Correlating Federal Reference Method and Continuous PM 2.5 Monitors in the MARAMA Region

Transcription

1 Correlating Federal Reference Method and Continuous PM 2.5 Monitors in the MARAMA Region July 1, 2005 Prepared by William G. Gillespie Senior Environmental Scientist Mid-Atlantic Regional Air Management Association

2 About MARAMA The Mid-Atlantic Regional Air Management Association is a voluntary, non-profit association of ten state and local air pollution control agencies. MARAMA's mission is to strengthen the skills and capabilities of member agencies and to help them work together to prevent and reduce air pollution in the Mid-Atlantic Region. MARAMA provides a cost-effective approach to regional collaboration and cooperation, the sharing of ideas, the analysis of data, and training of staff to implement common requirements. The following State and Local governments are MARAMA members: Delaware, the District of Columbia, Maryland, New Jersey, North Carolina, Pennsylvania, Virginia, West Virginia, Philadelphia, and Allegheny County, Pennsylvania. On the Cover: The PM2.5 Beta Attenuation Monitor at the Martin Luther King monitoring station in Wilmington, DE. Photograph courtesy of the Delaware Division of Air and Waste Management, Air Quality Management Section. For copies of this report contact: MARAMA Mid-Atlantic Regional Air Management Association Suite West 40 th Street Baltimore, MD Telephone Fax

3 Correlating Federal Reference Method and Continuous PM 2.5 Monitors in the MARAMA Region July 1, 2005 Prepared by William G. Gillespie Senior Environmental Scientist Mid-Atlantic Regional Air Management Association

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5 Preface This report summarizes the types of FRM and continuous PM 2.5 monitors operated by state and local air quality programs in the MARAMA Region, describes a method that can be used to correlate FRM and continuous monitors, and provides preliminary correlations for thirteen monitoring sites. A major goal of the report was to determine how well FRM and continuous monitors track each other at monitoring sites throughout the region and how FRM/continuous correlations are similar or different site-to-site. To accomplish this, a generally accepted method for correlating FRM and continuous data was consistently applied to monitoring data at each site studied. The analytical approach described in this report includes a practical method for identifying and evaluating potential outliers in correlation data. While the correlations developed in this report are adequate for the purposes of generally comparing the differences between FRM and continuous monitors operated in the region, they should be considered preliminary correlations. Although State and local staff have reviewed the data used to develop correlations found in this report, final quality assurance of the data had not been performed when these correlations were developed. Additional data, final quality-assured data, and other improvements or refinements could improve the quality of the correlations that were developed. Page ii

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7 Acknowledgements MARAMA would like to acknowledge the hard work and dedication of the state and local staff who operate and maintain air quality monitors in the MARAMA Region. These air quality professionals operate an extensive network of sophisticated instruments that run twenty-four hours a day, seven days a week. Keeping high-tech equipment operating at remote sites 24/7 during all kinds of weather is a challenging task under the best of circumstances. It has been especially challenging in recent years during which time state and local budgets have been cut, there have been requirements to establish new monitoring stations, and new instruments have been deployed. Without the hard work and dedication of state and local monitoring staff, we would not have the data needed to assess air quality, a fundamental requirement of all air quality programs. To prepare this report, MARAMA received data and assistance from many MARAMA members. MARAMA would like to acknowledge the following individuals who provided data or analyses used in this report or provided helpful or insightful comments and suggestions. Wayne Cornelius, North Carolina Department and Natural Resources Robert Day, District of Colombia Department of Health James P. Ebert, West Virginia Department of Environmental Protection Ted Erdman, United States Environmental Protection Agency, Region 3 Dirk Felton, New York State Department of Environmental Conservation Betsy Frey, Delaware Department of Natural Resources and Environmental Control Edwin Gluth, Maryland Department of the Environment Andrew Hass, United States Environmental Protection Agency, Region 3 John Haus, Maryland Department of the Environment Loretta Hayden, Philadelphia Department of Public Health Hoke Kimball, North Carolina Department and Natural Resources David Krask, Maryland Department of the Environment Alan Leston, Northeast States for Coordinated Air Use Management (NESCAUM) Jason Maranche, Allegheny County Health Department Tom McKenna, New Jersey Department of Environmental Protection George Mentzer, Pennsylvania Department of Environmental Protection Charlie Pietarinen, New Jersey Department of Environmental Protection Ron Savukas, Allegheny County Health Department Kassahun G. Sellassie, Ph.D., Air Management Services, Philadelphia, PA Matthew Seybold, Maryland Department of the Environment Joette Steger, North Carolina Department and Natural Resources Darrell Stern, Allegheny County Health Department Carolyn Stevens, Virginia Department of Environmental Quality Robert Vanderpool Ph.D., United States Environmental Protection Agency, Office of Research and Development Jeanne Wagner, West Virginia Department of Environmental Protection Chethana Devi from MARAMA prepared many of the graphs and plots in this report. Susan Stephenson of MARAMA assisted with layout and editing. Susan Wierman, MARAMA s Executive Director provided comments. This report was funded by the United States Environmental Protection Agency, Region 3, EPA grant number X and grant number PM Page iv

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9 Table of Contents List of Figures...viii List of Tables... xii List of Acronyms and Abbreviations...xiv 1.0 Executive Summary Introduction PM 2.5 Monitors in the MARAMA Region The Challenge of Correlating FRM and Continuous Monitors Methodology THE GENERAL APPROACH AN EXAMPLE ANALYSIS; CORRELATION OF THE MOUNDSVILLE, WV PM2.5 MONITORS Identify the Data Source and Perform Preliminary Calculations and Quality Assurance Graphically Examine the Data Prepare the Dataset for Analysis Calculate the Correlation Equation(s) Confirm Results Determine if the Correlation Equation is Adequate Improve the Correlation with Auxiliary Data if Possible State-by-State Correlations ALLEGHENY COUNTY DELAWARE MARYLAND NORTH CAROLINA NEW JERSEY PENNSYLVANIA VIRGINIA WEST VIRGINIA Conclusions and Recommendations GUIDANCE FOR DEVELOPING GOOD CORRELATIONS REGIONAL COMPARISONS THE LIMITATIONS OF A SEASONAL APPROACH IMPLICATIONS OF POOR CORRELATIONS ON EPA S AIRNOW PROGRAM AND AIR QUALITY FORECASTING AREAS FOR FURTHER WORK RECOMMENDATIONS Short-term Recommendations Long-term Recommendations...90 References Appendix A Appendix B Page vi

