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Enhanced aerosol particle growth sustained by high continental chlorine emission in India

Abstract

Many cities in India experience severe deterioration of air quality in winter. Particulate matter is a key atmospheric pollutant that impacts millions of people. In particular, the high mass concentration of particulate matter reduces visibility, which has severely damaged the economy and endangered human lives. But the underlying chemical mechanisms and physical processes responsible for initiating haze and fog formation remain poorly understood. Here we present the measurement results of chemical composition of particulate matter in Delhi and Chennai. We find persistently high chloride in Delhi and episodically high chloride in Chennai. These measurements, combined with thermodynamic modelling, suggest that in the presence of excess ammonia in Delhi, high local emission of hydrochloric acid partitions into aerosol water. The highly water-absorbing and soluble chloride in the aqueous phase substantially enhances aerosol water uptake through co-condensation, which sustains particle growth, leading to haze and fog formation. We therefore suggest that the high local concentration of gas-phase hydrochloric acid, possibly emitted from plastic-contained waste burning and industry, causes some 50% of the reduced visibility. Our work implies that identifying and regulating gaseous hydrochloric acid emissions could be critical to improve visibility and human health in India.

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Fig. 1: NR-PM1 chemical components measured by aerosol mass spectrometer and ACSM over India.
Fig. 2: Time series of NR-PM1, diel variations of chloride and scatter plots of Cl-to-OA versus BBOA-to-OA ratios over Delhi and Chennai.
Fig. 3: Thermodynamic modelling of the gas–particle partitioning of chloride in Delhi.
Fig. 4: The impact of chloride co-condensation on PM hygroscopic growth and cloud/fog formation.

Data availability

The NR-PM1 species from literature shown in Fig. 1 are available in Extended Data Fig. 1. All other data displayed in figures, including concentrations of NR-PM1 species measured by the ACSM in Delhi and Chennai in this study, and aerosol liquid water modelled by ISORROPIA II, are available in the figshare repository: https://doi.org/10.6084/m9.figshare.13277486. Source data are provided with this paper.

Code availability

The aerosol thermodynamic model ISORROPIA II is available at https://www.epfl.ch/labs/lapi/software/isorropia/. Other codes used in this paper are available from S.S.G. or P.L. upon reasonable request.

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Acknowledgements

S.S.G. acknowledges partial funding from the Ministry of Earth Sciences (sanction number MoES/16/04/2017-APHH (PROMOTE)), the Government of India, and the Department of Science and Technology (sanction number DST/CCP/CoE/141/2018C), the Government of India. This work was partially supported by the UK Natural Environment Research Council with grant reference numbers NE/P016480/1 and NE/P016472/1. P.L. acknowledges the start-up funding support from Georgia Insitute of Technology. Y.C. was funded under NERC grant number NE/P016405/1. All the authors are grateful to the APHH-PROMOTE team for providing logistic and experimental support during the campaign. Help from V. Kumar Soni, S. Singh and the staff at the India Meteorological Department, Delhi office, is specially acknowledged for logistic support during the campaign and providing the meteorological data. S.S.G. thankfully acknowledges Alfatech Services, New Delhi, for their generous technical support during the campaign. P.L. acknowledges fruitful discussions with N. L. Ng and L. G. Huey. L.J.M. acknowledges helpful discussions with D. Robie. We acknowledge A. Nenes for providing the ISORROPIA II model. U. Panda acknowledges CSIR for fellowship. S.S.G. was a recipient of the Fulbright Fellowship.

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Authors and Affiliations

Authors

Contributions

S.S.G. and P.L. designed the research. S.S.G., G.M. and H.C. conceptualized and planned the field campaign. U. Panda, S.S.R., A.S., S.M.K. and E.D. carried out the extensive field measurements and collected the ACSM data in Delhi and Chennai. S.S.G., P.L., U. Panda, S.M.K., R.R. and M.L.P. conducted ACSM data analysis. S.S.G., P.L., X.W., J.A., E.R.V. and G.M. conducted the ACSM data interpretation. P.L., S.S. and S.S.G. carried out the thermodynamic model simulations and conducted the data analysis and interpretation. P.L., Y.C., Y.W. and S.S.G. conducted the f(RH) and Köhler theory calculations and result intepretations. T.L. carried out the STILT simulations and performed the data interpretation with help from P.L. and S.S.G. S.S.G. and P.L. led the manuscript writing with specific inputs and edits from X.W., L.S., S.T.M., L.J.M., U. Pöschl, G.M., M.O.A. and H.C. All co-authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Sachin S. Gunthe or Pengfei Liu.

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Peer review information Nature Geoscience thanks Baerbel Sinha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Clare Davis; Rebecca Neely.

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Extended data

Extended Data Fig. 1 Summary table for the chemical species of non-refractory particulate matter (NR-PM1) in India reported in literature.

Table shows details about the various locations over India as represented in Fig. 1. Values show the absolute mass concentrations in µg m−3 (and fractions in %).

Extended Data Fig. 2 Summary table for the chemical species in NR-PM1 measured by the Aerosol Chemical Speciation Monitor (ACSM) in the present study.

Values show the mass concentration (µg m−3 ± standard deviation) and mass fraction in parentheses (%±standard deviation) of respective non-refractory PM species. Average temperature, humidity, and wind speed are also shown.

