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Asian Journal of Atmospheric Environment - Vol. 15 , No. 3

[ Review Article ]
Asian Journal of Atmospheric Environment - Vol. 15, No. 3
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Sep 2021
Received 03 Apr 2021 Revised 07 Jul 2021 Accepted 30 Aug 2021

A Review on the Techniques Used and Status of Equivalent Black Carbon Measurement in Two Major Asian Countries
Arpit Malik1), 2) ; Shankar G. Aggarwal1), 2), *
1)Environmental Sciences & Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K.S. Krishnan Marg, New Delhi 11012, India
2)Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Correspondence to : *Tel: +91 11 4560 8331 E-mail:

Copyright © 2021 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Black Carbon (BC) is a major pollutant that poses immediate health as well as long-term climatic threat to human civilization. Globally, India and China are considered to be among the significant contributors of carbonaceous aerosol. Therefore, in the recent past, several studies on BC measurements have been conducted and reported in both these countries. Optical absorbance measurement techniques which give BC mass by measuring light absorbance of aerosol have been used widely. Keeping these facts in mind, here an attempt has been made to realise the current state of Equivalent Black Carbon (EBC) measurement done in both countries. Eighty EBC measurement studies published in last 15 years (2005-2020) are analysed on the basis of technique, instrumentation and various important parameters involved in measurements. It is found that EBC measurements in India and China contain large uncertainties, and available data are metrologically insufficient to realise spatial distribution and long-term temporal variation precisely. Furthermore, MERRA-2 Surface Black Carbon (SBC) levels and EBC measurements are compared and evaluated for biases between spatial and temporal variation of modelled data and ground measurements. It is observed that standardization of measurement technique and parameters involved in measurement is the need of the hour. Lack of a reference method creates inconsistency and discrepancy among the measurements. Recommendations for selection of parameter/instrument and cautious measures are provided as conclusion based on this review to improve overall metrology of BC.

Keywords: Equivalent Black Carbon (EBC), Measurement techniques, 15 Years data trend, MERRA-2 Surface Black Carbon (SBC), Metrology of BC


Black Carbon (BC) is considered to be the second most important anthropogenic pollutant in terms of its climate forcing, amounting to a total climate forcing of +1.1 W m-2 (Bond et al., 2013). Moreover, BC contributes to a significant portion of fine inhalable particulate matter (PM2.5). A recent WHO report (Janssen et al., 2012) found short-term as well as long-term health effect estimates of BC to be slightly higher than PM10 or PM2.5. It has also been established that BC may be a better choice as indicator of harmful particulate substances from combustion sources (especially traffic) than undifferentiated PM mass ( Janssen et al., 2012, 2011). Therefore, it suggests that continuous BC measurement is necessary to better understand its health and climate effects, and also for regulatory purposes.

Present inventories contain large uncertainties in emission estimates of BC as emission factors associated with sources that are more prevalent in the developing world needs to be improved (Raga et al., 2018). Present models used to estimate BC emission are also needed to be further validated against the long-term measurement data. Raga et al. (2018) highlighted the differences in emission estimates by various models (SPEW, GAINS and RETRO). Although, BC data obtained from satellite observations correlate very well with ground-based measurements but concentration levels reported by satellite data are significantly different from ground data (Xu et al., 2020a; Qin et al., 2019; current study). Therefore, ground observations remain the primary source of analysing climatic and health effects of BC due to better quality of data as compared to that of satellite and modelled data. However, due to limited temporal and spatial BC data, and good correlation between satellite data and ground observations, satellite data have been used to define the spatial distribution of carbonaceous aerosol over India and China (Xu et al., 2020a; Rana et al., 2019).

