Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

• Published in: Journal of the Air & Waste Management Association (1995) 2002-01, Vol.52 (1), p.104-112
• Main Authors: Bortnick, Steven M., Coutant, Basil W., Eberly, Shelly I.
• Format: Article
• Language: English
• Subjects: Air Pollutants - analysis; Applied sciences; Atmospheric pollution; Cities; Environmental Monitoring - methods; Exact sciences and technology; Guideline Adherence; Linear Models; Particle Size; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; Quality Control
• ISSN: 1096-2247; 2162-2906
• DOI: 10.1080/10473289.2002.10470763

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM 2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area's (MSA's) AQI. 1 Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM 2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency's (EPA's) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity. 2 The chosen measure of model adequacy is r 2 , the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r 2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r 2 of 0.86, based on 214 paired observations.