Ozone Predictions in Atlanta,Georgia: Analysis of the 1999 Ozone Season

ABSTRACT

Ozone prediction has become an important activity in many U.S. ozone nonattainment areas. In this study, we describe the ozone prediction program in the Atlanta metropolitan area and analyze the performance of this program during the 1999 ozone-forecasting season. From May to September, a team of 10 air quality regulators, meteorologists, and atmospheric scientists made a daily prediction of the next-day maximum 8-hr average ozone concentration. The daily forecast was made aided by two linear regression models, a 3-dimensional air quality model, and the no-skill ozone persistence model. The team's performance is compared with the numerical models using several numerical indicators. Our analysis indicated that (1) the team correctly predicted next-day peak ozone concentrations 84% of the time, (2) the two linear regression models had a better performance than a 3-dimensional air quality model, (3) persistence was a strong predictor of ozone concentrations with a performance of 78%, and (4) about half of the team's wrong predictions could be prevented with improved meteorological predictions.     

Application of the Urban Airshed Model to Forecasting Next-Day Peak Ozone Concentrations in Atlanta,Georgia

ABSTRACT

Twenty-four to forty-eight-hour ozone air quality forecasts are increasingly being used in metropolitan areas to inform the public about potentially harmful air quality conditions. The forecasts are also behind “ozone action day” programs in which the public and private sectors are encouraged or mandated to alter activities that contribute to the formation of ground-level ozone. Presented here is a low-cost application of the Urban Airshed Model (UAM), an Eulerian 3-dimensional photochemical-transport grid model for generating next-day peak ozone concentration forecasts. During the summer of 1997, next-day peak ozone concentrations in Atlanta, GA, were predicted both by a team of eight forecasters and by the Urban Airshed Model in Forecast Mode (UAM-FM). Results are presented that compare the accuracy of the team and the UAM-FM. The results for the summer of 1997 indicate that the UAM-FM may be a better predictor of peak ozone concentrations when concentrations are high (> 0.095 ppmv), and the team may be a better predictor of ozone concentrations when concentrations are low (< 0.095 ppmv). The UAM-FM is also discussed in the context of other forecasting tools, primarily linear regression models and a no-skill, persistence-based technique.     

Application of the Urban Airshed Model to forecasting next-day peak ozone concentrations in Atlanta, Georgia

Twenty-four to forty-eight-hour ozone air quality forecasts are increasingly being used in metropolitan areas to inform the public about potentially harmful air quality conditions. The forecasts are also behind "ozone action day" programs in which the public and private sectors are encouraged or mandated to alter activities that contribute to the formation of ground-level ozone. Presented here is a low-cost application of the Urban Airshed Model (UAM), an Eulerian 3-dimensional photochemical-transport grid model for generating next-day peak ozone concentration forecasts. During the summer of 1997, next-day peak ozone concentrations in Atlanta, GA, were predicted both by a team of eight forecasters and by the Urban Airshed Model in Forecast Mode (UAM-FM). Results are presented that compare the accuracy of the team and the UAM-FM. The results for the summer of 1997 indicate that the UAM-FM may be a better predictor of peak ozone concentrations when concentrations are high (> 0.095 ppmv), and the team may be a better predictor of ozone concentrations when concentrations are low (< or = 0.095 ppmv). The UAM-FM is also discussed in the context of other forecasting tools, primarily linear regression models and a no-skill, persistence-based technique.     

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

ABSTRACT

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 spatiotem poral assessments of associations of ambient air pollutant concentrations with health outcome.     

Investigation into the use of satellite data in aiding characterization of particulate air quality in the Atlanta, Georgia metropolitan area

Poor air quality episodes occur often in metropolitan Atlanta, GA. The primary focus of this research is to assess the capability of satellites as a tool in characterizing air quality in Atlanta. Results indicate that intracity PM2.5 (particulate matter < or = 2.5 microm in aerodynamic diameter) concentrations show similar patterns as other U.S. urban areas, with the highest concentrations occurring within the city. PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) have higher values in the summer than spring, yet MODIS AOD doubles in the summer unlike PM2.5. Most (80%) of the Ozone Monitoring Instrument aerosol index (AI) is below 0.5 with little differences between spring and summer. Using this value as a constraint of the carbonaceous aerosol signal in the urban area, aerosol transport events such as wildfire smoke associated with higher positive AI values can be identified. The results indicate that MODIS AOD is well correlated with PM2.5 on a yearly and seasonal basis with correlation coefficients as high as 0.8 for Terra and 0.7 for Aqua. A possible alternative view of the PM2.5 and AOD relationship is seen through the use of AOD thresholds. These probabilistic thresholds provide a means to describe the air quality index (AQI) through the use of multiyear AOD records for a specific area. The National Ambient Air Quality Standards (NAAQS) are used to classify the AOD into different AQI codes and probabilistically determine thresholds of AOD that represent most of a specific AQI category. For example, 80% of cases of moderate AQI days have AOD values between 0.5 and 0.6. The development of AOD thresholds provides a useful tool for evaluating air quality from the use of satellites in regions where there are sparse ground-based measurements of PM2.5.     

