Data set associated with “A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements”

Wendt, Eric A.

Data set associated with “A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements”

dc.contributor.author	Wendt, Eric A.
dc.coverage.spatial	CSU Powerhouse Energy Campus (430 N College Ave, Fort Collins, CO 80524)	en_US
dc.coverage.spatial	NEON-CVALLA (N 40º09’39”, W 105º10’01”)	en_US
dc.coverage.temporal	2020	en_US
dc.date.accessioned	2021-03-04T16:38:58Z
dc.date.available	2021-03-04T16:38:58Z
dc.date.issued	2021
dc.description	This dataset includes two csv files containing the data associated with testing and validation of the AMODv2 particulate matter sampler. The first file, "amod_v2_test_deployment.csv" contains the results from a test deployment of 10 AMODv2 units in October 2020. The second file, "amod_v2_validation.csv" contains results from a validation experiment performed relative to a reference monitor perform fall 2020.	en_US
dc.description	Department of Mechanical Engineering
dc.description	Department of Environmental and Radiological Health Sciences
dc.description	Department of Atmospheric Science
dc.description.abstract	Atmospheric particulate matter smaller than 2.5 microns in diameter (PM2.5) impacts public health, the environment, and the climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM2.5 concentrations at a global scale. Remote sensing observations of aerosol optical depth (AOD) have been used to estimate surface-level PM2.5 for studies on human health and the Earth system. However, these estimates are uncertain due to a lack of measurements available to validate the derived PM2.5 products, which rely on the ratio of surface PM2.5 to AOD. Traditional monitoring of these two air quality metrics is costly and cumbersome, leading to a lack of surface monitoring networks with high spatial density. In part 1 of this series we described the development and validation of a first-generation device for low-cost measurement of AOD and PM2.5: The Aerosol Mass and Optical Depth (AMODv1) sampler. Part 2 of the series describes a citizen-science field deployment of the AMODv1 device. Here in part 3, we present an autonomous version of the AMOD, known as AMODv2, capable of unsupervised measurement of AOD and PM2.5 at 20-minute time intervals. The AMOD includes a set of four optically filtered photodiodes for multi-wavelength (current version at 440, 500, 675, and 870 nm) AOD, a Plantower PMS5003 sensor for time-resolved optical PM2.5 measurements, and a pump and cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. The AMODv2 uses low-cost motors and sensor data for autonomous sun alignment to provide the semi-continuous AOD measurements. Operators can connect to the AMODv2 over Bluetooth® and configure a sample using a smartphone application. A Wi-Fi module enables real-time data streaming and visualization on our website (csu-ceams.com). We present a sample deployment of 10 AMODv2s during a wildfire smoke event and demonstrate the ability of the instrument to capture changes in air quality at sub-hourly time resolution. We also present the results of an AOD validation campaign where AMODv2s were co-located with AERONET (Aerosol Robotics Network) instruments as the reference method at AOD levels ranging from 0.016-1.59. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.046, 0.057, 0.026, and 0.033 AOD units at 440 nm, 500 nm, 675 nm, and 870 nm, respectively. We identified individual unit bias as the primary source of error between AMODv2s and reference units and propose re-calibration to mitigate these biases. The AMODv2 is well suited for citizen-science and other high-spatial-density deployments due to its low cost, compact form, user-friendly interface, and high measurement frequency of AOD and PM2.5. These deployments could provide a rich air pollution data set for evaluating remote sensing observations, atmospheric modeling simulations, and provide communities with the information they need to implement effective public health and environmental interventions.	en_US
dc.description.sponsorship	This research was supported by NASA grant 80NSSC18M0120.	en_US
dc.format.medium	PDF
dc.format.medium	CSV
dc.identifier.uri	https://hdl.handle.net/10217/225291
dc.identifier.uri	http://dx.doi.org/10.25675/10217/225291
dc.language	English	en_US
dc.language.iso	eng	en_US
dc.publisher	Colorado State University. Libraries	en_US
dc.relation.ispartof	Research Data
dc.relation.isreferencedby	Wendt, E. A., Quinn, C., L'Orange, C., Miller-Lionberg, D. D., Ford, B., Pierce, J. R., Mehaffy, J., Cheeseman, M., Jathar, S. H., Hagan, D. H., Rosen, Z., Long, M., and Volckens, J.: A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements, Atmos. Meas. Tech., 14, 6023–6038, https://doi.org/10.5194/amt-14-6023-2021, 2021.	en_US
dc.subject	particulate matter	en_US
dc.subject	aerosol optical depth	en_US
dc.subject	air pollution	en_US
dc.title	Data set associated with “A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements”	en_US
dc.type	Dataset	en_US