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Rapid night-time nanoparticle growth in Delhi driven by biomass-burning emissions

Nature Geoscience volume 16, pages 224–230 (2023)Cite this article

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Abstract

Natural and anthropogenic biomass burning are among the major sources of particulate pollution worldwide that affects air quality, climate and human health. Delhi, one of the world’s most populated cities, experiences severe haze events caused by particulate pollution during winter, but the underlying pathways remain poorly understood. Here we observe intense and frequent nocturnal particle growth events during haze development in Delhi from measurements of aerosols and gases during January–February at the Indian Institute of Technology in Delhi. The particle growth events occur systematically despite the unfavourable condition for new-particle formation, including the lack of photochemical production of low-volatility vapours and considerable loss of vapours under extremely polluted conditions. We estimate that this process is responsible for 70% of the total particle-number concentration during haze. We identify that the condensation of primary organic vapours from biomass burning is the leading cause of the observed growth. The sharp decrease in night-time temperatures and rapid increase in biomass-burning emissions drive these primary organic vapours out of equilibrium, resulting in their condensation and the growth of nanoparticles into sizes relevant for haze formation. This high impact of primary biomass-burning emissions on night-time nanoparticle growth is unique compared with most urban locations globally, where low-volatility vapours formed through oxidation during the day drive particle growth and haze formation. As uncontrolled biomass burning for residential heating and cooking is rife in the Indo–Gangetic plain, we expect this growth mechanism to be a source of ultrafine particles, affecting the health of 5% of the world’s population and impacting the regional climate. Our work implies that regulating uncontrolled biomass-combustion emissions may help inhibit nocturnal haze formation and improve human health in India.

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Fig. 1: The phenomenology of non-NPG and NPG events.
Fig. 2: Growth-rate observations and their comparison with other reported values across the world.
Fig. 3: Size-resolved chemical speciation during nocturnal growth days.
Fig. 4: Identification of the potential pathway for nocturnal particle growth from the gas-phase composition.

Data availability

All the data displayed in figures, including concentrations of particle-number concentration and NR-PM1 species measured by the HR-ToF-AMS in Delhi in this study, are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.21932136. Source data are provided with this paper.

Code availability

Codes for the thermodynamic modelling conducting the analysis presented here can be obtained upon request from the corresponding author.

References

  1. IQAir. World Air Quality Report. 2019 World Air Qual. Rep. 1–35 (2019).

  2. Beig, G. et al. Anatomy of the winter 2017 air quality emergency in Delhi. Sci. Total Environ. 681, 305–311 (2019).

    Article  Google Scholar 

  3. Kanawade, V. P. et al. What caused severe air pollution episode of November 2016 in New Delhi? Atmos. Environ. 222, 117125 (2020).

    Article  Google Scholar 

  4. Ravindra, K. et al. Real-time monitoring of air pollutants in seven cities of North India during crop residue burning and their relationship with meteorology and transboundary movement of air. Sci. Total Environ. 690, 717–729 (2019).

    Article  Google Scholar 

  5. Zhang, R., Khalizov, A., Wang, L., Hu, M. & Xu, W. Nucleation and growth of nanoparticles in the atmosphere. Chem. Rev. 112, 1957–2011 (2012).

    Article  Google Scholar 

  6. Zhang, R. et al. Formation of urban fine particulate matter. Chem. Rev. 115, 3803–3855 (2015).

    Article  Google Scholar 

  7. Kulmala, M., Kerminen, V. M., Petäjä, T., Ding, A. J. & Wang, L. Atmospheric gas-to-particle conversion: why NPF events are observed in megacities? Faraday Discuss. 200, 271–288 (2017).

    Article  Google Scholar 

  8. Yao, L. et al. Atmospheric new particle formation from sulfuric acid and amines in a Chinese megacity. Science 361, 278–281 (2018).

    Article  Google Scholar 

  9. Salimi, F., Rahman, M. M., Clifford, S., Ristovski, Z. & Morawska, L. Nocturnal new particle formation events in urban environments. Atmos. Chem. Phys. 17, 521–530 (2017).

    Article  Google Scholar 

  10. Kanawade, V. P. et al. Observations of new particle formation at two distinct Indian subcontinental urban locations. Atmos. Environ. 96, 370–379 (2014).

