U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN. Implications Two long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.

[1]  J. Chow,et al.  Results of the "Carbon Conference" International Aerosol Carbon Round Robin Test Stage I , 2001 .

[2]  J. C. Cabada,et al.  Positive and Negative Artifacts in Particulate Organic Carbon Measurements with Denuded and Undenuded Sampler Configurations Special Issue of Aerosol Science and Technology on Findings from the Fine Particulate Matter Supersites Program , 2004 .

[3]  R M Harrison,et al.  Particulate matter in the atmosphere: which particle properties are important for its effects on health? , 2000, The Science of the total environment.

[4]  Barbara J. Turpin,et al.  Measuring and simulating particulate organics in the atmosphere: problems and prospects , 2000 .

[5]  Philip K Hopke,et al.  A Special Issue of JA&WMA Supporting Key Scientific and Policy- and Health-Relevant Findings from EPA’s Particulate Matter Supersites Program and Related Studies: An Integration and Synthesis of Results , 2008, Journal of the Air & Waste Management Association.

[6]  L. C Kenny,et al.  A DIRECT APPROACH TO THE DESIGN OF CYCLONES FOR AEROSOL-MONITORING APPLICATIONS , 2000 .

[7]  J. Chow,et al.  Loss of PM2.5 Nitrate from Filter Samples in Central California , 2005, Journal of the Air & Waste Management Association.

[8]  T. Bond,et al.  Light Absorption by Carbonaceous Particles: An Investigative Review , 2006 .

[9]  William C. Baker,et al.  The Measurement of Gas Flow Part II , 1983 .

[10]  Judith C. Chow,et al.  Comparison of IMPROVE and NIOSH Carbon Measurements , 2001 .

[11]  Judith C. Chow,et al.  The dri thermal/optical reflectance carbon analysis system: description, evaluation and applications in U.S. Air quality studies , 1993 .

[12]  J. Seinfeld,et al.  Atmospheric Chemistry and Physics: From Air Pollution to Climate Change , 1997 .

[13]  Naresh Kumar,et al.  Improved Light Extinction Reconstruction in Interagency Monitoring of Protected Visual Environments , 2005, Journal of the Air & Waste Management Association.

[14]  Barbara J. Turpin,et al.  Investigation of organic aerosol sampling artifacts in the los angeles basin , 1994 .

[15]  L. Gundel,et al.  A comparison of high volume and diffusion denuder samplers for measuring semivolatile organic compounds in the atmosphere. , 2000 .

[16]  R. A. Eldred,et al.  Loss of Particle Nitrate from Teflon Sampling Filters: Effects on Measured Gravimetric Mass in California and in the IMPROVE Network , 2004, Journal of the Air & Waste Management Association.

[17]  Judith C. Chow,et al.  Refining temperature measures in thermal/optical carbon analysis , 2005 .

[18]  T. G. Dzubay,et al.  Self Absorption Corrections for X-Ray Fluorescence Analysis of Aerosols , 1974 .

[19]  Spatial and Temporal Distribution of Atmospheric Nitric Acid and Particulate Nitrate Concentrations in the Los Angeles Area , 1992 .

[20]  Andrea C. McWilliams,et al.  Harmonization of Uncertainties of X-Ray Fluorescence Data for PM2.5 Air Filter Analysis , 2010, Journal of the Air & Waste Management Association.

[21]  K. T. Knapp,et al.  The nitric acid shootout: Field comparison of measurement methods , 1988 .

[22]  P. Solomon,et al.  Correction Methods for Organic Carbon Artifacts When Using Quartz-Fiber Filters in Large Particulate Matter Monitoring Networks: The Regression Method and Other Options , 2011, Journal of the Air & Waste Management Association.

[23]  Rudolf B. Husar,et al.  The National Ambient Air Monitoring Strategy: Rethinking the Role of National Networks , 2009, Journal of the Air & Waste Management Association.

[24]  J. Conny,et al.  Optimizing Thermal-Optical Methods for Measuring Atmospheric Elemental (Black) Carbon: A Response Surface Study , 2003 .

[25]  L. Folinsbee Human health effects of air pollution. , 1993, Environmental health perspectives.

[26]  Bong Mann Kim,et al.  Positive Organic Carbon Artifacts on Filter Medium During PM2.5 Sampling in the South Coast Air Basin , 2001 .

[27]  W. J. Mitchell,et al.  East versus West in the US: Chemical Characteristics of PM2.5 during the Winter of 1999 , 2001 .

[28]  L. Chen,et al.  Summary of Organic and Elemental Carbon/Black Carbon Analysis Methods and Intercomparisons , 2005 .

[29]  W. Malm,et al.  Uncertainties in PM2.5 Gravimetric and Speciation Measurements and What We Can Learn from Them , 2011, Journal of the Air & Waste Management Association.

[30]  P. Paatero,et al.  Source apportionment and analysis on ambient and personal exposure samples with a combined receptor model and an adaptive blank estimation strategy , 2006 .

[31]  Judith C. Chow,et al.  Source Apportionment: Findings from the U.S. Supersites Program , 2008, Journal of the Air & Waste Management Association.

[32]  Judith C. Chow,et al.  The IMPROVE_A Temperature Protocol for Thermal/Optical Carbon Analysis: Maintaining Consistency with a Long-Term Database , 2007, Journal of the Air & Waste Management Association.

[33]  William K. Modey,et al.  Comparison of integrated samplers for mass and composition during the 1999 Atlanta Supersites project , 2003, Journal of Geophysical Research.

[34]  T. Peters,et al.  Evaluation of PM2.5 Size Selectors Used in Speciation Samplers , 2001 .

