October 1999PM Data Analysis Workbook: Introduction1 Introduction to the PM Data Analysis Workbook Objectives of the PM Monitoring Program Critical Issues

October 1999 PM Data Analysis Workbook: Introduction 1

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

• PM2.5 Background

– PM2.5 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.


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 <2.5m (PM2.5) are 15 g/m3 annual and 65 g/m3 24-hr.

• PM2.5, fine particles, result from sources such as combustion and from the transformation of gaseous emissions such as sulfur dioxide (SO2), nitrogen oxide (NOx), and volatile organic compounds (VOCs).


IntroductionNature 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 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 PM2.5 standards, set at 15µg/m3 (annual) and 65 µg/m3 (24-hr), and to change the form of the 24-hr PM10 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.

PM2.5 composition. PM2.5 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 SO2, NOx, 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 PM2.5 monitoring. The goal of the PM2.5 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 PM2.5 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


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 and producer communities.

The on-line workbook and data analysis forum is available at http://capita.wustl.edu/PMFine/. Contributions to the workbook and

site are encouraged and welcome!


Why PM Data Analysis by Individual States?

• The new PM2.5 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, complex new PM2.5 data quantities,

– large uncertainties about causes and effects,

– immature nature and inherent complexity of analysis techniques,

– importance of both local and transport sources for PM2.5, and

– connections between PM2.5, 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 and modelers, health researchers, and policymakers

– multiple states, regions, nations, environmental groups and industrial stakeholdersPoirot, 1999


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 PM2.5

• Using PM Data to Assess Visibility (to be added later)

• Glossary

• Workbook References


Workbook Preparation Strategy (1 of 2)

• This workbook was designed to:

– Serve as a companion document to the PM2.5 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 PM2.5 data analysis (not a 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.


• 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)


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


PM2.5 Emission Sources

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 SO2 gas to sulfate-containing particles.

• Nitrate: results from a reversible gas/particle equilibrium between NH3, HNO3, and particulate ammonium nitrate.

• Ammonium: ammonium bisulfate, sulfate, and nitrate are most common from the irreversible reaction between H2SO4 and NH3.

Most PM mass in urban and nonurban areas can be explained by a combination of the following chemical components:

• 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 that contain >C20.

• Elemental carbon (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.


Common Emission Source Profiles

Example PM source profiles in development


Properties of Particulate Matter

• Physical, Chemical and Optical Properties

• Size Range of Particulate Matter (PM)• Mass Distribution of PM vs. Size:

PM10, PM2.5

• Fine and Coarse Particles

• Fine Particles: PM2.5

• Coarse Particle Fraction: PM10-PM2.5; Relationship of PM2.5 and PM10

• Chemical Composition of PM vs. Size

• Optical Properties of PM

Husar, 1999


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.


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 (0.1-10 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


Mass Distribution of PM vs. Size: PM10, PM2.5

• The mass distribution tends to be bi-modal with the saddle in the 1-3 m size range.

• PM10 refers to the fraction of the PM mass less than 10 m in diameter.

• PM2.5, or fine mass, refers to the fraction of the PM mass less than 2.5 m in size.

• The difference between PM10 and PM2.5 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 PM2.5.

• There is a natural division of atmospheric particulates into Fine and Coarse fraction based on particle size.

Fine Coarse

Husar, 1999


Fine and Coarse Particles

Adapted from: Seinfeld and Pandis, 1998


Fine Particles: PM2.5

• 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 PM2.5 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 PM2.5 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 PM2.5. 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.


Coarse Particle Fraction: PM10-PM2.5

• 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


Relationship of PM2.5 and PM10

• The historical PM2.5 network is sparse; thus, it is difficult to assess PM2.5 concentrations over the U.S.

• In many areas of the country, PM10 and PM2.5 are related because most of the PM10 is contributed by PM2.5. Evaluating the relationship between the two measurements provides information on PM2.5 concentrations in areas not monitored for PM2.5.

• PM2.5 comprises a larger fraction and has a more similar seasonal pattern in the northeastern U.S. than in southern California.