    Article  Google Scholar 

  11. Sebastian, M., Kanawade, V. P. & Pierce, J. R. Observation of sub-3 nm particles and new particle formation at an urban location in India. Atmos. Environ. 256, 118460 (2021).

    Article  Google Scholar 

  12. Gani, S. et al. Submicron aerosol composition in the world’s most polluted megacity: the Delhi Aerosol Supersite study. Atmos. Chem. Phys. 19, 6843–6859 (2019).

    Article  Google Scholar 

  13. Tobler, A. et al. Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi, India. Sci. Total Environ. 745, 140924 (2020).

    Article  Google Scholar 

  14. Gunthe, S. S. et al. Enhanced aerosol particle growth sustained by high continental chlorine emission in India. Nat. Geosci. https://doi.org/10.1038/s41561-020-00677-x (2021).

    Article  Google Scholar 

  15. Ojha, N. et al. On the widespread enhancement in fine particulate matter across the Indo–Gangetic Plain towards winter. Sci. Rep. 10, 5862 (2020).

    Article  Google Scholar 

  16. Sembhi, H. et al. Post-monsoon air quality degradation across northern India: assessing the impact of policy-related shifts in timing and amount of crop residue burnt. Environ. Res. Lett. 15, 104067 (2020).

    Article  Google Scholar 

  17. Andreae, M. O. & Merlet, P. Emission of trace gases and aerosols from biomass burning. Glob. Biogeochem. Cycles 15, 955–966 (2001).

  18. Bruns, E. A. et al. Identification of significant precursor gases of secondary organic aerosols from residential wood combustion. Sci. Rep. 6, 27881 (2016).

    Article  Google Scholar 

  19. Lim, C. Y. et al. Secondary organic aerosol formation from the laboratory oxidation of biomass burning emissions. Atmos. Chem. Phys. 19, 12797–12809 (2019).

    Article  Google Scholar 

  20. Kodros, J. K. et al. Rapid dark aging of biomass burning as an overlooked source of oxidized organic aerosol. Proc. Natl Acad. Sci. USA 117, 33028–33033 (2020).

    Article  Google Scholar 

  21. Decker, Z. C. J. et al. Nighttime chemical transformation in biomass burning plumes: a box model analysis initialized with aircraft observations. Environ. Sci. Technol. 53, 2529–2538 (2019).

    Article  Google Scholar 

  22. Wei, Y. et al. Nocturnal PM2.5 explosive growth dominates severe haze in the rural North China Plain. Atmos. Res. 242, 105020 (2020).

    Article  Google Scholar 

  23. Zhu, Y. et al. Comparative analysis of new particle formation events in less and severely polluted urban atmosphere. Atmos. Environ. 98, 655–664 (2014).

    Article  Google Scholar 

  24. An, J. et al. Characteristics of new particle formation events in Nanjing, China: effect of water-soluble ions. Atmos. Environ. 108, 32–40 (2015).

    Article  Google Scholar 

  25. Canagaratna, M. R. et al. Chemical and microphysical characterization of ambient aerosols with the aerodyne mass spectrometer. Mass Spectrom. Rev. 26, 185–222 (2007).

    Article  Google Scholar 

  26. Kulmala, M. et al. Is reducing new particle formation a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities? Faraday Discuss. https://doi.org/10.1039/d0fd00078g (2021).

  27. Chen, Y. et al. Ammonium chloride associated aerosol liquid water enhances haze in Delhi, India. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.2c00650 (2022).

  28. Xie, M., Hannigan, M. P. & Barsanti, K. C. Gas/particle partitioning of 2-methyltetrols and levoglucosan at an urban site in Denver. Environ. Sci. Technol. 48, 2835–2842 (2014).

    Article  Google Scholar 

  29. Wang, M. et al. Photo-oxidation of aromatic hydrocarbons produces low-volatility organic compounds. Environ. Sci. Technol. 54, 7911–7921 (2020).

    Article  Google Scholar 

  30. Nieminen, T., Lehtinen, K. E. J. & Kulmala, M. Sub-10 nm particle growth by vapor condensation—effects of vapor molecule size and particle thermal speed. Atmos. Chem. Phys. 10, 9773–9779 (2010).

    Article  Google Scholar 

  31. Rai, P. et al. Real-time measurement and source apportionment of elements in Delhi’s atmosphere. Sci. Total Environ. 742, 140332 (2020).

