Here is a list of publications related to PurpleAir projects. In addition to the U.S., especially California, there are several reports from Africa, Asia, Europe and Latin America. There are some innovative methods for determining calibration factors and improving human exposure estimates using land use regression and data mining techniques. There is a special emphasis, in terms of article numbers, on wildfire exposures and levels in underserved communities (environmental justice areas)
Amegah, A.K., Dakuu, G., Mudu, P., Jaakkola, J.J.K., (2021). Particulate matter pollution at traffic hotspots of Accra, Ghana: levels, exposure experiences of street traders, and associated respiratory and cardiovascular symptoms. Journal of Exposure Science and Environmental Epidemiology, 10.1038/s41370-021-00357-x.
Amegah, A.K., Dakuu, G., Mudu, P., Jaakkola, J.J.K., (2022). Particulate matter pollution at traffic hotspots of Accra, Ghana: levels, exposure experiences of street traders, and associated respiratory and cardiovascular symptoms. Journal of Exposure Science and Environmental Epidemiology, 32, 333-342. 10.1038/s41370-021-00357-x.
Ardon-Dryer, K., Dryer, Y., Williams, J.N., Moghimi, N., (2020). Measurements of PM2.5 with PurpleAir under atmospheric conditions. Atmospheric Measurement Techniques, 13, 5441-5458. 10.5194/amt-13-5441-2020.
Awokola, B., Okello, G., Johnson, O., Dobson, R., Ouédraogo, A.R., Dibba, B., Ngahane, M., Ndukwu, C., Agunwa, C., Marangu, D., Lawin, H., Ogugua, I., Eze, J., Nwosu, N., Ofiaeli, O., Ubuane, P., Osman, R., Awokola, E., Erhart, A., Mortimer, K., Jewell, C., Semple, S., (2022). Longitudinal ambient PM2.5 measurement at fifteen locations in eight Sub-Saharan African countries using low-cost sensors. Atmosphere, 13, 10.3390/atmos13101593.
Barkjohn, K.K., Gantt, B., Clements, A.L., (2021). Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor. Atmospheric Measurement Techniques, 14, 4617-4637. 10.5194/amt-14-4617-2021.
Barkjohn, K.K., Holder, A.L., Frederick, S.G., Clements, A.L., (2022). Correction and accuracy of PurpleAir PM2.5 measurements for extreme wildfire smoke. Sensors, 22, 10.3390/s22249669.
Bi, J.Z., Wildani, A., Chang, H.H., Liu, Y., (2020). Incorporating low-cost sensor Measurements into high-resolution PM2.5 modeling at a large spatial scale. Environmental Science & Technology, 54, 2152-2162. 10.1021/acs.est.9b06046.
Bi, J., Wallace, L.A., Sarnat, J.A., Liu, Y., (2021). Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data. Environmental Pollution, 276, 10.1016/j.envpol.2021.116763.
Bi, J., Carmona, N., Blanco, M.N., Gassett, A.J., Seto, E., Szpiro, A.A., Larson, T.V., Sampson, P.D., Kaufman, J.D., Sheppard, L., (2022). Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection. Environment International, 158, 10.1016/j.envint.2021.106897.
Caseiro, A., Schmitz, S., Villena, G., Jagatha, J.V., von Schneidemesser, E., (2022). Ambient characterisation of PurpleAir particulate matter monitors for measurements to be considered as indicative. Environmental Science: Atmospheres, 116, 10.1039/d2ea00085g.
Chow, F.K., Yu, K.A., Young, A., James, E., Grell, G.A., Csiszar, I., Tsidulko, M., Freitas, S., Pereira, G., Giglio, L., Friberg, M.D., Ahmadov, R., (2022). High-resolution smoke forecasting for the 2018 Camp fire in California. Bulletin of the American Meteorological Society, 103, E1531-E1552. 10.1175/BAMS-D-20-0329.1.
