Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets

Duvall, Rachelle M.; Hagler, Gayle; Clements, Andrea L.; Benedict, Kristen; Barkjohn, Karoline K.; Kilaru, V.; Hanley, Tim; Watkins, Nealson; Kaufman, Amanda; Kamal, Ali; Reece, Stephen; Fransioli, P.; Gerboles, Michel; Gillerman, G.; Habre, Rima; Hannigan, Michael P.; Ning, Zhi; Papapostolou, Vasileios; Pope, Ronald; Quintana, Penelope J.E.; Snyder, Janice Lam

doi:10.1016/j.atmosenv.2020.118099

Cited by 26 publications

(38 citation statements)

References 15 publications

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“…Dust impacts may be driving the underestimates on these days in the West. Because it is harder for larger particles to be sampled by the low flowrate fans, especially under higher windspeeds, and also because larger particles scatter less light per mass than smaller particles, this may be impossible to correct for with the hardware available on a PurpleAir (Duvall et al, 2020;Pawar and Sinha, 2020). Additional regional information from satellites or other sources may be able to improve these measurements in the future or more advanced sensor hardware may allow more accurate estimates.…”

Section: Aqimentioning

confidence: 99%

Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor

Barkjohn¹,

Gantt²,

Clements³

2020

Preprint

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Abstract. PurpleAir sensors which measure particulate matter (PM) are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 µg m−3 to 3 µg m−3 with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

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Section: Aqimentioning

confidence: 99%

Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor

Barkjohn¹,

Gantt²,

Clements³

2020

Preprint

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“…For optical particulate matter (PM) sensors, correction procedures are often needed due to both the changing optical properties of aerosols associated with both their physical and chemical characteristics (Levy Zamora et al, 2019;Tryner et al, 2019) and the influence of meteorological conditions including temperature and relative humidity (RH) (Jayaratne et al, 2018;Zheng et al, 2018). In addition, some air sensors have out-of-the box differences and low precision between sensors of the same model (Feenstra et al, 2019;Feinberg et al, 2018). Although collocation and local correction may be achievable for researchers and some air monitoring agencies, it is unattainable for many sensor users and community groups due to lack of access and proximity to regulatory monitoring sites.…”

Section: Introductionmentioning

confidence: 99%

“…The low cost of outdoor PurpleAir sensors (USD 230-260) has enabled them to be widely used with thousands of sensors publicly reporting across the US. Previous work has explored the performance and accuracy of PurpleAir sensors under outdoor ambient conditions in a variety of locations across the United States including in Colorado (Ardon-Dryer et al, 2020;Tryner et al, 2020a); Utah (Ardon-Dryer et al, 2020;Kelly et al, 2017;Sayahi et al, 2019); Pennsylvania (Malings et al, 2020); North Carolina (Magi et al, 2019); and in California, where the most work has occurred to date (Ardon-Dryer et al, 2020;Bi et al, 2020;Feenstra et al, 2019;Mehadi et al, 2020;Schulte et al, 2020;Lu et al, 2021). Their performance has been explored in a number of other parts of the world as well including in Korea (Kim et al, 2019), Greece (Stavroulas et al, 2020), Uganda (McFarlane et al, 2021), and Australia (Robinson, 2020).…”

Section: Introductionmentioning

confidence: 99%

Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor

2021

View full text Add to dashboard Cite

Abstract. PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10 000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12 000 24 h averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (US), including 16 states. Consistent with previous evaluations, under typical ambient and smoke-impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the US. A simple linear regression reduces much of this bias across most US regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 to 3 µg m−3, with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.

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“…( 2014 ) for satellite data and Giordano et al. ( 2021 ) and several recent workshop summaries that provide a comprehensive overview of the state of science of low‐cost sensors for AQ applications (Clements et al., 2017 ; Duvall et al., 2021 ; Williams et al., 2019 ).…”

Section: Satellites Low‐cost Sensors and Aq Forecastsmentioning

confidence: 99%

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

et al. 2021

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Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets

Cited by 26 publications

References 15 publications

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

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Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets

Cited by 26 publications

References 15 publications

Development and Application of a United States wide correction for PM&lt;sub&gt;2.5&lt;/sub&gt; data collected with the PurpleAir sensor

Development and Application of a United States wide correction for PM&lt;sub&gt;2.5&lt;/sub&gt; data collected with the PurpleAir sensor

Development and application of a United States-wide correction for PM&lt;sub&gt;2.5&lt;/sub&gt; data collected with the PurpleAir sensor

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

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Product

Resources

About

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor