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Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale

  • Jianzhao Bi
    Jianzhao Bi
    Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    More by Jianzhao Bi
    • , 
  • Avani Wildani
    Avani Wildani
    Department of Computer Science, Emory University, Atlanta, Georgia 30307, United States
    More by Avani Wildani
    • , 
  • Howard H. Chang
    Howard H. Chang
    Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    More by Howard H. Chang
    • , and 
  • Yang Liu*
    Yang Liu
    Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
    *E-mail: [email protected]
    More by Yang Liu
    Orcidhttp://orcid.org/0000-0001-5477-2186
Cite this: Environ. Sci. Technol. 2020, 54, 4, 2152–2162
Publication Date (Web):January 12, 2020
https://doi.org/10.1021/acs.est.9b06046
Copyright © 2020 American Chemical Society
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Abstract

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Low-cost air quality sensors are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM2.5 estimates. The calibration reduced PurpleAir’s systematic bias to ∼0 μg/m3 and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error. The temporal CV R2 did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM2.5 spatial details and hotspots.

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.9b06046.

  • Quality control for PurpleAir PM2.5 measurements (Section 1); evaluation of PurpleAir PM2.5 measurements (Section 2); nonlinearity of PurpleAir systematic bias (Section 3); validation of scale factor ρ (Section 4); PurpleAir calibration based on subsets of AQS stations (Table S1); HAC clustering feature space (Table S2); numbers and densities of continuous AQS stations (Table S3); spatial distribution of GWR slopes (Figure S1); clustered subdomains (Figure S2); ten-fold CV scatter plots (Figure S3); PM2.5 predictions at Ferguson Fire (Figure S4); PM2.5 predictions of the nonweighted model (Figure S5); PurpleAir dual-channel hourly measurements (Figure S6); AQS and PurpleAir hourly measurements (Figure S7); determination of scale factor ρ (Figure S8) (PDF)

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  2. Misti Levy Zamora, Colby Buehler, Hao Lei, Abhirup Datta, Fulizi Xiong, Drew R. Gentner, Kirsten Koehler. Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network. ACS ES&T Engineering 2022, 2 (5) , 780-793. https://doi.org/10.1021/acsestengg.1c00367
  3. Jianzhao Bi, K. Emma Knowland, Christoph A. Keller, Yang Liu. Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast. Environmental Science & Technology 2022, 56 (3) , 1544-1556. https://doi.org/10.1021/acs.est.1c05578
  4. Amirhosein Mousavi, Jun Wu. Indoor-Generated PM2.5 During COVID-19 Shutdowns Across California: Application of the PurpleAir Indoor–Outdoor Low-Cost Sensor Network. Environmental Science & Technology 2021, 55 (9) , 5648-5656. https://doi.org/10.1021/acs.est.0c06937
  5. Benjamin Krebs, Jennifer Burney, Joshua Graff Zivin, Matthew Neidell. Using Crowd-Sourced Data to Assess the Temporal and Spatial Relationship between Indoor and Outdoor Particulate Matter. Environmental Science & Technology 2021, 55 (9) , 6107-6115. https://doi.org/10.1021/acs.est.0c08469
  6. Stephanie E. Cleland, J. Jason West, Yiqin Jia, Stephen Reid, Sean Raffuse, Susan O’Neill, Marc L. Serre. Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5. Environmental Science & Technology 2020, 54 (21) , 13439-13447. https://doi.org/10.1021/acs.est.0c03761