Coker, E.S., Buralli, R., Manrique, A.F., Kanai, C.M., Amegah, A.K., Gouveia, N., (2022). Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research. Environmental Research, 214, 10.1016/j.envres.2022.113738.
Collier-Oxandale, A., Feenstra, B., Papapostolou, V., Polidori, A., (2022). AirSensor v1.0: Enhancements to the open-source R package to enable deep understanding of the long-term performance and reliability of PurpleAir sensors. Environmental Modelling and Software, 148, 10.1016/j.envsoft.2021.105256.
Connolly, R.E., Yu, Q., Wang, Z., Chen, Y.H., Liu, J.Z., Collier-Oxandale, A., Papapostolou, V., Polidori, A., Zhu, Y., (2022). Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale. Science of the Total Environment, 807, 10.1016/j.scitotenv.2021.150797.
Delp, W.W., Singer, B.C., (2020). Wildfire smoke adjustment factors for low-cost and professional PM2.5 monitors with optical sensors. Sensors, 20, 10.3390/s20133683.
Delp, W.W., Singer, B.C., (2020). Wildfire smoke adjustment factors for low-cost and professional pm2.5 monitors with optical sensors. Sensors (Switzerland), 20, 1-21. 10.3390/s20133683.
Dhammapala, R., Basnayake, A., Premasiri, S., Chathuranga, L., Mera, K., (2022). PM2.5 in Sri Lanka: Trend analysis, low-cost sensor correlations and spatial distribution. Aerosol and Air Quality Research, 22, 10.4209/aaqr.210266.
Karaoghlanian, N., Noureddine, B., Saliba, N., Shihadeh, A., Lakkis, I., (2022). Low cost air quality sensors “PurpleAir” calibration and inter-calibration dataset in the context of Beirut, Lebanon. Data in Brief, 41, 10.1016/j.dib.2022.108008.
Kelly, K.E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D., Martin, R., Butterfield, A., (2017). Ambient and laboratory evaluation of a low-cost particulate matter sensor. Environmental Pollution, 221, 491-500. 10.1016/j.envpol.2016.12.039.
Kim, S., Park, S., Lee, J., (2019). Evaluation of performance of inexpensive laser based PM2.5 sensor monitors for typical indoor and outdoor hotspots of South Korea. Applied Sciences-Basel, 9, 10.3390/app9091947.
Ko, K., Cho, S., Rao, R.R., (2020). Performance evaluation of low-cost purpleair sensors in ambient air, Webb, G., Zhang, Z., Tseng, V.S., Williams, G., Vlachos, M., Cao, L. (Eds.), Proceedings, 7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 563-568.
Kosmopoulos, G., Salamalikis, V., Pandis, S.N., Yannopoulos, P., Bloutsos, A.A., Kazantzidis, A., (2020). Low-cost sensors for measuring airborne particulate matter: Field evaluation and calibration at a South-Eastern European site. Science of the Total Environment, 748, 10.1016/j.scitotenv.2020.141396.
Kramer, A.L., Liu, J., Li, L., Connolly, R., Barbato, M., Zhu, Y., (2023). Environmental justice analysis of wildfire-related PM2.5 exposure using low-cost sensors in California. Science of the Total Environment, 856, 10.1016/j.scitotenv.2022.159218.
Kumar, V., Malyan, V., Sahu, M., (2022). Significance of meteorological feature selection and seasonal Variation on performance and calibration of a low-cost particle sensor. Atmosphere, 13, 10.3390/atmos13040587.
Li, J., Mattewal, S.K., Patel, S., Biswas, P., (2020). Evaluation of nine low-cost-sensor-based particulate matter monitors. Aerosol and Air Quality Research, 20, 254-270. 10.4209/aaqr.2018.12.0485.
Liang, Y., Sengupta, D., Campmier, M.J., Lunderberg, D.M., Apte, J.S., Goldstein, A.H., (2021). Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California. Proceedings of the National Academy of Sciences of the United States of America, 118, 10.1073/pnas.2106478118.
