October 1999PM Data Analysis Workbook: Introduction1 Introduction to the PM Data Analysis Workbook Objectives of the PM Monitoring Program Critical Issues for Data Uses and Interpretation Motivating Examples References Introduction Workbook Content PM 2.5 Background –Common PM 2.5 Emission Sources –Properties of PM –PM Formation in the Atmosphere –Atmospheric Transport of PM The objective of the workbook is to guide federal, state, and local agencies and other interested people in using particulate matter data to meet their objectives.

October 1999PM Data Analysis Workbook: Introduction2 Introduction Particulate matter (PM) is a general term for a mixture of solid particles and liquid droplets found in the air. Scientific studies show a link between PM and a series of significant health effects. The new standards for particles <2.5  m (PM 2.5 ) are 15  g/m 3 annual and 65  g/m 3 24-hr. PM 2.5, fine particles, result from sources such as combustion and from the transformation of gaseous emissions such as sulfur dioxide (SO 2 ), nitrogen oxide (NO x ), and volatile organic compounds (VOCs).

October 1999PM Data Analysis Workbook: Introduction3 Introduction Nature and sources of particulate matter (PM). Particulate matter is the general term used for a mixture of solid particles and liquid droplets found in the air. These particles, which come in a wide range of sizes, originate from many different stationary, area, and mobile sources as well as from natural sources. They may be emitted directly by a source or formed in the atmosphere by the transformation of gaseous emissions. Their chemical and physical compositions vary depending on location, time of year, and meteorology. Health and other effects of PM. Scientific studies show a link between PM (alone or combined with other pollutants in the air) and a series of significant health effects. These health effects include premature death, increased hospital admissions and emergency room visits, increased respiratory symptoms and disease, and decreased lung function, and alterations in lung tissue and structure and in respiratory tract defense mechanisms. Sensitive groups that appear to be at greater risk to such effects include the elderly, individuals with cardiopulmonary disease such as asthma, and children. In addition to health problems, particulate matter is the major cause of reduced visibility in many parts of the United States. Airborne particles also can cause soiling and damage to materials. New PM standards. The primary (health-based) standards were revised to add two new PM 2.5 standards, set at 15µg/m 3 (annual) and 65 µg/m 3 (24-hr), and to change the form of the 24-hr PM 10 standard. The selected levels are based on the judgment that public health will be protected with an adequate margin of safety. The secondary (welfare-based) standards were revised by making them identical to the primary standards. In conjunction with the Regional Haze Program, the secondary standards will protect against major PM welfare effects, such as visibility impairment, soiling, and materials damage. PM 2.5 composition. PM 2.5 consists of those particles that are less than 2.5 micrometers in diameter. They are also referred to as "fine" particles, while those between 2.5 and 10 µ m are known as "coarse" particles. Fine particles result from fuel combustion from motor vehicles, power generation, and industrial facilities and from residential fireplaces and wood stoves. Fine particles can also be formed in the atmosphere by the transformation of gaseous emissions such as SO 2, NO x, and VOCs. Coarse particles are generally emitted from sources such as vehicles traveling on unpaved roads, materials handling, crushing and grinding operations, and windblown dust. Goals of PM 2.5 monitoring. The goal of the PM 2.5 monitoring program is to provide ambient data that support the nation's air quality programs, including both mass measurements and chemically resolved, or speciated, data. Data from this program will be used for PM 2.5 National Ambient Air Quality Standard (NAAQS) comparisons, development and tracking of implementation plans, assessments for regional haze, assistance for studies of health effects, and other ambient PM research activities. U.S. EPA, 1999a

October 1999PM Data Analysis Workbook: Introduction4 PM Data Analysis Workbook: Design Goals Relevant. The workbook should contain material that state PM data analysts need and omit material that they don’t need. Technically sound. The workbook should be prepared and agreed upon by experienced PM analysts. Educational. The workbook content should be presented in a manner that enables state PM data analysts to learn relevant new PM analysis techniques. Practical. Beyond theory, the workbook should contain practical advice and access to examples, tools and methods. Gateway. The core workbook should be a gateway to additional on-line resources. Evolving. The on-line and hard copy workbooks should improve in time through feedback from the user communities. The on-line workbook and data analysis forum is available at Contributions to the workbook and site are encouraged and welcome!

October 1999PM Data Analysis Workbook: Introduction5 Why PM Data Analysis by Individual States? The new PM 2.5 regulations will further increase the need to better understand the nature, causes, effects, and reduction strategies for PM. States collecting data have unique “local” perspectives on data quality, meteorology, and sources, and in articulating policy-relevant data analysis questions. States also face: –large quantities of complex new PM 2.5 data, –large uncertainties about causes and effects, –immature nature and inherent complexity of analysis techniques, –importance of both local and transport sources for PM 2.5, and –connections between PM 2.5, visibility, ozone, climate change, and toxics. Collaborative data analysis is needed to develop and support linkages between: –data analysis “experts,” “novices,” and “beginners” –data analysts, modelers, health researchers, and policymakers –multiple states, regions, nations, environmental groups and industrial stakeholders Poirot, 1999

