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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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.
Codes for the thermodynamic modelling conducting the analysis presented here can be obtained upon request from the corresponding author.
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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.
The authors declare no competing interests.
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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).
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