On performing long-term predictions of ozone using the SOMS model

This study examined the potential of using the Simplified Ozone Modeling System (SOMS) (Venkatram et al., 1994. Atmospheric Environment 28, 3665–3678) to generate long-term ozone predictions that may be used to complement the results from more complex air quality models for creating control strategies and establishing long-term trends. A sensitivity study was performed using SOMS to study the application of a model, which is an exponential function of temperature, to estimate the intra-annual biogenic VOC concentration at the receptor in a 1-year run (i.e. 1988). The predictions were made for a core urban site in Baltimore, Maryland. After the sensitivity analyses was completed, the daily maximum ozone concentration (DMOC) was predicted for a 3-year (1987–1989) period for the Baltimore site. The results of the 3-year model prediction were compared with observations.     

Ozone response to precursor controls: comparison of data analysis methods with the predictions of photochemical air quality simulation models

Comparisons were made between the predictions of six photochemical air quality simulation models (PAQSMs) and three indicators of ozone response to emission reductions: the ratios of O₃/NO_z and O₃/NO_y and the extent of reaction. The values of the two indicator ratios and the extent of reaction were computed from the model-predicted mixing ratios of ozone and oxidized nitrogen species and were compared to the changes in peak 1 and 8 h ozone mixing ratios predicted by the PAQSMs. The ozone changes were determined from the ozone levels predicted for base-case emission levels and for reduced emissions of volatile organic compounds (VOCs) and oxides of nitrogen (NO_x). For all simulations, the model-predicted responses of peak 1 and 8 h ozone mixing ratios to VOC or NO_x emission reductions were correlated with the base-case extent of reaction and ratios of O₃/NO_z and O₃/NO_y. Peak ozone values increased following NO_x control in 95% (median over all simulations) of the high-ozone (>80 ppbv hourly mixing ratio in the base-case) grid cells having mean afternoon O₃/NO_z ratios less than 5 : 1, O₃/NO_y less than 4 : 1, or extent less than 0.6. Peak ozone levels decreased in response to NO_x reductions in 95% (median over all simulations) of the grid cells having peak hourly ozone mixing ratios greater than 80 ppbv and where mean afternoon O₃/NO_z exceeded 10 : 1, O₃/NO_y was greater than 8 : 1, or extent exceeded 0.8. Ozone responses varied in grid cells where O₃/NO_z was between 5 : 1 and 10 : 1, O₃/NO_y was between 4 : 1 and 8 : 1, or extent was between 0.6 and 0.8. The responses in such grid cells were affected by ozone responses in upwind grid cells and by the changes in ozone levels along the upwind boundaries of the modeling domains.     

Trends in on-road vehicle emissions and ambient air quality in Atlanta,Georgia, USA,from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO_x, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NO_x emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO_x in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NO_x, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.

Implications:Large reductions in on-road vehicle emissions of CO and NO_x in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO_x during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.     

Short-term temporal variation in PM2.5 mass and chemical composition during the Atlanta Supersite Experiment, 1999

Measurements in urban Atlanta of transient aerosol events in which PM2.5 mass concentrations rapidly rise and fall over a period of 3-6 hr are reported. The data are based on new measurement techniques demonstrated at the U.S. Environmental Protection Agency (EPA) Atlanta Supersite Experiment in August 1999. These independent instruments for aerosol chemical speciation of NO3-, SO4(2-), NH4+, and organic and elemental carbon (OC and EC), reconstructed the observed hourly dry PM2.5 mass to within 20% or better. Data from the experiment indicated that transient PM2.5 events were ubiquitous in Atlanta and were typically characterized by a sudden increase of EC (soot) and OC in the early morning or SO4(2-) in the late afternoon. The frequent temporal decoupling of these events provides insights into their origins, suggesting mobile sources in metro Atlanta as the main contributor to early morning PM2.5 and more regionally located point SO2 sources for afternoon PM2.5 events. The transient events may also have health implications. New data suggest that short-term PM2.5 exposures may lead to adverse health effects. Standard integrated filter-based techniques used in PM2.5 compliance monitoring networks and in most past PM2.5 epidemiologic studies collect samples over 24-hr periods and thus are unable to capture these transient events. Moreover, health-effects studies that focus on daily PM2.5 mass alone cannot evaluate the health implications of the unique and variable chemical properties of these episodes.     