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11 List of Figures Figure 1. PM 2.5 Monitors in the MARAMA Region...6 Figure Annual PM 2.5 Design Values in the Mid-Atlantic Region...7 Figure Annual PM 2.5 Design Values in the Mid-Atlantic Region...8 Figure 4. PM 2.5 Nonattainment Areas in the MARAMA Region...9 Figure 5. FRM PM 2.5 Monitors in the MARAMA Region...13 Figure 6. Continuous PM 2.5 Monitors in the MARAMA Region...13 Figure 7. FRM & Continuous PM 2.5 Monitor Time Series, Old Town, Baltimore, MD...15 Figure 8. FRM & Continuous PM 2.5 Monitor Time Series, Liberty, Allegheny County, PA...16 Figure 9. Moundsville, WV, Time Series, Figure 10. Moundsville, WV, Time Series, Figure 11. Moundsville, WV, Time Series, Figure 12. Moundsville, WV, Scatter Plot with Outliers, All Seasons...24 Figure 13. Moundsville, WV, Residuals...24 Figure 14. Moundsville, WV, Semi-Studentized Residuals...25 Figure 15. Moundsville, WV, Difference Plot (FRM - TEOM)...26 Figure 16. Moundsville, WV, TEOM Histogram...27 Figure 17. Moundsville, WV, FRM Histogram...27 Figure 18. Moundsville, WV, TEOM Histogram, Log-Transformed...28 Figure 19. Moundsville, WV, FRM Histogram, Log-Transformed...28 Figure 20. Moundsville, WV, Log-Transformed Correlation, All Seasons...29 Figure 21. Moundsville, WV, Correlation, Spring...30 Figure 22. Moundsville, WV, Correlation, Summer...30 Figure 23. Moundsville, WV, Correlation, Fall...31 Figure 24. Moundsville, WV, Correlation, Winter...31 Figure 25. Moundsville, WV, FRM and Estimated FRM Plot, Winter...32 Figure 26. Lawrenceville, Allegheny County, Spring Correlation...36 Figure 27. Lawrenceville, Allegheny County, Summer Correlation...36 Figure 28. Lawrenceville, Allegheny County, Fall Correlation...37 Figure 29. Lawrenceville, Allegheny County, Winter Correlation...37 Figure 30. Lawrenceville, Allegheny County, Difference Plot (FRM-TEOM)...38 Figure 31. Lawrenceville, Allegheny County, Time Series...38 Figure 32. Liberty, Allegheny County, Spring Correlation...40 Figure 33. Liberty, Allegheny County, Summer Correlation...40 Figure 34. Liberty, Allegheny County, Fall Correlation...41 Figure 35. Liberty, Allegheny County, Winter Correlation...41 Page viii

12 Figure 36. Liberty, Allegheny County, Difference Plot (FRM - TEOM)...42 Figure 37. Liberty, Allegheny County, Time Series...42 Figure 38. MLK, Wilmington, DE, Spring Correlation...44 Figure 39. MLK, Wilmington, DE, Summer Correlation...44 Figure 40. MLK, Wilmington, DE, Fall Correlation...45 Figure 41. MLK, Wilmington, DE, Winter Correlation...45 Figure 42. MLK, Wilmington, DE, Difference Plot (FRM - TEOM)...46 Figure 43. MLK, Wilmington, DE, Time Series...46 Figure 44. Old Town, Baltimore, MD, Spring Correlation...48 Figure 45. Old Town, Baltimore, MD, Summer Correlation...48 Figure 46. Old Town, Baltimore, MD, Fall Correlation...49 Figure 47. Old Town, Baltimore, MD, Winter Correlation...49 Figure 48. Old Town, Baltimore, MD, Difference Plot (FRM - TEOM)...50 Figure 49. Old Town, Baltimore, MD, Time Series...50 Figure 50. Garinger, Charlotte, NC, Spring Correlation...52 Figure 51. Garinger, Charlotte, NC, Summer Correlation...52 Figure 52. Garinger, Charlotte, NC, Fall Correlation...53 Figure 53. Garinger, Charlotte, NC, Winter Correlation...53 Figure 54. Garinger, Charlotte, NC, Difference Plot (FRM - TEOM)...54 Figure 55. Garinger, Charlotte, NC, Times Series...54 Figure 56. Camden Lab, Camden, NJ, Spring Correlation...56 Figure 57. Camden Lab, Camden, NJ, Summer Correlation...56 Figure 58. Camden Lab, Camden, NJ, Fall Correlation...57 Figure 59. Camden Lab, Camden, NJ, Winter Correlation...57 Figure 60. Camden Lab, Camden, NJ, Difference Plot (FRM - TEOM)...58 Figure 61. Camden Lab, Camden, NJ, Time Series...58 Figure 62. Elizabeth Lab, Elizabeth, NJ, Spring Correlation...60 Figure 63. Elizabeth Lab, Elizabeth, NJ, Summer Correlation...60 Figure 64. Elizabeth Lab, Elizabeth, NJ, Fall Correlation...61 Figure 65. Elizabeth Lab, Elizabeth, NJ, Winter Correlation...61 Figure 66. Elizabeth Lab, Elizabeth, NJ, Difference Plot (FRM - TEOM)...62 Figure 67. Elizabeth Lab, Elizabeth, NJ, Time Series...62 Figure 68. Arendtsville, PA, Spring Correlation...64 Figure 69. Arendtsville, PA, Summer Correlation...64 Figure 70. Arendtsville, PA, Fall Correlation...65 Figure 71. Arendtsville, PA, Winter Correlation...65 Figure 72. Arendtsville, PA, Difference Plot (FRM - TEOM)...66 Page ix