    Article  Google Scholar 

  32. Kuttippurath, J. et al. Record high levels of atmospheric ammonia over India: spatial and temporal analyses. Sci. Total Environ. 740, 139986 (2020).

    Article  Google Scholar 

  33. Aiken, A. C. et al. Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0)—part 1: fine particle composition and organic source apportionment. Atmos. Chem. Phys. 9, 6633–6653 (2009).

    Article  Google Scholar 

  34. Allan, J. D. et al. A generalised method for the extraction of chemically resolved mass spectra from Aerodyne aerosol mass spectrometer data. J. Aerosol Sci. 35, 909–922 (2004).

    Article  Google Scholar 

  35. Canagaratna, M. R. et al. Elemental ratio measurements of organic compounds using aerosol mass spectrometry: characterization, improved calibration, and implications. Atmos. Chem. Phys. 15, 253–272 (2015).

    Article  Google Scholar 

  36. Jayne, J. T. et al. Development of an aerosol mass spectrometer for size and composition analysis of submicron particles. Aerosol Sci. Technol. 33, 49–70 (2000).

    Article  Google Scholar 

  37. Kumar, V. et al. Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry. Atmos. Chem. Phys. 22, 7739–7761 (2022).

    Article  Google Scholar 

  38. Singh, A. et al. Sources and characteristics of light-absorbing fine particulates over Delhi through the synergy of real-time optical and chemical measurements. Atmos. Environ. 252, 118338 (2021).

    Article  Google Scholar 

  39. Lalchandani, V. et al. Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter. Sci. Total Environ. 770, 145324 (2021).

    Article  Google Scholar 

  40. Middlebrook, A. M., Bahreini, R., Jimenez, J. L. & Canagaratna, M. R. Evaluation of composition-dependent collection efficiencies for the Aerodyne aerosol mass spectrometer using field data. Aerosol Sci. Technol. 46, 258–271 (2012).

    Article  Google Scholar 

  41. Drinovec, L. et al. The ‘dual-spot’ Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation. Atmos. Meas. Tech. 8, 1965–1979 (2015).

    Article  Google Scholar 

  42. Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev. 8, 1339–1356 (2015).

    Article  Google Scholar 

  43. Lopez-Hilfiker, F. D. et al. A novel method for online analysis of gas and particle composition: description and evaluation of a filter inlet for gases and AEROsols (FIGAERO). Atmos. Meas. Tech. 7, 983–1001 (2014).

    Article  Google Scholar 

  44. Haslett, S. L. et al. The radiative impact of out-of-cloud aerosol hygroscopic growth during the summer monsoon in southern West Africa. Atmos. Chem. Phys. 19, 1505–1520 (2019).

    Article  Google Scholar 

  45. Shen, X. J. et al. Variations in submicron aerosol liquid water content and the contribution of chemical components during heavy aerosol pollution episodes in winter in Beijing. Sci. Total Environ. 693, 133521 (2019).

    Article  Google Scholar 

  46. Fuchs, N. A. & Sutugin, A. G. in Topics in Current Aerosol Research (eds Hidy, G. M. & Brock, J. R.) 1–60 (Pergamon, 1971).

  47. Dal Maso, M. et al. Formation and growth of fresh atmospheric aerosols: eight years of aerosol size distribution data from SMEAR II, Hyytiälä, Finland. Boreal Environ. Res. 10, 323–336 (2005).

    Google Scholar 

  48. Hussein, T. et al. Observation of regional new particle formation in the urban atmosphere. Tellus B 60, 509–521 (2008).

    Article  Google Scholar 

  49. Zhou, Y. et al. Variation of size-segregated particle number concentrations in wintertime Beijing. Atmos. Chem. Phys. 20, 1201–1216 (2020).

    Article  Google Scholar 

  50. DeCarlo, P. F., Slowik, J. G., Worsnop, D. R., Davidovits, P. & Jimenez, J. L. Particle morphology and density characterization by combined mobility and aerodynamic diameter measurements. Part 1: theory. Aerosol Sci. Technol. 38, 1185–1205 (2004).

    Article  Google Scholar 

  51. Paatero, P. The Multilinear Engine—a table-driven, least squares program for solving multilinear problems, including the n-Way Parallel Factor Analysis Model. J. Comput. Graph. Stat. 8, 854–888 (1999).

    Google Scholar 

  52. Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U. & Prévôt, A. S. H. SoFi, an IGOR-based interface for the efficient use of the generalized Multilinear Engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data. Atmos. Meas. Tech. 6, 3649–3661 (2013).

