Ting Zhang1, Steven N Chillrud2, Masha Pitiranggon2, James Ross2, Junfeng Ji3, Beizhan Yan4. 1. Lamont Doherty Earth Observatory of Columbia University, 16 Rt. 9W, Palisades, NY 10964, USA; Key Laboratory of Surficial Geochemistry, Ministry of Education, Nanjing University, 163 Xianlin Ave, Qixia, Nanjing 210023, China. 2. Lamont Doherty Earth Observatory of Columbia University, 16 Rt. 9W, Palisades, NY 10964, USA. 3. Key Laboratory of Surficial Geochemistry, Ministry of Education, Nanjing University, 163 Xianlin Ave, Qixia, Nanjing 210023, China. 4. Lamont Doherty Earth Observatory of Columbia University, 16 Rt. 9W, Palisades, NY 10964, USA. Electronic address: yanbz@ldeo.columbia.edu.
Abstract
BACKGROUND: Fine particulate matter (PM2.5) is associated with various adverse health outcomes. The MicroPEM (RTI, NC), a miniaturized real-time portable particulate sensor with an integrated filter for collecting particles, has been widely used for personal PM2.5 exposure assessment. Five-day deployments were targeted on a total of 142 deployments (personal or residential) to obtain real-time PM2.5 levels from children living in New York City and Baltimore. Among these 142 deployments, 79 applied high-efficiency particulate air (HEPA) filters in the field at the beginning and end of each deployment to adjust the zero level of the nephelometer. However, unacceptable baseline drift was observed in a large fraction (> 40%) of acquisitions in this study even after HEPA correction. This drift issue has been observed in several other studies as well. The purpose of the present study is to develop an algorithm to correct the baseline drift in MicroPEM based on central site ambient data during inactive time periods. METHOD: A running baseline & gravimetric correction (RBGC) method was developed based on the comparison of MicroPEM readings during inactive periods to ambient PM2.5 levels provided by fixed monitoring sites and the gravimetric weight of PM2.5 collected on the MicroPEM filters. The results after RBGC correction were compared with those using HEPA approach and gravimetric correction alone. Seven pairs of duplicate acquisitions were used to validate the RBGC method. RESULTS: The percentages of acquisitions with baseline drift problems were 42%, 53% and 10% for raw, HEPA corrected, and RBGC corrected data, respectively. Pearson correlation analysis of duplicates showed an increase in the coefficient of determination from 0.75 for raw data to 0.97 after RBGC correction. In addition, the slope of the regression line increased from 0.60 for raw data to 1.00 after RBGC correction. CONCLUSIONS: The RBGC approach corrected the baseline drift issue associated with MicroPEM data. The algorithm developed has the potential for use with data generated from other types of PM sensors that contain a filter for weighing as well. In addition, this approach can be applied in many other regions, given widely available ambient PM data from monitoring networks, especially in urban areas.
BACKGROUND: Fine particulate matter (PM2.5) is associated with various adverse health outcomes. The MicroPEM (RTI, NC), a miniaturized real-time portable particulate sensor with an integrated filter for collecting particles, has been widely used for personal PM2.5 exposure assessment. Five-day deployments were targeted on a total of 142 deployments (personal or residential) to obtain real-time PM2.5 levels from children living in New York City and Baltimore. Among these 142 deployments, 79 applied high-efficiency particulate air (HEPA) filters in the field at the beginning and end of each deployment to adjust the zero level of the nephelometer. However, unacceptable baseline drift was observed in a large fraction (> 40%) of acquisitions in this study even after HEPA correction. This drift issue has been observed in several other studies as well. The purpose of the present study is to develop an algorithm to correct the baseline drift in MicroPEM based on central site ambient data during inactive time periods. METHOD: A running baseline & gravimetric correction (RBGC) method was developed based on the comparison of MicroPEM readings during inactive periods to ambient PM2.5 levels provided by fixed monitoring sites and the gravimetric weight of PM2.5 collected on the MicroPEM filters. The results after RBGC correction were compared with those using HEPA approach and gravimetric correction alone. Seven pairs of duplicate acquisitions were used to validate the RBGC method. RESULTS: The percentages of acquisitions with baseline drift problems were 42%, 53% and 10% for raw, HEPA corrected, and RBGC corrected data, respectively. Pearson correlation analysis of duplicates showed an increase in the coefficient of determination from 0.75 for raw data to 0.97 after RBGC correction. In addition, the slope of the regression line increased from 0.60 for raw data to 1.00 after RBGC correction. CONCLUSIONS: The RBGC approach corrected the baseline drift issue associated with MicroPEM data. The algorithm developed has the potential for use with data generated from other types of PM sensors that contain a filter for weighing as well. In addition, this approach can be applied in many other regions, given widely available ambient PM data from monitoring networks, especially in urban areas.
Authors: L Morawska; A Afshari; G N Bae; G Buonanno; C Y H Chao; O Hänninen; W Hofmann; C Isaxon; E R Jayaratne; P Pasanen; T Salthammer; M Waring; A Wierzbicka Journal: Indoor Air Date: 2013-05-13 Impact factor: 5.770
Authors: P J Quintana; B S Samimi; M T Kleinman; L J Liu; K Soto; G Y Warner; C Bufalino; J Valencia; D Francis; M H Hovell; R J Delfino Journal: J Expo Anal Environ Epidemiol Date: 2000 Sep-Oct
Authors: Beizhan Yan; Daniel Kennedy; Rachel L Miller; James P Cowin; Kyung-Hwa Jung; Matt Perzanowski; Marco Balletta; Federica P Perera; Patrick L Kinney; Steven N Chillrud Journal: Atmos Environ (1994) Date: 2011-12 Impact factor: 4.798
Authors: Kyung Hwa Jung; Molini M Patel; Kathleen Moors; Patrick L Kinney; Steven N Chillrud; Robin Whyatt; Lori Hoepner; Robin Garfinkel; Beizhan Yan; James Ross; David Camann; Frederica P Perera; Rachel L Miller Journal: Atmos Environ (1994) Date: 2010-11-01 Impact factor: 4.798
Authors: Darby W Jack; Kwaku Poku Asante; Blair J Wylie; Steve N Chillrud; Robin M Whyatt; Kenneth A Ae-Ngibise; Ashlinn K Quinn; Abena Konadu Yawson; Ellen Abrafi Boamah; Oscar Agyei; Mohammed Mujtaba; Seyram Kaali; Patrick Kinney; Seth Owusu-Agyei Journal: Trials Date: 2015-09-22 Impact factor: 2.279
Authors: Ting Zhang; Steven N Chillrud; Qiang Yang; Masha Pitiranggon; James Ross; Frederica Perera; Junfeng Ji; Avrum Spira; Patrick N Breysse; Charles E Rodes; Rachel Miller; Beizhan Yan Journal: Indoor Air Date: 2019-12-11 Impact factor: 5.770
Authors: Jonathan Thornburg; Yuliya Halchenko; Michelle McCombs; Nalyn Siripanichgon; Erin Dowell; Seung-Hyun Cho; Jennifer Egner; Vicki Sayarath; Margaret R Karagas Journal: Int J Environ Res Public Health Date: 2021-11-18 Impact factor: 4.614