13 Figure 73. Arendtsville, PA, Time Series...66 Figure 74. Harrisburg, PA, Spring Correlation...68 Figure 75. Harrisburg, PA, Summer Correlation...68 Figure 76. Harrisburg, PA, Fall Correlation...69 Figure 77. Harrisburg, PA, Winter Correlation...69 Figure 78. Harrisburg, PA, Difference Plot (FRM - TEOM)...70 Figure 79. Harrisburg, PA, Time Series...70 Figure 80. Norristown, PA, Spring Correlation...72 Figure 81. Norristown, PA, Summer Correlation...72 Figure 82. Norristown, PA, Fall Correlation...73 Figure 83. Norristown, PA, Winter Correlation...73 Figure 84. Norristown, PA, Difference Plot (FRM - TEOM)...74 Figure 85. Norristown, PA, Time Series...74 Figure 86. Hampton, VA, Spring Correlation...76 Figure 87. Hampton, VA, Summer Correlation...76 Figure 88. Hampton, VA, Fall Correlation...77 Figure 89. Hampton, VA, Winter Correlation...77 Figure 90. Hampton, VA, Difference Plot (FRM - TEOM)...78 Figure 91. Hampton, VA, Time Series...78 Figure 92. Richmond, VA, Spring Correlation...80 Figure 93. Richmond, VA, Summer Correlation...80 Figure 94. Richmond, VA, Fall Correlation...81 Figure 95. Richmond, VA Winter Correlation...81 Figure 96. Richmond, VA, Difference Plot (FRM - TEOM)...82 Figure 97. Richmond, VA, Time Series...82 Figure 98. Moundsville, WV, Spring Correlation...84 Figure 99. Moundsville, WV, Summer Correlation...84 Figure 100. Moundsville, WV, Fall Correlation...85 Figure 101. Moundsville, WV, Winter Correlation...85 Figure 102. Moundsville, WV, Difference Plot (FRM - TEOM)...86 Figure 103. Moundsville, WV, Time Series...86 Page x

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15 List of Tables Table 1. PM2.5 Monitors in the MARAMA Region...11 Table 2. FRM PM2.5 Monitors in the MARAMA Region...12 Table 3. Continuous Monitors in the MARAMA Region...14 Table 4. Months Included in Each Seasonal Analysis...29 Table 5. Camden, NJ Correlations With and Without Temperature Included in the Regression Analysis...33 Table 6. Correlation Equations for Lawrenceville, Allegheny County, PA...35 Table 7. Correlation Equations for Liberty, Allegheny County, PA...39 Table 8. Correlation Equations for MLK, Wilmington, DE...43 Table 9. Correlation Equations for Old Town, Baltimore, MD...47 Table 10. Correlation Equations for Garinger, Charlotte, NC...51 Table 11. Correlation Equations for Camden Lab, Camden, NJ...55 Table 12. Comparison of Correlations Performed With and Without Temperature Data, Camden, NJ...59 Table 13. Correlation Equations for Elizabeth Lab, Elizabeth, NJ...59 Table 14. Correlation Equations for Arendtsville, PA...63 Table 15. Correlation Equations for Harrisburg, PA...67 Table 16. Correlation Equations for Norristown, PA...71 Table 17. Correlation Equations for Hampton, VA...75 Table 18. Correlation Equations for Richmond, VA...79 Table 19. Correlation Equations for Moundsville, WV...83 Table 20. Correlation Equations Sorted by R Table 21. Correlation Equations Sorted by Slope...94 Page xii

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17 List of Acronyms and Abbreviations AIRNow A U.S. EPA web site at that provides near real-time air quality data and forecasts AQI Air Quality Index ASCII American Standard Code for Information Interchange BAM Beta Attenuation Monitor C Centigrade CFR Code of Federal Regulations EPA U.S. Environmental Protection Agency FDMS TM Filter Dynamics Measurement System, a trademark of Rupprecht & Patashnick, Co., Inc. FRM Federal Reference Method MARAMA Mid-Atlantic Regional Air Management Association NAAQS National Ambient Air Quality Standards NESCAUM Northeast States for Coordinated Air Use Management µg/m 3 micrograms per cubic meter PM Particulate matter PM 2.5 Air borne particles with a mean aerometric diameter equal to or smaller than 2.5 micrometers R 2 the coefficient of determination or the square of the coefficient of correlation. A statistical measure of the degree of correlation. SOP Standard Operating Procedure TEOM Tapered Element Oscillating Microbalance, a registered trademark of Rupprecht & Patashnick Co., Inc. Page xiv

