Evaluations of the chemical mass balance method for determining contributions of gasoline and diesel exhaust to ambient carbonaceous aerosols

The US. Department of Energy Gasoline/Diesel PM Split Study was conducted to assess the sources of uncertainties in using an organic compound-based chemical mass balance receptor model to quantify the relative contributions of emissions from gasoline (or spark ignition [SI]) and diesel (or compression ignition [CI]) engines to ambient concentrations of fine particulate matter (PM2.5) in California's South Coast Air Basin (SOCAB). In this study, several groups worked cooperatively on source and ambient sample collection and quality assurance aspects of the study but worked independently to perform chemical analysis and source apportionment. Ambient sampling included daily 24-hr PM2.5 samples at two air quality-monitoring stations, several regional urban locations, and along freeway routes and surface streets with varying proportions of automobile and truck traffic. Diesel exhaust was the dominant source of total carbon (TC) and elemental carbon (EC) at the Azusa and downtown Los Angeles, CA, monitoring sites, but samples from the central part of the air basin showed nearly equal apportionments of CI and SI. CI apportionments to TC were mainly dependent on EC, which was sensitive to the analytical method used. Weekday contributions of CI exhaust were higher for Interagency Monitoring of Protected Visual Environments (IMPROVE; 41+/-3.7%) than Speciation Trends Network (32+/-2.4%). EC had little effect on SI apportionment. SI apportionments were most sensitive to higher molecular weight polycyclic aromatic hydrocarbons (indeno[123-cd]pyrene, benzo(ghi)perylene, and coronene) and several steranes and hopanes, which were associated mainly with high emitters. Apportionments were also sensitive to choice of source profiles. CI contributions varied from 30% to 60% of TC when using individual source profiles rather than the composites used in the final apportionments. The apportionment of SI vehicles varied from 1% to 12% of TC depending on the specific profile that was used. Up to 70% of organic carbon (OC) in the ambient samples collected at the two fixed monitoring sites could not be apportioned to directly emitted PM emissions.     

Application of the factor-analysis receptor model to simulated urban- and regional-scale data sets

Factor analysis has been used extensively to model the sources of ambient aerosol. In this study, simple urban- and regional-scale simulations showed that factor analysis may not always produce reliable results. The accuracy of apportionments of total mass depended on the rotation scheme used to transform the factors. Varimax-rotated solutions were generally independent of the degree of random error in the data, but were sensitive to collinearities in profiles, correlations of source strengths, and magnitudes of source strengths. Target-transformation factor analysis was more successful than varimax rotation when targets were similar to true profiles.In simple regional simulations, midwestern and northeastern sources were resolved qualitatively by varimax-rotation and quantitatively by target-transformation factor analysis. Signatures determined from principal-component analysis of ambient data in the northeastern U.S. and the Arctic resembled those determined independently.     

Source apportionment of indoor air pollution

An understanding of the relative contributions from important pollutant sources to human exposures is necessary for the design and implementation of effective control strategies. In the past, societal efforts to control air pollution have focused almost exclusively on the outdoor (ambient) environment. As a result, substantial amounts of time and money have been spent to limit airborne discharges from mobile and stationary sources. Yet it is now recognized that exposures to elevated pollutant concentrations often occur as a result of indoor, rather than outdoor, emissions. While the major indoor sources have been identified, their relative impacts on indoor air quality have not been well defined. Application of existing source apportionment models to nonindustrial indoor environments is only just beginning. It is possible that these models might be used to distinguish between indoor and outdoor emissions, as well as to distinguish among indoor sources themselves. However, before the feasibility and suitability of source-apportionment methods for indoor applications can be assessed adequately, it is necessary to take account of model assumptions and associated data requirements. This paper examines the issue of indoor source apportionment and reviews the need for emission characterization studies to support such source-apportionment efforts.     

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.     

Another Look at New York City’s Air Pollution Problem

An analysis of the ambient air quality in New York City over the past several years has been made. The various sources of the contaminants are identified and evaluated as to their effects on ambient air quality. Meteorological data have been analyzed to develop insight into the influence of weather conditions upon ground level pollution concentrations. The results of these analyses are employed to indicate the approaches that will be most effective in improving air quality.     