October 1999PM Data Analysis Workbook: Introduction6 Workbook Content Introduction Ensuring High Quality Data Quantifying PM NAAQS Attainment Status Characterizing Ambient PM Concentrations and Processes Quantifying Trends in PM and its Precursors Quantifying the Contribution of Important Sources to PM Concentrations Evaluating PM and Precursor Emission Inventories Identifying Control Strategies to Meet the NAAQS for PM 2.5 Using PM Data to Assess Visibility (to be added later) Glossary Workbook References

October 1999PM Data Analysis Workbook: Introduction7 Workbook Preparation Strategy (1 of 2) This workbook was designed to: –Serve as a companion document to the PM 2.5 Data Analysis Workshops. –Reflect a snapshot in time of the workbook available on the website. By design, the website will have the most current information. –Serve as an overview to the large topic of PM 2.5 data analysis (not an official guidance document). For some topics, more information is provided by adding pages in 12 point font. A summary page in larger, presentation- friendly font is typically given to summarize these information-laden pages.

October 1999PM Data Analysis Workbook: Introduction8 Workshop presenters will use most, but not all, of the workbook pages in their presentations. The goal is that workshop attendees will walk away with all the presentation materials and more. The document was prepared in landscape format using a single software package to facilitate the presentation, HTML transfer, and printing of the hard copy document. Each topic area could be an entire workbook on its own. The web version of the workbook will eventually contain active links to methods, tools, data, and references. References are provided for readers who want more detail. Workbook Preparation Strategy (2 of 2)

October 1999PM Data Analysis Workbook: Introduction9 Using the Workbook Decision matrix to be used to identify example activities that will help the analyst meet policy-relevant objectives. To use the matrix, find your policy-relevant objective at the top left. Follow this line across to see which example activities will be useful to meet the objective. For each of these activities, look down the column to see which data and data analysis tools are useful for the activity. Adapted from Main et al., 1998

October 1999PM Data Analysis Workbook: Introduction10 PM 2.5 Background Primary PM (directly emitted): –Suspended dust –Sea salt –Organic carbon –Elemental carbon –Metals from combustion –Small amounts of sulfate and nitrate Gases that form PM in the atmosphere (secondary PM): –Sulfur dioxide (SO 2 ): forms sulfates –Nitrogen oxides (NO x ): forms nitrates –Ammonia (NH 3 ): forms ammonium compounds –Volatile organic compounds (VOC): forms organic carbon compounds PM is composed of a mixture of primary and secondary compounds. Emissions that Contribute to PM Mass

October 1999PM Data Analysis Workbook: Introduction11 NaCl: Salt is found in PM near sea coasts, open playas, and after de-icing materials are applied. Organic carbon (OC): consists of hundreds of separate compounds containing mainly carbon, hydrogen and oxygen. Elemental carbon (EC): Composed of carbon without much hydrocarbon or oxygen. EC is black, often called soot. Liquid Water: soluble nitrates, sulfates, ammonium, sodium, other inorganic ions, and some organic material absorb water vapor from the atmosphere. Major PM 2.5 Components Chow and Watson, 1997 Geological material: suspended dust consists mainly of oxides of Al, Si, Ca, Ti, Fe, and other metal oxides. Sulfate: results from conversion of SO 2 gas to sulfate-containing particles. Nitrate: results from a reversible gas/particle equilibrium between NH 3, HNO 3, and particulate ammonium nitrate. Ammonium: ammonium bisulfate, sulfate, and nitrate are most common. Most PM mass in urban and nonurban areas is composed of a combination of the following chemical components:

October 1999PM Data Analysis Workbook: Introduction12 Common PM 2.5 Emission Sources: Profiles Fujita, 1998

October 1999PM Data Analysis Workbook: Introduction13 Properties of PM Physical, Chemical and Optical Properties Size Range of Particulate Matter (PM) Mass Distribution of PM vs. Size: PM 10, PM 2.5 Fine and Coarse Particles Fine Particles: PM 2.5 Coarse Particle Fraction: PM 10 -PM 2.5 ; Relationship of PM 2.5 and PM 10 Chemical Composition of PM vs. Size Internal and External Mixtures Optical Properties of PM Husar, 1999

October 1999PM Data Analysis Workbook: Introduction14 Physical, Chemical and Optical Properties PM is characterized by its physical, chemical, and optical properties. Physical properties include particle size and shape. Particle size refers to particle diameter or “equivalent” diameter for odd-shaped particles. Particles may be liquid droplets, regular or irregular shaped crystals, or aggregates of odd shape. Particle chemical composition may vary including dilute water solutions of acids or salts, organic liquids, earth's crust materials (dust), soot (unburned carbon), and toxic metals. Optical properties determine the visual appearance of dust, smoke, and haze and include light extinction, scattering, and absorption. The optical properties are determined by the physical and chemical properties of the ambient PM. Each PM source type produces particles with a specific physical, chemical, and optical signature. Hence, PM may be viewed as several pollutants since each PM type has its own properties and sources and may require different controls.