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19 1.0 Executive Summary 1.0 Executive Summary Since the promulgation of the National Ambient Air Quality Standards for fine particulate matter (PM 2.5 ) in 1997, there has been increasing interest in how to measure this pollutant in the atmosphere. Currently, there are a variety of methods for monitoring PM 2.5. EPA Federal Reference Methods (FRM) for PM 2.5 are filter-based methods that generally produce data of good accuracy and precision. Unfortunately, there is a four to twelve week delay between the time an FRM monitor makes a measurement and the date the data are available. Because of this delay, FRM monitors cannot be used to calculate the Air Quality Index (AQI) or alert air quality managers or the public to poor or deteriorating air quality. FRM monitoring networks for PM 2.5 are also expensive and labor intensive to operate and maintain. Continuous monitors, which use a variety of techniques to measure PM 2.5, have the advantage of providing near real-time data. If their data can be correlated to Federal Reference Method measurements, continuous data can be used to calculate the AQI and provide real-time information about air quality. On October 1, 2003, EPA and state and local agencies implemented year-round air quality forecasting in major cities across the United States. The program considers all criteria pollutants in air quality forecasts, with special emphasis on ozone and PM 2.5 the pollutants frequently responsible for poor air quality. With the initiation of the year-round forecasting program, there has been increasing interest in correlating continuous PM 2.5 monitoring data with FRM monitoring data. To gain a better understanding of the nature of PM 2.5 measurements, and to help support forecasting programs in MARAMA states, MARAMA s Executive Board asked MARAMA to work with MARAMA members to correlate FRM and continuous monitors in the MARAMA Region. This report provides information about the types of PM 2.5 monitors operated in the MARAMA Region and summarizes early work to correlate data from FRM and continuous monitors. With the assistance of state and local agencies, MARAMA developed correlations for thirteen monitoring sites. Correlations were developed for monitors in Allegheny County, PA; Arendtsville, PA; Baltimore, MD; Camden and Elizabeth, NJ; Charlotte, NC; Hampton, VA; Harrisburg, PA; Moundsville, WV; Norristown, PA; Richmond, VA; and Wilmington, DE. In developing correlations, MARAMA followed the approach outlined in the EPA document: Data Quality Objectives (DQOs) for Relating Federal Reference Method (FRM) and Continuous PM 2.5 Measurements to Report an Air Quality Index (AQI), EPA-454/B , November At all the sites studied, good correlation equations could be established for the summer months of June, July, and August. Correlations for the summer months produced coefficients of determination (R 2 values) of or greater. In general, during summer months, TEOM continuous monitors overstate FRM monitors by small amounts, but good correlation equations can be developed to correct for these differences. In the winter, the degree of correlation between TEOM monitors and FRM monitors is much poorer than in the summer in most cases. At six monitoring sites where TEOM monitors are operated, R 2 values for winter correlations were less than 0.80 the accept/reject criteria established in EPA s Data Quality Objectives document. Correlations for winter months also revealed that fairly large adjustments must be applied to TEOM measurements to make these measurements FRM-like. At nine sites, winter TEOM measurement must be adjusted as much as 16 to 42 percent. Page 1

20 1.0 Executive Summary While only limited data was available at the time of the study to evaluate the FDMS TEOM and Met One BAM, these instruments appear to be continuous monitors that correlate well with FRM monitors regardless of season. While correlations for these instruments were strong, the FDMS TEOM and Met One BAM overstate FRM measurements by significant amounts. Fairly large adjustments must be applied to FDMS TEOM and Met One BAM data to make these data FRM-like. While the seasonal approach to correlating monitors is valuable and an improvement over simply applying a correlation equation based on an entire year of data, it has limitations. At some locations, it may be inadequate in winter or other cold weather periods when strong correlations cannot be established. Seasonal correlations may also be inaccurate when applied during changes of season or during periods of unseasonable weather. In the short-term, MARAMA members recommend taking the following steps to improve PM 2.5 monitoring in the region. State and local agencies should continue their effort to understand the differences between their FRM and continuous PM 2.5 monitors. Continuing to collect good data from stable, well-maintained equipment will help further explain differences in monitoring data. State and local agencies should continue their evaluations of cold Beta Attenuation Monitors. These monitors show promise of producing real-time data that is well correlated with FRM measurements. State and local agencies should continue to deploy and operate Filter Dynamic Measurement System (FDMS) Tapered Element Oscillating Microbalance (TEOM) monitors. While these instruments are relatively expensive compared to other continuous instruments and offer some challenges from an operational point-of-view, they appear to provide data that is well correlated with FRM measurements. Where data are available, studies should be conducted to determine whether ambient temperature, relative humidity, or other meteorological data can be used to improve correlations between continuous and FRM monitors. These studies may provide improved correlations and useful information on how PM 2.5 concentrations vary with these parameters. On the other hand, temperature and/or other meteorological parameters may not improve correlations or provide good on the fly corrections to continuous data since measurement disparities appear to be linked to the presence or absence of semivolatile species that occur independent of any single, readily-available meteorological parameter. Auxiliary data that might improve correlations are data from continuous speciation monitors. Statistical analysis of FRM and continuous monitoring data, along with continuous speciation data might allow a determination as to which species or class of compounds cause disparities between the monitors. This is another area where additional Page 2

21 1.0 Executive Summary work could lead to improved correlations, or better still, improved monitoring methods that measure the same atmospheric constituents under the same atmospheric conditions. In the long-term, MARAMA members recommend the development and approval of robust federal continuous methods that accurately measure PM 2.5 concentrations. Large-scale deployment of continuous monitors that are Federal Reference Methods could produce significant savings in equipment and personnel costs. Large-scale use of approved continuous methods for PM 2.5 would improve our knowledge and understanding of PM 2.5 pollution and greatly facilitate PM 2.5 mapping and forecasting. Page 3

