Air Quality Screening Model with Long-Term Averaging

A simple screening model is presented for estimating maximum ground level concentrations of air pollutants from single elevated buoyant sources of emissions. The model, which incorporates plausible error margins, is based on the Gaussian dispersion formula. Maximum longer-term (3, 8, 24 h) concentrations are estimated using a joint probability analysis of the persistence of meteorological worst case events.     

A Cost Model for Air Quality Monitoring Systems

A model was developed for remote terminal use to compare the costs of alternate designs of air quality monitoring networks with varying sophistication, ranging from totally manual to completely automated systems. Of special interest is the isolation of manual sample analysis and manual data analysis for comparison with instrumental sensors and automated data processing.

The model allows for 10 levels of sophistication, and 6 were used in sample runs. As many as 50 samplingsite locations, with three different site types, may be specified, and each site type may have any configuration of chemical and/or meteorological sensors. Amortization, labor rates, instrument costs and lifetimes, telephone line charges, and other variables are readily changed by the user as desired.

It was concluded that for systems with fewer than 20–25 sensors, fully automated systems may not be justified on cost alone, at least for producing the same data (hourly) as the manual or semiautomatic systems. (Much more than hourly data is obtained with the automated system, of course.) For larger systems, labor costs put the nonautomated systems at a disadvantage after a few years of operation.     

An Index and Model of Human Response to Air Quality

This study undertook an empirical Investigation of human response to air quality. Home interviews of 475 respondents living in 22 neighborhoods of Los Angeles County had elicited information on respondent socioeconomic characteristics, behavioral patterns, and measures of human response to air quality. This data base was then augmented with nine measures of actual air quality for six time frames for each neighborhood.

An observer-based air quality index (OBAQI) was constructed based upon which combination of air quality variables correlated best with the percentage of neighborhood respondents who perceived “smoggy air.” The best combination (OBAQI 3) consisted of prevailing visibility, O₃, and SO₂, each measured as the annual number of days that a selected standard had been equalled or exceeded. Subsequently, multiple regression models were constructed using this index as a predictor of aggregated perception of air quality. In addition to a general model for all neighborhoods, separate models were constructed for clusters of neighborhoods of similar micrometeorology as approximated by uniform elevations and/or coastal distances. Zones of homogeneous air quality and micrometeorology were then defined. Within these zones variation in four measures of human response to air quality was associated with respondent socioeconomic characteristics and behavioral patterns. Both the index and model could prove useful in gauging public response to proposed actions of air quality management districts.     

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.

Implications: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.     

Evaluation of Episode Control Schemes Through Air Quality Data Analysis

Prediction performance of various air pollution episode models are first compared with that of a persistence model which is based on the assumption that present concentrations persist to a future time. The comparisons are made by computing a correlation coefficient for different lead times between the observed and predicted values, and an auto-correlation function of the air quality data to which the episode model is applied. The persistence of high levels of air pollution is next examined, using existing air quality data, by constructing frequency distributions of air pollution episode duration for various concentration thresholds. Based on the results of persistence analysis, the flaws of currently used episode management schemes are discussed and some alternative episode management schemes are presented. Methodologies and parameters to evaluate the anticipated performances of episode management schemes are developed and some examples are worked out. In conclusion, it is suggested that a combination of episode persistence analysis and air pollution meteorological forecasting could lead to a workable air pollution episode management scheme.     

Performance and Diagnostic Evaluation of Ozone Predictions by the Eta-Community Multiscale Air Quality Forecast System during the 2002 New England Air Quality Study

Abstract

A real-time air quality forecasting system (Eta-Community Multiscale Air Quality [CMAQ] model suite) has been developed by linking the National Centers for Environmental Estimation Eta model to the U.S. Environmental Protection Agency (EPA) CMAQ model. This work presents results from the application of the Eta-CMAQ modeling system for forecasting ozone (O₃) over the Northeastern United States during the 2002 New England Air Quality Study (NEAQS). Spatial and temporal performance of the Eta-CMAQ model for O₃ was evaluated by comparison with observations from the EPA Air Quality System (AQS) network. This study also examines the ability of the model to simulate the processes governing the distributions of tropospheric O₃ on the basis of the intensive datasets obtained at the four Atmospheric Investigation, Regional Modeling, Analysis, and Estimation (AIRMAP) and Harvard Forest (HF) surface sites. The episode analysis reveals that the model captured the buildup of O₃ concentrations over the northeastern domain from August 11 and reproduced the spatial distributions of observed O₃ very well for the daytime (8:00 p.m.) of both August 8 and 12 with most of normalized mean bias (NMB) within [H11006]20%. The model reproduced 53.3% of the observed hourly O₃ within a factor of 1.5 with NMB of 29.7% and normalized mean error of 46.9% at the 342 AQS sites.The comparison of modeled and observed lidar O₃ vertical profiles shows that whereas the model reproduced the observed vertical structure, it tended to overestimate at higher altitude. The model reproduced 64 –77% of observed NO₂ photolysis rate values within a factor of 1.5 at the AIRMAP sites. At the HF site, comparison of modeled and observed O₃/nitrogen oxide (NO_x) ratios suggests that the site is mainly under strongly NO_x-sensitive conditions (>53%). It was found that the modeled lower limits of the O₃ production efficiency values (inferred from O₃-CO correlation) are close to the observations.     

