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

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.     

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.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Prediction of ozone concentrations in Oporto city with statistical approaches

The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.     

Stochastic model to forecast ground-level ozone concentration at urban and rural areas

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,1)24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.     

A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area

Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested.     

The potential of statistical state space models in urban ozone forecasting

State space models for tropospheric urban ozone prediction are introduced and compared with linear regression models. The linear and non-linear state space models make accurate short-term predictions of the ozone dynamics. The average prediction error one hour in advance is 7 μg/m³ and increases logarithmically with time until it reaches 26 μg/m³ after 30 days. For a given sequence of solar radiation inputs, predictions converge exponentially with a time scale of 8 hours, so that the model is insensitive to perturbations of more than 150 μg/m³ O₃. The slow increase of the prediction error in addition to the uniqueness of the prediction are encouraging for applications of state space models in forecasting ozone levels when coupled with a model that predicts total radiation. Since a radiation prediction model will be more accurate during cloud-free conditions, in addition to the fact that the state space models perform better during the summer months, state space models are suitable for applications in sunny environments.     

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.     

Evaluating the performance of regional-scale photochemical modeling systems: Part II—ozone predictions

In this paper, the concept of scale analysis is applied to evaluate ozone predictions from two regional-scale air quality models. To this end, seasonal time series of observations and predictions from the RAMS3b/UAM-V and MM5/MAQSIP (SMRAQ) modeling systems for ozone were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the scale analysis approach can help improve our understanding of the models’ performance. The results indicate that UAM-V underestimates the total variance (energy) of the ozone time series when compared with observations, but shows a higher mean value than the observations. On the other hand, MAQSIP is able to better reproduce the average energy and mean concentration of the observations. However, both modeling systems do not capture the amount of variability present on the intra-day time scale primarily due to the grid resolution used in the models. For both modeling systems, the correlations between the predictions and observations are insignificant for the intra-day component, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and baseline components. This better model performance on longer time scales suggests that current regional-scale models are most skillful in characterizing average patterns over extended periods, rather than in predicting concentrations at specific locations, during 1–2 day episodic events. In addition, we discuss the implications of these results to using the model-predicted daily maximum ozone concentrations in the regulatory framework in light of the uncertainties introduced by the models’ poor performance on the intra-day and diurnal time scales.     

Accuracy and reliability of an automated air quality forecast system for ozone in seven Kentucky metropolitan areas

An automated forecast system for ozone in seven Kentucky metropolitan areas has been operational since 2004. The forecast system automatically downloads the required input data twice each day, produces next-day forecasts of metro area peak 8-h average ozone concentration using a computer coded hybrid nonlinear regression (NLR) model, and posts the results on a website. The automated models were similar to previous NLR models, first applied to forecasting ozone in the Louisville metro area. The forecast system operated reliably during the 2004 and 2005 O₃ seasons, producing at least one forecast per day better than 99% of the time. The forecast accuracy of the automated system was good. For all 2004 and 2005 forecasts, the mean absolute error was equal to 8.7 ppb, or 15.6% of the overall mean concentration. The overall detection rate of air quality standard exceedences was 56%, and the overall false alarm rate was 42%. In Louisville, the performance of the automated system was comparable to that of expert forecasters using the NLR model as a forecast tool.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

An enhanced ozone forecasting model using air mass trajectory analysis

An enhanced ozone forecasting model using nonlinear regression and an air mass trajectory parameter has been developed and field tested. The model performed significantly better in predicting daily maximum 1-h ozone concentrations during a five-year model calibration period (1993–1997) than did a previously reported regression model. This was particularly true on the 28 “high ozone” days ([O₃]>120 ppb) during the period, for which the mean absolute error (MAE) improved from 21.7 to 12.1 ppb. On the 77 days meteorologically conducive to high ozone, the MAE improved from 12.2 to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 to 8.35 ppb. The model was field-tested during the 1998 ozone season, and performed about as expected. Using actual meteorological data as input for the ozone predictions, the MAE for the season was 11.0 ppb. For the daily ozone forecasts, which used meteorological forecast data as input, the MAE was 13.4 ppb. The high ozone days were all anticipated by the ozone forecasters when the model was used for next day forecasts.     

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.     

