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.     

Assessment of a regulatory model's performance relative to large spatial heterogeneity in observed ozone in Houston,Texas

In Houston, some of the highest measured 8-hr ozone (O₃) peaks are characterized by sudden increases in observed concentrations of at least 40 ppb in 1 hr, or 60 ppb in 2 hr. Measurements show that these large hourly changes appear at only a few monitors and span a narrow geographic area, suggesting a spatially heterogeneous field of O₃ concentrations. This study assessed whether a regulatory air quality model (AQM) can simulate this observed behavior. The AQM did not reproduce the magnitude or location of some of the highest observed hourly O₃ changes, and it also failed to capture the limited spatial extent. On days with measured large hourly changes in O₃ concentrations, the AQM predicted high O₃ over large regions of Houston, resulting in overpredictions at several monitors. This analysis shows that the model can make high O₃, but on these days the predicted spatial field suggests that the model had a different cause. Some observed large hourly changes in O₃ concentrations have been linked to random releases of industrial volatile organic compounds (VOCs). In the AQM emission inventory, there are several emission events when an industrial point source increases VOC emissions in excess of 10,000 mol/hr. One instance increased predicted downwind O₃ concentrations up to 25 ppb. These results show that the modeling system is responsive to a large VOC release, but the timing and location of the release, and meteorological conditions, are critical requirements. Attainment of the O₃ standard requires the use of observational data and AQM predictions. If the large observed hourly changes are indicative of a separate cause of high O₃, then the model may not include that cause, which might result in regulators enacting control strategies that could be ineffective.

Implications To show the attainment of the O₃ standard, the U.S. Environmental Protection Agency (EPA) requires the use of observations and model predictions under the assumption that simulations are capable of reproducing observed phenomena. The regulatory model is unable to reproduce observed behavior measured in the observational database. If the large observed hourly changes were indicative of a separate cause of high O₃, then the model would not include that cause. Inaccurate model predictions may prompt air quality regulators to enact control strategies that are effective in the modeling system, but prove ineffective in the real world.     

Summertime photochemical ozone formation in Santiago,Chile

The city of Santiago, Chile experiences frequent high pollution episodes and as a consequence very high ozone concentrations, which are associated with health problems including increasing daily mortality and hospital admissions for respiratory illnesses. The development of ozone abatement strategies requires the determination of the potential of each pollutant to produce ozone, taking into account known mechanisms and chemical kinetics in addition to ambient atmospheric conditions. In this study, the photochemical formation of ozone during a summer campaign carried out from March 8–20, 2005 has been investigated using an urban photochemical box model based on the Master Chemical Mechanism (MCMv3.1). The MCM box model has been constrained with 10 min averages of simultaneous measurements of HONO, HCHO, CO, NO, j(O¹D), j(NO₂), 31 volatile organic compounds (VOCs) and meteorological parameters. The O₃–NO_x–VOC sensitivities have been determined by simulating ozone formation at different VOC and NO_x concentrations. Ozone sensitivity analyses showed that photochemical ozone formation is VOC-limited under average summertime conditions in Santiago. The results of the model simulations have been compared with a set of potential empirical indicator relationships including H₂O₂/HNO₃, HCHO/NO_y and O₃/NO_z. The ozone forming potential of each measured VOC has been determined using the MCM box model. The impacts of the above study on possible summertime ozone control strategies in Santiago are discussed.     

Modeling the long-term frequency distribution of regional ozone concentrations

Efficient methods are developed for modeling emissions – air quality relationships that govern ozone and NO₂ concentrations over very long periods of time. A baseline model evaluation study is conducted to assess the accuracy and speed with which the relationship between pollutant emissions and the frequency distribution of O₃ concentrations throughout the year can be computed along with annual average NO₂ values using a deterministic photochemical airshed model driven by automated objective analysis of measured meteorological parameters. Methods developed are illustrated by application to the air quality situation that exists in Southern California. Model performance statistics for O₃ are similar to the results obtained in previous short-term episodic model evaluation studies that were based on hand-crafted meteorological inputs that are supplemented by expensive field measurement campaigns. Model predictions at one of the highest NO₂ concentration sites in the US indicate that measured violation of the US annual average NO₂ air quality standard at that site occurs because other species such as HNO₃ and PAN are measured as if they were NO₂ by the chemiluminescent NO_x monitors in current use.     