  7. . Overview of extreme weather events, impacts and forecasting techniques. 2023,,, 1-86. https://doi.org/10.1016/B978-0-12-820124-4.00009-8
  8. Wei-Ting Hsu, Chun-Hung Ku, Mu-Jean Chen, Chih-Da Wu, Shih-Chun Candice Lung, Yu-Cheng Chen. Model development and validation of personal exposure to PM2.5 among urban elders. Environmental Pollution 2023, 316 , 120538. https://doi.org/10.1016/j.envpol.2022.120538
  9. Zehui Li, Zizhen Ma, Zhan Zhang, Lingling Zhang, Enze Tian, Haiteng Zhang, Ruiyao Yang, Diwei Zhu, Hui Li, Ziyi Wang, Yinglei Zhang, Pingchuan Xu, Yuexin Xu, Dongbin Wang, Gang Wang, Minjung Kim, Yi Yuan, Xiaohui Qiao, Mingjie Li, Yangyang Xie, Shaojun Guo, Kaihui Liu, Jingkun Jiang. High-density volatile organic compound monitoring network for identifying pollution sources. Science of The Total Environment 2023, 855 , 158872. https://doi.org/10.1016/j.scitotenv.2022.158872
  10. Amber L. Kramer, Jonathan Liu, Liqiao Li, Rachel Connolly, Michele Barbato, Yifang Zhu. Environmental justice analysis of wildfire-related PM2.5 exposure using low-cost sensors in California. Science of The Total Environment 2023, 856 , 159218. https://doi.org/10.1016/j.scitotenv.2022.159218
  11. Qiang Pu, Eun-Hye Yoo. A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data. Environmental Pollution 2022, 315 , 120419. https://doi.org/10.1016/j.envpol.2022.120419
  12. Sathish Swaminathan, Anand Vijay Sankar Guntuku, Sumeer S, Amita Gupta, Raghunathan Rengaswamy. Data science and IoT based mobile monitoring framework for hyper-local PM2.5 assessment in urban setting. Building and Environment 2022, 225 , 109597. https://doi.org/10.1016/j.buildenv.2022.109597
  13. Samuel Ogunjo, Adeyemi Olusola, Israel Orimoloye. Association Between Weather Parameters and SARS‐CoV‐2 Confirmed Cases in Two South African Cities. GeoHealth 2022, 6 (11) https://doi.org/10.1029/2021GH000520
  14. Shahir Masri, Yufang Jin, Jun Wu. 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 2022, 10 (10) , 145. https://doi.org/10.3390/cli10100145
  15. Jianzhao Bi, Christopher Zuidema, David Clausen, Kipruto Kirwa, Michael T. Young, Amanda J. Gassett, Edmund Y. W. Seto, Paul D. Sampson, Timothy V. Larson, Adam A. Szpiro, Lianne Sheppard, Joel D. Kaufman. Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework. Environmental Health Perspectives 2022, 130 (9) https://doi.org/10.1289/EHP10889
  16. Jiayang He, Ching-Hsuan Huang, Nanhsun Yuan, Elena Austin, Edmund Seto, Igor Novosselov. Network of low-cost air quality sensors for monitoring indoor, outdoor, and personal PM2.5 exposure in Seattle during the 2020 wildfire season. Atmospheric Environment 2022, 285 , 119244. https://doi.org/10.1016/j.atmosenv.2022.119244
  17. Lance A. Wallace, Tongke Zhao, Neil E. Klepeis. Indoor contribution to PM 2 .5 exposure using all PurpleAir sites in Washington, Oregon, and California. Indoor Air 2022, 32 (9) https://doi.org/10.1111/ina.13105
  18. Lance Wallace, Tongke Zhao, Neil E. Klepeis. 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 2022, 22 (13) , 4741. https://doi.org/10.3390/s22134741
  19. Tianjun Lu, Yisi Liu, Armando Garcia, Meng Wang, Yang Li, German Bravo-villasenor, Kimberly Campos, Jia Xu, Bin Han. 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 2022, 19 (14) , 8777. https://doi.org/10.3390/ijerph19148777
  20. Yuxia Cui, Kristin M. Eccles, Richard K. Kwok, Bonnie R. Joubert, Kyle P. Messier, David M. Balshaw. Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities. Toxics 2022, 10 (7) , 403. https://doi.org/10.3390/toxics10070403
