Articles | Volume 11, issue 8
https://doi.org/10.5194/amt-11-4823-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-4823-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments
Tongshu Zheng
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
Michael H. Bergin
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
Karoline K. Johnson
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
Sachchida N. Tripathi
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India
Shilpa Shirodkar
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India
Matthew S. Landis
US Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
Ronak Sutaria
Center for Urban Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
David E. Carlson
Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA
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Shoubhik Chakraborty, Sachchida Nand Tripathi, Davender Sethi, Akanksha Lakra, Ambasht Kumar, Pranjal Kumar Srivastava, Nihal Thukarama Rao, Avnish Tripathi, and Purushottam Kar
EGUsphere, https://doi.org/10.5194/egusphere-2025-5677, https://doi.org/10.5194/egusphere-2025-5677, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This paper proposes a novel source apportionment paradigm that predicts the relative contributions of different air-pollution sources using a machine-learning framework applied to data obtained from low-cost sensor units. A key strength of this approach is its ability to support a dense network of low-cost sensor units spanning wide geographical areas and providing source apportionment results in real time, thus helping policymakers take regulatory action to curb air pollution in real time.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
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Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Zhenyu Zhang, Jing Li, Huizheng Che, Yueming Dong, Oleg Dubovik, Thomas Eck, Pawan Gupta, Brent Holben, Jhoon Kim, Elena Lind, Trailokya Saud, Sachchida Nand Tripathi, and Tong Ying
Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025, https://doi.org/10.5194/acp-25-4617-2025, 2025
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We used ground-based remote sensing data from the Aerosol Robotic Network to examine long-term trends in aerosol characteristics. We found aerosol loadings generally decreased globally, and aerosols became more scattering. These changes are closely related to variations in aerosol compositions, such as decreased anthropogenic emissions over East Asia, Europe, and North America; increased anthropogenic sources over northern India; and increased dust activity over the Arabian Peninsula.
Ashutosh K. Shukla, Sachchida N. Tripathi, Shamitaksha Talukdar, Vishnu Murari, Sreenivas Gaddamidi, Manousos-Ioannis Manousakas, Vipul Lalchandani, Kuldeep Dixit, Vinayak M. Ruge, Peeyush Khare, Mayank Kumar, Vikram Singh, Neeraj Rastogi, Suresh Tiwari, Atul K. Srivastava, Dilip Ganguly, Kaspar Rudolf Daellenbach, and André S. H. Prévôt
Atmos. Chem. Phys., 25, 3765–3784, https://doi.org/10.5194/acp-25-3765-2025, https://doi.org/10.5194/acp-25-3765-2025, 2025
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Our study delves into the elemental composition of aerosols at three sites across the Indo-Gangetic Plain (IGP), revealing distinct patterns during pollution episodes. We found significant increases in chlorine (Cl)-rich and solid fuel combustion (SFC) sources, indicating dynamic emission sources, agricultural burning impacts, and meteorological influences. Surges in Cl-rich particles during cold periods highlight their role in particle growth under high-relative-humidity conditions.
Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 17, 1651–1664, https://doi.org/10.5194/amt-17-1651-2024, https://doi.org/10.5194/amt-17-1651-2024, 2024
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This research focuses on the optimal placement of hybrid instruments (sensors and monitors) to maximize satisfaction function considering population, PM2.5 concentration, budget, and other factors. Two algorithms are developed in this study: a genetic algorithm and a greedy algorithm. We tested these algorithms on various regions. The insights of this work aid in quantitative placement of air quality monitoring instruments in large cities, moving away from ad hoc approaches.
Wei Huang, Cheng Wu, Linyu Gao, Yvette Gramlich, Sophie L. Haslett, Joel Thornton, Felipe D. Lopez-Hilfiker, Ben H. Lee, Junwei Song, Harald Saathoff, Xiaoli Shen, Ramakrishna Ramisetty, Sachchida N. Tripathi, Dilip Ganguly, Feng Jiang, Magdalena Vallon, Siegfried Schobesberger, Taina Yli-Juuti, and Claudia Mohr
Atmos. Chem. Phys., 24, 2607–2624, https://doi.org/10.5194/acp-24-2607-2024, https://doi.org/10.5194/acp-24-2607-2024, 2024
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We present distinct molecular composition and volatility of oxygenated organic aerosol particles in different rural, urban, and mountain environments. We do a comprehensive investigation of the relationship between the chemical composition and volatility of oxygenated organic aerosol particles across different systems and environments. This study provides implications for volatility descriptions of oxygenated organic aerosol particles in different model frameworks.