Husar, 1999


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


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 (0.2 < D < 1 m) scatter over ten times more than 10 g of coarse particles (D > 2.5 m)

Husar, 1999


• Sulfate Formation in the Atmosphere

• Sulfate Formation in Clouds

• Season SO2--to-Sulfate Transformation Rate

• Residence Time of Sulfur and Organics

•Nitrate Formation in the Atmosphere

PM Formation in the Atmosphere


• The condensation of H2SO4 molecules results in the accumulation and growth of particles in the 0.1-1.0 m size range – hence the name “accumulation-mode” particles.

Sulfate Formation in the Atmosphere • Sulfates constitute about half of the PM2.5 in the eastern U.S. Virtually all the

ambient sulfate (99%) is secondary, formed within the atmosphere from SO2.

• About half of the SO2 oxidation to sulfate occurs in the gas phase through photochemical oxidation in the daytime. NOx and hydrocarbon emissions tend to enhance the photochemical oxidation rate.

Husar, 1999


• 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 PM2.5.

Sulfate Formation in Clouds• At least half of the SO2 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 SO2, get scavenged by the water droplets and rapidly oxidize to sulfate. Husar, 1999


Season SO2-to-Sulfate Transformation Rate

SO2-to-sulfate transformation rates peak in the summer due

to enhanced summertime photochemical oxidation and SO2 oxidation in clouds.

Husar, 1999

Transformation rates derived from the CAPITA Monte Carlo Model, Schichteland Husar (1997).


Residence Time of Sulfur and Organics.

• SO2 is depleted mostly by dry deposition (2-3%/hr) and also by conversion to sulfate (up to 1%/hr). This gives SO2 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 SO2 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 PM2.5 have a similar conversion rate, removal rate, and atmospheric residence time.

Husar, 1999


Nitrate Formation in the Atmosphere• The NO2 portion of NOx can be converted to nitric acid (HNO3) by

reaction with hydroxyl radicals (OH) during the day.– The reaction of OH with NO2 is about 10 times faster than the OH

reaction with SO2. – The peak daytime conversion rate of NO2 to HNO3 in the gas phase

is about 10 to 50% per hour.• During the nighttime, NO2 is converted into HNO3 by a series of

reactions involving ozone and the nitrate radical (NO3).• HNO3 reacts with ammonia to form particulate ammonium nitrate

(NH4NO3).• About 1/3 of anthropogenic NOx emissions in the U.S. are estimated to

be removed by wet deposition.


PM, Ozone, and Other Pollutants (1 of 2)

• The formation of a substantial fraction of fine particles can depend on the gas phase reactions which also produce ozone. – Concentrations of OH radicals, ozone, and hydrogen

peroxide (H2O2), formed by gas phase reactions involving VOCs and NOx, depend on the concentrations of the reactants and on meteorological conditions including temperature, solar radiation, wind speed, mixing volume, and passage of high pressure systems.

NESCAUM, 1992


PM, Ozone, and Other Pollutants (2 of 2)

• Bullets discussing PM link to health, acid precipitation, visibility in development


Summary of Factors Influencing PM Concentrations (1 of 2)

• Meteorology– Meteorological parameters important to PM concentration fluctuations include:

temperature, relative humidity, mixing heights, wind speed, and wind direction.– Seasonal changes in meteorology affect diurnal, seasonal, and chemical patterns

of PM.

Chu and Cox, 1998


Summary of Factors Influencing PM Concentrations (2 of 2)

• Bullets on Emissions in development

– Hot spots

– Brief overview of how some PM species are tied to sources (e.g., Na = marine when near coast but also road salt in winter.

– Ni, V oil combustion


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


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 MechanismsPollutants are transported by the atmospheric flow field which

consists of the mean flow and the fluctuating turbulent flow.

Husar, 1999


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 Dilution Vertical 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


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 PM2.5 in the eastern U.S. is from SO2 emitted by power plants.

Husar, 1999


Long-range Transport• In many remote areas of the U.S., high concentrations of PM2.5 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 PM2.5 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


Atmospheric Residence Time and Spatial Scales

• PM2.5 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 0.1-1.0 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, PM2.5 particles are transported 1000 or more km from the source of their precursor gases.