Effects of 10% biofuel substitution on ground level ozone formation in Bangkok,Thailand

The Thai Government's search for alternatives to imported petroleum led to the consideration of mandating 10% biofuel blends (biodiesel and gasohol) by 2012. Concerns over the effects of biofuel combustion on ground level ozone formation in relation to their conventional counterparts need addressing. Ozone formation in Bangkok is explored using a trajectory box model. The model is compared against O₃, NO, and NO₂ time concentration data from air monitoring stations operated by the Thai Pollution Control Department. Four high ozone days in 2006 were selected for modeling. Both the traditional trajectory approach and a citywide average approach were used. The model performs well with both approaches but slightly better with the citywide average. Highly uncertain and missing data are derived within realistic bounds using a genetic algorithm optimization. It was found that 10% biofuel substitution will lead to as much as a 16 ppb peak O₃ increase on these four days compared to a 48 ppb increase due to the predicted vehicle fleet size increase between 2006 and 2012. The approach also suggests that when detailed meteorological data is not available to run three dimensional airshed models, and if the air is stagnant or predominately remains over an urban area during the day, that a simple low cost trajectory analysis of O₃ formation may be applicable.     

Trends in Meteorologically Adjusted Ozone Concentrations in Six Kentucky Metro Areas, 1998–2002

Abstract

Meteorologically adjusted ozone (O₃) concentrations during five recent O₃ seasons (1998-2002) were computed for six Kentucky metro areas using a nonlinear regression model originally developed for forecasting ground-level O₃ concentrations. The meteorological adjustment procedure was based on modifying actual measured O₃ concentrations according to model-predicted responses to climate departures with respect to a reference year. For all six Kentucky metro areas, meteorologically adjusted O₃ concentrations declined over the five-year period. The linear best-fit rate of decline in mean adjusted O₃ concentrations ranged from 0.9 to 2.6 ppb/yr for these metro areas; the average rate of decline was 1.6 ppb/yr. The rates of decline in meteorologically adjusted extreme value (e.g., 95th percentile) concentrations were approximately the same, but there is greater statistical uncertainty concerning the extreme value trends.     

Application of Artificial Neural Networks to Modeling and Prediction of Ambient Ozone Concentrations

ABSTRACT

The deterministic modeling of ambient O₃ concentrations is difficult because of the complexity of the atmospheric system in terms of the number of chemical species; the availability of accurate, time-resolved emissions data; and the required rate constants. However, other complex systems have been successfully approximated using artificial neural networks (ANNs). In this paper, ANNs are used to model and predict ambient O₃ concentrations based on a limited number of measured hydrocarbon species, NO_x compounds, temperature, and radiant energy. In order to examine the utility of these approaches, data from the Coastal Oxidant Assessment for Southeast Texas (COAST) program in Houston, TX, have been used. In this study, 53 hydrocarbon compounds, along with O₃, nitrogen oxides, and meteorological data were continuously measured during summer 1993. Steady-state ANN models were developed to examine the ability of these models to predict current O₃ concentrations from measured VOC and NO concentrations. To predict the future concentrations of O₃, dynamic models were also explored and were used for extraction of chemical information such as reactivity estimations for the VOC species.

The steady-state model produced an approximation of O₃ data and demonstrated the functional relationship between O₃ and VOC-NO_x concentrations. The dynamic models were able to the adequately predict the O₃ concentration and behavior of VOC-NO_x-O₃ system a number of hourly intervals into the future. For 3 hr into the future, O₃ concentration could be predicted with a root-mean squared error (RMSE) of 8.21 ppb. Extending the models further in time led to an RMSE of 11.46 ppb for 5-hr-ahead values. This prediction capability could be useful in determining when control actions are needed to maintain measured concentrations within acceptable value ranges.     

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.     