October 1999PM Data Analysis Workbook: Introduction15 Size Range of Particulate Matter The size of PM particles ranges from about tens of nanometers (nm) (which corresponds to molecular aggregates) to tens of microns (1  m  the size of human hair). The smallest particles are generally more numerous, and the number distribution of particles generally peaks below 0.1  m. The size range below 0.1  m is also referred to as the ultrafine range. The largest particles (  m) are small in number but contain most of the PM volume (mass). The volume (mass) distribution can have two or three peaks (modes). The bi-modal mass distribution has two peaks. The peak of the PM surface area distribution is always between the number and the volume peaks. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction16 Mass Distribution of PM vs. Size: PM 10, PM 2.5 The mass distribution tends to be bi-modal with the saddle in the 1-3  m size range. PM 10 refers to the fraction of the PM mass less than 10  m in diameter. PM 2.5, or fine mass, refers to the fraction of the PM mass less than 2.5  m in size. The difference between PM 10 and PM 2.5 constitutes the coarse fraction. The fine and coarse particles have different sources, properties, and effects. Many of the known environmental impacts (health, visibility, acid deposition) are attributed to PM 2.5. There is a natural division of atmospheric particulates into Fine and Coarse fraction based on particle size. FineCoarse Husar, 1999

October 1999PM Data Analysis Workbook: Introduction17 Fine and Coarse Particles Adapted from: Seinfeld and Pandis, 1998

October 1999PM Data Analysis Workbook: Introduction18 Fine Particles: PM 2.5 Fine particles (  2.5  m) result primarily from combustion of fossil fuels in industrial boilers, automobiles, and residential heating systems. A significant fraction of the PM 2.5 mass over the U.S. is produced in the atmosphere through gas-particle conversion of precursor gases such as sulfur oxides, nitrogen oxides, organics, and ammonia. The resulting secondary PM products are sulfates, nitrates, organics, and ammonium. Some PM 2.5 is emitted as primary emissions from industrial activities and motor vehicles, including soot (unburned carbon), trace metals, and oily residues. Fine particles are mostly droplets, except for soot which is in the form of chain aggregates. Over the industrialized regions of the U.S., anthropogenic emissions from fossil fuel combustion contribute most of the PM 2.5. In remote areas, biomass burning, windblown dust, and sea salt also contribute. Fine particles can remain suspended for long periods (days to weeks) and contribute to ambient PM levels hundreds of km away from where they are formed.

October 1999PM Data Analysis Workbook: Introduction19 Coarse Particle Fraction: PM 10 -PM 2.5 Coarse particles (2.5 to 10  m) are generated by mechanical processes that break down crustal material into dust that can be suspended by the wind, agricultural practices, and vehicular traffic on unpaved roads. Coarse particles are primary in that they are emitted as windblown dust and sea spray in coastal areas. Anthropogenic coarse particle sources include flyash from coal combustion and road dust from automobiles. The chemical composition of the coarse particle fraction is similar to that of the earth's crust or the sea, but sometimes coarse particles also carry trace metals and nitrates. Coarse particles are removed from the atmosphere by gravitational settling, impaction to surfaces, and scavenging by precipitation. Their atmospheric residence time is generally less than a day, and their typical transport distance is below a few hundred km. Some dust storms tend to lift the dust to several km altitude, which increases the transport distance to many thousand km. Albritton and Greenbaum, 1998

October 1999PM Data Analysis Workbook: Introduction20 Relationship of PM 2.5 and PM 10 PM 10 and PM 2.5 are related to each other when most of the PM 10 is contributed by PM 2.5 (e.g., Northeast example above). Different areas and/or different seasons may have different relationships between PM 2.5 and PM 10. PM 2.5 comprises a larger fraction of PM 10 in the northeastern U.S. than in southern California. PM 2.5 seasonal patterns are similar to those for PM 10 in the northeast; seasonal patterns of PM 2.5 and PM 10 differ in Southern California. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction21 Chemical Composition of PM vs. Size The chemical species that make up the PM occur at different sizes. For example in Los Angeles, ammonium and sulfate occur in the fine mode, <2.5  m in diameter. Carbonaceous soot, organic compounds, and trace metals tend to be in the fine particle mode. The sea salt components, sodium and chloride, occur in the coarse fraction, > 2.5  m. Wind-blown and fugitive dust are also found mainly in the coarse mode. Nitrates may occur in fine and coarse modes. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction22 Internal and External Mixtures of Particles During their multi-day atmospheric residence time, particles from different sources and with different compositions are mixed together by a range of atmospheric processes. The resulting particles can be either external or internal mixtures. In an external mixture, the particle composition will be non-uniform because the components from different sources remain separate (e.g., a soot particle inside a sulfate droplet, as illustrated by the electron micrograph below). In an internal mixture, the particle composition is uniform because the individual components are completely mixed. The main process that produces internal mixtures is processing by water such as in fog and/or cloud scavenging and subsequent evaporation. Electron micrograph of a PM 2.5 droplet residue. Evidently, the droplet contained a solid particle, possibly soot. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction23 Optical Properties of PM Particles effectively scatter and absorb solar radiation. The scattering efficiency per PM mass is highest at about 0.5  m. This is why, for example, 10  g of fine particles (  m) Husar, 1999

October 1999PM Data Analysis Workbook: Introduction24 Sulfate Formation in the Atmosphere Sulfate Formation in Clouds Seasonal SO 2- -to-Sulfate Transformation Rate Residence Time of Sulfur and Organics Nitrate Formation in the Atmosphere Links to Ozone Formation, Health, and Visibility PM Formation in the Atmosphere