Liu, J., Banerjee, S., Oroumiyeh, F., Shen, J., del Rosario, I., Lipsitt, J., Paulson, S., Ritz, B., Su, J., Weichenthal, S., Lakey, P., Shiraiwa, M., Zhu, Y., Jerrett, M., (2022). Cokriging with a low-cost sensor network to estimate spatial variation of brake and tire-wear metals and oxidative stress potential in Southern California. Environment International, 168, 10.1016/j.envint.2022.107481.
Lu, Y.G., Giuliano, G., Habre, R., (2021). Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. Environmental Research, 195, 10.1016/j.envres.2020.110653.
Lu, T., Bechle, M.J., Wan, Y., Presto, A.A., Hankey, S., (2022). Using crowd-sourced low-cost sensors in a land use regression of PM2.5 in 6 US cities. Air Quality, Atmosphere and Health, 10.1007/s11869-022-01162-7.
Lu, T., Bechle, M.J., Wan, Y., Presto, A.A., Hankey, S., (2022). Using crowd-sourced low-cost sensors in a land use regression of PM2.5 in 6 US cities. Air Quality, Atmosphere and Health, 15, 667-678. 10.1007/s11869-022-01162-7.
Lu, T., Liu, Y., Garcia, A., Wang, M., Li, Y., Bravo-villasenor, G., Campos, K., Xu, J., Han, B., (2022). Leveraging citizen science and low-cost sensors to characterize air pollution exposure of disadvantaged communities in southern California. International Journal of Environmental Research and Public Health, 19, 10.3390/ijerph19148777.
Magi, B.I., Cupini, C., Francis, J., Green, M., Hauser, C., (2020). Evaluation of PM2.5 measured in an urban setting using a low-cost optical particle counter and a Federal Equivalent Method Beta Attenuation Monitor. Aerosol Science and Technology, 54, 147-159. 10.1080/02786826.2019.1619915.
Malings, C., Tanzer, R., Hauryliuk, A., Saha, P.K., Robinson, A.L., Presto, A.A., Subramanian, R., (2020). Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation. Aerosol Science and Technology, 54, 160-174. 10.1080/02786826.2019.1623863.
Masoud, S., Mariscal, N., Huang, Y., Zhu, M., (2021). A sensor-based data driven framework to investigate PM2.5in the greater Detroit area. Ieee Sensors Journal, 21, 16192-16200. 10.1109/JSEN.2021.3076041.
Masri, S., Jin, Y., Wu, J., (2022). Compound risk of air pollution and heat days and the influence of wildfire by SES across California, 2018–2020: Implications for environmental justice in the context of climate change. Climate, 10, 10.3390/cli10100145.
MatLab, (2019). How to Analyze IoT Data in ThingSpeak. YouTube. How to Analyze IoT Data in ThingSpeak - YouTube
MatLab, (2019). Air quality measurements and visualizations. MatLab. Air Quality Measurements and Visualizations - File Exchange - MATLAB Central
May, N.W., Dixon, C., Jaffe, D.A., (2021). Impact of wildfire smoke events on indoor air quality and evaluation of a low-cost filtration method. Aerosol and Air Quality Research, 21, 10.4209/aaqr.210046.
McFarlane, C., Isevulambire, P.K., Lumbuenamo, R.S., Ndinga, A.M.E., Dhammapala, R., Jin, X., McNeill, V.F., Malings, C., Subramanian, R., Westervelt, D.M., (2021). First measurements of ambient OM2.5 in kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo using field-calibrated low-cost sensors. Aerosol and Air Quality Research, 21, 10.4209/aaqr.200619.
McFarlane, C., Raheja, G., Malings, C., Appoh, E.K.E., Hughes, A.F., Westervelt, D.M., (2021). Application of Gaussian mixture regression for the correction of low cost PM2.5 monitoring dData in Accra, Ghana. Acs Earth and Space Chemistry, 5, 2268-2279. 10.1021/acsearthspacechem.1c00217.