Husar, 1999


Residence Time Dependence on Height

• The PM2.5 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


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


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


Overview of National PM2.5 Network


Site Category Projected Number Major PurposeCore Sites 850 Reference

50 ContinuousMin. required for attainment designations.Continuous needed for air quality index (AQI).

Spatial Averaging/Special Purpose

200 Referenceother

States requested additional monitors for spatialaverages or other specific monitoring needs.

Continuous 50 Continuous AQI reporting and further delineation ofsource/exposure patterns.

IMPROVE 100 additionalIMPROVE monitors

Supports regional haze rules in class I areasand PM transport assessment. Speciation.

Routine chemicalspeciation

50 requiredUp to 250 additional

Trends, source apportionment, modelevaluation, strategy effectiveness, riskassessment, better understanding ofatmospheric processes.

Super site studyareas

4 to 7 areas withresearch gradeequipment

Intensive work on source receptor relationshipsin areas representative of PM issues; healthrisk, monitor advances.


PM2.5 Implementation Update

• The bulk of all compliance and continuous monitoring sites are to be established by December 31, 1999.

• The chemical speciation sites will begin operation by November 1999, and installations will continue through December 31, 2000.

• The IMPROVE sites were to have been deployed by December 31, 1999; however, this schedule has been delayed.

• The super sites began in 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


PM2.5 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 regime.

Byrd, 1999


Critical Issues for Data Uses and Interpretation

• Sampling losses on the order of 30% of the annual federal standard for PM2.5 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).

• Add bullet on organic carbon losses.

• Discuss how these issues relate to data interpretation and can affect uses of the data.

• Some analyses require data collected on a less than 24-hr basis because of the changes in photochemistry, emissions and meteorology that occur during a 24-hr period.


Site Types

The larger check marks reflect the primary use of the data.


PM2.5 Monitoring ObjectivesNetworkElement NAAQS

Comparison

PublicInformation

(AQI)SIP

Development

AssessSIPS,Trends

Health/Exposure

AssessVisibility

MethodsTesting

FRM Mass

ContinuousMass

Speciation(NAMS)

Speciation(State)

IMPROVE Supersites


Data Collected



Sampling Artifacts, Interferences, and Limitations


Monitoring Issue Possible Effects Possible SolutionInlet surface deposition Nitric acid loss

suspended particle attractionCoat inlet surface with PFA.Use non-plastic surface.

Nitrate particlevolatilization

Loss of nitrate during/aftersampling

Proper ventilation and cooling ofsampler; store filters in sealedcontainers and refrigerate.

Organic carbonvolatilization

Collection of gas phase organics(+ bias); loss of particle phaseorganics (- bias)

Use denuder to remove gas phaseorganics and back-up sorbent toassess semi-volatiles.

Sample moisture Bias in mass measurements Control relative humidityequilibration ranges.

Electrostatic charge Bias in mass measurements Use radioactive antistatic strip.Passive deposition(windblown dust)

Bias (+) mass measurements Reduce filter residence time insampler.

Handlingcontamination

Invalid samples Use proper standard operatingprocedures.

Filter media artifacts Nitrate loss, SO2 conversion Denuder use and maintenance.


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.


Data Validation Continues During Data Analysis

• Two source apportionment models were applied to PM2.5 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


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/m3 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/m3 annual mean at that site is included in the spatial mean.

• The 3-yr mean rounds to 14.4 g/m3 which is less than the level of the standard of 15.0 g/m3.

A PM2.5 network with annual means calculated from quarterly means

Site 1 Site 2 Site 3 Site 4Spatial mean

Year 1Ann. mean

12.7 12.7

% Comp. 80 0 0 0

Year 2Ann. mean

12.6 17.5 15.2 15.05

% Comp. 90 83 38 0

Year 3Ann. mean

12.5 18.5 14.1 16.9 15.50

% Comp. 90 80 85 503-Year mean

14.42

Fitz-Simmons, 1999


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 PM2.5 concentrations can occur during any time of year (e.g., winter wood smoke, summer photochemical event, etc.).

Poirot et al., 1999


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 PM10 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.

