Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Evaluation of Absorption Sampling Devices

The information presented in this paper is directed to those with the responsibility of designing and operating air quality monitoring networks. An analytical model for location of monitor sites based upon maximizing a sum of coverage factors for each source is developed. An heuristic solution method from the facilities location analysis literature is used for solution of the model. Results of an example problem are presented and compared with the monitoring network currently In place. The model is shown to be a valuable addition to the methods available to the air quality monitor network designer. Needs for further research are pointed out.     

A Qualitative Approach to Evaluating the Anticipated Reliability of a Photochemical Air Quality Simulation Model for a Selected Application

ABSTRACT

Photochemical air quality simulation models are now used widely in evaluating the merits of alternative emissions control strategies on spatial scales from metropolitan to sub-continental. Greatly varying levels of resources have been available to support modeling, from relatively comprehensive databases and evaluation of performance to a paucity of aerometric data for developing model inputs. Where data are sparse, many alternative outcomes are consistent with the knowledge at hand. Where performance evaluation is inadequately supported, the probability of error may be high. In each instance, uncertainties may be large when compared with the signal of interest, and thus confidence in the reliability of the model as an estimator of future air quality may come into question.

This paper proposes a qualitative procedure for assessing whether a particular application of a modeling system is likely to be potentially unreliable, suggesting that either (1) modification and further evaluation is needed, if supportable, prior to adoption for regulatory application; or (2) the model should not be used if improvement is not supportable. The procedure is proposed for use by policy-makers, staffs of public agencies, air quality managers, environmental staffs of industrial organizations, and other interested parties. The proposed use of the procedure is (1) to assess, a priori, whether a proposed application is likely to be judged questionable or unacceptably uncertain in outcome; and (2) to provide, a posteriori, a basis for judging quickly the likely quality of model performance. The procedure is presented with tropospheric ozone as the pollutant of concern. With adjustments, however, the procedure should be applicable for particu-late matter and other pollutants of interest.     

Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis Function Networks

Abstract

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.     

Multi-adsorbent Preconcentration and Gas Chromatographic Analysis of Air Toxics with an Automated Collection/Analytical System

Section 507 of the 1990 Clean Air Act Amendments (CAAA) requires states to develop a small business stationary source and environmental compliance assistance program to aid small businesses impacted by applicable air quality regulations. In general, the program consists of three main components: (1) a small business assistance program (SBAP) for providing compliance and technical assistance to small businesses; (2) an ombudsman for providing direct oversight to the SBAP; and (3) a compliance advisory panel consisting of members from both the public and private sector responsible for determining the overall effectiveness of the SBAP. The key component of the program for assisting small businesses is the SBAP. Providing the necessary assistance to small businesses regarding such issues as permit applicability, rights under the CAAA and emission control options will require the SBAP to develop both proactive and reactive components. The proactive component involves providing outreach services in the form of collecting and disseminating compliance and technical information to small businesses. The reactive component includes the establishment of an information clearinghouse for handling the many inquiries from members of the small business community who have never been subject to air pollution control regulations. States without the available resources to fully implement an SBAP may need to rely on contractor assistance. This paper briefly describes the establishment of the program, provides an overview of each of the program components, discusses many of the ways in which states may implement both the proactive and reactive components of the SBAP, and lists the types of contractors most suitable for SBAP assistance.     

基于指标规范值的室内空气客观评价的概率神经网络模型

为了建立简单、普适、通用的概率神经网络的室内空气评价模型,在适当设定室内空气各项指标的参照值及指标值的规范变换式基础上,使室内空气同级标准不同指标的规范值差异尽可能小,从而用规范值表示的各指标都可用同一个规范指标"等效"替代。因此,概率神经网络隐层各类模式的基函数中心矢量的各指标分量值与同级标准所有15项指标规范值的均值等同。将基于指标规范值的概率神经网络模型用于室内空气的评价实例进行检验,验证了该模型的普适性、通用性和简便性。     

Characterization of urban air quality using GIS as a management system

Keeping the air quality acceptable has become an important task for decision makers as well as for non-governmental organizations. Particulate and gaseous emissions of pollutant from industries and auto-exhausts are responsible for rising discomfort, increasing airway diseases, decreasing productivity and the deterioration of artistic and cultural patrimony in urban centers. A model to determine the air quality in urban areas using a geographical information system will be presented here. This system permits the integration, handling, analysis and simulation of spatial and temporal data of the ambient concentration of the main pollutant. It allows the users to characterize and recognize areas with a potential increase or improvement in its air pollution situation. It is also possible to compute past or present conditions by changing basic input information as traffic flow, or stack emission rates. Additionally the model may be used to test the compliance of local standard air quality, to study the environmental impact of new industries or to determine the changes in the conditions when the vehicle circulation is increased.     

