Elsevier

Atmospheric Research

Volume 200, 1 February 2018, Pages 88-96
Atmospheric Research

Most probable mixing state of aerosols in Delhi NCR, northern India

https://doi.org/10.1016/j.atmosres.2017.09.018Get rights and content

Highlights

  • Modulation of aerosol forcing by mixing state is examined at megacity Delhi.

  • External mixing seems reasonable only in the pre-monsoon season.

  • Core-shell mixing state is most probable in the remaining seasons.

  • Largest enhancement in TOA warming is observed for internal mixing and BC coating over dust.

Abstract

Unknown mixing state is one of the major sources of uncertainty in estimating aerosol direct radiative forcing (DRF). Aerosol DRF in India is usually reported for external mixing and any deviation from this would lead to high bias and error. Limited information on aerosol composition hinders in resolving this issue in India. Here we use two years of aerosol chemical composition data measured at megacity Delhi to examine the most probable aerosol mixing state by comparing the simulated clear-sky downward surface flux with the measured flux. We consider external, internal, and four combinations of core-shell (black carbon, BC over dust; water-soluble, WS over dust; WS over water-insoluble, WINS and BC over WINS) mixing. Our analysis reveals that choice of external mixing (usually considered in satellite retrievals and climate models) seems reasonable in Delhi only in the pre-monsoon (Mar-Jun) season. During the winter (Dec-Feb) and monsoon (Jul-Sep) seasons, ‘WS coating over dust’ externally mixed with BC and WINS appears to be the most probable mixing state; while ‘WS coating over WINS’ externally mixed with BC and dust seems to be the most probable mixing state in the post-monsoon (Oct–Nov) season. Mean seasonal TOA (surface) aerosol DRF for the most probable mixing states are 4.4 ± 3.9 (− 25.9 ± 3.9), − 16.3 ± 5.7 (− 42.4 ± 10.5), 13.6 ± 11.4 (− 76.6 ± 16.6) and − 5.4 ± 7.7 (− 80.0 ± 7.2) W m 2 respectively in the pre-monsoon, monsoon, post-monsoon and winter seasons. Our results highlight the importance of realistic mixing state treatment in estimating aerosol DRF to aid in policy making to combat climate change.

Introduction

Aerosol direct radiative forcing (DRF) has large uncertainty due to various factors like error in composition, scale height, mixing state, particle morphology etc. (Boucher et al., 2013). Aerosols which are emitted from a variety of sources are mixed in different ways during its transport, thus changing their optical properties (Chandra et al., 2004, Dey et al., 2008, Srivastava and Ramachandran, 2013). In external mixing, different aerosol species (each particle is of homogeneous composition) do not interact with each other physically or chemically (case 1 in Fig. 1). One extreme is internal mixing (Case 2 of Fig. 1), where particles of homogeneous composition are mixed internally so that the composition of each composite particle is weighted average of individual homogeneous particles. In between, we have core-shell mixing (remaining cases in Fig. 1), where one particular species (e.g. dust) is coated by another species (e.g. black carbon, BC). Observations suggest that fully internal mixing may not be realistic, rather the reality is somewhere between external and core-shell mixing or a combination of the two (Jacobson et al., 2000). Therefore, it is important to understand the role of mixing state in modulating aerosol DRF (Jacobson, 2001, Wang et al., 2010) and its feedback on atmosphere, especially at regional scale where aerosol characteristics are highly variable.

Indian subcontinent has been identified as a regional aerosol hotspot and one such place, where aerosol optical depth (AOD) shows large seasonal and spatial variability depending on the synoptic meteorology, emission characteristics and topography (Dey and Di Girolamo, 2010 and the references therein). Contrary to a decreasing global trend (Mishchenko et al., 2007), AOD shows an increasing trend in India in the recent times (Dey et al., 2011, Krishna Moorthy et al., 2013), therefore making aerosols a critical component in understanding the regional climate variability (Sanap and Pandithurai, 2015).

Direct measurement of aerosol composition covering all seasons continuously at one location is limited in the Indian subcontinent. In absence of detailed aerosol composition, researchers mostly use an indirect approach to estimate top-of-the-atmosphere (TOA) and surface DRF. Aerosol composition is inferred through an iterative process, where the respective number concentrations of individual species are tuned by matching the spectral AOD calculated by Mie theory with direct measurements, either by ground-based radiometers (e.g. Singh et al., 2005, Dumka et al., 2014, More et al., 2013) or satellite-derived aerosol products (Dumka et al., 2014, More et al., 2013). The spectral optical properties are then utilized for estimating aerosol DRF using radiative transfer model (e.g. Singh et al., 2005, Dey and Tripathi, 2008, Singh et al., 2010, Srivastava et al., 2012, Srivastava et al., 2014b). External mixing state is usually considered for calculating aerosol spectral optical properties in such cases and therefore any error due to wrong choice of mixing state (i.e. if mixing state deviates from external mixing in reality) infiltrates into the aerosol DRF estimate. In addition, the accuracy of inferred composition from such process depends on robustness of matching criteria and choice of individual species in bulk aerosol composition. Therefore, it is difficult to ascertain the probable mixing state, when inferred composition itself has large uncertainty.