Effects of Instrument Precision and Spatial Variability on the Assessment of the Temporal Variation of Ambient Air Pollution in Atlanta,Georgia

Abstract

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO₂, CO, NO_x, and O₃) and fine particulate matter ([PM_2.5] PM_2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7–40% of the temporal variation in the daily pollutant measures and was largest for the PM_2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO₂, CO, NO_x, and EC). Population–weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O₃ to 70% of the temporal variation for SO₂ and EC. Wind rose plots, corrected for diurnal and seasonal pattern effects, are used to demonstrate the impacts of local sources on monitoring station data. The results presented are being used to quantify the impacts of instrument precision and spatial variability on the assessment of health effects of ambient air pollution in Atlanta and are relevant to the interpretation of results from time series health studies that use data from fixed monitors.     

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind     

Comparison of Ozone Temporal Scales for Large Urban,Small Urban,and Rural Areas in Georgia

Abstract

Ground‐level ozone (O₃) time series are characterized by the sum of several distinct temporal scales: long‐term, seasonal, synoptic, diurnal (daily), and intraday variation. In this study, the authors use a Kolmorogov‐Zurbenko filter to separate the 1981–2001 O₃ time‐series from many sites in and around Georgia into these various components. The authors compare the temporal components to examine differences between small and large metropolitan areas and between urban and rural areas. They then focus on the synoptic component to define a predominant transport region or airshed for each site.     

Comparison of ozone temporal scales for large urban, small urban, and rural areas in Georgia

Ground-level ozone (O3) time series are characterized by the sum of several distinct temporal scales: long-term, seasonal, synoptic, diurnal (daily), and intraday variation. In this study, the authors use a Kolmorogov-Zurbenko filter to separate the 1981-2001 O3 time-series from many sites in and around Georgia into these various components. The authors compare the temporal components to examine differences between small and large metropolitan areas and between urban and rural areas. They then focus on the synoptic component to define a predominant transport region or airshed for each site.     

Understanding the emission impacts of high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experience from Atlanta,Georgia

Converting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011. The analyses employed the Motor Vehicle Emission Simulator (MOVES) for project-level analysis with monitored changes in vehicle activity data collected by Georgia Tech researchers for the Georgia Department of Transportation (GDOT). During the quarterly field data collection from 2010 to 2012, more than 1.5 million license plates were observed and matched to vehicle class and age information using the vehicle registration database. The study also utilized the 20-sec, lane-specific traffic operations data from the Georgia NaviGAtor intelligent transportation system, as well as a direct feed of HOT lane usage data from the State Road and Tollway Authority (SRTA) managed lane system. As such, the analyses in this paper simultaneously assessed the impacts associated with changes in traffic volumes, on-road operating conditions, and fleet composition before and after the conversion. Both greenhouse gases and criteria pollutants were examined.

Implications: A straight before-after analysis showed about 5% decrease in air pollutants and carbon dioxide (CO₂). However, when the before-after calendar year of analysis was held constant (to account for the effect of 1 yr of fleet turnover), mass emissions at the analysis site during peak hours increased by as much as 17%, with little change in CO₂. Further investigation revealed that a large percentage decrease in criteria pollutants in the straight before-after analysis was associated with a single calendar year change in MOVES. Hence, the Atlanta, Georgia, results suggest that an HOV-to-HOT conversion project may have increased mass emissions on the corridor. The results also showcase the importance of obtaining on-road data for emission impact assessment of HOV-to-HOT conversion projects.     

Ozone pollution and ozone biomonitoring in European cities. Part I: Ozone concentrations and cumulative exposure indices at urban and suburban sites