  21. Pei-Yi Wong, Hsiao-Yun Lee, Ling-Jyh Chen, Yu-Cheng Chen, Nai-Tzu Chen, Shih-Chun Candice Lung, Huey-Jen Su, Chih-Da Wu, Jose Guillermo Cedeno Laurent, Gary Adamkiewicz, John D. Spengler. An alternative approach for estimating large-area indoor PM2.5 concentration – A case study of schools. Building and Environment 2022, 219 , 109249. https://doi.org/10.1016/j.buildenv.2022.109249
  22. Faraz Enayati Ahangar, Jeanette Cobian-Iñiguez, Ricardo Cisneros. Combining Regulatory Instruments and Low-Cost Sensors to Quantify the Effects of 2020 California Wildfires on PM2.5 in San Joaquin Valley. Fire 2022, 5 (3) , 64. https://doi.org/10.3390/fire5030064
  23. Ziyang Jiang, Tongshu Zheng, Mike Bergin, David Carlson. Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery. Science of Remote Sensing 2022, 5 , 100052. https://doi.org/10.1016/j.srs.2022.100052
  24. Adeel Khan, Sumit Sharma, Kaushik Roy Chowdhury, Prateek Sharma. A novel seasonal index–based machine learning approach for air pollution forecasting. Environmental Monitoring and Assessment 2022, 194 (6) https://doi.org/10.1007/s10661-022-10092-x
  25. Richard Bluhm, Pascal Polonik, Kyle S. Hemes, Luke C. Sanford, Susanne A. Benz, Morgan C. Levy, Katharine L. Ricke, Jennifer A. Burney. Disparate air pollution reductions during California’s COVID-19 economic shutdown. Nature Sustainability 2022, 5 (6) , 509-517. https://doi.org/10.1038/s41893-022-00856-1
  26. Shahir Masri, Jose Rea, Jun Wu. Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study. Atmosphere 2022, 13 (5) , 722. https://doi.org/10.3390/atmos13050722
  27. Madeleine I. G. Daepp, Alex Cabral, Vaishnavi Ranganathan, Vikram Iyer, Scott Counts, Paul Johns, Asta Roseway, Charlie Catlett, Gavin Jancke, Darren Gehring, Chuck Needham, Curtis von Veh, Tracy Tran, Lex Story, Gabriele D'Amone, Bichlien H Nguyen. Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. 2022,,, 28-40. https://doi.org/10.1109/IPSN54338.2022.00010
  28. Tianjun Lu, Matthew J. Bechle, Yanyu Wan, Albert A. Presto, Steve Hankey. Using crowd-sourced low-cost sensors in a land use regression of PM2.5 in 6 US cities. Air Quality, Atmosphere & Health 2022, 15 (4) , 667-678. https://doi.org/10.1007/s11869-022-01162-7
  29. Lance Wallace. 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 2022, 22 (7) , 2755. https://doi.org/10.3390/s22072755
  30. Yi Sun, Amirhosein Mousavi, Shahir Masri, Jun Wu. Socioeconomic Disparities of Low-Cost Air Quality Sensors in California, 2017–2020. American Journal of Public Health 2022, 112 (3) , 434-442. https://doi.org/10.2105/AJPH.2021.306603
  31. Bryan N. Vu, Jianzhao Bi, Wenhao Wang, Amy Huff, Shobha Kondragunta, Yang Liu. 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 2022, 271 , 112890. https://doi.org/10.1016/j.rse.2022.112890
  32. Rachel E. Connolly, Qiao Yu, Zemin Wang, Yu-Han Chen, Jonathan Z. Liu, Ashley Collier-Oxandale, Vasileios Papapostolou, Andrea Polidori, Yifang Zhu. 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 2022, 807 , 150797. https://doi.org/10.1016/j.scitotenv.2021.150797
  33. Laura Gladson, Nicolas Garcia, Jianzhao Bi, Yang Liu, Hyung Joo Lee, Kevin Cromar. Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018. Atmosphere 2022, 13 (1) , 85. https://doi.org/10.3390/atmos13010085