A recent report published by Global Carbon Project has found that China and India are the primary contributors of carbonaceous aerosol (Friedlingstein et al., 2019). Various emission inventories (Klimont et al., 2017; Paliwal et al., 2016; Wang et al., 2012; Bond et al., 2007) have also found both countries to be the largest contributors to global BC aerosols. Hence it is well established that China and India are considered to be largest emitters of BC. Therefore, accurate measurement and assessment of BC is of great significance in both countries. The effect of BC on climate is mainly assessed by analysing optical properties of BC (Bond et al., 2013) and most of the continuous real time measurement instrument also gives BC mass by measuring optical properties. In India and China also, most of the long-term continuous BC measurements are done by measuring optical absorbance of aerosol using different filter-based instruments (see references in Table 1).

Table 1. 
Summary of filter-based instruments.
Name of
Important parameters Special feature
Uncertainty/Accuracy (%) References for
detailed working
MAC value
(m2 g-1)
detection limit
(ng m-3)
(Multi Angle
Absorption Photometer)
670 6.6 <50 Optical scattering is also
measured along with
12 (βabs),
(Petzold and Schonlinner, 2004)
25 (MBC),
(Petzold and Schonlinner, 2004)
Petzold and Schonlinner, 2004;
Petzold et al., 2002
(Continuous Soot
Monitoring System)
565 10 40 Heated inlet provides
stabilized MAC value
12 (βabs),
(Miyazaki et al., 2008)
15 (MBC),
(Ohata et al., 2021)
Kondo et al., 2011, 2009;
Miyazaki et al., 2008;
Ohata et al., 2019
370, 470, 520,
590, 660, 880,
39.5, 31.1, 28.1,
24.8, 22.2, 16.6,
100 7-Wavelength operation
and dual spot
5-40 (MBC),
(Corrigen et al., 2006)
36 (βabs),
( Backman et al., 2017;
Sharma et al., 2017a)
Hansen and Schnell, 2005;
Sedlacek, 2016
Carbon Monitor)
370, 430, 470,
525, 565, 590,
660, 700, 880,
NM 8 10-Waveength operation
and very low detection
NM Xue et al., 2018
(Particle Soot
Absorption Photometer)
467, 530, 660 NM 100 3-Wavelength operation
makes it compatible with
Up to 50 (MBC),
(Springston, 2018)
Springston, 2018;
Virkkula et al., 2010, 2005

Following the recommendation proposed in previous studies (Lack et al., 2014; Petzold et al., 2013) specific term EBC is used in this review to denote BC mass derived by measuring optical absorbance of aerosol. The general term BC is used here to denote all type of carbonaceous aerosol (except brown and organic carbon) irrespective of technique used for measurement. Despite high relevance of ground measurements of EBC, at present there is no overall agreed reference method of EBC measurement (Petzold et al., 2013). As a result, measurements reported may have suffered from personal biases and involved high uncertainties. A considerable number of studies on ground measurements of EBC have been published across India and China in the past decades. Realising the significance of real-time ground EBC measurements in both countries, eighty of these EBC studies (see references in Tables 2 and 3) published in the last 15 years (2005-2020) are analysed/examined to determine the various factors and parameters that impact the measurement.