Source Fingerprints for Receptor Modeling of Volatile Organics

The development of receptor models for the determination of the sources of an ambient air pollutant requires that the composition of the pollutant at the point of emission be known. For this study, composition information for 10 sources of volatile organic compounds (VOC) were evaluated and source fingerprints developed. The source categories include motor vehicles, gasoline vapor, petroleum refineries, architectural coatings, graphic arts, waste-water treatment, vapor degreasing, drycleaning, automobile assembly (including body painting), and polyethylene production. The fingerprints are presented for a group of 23 compounds. These compounds were selected for a variety of reasons including ease of measurement in the ambient environment, compound toxicity, reactivity, and usefulness in previous receptor modeling applications. In general, the data for sources of VOC are remarkably consistent from study to study. Because the profiles for many of the sources of VOC are controlled by physical and chemical processes (e.g. combustion) and not raw material composition, the fingerprints have general applicability.     

Characterization of ambient fine particles in the northwestern area and Anchorage, Alaska

Ambient PM2.5 (particulate matter less than 2.5 microm in aerodynamic diameter) in the northwestern United States and Alaska is dominated by carbonaceous compounds associated with wood burning and transportation sources. PM2.5 source characterization studies analyzing recent PM2.5 speciation data have not been previously reported for these areas. In this study, ambient PM2.5 speciation samples collected at two monitoring sites located in the northwestern area, Olympic Peninsula, WA, and Portland, OR, and one monitoring site located in Anchorage, AK, were characterized through source apportionments. Gasoline vehicle, secondary sulfate, and wood smoke were the largest sources of PM2.5 collected at the Anchorage, Olympic, and Portland monitoring sites, respectively. Secondary sulfates showed an April peak at Anchorage and a November peak at Portland that are likely related to the increased photochemical reaction and long-range transport in Anchorage and meteorological stagnation in Portland. Secondary nitrate at the Olympic site showed a weak summer high peak that could be caused by seasonal tourism in the national park. Backward trajectories suggested that the elevated aged sea salt concentrations at the Portland monitoring site could be regional transport of sea salt that passed through other contaminated air sheds along the coast. Oil combustion emissions that might originate from ships and ferries were observed at the Olympic monitoring site.     

Source identifications of airborne fine particles using positive matrix factorization and U.S. Environmental Protection Agency positive matrix factorization

The widely used source apportionment model, positive matrix factorization (PMF2), has been applied to various air pollution data. Recently, U.S. Environmental Protection Agency (EPA) developed EPA positive matrix factorization (PMF), a version of PMF that will be freely distributed by EPA. The objectives of this study were to conduct source apportionment studies for particulate matter less than 2.5 microm in aerodynamic diameter (PM(2.5)) speciation data using PMF2 and EPA PMF (version 1.1) and to compare identified sources between the two models. In the present study, ambient PM(2.5) compositional datasets of 24-hr integrated samples collected at EPA Speciation Trends Network monitoring sites in Chicago, IL, and Portland, OR, were analyzed. Both PMF2 and EPA PMF extracted eight sources for the Chicago data and 10 sources for the Portland data. The model-resolved source profiles were similar between two models for both datasets. However, in several sources, the average contributions did not agree well and the time series contributions were not highly correlated. The differences between PMF2 and EPA PMF solutions were caused by the different least-square algorithm and the different nonnegativity constraints. Most of the average source contributions resolved by both models were within 5-95% uncertainty provided by EPA PMF, indicating that the sources resolved by both models were reproducible.     

developing a practical dispersion model for an air quality region

A series of computer models have been developed to predict air quality in the New York/New Jersey/Connecticut Air Quality Region. Efforts have been directed at models which have a shorter time scale than climatological models, and which are capable of providing better recommendations for effective abatement and planning, but use input data presently available.

The basic dispersion model for these investigations is a steady-state,nondivergent Gaussian-type model. A modified inventory of SO₂ sources,based on published data for the New York/New Jersey/Connecticut Air Quality Region, was prepared for use with the model. The basic model has been subjected to various internal sensitivity analyses, in which was isolated the variation produced in the pollutant concentration by a given change in each of the factors that contribute, e.g., wind speed, wind direction,mixing depth, stability conditions, source strengths, and grid size for the area sources.

To date, validation tests of the model have been made against the July and August 1969 data for the ten telemetering stations of the New York City Aerometric Network. Hourly as well as averaged concentrations were considered. Various sets of meteorological data from the network stations and the three area airports, were compared and tested. Additional tests, particularly for the winter season, are needed to substantiate the preliminary conclusions suggested by the results to date.

Considerable insight into the relative importance of model components has been acquired from the sensitivity studies. Furthermore the validation results lend support to the belief that a reasonably simple, practical dispersion model can be developed for the region.     