October 1999PM Data Analysis Workbook: Introduction25 The condensation of H 2 SO 4 molecules results in the accumulation and growth of particles in the  m size range – hence the name “accumulation-mode” particles. Sulfate Formation in the Atmosphere Sulfates constitute about half of the PM 2.5 in the eastern U.S. Virtually all the ambient sulfate (99%) is secondary, formed within the atmosphere from SO 2. About half of the SO 2 oxidation to sulfate occurs in the gas phase through photochemical oxidation in the daytime. NO x and hydrocarbon emissions tend to enhance the photochemical oxidation rate. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction26 Only a small fraction of the cloud droplets rain out; most droplets evaporate at night and leave a sulfate residue or “convective debris”. Most elevated layers above the mixing layer are pancake-like cloud residues. Such cloud “processing” is responsible for internally mixing PM particles from many different sources. It is also believed that such “wet” processes are significant in the formation of the organic fraction of PM 2.5. Sulfate Formation in Clouds At least half of the SO 2 oxidation takes place in cloud droplets as air molecules pass through convective clouds at least once every summer day. Within clouds, the soluble pollutant gases, such as SO 2, get scavenged by the water droplets and rapidly oxidize to sulfate. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction27 Season SO 2 -to-Sulfate Transformation Rate SO 2 -to-sulfate transformation rates peak in the summer due to enhanced summertime photochemical oxidation and SO 2 oxidation in clouds. Husar, 1999 Transformation rates derived from the CAPITA Monte Carlo Model, Schichtel and Husar (1997).

October 1999PM Data Analysis Workbook: Introduction28 Residence Time of Sulfur and Organics SO 2 is depleted mostly by dry deposition (2-3%/hr) and also by conversion to sulfate (up to 1%/hr). This gives SO 2 an atmospheric residence time of only 1 to 1.5 days. It takes about a day to form the sulfate PM. Once formed, sulfate is removed mostly by wet deposition at a rate of 1-2 %/hr yielding a residence time of 3 to 5 days. Overall, sulfur as SO 2 and sulfate is removed at a rate of 2-3%/hr, which corresponds to a residence time of 2-4 days. These processes have at least a factor of two seasonal and geographic variation. It is believed that the organics in PM 2.5 have a similar conversion rate, removal rate, and atmospheric residence time. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction29 Nitrate Formation and Removal in the Atmosphere NO 2 can be converted to nitric acid (HNO 3 ) by reaction with hydroxyl radicals (OH) during the day. –The reaction of OH with NO 2 is about 10 times faster than the OH reaction with SO 2. –The peak daytime conversion rate of NO 2 to HNO 3 in the gas phase is about 10 to 50% per hour. During the nighttime, NO 2 is converted into HNO 3 by a series of reactions involving ozone and the nitrate radical. HNO 3 reacts with ammonia to form particulate ammonium nitrate (NH 4 NO 3 ). About 1/3 of anthropogenic NO x emissions in the U.S. are estimated to be removed by wet deposition. Thus, PM nitrate can be formed at night and during the day; daytime photochemistry also forms ozone.

October 1999PM Data Analysis Workbook: Introduction30 PM and Ozone (1 of 2) The formation of a substantial fraction of secondary PM 2.5 depends on photochemical gas phase reactions which also produce ozone. –Concentrations of OH radicals, ozone, and hydrogen peroxide (H 2 O 2 ), formed by gas phase reactions involving VOCs and NO x, depend on the concentrations of the reactants and on meteorological conditions including temperature, solar radiation, wind speed, mixing volume, and synoptic weather conditions. NESCAUM, 1992

October 1999PM Data Analysis Workbook: Introduction31 PM and Ozone (2 of 2) An illustration of some of the environmental factors that influence the production of ozone and secondary PM formation. Meteorological (e.g., mixing heights, transport) and chemical conditions (e.g., emissions composition and intensity) affect the concentration of secondary PM and ozone precursors. RRWG Policy Team, 1999

October 1999PM Data Analysis Workbook: Introduction32 PM, Health, and Visibility Human health research indicates that PM mass correlates with sickness and death. The components of PM that cause these health effects are not known. Fine particles and/or coarse particles may contribute to these health effects. Visibility, the distance one can distinguish a target, is influenced by lighting, contrast of the target to the background, and most importantly, the size, color, and concentration of the particles between the observer and the target. Thus, we need to better understand the chemical and physical characteristics and the formation of PM in order to identify the links between and reduce the influence of PM on health and visibility.

October 1999PM Data Analysis Workbook: Introduction33 Summary of Factors Influencing PM Concentrations: Meteorology Meteorological parameters important to PM concentration variations include: temperature, relative humidity, mixing heights, wind speed, and wind direction. Seasonal changes in meteorology effect diurnal, seasonal, and chemical patterns of PM. Chu and Cox, 1998

October 1999PM Data Analysis Workbook: Introduction34 Summary of Factors Influencing PM Concentrations: Emissions Time patterns of emissions –Diurnal patterns (e.g., traffic, biogenics) –Weekday/weekend patterns Source type and location of emissions –Point vs. area vs. mobile source emissions –Height of emissions Primary PM emissions vs. secondary PM Chemical composition (e.g., Ni and V from oil, Se from coal, Na from sea salt or winter road salt) Temporal, spatial, and chemical emissions characteristics influence PM concentrations and provide clues to source contributions.