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23 2.0 Introduction 2.0 Introduction On July 17, 1997, the U.S. Environmental Protection Agency (EPA) revised the National Ambient Air Quality Standards (NAAQS) for particulate matter (PM). After reviewing peerreviewed scientific studies, EPA determined that modifications to the existing particulate matter standard were necessary to protect public health and the environment. EPA revised the primary, health-based standard by creating a new annual PM 2.5 standard set at 15 micrograms per cubic meter and a new 24-hour PM 2.5 standard set at 65 micrograms per cubic meter. The new standards applied to air borne particles with a mean aerometric diameter equal to or smaller than 2.5 micrometers. These small particles, referred to as PM 2.5, were deemed to be the particles in the air that were of greatest concern in terms of public health. At the same time that EPA promulgated the new PM 2.5 standards, EPA announced new monitoring requirements to support the new standards. EPA established new Federal Reference Methods (FRM) for measuring PM 2.5 and put in place new criteria for siting monitors and new procedures for operating monitoring networks and quality assuring data. Soon after, states and local agencies began to establish PM 2.5 monitoring networks in conformance with EPA requirements. The first PM 2.5 FRM monitors were in place in Today, state and local air quality agencies and other organizations operate large networks to monitor PM 2.5 concentrations in the atmosphere. Many different makes, models and types of PM 2.5 monitors have been deployed. Most monitors are filter-based FRM monitors that measure PM 2.5 mass by passing a measured volume of air through a pre-weighed filter. Generally, FRM monitors produce data of good accuracy and precision. FRM monitors have a drawback however. Data from these monitors are not available until four to twelve weeks after the measurement was made. The slow delivery of data makes them unsuitable for use in calculating the Air Quality Index (AQI) or for alerting the public on days when PM 2.5 air quality is poor. Continuous monitors are monitors that measure PM 2.5 concentration hour-by-hour or in even shorter time periods. To provide timely PM 2.5 data, state and local agencies have deployed these monitors in some key locations. Figure 1 shows the location of PM 2.5 FRM and continuous monitors in the MARAMA Region. Continuous monitoring methods have not yet been approved as Federal Reference Methods for measuring PM 2.5. As a result, continuous monitors cannot be used to establish an area s compliance with the PM 2.5 NAAQS. Continuous monitoring data is valuable, however, because it is available in near real-time, and can be used to calculate the AQI and warn the public of poor or deteriorating air quality. Since the AQI is based on the Federal Reference Method and data from FRM monitors is not available in real-time, EPA has encouraged air quality agencies to develop correlations between their FRM and continuous PM 2.5 monitors. In this way, state and local agencies obtain FRM-like data in a timely fashion, enabling them to make statements about current PM 2.5 air quality. Continuous PM 2.5 data is critical to outreach programs that provide the public with information about air quality. It is also important to meteorologists who forecast air quality. To make an air Page 5

24 2.0 Introduction quality forecast, a meteorologist needs both meteorological and air quality data. Important meteorological data include: observed and predicted wind speed and direction, relative humidity Figure 1. PM 2.5 Monitors in the MARAMA Region and other data at the earth s surface and aloft. Meteorologists also need information on the stability of the atmosphere and information regarding frontal boundaries and the general synoptic pattern in place (or predicted) over the forecast area. Finally, and equally important, meteorologists need real-time air quality data. To issue a reliable forecast, they need current PM 2.5 concentrations in their forecast area as well as concentrations from upwind areas. Without knowing current concentrations, it is difficult to predict future levels. Interest in correlating FRM and continuous PM 2.5 monitoring data has been strong in recent years because state and local air quality agencies have been implementing PM 2.5 forecasting programs to support EPA s new year-round forecasting program. EPA asked states to initiate year-round forecasting programs October 1, 2003 in response to the USA Today newspaper s interest in carrying air quality information on their weather page year-round. EPA was also asked to provide air quality forecasts year-round by weather service providers such as the Weather Channel. Before October 1, 2003, air quality forecasting in most parts of the country ended with the end of the ozone season. Today, air quality forecasts are developed for PM 2.5 throughout the year in many major metropolitan areas. States and local air quality agencies have expended significant resources in putting these year-round forecasting programs in place. In addition to the need to provide year-round forecasts, state and local agencies in the MARAMA Region are interested in monitoring PM 2.5 pollution and the relationship between Page 6

25 2.0 Introduction FRM and continuous data, because of observed exceedances of the annual PM 2.5 standard in the region. Areas where exceedances of the annual standard have been observed are shown in Figures 2 and 3. As the figures show, some counties exceed the annual standard regardless of which three-year period is considered. Other counties are sensitive to which data are used to determine the design value indicating the county is close to the annual standard. In counties close to the annual standard, during any three-year period, some PM 2.5 monitors in the county may exceed the standard while others may be below the standard. Figure 3 shows that by 2002 many counties had sufficient data to calculate a design value for the annual standard. Figure Annual PM 2.5 Design Values in the Mid-Atlantic Region Page 7

26 2.0 Introduction Figure Annual PM 2.5 Design Values in the Mid-Atlantic Region On December 17, 2004, EPA designated areas in the United States that do not meet the NAAQS for PM 2.5. The designations were based on monitoring data. Because EPA s designations occurred close to the end of 2004, EPA provided states an opportunity to have data considered in the final designation process. After reviewing certified, qualityassured data for , EPA found that eight areas previously identified as not attaining the PM 2.5 standards were attaining the standards. Figure 4 depicts final designations for PM 2.5 including the changes that resulted from using data. Page 8

27 2.0 Introduction Figure 4. PM 2.5 Nonattainment Areas in the MARAMA Region To help MARAMA members better understand the nature of the PM 2.5 problem in the MARAMA Region, and to help develop forecasting programs in MARAMA states, MARAMA s Executive Board asked MARAMA to work with MARAMA members to correlate FRM and continuous monitors in the MARAMA Region. This work was done to support PM 2.5 forecasters and to evaluate continuous PM 2.5 monitoring methods. This work was supported by a grant from EPA Region 3. Correlation work and other efforts to evaluate continuous data may eventually allow states to replace some FRM monitors with continuous monitors as recommended by the National Academy of Sciences. In general, states are eager to replace FRM PM 2.5 monitors with continuous monitors because of the high cost and labor intensive nature of FRM monitors. Page 9