MONTUE

WED

THU FRISAT

SUN

Day of the Week

0

5

10

15

20

Die

sel P

M1 0

+ N

MO

G, u

g /m

3 Dis tribution of day-t ime diesel engine concentrations by day of the week.

Lin et al., 1993

Chicago80 samples1990-1991


Seasonal Pattern of PM2.5

• 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 PM2.5 mass and its elemental constituents can provide insights into these causal factors.

• The season with the highest concentrations is a good candidate for PM2.5 control actions.

Schichtel, 1999a


Seasonal PM2.5 Dependence on Elevation in the Appalachian Mountains

• In August, the PM2.5 concentrations are independent of elevation to at least 1200 m. Above 1200 m, PM2.5 concentrations decrease.

• In January, PM2.5 concentrations decrease between sites at 300 and 800 m by about 50% . PM2.5 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


Seasonal Maps of PM2.5 (1994-1996)

• These maps illustrate the regional differences in PM. The same control strategies may not be effective if applied on a national scale.

• The PM2.5 concentrations peak during the summer (Q3) in the eastern U.S. The PM2.5 concentrations peak in the winter (Q1) in populated regions of the Southwest and in the San Joaquin Valley in California.

Falk, 1999

http://capita.wustl.edu/PMFine/Workgroup/Status%26Trends/Reports/In-progress/PM25Maps/FMCPMQ1g.gif





PM10 in the U.S. During the Central American Smoke Event

24-hr PM10 concentrations in g/m3 are shown for several cities. The likely smoke impact on these cities is highlighted.

The vertical line is at 65 g/m3 in each figure. Husar, 1999


IMPROVE Network PM2.5 Trends 1988-1997

• The map shows the annual trends in overall PM2.5 concentration for 1988-1997, at 34 monitoring sites in the continental U.S. which have been recording PM2.5 concentrations for over six years.

• The site labels are the annual trends of PM2.5 concentrations at each site. The data were deseasonalized to "take out" the seasonal cycle of PM2.5.

Frechtel et al., 1999


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 4th weekend are highlighted.

Microsoft Excel used to prepare scatter plot and calculate regression coefficients.

Poirot, (1998)

Concentrations of PM2.5 iron with silicon, aluminum, and potassium at Chiricahua National Park in Arizona.


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 dayPoirot et al. (1998)


UNMIX Analysis• UNMIX was applied to PM2.5 data

collected at Underhill, VT, during 1988-1995.

• 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.


PMF Analysis

• The highest average PM2.5 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).

NWAA BELA GAAR DENA YUCH WRST KATM

Con

cent

rati

on (

g m

-3)

0

1

2

3

4

5

6 A

B

Figure 20

All data

SitesNWAA BELA GAAR DENA YUCH WRST KATM

Con

cent

rati

on (

g m

-3)

0

1

2

3

4

5

6 October - June (left)July - September (right)

S - anthropogenicBC-Na-S - anthropogenicBC-H+-K - forest fires, local

Pb-Br - motor vehiclesZn-Cu - incinerationSi - soil, coal combustionAl-Si - soilCl-Na - sea saltresidual mass

B

Polissar et al., 1998Stacked bar plots preparedusing a spreadsheet program.


Case Study: Top-Down Emissions Evaluation

PM/NOx

0.0

10.0

20.0

Ba kers fie ld Fre sn o

(a) (b)

(c) (d)

NH3/NOx

Primary PM10/NOx

City #1 City #2

Top-down comparison of ambient- and emissions-derived primary PM10/NOx in two cities.

Ambient Ratio

Emission Inventory Ratio

Comparison of the ambient- and emissions-derived PM10/NOx ratios in two cities are quite different. It appears as though PM10 is overestimated in the emission inventory by approximately a factor of two. Recommendation: the PM10 portion of the inventory should be investigated from the bottom-up.

Note that this example corresponds to PM10; a similarcomparison could be made for PM2.5

Haste et. al., 1998


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 PM2.5 in the ambient air while the emission inventory data shows dust being the main contributor to PM2.5. These types of discrepancies are

important to consider prior to control strategy development.

Dust15%

Mobile Sources

65%

Other20%

CMB PM2.5

Source Apportionment

Emission Inventory PM2.5

Source Apportionment

Dust80%

Mobile Sources

4%

Other16%

Lurmann et. al., 1999Watson et. al., 1998


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 200-250 (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.