October 1999PM Data Analysis Workbook: Introduction35 Atmospheric Transport of PM Transport Mechanisms Influence of Transport on Source Regions Plume Transport Long-range Transport Atmospheric Residence Time and Spatial Scales Residence Time Dependence on Height Range of Transport

October 1999PM Data Analysis Workbook: Introduction36 The three major airmass source regions that influence North America are the northern Pacific, the Arctic, and the tropical Atlantic. During the summer, the eastern U.S. is influenced by the tropical airmass from the Gulf of Mexico. The three transport processes that shape regional dispersion are wind shear, veer, and eddy motion. Homogeneous hazy airmasses are created through shear and veer at night followed by vigorous vertical mixing during the day. Transport Mechanisms Pollutants are transported by the atmospheric flow field which consists of the mean flow and the fluctuating turbulent flow. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction37 Low wind speeds over a source region allows for pollutants to accumulate. High wind speeds ventilate a source region preventing local emissions from accumulating. Horizontal DilutionVertical Dilution In urban areas, during the night and early morning, the emissions are trapped by poor ventilation. In the afternoon, vertical mixing and horizontal transport tend to dilute the concentrations. Influence of Transport on Source Regions Husar, 1999

October 1999PM Data Analysis Workbook: Introduction38 Plume Transport Plume transport varies diurnally from a ribbon-like layer near the surface at night to a well-mixed plume during the daytime. Even during the daytime mixing, individual power plant plumes remain coherent and have been tracked for 300+ km from the source. Most of the plume mixing is due to nighttime lateral dispersion followed by daytime vertical mixing. Much of the man-made PM 2.5 in the eastern U.S. is from SO 2 emitted by power plants. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction39 Long-range Transport In many remote areas of the U.S., high concentrations of PM 2.5 have been observed. Such events have been attributed to long-range transport. Long-range transport events occur when there is an airmass stagnation over a source region, such as the Ohio River Valley, and the PM 2.5 accumulates. Following the accumulation, the hazy airmass is transported to the receptor areas. Satellite and surface observations of fine particles in hazy airmasses provide a clear manifestation of long-range pollutant transport over eastern North America. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction40 Atmospheric Residence Time and Spatial Scales PM 2.5 sulfates reside 3 to 5 days in the atmosphere. Ultrafine 0.1  m coagulate while coarse particles above 10  m settle out more rapidly. PM in the  m size range has the longest residence time because it neither settles nor coagulates. Atmospheric residence time and transport distance are related by the average wind speed, about 5 m/s. Residence time of several days yields “long- range transport” and more uniform spatial pattern. On average, PM 2.5 particles are transported 1000 or more km from the source of their precursor gases. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction41 Residence Time Dependence on Height The PM 2.5 residence time increased with height. Within the atmospheric boundary layer (the lowest 1-2 km), the residence time is 3 to 5 days. If aerosols are lifted to 1-10 km in the troposphere, they are transported for weeks and many thousand miles before removal. The lifting of boundary layer air into the free troposphere occurs by deep convective clouds and by converging airmasses near weather fronts. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction42 Range of Transport The residence time determines the range of transport. For example, given a residence time of 4 days (~100 hrs) and a mean transport speed of 10 mph, the transport distance is about 1000 miles. The range of transport determines the “region of influence” of specific sources. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction43 Objectives of the PM Monitoring Program The primary objective of the PM monitoring program is to provide ambient data that support the nation’s air quality program objectives. At a minimum, this includes: –Determine whether health and welfare standards (NAAQS) are met. –Assess annual and seasonal spatial characterization of PM. –Track progress of the nation and specific areas in meeting Clean Air Act requirements (provided, for example, through national trends analyses). –Develop emission control strategies. Homolya et al., 1998

October 1999PM Data Analysis Workbook: Introduction44 Overview of National PM 2.5 Network Homolya et al., 1998

October 1999PM Data Analysis Workbook: Introduction45 PM 2.5 Implementation Update The bulk of all compliance and continuous monitoring sites are to be established by December 31, The first chemical speciation sites will begin operation by November 1999, and installations will continue through December 31, The IMPROVE sites were to have been deployed by December 31, 1999; however, this schedule has been delayed. Operation of the Super-sites began with Atlanta in August 1999; the site in Fresno will be next, followed by the remaining areas (to be announced once grants are awarded). Byrd, 1999

October 1999PM Data Analysis Workbook: Introduction46 PM 2.5 Sampling Schedule Compliance sites [those with federal reference method samples (FRMS)] will operate largely on an everyday or one-in-three-day schedule. Some sites will operate on a one-in-six-day schedule. Continuous sites will operate every day. Fifty-four speciation sites will operate on a one-in-three- day schedule. The remaining sites will operate on a one-in-six-day or episodic schedule, depending on data needs. The IMPROVE sampling schedule will ultimately match a one-in-three-day schedule. Byrd, 1999

October 1999PM Data Analysis Workbook: Introduction47 Site Types The larger check marks reflect the primary use of the data. Homolya et al., 1998

October 1999PM Data Analysis Workbook: Introduction48 Data Collected Homolya et al., 1998

October 1999PM Data Analysis Workbook: Introduction49 Sampling Artifacts and Interferences (1 of 2) Homolya et al., 1998

October 1999PM Data Analysis Workbook: Introduction50 Sampling Artifacts and Interferences (2 of 2) Organic gas adsorption (positive bias) comprised up to 50% of the organic carbon measured on quartz-fiber filters in southern California (Turpin et al., 1994). These studies also indicated that adsorption was much more important than organic particle volatilization (negative bias). Sampling losses on the order of 30% of the annual federal standard for PM 2.5 may be expected due to volatilization of ammonium nitrate in those areas of the country where nitrate is a significant contributor to the fine particle mass and where ambient temperatures tend to be warm (Hering and Cass, 1999).