Table 2. 
Summary of studies reported from China.
S. No Measurement site Measurement period Concentration mean (μg m-3) Instrument used Wavelength (nm) MAC (m2 g-1) Source Correction method Reference
1 Beijing (U) 2003 8.80 (Summer)
11.4 (Winter)
AE-16 880 16.6 TSP
NM LOU et al., 2007
2 Beijing (U) Every late autumn 1996-2004 20 (1996-2000)
16 (2000-2004)
Integrating plate NM NM NM NM Gengchen et al., 2005
3 Beijing (U) (5sites) Sep 2005-Aug 2007 4.21-7.86 (Day)
3.39±10.2 (Night)
SPECORD50 650 NM PM2.5 Filter immersion Schleicher et al., 2013
4 Beijing (U) (2sites) 2-14 Aug 2009 and 1-11 Dec 2009 8.1-12.3 (Aug)
16.1-17.9 (Dec)
AE-51 880 NM TSP Virkkula 2007 Song et al., 2013
5 Beijing (U) 2005-2013 4.34 SPECORD50 and SPECORD50+ 650 NM PM2.5 Filter immersion Chen et al., 2016
6 Beijing (U) 2010-2014 3.67 AE-31 880 16.6 PM2.5 NM Liu et al., 2016
7 Beijing (U) 2014 4.4±3.7 AE-31 NM NM PM2.5 Virkkula 2007 Ji et al., 2017
8 Beijing (U) Nov 2014-Jan 2015 2.72±2.63 (Nov 3-12)
3.83±4.08 (Nov 12-Jan 28)
AE-31 880 16.6 PM2.5 NU Wang et al., 2017c
9 Beijing (U) Dec 2015-Feb 2016 5.31±6.26 AE-33 880 7.77 NM Drinovec 2015 Liu et al., 2018
10 Beijing (U) Nov 2014
Nov 2015
Nov 2016
AE-33 880 7.77 NM Drinovec 2015 Qin et al., 2019
11 Beijing (SU) (2 sites) 2016 3.4-5.1 AE-31 NM 16.6 PM2.5 Schmid 2006
Drinovec 2015
Xia et al., 2020
12 Beijing (U) IAP 2015-2017 3.5 AE-31 NM 16.6 PM2.5 Schmid 2006
Drinovec 2015
Xia et al., 2020
13 Beijing (U) Jan-Mar 2017 4.6 (Near-UV)
3.1 (Visible)
2.6 (Near-IR)
Multi-spectrum carbon monitor 370 (near-UV)
420-700 (Visible)
880, 950 (near-IR)
NM PM2.5 Inter-Comparison Xue et al., 2018
14 Baoji (U) 2015 2.9 ±1.7 AE-31 NM NM PM2.5 Virkkula 2007 Zhou et al., 2018
15 Guangzhou (U) Dec 2007-Dec 2008 4.7 AE-31 880 16.6 PM2.5 NM Chen et al., 2014
16 Hangzhou (U) Jan-Mar 2020 1.29-2.3 AE-31 NM NM NM Weingartner 2003 Xu et al., 2020a
17 Hefei (U) Jun 2012-May 2013 3.5±2.5 AE-31 880 NM NM NM Zhang et al., 2015
18 Jiaxing 26 Sep-30 Nov 5.1 AE-31 NM NM NM NM Shen et al., 2015
19 Lanzhou (U) Jul-Aug 2012 2.94 MAAP 670 NM PM2.5 NM Xu et al., 2014
20 Nanjing (U) 2012 4.1575±2.6 AE-31 880 NM TSP Virkkula 2007 Zhuang et al., 2014
21 Nanjing (U) Dec 2017-Nov 2018 2.78±1.96 MAAP 670 NM TSP NM Zhang et al., 2020a
22 Nanjing (U) 2015-2016 2.2±1.3 AE-33 880 16.6 PM2.5 Drinovec 2015 Xiao et al., 2020
23 Panyu, Guangzhou (U) 2004-2007 3.1-14.8 AE-31 880 8.28 PM10 NM Wu et al., 2009
24 Xian (U) Sep 2003-Aug 2005 14.7±9.5 AE-16 880 16.6 PM2.5 NM Cao et al., 2009
25 Xian (U) 2003-2006 12.7±8.3 AE-16 880 NM NM NM Zhao et al., 2015
26 Xuzhou(U) May 2014-Jul 2016 2.24 AE-42 880 NM NM Virkkula 2007 Chen et al., 2020
