PM Fine Standards and Implied Modeling/Analysis Needs

ABSTRACT

New National Ambient Air Quality Standards (NAAQS) were promulgated for fine particulate matter (FPM) in July 1997. This paper summarizes likely timing for implementing programs to meet these standards, which have a bearing on future modeling/analysis needs. The paper notes technical requirements implied by the nature of the NAAQS, as well as feedback the agency has received concerning modeling/analysis through Federal Advisory Committee Act (FACA) subcommittee work groups. Conclusions and recommendations drawn from recently completed U.S. Environmental Protection Agency (EPA)- sponsored workshops on modeling and other source attribution techniques are also described. Efforts to respond to needs implied by the NAAQS and feedback are noted by outlining major topics and issues that future guidance on use of modeling and other analyses used for attainment demonstrations will need to address. The paper concludes by highlighting several as yet unmet modeling/analysis needs to support a well-founded strategy for meeting air quality goals for FPM. These are suggested as potential areas for policy-relevant research.     

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.     

Analysis of Air Quality Observations with the Aid of the Source-Receptor Relationship Approach

Abstract

In this study, an attempt was made to analyze time series of air quality measurements (O₃, SO₂, SO₄ ^2?NO_x) conducted at a remote place in the eastern Mediterranean (Finokalia at Crete Island in 1999) to obtain concrete information on potential contributions from emission sources. For the definition of a source-receptor relationship, advanced meteorological and dispersion models appropriate to identify “areas of influence” have been used. The model tools used are the Regional Atmospheric Modeling System and the Lagrangian-type particle dispersion model (forward and backward in time), with capabilities to derive influence functions and definition of “areas of influence.” When high levels of pollutants have been measured at the remote location of Finokalia, particles are released from this location (receptor) and traced backward in time. The influence function derived from particle distributions characterizes dispersion conditions in the atmosphere and also provides information on potential contributions from emission sources within the modeling domain to this high concentration. As was shown in the simulation results, the experimental site of Finokalia in Crete is influenced during the selected case studies, primarily by pollutants emitted from the urban conglomerate of Athens. Secondarily, it is influenced by polluted air masses arriving from Italy and/or the Black Sea Region. For some specific cases, air pollutants monitored at Finokalia were possibly related to war activities in the West Balkan Region (Kosovo).     

Air Quality Modeling and Decisions for Ozone Reduction Strategies

Abstract

Despite the widespread application of photochemical air quality models (AQMs) in U.S. state implementation planning (SIP) for attainment of the ambient ozone standard, documentation for the reliability of projections has remained highly subjective. An “idealized” evaluation framework is proposed that provides a means for assessing reliability. Applied to 18 cases of regulatory modeling in the early 1990s in North America, a comparative review of these applications is reported. The intercomparisons suggest that more than two thirds of these AQM applications suffered from having inadequate air quality and meteorological databases. Emissions representations often were unreliable; uncertainties were too high. More than two thirds of the performance evaluation efforts were judged to be substandard compared with idealized goals. Meteorological conditions chosen according regulatory guidelines were limited to one or two cases and tended to be similar, thus limiting the extent to which public policy makers could be confident that the emission controls adopted would yield attainment for a broad range of adverse atmospheric conditions. More than half of the studies reviewed did not give sufficient attention to addressing the potential for compensating errors. Corroborative analyses were conducted in only one of the 18 studies reviewed. Insufficient attention was given to the estimation of model and/or input database errors, uncertainties, or variability in all of the cases examined. However, recent SIP and policy‐related regional modeling provides evidence of substantial improvements in the underlying science and available modeling systems used for regulatory decision making. Nevertheless, the availability of suitable databases to support increasingly sophisticated modeling continues to be a concern for many locations. Thus, AQM results may still be subject to significant uncertainties. The evaluative process used here provides a framework for modelers and public policy makers to assess the adequacy of contemporary and future modeling work.     

Lagrangian Dispersion Modeling of Vehicular Emissions from a Highway in Complex Terrain

Abstract

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada’s Regional Air Quality Deterministic Prediction System

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr.

Implications: This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.     

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi,China

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM₁₀], sulfur dioxide [SO₂], and nitrogen oxides [NO_x]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants.

Implications: The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality.

Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.     

Objectives and Design of Central California's 1995 Integrated Monitoring Study of the California Regional PM10/PM2.5 Air Quality Study

ABSTRACT

The 1995 Integrated Monitoring Study (IMS95) is part of the Phase 1 planning efforts for the California Regional PM_10/PM_2.5 Air Quality Study. Thus, the overall objectives of IMS95 are to (1) fill information gaps needed for planning an effective field program later this decade; (2) develop an improved conceptual model for pollution buildup (PM10, PM2.5, and aerosol precursors) in the San Joaquin Valley; (3) develop a uniform air quality, meteorological, and emissions database that can be used to perform initial evaluations of aerosol and fog air quality models; and (4) provide early products that can be used to help with the development of State Implementation Plans for PM_10. Consideration of the new particulate matter standards were also included in the planning and design of IMS95, although they were proposed standards when IMS95 was in the planning process.     