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29 3.0 PM 2.5 Monitors in the MARAMA Region 3.0 PM 2.5 Monitors in the MARAMA Region Approximately 287 PM 2.5 monitors are operated in the MARAMA Region. About 65 percent are FRM monitors, 15 percent are speciation monitors, 16 percent are continuous monitors, and 4 percent are IMPROVE protocol monitors. Table 1 summarizes PM 2.5 monitors by type. Table 1. PM2.5 Monitors in the MARAMA Region Monitor Type Number of Monitors Percent FRM Speciation Continuous IMPROVE Protocol Total In the MARAMA Region, about 76 percent of the FRM monitors are Rupprecht and Patashnick (R&P) 2025 monitors. Twenty-three percent of the FRM monitors are Anderson RAAS-300 monitors. Allegheny County, New Jersey, North Carolina, Pennsylvania, Philadelphia, Virginia and West Virginia operate R&P instruments. Delaware, Maryland, and Washington, DC operate Anderson instruments. Table 2 summarizes the types of FRM monitors operated in the MARAMA Region. As the table shows, most FRM monitors have sample frequencies of once in three days. Only about 18 percent of the FRM monitors are operated daily. The lack of daily FRM data and the spatial distribution of the FRM network make the mapping of past PM 2.5 episodes over the region difficult. In some areas of the region, monitors are operated in fairly close proximity to each other. In other areas, monitors are far apart or nonexistent. While the spatial distribution of the PM 2.5 monitoring network may meet regulatory requirements, it does not lend itself to the mapping and analysis of regional PM 2.5 episodes. Figure 5 shows PM 2.5 FRM monitoring sites operated in the MARAMA states. Forty-six continuous PM 2.5 monitors are operated in the MARAMA Region. As one can see in Figure 6, many of these monitors are located in the urban corridor from Northern Virginia to Elizabeth, NJ. North Carolina operates a dispersed network of continuous monitors providing continuous data in a variety of locations. Figure 6 also shows, there are large areas of the MARAMA Region without continuous monitors. Table 3 summarizes the continuous monitors operated in the MARAMA Region. Most continuous monitors are R&P Tapered Element Oscillating Microbalance (TEOM ) units. Most TEOM instruments in the Region operate at 50 degrees C. Pennsylvania s TEOMs at Arendtsville, Kittanning and Easton are operated at 30 degrees C, however. The TEOMs use factory presets of 3 percent and 3 µg/m 3. In recent years, state and local agencies have installed Filter Dynamics Measurement System (FDMS TM ) TEOMs and Beta Attenuation Monitors (BAMs) at many sites. Twelve FDMS TEOMs have been installed in the MARAMA Region. Most of these monitors are operated by New Jersey and Pennsylvania. FDMS TEOMs operate at 30 degrees C. Eight Beta Attenuation Monitors (BAMs) are operated by state and local agencies in the MARAMA Region. Delaware Page 11

30 3.0 PM 2.5 Monitors in the MARAMA Region operates four Anderson BAMs. The Pennsylvania Department of Environmental Protection and Philadelphia s Air Management Services each operate two Met-One 1020 BAMs. Table 2. FRM PM2.5 Monitors in the MARAMA Region State Collection Frequency (Days) Make Model No. of Monitors Allegheny County, PA 1 R&P R&P R&P Delaware 1 Andersen RAAS Andersen RAAS Andersen RAAS Washington, DC 1 Andersen RAAS Andersen RAAS Andersen RAAS Maryland 1 Andersen RAAS Andersen RAAS Andersen RAAS New Jersey 1 R&P R&P R&P North Carolina 1 R&P R&P R&P Pennsylvania 1 R&P 2025A 11 3 R&P 2025A 13 6 R&P 2025A 4 Philadelphia 1 Andersen RAAS R&P Andersen RAAS Andersen RAAS Virginia 1 R&P R&P West Virginia R&P Totals by Filter Collection Frequency Total FRM Monitors 185 Page 12

31 3.0 PM 2.5 Monitors in the MARAMA Region Figure 5. FRM PM 2.5 Monitors in the MARAMA Region Figure 6. Continuous PM 2.5 Monitors in the MARAMA Region Page 13

32 3.0 PM 2.5 Monitors in the MARAMA Region Table 3. Continuous Monitors in the MARAMA Region State Make Model Number of Monitors Allegheny County, PA R&P 1400ab 2 Delaware Andersen BAM Series FH 62 C14 4 Washington, DC R&P 1400ab 1 Maryland R&P 1400ab 1 R&P FDMS TEOM 1 New Jersey R&P FDMS TEOM 5 North Carolina R&P 1400ab 14 Pennsylvania R&P 1400ab 3 R&P FDMS TEOM 5 Met One BAM Philadelphia, PA Met One BAM Virginia R&P 1400ab 5 West Virginia R&P FDMS TEOM 1 Total 46 Page 14

33 4.0 The Challenge of Correlating FRM and Continuous Monitors 4.0 The Challenge of Correlating FRM and Continuous Monitors Correlating continuous PM 2.5 monitors with collocated or nearby FRM monitors is a fairly straightforward process. Before applying statistical techniques and developing correlation equations, however, it is useful to visually examine the data. The following example explores the relationship between FRM and continuous data over time in Baltimore, MD. Figure 7 is a time series plot of the FRM and continuous monitors located at the Old Town monitoring site in Baltimore, MD for the period from May 16, 2002 to December 31, FRM data are shown as individual points. Continuous data are shown as a solid blue line. Over the seven and half month period, the continuous PM 2.5 monitor appears to track the FRM monitor well. In the late fall and winter, however, the continuous monitor appears to underreport PM 2.5 concentrations, especially during episodes when PM 2.5 concentrations are elevated. Figure 8, a time series plot for the Liberty monitor in Allegheny County, PA reveals similar wintertime behavior. It is understandable that FRM and continuous PM 2.5 monitors produce somewhat different results. The methods use different techniques to measure PM 2.5 mass. The FRM is a gravimetric method for measuring PM 2.5 mass. In FRM instruments, ambient air is drawn through filters that have been pre-weighed in a laboratory under carefully controlled conditions. After 24-hours of sampling, the filter cassette is stored in the sampling instrument for pickup by a field technician. On a predetermined schedule, field technicians retrieve the filters. The filters are then weighed under controlled conditions. By subtracting the initial filter weight from the final filter weight, the mass collected over the 24-hour sampling period can be determined. Dividing the measured mass by the measured volume of air sampled yields an average concentration measured over the sampling period TEOM FRM 60 Concentration (ug/m3) /1/02 6/1/02 7/1/02 8/1/02 9/1/02 10/1/02 11/1/02 12/1/02 Figure 7. FRM & Continuous PM 2.5 Monitor Time Series, Old Town, Baltimore, MD Page 15