Matthias Kohl, Jos Lelieveld, Sourangsu Chowdhury, Sebastian Ehrhart, Disha Sharma, Yafang Cheng, Sachchida Nand Tripathi, Mathew Sebastian, Govindan Pandithurai, Hongli Wang, and Andrea Pozzer
Atmos. Chem. Phys., 23, 13191–13215, https://doi.org/10.5194/acp-23-13191-2023, https://doi.org/10.5194/acp-23-13191-2023, 2023
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Knowledge on atmospheric ultrafine particles (UFPs) with a diameter smaller than 100 nm is crucial for public health and the hydrological cycle. We present a new global dataset of UFP concentrations at the Earth's surface derived with a comprehensive chemistry–climate model and evaluated with ground-based observations. The evaluation results are combined with high-resolution primary emissions to downscale UFP concentrations to an unprecedented horizontal resolution of 0.1° × 0.1°.
Sophie L. Haslett, David M. Bell, Varun Kumar, Jay G. Slowik, Dongyu S. Wang, Suneeti Mishra, Neeraj Rastogi, Atinderpal Singh, Dilip Ganguly, Joel Thornton, Feixue Zheng, Yuanyuan Li, Wei Nie, Yongchun Liu, Wei Ma, Chao Yan, Markku Kulmala, Kaspar R. Daellenbach, David Hadden, Urs Baltensperger, Andre S. H. Prevot, Sachchida N. Tripathi, and Claudia Mohr
Atmos. Chem. Phys., 23, 9023–9036, https://doi.org/10.5194/acp-23-9023-2023, https://doi.org/10.5194/acp-23-9023-2023, 2023
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In Delhi, some aspects of daytime and nighttime atmospheric chemistry are inverted, and parodoxically, vehicle emissions may be limiting other forms of particle production. This is because the nighttime emissions of nitrogen oxide (NO) by traffic and biomass burning prevent some chemical processes that would otherwise create even more particles and worsen the urban haze.
Vaishali Jain, Nidhi Tripathi, Sachchida N. Tripathi, Mansi Gupta, Lokesh K. Sahu, Vishnu Murari, Sreenivas Gaddamidi, Ashutosh K. Shukla, and Andre S. H. Prevot
Atmos. Chem. Phys., 23, 3383–3408, https://doi.org/10.5194/acp-23-3383-2023, https://doi.org/10.5194/acp-23-3383-2023, 2023
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This research chemically characterises 173 different NMVOCs (non-methane volatile organic compounds) measured in real time for three seasons in the city of the central Indo-Gangetic basin of India, Lucknow. Receptor modelling is used to analyse probable sources of NMVOCs and their crucial role in forming ozone and secondary organic aerosols. It is observed that vehicular emissions and solid fuel combustion are the highest contributors to the emission of primary and secondary NMVOCs.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
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Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Varun Kumar, Stamatios Giannoukos, Sophie L. Haslett, Yandong Tong, Atinderpal Singh, Amelie Bertrand, Chuan Ping Lee, Dongyu S. Wang, Deepika Bhattu, Giulia Stefenelli, Jay S. Dave, Joseph V. Puthussery, Lu Qi, Pawan Vats, Pragati Rai, Roberto Casotto, Rangu Satish, Suneeti Mishra, Veronika Pospisilova, Claudia Mohr, David M. Bell, Dilip Ganguly, Vishal Verma, Neeraj Rastogi, Urs Baltensperger, Sachchida N. Tripathi, André S. H. Prévôt, and Jay G. Slowik
Atmos. Chem. Phys., 22, 7739–7761, https://doi.org/10.5194/acp-22-7739-2022, https://doi.org/10.5194/acp-22-7739-2022, 2022
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Here we present source apportionment results from the first field deployment in Delhi of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF). The EESI-TOF is a recently developed instrument capable of providing uniquely detailed online chemical characterization of organic aerosol (OA), in particular the secondary OA (SOA) fraction. Here, we are able to apportion not only primary OA but also SOA to specific sources, which is performed for the first time in Delhi.
Himadri Sekhar Bhowmik, Ashutosh Shukla, Vipul Lalchandani, Jay Dave, Neeraj Rastogi, Mayank Kumar, Vikram Singh, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 15, 2667–2684, https://doi.org/10.5194/amt-15-2667-2022, https://doi.org/10.5194/amt-15-2667-2022, 2022
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This study presents comparisons between online and offline measurements of both refractory and non-refractory aerosol. This study shows differences between the measurements, related to either the limitations of the instrument (e.g., aerosol mass spectrometer only observing non-refractory aerosol) or known interferences with the technique (e.g., volatilization or reactions). The findings highlight the measurement methods' accuracy and imply the particular type of measurements needed.
