Literature DB >> 26487426

Determining PM2.5 calibration curves for a low-cost particle monitor: common indoor residential aerosols.

Philip J Dacunto1, Neil E Klepeis2, Kai-Chung Cheng3, Viviana Acevedo-Bolton3, Ruo-Ting Jiang3, James L Repace4, Wayne R Ott3, Lynn M Hildemann3.   

Abstract

Real-time particle monitors are essential for accurately estimating exposure to fine particles indoors. However, many such monitors tend to be prohibitively expensive for some applications, such as a tenant or homeowner curious about the quality of the air in their home. A lower cost version (the Dylos Air Quality Monitor) has recently been introduced, but it requires appropriate calibration to reflect the mass concentration units required for exposure assessment. We conducted a total of 64 experiments with a suite of instruments including a Dylos DC1100, another real-time laser photometer (TSI SidePak™ Model AM-510 Personal Aerosol Monitor), and a gravimetric sampling apparatus to estimate Dylos calibration factors for emissions from 17 different common indoor sources including cigarettes, incense, fried bacon, chicken, and hamburger. Comparison of minute-by-minute data from the Dylos with the gravimetrically calibrated SidePak yielded relationships that enable the conversion of the raw Dylos particle counts less than 2.5 μm (in #/0.01 ft(3)) to estimated PM2.5 mass concentration (e.g. μg m(-3)). The relationship between the exponentially-decaying Dylos particle counts and PM2.5 mass concentration can be described by a theoretically-derived power law with source-specific empirical parameters. A linear relationship (calibration factor) is applicable to fresh or quickly decaying emissions (i.e., before the aerosol has aged and differential decay rates introduce curvature into the relationship). The empirical parameters for the power-law relationships vary greatly both between and within source types, although linear factors appear to have lower uncertainty. The Dylos Air Quality Monitor is likely most useful for providing instantaneous feedback and context on mass particle levels in home and work situations for field-survey or personal awareness applications.

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Year:  2015        PMID: 26487426     DOI: 10.1039/c5em00365b

Source DB:  PubMed          Journal:  Environ Sci Process Impacts        ISSN: 2050-7887            Impact factor:   4.238


  12 in total

1.  Randomized Trial to Reduce Air Particle Levels in Homes of Smokers and Children.

Authors:  Suzanne C Hughes; John Bellettiere; Benjamin Nguyen; Sandy Liles; Neil E Klepeis; Penelope J E Quintana; Vincent Berardi; Saori Obayashi; Savannah Bradley; C Richard Hofstetter; Melbourne F Hovell
Journal:  Am J Prev Med       Date:  2018-01-02       Impact factor: 5.043

2.  Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments.

Authors:  Karoline K Johnson; Michael H Bergin; Armistead G Russell; Gayle S W Hagler
Journal:  Aerosol Air Qual Res       Date:  2018       Impact factor: 3.063

Review 3.  Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Authors:  Lidia Morawska; Phong K Thai; Xiaoting Liu; Akwasi Asumadu-Sakyi; Godwin Ayoko; Alena Bartonova; Andrea Bedini; Fahe Chai; Bryce Christensen; Matthew Dunbabin; Jian Gao; Gayle S W Hagler; Rohan Jayaratne; Prashant Kumar; Alexis K H Lau; Peter K K Louie; Mandana Mazaheri; Zhi Ning; Nunzio Motta; Ben Mullins; Md Mahmudur Rahman; Zoran Ristovski; Mahnaz Shafiei; Dian Tjondronegoro; Dane Westerdahl; Ron Williams
Journal:  Environ Int       Date:  2018-04-26       Impact factor: 9.621

4.  Effects of aerosol particle size on the measurement of airborne PM2.5 with a low-cost particulate matter sensor (LCPMS) in a laboratory chamber.

Authors:  Temitope Oluwadairo; Lawrence Whitehead; Elaine Symanski; Cici Bauer; Arch Carson; Inkyu Han
Journal:  Environ Monit Assess       Date:  2022-01-06       Impact factor: 2.513

5.  Fine Particle Sensor Based on Multi-Angle Light Scattering and Data Fusion.

Authors:  Wenjia Shao; Hongjian Zhang; Hongliang Zhou
Journal:  Sensors (Basel)       Date:  2017-05-04       Impact factor: 3.576

6.  Fine particles in homes of predominantly low-income families with children and smokers: Key physical and behavioral determinants to inform indoor-air-quality interventions.

Authors:  Neil E Klepeis; John Bellettiere; Suzanne C Hughes; Benjamin Nguyen; Vincent Berardi; Sandy Liles; Saori Obayashi; C Richard Hofstetter; Elaine Blumberg; Melbourne F Hovell
Journal:  PLoS One       Date:  2017-05-17       Impact factor: 3.240

7.  Evaluation of micro-well collector for capture and analysis of aerosolized Bacillus subtilis spores.

Authors:  Jiayang He; Nicola K Beck; Alexandra L Kossik; Jiawei Zhang; Edmund Seto; John Scott Meschke; Igor Novosselov
Journal:  PLoS One       Date:  2018-05-30       Impact factor: 3.240

8.  Characteristics of Indoor PM2.5 Concentration in Gers Using Coal Stoves in Ulaanbaatar, Mongolia.

Authors:  Miyoung Lim; Sainnyambuu Myagmarchuluun; Hyunkyung Ban; Yunhyung Hwang; Chimedsuren Ochir; Delgerzul Lodoisamba; Kiyoung Lee
Journal:  Int J Environ Res Public Health       Date:  2018-11-12       Impact factor: 3.390

9.  Biomass-fuelled improved cookstove intervention to prevent household air pollution in Northwest Ethiopia: a cluster randomized controlled trial.

Authors:  Mesafint Molla Adane; Getu Degu Alene; Seid Tiku Mereta
Journal:  Environ Health Prev Med       Date:  2021-01-04       Impact factor: 3.674

10.  Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public.

Authors:  Christopher Carlsten; Sundeep Salvi; Gary W K Wong; Kian Fan Chung
Journal:  Eur Respir J       Date:  2020-06-04       Impact factor: 16.671

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