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.     

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.     

Modeling Analysis of Power Plants for Compliance Extensions in 51 Air Quality Control Regions

Past studies indicate a nationwide potential low-sulfur coal supply deficit in 1975 arising from extremely low-sulfur State Implementation Plan requirements which cannot ail be met in time by available coal and gas cleaning technology. One means to alleviate this net deficit would be to grant variances where at least primary air quality standards would be maintained.

An extensive modeling analysis was conducted by EPA and Walden Research on a large number of power plants in 51 AQCRs located in 20 states to determine if compliance extensions at these plants could significantly reduce the projected deficit of lowsulfur coal. Using simulation modeling, air quality impact at each plant for projected 1975 operations was determined with application of SIP regulatory requirements and with a full variance from SIP requirements for coal-fired boilers. The results from this investigation indicate that the attainment of primary SO2 air quality standards for the coal-fired plants would probably not be jeopardized by the application of full variance status to 34% of the plants and limited variance status to an additional 22% of the plants. No variance is appropriate for the remaining plants. The projected annual reduction In low-sulfur coal demand (less than 1.0% sulfur) is approximately 137 million tons. The projected shift in the average coal sulfur distribution is from 1.2% under SIP status to 2.1% under the applicable variance status. The power plant variance strategy appears, then, to offer a potentially feasible approach toward alleviating the low-sulfur coal deficit problem without jeopardizing attainment of primary air quality standards. It should be emphasized that compliance extensions are not the only way, or the most desirable way, of dealing with this problem. The final selection of a strategy for a given state or AQCR and the implementation of that strategy involve many questions and policy matters beyond the scope of this study.     

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.     

Results of a Receptor Modeling Feasibility Study

This paper summarizes the results of an Electric Power Research Institute funded research effort to determine the feasibility of using receptor models for the apportionment of power plant contributions to air quality, deposition quality, and light extinction on local and regional scales. Sufficient information currently exists (or was developed during the course of this study) to establish feasibility for the apportionment of power plant contributions to local-scale air quality and to strongly suggest the usefulness of receptor modeling for regional-scale air quality, light extinction, and dry deposition quality apportionment. Insufficient information existed to determine whether or not receptor modeling can be useful for the allocation of wet deposition quality.

A series of seven future research recommendations were prepared for the purpose of advancing receptor modeling from theoretical feasibility to practical utility. Two recommendations address model evaluation: (1) prepare a user-oriented receptor model application and testing package, and (2) perform computer simulation testing of past, current, and proposed receptor model applications. Two recommendations address model development: (1) develop a receptor model requiring minimal information about source profiles, and (2) develop a “hybrid” model (i.e., a model that combines source and receptor oriented methods). Finally, three recommendations address source characterization: (1) develop procedures for individual particle characterization, (2) develop sampling and analysis methods for source profile determination, and (3) measure source profiles of coal- and oil-fired power plants, and other sources that confound identification of emissions from coal- and oilfired power plants.     

Mapping air quality zones for coastal urban centers

This study presents a new method that incorporates modern air dispersion models allowing local terrain and land–sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year’s worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.

Implications: A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land–sea breeze effects that can be used to manage local air quality in areas of similar microclimates.     

An Efficient Gaussian-Plume Multiple-Source Air Quality Algorithm

The information presented in this paper is directed to air pollution scientists with an interest in applying air quality simulation models. RAM is the three letter designation for this efficient Gaussian-plume multiple-source air quality algorithm. RAM is a method of estimating short-term dispersion using the Gaussian steady-state model. This algorithm can be used for estimating air quality concentrations of relatively stable pollutants for averaging times from an hour to a day in urban areas from point and area sources. The algorithm is applicable for locations with level or gently rolling terrain where a single wind vector for each hour is a good approximation to the flow over the source area considered. Calculations are performed for each hour. Hourly meteorological data required are wind direction, wind speed, stability class, and mixing height. Emission information required of point sources consists of source coordinates, emission rate, physical height, stack gas volume flow and stack gas temperature. Emission information required of area sources consists of south-west corner coordinates, source area, total area emission rate and effective area source height. Computation time is kept to a minimum by the manner in which concentrations from area sources are estimated using a narrow plume hypothesis and using the area source squares as given rather than breaking down all sources to an area of uniform elements. Options are available to the user to allow use of three different types of receptor locations: 1 ) those whose coordinates are input by the user, 2) those whose coordinates are determined by thé model and are downwind óf significant point and area sources where maxima are likely to occur, and 3) those whose coordinates are determined by the model to give good area coverage of a specific portion of the region. Computation time is also decreased by keeping the number of receptors to a minimum.     