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 3750

1

2

3

4

5Mean Daily Concentrations

Model Ambient

Julian Day (1990)

Sulfate (g/m3)

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375-0.5

0.0

0.5

1.0

1.5

2.0

2.5Mean Daily Sulfate Comparison

Julian Day (1990)

Bias (g/m3)

Adapted from Wayland (1998)


ReferencesAlbritton 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. 320-382.

Chow J.C. and Watson J.G. (1997) Guideline on speciated particulate monitoring. Report prepared by Desert Research Institute and available at http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/drispec.pdf

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 91st Annual Meeting & Exhibition, June 14-18, 1998, San Diego, California.

Falk S. (1999) PM2.5 topic summaries available at: http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/UrbanSpatialPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/NationalSpatialPattern/sld001.htm

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 http://capita.wustl.edu/PMFine/Workgroup/Status&Trends/Reports/Completed/LongTermIMPROVE/LongTermIMPROVE.html

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-997211-1800-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. 725-733.

Homolya J.B., Rice J., Scheffe R.D. (1998) PM2.5 speciation - objectives, requirements, and approach. Presentation. September.


ReferencesHusar, R. (1999) Draft PM2.5 topic summaries available at http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMProperties/

sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/Pm25Formation/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMTransport/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMOrigin/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PM10PM25Relationship/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMAnalysis/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/Pm25TransportROI/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/DiurnalPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/WeeklyPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMGlobalContPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/NaturalEvents/sld001.htm

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, <http://www.epa.gov/oar/oaqps/pams/analysis> STI-997280-1824, June.

NESCAUM (1992) 1992 Regional Ozone Concentrations in the Northeastern United States. Paper available at http://capita.wustl.edu/neardat/reports/TechnicalReports/NEozone92/avoztitl.html

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. 19045-19057.

Poirot R., A. Leston, C. Michaelsen (1999) August 1995 forest fire impacts in New England and Atlantic Canada. Report available at http://capita.wustl.edu/NEARDAT/Reports/TechnicalReports/smoke895/895smoke.htm


ReferencesPoirot 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 http://capita.wustl.edu/neardat/Reports/TechnicalReports/lakchamp/lchmpair.htm

Poirot R. (1998) Tracers of opportunity: Potassium. Paper available at http://capita.wustl.edu/PMFine/Workgroup/SourceAttribution/Reports/In-progress/Potass/ktext.html

Poirot, R. (1999) Draft PM2.5 topic summary available at http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/PMAnlysisByStates/sld001.htm

Poirot R. (1999b) personal communication

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 http://capita.wustl.edu/CAPITA/CapitaReports/MonteCarlo/MonteCarlo.html

Schichtel B. and Husar R. (1997) Derivation of SO2 – SO42- Transformation and Deposition Rate Coefficients Over The Eastern US

using a Semi-Empirical Approach. Paper available at http://capita.wustl.edu/capita/capitareports/mcarlokinetics/mcrateco4_AWMAPres.html

Schichtel B.A. (1999a) PM2.5 topic summaries available at: http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/SeasonalPattern/sld001.htm http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/ElevationDep/sld001.htm http://capita.wustl.edu/CAPITA/CapitaReports/USVisiTrend/80_95/USVistrnd80_95/index.htm http://capita.wustl.edu/Central-America/reports/SmokeSum/SmokeSumApr99/index.htm

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.

U.S. EPA (1999a) Particulate matter (PM2.5) speciation guidance document. Available at http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/specpln3.pdf


ReferencesU.S. EPA (1999b) General Information regarding PM2.5 data analysis posted on the EPA Internet web site

http://www.epa.gov/oar/oaqps/pm25/general.html

U.S. EPA (1998) Fact sheet on PM data handling available at http://ttnwww.rtpnc.epa.gov/naaqsfin/fs122398.htm

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 - 1990 Base case simulation: model performance evaluation. Draft report prepared by USEPA OAQPS, Research Triangle Park, NC, March.

Documents

October 1999PM Data Analysis Workbook: Introduction1 Introduction to the PM Data Analysis Workbook Objectives of the PM Monitoring Program Critical Issues