  34. Aziemah Zulkifli<sup>+</sup>, Nurul Latiffah Abd Rani<sup>+</sup>, Raisya Nur Syazmeen Abdul Mutalib, Ruaraidh Dobson, Tengku Azmina Engku Ibrahim, Norul Hernani Abd Latif, Rachel O’Donnell, Isabelle Uny, Emilia Zainal Abidin, Sean Semple. Measuring secondhand smoke in homes in Malaysia: A feasibility study comparing indoor fine particulate (PM2.5) concentrations following an educational feedback intervention to create smoke-free homes during the COVID-19 pandemic. Tobacco Induced Diseases 2022, 20 (July) , 1-13. https://doi.org/10.18332/tid/150338
  35. Jianzhao Bi, Nancy Carmona, Magali N. Blanco, Amanda J. Gassett, Edmund Seto, Adam A. Szpiro, Timothy V. Larson, Paul D. Sampson, Joel D. Kaufman, Lianne Sheppard. Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection. Environment International 2022, 158 , 106897. https://doi.org/10.1016/j.envint.2021.106897
  36. Priyanka deSouza, Ralph Kahn, Tehya Stockman, William Obermann, Ben Crawford, An Wang, James Crooks, Jing Li, Patrick Kinney. Calibrating networks of low-cost air quality sensors. Atmospheric Measurement Techniques 2022, 15 (21) , 6309-6328. https://doi.org/10.5194/amt-15-6309-2022
  37. Lance Wallace, Philip K. Hopke. Measuring Particle Concentrations and Composition in Indoor Air. 2022,,, 517-567. https://doi.org/10.1007/978-981-16-7680-2_19
  38. Dimitrios M. Vlachogiannis, Yanyan Xu, Ling Jin, Marta C. González. Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study. Applied Network Science 2021, 6 (1) https://doi.org/10.1007/s41109-021-00373-8
  39. , Satchit Balsari, Mathew V. Kiang, Caroline O. Buckee. Data in Crisis — Rethinking Disaster Preparedness in the United States. New England Journal of Medicine 2021, 385 (16) , 1526-1530. https://doi.org/10.1056/NEJMms2104654
  40. Yougeng Lu. Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data. Environmental Research 2021, 201 , 111549. https://doi.org/10.1016/j.envres.2021.111549
  41. Alexandra A. Chudnovsky. Monitoring Air Pollution in the Urban Environment by Remote Sensing. 2021,,, 391-422. https://doi.org/10.1002/9781119625865.ch18
  42. Yutong Liang, Deep Sengupta, Mark J. Campmier, David M. Lunderberg, Joshua S. Apte, Allen H. Goldstein. Wildfire smoke impacts on indoor air quality assessed using crowdsourced data in California. Proceedings of the National Academy of Sciences 2021, 118 (36) https://doi.org/10.1073/pnas.2106478118
  43. Gung-Hwa Hong, Thi-Cuc Le, Jing-Wei Tu, Chieh Wang, Shuenn-Chin Chang, Jhih-Yuan Yu, Guan-Yu Lin, Shankar G. Aggarwal, Chuen-Jinn Tsai. Long-term evaluation and calibration of three types of low-cost PM2.5 sensors at different air quality monitoring stations. Journal of Aerosol Science 2021, 157 , 105829. https://doi.org/10.1016/j.jaerosci.2021.105829
  44. Susan M. O’Neill, Minghui Diao, Sean Raffuse, Mohammad Al-Hamdan, Muhammad Barik, Yiqin Jia, Steve Reid, Yufei Zou, Daniel Tong, J. Jason West, Joseph Wilkins, Amy Marsha, Frank Freedman, Jason Vargo, Narasimhan K. Larkin, Ernesto Alvarado, Patti Loesche. A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires. Journal of the Air & Waste Management Association 2021, 71 (7) , 791-814. https://doi.org/10.1080/10962247.2021.1891994
  45. Lance Wallace, Jianzhao Bi, Wayne R. Ott, Jeremy Sarnat, Yang Liu. Calibration of low-cost PurpleAir outdoor monitors using an improved method of calculating PM. Atmospheric Environment 2021, 256 , 118432. https://doi.org/10.1016/j.atmosenv.2021.118432
  46. Temuulen Enebish, Khang Chau, Batbayar Jadamba, Meredith Franklin. Predicting ambient PM2.5 concentrations in Ulaanbaatar, Mongolia with machine learning approaches. Journal of Exposure Science & Environmental Epidemiology 2021, 31 (4) , 699-708. https://doi.org/10.1038/s41370-020-0257-8