Sulfur dioxide dispersion and source contribution to receptors of downtown Patras, Greece

Background The development of the city of Patras, including harbour relocation, in conjunction with the protection of the regional ecosystems, requires air quality assessment and management. For this reason, a model applicable in the Patras area is necessary and valuable. The goal of this study was to validate a model suitable for predicting the dispersion of sulfur dioxide (SO₂), based on particular activity, topography and weather conditions. Methods We used the US-EPA ISCLT3 integral dispersion model to predict SO₂ concentrations for Patras, Greece. We assumed that the major contribution to Patras air pollution came from central heating, harbour and traffic. We calculated traffic emissions using COPERTIII. Results and Discussion Assigning suitable values of the mixing height, the model predicted the local and spatial distribution of the mean monthly SO₂ concentrations in downtown Patras, as well computed the contribution of the SO₂ emissions originating from each particular source at each receptor location on a seasonal and annual basis. The comparison between predictions and measurements shows that the model performance for estimating the SO₂ concentrations and period pattern is satisfactory. Conclusion The mixing height was the critical parameter for calibrating the model. Model validation promises satisfactory predictions for SO₂ pollution levels on monthly basis. Recommendations and Outlook The model could be used in predicting SO₂ concentrations and source contribution for several downtown Patras receptors using pertinent meteorological and emission information. It could be also extended to predict the dispersion of other primary air pollutants. The calibrated model predictions could be used to fill gaps in monitoring data, saving money and time, and help in assess and manage air quality as Patras develops.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 4. A Three-Parameter Averaging-Time Model

Urban air pollutant concentration data often tend to fit a two-parameter averaging-time model having three characteristics: (1) pollutant concentrations are (two-parameter) lognormally distributed for all averaging times; (2) median concentrations are proportional to averaging time raised to an exponent; and (3) maximum concentrations are approximately inversely proportional to averaging time raised to an exponent. Concentration data obtained near many isolated point sources and in some urban areas often do not fit a two-parameter lognormal distribution. An increment (either positive or negative) can be added to each such concentration in order to fit the data instead to a three-parameter lognormal distribution. This increment has been incorporated as the third parameter in a new three-parameter averaging-time model that can be used in both point-source and urban settings. Examples show how this new model can be used to analyze SO₂ concentration data obtained near a point source to determine the degree of emission reduction needed to achieve the national ambient air quality standards.     

Sequential box models for indoor air quality: Application to airliner cabin air quality

In this paper we present the development and application of a model for indoor air quality. The model represents a departure from the standard box models typically used for indoor environments which has applicability in residences and office buildings. The model has been developed for a physical system consisting of sequential compartments which communicate only with adjacent compartments. Each compartment may contain various source and sink terms for a pollutant as well as leakage, and air transfer from adjacent compartments. The mathematical derivation affords rapid calculation of equilibrium concentrations in an essentially unlimited number of compartments. The model has been applied to air quality in the passenger cabin of three commercial aircraft. Simulations have been performed for environmental tobacco smoke (ETS) under two scenarios, CO₂ and water vapor. Additionally, concentrations in one aircraft have been simulated under conditions different from the standard configuration. Results of the simulations suggest the potential for elevated concentrations of ETS in smoking sections of non-air-recirculating aircraft and throughout the aircraft when air is recirculated. Concentrations of CO₂ and water vapor are consistent with expected results. We conclude that this model may be a useful tool in understanding indoor air quality in general and on aircraft in particular.     

Design of an optimum air monitoring network for exposure assessments

Nonlinear programming techniques are frequently used to design optimum monitoring networks. These mathematically rigorous techniques are difficult to implement or cumbersome when considering other design criteria. This paper presents a more pragmatic approach to the design of an optimal monitoring network to estimate human exposure to hazardous air pollutants. In this approach, an air quality simulation model is used to produce representative air quality patterns, which are then combined with population patterns to obtain typical exposure patterns. These combined patterns are used to determine ‘figures of merit’ for each potential monitoring site, which are used to identify and rank the most favorable sites. The spatial covariance structure of the air quality patterns is used to draw a ‘sphere of influence’ around each site to identify and eliminate redundant monitoring sites. This procedure determines the minimum number of sites required to achieve the desired spatial coverage. This methodology was used to design an optimal ambient air monitoring network for assessing population exposure to hazardous pollutants in the southeastern Ohio River valley.