Source apportionment of ambient particles in steubenville,oh using specific rotation factor analysis

A statistical approach, Specific Rotation Factor Analysis (SRFA), has been developed to identify and apportion sources of ambient air pollutants. To increase the statistical weight of the source tracer elements, this technique is based on the analysis of the covariance matrix. The obtained eigenvectors are obliquely rotated in order to maximize the loadings of the tracer quantities of the corresponding source (specific rotations). Source contributions and profiles are estimated by regressing elemental and mass concentrations on the factor loadings.The ability of the SRFA technique to resolve major aerosol sources and determine their profiles and contributions at the receptor site is demonstrated by applying it to a subset of fine particle elemental and mass concentration data from Steubenville, OH.     

Air emission inventories in North America: a critical assessment

Although emission inventories are the foundation of air quality management and have supported substantial improvements in North American air quality, they have a number of shortcomings that can potentially lead to ineffective air quality management strategies. Major reductions in the largest emissions sources have made accurate inventories of previously minor sources much more important to the understanding and improvement of local air quality. Changes in manufacturing processes, industry types, vehicle technologies, and metropolitan infrastructure are occurring at an increasingly rapid pace, emphasizing the importance of inventories that reflect current conditions. New technologies for measuring source emissions and ambient pollutant concentrations, both at the point of emissions and from remote platforms, are providing novel approaches to collecting data for inventory developers. Advances in information technologies are allowing data to be shared more quickly, more easily, and processed and compared in novel ways that can speed the development of emission inventories. Approaches to improving quantitative measures of inventory uncertainty allow air quality management decisions to take into account the uncertainties associated with emissions estimates, providing more accurate projections of how well alternative strategies may work. This paper discusses applications of these technologies and techniques to improve the accuracy, timeliness, and completeness of emission inventories across North America and outlines a series of eight recommendations aimed at inventory developers and air quality management decision-makers to improve emission inventories and enable them to support effective air quality management decisions for the foreseeable future.     

裸土风蚀型开放源起尘机制研究进展

中国北方多个城市空气颗粒物来源解析结果表明,开放源是空气颗粒物污染的主要来源,裸土风蚀型开放源是主要的排放源类.对裸土风蚀型开放源起尘机制进行了诠释,提出了裸土风蚀型开放源、裸土风蚀起尘、裸土风蚀尘的概念,并对裸土风蚀型开放源的风蚀影响因素进行了研究,为城市空气质量达标及空气颗粒物污染防治提供科学参考.     

The Nationwide Program for Maintenance of Air Quality

The initial state implementation plans concentrated on attainment of the ambient air quality standards in the relatively polluted areas of the country. Many of these plans must now be modified to ensure that the ambient standards will be maintained for the foreseeable future, and to ensure that significant deterioration of air quality in clean areas of the country is prevented.

The existing implementation plans currently contain many measures which are applicable to the maintenance and deterioration efforts, but additional measures must also be developed. Many of these additional measures will involve future planning activities—most common of which will be land use planning activities.

The point is made that, after existing sources have reduced their emissions to the lowest practical level, further air pollution control can only be accomplished by implementing rational planning procedures for management of any new sources of air pollution. This will require extensive cooperation among the air pollution control community; regional, state, and local planning agencies; state and local governments; and the general public in order to ensure that future land use plans include appropriate air quality considerations.     

Air Emission Inventories in North America: A Critical Assessment

Abstract

Although emission inventories are the foundation of air quality management and have supported substantial improvements in North American air quality, they have a number of shortcomings that can potentially lead to ineffective air quality management strategies. Major reductions in the largest emissions sources have made accurate inventories of previously minor sources much more important to the understanding and improvement of local air quality. Changes in manufacturing processes, industry types, vehicle technologies, and metropolitan infrastructure are occurring at an increasingly rapid pace, emphasizing the importance of inventories that reflect current conditions. New technologies for measuring source emissions and ambient pollutant concentrations, both at the point of emissions and from remote platforms, are providing novel approaches to collecting data for inventory developers. Advances in information technologies are allowing data to be shared more quickly, more easily, and processed and compared in novel ways that can speed the development of emission inventories. Approaches to improving quantitative measures of inventory uncertainty allow air quality management decisions to take into account the uncertainties associated with emissions estimates, providing more accurate projections of how well alternative strategies may work. This paper discusses applications of these technologies and techniques to improve the accuracy, timeliness, and completeness of emission inventories across North America and outlines a series of eight recommendations aimed at inventory developers and air quality management decision-makers to improve emission inventories and enable them to support effective air quality management decisions for the foreseeable future.     