The Chemical and Meteorological Conditions Associated with High and Low Ozone Concentrations in Southeastern Michigan and Nearby Areas of Ontario

This analysis represents the first characterization of the photochemistry and transport of ozone in the Detroit metropolitan area and provides a basis for comparing data for Detroit to that for other cities. The characterization is based on a comprehensive set of meteorological and chemical measurements obtained at a site in the urban core of Detroit during the summer of 1981, together with measurements of O₃, nitrogen oxides (NO _X ), and nonmethane organic compounds (NMOC) from rural, suburban, and urban areas in southeastern Michigan and adjacent areas of Ontario.

For the quartile (23 days) with highest ozone maxima (97-180 ppb), the maxima occurred 10-70 km north-northeast of the city on days that were warm and hazy with light southsouthwest winds. On such days there was a marked accumulation of ozone precursors (NMOC and NO_X) in the early morning, as well as a rapid chemical removal of NO _X (NO _X half-life of ～5 h) from morning to midday. The timing of the daily ozone increase across the study region suggests that local photochemical generation in a moving plume was responsible for more than half of the ozone measured downwind. However, there was also evidence that ozone transported into Detroit as part of the regional background was a significant part of the O₃ maxima on high ozone days. The average contributions of photochemistry and transport for the 23 days with the highest ozone maxima were estimated to be 57 ppb and 47 ppb, respectively.     

Analysis of ozone and VOCs measured in Shanghai: A case study

Shanghai Meteorological Administration has established a volatile organic compounds (VOCs) laboratory and an observational network for VOCs and ozone (O₃) measurements in the city of Shanghai. In this study, the measured VOCs and O₃ concentrations from 15 November (15-Nov) to 26 November (26-Nov) of 2005 in Shanghai show that there are strong day-to-day and diurnal variations. The measured O₃ and VOCs concentrations have very different characterizations between the two periods. During 15-Nov to 21-Nov (defined as the first period), VOCs and O₃ concentrations are lower than the values during 22-Nov to 28-Nov (defined as the second period). There is a strong diurnal variation of O₃ during the second period with maximum concentrations of 40–80 ppbv at noontime, and minimum concentrations at nighttime. By contrast, during the first period, the diurnal variation of O₃ is in an irregular pattern with maximum concentrations of only 20–30 ppbv. The VOC concentrations change rapidly from 30–50 ppbv during the first period to 80–100 ppbv during the second period. Two chemical models are applied to interpret the measurements. One model is a regional chemical/dynamical model (WRF-Chem) and another is a detailed chemical mechanism model (NCAR MM). Model analysis shows that the meteorological conditions are very different between the two periods, and are mainly responsible for the different chemical characterizations of O₃ and VOCs between the two periods. During the first period, meteorological conditions are characterized by cloudy sky and high-surface winds in Shanghai, resulting in a higher nighttime planetary boundary layer (PBL) and faster transport of air pollutants. By contrast, during the second period, the meteorological conditions are characterized by clear sky and weak surface winds, resulting in a lower nighttime PBL and slower transport of air pollutants. The chemical mechanism model calculation shows that different VOC species has very different contributions to the high-ozone concentrations during the second period. Alkane (40 ppbv) and aromatic (30 ppbv) are among the highest VOC concentrations observed in Shanghai. The analysis suggests that the aromatic is a main contributor for the O₃ chemical production in Shanghai, with approximately 79% of the O₃ being produced by aromatic. This analysis implies that future increase in VOC (especially aromatic) emissions could lead to significant increase in O₃ concentrations in Shanghai.     

Air Quality Modeling in the South Coast Air Basin of California: What Do the Numbers Really Mean?

Abstract

This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O₃) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O₃ concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O₃ concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O₃ concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O₃ concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O₃ predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O₃ concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.     