Evaluation of the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) performance in hindcast and forecast of ground-level ozone

This paper presents the first attempt to apply the Mesoscale Meteorological Model (MM5)-Community Multi-Scale Air Quality Model (CMAQ) model system to simulate ground-level ozone (O3) over the continental Southeast Asia (CSEA) region for both hindcast and forecast purposes. Hindcast simulation was done over the CSEA domain for two historical O3 episodes, January 26-29, 2004 (January episode, northeast monsoon) and March 24-26, 2004 (March episode, southwest monsoon). Experimental forecast was done for next-day hourly O3 during January 2006 over the central part of Thailand (CENTHAI). Available data from 20 ambient monitoring stations in Thailand and 3 stations in Ho Chi Minh City, Vietnam, were used for the episode analysis and for the model performance evaluation. The year 2000 anthropogenic emission inventory prepared by the Center for Global and Regional Environmental Research at the University of Iowa was projected to the simulation year on the basis of the regional average economic growth rate. Hourly emission in urban areas was prepared using ambient carbon monoxide concentration as a surrogate for the emission intensity. Biogenic emissions were estimated based on data from the Global Emissions Inventory Activity. Hindcast simulations (CSEA) were performed with 0.5 degree x 0.5 degree resolution, whereas forecast simulations (CENTHAI) were done with 0.1 degree x 0.1 degree hourly emission input data. MM5-CMAQ model system performance during the selected episodes satisfactorily met U.S. Environmental Protection Agency criteria for O3 for most simulated days. The experiment forecast for next-day hourly O3 in January 2006 yielded promising results. Modeled plumes of ozone in both hindcast and forecast cases agreed with the main wind fields and extended over considerable downwind distances from large urban areas.     

A Short-Term Air Quality Model for Sulfur Dioxide in Louisville,Kentucky

The conventional gausslan plume equation for ground level concentrations was used to estimate hourly average sulfur dioxide concentrations at selected points in Louisville, KY, on specific days during 1973. Area emission sources were not included in the model since they are not substantial. The trajectory of the emissions from each continuous point source was calculated by a procedure that allowed for spatial variability in wind direction. All other meteorological parameters were held constant during each hour. The twenty-four individual hourly estimates at each location for a given day were arithmetically averaged yielding a daily mean. The model predictions were compared to actual measurements conducted by Jefferson County Air Pollution Control District personnel using the West-Gaeke sampling procedure. The sample correlation coefficient for all predictions was low, but after only about 30% of the predictions were eliminated on statistical grounds, the sample correlation coefficient was increased to 0.72. The statistical analysis appeared to discard a reasonable number of predictions on the basis of observed variability in the measured air quality.     

Ozone Air Quality Models

Abstract

The field of ozone air quality modeling, or as it is commonly referred to, photochemical air quality modeling, has undergone rapid change in recent years. Improvements in model components, as well as in methods of interpreting model performance, have contributed to this change. Attendant with this rapid change has been a growing need for those developing and using air quality models and policy makers to have a common understanding of the use and role of models in the decision making process. This Critical Review highlights recent advances and continuing problem areas in photochemical air quality modeling. Emphasis is placed on the components and input data for such models, model performance evaluation, and the implications for their use in regulatory decisions.     