Compilation and assessment of recent positive matrix factorization and UNMIX receptor model studies on fine particulate matter source apportionment for the eastern United States

In 1997, the U.S. Environmental Protection Agency (EPA) revised its particulate matter standards to include an annual standard for fine particulate matter (PM2.5; 15 microg/m3) and a 24-hr standard (65 microg/m3). The 24-hr standard was lowered to 35 microg/m3 in 2006 in an effort to further reduce overall ambient PM2.5 concentrations. Identifying and quantifying sources of particulate matter affecting a particular location through source apportionment methods is now an important component of the information available to decision makers when evaluating the new standards. This literature compilation summarizes a subset of the source apportionment research and general findings on fine particulate matter in the eastern half of the United States using Positive Matrix Factorization. The results between studies are generally comparable when comparable datasets are used; however, methodologies vary considerably. Commonly identified source categories include: secondary sulfate/coal burning (sometimes over 50% of total mass), secondary organic carbon/mobile sources, crustal sources, biomass burning, nitrate, various industrial processes, and sea salt. The source apportionment tools and methodologies have passed the proof-of-concept stage and are now being used to understand the ambient composition of particulate matter for sites across the United States and the spatial relationship of sources to the receptor. Recommendations are made for further and standardized method development for source apportionment studies, and specific research areas of interest for the eastern United States are proposed.     

Determining Reduced-Emission Goals Needed to Achieve Air Quality Goals— A Hypothetical Case

Air management steps involved in determining reduced-emission goals include determining the effects of various pollutant concentrations on man, animals, plants, and property; deciding which effects to prevent; selecting ambient air quality goals that will prevent these effects; measuring and evaluating pollutant concentrations from sources and in the ambient atmosphere; calculating over-all source reductions needed to achieve selected ambient air quality goals; and finally, determining reduced-emission goals for the various source types. Examples are cited of the various decisions and actions involved in determining a set of reduced-emission goals for stationary and mobile combustion sources.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

Variations in source apportionments of nutrient load among seasons and hydrological years in a semi-arid watershed: GWLF model results

Quantifying source apportionments of nutrient load and their variations among seasons and hydrological years can provide useful information for watershed nutrient load reduction programs. There are large seasonal and inter-annual variations in nutrient loads and their sources in semi-arid watersheds that have a monsoon climate. The Generalized Watershed Loading Function model was used to simulate monthly nutrient loads from 2004 to 2011 in the Liu River watershed, Northern China. Model results were used to investigate nutrient load contributions from different sources, temporal variations of source apportionments and the differences in the behavior of total nitrogen (TN) and total phosphorus (TP). Examination of source apportionments for different seasons showed that point sources were the main source of TN and TP in the non-flood season, whereas contributions from diffuse sources, such as rural runoff, soil erosion, and urban areas, were much higher in the flood season. Furthermore, results for three typical hydrological years showed that the contribution ratios of nutrient loads from point sources increased as streamflow decreased, while contribution ratios from rural runoff and urban area increased as streamflow increased. Further, there were significant differences between TN and TP sources on different time scales. Our findings suggest that priority actions and management measures should be changed for different time periods and hydrological conditions, and that different strategies should be used to reduce loads of nitrogen and phosphorus effectively.     

Design of Monitor Networks to Meet Multiple Criteria

For most monitoring networks, demonstration of compliance with ambient air quality standards is only one of many interrelated purposes served. The network also may be needed (1) to assess current air quality, (2) to assess regional background air quality, (3) to determine individual source “culpability,” (4) to validate or calibrate a particular dispersion model, (5) to assess effectiveness of a control strategy, or (6) to determine the risk of damage to certain critical or sensitive receptors. Monitoring to support compliance with Prevention of Significant Deterioration regulations is now also an important issue for many new sources. The same network may have to perform many of these functions for several pollutants that have different source locations and different characteristic averaging times. This paper presents a method for systematically addressing each of these concerns. The method includes use of a statistical computer model, MONITOR. This model provides quantitative estimates of relative probabilities in order to assess whether a particular network design will meet each of these criteria. A sample application of the method is provided.     

Selected International Receptor-Based Air Quality Standards

Abstract

The ambient air quality standards (AAQS) of twenty-one nations for eight commonly regulated substances are presented. Many countries are adding a receptor-based component to their air quality management, which traditionally have been emission oriented. Automation of air quality monitoring stations has meant that local air quality evaluation can now be more easily achieved. However, a majority of countries have no active air quality standards (emission or receptor-based) or ambient air quality monitoring. One possible monitoring procedure is outlined and the variation in international standards is discussed.