  47. Kipruto Kirwa, Adam A. Szpiro, Lianne Sheppard, Paul D. Sampson, Meng Wang, Joshua P. Keller, Michael T. Young, Sun-Young Kim, Timothy V. Larson, Joel D. Kaufman. Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Current Environmental Health Reports 2021, 121 https://doi.org/10.1007/s40572-021-00310-y
  48. Amirhosein Mousavi, Yiting Yuan, Shahir Masri, Greg Barta, Jun Wu. 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 2021, 18 (11) , 5735. https://doi.org/10.3390/ijerph18115735
  49. Hugo Wai Leung Mak, Yun Fat Lam. Comparative assessments and insights of data openness of 50 smart cities in air quality aspects. Sustainable Cities and Society 2021, 69 , 102868. https://doi.org/10.1016/j.scs.2021.102868
  50. Priyanka deSouza, Patrick L. Kinney. On the distribution of low-cost PM2.5 sensors in the US: demographic and air quality associations. Journal of Exposure Science & Environmental Epidemiology 2021, 31 (3) , 514-524. https://doi.org/10.1038/s41370-021-00328-2
  51. Jianzhao Bi, Lance A. Wallace, Jeremy A. Sarnat, Yang Liu. Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data. Environmental Pollution 2021, 276 , 116763. https://doi.org/10.1016/j.envpol.2021.116763
  52. Tongshu Zheng, Michael Bergin, Guoyin Wang, David Carlson. Local PM2.5 Hotspot Detector at 300 m Resolution: A Random Forest–Convolutional Neural Network Joint Model Jointly Trained on Satellite Images and Meteorology. Remote Sensing 2021, 13 (7) , 1356. https://doi.org/10.3390/rs13071356
  53. Yougeng Lu, Genevieve Giuliano, Rima Habre. Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. Environmental Research 2021, 195 , 110653. https://doi.org/10.1016/j.envres.2020.110653
  54. Pengfei Han, Han Mei, Di Liu, Ning Zeng, Xiao Tang, Yinghong Wang, Yuepeng Pan. Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2. Sensors 2021, 21 (1) , 256. https://doi.org/10.3390/s21010256
  55. Karoline K. Barkjohn, Brett Gantt, Andrea L. Clements. Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor. Atmospheric Measurement Techniques 2021, 14 (6) , 4617-4637. https://doi.org/10.5194/amt-14-4617-2021
  56. Lance Wallace, Philip Hopke. Measuring Particle Concentration and Compositions in Indoor Air. 2021,,, 1-55. https://doi.org/10.1007/978-981-10-5155-5_19-1
  57. Lance Wallace, Philip K. Hopke. Measuring Particle Concentrations and Composition in Indoor Air. 2021,,, 1-51. https://doi.org/10.1007/978-981-10-5155-5_19-2
  58. Hyung Joo Lee. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review. Asian Journal of Atmospheric Environment 2020, 14 (4) , 319-334. https://doi.org/10.5572/ajae.2020.14.4.319
  59. Hone-Jay Chu, Muhammad Zeeshan Ali, Yu-Chen He. Spatial calibration and PM2.5 mapping of low-cost air quality sensors. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-79064-w
  60. Iasonas Stavroulas, Georgios Grivas, Panagiotis Michalopoulos, Eleni Liakakou, Aikaterini Bougiatioti, Panayiotis Kalkavouras, Kyriaki Fameli, Nikolaos Hatzianastassiou, Nikolaos Mihalopoulos, Evangelos Gerasopoulos. Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece. Atmosphere 2020, 11 (9) , 926. https://doi.org/10.3390/atmos11090926
  61. Karin Ardon-Dryer, Yuval Dryer, Jake N. Williams, Nastaran Moghimi. Measurements of PM2.5 with PurpleAir under atmospheric conditions. Atmospheric Measurement Techniques 2020, 13 (10) , 5441-5458. https://doi.org/10.5194/amt-13-5441-2020

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