In the frame of a European research project on air quality in urban agglomerations, data on ozone concentrations from 23 automated urban and suburban monitoring stations in 11 cities from seven countries were analysed and evaluated. Daily and summer mean and maximum concentrations were computed based on hourly mean values, and cumulative ozone exposure indices (Accumulated exposure Over a Threshold of 40 ppb (AOT40), AOT20) were calculated. The diurnal profiles showed a characteristic pattern in most city centres, with minimum values in the early morning hours, a strong rise during the morning, peak concentrations in the afternoon, and a decline during the night. The widest amplitudes between minimum and maximum values were found in central and southern European cities such as Düsseldorf, Verona, Klagenfurt, Lyon or Barcelona. In the northern European cities of Edinburgh and Copenhagen, by contrast, maximum values were lower and diurnal variation was much smaller. Based on ozone concentrations as well as on cumulative exposure indices, a clear north–south gradient in ozone pollution, with increasing levels from northern and northwestern sites to central and southern European sites, was observed. Only the Spanish cities did not fit this pattern; there, ozone levels were again lower than in central European cities, probably due to the direct influence of strong car traffic emissions. In general, ozone concentrations and cumulative exposure were significantly higher at suburban sites than at urban and traffic-exposed sites. When applying the newly established European Union (EU) Directive on ozone pollution in ambient air, it was demonstrated that the target value for the protection of human health was regularly surpassed at urban as well as suburban sites, particularly in cities in Austria, France, northern Italy and southern Germany. European target values and long-term objectives for the protection of vegetation expressed as AOT40 were also exceeded at many monitoring sites.     

Ozone uptake by citrus trees exposed to a range of ozone concentrations

The Citrus genus includes a large number of species and varieties widely cultivated in the Central Valley of California and in many other countries having similar Mediterranean climates. In the summer, orchards in California experience high levels of tropospheric ozone, formed by reactions of volatile organic compounds (VOC) with oxides of nitrogen (NO_x). Citrus trees may improve air quality in the orchard environment by taking up ozone through stomatal and non-stomatal mechanisms, but they may ultimately be detrimental to regional air quality by emitting biogenic VOC (BVOC) that oxidize to form ozone and secondary organic aerosol downwind of the site of emission. BVOC also play a key role in removing ozone through gas-phase chemical reactions in the intercellular spaces of the leaves and in ambient air outside the plants. Ozone is known to oxidize leaf tissues after entering stomata, resulting in decreased carbon assimilation and crop yield. To characterize ozone deposition and BVOC emissions for lemon (Citrus limon), mandarin (Citrus reticulata), and orange (Citrus sinensis), we designed branch enclosures that allowed direct measurement of fluxes under different physiological conditions in a controlled greenhouse environment. Average ozone uptake was up to 11 nmol s^?1 m^?2 of leaf. At low concentrations of ozone (40 ppb), measured ozone deposition was higher than expected ozone deposition modeled on the basis of stomatal aperture and ozone concentration. Our results were in better agreement with modeled values when we included non-stomatal ozone loss by reaction with gas-phase BVOC emitted from the citrus plants. At high ozone concentrations (160 ppb), the measured ozone deposition was lower than modeled, and we speculate that this indicates ozone accumulation in the leaf mesophyll.     

Statistical analysis of winter ozone exceedances in the Uintah Basin,Utah, USA

Because of the confluence of several factors (persistent multiday inversions, petroleum production, and snow cover), the Uintah Basin of eastern Utah, USA, exhibits high concentrations of winter ozone. A regression analysis is presented that successfully predicts daily ozone concentration with a standard error of about 11 ppb. It also predicts with 90% accuracy whether any given day will exceed the National Ambient Air Quality Standard for ozone, 70 ppb. An analysis is introduced for calculating a “pseudo-lapse rate,” a determination of inversion intensity in the absence of sounding data. By combining the model with historical meteorological data, it is possible to make long-range predictions about ozone formation. The odds of observing no exceedance days in any given season are 38%. The odds of only three or fewer exceedance days in any given season are 46%.

Implications: This paper provides an improved understanding of the scientific underpinnings of the winter ozone phenomenon and an ability to make long-range predictions.     

Influence of ship emissions on ozone concentrations around coastal areas during summer season

The influence of ship emissions on ozone (O₃) concentrations in a coastal area (CA) including Busan port, Korea was examined based on a numerical modeling approach during a high O₃ episode. The analysis was performed by two sets of simulation scenarios: (1) with ship emissions (e.g., TOTAL case) and (2) without ship emissions (e.g., BASE case). A process analysis (PA) (the integrated processes rate (IPR) and integrated reaction rate (IRR) analyses) was used to evaluate the relative contributions of individual physical and chemical processes in O₃ production in and around the CA (e.g., sites of Dong Sam (DS) and Dae Yeon (DY)). The model study suggested the possibility that pollutant gases emitted from the ships traversing Busan port can exert a direct impact on the O₃ concentration levels in the CA. Largest impacts of ship emissions on the O₃ concentrations were predicted at the coast (up to 15 ppb) and at inland locations (about 5 ppb) due to both the photochemical production of pollutant gases emitted from the ships and meteorological conditions. From the PA, the photochemical production of O₃ (P(O₃)) due to ship emissions in the CA was found to increase by a mean of 1.5 ppb h^?1 (especially by ≥10 ppb h^?1 at the DS site) during the day.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.