    Article  Google Scholar 

  53. Koo, B., Knipping, E. & Yarwood, G. 1.5-Dimensional volatility basis set approach for modeling organic aerosol in CAMx and CMAQ. Atmos. Environ. 95, 158–164 (2014).

    Article  Google Scholar 

  54. Donahue, N. M., Epstein, S. A., Pandis, S. N. & Robinson, A. L. A two-dimensional volatility basis set: 1. Organic-aerosol mixing thermodynamics. Atmos. Chem. Phys. 11, 3303–3318 (2011).

    Article  Google Scholar 

  55. Donahue, N. M., Kroll, J. H., Pandis, S. N. & Robinson, A. L. A two-dimensional volatility basis set—Part 2: diagnostics of organic-aerosol evolution. Atmos. Chem. Phys. 12, 615–634 (2012).

    Article  Google Scholar 

  56. Donahue, N. M. et al. Aging of biogenic secondary organic aerosol via gas-phase OH radical reactions. Proc. Natl Acad. Sci. USA 109, 13503–13508 (2012).

    Article  Google Scholar 

  57. Sheehan, P. E. & Bowman, F. M. Estimated effects of composition on secondary organic aerosol mass concentrations. Environ. Sci. Technol. 35, 2129–2135 (2001).

    Article  Google Scholar 

  58. May, A. A. et al. Gas-particle partitioning of primary organic aerosol emissions: 3. Biomass burning. J. Geophys. Res. Atmos. 118, 11327–11338 (2013).

    Article  Google Scholar 

  59. Kulmala, M. et al. On the mode-segregated aerosol particle number concentration load: contributions of primary and secondary particles in Hyytiälä and Nanjing. Boreal Environ. Res. 21, 319–331 (2016).

    Google Scholar 

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Acknowledgements

We thank the Central Pollution Control Board (CPCB), Government of India, for providing financial support for this experiment. S.N.T. gratefully acknowledges their support in conducting this research under grant no. AQM/Source apportionment_EPC Project/2017. S.M. and S.N.T. also acknowledge financial support received under Centre of Excellence Advanced Technologies for Monitoring Air-quality iNdicators (ATMAN) approved by PSA office, Government of India and supported by a group of philanthropic funders, including the Bloomberg Philanthropies, the Children’s Investment Fund Foundation (CIFF), the Open Philanthropy and the Clean Air Fund. A.S.H.P. acknowledges the following project: the SDC Clean Air Project in India (grant no. 7F-10093.01.04). M.K. acknowledges ACCC Flagship funded by the Academy of Finland grant number 337549, Academy professorship funded by the Academy of Finland (grant no. 302958), Academy of Finland projects no. 1325656, 316114 and 325647, ‘Quantifying carbon sink, CarbonSink+ and their interaction with air quality’ INAR project funded by Jane and Aatos Erkko Foundation, European Research Council (ERC) project ATM-GTP contract no. 742206. G.C. acknowledges the support of the European Research Council with the project CHAPAs no. 850614. V.P.K. acknowledges support by the Department of Science & Technology (DST)-Science Engineering Research Board (SERB; ECR/2016/001333) and DST-Climate Change Division Program (Aerosol/89/2017). S.N.T. acknowledges the support of India Meteorological department (IMD, New Delhi) for providing visibility data. K.R.D. acknowledges support by the Swiss National Science Foundation Ambizione grant PZPGP2_201992.

Author information

Author notes

  1. Varun Kumar

    Present address: Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France

  2. Atinderpal Singh

    Present address: Department of Environmental Studies, University of Delhi, Delhi, India

  3. Deepika Bhattu

    Present address: Department of Civil and Infrastructure Engineering, Indian Institute of Technology Jodhpur, Jodhpur, India

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Suneeti Mishra & Sachchida Nand Tripathi

  2. Centre for Earth, Ocean, and Atmospheric Sciences, University of Hyderabad, Hyderabad, India

    Vijay P. Kanawade

  3. Department of Environmental Science, Stockholm University, Stockholm, Sweden

    Sophie L. Haslett & Claudia Mohr

  4. Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland

    Lubna Dada, Varun Kumar, Deepika Bhattu, Kaspar R. Daellenbach, Jay G. Slowik, Imad El-Haddad & Andre S. H. Prevot