A Microcomputer-based Forecasting Model: Potential Applications for Emergency Response Plans and Air Quality Studies

Most atmospheric transport and diffusion models within emergency response systems have very limited physics and are forced to rely on the assumption that wind and turbulence conditions at the time of the release will be representative over the period for which dispersion must be predicted. For releases where the principal concern is about the first few kilometers of travel, such an assumption is appropriate. However, for large accidental releases during stable conditions, the plume may travel for several hours before it is diluted to safe levels and the assumption of persistence may be inappropriate, particularly for transport in complex terrain. Under these circumstances, a model that can forecast changes in wind and turbulence conditions is required. We have installed such a model on microcomputers and tested it in complex terrain near Salt Lake City. One-hour tracer releases produced surface concentrations that remain high for much longer times than that expected based on one hour’ travel time with the mean wind at the source height. Furthermore, relatively large concentrations were found at distances of over 40 km from the source. The model was generally able to reproduce the principal features described by the measurements, although some effects of subgrid scale terrain were missed.     

Two-Stage Fuzzy-Stochastic Robust Programming: A Hybrid Model for Regional Air Quality Management

Abstract

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.     

Calculating Air Quality and Its Control

Air quality is shown as a function of averaging times of five minutes to one year for carbon monoxide, hydrocarbons, nitric oxide, nitrogen dioxide, nitrogen oxides, oxidant, and sulfur dioxide in Chicago, Cincinnati, Los Angeles, New Orleans, Philadelphia, San Francisco, and Washington, D. C. Concentrations are approximately lognormally distributed for all pollutants in all cities for all averaging times. Maximum concentration is inversely proportional to averaging time to an exponent. The exponent is a function of the standard geometric deviation. General air quality and control parameters are derived and shown for one example, nitrogen oxides in Washington, D. C. These values are compared to one air quality standard.     

Guidance for the Performance Evaluation of Three-Dimensional Air Quality Modeling Systems for Particulate Matter and Visibility

ABSTRACT

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility is presented. Four levels are considered: operational, diagnostic, mechanistic, and probabilistic evaluations. First, a comprehensive model evaluation should be conducted in at least two distinct geographical locations and for several meteorological episodes. Next, streamlined evaluations can be conducted for other similar applications if the comprehensive evaluation is deemed satisfactory. In all cases, the operational evaluation alone is insufficient, and some diagnostic evaluation must always be carried out. Recommendations are provided for designing field measurement programs that can provide the data needed for such model performance evaluations.     

Ambient Air Quality and Automotive Emission Control

This paper is concerned with uncertainties involved in projecting ambient air quality. Ambient air quality was projected by assuming a linear dependence on estimated future emissions. Future automotive emissions were estimated by a method recommended by EPA. Projections were made for the locations reported to have the highest ambient air concentrations of each pollutant; Chicago for carbon monoxide and the California South Coast Air Basin for hydrocarbon and oxidant. The sensitivity of the projections to several input parameters was determined.

The uncertainty in projection of air quality due to the use of a maximum, once-per-year concentration is large. For example, the reduction in total CO emissions in Chicago in 1975, necessary to meet the air quality standard, was as high as 68% or as low as 26%, depending on whether the historic high, 8 hr average concentration of 44 ppm or the 1970 maximum of 21 ppm was used. The effects of uncertainties in growth rates and fraction of emissions attributed to the automobile were also sizeable. Differences in automotive growth rate had a large near-term effect on projected concentrations, while differences in nonautomotive growth rate or fraction of emissions attributed to the automobile had a large long-term effect. The effect of 1975 interim automotive emission standards on projected air quality was negligible when compared with projected air quality based on the previous Federal automotive emission standards for 1975.     

Air Quality Control Equipment Guarantees

The purpose of this paper is to discuss considerations regarding guarantees of air quality control equipment. Topics include specification design parameters to cover ranges of expected operating conditions, negotiation of performance guarantees, design changes during construction, reliability, performance testing and comparison of test conditions to the worst expected conditions for which the guarantees apply. Comments are also offered regarding corrective actions in event the guarantees are not complied with.     

Non-Methane Hydrocarbon Air Quality Measurements

It is important in the implementation of the air quality standard for ozone/oxidants and non-methane hydrocarbons to develop quantitative relationships between these pollutants in air quality regions. Analyses for ambient air non-methane hydrocarbon give a direct measure of the progress in control of hydrocarbon emissions and in the reduction of oxidant/ozone concentration levels. Total hydrocarbon concentrations are much more available than non-hydrocarbon levels. An empirical relationship between total hydrocarbons and non-methane hydrocarbons has been obtained from measurements at both west and east coast sites in the U. S. The comparability of measurements from flame ionization analyzers and gas chromatography has been demonstrated. Either analytical technique can be applied to samples collected at monitoring sites to provide the 6-9 A.M. non-methane hydrocarbon aerometric results specified in the air quality standards.