Mehadi, A., Moosmuller, H., Campbell, D.E., Ham, W., Schweizer, D., Tarnay, L., Hunter, J., (2020). Laboratory and field evaluation of real-time and near real-time PM2.5 smoke monitors. Journal of the Air & Waste Management Association, 70, 158-179. 10.1080/10962247.2019.1654036.
MIT, (2023). purpleair 0.0.4. Massachusetts Institute of Technology, Cambridge, MA. purpleair · PyPI
Mousavi, A., Wu, J., (2021). Indoor-generated PM2.5 during COVID-19 shutdowns across California: Application of the PurpleAir indoor-outdoor low-cost sensor network. Environmental Science and Technology, 55, 5648-5656. 10.1021/acs.est.0c06937.
Mousavi, A., Yuan, Y., Masri, S., Barta, G., Wu, J., (2021). Impact of 4th of July fireworks on spatiotemporal PM2.5 concentrations in California based on the Purpleair sensor network: Implications for policy and environmental justice. International Journal of Environmental Research and Public Health, 18, 10.3390/ijerph18115735.
Mullen, C., Flores, A., Grineski, S., Collins, T., (2022). Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County. Environmental Research, 206, 10.1016/j.envres.2021.112612.
Nilson, B., Jackson, P.L., Schiller, C.L., Parsons, M.T., (2022). Development and evaluation of correction models for a low-cost fine particulate matter monitor. Atmospheric Measurement Techniques, 15, 3315-3328. 10.5194/amt-15-3315-2022.
Ouimette, J.R., Malm, W.C., Schichtel, B.A., Sheridan, P.J., Andrews, E., Ogren, J.A., Arnott, W.P., (2022). Evaluating the PurpleAir monitor as an aerosol light scattering instrument. Atmospheric Measurement Techniques, 15, 655-676. 10.5194/amt-15-655-2022. AMT - Evaluating the PurpleAir monitor as an aerosol light scattering instrument
Proma, R.A., Sumpter, M., Lugo, H., Friedman, E., Huq, K.T., Rosen, P., (2021). CleanAirNowKC: Building community power by improving data accessibility, Proceedings, 1st IEEE Workshop on Visualization for Social Good, VIS4Good 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1-5.
PurpleAir, (2018). Using PurpleAir data. Salt Lake City, UT. Using PurpleAir Data - Google Docs
PurpleAir, (2021). API - PurpleAir: A RESTful API for PurpleAir sensors. PurpleAir, Salt Lake City, UT. https://api.purpleair.com/
PurpleAir, (2023). PurpleAir: Air quality monitoring. Salt Lake City, UT. https://www.purpleair.com/
PurpleAir, (2023). Making API calls with the PurpleAir API. Salt Lake City, UT. Making API Calls with the PurpleAir API
Raheja, G., Sabi, K., Sonla, H., Gbedjangni, E.K., McFarlane, C.M., Hodoli, C.G., Westervelt, D.M., (2022). A network of field-calibrated low-cost sensor measurements of PM2.5in Lomé, Togo, over one to two years. Acs Earth and Space Chemistry, 6, 1011-1021. 10.1021/acsearthspacechem.1c00391.
Regmi, J., Poudyal, K.N., Pokhrel, A., Malakar, N., Gyawali, M., Tripathee, L., Rai, M., Ramachandran, S., Wilson, K., Aryal, R., (2023). Analysis of surface level PM2.5 measured by low-cost sensor and satellite-based column aerosol optical depth (AOD) over Kathmandu. Aerosol and Air Quality Research, 23, 10.4209/aaqr.220311.
Robinson, D.L., (2020). Accurate, low cost PM2.5 measurements demonstrate the large spatial variation in wood smoke pollution in regional Australia and improve modeling and estimates of health costs. Atmosphere, 11, 10.3390/atmos11080856.