A comparison study of three urban air pollution models

The predictions of three urban air pollution models with varying degrees of mathematical and computational complexities are compared against the hourly SO₂ ground-level concentrations observed on 10 winter nights of the RAPS experiment in St. Louis. The emphasis in this study is on the prediction of urban area source concentrations. Statistics for the paired comparison of predictions of each model with the observations are presented. The RAM and the ATDL model with stable diffusion coefficients overestimated the observed night-time concentrations. The results show that the performance of the ATDL model with near-neutral diffusion coefficients is comparable to the more sophisticated 3-D grid numerical model.     

Modelling the photochemical pollution over the metropolitan area of Porto Alegre,Brazil

The main purpose of this study is to evaluate the photochemical pollution over the Metropolitan Area of Porto Alegre (MAPA), Brazil, where high concentrations of ozone have been registered during the past years. Due to the restricted spatial coverage of the monitoring air quality network, a numerical modelling technique was selected and applied to this assessment exercise. Two different chemistry-transport models – CAMx and CALGRID – were applied for a summer period, driven by the MM5 meteorological model. The meteorological model performance was evaluated comparing its results to available monitoring data measured at the Porto Alegre airport. Validation results point out a good model performance. It was not possible to evaluate the chemistry models performance due to the lack of adequate monitoring data. Nevertheless, the model intercomparison between CAMx and CALGRID shows a similar behaviour in what concerns the simulation of nitrogen dioxide, but some discrepancies concerning ozone. Regarding the fulfilment of the Brazilian air quality targets, the simulated ozone concentrations surpass the legislated value in specific periods, mainly outside the urban area of Porto Alegre. The ozone formation is influenced by the emission of pollutants that act as precursors (like the nitrogen oxides emitted at Porto Alegre urban area and coming from a large refinery complex) and by the meteorological conditions.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

An assessment of the impact of California's Phase 2 Reformulated Gasoline on ozone air quality

California's Phase 2 Reformulated Gasoline (CaRFG), introduced early in 1996, represents an important step toward attainment of ozone standards. Studies of vehicle emissions and ambient air quality data have reported substantial reductions of ozone precursors due to CaRFG. This study uses daily measurements of regional ozone and meteorology to estimate the effect of CaRFG on ozone concentrations in three areas of California. In each area, a regression model was used to partially account for the daily effects of meteorology on area-wide ozone maxima for May-October. The statistical models are based on combinations of air temperature aloft (approximately 5000 ft), surface air temperatures, and surface wind speeds. Estimated ozone benefits were attributed to CaRFG after accounting for meteorology, which improved the precision of the estimates by approximately 37-57% based on a resampling analysis. The ozone benefits were calculated as the difference in ozone times the proportion of the reductions of hydrocarbons and nitrogen oxides attributed to CaRFG by the best available emission inventories. Ozone benefits attributed to CaRFG (with approximately 90% confidence) are 8-13% in the Los Angeles area, -2-6% in the San Francisco Bay area overall with greater benefits in two major subregions, and 3-15% in the Sacramento area.     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Meteorological factors of ozone predictability at Houston, Texas

Several ozone modeling approaches were investigated to determine if uncertainties in the meteorological data would be sufficiently large to limit the application of physically realistic ozone (O3) forecast models. Three diagnostic schemes were evaluated for the period of May through September 1997 for Houston, TX. Correlations between measured daily maximum and model calculated O3 air concentrations were found to be 0.70 using a linear regression model, 0.65 using a non-advective box model, and 0.49 using a three-dimensional (3-D) transport and dispersion model. Although the regression model had the highest correlation, it showed substantial underestimates of the highest concentrations. The box model results were the most similar to the regression model and did not show as much underestimation. The more complex 3-D modeling approach yielded the worst results, likely resulting from O3 maxima that were driven by local factors rather than by the transport of pollutants from outside of the Houston domain. The highest O3 concentrations at Houston were associated with light winds and meandering trajectories. A comparison of the gridded meteorological data used by the 3-D model to the observations showed that the wind direction and speed values at Houston differed most on those days on which the O3 underestimations were the greatest. These periods also tended to correspond with poor precipitation and temperature estimates. It is concluded that better results are not just obtained through additional modeling complexity, but there needs to be a comparable increase in the accuracy of the meteorological data.