34 4.0 The Challenge of Correlating FRM and Continuous Monitors TEOM FRM 60 Concentration (ug/m3) /1/02 2/1/02 3/1/02 4/1/02 5/1/02 6/1/02 7/1/02 8/1/02 9/1/02 10/1/02 11/1/02 Figure 8. FRM & Continuous PM 2.5 Monitor Time Series, Liberty, Allegheny County, PA The measurement principle for FRM monitors is sound, and measurement results obtained from these monitors are generally good. There are however sampling and filter issues that contribute to measurement uncertainty. Some of the mass measured by FRM monitors is in the form of volatile species including water, organic carbon species and nitrate species. The amount of these species collected depends on temperature, humidity, filter history, and other factors. Filter history includes such things as how long a filter is stored and under what conditions it is stored. From an operational point of view, FRM monitors are labor intensive. Field technicians must pickup and transport filters to laboratories for weighing. Errors can and do occur in the handling, transport, and weighing of filters. FRM monitors require climate controlled weighing rooms, sophisticated balances, and the staff to run them. The entire monitoring system, including the monitors, filter transport, weighing operations, etc., adds to the cost of the method. Some MARAMA states estimate that the cost of a 24-hour sample from an FRM monitor is three to four times the cost of a 24-hour sample from a continuous monitor. The frequency of FRM sample collection affects the cost per sample. If an FRM monitor only operates one day in six, the cost of each 24-hour sample can be ten times the cost of a 24-hour sample from a continuous monitor. Three major types of continuous instruments are used to measure PM 2.5 concentrations in the MARAMA Region: the TEOM Series 1400a Ambient Particulate Monitor, TEOMs equipped with Series 8500 Filter Dynamics Measurement Systems (FDMS TM ), and Beta Attenuation Monitors (BAMs). TEOM instruments use a Tapered Element Oscillating Microbalance, a patented micro-weighing technology, to measure PM mass. TEOM monitors measure mass by relating the change in frequency of a filter oscillating on the end of a tapered element to the mass in the sampled air stream. Since standard TEOM monitors condition sampled air to 30 or 50 degrees C, during cold weather they may partially volatilize some components of the PM 2.5 mass Page 16

35 4.0 The Challenge of Correlating FRM and Continuous Monitors including particle bound water, nitrates, and organic carbon species. As a result, TEOM monitors may under report PM 2.5 mass relative to FRM monitor measurements during cold weather periods. The FDMS TEOM monitors were designed to measure PM 2.5 mass constituents that may elude detection in standard hot (30 or 50 degree C) TEOMs under some conditions. The FDMS system measures PM 2.5 mass concentration by passing sample air through two alternating measurement paths. The first path, the path found in a standard TEOM monitor, includes a size selective inlet, a diffusion dryer unit, and the TEOM s sensor unit. The mass measurement made at the end of this path is referred to as the base mass. The second path takes sample air through the same size selective inlet and dryer as in the first path, but before the TEOM monitor sensor unit, the air is redirected through a purge filter and conditioning unit operated at 4 degrees C. The purge filter and conditioning unit remove all particulate matter in the sample stream. The mass measurement made at the end of this path is referred to as the reference mass. Every 6 minutes, the FDMS TEOM switches between the base and reference mass paths. By subtracting the reference mass measurement from the base mass measurement, the FDMS system determines the total atmospheric aerosol mass concentration including volatile and semi-volatile components. Beta Attenuation Monitors (BAMs) pass sample air through either a paper filter tape or Teflon filter tape to make PM 2.5 mass measurements. Sampled and un-sampled sections of the filter tape are exposed to a stream of Beta particles. Since the attenuation of Beta particles is proportional to the mass collected on the tape, mass measurements can be made. Dividing the measured mass by the measured volume of air sampled, produces an average concentration measured over the sampling period. As with any PM 2.5 mass instrument, there are operational issues that affect BAM measurements. Leak and airflow issues can compromise data quality. The tape can break or tear, causing erroneous or lost data. In spite of these and other technical issues, Beta Attenuation Monitors operated in the cold BAMs configuration produce good quality data. Cold BAMs are Beta Attenuation Monitors operated at near ambient conditions except when temperatures fall to near 0 degrees C. Near 0 degrees C, smart heaters come on to maintain the instrument enclosure above the BAM s rated operating temperature. To achieve near ambient conditions, cold BAMs are operated in enclosures outside a typical monitoring shelter. To avoid condensation problems, BAMs have heaters that maintain sample air at 45 percent relative humidity. The California Air Resources Board recently adopted state approved methods for measuring continuous PM 2.5 that rely on: Anderson Model FH 62 C14 BAMs, Met One Model 1020 BAMs, and R&P Series 8500 FDMS TEOM monitors. Page 17