October 1999PM Data Analysis Workbook: Introduction51 Critical Issues for Data Interpretation Issues to be considered when planning and performing data interpretation: –Data availability (mass, ions, metals, organic carbon, speciated organic carbon, etc.) –Data quality (standard operating procedures, audits, accuracy and precision, data validation) –Sampling artifacts and interferences (organic carbon volatilization, nitrate volatilization, moisture) –Data representativeness for planned analysis (nearby sources vs. regional background) –Sampling duration (use of 24-hr data to investigate diurnal changes in photochemistry, emissions and meteorology) –Sampling frequency (use of 1-in-6 day data to investigate many episodes of high PM) –Availability of complementary data (PM precursor, meteorological, and visibility data) Use the decision matrix to proceed from policy-relevant objectives, to data analysis activities, to applicable data and tools.

October 1999PM Data Analysis Workbook: Introduction52 Motivating Examples The following pages are excerpts from other chapters in this workbook. These examples illustrate key PM data analysis and validation issues. Meaningful data analyses: –Begin with the collection and reporting of valid data. –Proceed through an understanding of the chemical and physical processes related to PM formation, transport, and removal. –Evolve as more analysis techniques are applied to the data to obtain a consensus view of attainment and control issues.

October 1999PM Data Analysis Workbook: Introduction53 Data Validation Continues During Data Analysis Two source apportionment models were applied to PM 2.5 data collected in Vermont, and the results of the models were compared. Excellent agreement for the selenium source was observed for part of the data while the rest of the results did not agree well. Further investigation showed that the period of good agreement coincided with a change in laboratory analysis (with an accompanying change in detection limit and measurement uncertainty - the two models treat these quantities differently.) Poirot, 1999b

October 1999PM Data Analysis Workbook: Introduction54 Annual Standards Calculation Annual means are averaged across sites (spatial mean) before averaging across years. This calculation assumes the site with 38% data completeness (Site 3, year 2) had less than 11 samples in each quarter. Thus, the 15.2  g/m 3 annual mean was left out of the spatial mean calculation. If we also assume that the site with 50% data completeness (Site 4, year 4) resulted in all quarters with at least 11 samples, then the 16.9  g/m 3 annual mean at that site is included in the spatial mean. The 3-yr mean rounds to 14.4  g/m 3 which is less than the level of the standard of 15.0  g/m 3. A PM 2.5 network with annual means calculated from quarterly means Fitz-Simmons, 1999

October 1999PM Data Analysis Workbook: Introduction55 Episodic Patterns in PM Investigations of episodes of high PM concentrations are necessary in order to understand the meteorological conditions and possible PM and precursor sources that lead to the high concentrations. Unlike ozone episodes which typically occur during the summer, episodes of high PM 2.5 concentrations can occur during any time of year (e.g., winter wood smoke, summer photochemical event, etc.). Poirot et al., 1999

October 1999PM Data Analysis Workbook: Introduction56 Day-of-Week Cycle in PM Emissions Example day of week pattern of diesel engine emissions in Chicago, Illinois as determined by chemical mass balance model. Though the CMB fit was performed using PM 10 and nonmethane organic gas (NMOG) data, diesel emissions in this case were nearly 100% particulate matter. Note that Saturday and Sunday diesel emissions are statistically significantly lower than Monday through Friday. Lin et al., 1993 Chicago 80 samples

October 1999PM Data Analysis Workbook: Introduction57 Seasonal Pattern of PM 2.5 The seasonal cycle results from changes in PM background levels, emissions, atmospheric dilution, and chemical reaction, formation, and removal processes. Examining the seasonal cycles of PM 2.5 mass and its elemental constituents can provide insights into these causal factors. The season with the highest concentrations is a good candidate for PM 2.5 control actions. Schichtel, 1999a

October 1999PM Data Analysis Workbook: Introduction58 Seasonal PM 2.5 Dependence on Elevation in the Appalachian Mountains In August, the PM 2.5 concentrations are independent of elevation to at least 1200 m. Above 1200 m, PM 2.5 concentrations decrease. In January, PM 2.5 concentrations decrease between sites at 300 and 800 m by about 50%. PM 2.5 concentrations are approximately constant from 800 m to 1200 m and decrease another ~50% from 1200 to 1700 m. Monitor locations and topography Schichtel, 1999a

October 1999PM Data Analysis Workbook: Introduction59 Seasonal Maps of PM 2.5 ( ) These maps illustrate the regional differences in PM. The same control strategies may not be effective if applied on a national scale. The PM 2.5 concentrations peak during the summer (Q3) in the eastern U.S. The PM 2.5 concentrations peak in the winter (Q1) in populated regions of the Southwest and in the San Joaquin Valley in California. Falke, 1999