Aerosol optical properties are well studied in the western IGB (Tiwari et al., 2015). For example, Lodhi et al. (2013) examined AOD climatology, seasonal variability and trend for the period 2001–2012 at Delhi NCR using long-term ground-based measurements, while Srivastava et al. (2014a) established satellite-based aerosol climatology for the last decade. In another study, Srivastava et al. (2014b) characterized major aerosol types in Delhi NCR using a combination of fine-mode fraction and single scattering albedo (SSA). Diurnal and seasonal variations of carbonaceous aerosols and their emission sources were also examined at Delhi (Srivastava et al., 2014b, Tyagi et al., 2017). Singh et al. (2010) estimated ‘clear-sky’ aerosol DRF over Delhi for one year period using AOD spectrum measured by a passive radiometer and other optical properties (e.g. SSA and asymmetry parameter) estimated by Optical Properties of Aerosols and Clouds (OPAC) model. Key findings of these studies can be summarized as follows: (1) AOD peaks during the pre-monsoon season (Mar–Jun) along with a reduction in Angstrom parameter indicating an increase in coarse dust particles in columnar burden; (2) AOD continues to remain high during the dry phase of monsoon (Jul–Sep) season because of persistent dust transport and high anthropogenic emission; however measurement of AOD by passive sensor is biased towards ‘clear-sky’ condition and fails to quantify the washout by rain; (3) dust is ubiquitously present in Delhi NCR with varying source seasonally; (4) absorbing BC shows strong seasonal and diurnal variation with highest value observed during the winter (Dec-Feb) season because of stable boundary layer; (5) concentration of aerosols smaller than 10 μm (PM10) and 2.5 μm (PM2.5) is 5 times larger than the Indian air quality standard and 20 times than World Health Organization standard; and (6) very large aerosol DRF at TOA (varies from − 1.4 ± 0.4 to 21 ± 2 W m 2) and surface (varies from − 46 ± 8 to − 110 ± 20 W m 2) are observed. All these studies assumed ‘external mixing’ in computing optical properties and DRF. If aerosol mixing state is not external throughout the year, previous estimates would have large uncertainty. Previous attempts to infer aerosol mixing state in the Indo-Gangetic Basin have focused on the central and eastern parts of the Indo-Gangetic Basin (IGB) (Dey et al., 2008, Srivastava and Ramachandran, 2013). These studies considered spectral aerosol optical properties retrieved by AERONET sunphotometer to constrain inferred aerosol composition. Here, we focus on Delhi national capital region (NCR), in the western part of the IGB.

In the present study, we take advantage of detailed chemical measurement of aerosol samples collected at Delhi NCR for a two-year (2007–2008) period. These data are used to estimate the aerosol DRF in ‘clear-sky’ condition. Further, we examine the modulation of surface reaching flux by different mixing states and compare with in-situ measurement to infer the most probable aerosol mixing state in each season. Our results provide an insight into the importance of treatment of appropriate mixing state in estimation of aerosol DRF.

Section snippets

Approach

Our approach has the following major steps. First, we group the chemical data quantitatively into four major aerosol types. Aerosol spectral optical properties are calculated for various mixing states as described in the following sub-sections. The optical properties are used as inputs to a radiative transfer model to estimate ‘clear-sky’ aerosol DRF at the TOA and surface. Finally, the surface reaching ‘clear-sky’ flux in presence of aerosols for various mixing states is compared against

Results and discussion

Mean (± 1σ) seasonal surface fluxes measured by IMD in ‘clear-sky’ condition in Delhi NCR are 273.6 ± 23.9, 265.9 ± 23.8, 183.2 ± 29.5 and 161.2 ± 26.9 W m 2 respectively for the pre-monsoon, monsoon, post-monsoon and winter seasons. We note that although June is considered as a monsoon month in all-India context, monsoon arrives at Delhi NCR by end of June to early July, and hence it is included in the pre-monsoon season in this work. Percentage departure of estimated mean seasonal surface fluxes for

Summary and conclusions

We examine and report the modulation of aerosol DRF at Delhi NCR (an urban site in the Indo-Gangetic Basin in northern India) by mixing state in clear-sky condition utilizing measured aerosol composition for two years period (2007–2008).

The main findings are summarized as follows:

  • 1.

    External mixing, the preferred choice in models and in estimation of aerosol DRF, seems valid only in the pre-monsoon season in Delhi NCR.

  • 2.

    ‘WS coating over dust’ externally mixed with BC and WINS is inferred as the most

Acknowledgements

The work is partially supported by grant from Climate Change Program of Department of Science and Technology, Govt. of India under contract DST/CCP/PR/11/2011 through research project operational at IIT Delhi (RP2580). We thank the anonymous reviewers for constructive comments that helped improving the manuscript.

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