36 5.0 Methodology Page 18

37 5.0 Methodology 5.0 Methodology 5.1 The General Approach In developing correlations between FRM and continuous PM 2.5 monitors, MARAMA applied an approach very similar to the approach outlined in the EPA publication, Data Quality Objectives (DQOs) for Relating Federal Reference Method (FRM) and Continuous PM 2.5 Measurements to Report an Air Quality Index (AQI), EPA-454/B , November The MARAMA approach involved the seven steps listed below. 1. Identify the data source and perform preliminary calculations and quality assurance 2. Graphically examine the data 3. Prepare the dataset for analysis 4. Develop the correlation equations and examine quality and appropriateness of these equations 5. Confirm results 6. Determine if the correlation equation is adequate 7. Improve the correlation(s) with auxiliary data if possible It should be noted that other methods are available to correlate FRM and continuous data. Michael Rizzo from EPA Region 5 along with Peter Scheff from the University of Illinois and William Kaldy from Hamilton County Ohio have developed the Knot Method for correlating FRM and continuous monitors. Dirk Felton with New York Department of Environmental Conservation (NY DEC) has developed two methods for correlating FRM and continuous monitors. The Julian Day Correction first estimates the portion of bias between FRM and continuous data that is due to season. Once the seasonal portion of the bias has been estimated and accounted for, linear regression is applied to the daily, seasonally adjusted data. Dirk Felton has also developed a Continuous Temperature Correction Method that uses average daily temperature to correlate FRM and continuous monitors. Both NY DEC methods have the benefit of avoiding the step changes that occur when applying correlation equations developed for specific seasons. The NY DEC methods are also practical from an operational standpoint in that they can be programmed into a monitoring site data logger. MARAMA chose to apply a seasonal approach very similar to the method described in EPA s Data Quality Objectives document because it is a logical approach, it applies statistically sound methods, it is easy to apply, analyses are easily updated as more data becomes available, and the method is used and readily understood by other data analysts in the region and nationally. While the seasonal correlations are difficult to implement operationally at monitoring sites, they clearly reveal the seasonal nature of the correlation problem. Season-by-season correlations are explicit in showing when correlations between monitors are good or not so good. The seasonal approach also allows for site-to-site, intra-regional comparisons that are interesting. In this analysis, spring includes March, April, May; summer includes June, July and August; Fall includes September, October and November; and winter includes December, January and February. Page 19

38 5.0 Methodology 5.2 An Example Analysis; Correlation of the Moundsville, WV PM2.5 Monitors Identify the Data Source and Perform Preliminary Calculations and Quality Assurance In MARAMA s correlation work, data were obtained from state and local air quality agencies. While monitoring data can be obtained from EPA s Aerometric Information and Retrieval System (AIRS), MARAMA chose to work closely with state and local air monitoring staff to obtain the needed data. State and local staff who operate monitoring networks and quality assure monitoring data have good knowledge of the data they collect. In many cases, they had already performed correlations for their collocated FRM and continuous monitors. In other cases, they asked MARMA for assistance in developing correlations. In all instances, they were an enormous resource providing useful insights into the operation of the monitoring equipment. MARAMA developed correlations for collocated FRM and continuous monitors. No attempt was made to establish correlations between an FRM monitor located at one site and a continuous monitor at another. To improve the quality of each correlation, MARAMA tried to obtain the largest amount of data possible for each monitoring site. MARAMA preformed some preparatory tasks before plotting or graphically exploring the data. These tasks included: averaging hourly continuous monitoring data to develop 24-hour average values for comparison with FRM monitor values, associating 24-hour average continuous monitor data with the correct 1-in-3 or 1-in-6 day FRM monitor 24 hour average data, calculating average daily temperature from hourly temperature measurements, and looking for excessively high or negative mass concentrations. State and local agencies delivered FRM and continuous data to MARAMA in electronic files, usually in Microsoft Excel file format. In some cases, data was provided in American Standard Code for Information Interchange (ASCII) text files Graphically Examine the Data Time Series Plots After receiving a dataset, the data were plotted to look for missing data, long-term trends, seasonal variation, outliers and other features of the data. First, time series were developed. Figures 9 through 11 show time series for the collocated FRM and continuous monitors at Moundsville, WV. Figure 9, the time series for the 2001 data, reveals that the monitors track each other fairly well. While the TEOM monitor reports slightly higher values than the FRM monitor from April through much of May, from June through December the TEOM consistently reports values less than the FRM monitor. The time series reveals a few missing FRM data points in late July. Page 20

39 5.0 Methodology The 2002 time series, Figure 10, continues to show good general agreement between the monitors. Again, the TEOM appears to report values somewhat above the FRM monitor in the spring, in this case from April through early June. At all other times, the TEOM reports values slightly less than the FRM monitor. The high FRM monitor value of 41 micrograms per cubic meter (ug/m 3 ) reported on December 7, 2002 appears to be an outlier. The final time series for 2003, Figure 11, shows a high TEOM reading on April 15 that may be an outlier. Otherwise, the monitors show good relative agreement with the TEOM reporting slightly lower values than the FRM monitor except during a short period in the spring TEOM FRM Concentration (ug/m3) /1/2001 2/1/2001 3/1/2001 4/1/2001 5/1/2001 6/1/2001 7/1/2001 8/1/2001 9/1/ /1/ /1/ /1/2001 Figure 9. Moundsville, WV, Time Series, 2001 Page 21

40 5.0 Methodology TEOM FRM Concentration (ug/m3) /1/2002 2/1/2002 3/1/2002 4/1/2002 5/1/2002 6/1/2002 7/1/2002 8/1/2002 9/1/ /1/ /1/ /1/2002 Figure 10. Moundsville, WV, Time Series, TEOM FRM Concentration (ug/m3) /1/2003 2/1/2003 3/1/2003 4/1/2003 5/1/2003 6/1/2003 7/1/2003 8/1/2003 9/1/ /1/ /1/ /1/2003 Figure 11. Moundsville, WV, Time Series, 2003 Taken together, the three time series plots provide a good general understanding of the data over the monitoring period. These data, that compare a 1-in-3 day FRM monitor with a continuous monitor, show that PM 2.5 concentrations vary from measurement to measurement. The largest swings in concentration occur in the summer. Highest concentrations also occur in the summer Page 22