Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6051-2022
https://doi.org/10.5194/amt-15-6051-2022
Research article
 | 
21 Oct 2022
Research article |  | 21 Oct 2022

Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution

Sahil Bhandari, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz

Related authors

Contributions of primary sources to submicron organic aerosols in Delhi, India
Sahil Bhandari, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz
Atmos. Chem. Phys., 22, 13631–13657, https://doi.org/10.5194/acp-22-13631-2022,https://doi.org/10.5194/acp-22-13631-2022, 2022
Short summary
Particle number concentrations and size distribution in a polluted megacity: the Delhi Aerosol Supersite study
Shahzad Gani, Sahil Bhandari, Kanan Patel, Sarah Seraj, Prashant Soni, Zainab Arub, Gazala Habib, Lea Hildebrandt Ruiz, and Joshua S. Apte
Atmos. Chem. Phys., 20, 8533–8549, https://doi.org/10.5194/acp-20-8533-2020,https://doi.org/10.5194/acp-20-8533-2020, 2020
Short summary
Air mass physiochemical characteristics over New Delhi: impacts on aerosol hygroscopicity and cloud condensation nuclei (CCN) formation
Zainab Arub, Sahil Bhandari, Shahzad Gani, Joshua S. Apte, Lea Hildebrandt Ruiz, and Gazala Habib
Atmos. Chem. Phys., 20, 6953–6971, https://doi.org/10.5194/acp-20-6953-2020,https://doi.org/10.5194/acp-20-6953-2020, 2020
Short summary
Sources and atmospheric dynamics of organic aerosol in New Delhi, India: insights from receptor modeling
Sahil Bhandari, Shahzad Gani, Kanan Patel, Dongyu S. Wang, Prashant Soni, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz
Atmos. Chem. Phys., 20, 735–752, https://doi.org/10.5194/acp-20-735-2020,https://doi.org/10.5194/acp-20-735-2020, 2020
Short summary
Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study
Shahzad Gani, Sahil Bhandari, Sarah Seraj, Dongyu S. Wang, Kanan Patel, Prashant Soni, Zainab Arub, Gazala Habib, Lea Hildebrandt Ruiz, and Joshua S. Apte
Atmos. Chem. Phys., 19, 6843–6859, https://doi.org/10.5194/acp-19-6843-2019,https://doi.org/10.5194/acp-19-6843-2019, 2019
Short summary

Related subject area

Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Inversion algorithm of black carbon mixing state based on machine learning
Zeyuan Tian, Jiandong Wang, Jiaping Wang, Chao Liu, Jia Xing, Jinbo Wang, Zhouyang Zhang, Yuzhi Jin, Sunan Shen, Bin Wang, Wei Nie, Xin Huang, and Aijun Ding
Atmos. Meas. Tech., 18, 1149–1162, https://doi.org/10.5194/amt-18-1149-2025,https://doi.org/10.5194/amt-18-1149-2025, 2025
Short summary
Implementation of Real-Time Source Apportionment Approaches Using the ACSM-Xact-Aethalometer (AXA) Set-Up with SoFi RT: The Athens Case Study
Manousos Ioannis Manousakas, Olga Zografou, Francesco Canonaco, Evangelia Diapouli, Stefanos Papagiannis, Maria Gini, Vasiliki Vasilatou, Anna Tobler, Stergios Vratolis, Jay G. Slowik, Kaspar R. Daellenbach, André S. H. Prevot, and Konstantinos Eleftheriadis
EGUsphere, https://doi.org/10.5194/egusphere-2025-542,https://doi.org/10.5194/egusphere-2025-542, 2025
Short summary
Performance evaluation of Atmotube PRO sensors for air quality measurements in an urban location
Aishah I. Shittu, Kirsty J. Pringle, Stephen R. Arnold, Richard J. Pope, Ailish M. Graham, Carly Reddington, Richard Rigby, and James B. McQuaid
Atmos. Meas. Tech., 18, 817–828, https://doi.org/10.5194/amt-18-817-2025,https://doi.org/10.5194/amt-18-817-2025, 2025
Short summary
Development and validation of a NOx+ ratio method for the quantitative separation of inorganic and organic nitrate aerosol using CV-UMR-ToF-ACSM
Farhan R. Nursanto, Douglas A. Day, Roy Meinen, Rupert Holzinger, Harald Saathoff, Jinglan Fu, Jan Mulder, Ulrike Dusek, and Juliane L. Fry
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-191,https://doi.org/10.5194/amt-2024-191, 2025
Revised manuscript accepted for AMT
Short summary
Retrieval of Bulk Hygroscopicity From PurpleAir PM2.5 Sensor Measurements
Jillian Psotka, Emily Tracey, and Robert Sica
EGUsphere, https://doi.org/10.5194/egusphere-2024-3618,https://doi.org/10.5194/egusphere-2024-3618, 2024
Short summary

Cited articles

Abdullahi, K. L., Delgado-Saborit, J. M., and Harrison, R. M.: Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: a review, Atmos. Environ., 71, 260–294, https://doi.org/10.1016/j.atmosenv.2013.01.061, 2013. 
Allan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, https://doi.org/10.5194/acp-10-647-2010, 2010. 
Amato, F. and Hopke, P. K.: Source apportionment of the ambient PM2.5 across St. Louis using constrained positive matrix factorization, Atmos. Environ., 46, 329–337, https://doi.org/10.1016/j.atmosenv.2011.09.062, 2012. 
Amato, F., Pandolfi, M., Escrig, A., Querol, X., Alastuey, A., Pey, J., Perez, N., and Hopke, P. K.: Quantifying road dust resuspension in urban environment by Multilinear Engine: a comparison with PMF2, Atmos. Environ., 43, 2770–2780, https://doi.org/10.1016/j.atmosenv.2009.02.039, 2009. 
Amil, N., Latif, M. T., Khan, M. F., and Mohamad, M.: Seasonal variability of PM2.5 composition and sources in the Klang Valley urban-industrial environment, Atmos. Chem. Phys., 16, 5357–5381, https://doi.org/10.5194/acp-16-5357-2016, 2016. 
Short summary
We present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization. We report results for four example time periods in two seasons (winter and monsoon 2017) in Delhi, India. Compared to the traditional approach, we extract a larger number of factors that represent the expected sources of primary organic aerosol. This method can capture diurnal time series patterns of sources at low computational cost.
Share