October 1999PM Data Analysis Workbook: Introduction60 PM 10 in the U.S. During the Central American Smoke Event 24-hr PM 10 concentrations in  g/m 3 are shown for several cities. The likely smoke impact on these cities is highlighted. The vertical line is at 65  g/m 3 in each figure. Husar, 1999

October 1999PM Data Analysis Workbook: Introduction61 Combining Spatial and Temporal Trends The map shows the annual trends in overall PM 2.5 concentration for , at 34 monitoring sites in the continental U.S. which have been recording PM 2.5 concentrations for over six years. The site labels are the annual trends of PM 2.5 concentrations at each site. The data were deseasonalized to "take out" the seasonal cycle of PM 2.5. Frechtel et al., 1999

October 1999PM Data Analysis Workbook: Introduction62 Discerning Natural vs. Anthropogenic Sources Using Spatial and Temporal Analyses Fe and Al concentrations strongly correlate, suggesting a common source influence. Ratios are consistent with soil. Fe and K concentrations do not correlate as well. The lower K:Fe ratio of 0.6 is indicative of soil. Higher ratios are consistent with woodsmoke. Data corresponding to the July 4 th weekend are highlighted. Microsoft Excel used to prepare scatter plot and calculate regression coefficients. Poirot, (1998) Concentrations of PM 2.5 iron with silicon, aluminum, and potassium at Chiricahua National Park in Arizona.

October 1999PM Data Analysis Workbook: Introduction63 Air Mass History Analysis Upwind probability plots for high arsenic concentrations have a strong NW orientation at all three sites, pointing directly toward a smelter region. The location of several large smelters are also identified in the plots, with the smelter identified as a green dot appearing to be the most likely contributor (the yellow dot is the receptor location). High arsenic levels paper to be excellent tracers for influence in the Lake Champlain Basin from the smelter region. Upwind probabilities for high aerosol arsenic at three Champlain Basin sites Shaded areas show 20%, 40%, and 60% of upwind probability on highest concentration day Poirot et al. (1998)

October 1999PM Data Analysis Workbook: Introduction64 UNMIX Analysis UNMIX was applied to PM 2.5 data collected at Underhill, VT, during Six “sources” were identified using mass (MF), particle absorption (BABS), arsenic (As), calcium (Ca), iron (Fe), nickel (Ni), selenium (Se), silicon (Si), total sulfur (S), and non- soil potassium (KNON). The “sources” were further investigated by performing back trajectories and investigating time series. The smelter (“smelt”) source, oil combustion, and winter coal combustion source trajectories are consistent with known emission patterns. Poirot (1999) Values represent the % of the element accounted for by the source.

October 1999PM Data Analysis Workbook: Introduction65 PMF Analysis The highest average PM 2.5 concentration at the Bering Land Bridge site (BELA) may be due to the strong influence of aerosol emissions from local pollution sources in nearby Nome plus PM transported into the region. Note the large seasonal difference in the forest fire factor at Gates of the Arctic (GAAR). Polissar et al., 1998 Stacked bar plots prepared using a spreadsheet program.

October 1999PM Data Analysis Workbook: Introduction66 Case Study: Top-Down Emissions Evaluation Primary PM 10 /NO x City #1City #2 Top-down comparison of ambient- and emissions-derived primary PM 10 /NO x in two cities. Ambient Ratio Emission Inventory Ratio Comparison of the ambient- and emissions-derived PM 10 /NO x ratios in two cities are quite different. It appears as though PM 10 is overestimated in the emission inventory by approximately a factor of two. Recommendation: the PM 10 portion of the inventory should be investigated from the bottom-up. Note that this example corresponds to PM 10 ; a similar comparison could be made for PM 2.5 Haste et. al., 1998

October 1999PM Data Analysis Workbook: Introduction67 Case Study: Using CMB to Assess Emission Estimates and Source Apportionment Comparison of CMB modeling results and emission inventory source apportionment are very different. The results of CMB modeling show that mobile sources are responsible for a much larger percentage of PM 2.5 in the ambient air while the emission inventory data shows dust being the main contributor to PM 2.5. These types of discrepancies are important to consider prior to control strategy development. CMB PM 2.5 Source Apportionment Emission Inventory PM 2.5 Source Apportionment Lurmann et. al., 1999 Watson et. al., 1998

October 1999PM Data Analysis Workbook: Introduction68 Model Performance Evaluation Mean daily variation in sulfate predictions and observations in this example show that the model predictions were greater than the ambient observations during most of the year. The largest over-predictions occurred on Julian days (mid- to late summer). There are some occurrences when the model under-predicts. The tendency for over- prediction is most easily seen in the bias display. Sulfate (  g/m 3 )Bias (  g/m 3 ) Adapted from Wayland (1998)

October 1999PM Data Analysis Workbook: Introduction69 Summary This workbook will serve as a companion document to the PM 2.5 Data Analysis Workshop, will reflect a snapshot in time of the workbook available on the website, and will serve as an overview to the large topic of PM 2.5 data analysis. PM 2.5 data can be used to meet a wide range of objectives. The on-line workbook and data analysis forum is available at Contributions to the workbook and site are encouraged and welcome!