Long term changes in nitrogen oxides and volatile organic compounds in Toronto and the challenges facing local ozone control

Ground level ozone represents a significant air quality concern in Toronto, Canada, where the national 65 ppb 8-h standard is repeatedly exceeded during the summer. Here we present an analysis of nitrogen dioxide (NO₂), ozone (O₃), and volatile organic compound (VOC) data from federal and provincial governmental monitoring sites from 2000 to 2007. We show that summertime VOC reactivity and ambient concentrations of NO₂ have decreased over this period of time by up to 40% across Toronto and the surrounding region. This has not resulted in significant summertime ozone reductions, and in some urban areas, it appears to be increasing. We discuss the competing effects of decreased ozone titration leading to an increase in O₃, and decreased local ozone production, both caused by significant decreases in NO_x concentrations. In addition, by using local meteorological data, we show that annual variability in summer ozone correlates strongly with maximum daily temperatures, and we explore the effect of atmospheric transport from the southwest which has a significant influence on early morning levels before local production begins. A mathematical model of instantaneous ozone production is presented which suggests that, given the observed decreases in NO_x and VOC reactivity, we would not expect a significant change in local ozone production under photochemically relevant conditions. These results are discussed in the context of Toronto's recent commitment to cutting local smog-causing pollutants by 20% by 2012.     

The effect of variability in industrial emissions on ozone formation in Houston, Texas

Ambient observations have indicated that high concentrations of ozone observed in the Houston/Galveston area are associated with plumes of highly reactive hydrocarbons, mixed with NO_x, from industrial facilities. Ambient observations and industrial process data, such as mass flow rates for industrial flares, indicate that the VOCs associated with these industrial emissions can have significant temporal variability. To characterize the effect of this variability in emissions on ozone formation in Houston, data were collected on the temporal variability of industrial emissions or emission surrogates (e.g., mass flow rates to flares). The observed emissions variability was then used to construct regionwide emission inventories with variable industrial emissions, and the impacts of the variability on ozone formation were examined for two types of meteorological conditions, both of which lead to high ozone concentrations in Houston. The air quality simulations indicate that variability in industrial emissions has the potential to cause increases and decreases of 10–52 ppb (13–316%), or more, in ozone concentration. The largest of these differences are restricted to regions of 10–20 km², but the variability also has the potential to increase regionwide maxima in ozone concentrations by up to 12 ppb.     

Parameter Evaluation and Model Validation of Ozone Exposure Assessment Using Harvard Southern California Chronic Ozone Exposure Study Data

Abstract

To examine factors influencing long‐term ozone (O₃) exposures by children living in urban communities, the authors analyzed longitudinal data on personal, indoor, and outdoor O₃ concentrations, as well as related housing and other questionnaire information collected in the one‐year‐long Harvard Southern California Chronic Ozone Exposure Study. Of 224 children contained in the original data set, 160 children were found to have longitudinal measurements of O₃ concentrations in at least six months of 12 months of the study period. Data for these children were randomly split into two equal sets: one for model development and the other for model validation. Mixed models with various variance‐covariance structures were developed to evaluate statistically important predictors for chronic personal ozone exposures. Model predictions were then validated against the field measurements using an empirical best‐linear unbiased prediction technique.The results of model fitting showed that the most important predictors for personal ozone exposure include indoor O₃ concentration, central ambient O₃ concentration, outdoor O₃ concentration, season, gender, outdoor time, house fan usage, and the presence of a gas range in the house. Hierarchical models of personal O₃ concentrations indicate the following levels of explanatory power for each of the predictive models: indoor and outdoor O₃ concentrations plus questionnaire variables, central and indoor O₃ concentrations plus questionnaire variables, indoor O₃ concentrations plus questionnaire variables, central O₃ concentrations plus questionnaire variables, and questionnaire data alone on time activity and housing characteristics. These results provide important information on key predictors of chronic human exposures to ambient O₃ for children and offer insights into how to reliably and cost‐effectively predict personal O₃ exposures in the future. Furthermore, the techniques and findings derived from this study also have strong implications for selecting the most reliable and cost‐effective exposure study design and modeling approaches for other ambient pollutants, such as fine particulate matter and selected urban air toxics.     

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.     

