Michael E. Chang

On using inverse methods for resolving emissions with large spatial inhomogeneities

We apply an inverse method to estimate the carbon monoxide emissions in Atlanta, Georgia. The resultant carbon monoxide inventory is unrealistically characterized by temporal oscillations on the frequency of 1 hour. We suspect that the difficulty in deducing the emissions is due to inhomogeneities in the spatial distribution of the emissions. In several controlled experiments we reproduce the oscillations by introducing relatively small errors into the spatial distribution of the emission inventory. In similar experiments with isoprene in which the emissions are more homogeneous, we do not find this problem. These results are discussed in the context of previous inverse studies of carbon monoxide and isoprene.

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.

The use of survey data to investigate ozone sensitivity to point sources

Abstract During the Southern Oxidants Studys 1992 Atlanta Intensive, a survey was conducted to improve the emission estimates from point sources for the Atlanta metropolitan region. The survey consisted of a questionnaire and a daily activity log for the largest point sources in the region. The point source information was used to compare a 1992 typical summer days emissions with a specific days emissions (10 August 1992). Both emission inventories indicate that about 90% of point source nitrogen oxides (NO x ) emissions were from power plants. Furthermore, our results show that the daily variation of point source NO x emissions during the Intensive Study was mostly due to the emissions from the power plants. The daily variation of NO x emissions with respect to a typical summer day was as much as 24%. Although the day-to-day variability in point source VOC emissions was as much as 28%, their contribution to the total VOC was not significant. Finally, we evaluate the impact of NO x emissions from power plants on ozone concentrations. Air quality model simulations show significantly different ozone concentrations depending on power plant location.

Using synoptic classification to evaluate an operational air quality forecasting system in Atlanta

Since 2006, a team of forecasters in Georgia (USA) has been using the high–resolution air quality forecasting system (Hi–Res) as an aid for making ozone (O3) and fine particulate matter (PM2.5) forecasts. Here, we examine Hi–Res’s O3 and PM2.5 forecasting performance for the Atlanta metropolitan area during the summers of 2006–2009. A classificatory evaluation approach was adopted. The spatial synoptic classification (SSC) calendar for Atlanta was used to cluster the forecasting days into typical summer weather types of dry moderate, dry tropical, moist moderate, moist tropical, and a transition class. The forecasting days were also classified according to emissions conditions as special weekdays (Monday and Friday), typical weekdays and weekends/holidays. Evaluation of forecasts during 2006– 2009 shows that O3 performance was worse on moist days and better on dry days. This is an important concern for forecasters since a sizeable number of days that exceeded the National Ambient Air Quality Standard (NAAQS) for O3 were observed under moist tropical weather type during the period. On the other hand, PM2.5 performance during 2006–2008 was opposite – worse on dry days, especially on dry tropical days, and better on moist days. This too is a concern since higher concentrations of PM2.5 were observed to occur on dry days. In 2009, PM2.5 forecasting performance on dry days was improved significantly by integrating a new secondary organic aerosol (SOA) module into the system. As a result, the differences in PM2.5 forecasting performance between dry and moist days were diminished. Other results of this study, suggest that a relatively larger forecasting error on weekends/holidays may be due to higher uncertainties in emission estimates on those days. To a lesser extent, this was also true on special weekdays because of the greater variations in rush hour emissions relative to typical weekdays.

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.

Chapter 2.14 Forecasting ozone and PM2.5 in southeastern U.S.

Abstract An air quality forecasting system was developed to aid the operational ozone and PM 2.5 forecasting in Atlanta, Georgia. The system is based on three dimensional models for weather and air quality prediction and provides high resolution locally. A preliminary evaluation shows that the system has the potential of producing reliable forecasts.

Source-Impact Forecasting for Dynamic Air Quality Management: Application to Prescribed Burn Management

The newly developed weather-based prescribed burn forecasting capability presents new opportunities for dynamic air quality management. Forecasting of burn emissions has been incorporated into the HiRes-2 Air Quality Forecasting System. Forecasts are being produced daily for air quality and the impacts of power plant, traffic and prescribed burn emissions. The ultimate goal is to integrate these air quality forecasts into the burn permitting operations.