October 1999PM Data Analysis Workbook: Introduction70 References Albritton D.L. and Greenbaum D.S. (1998) Atmospheric observations: Helping build the scientific basis for decisions related to airborne particulate matter. Chow J.C. (1995) Measurement methods to determine compliance with ambient air quality standards for suspended particles. J. Air & Waste Manage., 45, pp Chow J.C. and Watson J.G. (1997) Guideline on speciated particulate monitoring. Report prepared by Desert Research Institute and available at Chu S. and W. Cox (1998) Relationship of PM fine to Ozone and Meteorology. Paper 98-RA90A.03 presented at the Air & Waste Management Association's 91 st Annual Meeting & Exhibition, June 14-18, 1998, San Diego, California. Falke S. (1999) PM 2.5 topic summaries available at: Fitz-Simmons T. (1999) How to calculate the particulate NAAQS. Paper presented at the National AIRS conference, San Francisco, May. Frechtel P., Eberly S., Cox W. (1999) PM-Fine Trends at Long-Term IMPROVE Sites. Paper available at ROVE.html Fujita E.M. (1998) MAG Brown Cloud Study Source Attribution of PM2.5. Final report prepared by Desert Research Institute for Maricopa Assoc. of Governments, Phoenix, AZ. December. Haste T.L., Chinkin L.R., Kumar N., Lurmann F.W., and Hurwitt, S.B. (1998) Use of ambient data collected during IMS95 to evaluate a regional emission inventory for the San Joaquin Valley. Final report prepared for the San Joaquin Valleywide Air Pollution Study Agency and the California Air Resources Board, Sacramento, CA by Sonoma Technology, Inc., Petaluma, CA, STI FR, July. Hering S. and Cass G. (1999) the magnitude of bias in the measurement of PM2.5 arising from volatilization of particulate nitrate from Teflon filters. J. Air & Waste Manage. Assoc., 49, pp Homolya J.B., Rice J., Scheffe R.D. (1998) PM2.5 speciation - objectives, requirements, and approach. Presentation. September.

October 1999PM Data Analysis Workbook: Introduction71 References Husar, R. (1999) Draft PM2.5 topic summaries available at Lin J. Scheff P.A., and Wadden R.A. (1993) Development of a two-phase receptor model for VOC and PM10 air pollution sources in Chicago. Paper 93-A487 presented at the 86th annual meeting of the Air & Waste Management Assoc., Denver, June. Lurmann F.W., et. al., (1999) Personal communication. Main H.H., Chinkin L.R., and Roberts P.T. (1998) PAMS data analysis workshops: illustrating the use of PAMS data to support ozone control programs. Web page prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC by Sonoma Technology, Inc., Petaluma, CA, STI , June. NESCAUM (1992) 1992 Regional Ozone Concentrations in the Northeastern United States. Paper available at Polissar A.V., Hopke P.K., Paatero P., Malm W.C., Sisler J.F. (1998) Atmospheric aerosol over Alaska 2. Elemental composition and sources. J. Geophysical Research, Vol. 103, No. D15, pp Poirot R., A. Leston, C. Michaelsen (1999) August 1995 forest fire impacts in New England and Atlantic Canada. Report available at

October 1999PM Data Analysis Workbook: Introduction72 References Poirot R., P. Wishinski, B. Schichtel, and P. Girton (1998) Air trajectory pollution climatology for the Lake Champlain Basin. Draft paper presented at 1998 symposium of the Lake Champlain Research Consortium. Available at Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at Poirot, R. (1999) Draft PM2.5 topic summary available at Poirot R. (1999b) personal communication Reactivity Research Work Group Policy Team (1999) VOC Reactivity Policy White Paper. Prepared for the Reactivity Research Work Group, October. Schichtel B. and Husar R. (1995) Regional simulation of atmospheric pollutants with the Capita Monte Carlo Model. Prepared by the Center for air Pollution and Trend Analysis, Washington University, St. Louis, MO. September. Available at Schichtel B. and Husar R. (1997) Derivation of SO 2 – SO 4 2- Transformation and Deposition Rate Coefficients Over The Eastern US using a Semi-Empirical Approach. Paper available at Schichtel B.A. (1999a) PM 2.5 topic summaries available at: Seinfeld J.H. and Pandis S.N. (1998) Atmospheric chemistry and physics: from air pollution to climate change. John Wiley and Sons, Inc., New York, New York. Turpin B.J., Huntzicker J.J., and Hering S.V. (1994) Investigation of organic aerosol sampling artifacts in the Los Angeles basin. Atmos. Environ., 28, pp

October 1999PM Data Analysis Workbook: Introduction73 References U.S. EPA (1999a) Particulate matter (PM2.5) speciation guidance document. Available at U.S. EPA (1999b) General Information regarding PM2.5 data analysis posted on the EPA Internet web site U.S. EPA (1998) Fact sheet on PM data handling available at Watson J.G., Fujita E.M., Chow J.C., Richards L.W., Neff W., and Dietrich D. (1998) Northern Front Range Air Quality Study. Final report prepared for Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO by Desert Research Institute, Reno, NV. Wayland R.J. (1999) REMSAD Base case simulation: model performance evaluation. Draft report prepared by USEPA OAQPS, Research Triangle Park, NC, March.