Improved Spatial Allocation Methodology for On-Road Mobile Emissions

ABSTRACT

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

The Location of Maximum Exposure Due to An Instantaneous Release from a Point Source

An international specialty conference, jointly sponsored by the Air &; Waste Management Association (A&;WMA) and the U.S. Environmental Protection Agency (EPA), entitled “PM₁₀ Standards and Nontraditional Particulate Source Controls,” was held in Scottsdale, Arizona, January 12-15, 1992. The conference included 92 presentations in 17 technical sessions. Eighty-one peer-reviewed technical papers, two keynote addresses and one panel session summary describing novel applications, measurement processes, modeling techniques and control measures for nontraditional pollution sources are assembled in the Transactions¹. The technical issues addressed during the conference included: (1) measurement methods and data bases; (2) emissions source characterization; (3) source apportionment of nontraditional sources; (4) fugitive dust characterization and control technologies; (5) vegetative burning characterization and control technologies; (6) sources and controls of secondary aerosol and motor vehicle precursors; and (7) regulatory policies and State Implementation Plan (SIP) development. This paper gives an overview of the technical program     

A Maximum Entropy Approach to Estimating Emissions

ABSTRACT

A maximum information entropy method of calculating probabilistic estimates of volatile organic compound (VOC) emissions by the wood furniture and fixture coating industry is presented. The maximum entropy approach is used to produce minimally biased probability distributions for number of firms, coating use, and coating emission factors from existing summary statistics. These distributions are combined to estimate VOC emissions. The maximum entropy emissions estimate provides information to support probabilistic modeling of regional air quality, probabilistic assessment of emission reduction strategies, and risk assessments. Accurate estimation of emission distributions produces more informed regulatory decisionmaking, risk comparisons, and regulatory and scientific priority setting.     

Assessment of the emissions and air quality impacts of biomass and biogas use in California

It is estimated that there is sufficient in-state “technically” recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NO_x) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality.

Implications:?This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.     

Health Effects of Fine Particulate Air Pollution: Lines that Connect

Abstract

Efforts to understand and mitigate the health effects of particulate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different time-scales of exposure and in the exploration of the shape of the concentration-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiological pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality standards.     

Freight shipment modal split and its environmental impacts: An exploratory study

Freight transportation activities are responsible for a large share of air pollution and greenhouse gas emissions in the United States. Various freight transportation modes have significantly different impacts on air quality and environmental sustainability, and this highlights the need for a better understanding of interregional freight shipment mode choices. This paper develops a binomial logit market share model to predict interregional freight modal share between truck and rail as a function of freight and shipment characteristics. This model can be used to estimate the impacts of various factors, such as oil price, on shippers’ mode choice decisions. A set of multiyear freight and geographical information databases was integrated to construct regression models for typical freight commodities. The atmospheric impact levels incurred by different freight modal choice decisions are analyzed to provide insights on the relationship among freight modal split, oil price change, and air quality.

Implications:

Freight transportation has become a major source of energy consumption and air pollution, and emissions rates vary significantly across different modes. Understanding freight shipment mode choice under various economic and engineering factors will help assess the environmental impacts of freight shipment systems at the national level. This paper develops a binomial logit model for two dominating modes (truck and rail) and shows how this model is incorporated into an environmental impact analysis. The framework will be useful to policy makers to assess the impacts of freight movements on air quality and public health and to mitigate those adverse impacts.     

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.     

Updated methods for assessing the impacts of nearby gas drilling and production on neighborhood air quality and human health

An explosive growth in natural gas production within the last decade has fueled concern over the public health impacts of air pollutant emissions from oil and gas sites in the Barnett and Eagle Ford shale regions of Texas. Commonly acknowledged sources of uncertainty are the lack of sustained monitoring of ambient concentrations of pollutants associated with gas mining, poor quantification of their emissions, and inability to correlate health symptoms with specific emission events. These uncertainties are best addressed not by conventional monitoring and modeling technology, but by increasingly available advanced techniques for real-time mobile monitoring, microscale modeling and source attribution, and real-time broadcasting of air quality and human health data over the World Wide Web. The combination of contemporary scientific and social media approaches can be used to develop a strategy to detect and quantify emission events from oil and gas facilities, alert nearby residents of these events, and collect associated human health data, all in real time or near-real time. The various technical elements of this strategy are demonstrated based on the results of past, current, and planned future monitoring studies in the Barnett and Eagle Ford shale regions.

Implications: Resources should not be invested in expanding the conventional air quality monitoring network in the vicinity of oil and gas exploration and production sites. Rather, more contemporary monitoring and data analysis techniques should take the place of older methods to better protect the health of nearby residents and maintain the integrity of the surrounding environment.     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.