Chandan Sarangi, TC Chakraborty, Sachchidanand Tripathi, Mithun Krishnan, Ross Morrison, Jonathan Evans, and Lina M. Mercado
Atmos. Chem. Phys., 22, 3615–3629, https://doi.org/10.5194/acp-22-3615-2022, https://doi.org/10.5194/acp-22-3615-2022, 2022
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Transpiration fluxes by vegetation are reduced under heat stress to conserve water. However, in situ observations over northern India show that the strength of the inverse association between transpiration and atmospheric vapor pressure deficit is weakening in the presence of heavy aerosol loading. This finding not only implicates the significant role of aerosols in modifying the evaporative fraction (EF) but also warrants an in-depth analysis of the aerosol–plant–temperature–EF continuum.
Karoline K. Barkjohn, Brett Gantt, and Andrea L. Clements
Atmos. Meas. Tech., 14, 4617–4637, https://doi.org/10.5194/amt-14-4617-2021, https://doi.org/10.5194/amt-14-4617-2021, 2021
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Although widely used, air sensor measurements are often biased. In this work we develop a correction with a relative humidity term that reduces the bias and improves consistency between different United States regions. 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 and has the potential to be successfully used in other air quality and public health applications.
Karn Vohra, Eloise A. Marais, Shannen Suckra, Louisa Kramer, William J. Bloss, Ravi Sahu, Abhishek Gaur, Sachchida N. Tripathi, Martin Van Damme, Lieven Clarisse, and Pierre-F. Coheur
Atmos. Chem. Phys., 21, 6275–6296, https://doi.org/10.5194/acp-21-6275-2021, https://doi.org/10.5194/acp-21-6275-2021, 2021
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We find satellite observations of atmospheric composition generally reproduce variability in surface air pollution, so we use their long record to estimate air quality trends in major UK and Indian cities. Our trend analysis shows that pollutants targeted with air quality policies have not declined in Delhi and Kanpur but have in London and Birmingham, with the exception of a recent and dramatic increase in reactive volatile organics in London. Unregulated ammonia has increased only in Delhi.
Russell W. Long, Andrew Whitehill, Andrew Habel, Shawn Urbanski, Hannah Halliday, Maribel Colón, Surender Kaushik, and Matthew S. Landis
Atmos. Meas. Tech., 14, 1783–1800, https://doi.org/10.5194/amt-14-1783-2021, https://doi.org/10.5194/amt-14-1783-2021, 2021
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This manuscript details field and laboratory-based evaluations of ozone monitoring methods in smoke. UV photometry, the most widely used measurement method for ozone in ambient air, was shown to suffer from a severe positive interference when operated in the presence of smoke, while chemiluminescence-based methods were shown to be free of interferences. The results detailed in this paper will provide monitoring agencies with the tools needed to address smoke-related ozone measurement challenges.
Pragati Rai, Jay G. Slowik, Markus Furger, Imad El Haddad, Suzanne Visser, Yandong Tong, Atinderpal Singh, Günther Wehrle, Varun Kumar, Anna K. Tobler, Deepika Bhattu, Liwei Wang, Dilip Ganguly, Neeraj Rastogi, Ru-Jin Huang, Jaroslaw Necki, Junji Cao, Sachchida N. Tripathi, Urs Baltensperger, and André S. H. Prévôt
Atmos. Chem. Phys., 21, 717–730, https://doi.org/10.5194/acp-21-717-2021, https://doi.org/10.5194/acp-21-717-2021, 2021
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We present a simple conceptual framework based on elemental size distributions and enrichment factors that allows for a characterization of major sources, site-to-site similarities, and local differences and the identification of key information required for efficient policy development. Absolute concentrations are by far the highest in Delhi, followed by Beijing, and then the European cities.
Ravi Sahu, Ayush Nagal, Kuldeep Kumar Dixit, Harshavardhan Unnibhavi, Srikanth Mantravadi, Srijith Nair, Yogesh Simmhan, Brijesh Mishra, Rajesh Zele, Ronak Sutaria, Vidyanand Motiram Motghare, Purushottam Kar, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 14, 37–52, https://doi.org/10.5194/amt-14-37-2021, https://doi.org/10.5194/amt-14-37-2021, 2021
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A unique feature of our low-cost sensor deployment is a swap-out experiment wherein four of the six sensors were relocated to different sites in the two phases. The swap-out experiment is crucial in investigating the efficacy of calibration models when applied to weather and air quality conditions vastly different from those present during calibration. We developed a novel local calibration algorithm based on metric learning that offers stable and accurate calibration performance.
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Short summary
Low-cost particulate matter sensors are promising tools for supplementing existing air quality monitoring networks but their performance under field conditions is not well understood. We characterized how well Plantower PMS3003 sensors measure PM2.5 in a wide range of ambient conditions against different reference sensors. When a more precise reference method is used for calibration and proper RH corrections are made, our work suggests PMS3003's can measure PM2.5 within ~ 10 % of ambient values.
Low-cost particulate matter sensors are promising tools for supplementing existing air quality...