[35]  Philip K. Hopke,et al.  Recent developments in receptor modeling , 2003 .

[36]  J. Chow,et al.  Quantification of PM 2.5 organic carbon sampling artifacts in US networks , 2010 .

[37]  N. Hyslop,et al.  An empirical approach to estimating detection limits using collocated data. , 2008, Environmental science & technology.

[38]  Andrea C. McWilliams,et al.  Harmonization of Interlaboratory X-ray Fluorescence Measurement Uncertainties Detailed Discussion Paper , 2006 .

[39]  Allen L. Robinson,et al.  Positive and Negative Artifacts in Particulate Organic Carbon Measurements with Denuded and Undenuded Sampler Configurations Special Issue of Aerosol Science and Technology on Findings from the Fine Particulate Matter Supersites Program , 2004 .

[40]  B. Jessiman CHAPTER 2 Health Context for Management of Particulate Matter , 2004 .

[41]  Yuan Cheng,et al.  Intercomparison of thermal-optical methods for the determination of organic and elemental carbon: influences of aerosol composition and implications. , 2011, Environmental science & technology.

[42]  Grinding Facility,et al.  Office Of Air Quality Planning And Standards , 1976 .

[43]  J. Chow,et al.  Ambient Aerosol Sampling , 2011 .

[44]  J. Lewtas,et al.  Comparison of Sampling Methods for Semi-Volatile Organic Carbon Associated with PM2.5 , 2001 .

[45]  P. Hopke,et al.  Air pollution and health: bridging the gap from sources to health outcomes: conference summary , 2012, Air Quality, Atmosphere & Health.

[46]  Tami C. Bond,et al.  Calibration and Intercomparison of Filter-Based Measurements of Visible Light Absorption by Aerosols , 1999 .

[47]  Per Capita,et al.  About the authors , 1995, Machine Vision and Applications.

[48]  Yanbo Pang,et al.  Effect of Semivolatile Material on PM 2.5 Measurement by the PM 2.5 Federal Reference Method Sampler at Bakersfield, California , 2002 .

[49]  Hans Moosmüller,et al.  Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. , 2004, Environmental science & technology.

[50]  Christian Hogrefe,et al.  Assessing the Comparability of Ammonium, Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks , 2005 .

[51]  N. Hyslop,et al.  Estimating measurement uncertainty in an ambient sulfate trend , 2005 .

[52]  L. Chen,et al.  Methods to Assess Carbonaceous Aerosol Sampling Artifacts for IMPROVE and Other Long-Term Networks , 2009, Journal of the Air & Waste Management Association.

[53]  J C Chow,et al.  Measurement methods to determine compliance with ambient air quality standards for suspended particles. , 1995, Journal of the Air & Waste Management Association.

[54]  Prakash Doraiswamy,et al.  Advances in Integrated and Continuous Measurements for Particle Mass and Chemical Composition , 2008, Journal of the Air & Waste Management Association.

[55]  P. Solomon Air Pollution and Health: Bridging the Gap from Sources to Health Outcomes , 2011, Environmental health perspectives.

[56]  Ronald E. Hester,et al.  Receptor modeling for air quality management , 1997 .

[57]  P. Hopke Chapter 6 – Scanning Electron Microscopy , 1991 .

[58]  S. Hering,et al.  Field assessment of the dynamics of particulate nitrate vaporization using differential TEOM® and automated nitrate monitors , 2004 .

[59]  P. Hopke,et al.  The U.S. Environmental Protection Agency’s Particulate Matter Supersites Program: An Integrated Synthesis of Scientific Findings and Policy- and Health-Relevant Insights , 2008, Journal of the Air & Waste Management Association.

[60]  J. Huntzicker,et al.  Vapor adsorption artifact in the sampling of organic aerosol: Face velocity effects , 1986 .

[61]  P. Hopke,et al.  Estimation of Organic Carbon Blank Values and Error Structures of the Speciation Trends Network Data for Source Apportionment , 2005, Journal of the Air & Waste Management Association.

[62]  M. L. Laucks Aerosol Technology Properties, Behavior, and Measurement of Airborne Particles , 2000 .

[63]  W. Malm,et al.  Loss of Fine Particle Ammonium from Denuded Nylon Filters , 2006 .

[64]  G. Cass,et al.  The Magnitude of Bias in the Measurement of PM25 Arising from Volatilization of Particulate Nitrate from Teflon Filters. , 1999, Journal of the Air & Waste Management Association.

[65]  W. John,et al.  A cyclone for size-selective sampling of ambient air , 1980 .

[66]  James J. Schauer,et al.  Source apportionment of airborne particulate matter using organic compounds as tracers , 1996 .

[67]  N. Hyslop,et al.  An evaluation of interagency monitoring of protected visual environments (IMPROVE) collocated precision and uncertainty estimates , 2008 .

[68]  L. C. Kenny,et al.  Development of a Sharp-Cut Cyclone for Ambient Aerosol Monitoring Applications , 2000 .

[69]  A. Dillner,et al.  Particulate Matter Sample Deposit Geometry and Effective Filter Face Velocities , 2009, Journal of the Air & Waste Management Association.

[70]  W. Koch,et al.  Aspiration and sampling efficiencies of the TSP and louvered particulate matter inlets. , 2005, Journal of environmental monitoring : JEM.

[71]  Richard Scheffe,et al.  Key scientific findings and policy- and health-relevant insights from the U.S. Environmental Protection Agency's Particulate Matter Supersites Program and related studies: an integration and synthesis of results. , 2008, Journal of the Air & Waste Management Association.

[72]  H. Johnson,et al.  A comparison of 'traditional' and multimedia information systems development practices , 2003, Inf. Softw. Technol..