  5. Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, Helsinki, Finland

    Giancarlo Ciarelli & Markku Kulmala

  6. Geosciences Division, Physical Research Laboratory, Ahmedabad, India

    Atinderpal Singh & Neeraj Rastogi

  7. Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India

    Dilip Ganguly

  8. Atmospheric Sciences, Centre for Pollution Control Board, Delhi, India

    Prashant Gargava

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Contributions

S.N.T. and A.S.H.P conceptualized, designed, and planned the experiments. S.M., V.K., J.G.S., S.L.H. and A.S. prepared the measurement facility and handled instrumentation and data collection. S.M., V.K., S.L.H., V.P.K., L.D., C.M., and I.E.-H. analysed the data. I.E.-H. and G.C. performed modelling. S.N.T., A.S.H.P, M.K., I.E-H., C.M., N.R., D.B., K.R.D., D.G. and P.G. contributed to the scientific discussion. S.M. led the paper writing with specific inputs and edits from I.E.-H., C.M., S.N.T., and V.P.K. All co-authors reviewed and commented on the paper.

Corresponding authors

Correspondence to Sachchida Nand Tripathi, Imad El-Haddad or Andre S. H. Prevot.

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Extended data

Extended Data Fig. 1 A classical event.

A typical particle nocturnal growth event of 16th January 2019 (one of the 27 growth event days).

Extended Data Fig. 2 Characteristics of all growth days.

a) Average number size distribution for all NPG days along with their mode variation, color-coded with their respective condensation-sink values, b) fractional contributions to total PM1 and organic factors alone, c) diel variation of organics (mean ±1σ) (left) and stacked variation of organic factors (right), d) diel variation of inorganics (mean ±1σ) and aerosol water content (AWC) (left) and stacked variation of nitrate, sulphate, ammonium, and chloride (right), e) black carbon ((mean ± 1σ) and carbon monoxide, f) RH, temperature and visibility. The line and the error bars represent the mean and ±1 s.d. (n Number=27).

Extended Data Fig. 3 Particle mass and Number variation.

Diurnal variation of size-segregated particle number and mass concentrations on NPG event days.

Extended Data Fig. 4 Regionality of all growth days.

Time-evolution of particle number-size distributions measured at urban (IITD) and at a sub-urban industrial (MRIU) location during January-February 2019. The urban background site (MRIU) is 20 Km downwind of our site.

Extended Data Fig. 5 OA-PMF factor solutions.

a) PMF factor signals for the 5-factor solution, b) factor signals up to m/z 100.

Extended Data Fig. 6 OA-PMF contribution and variation.

a) Fractional aerosol contribution of total PM1 and organic factors from HR-ToF-AMS PMF during the study period, b) time series of chemical species and organic factors, with elevated nocturnal growth days highlighted.

Extended Data Fig. 7 OA-PMF factors variation.

a) OA-PMF factors along with PM1 variation b) chemical species (organics, nitrate, ammonium, sulphate and chloride) during the NPG events.

Extended Data Fig. 8 GR correlation.

Correlation of a) total growth rates of organics from HR-ToF-AMS to SMPS growth rates, b) scatter plot of growth rate from SMPS data (GRSMPS > 100 nm), and the corresponding growth rate of organics, ammonium and chloride from HR-ToF-AMS data (GRAMS > 100 nm), c) scatter plot of growth rates (GRSMPS > 100 nm) of secondary inorganics from HR-ToF-AMS to SMPS growth rates for all growth events, b, c) The lines indicate the slope of fit of each species.

Extended Data Fig. 9 Gas-phase PMF results.

a,b) Fractional contribution of the gas-phase PMF factors during the entire study period (a) and on the NPG days (b). c) Diel variation of the levoglucosan signal in the gas phase and particle phase.

Extended Data Fig. 10 Relative contribution.

a) Accumulation mode concentration for all the NPG days as a function of the BC concentration, in the constrained fittings, b) The fraction of primary particles in the accumulation mode (>100 nm) estimated using the BC tracer method (Methods).

Supplementary information

Supplementary Information

Supplementary Tables 1–4, Texts 1–4 and references.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Mishra, S., Tripathi, S.N., Kanawade, V.P. et al. Rapid night-time nanoparticle growth in Delhi driven by biomass-burning emissions. Nat. Geosci. 16, 224–230 (2023). https://doi.org/10.1038/s41561-023-01138-x

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