Singer, B.C., Delp, W.W., (2018). Response of consumer and research grade indoor air quality monitors to residential sources of fine particles. Indoor Air, 28, 624-639. 10.1111/ina.12463.
Sun, Y., Mousavi, A., Masri, S., Wu, J., (2022). Socioeconomic disparities of low-cost air quality sensors in California, 2017-2020. American Journal of Public Health, 112, 434-442. 10.2105/AJPH.2021.306603.
Tryner, J., L’Orange, C., Mehaffy, J., Miller-Lionberg, D., Hofstetter, J.C., Wilson, A., Volckens, J., (2020). Laboratory evaluation of low-cost PurpleAir PM monitors and in-field correction using co-located portable filter samplers. Atmospheric Environment, 220, 10.1016/j.atmosenv.2019.117067. ://WOS:000501407200020
Vu, B.N., Bi, J., Wang, W., Huff, A., Kondragunta, S., Liu, Y., (2022). Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California. Remote Sensing of Environment, 271, 10.1016/j.rse.2022.112890.
Wallace, L., Ott, W., Zhao, T., Cheng, K.C., Hildemann, L., (2020). Secondhand exposure from vaping marijuana: Concentrations, emissions, and exposures determined using both research-grade and low-cost monitors. Atmospheric Environment: X, 8, 10.1016/j.aeaoa.2020.100093.
Wallace, L., Bi, J., Ott, W.R., Sarnat, J., Liu, Y., (2021). Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM2.5. Atmospheric Environment, 256, 10.1016/j.atmosenv.2021.118432.
Wallace, L., (2022). Intercomparison of PurpleAir sensor performance over three years indoors and outdoors at a home: Bias, precision, and limit of detection using an improved algorithm for calculating PM2.5. Sensors, 22, 10.3390/s22072755.
Wallace, L., Zhao, T., Klepeis, N.E., (2022). Calibration of PurpleAir PA-I and PA-II monitors using daily mean PM2.5 concentrations measured in California, Washington, and Oregon from 2017 to 2021. Sensors, 22, 10.3390/s22134741.
Wallace, L.A., Zhao, T., Klepeis, N.E., (2022). Indoor contribution to PM2.5 exposure using all PurpleAir sites in Washington, Oregon, and California. Indoor Air, 32, 10.1111/ina.13105.
Wang, Z.Q., Delp, W.W., Singer, B.C., (2020). Performance of low-cost indoor air quality monitors for PM2.5 and PM10 from residential sources. Building and Environment, 171, 10.1016/j.buildenv.2020.106654.
Watson, J.G., Sheth, S., (2019). Dust mitigation plan related to closure construction for RMC Pacific Materials (CEMEX) in Davenport, CA. Desert Research Institute for RMC Pacific Materials, Reno, NV. https://www.researchgate.net/publication/367380333_Dust_Mitigation_Plan_related_to_Closure_Construction_for_RMC_Pacific_Materials_CEMEX_in_Davenport_CA_Prepared_by
https://www.sccoplanning.com/Portals/2/County/Appendix%205%20Dust%20Mitigation%20Plan%20(Watson%20and%20Sheth%20May%2030%2C%202019).pdf
Whitty, R.C.W., Ilyinskaya, E., Mason, E., Wieser, P.E., Liu, E.J., Schmidt, A., Roberts, T., Pfeffer, M.A., Brooks, B., Mather, T.A., Edmonds, M., Elias, T., Schneider, D.J., Oppenheimer, C., Dybwad, A., Nadeau, P.A., Kern, C., (2020). Spatial and temporal Variations in SO2 and PM2.5 levels around Kilauea volcano, Hawai’i During 2007-2018. Frontiers in Earth Science, 8, 10.3389/feart.2020.00036.
Zimmerman, N., (2022). Tutorial: Guidelines for implementing low-cost sensor networks for aerosol monitoring. Journal of Aerosol Science, 159, 10.1016/j.jaerosci.2021.105872.
