior and subordinates' attitudes and behavior. Moderated regression analysis revealed that the length of time a subordinate had served under the same supervisor influenced the relationship between supportive and directive leader behaviors and follower performance. Implications of these findings were discussed, focusing on the exchange process between individual leaders and followers.     

Field method comparison between passive air samplers and continuous monitors for Vocs and NO2 in El Paso, Texas

This study evaluates the performance of Model 3300 Ogawa Passive Nitrogen Dioxide (NO2) Samplers and 3M 3520 Organic Vapor Monitors (OVMs) by comparing integrated passive sampling concentrations to averaged hourly NO2 and volatile organic compound (VOC) measurements at two sites in El Paso, TX. Sampling periods were three time intervals (3-day weekend, 4-day weekday, and 7-day weekly) for three consecutive weeks. OVM concentrations were corrected for ambient pressure to account for higher elevation. Precise results (< 5% relative standard deviation, RSD) were found for NO2 measurements from collocated Ogawa samplers. Reproducibility was lower from duplicate OVMs for BTEX (benzene, toluene, ethylbenzene, and xylene isomers) VOCs (> or = 77% RSD for 2-day samples) with better precision for longer sampling periods. Comparison of Ogawa NO2 samplers with chemiluminescence measurements averaged over the same time period suggested potential calibration problems with the chemiluminescence analyzer. For BTEX species, generally good agreement was obtained between OVMs and automated-gas chromatograph (auto-GC) measurements. The OVMs successfully tracked increasing levels of VOCs recorded by the auto-GCs. However, except for toluene, OVM BTEX measurements generally exceeded their continuous counterparts with a mean bias of 5-10%. Although interpretation of the study results was limited due to small sample sizes, diffusion barrier influences caused by shelters that housed OVMs and differences in sampling heights between OVMs and auto-GC inlet may explain the overestimation.     

Spatial gradients and source apportionment of volatile organic compounds near roadways

Concentrations of 55 volatile organic compounds (VOCs) (C₂–C₁₂) are reported near a highway in Raleigh, NC. Thirty-minute samples were collected at eight locations, ranging from approximately 10–100 m perpendicular from the roadway. The highest concentrations of VOCs were generally measured closest to the roadway, and concentrations decreased exponentially with increasing distance from the roadway. The highest mean concentration for individual VOCs were for ethylene (3.10 ppbv) (mean concentration at x = 13 m), propane (2.27 ppbv), ethane (1.91 ppbv), isopentane (1.54 ppbv), toluene (0.95 ppbv), and n-butane (0.89 ppbv). Concentrations at the nearest roadway location (x = 13 m) were generally between 2.0 and 1.5 times those from the farthest roadway location (x = 92 m). The data were apportioned into four source categories using the EPA Chemical Mass Balance Model (CMB8.2): motor vehicle exhaust, compressed natural gas, propane gas, and evaporative gasoline. The majority of the VOCs resulted from motor vehicle exhaust (67 ± 12%) (% of total VOC at x = 13 m ± S.D.). Compressed natural gas, propane gas, and evaporative gasoline accounted for approximately 15%, 7% and 1% of the total VOC emissions, respectively, at x = 13 m.