Measurement of surface ozone and its precursors in an urban area in South Brazil

Weekly and seasonal variations of surface ozone and their precursors – nitrogen oxides, carbon monoxide-associated with meteorological parameters (wind direction, temperature, solar radiation) – are reported. Measurements were performed continuously during 2006 at two sampling stations located in the metropolitan area of Porto Alegre, Brazil. Results have shown that O₃ concentrations remained almost constant between weekdays. Levels of NO_x precursors decreased especially on Sundays, due to lighter traffic. The seasonal variation has shown a maximum O₃ concentration during summer and spring while NO_x and NO₂ have maxima at the colder months. The daily cycle of highest ozone concentrations reveals a lower nightly level and an inverse relation between O₃ and NO_x, evidencing the photochemical formation of O₃. There are seasonal variation and source heterogeneity.     

Variations of surface O3 in August at a rural site near Shanghai: influences from the West Pacific subtropical high and anthropogenic emissions

Large day-to-day variability in O₃ and CO was observed at Chongming, a remote rural site east of Shanghai, in August 2010. High ozone periods (HOPs) that typically lasted for 3?C5?days with daily maximum ozone exceeding 102?ppb were intermittent with low ozone periods (LOPs) with daily maximum ozone less than 20?ppb. The correlation analysis of ozone with meteorological factors suggests that the large variations of surface ozone are driven by meteorological conditions correlated with the changes in the location and intensity of the west Pacific subtropical high (WPSH) associated with the East Asian summer monsoon (EASM). When the center of WPSH with weaker intensity is to the southeast of Chongming site, the mixing ratios and variability of surface ozone are higher. When the center of WPSH with stronger intensity is to the northeast of Chongming site, the mixing ratios and variability of surface ozone are lower. Sensitivity simulations using the GEOS-Chem chemical transport model indicate that meteorological condition associated with WPSH is the primary factor controlling surface ozone at Chongming in August, while local anthropogenic emissions make significant contributions to surface ozone concentrations only during HOP.     

Ozone Formation in California's San Joaquin Valley: A Critical Assessment of Modeling and Data Needs

ABSTRACT

Data from the 1990 San Joaquin Valley Air Quality Study/ Atmospheric Utility Signatures, Predictions, and Experiments (SJVAQS/AUSPEX) field program in California's San Joaquin Valley (SJV) suggest that both urban and rural areas would have difficulty meeting an 8-hr average O₃ standard of 80 ppb. A conceptual model of O₃ formation and accumulation in the SJV is formulated based on the chemical, meteorological, and tracer data from SJVAQS/ AUSPEX. Two major phenomena appear to lead to high O₃ concentrations in the SJV: (1) transport of O₃ and precursors from upwind areas (primarily the San Francisco Bay Area, but also the Sacramento Valley) into the SJV, affecting the northern part of the valley, and (2) emissions of precursors, mixing, transport (including long-range transport), and atmospheric reactions within the SJV responsible for regional and urban-scale (e.g., downwind of Fresno and Bakersfield) distributions of O₃. Using this conceptual model, we then conduct a critical evaluation of the meteorological model and air quality model. Areas of model improvements and data needed to understand and properly simulate O₃ formation in the SJV are highlighted.     

Systematic Development of an Artificial Neural Network Model for Real-Time Prediction of Ground-Level Ozone in Edmonton,Alberta, Canada

Abstract

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O₃) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O₃ events. The models are used to determine the meteorological conditions and precursors that most affect O₃ concentrations. O₃ time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O₃ prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide.     

Modeling alkene chemistry using condensed mechanisms for conditions relevant to southeast Texas,USA

Alkenes are important in photochemical smog formation in southeast Texas due to their high emissions, especially from industrial sources in and around Houston, and their high reactivities. Therefore, properly characterizing the chemistry of alkenes in condensed mechanisms used in regional photochemical models is important in understanding the formation of ozone and other photochemical air pollutants in Houston. The performance of three versions of the SAPRC condensed chemical mechanism family, for predicting ozone and radical formation, was compared. Simulations were compared to environmental chamber data and ambient data. The analyses showed that separately modeling individual alkenes reactions (especially propene for southeast Texas) has the potential to lead to more accurate simulations of alkene chemistry. Caution must be exercised in un-lumping, however. Testing with different formulations of the 1-butene + O₃ reaction demonstrated the complexity and interconnectedness in choices of stoichiometric parameters for un-lumped species and the extent to which lumped mechanisms are un-lumped.