Extended Data Fig. 3 Distribution of organic and inorganic mass fraction of different chemical species in NR-PM1 and further fractions of different organic aerosol (OA) factors identified by positive matrix factorization (PMF) analysis.

The contributions in the form of pie charts are shown for entire campaign, the periods associated with high chloride episode, morning hours (04:00–09:00 am) during the high chloride episode (when partitioning to the particle phase is favourable), and the low chloride days. The left and right panels indicated the fractions for Delhi and Chennai, respectively. The identified primary OA (POA) factors include hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and cooking OA (COA; Delhi only). The identified oxygenated OA (OOA) factors are oxidized primary OA (OPOA; Delhi only), less-oxidized oxygenated OA (LO-OOA; Chennai only), and more oxidized oxygenated OA (MO-OOA). The individual mass fractions (in %) and total NR-PM1 mass is marked in the respective panels.

Extended Data Fig. 4 Scatter plot of mass fraction of NR-PM1 versus absolute mass of NR-PM1 (Org, \({\mathrm{NO}}_3^ - ,\,{\mathrm{NH}}_4^ + ,\,{\mathrm{SO}}_4^ - ,\) and \({\mathrm{Cl}}^ -\)) for Delhi.

(a) Total NR-PM1 mass versus chloride mass fraction (b) Total NR-PM1 mass versus sulphate mass fraction (c) Total NR-PM1 mass versus nitrate mass fraction, and (d) Total NR-PM1 mass versus organic mass fraction. The square points in each panel represent the mean values for different concentration bins and the error bars extend from lower to upper standard deviation.

Extended Data Fig. 5 Averaged diel variations of non-refractory chemical components and respective fraction in submicrometer particulate matter (NR-PM1; Org, \({\mathrm{NO}}_3^ - ,\,{\mathrm{NH}}_4^ + ,\,{\mathrm{SO}}_4^ - ,\) and \({\mathrm{Cl}}^ -\)) measured in Delhi and Chennai.

(a) Stacked diel variations in absolute mass concentrations of chemical components in NR-PM1 measured by ACSM during the field campaign in Delhi. (b) Stacked diel variations in the mass fraction of NR-PM1 for Delhi as derived from absolute mass concentrations. (c) Same as (a) for Chennai. (d) Same as (b) for Chennai.

Extended Data Fig. 6 Scatter plot of HCl:BC versus HCl:OC ratios for various type of burning as collected from literature, compared with measured ratios from the present observations from Delhi and Chennai.

The fuels for which the emission ratios are derived includes open biomass burning, biofuel burning, open burning of mixed garbage, and burning of plastic waste. The purple dots and corresponding square in the graph represents the HCl:BC and HCl:OC ratios for Delhi during the entire period and averaged over P1 period, respectively. The green dots and corresponding square are similar like purple colour but for period P2. The orange dots and corresponding square is again similar to purple and green dots but for Chennai representing the Bhogi period. The total HCl concentration was inferred from the particulate chloride observed during early morning hours when HCl was expected to partition into the particle phase. The squares represent the arithmetic mean of measured points during respective periods of measurements for corresponding sites and the error bars extend to the 10th and 90th percentiles.

Extended Data Fig. 7 Sensitivity of the measurement site with respect to nearby potential emission sources.

Sensitivity is derived using the Stochastic Time-Inverted Lagrangian Transport (STILT) model. (a) Sensitivity during P1: high OA high Cl period, (b) for period P2: high OA low Cl period, and (c) the difference in the sensitivity between P1 and P2. The map in the upper right corner shows the model domain over the India map. The STILT model was driven by the meteorological data from Global Data Assimilation System (GDAS) with a spatial resolution of 0.5° × 0.5°. The corresponding back-trajectory footprints were resampled at a finer resolution of 0.01° × 0.01°. Note that the colour bar is in the logarithm scale.

Extended Data Fig. 8 Scatter plot of relative humidity (RH in %) and temperature (in °C) versus particle phase chloride (μg m−3) and mass fraction of chloride in NR-PM1 over Delhi.

(a) Mass concentration of particle phase chloride plotted against relative humidity. The data points represented in the scatter plot are scaled by temperature. (b) Chloride mass fraction in NR-PM1 plotted versus relative humidity (c) same as (a) but plotted against temperature (in °C) and scaled by RH, and (d) same as (b) but plotted against temperature (in °C) and scaled by RH.

Extended Data Fig. 9 Mass of organic, inorganic species, and water content as a function of relative humidity.

Organic, inorganic mass fraction and water content in NR-PM1 over Delhi as a function of relative humidity (%) as derived from ISORROPIA model calculations distinctly showing a strong increase in chloride and water contents in NR-PM1 with increasing relative humidity.

Source data

Source Data Fig. 1

Raw data for Fig. 1 including all panels.

Source Data Fig. 2

Raw data for Fig. 2 including all panels.

Source Data Fig. 3

Raw data for Fig. 3 including all panels.

Source Data Fig. 4

Raw data for Fig. 4 including all panels.

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Gunthe, S.S., Liu, P., Panda, U. et al. Enhanced aerosol particle growth sustained by high continental chlorine emission in India. Nat. Geosci. 14, 77–84 (2021). https://doi.org/10.1038/s41561-020-00677-x

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