27 Shanghai (CU) (Century Park) Apr 2007-Mar 2010 3.83 AE-31 880 16.6 TSP NU Wang et al., 2014
28 Shanghai (CU) (Xujiahui) Jan 2010-Dec 2011 3.8±2.3 (2010)
3.3±2.1 (2011)
AE-21 880 NM PM2.5 Park 2010 Feng et al., 2014
29 Shanghai (CU) (Fudan University) Jan 2011-Jan 2012 2.33 AE-31 880 NM NM Intercomparison Zha et al., 2014
30 Nanjing (SU) Nov 2008-Apr 2010 1.114-19.408 AE-21 880 NM PM2.5 NM Tang et al., 2011
31 Taicang (SU) Shanghai 27 Jun-31 Jul 5.47±4.00 AE-21 880 16.6 PM2.5 NM Zhou et al., 2009
32 Tongyu (SU) Mar-Dec 2008 2.52 AE-31 880 NM NM Inter-Comparison Cheng et al., 2010
33 Xianghe (SU) Dec 2017-Jan 2018 3.6±4.0 AE-33 880 NM PM2.5 Inter-Comparison Wang et al., 2020
34 Xianghe (SU) 2013-2015 5.39±4.44 (2013)
4.93±4.28 (2014)
AE-31 880 16.6 PM10 Virkkula 2007
Schmid 2006
Ran et al., 2016
35 Chang Ping Beijing (R) 27 Jun-31 Jul 2.37±1.79 AE-21 880 16.6 PM2.5 NM Zhou et al., 2009
36 Miyun Beijing (R) Apr 2003-Jan 2005 2.12 ±1.62 AE-31 880 8.28 TSP Inter-Comparison Yan et al., 2008
37 Miyun Beijing (R) 23-27Aug 2009
1-11 Dec 2009
1.9 (Aug)
7.7 (Dec)
AE-51 880 NM TSP Virkkula 2007 Song et al., 2013
38 Miyun Beijing(R) Apr-Oct 2010 2.26±2.33 COSMOS 565 NM PM1 NM Wang et al., 2011
39 Nancun (R) Guangzhou May-Jun 2008
Dec 2008-Jan 2009
2.33-15.97 AE-16 880 16.6 PM10 Inter-Comparison Wu et al., 2013
40 Hok Tsui (Re) Jun 2004-May 2005 2.4±1.8 AE-42 880 16.6 PM2.5 NU Cheng et al., 2006
41 Maofengshan (R) Guangzhou May-Jun 2008
Dec 2008-Jan 2009
AE-31 880 16.6 PM10 Inter-Comparison Wu et al., 2013
42 Northwestern Qilian Shan (Re) May 2009-May 2011 0.048 AE-31 880 16.6 TSP NU Zhao et al., 2012
43 Tibet (Re) (NamCo region) 2010-2014 0.074±0.050 AE-31 880 16.6 TSP NU Zhang et al., 2017
44 The Qomolangma Station, Mt. Everest May 2015-May 2017 0.30±0.34 AE-33 880 NM NM Drinovec 2015 Chen et al., 2018
45 Yongxing Island, China May-Jun 2008
Dec 2008-Jan 2009
AE-31 880 16.6 PM2.5
Inter-Comparison Wu et al., 2013

Table 3. 
Summary of studies reported from India.
S. No Measurement site Measurement period Concentration mean (μg m-3) Instrument used Wavelength (nm) MAC (m2 g-1) Source Correction method Reference
1 Agra (U) May 2014-Apr 2015 9.5 AE-33 880 NM TSP Drinovec 2015 Gupta et al., 2017
2 Ahmedabad, Gujarat (U) 2008 2.1±0.8-11.6±2.9 AE-31 880 16.6 TSP Bodhaine 1995
Bond and Bergstrom 2006
Ramachandrn and Kedia, 2010
3 Bhubaneshwar (U) Odisha Jul 2007-Jun 2008 3.6±0.1 AE-21 NM NM TSP NM Das et al., 2009
4 Bengaluru Karnataka (U) 2003-2008 1.4±0.9-4.2±1.6 AE-20 880 NM PM10 NM Satheesh et al., 2011
5 Delhi (U) Dec 2004 29±14 AE-42 NM NM TSP Weingartner 2003 Ganguly et al., 2006
6 Delhi (U) 2006 14.8 AE-42 880 NM NM NM Bano et al., 2011
7 Delhi (U) 2007 14.0±12.0 AE-42 880 16.6 TSP NM Tiwari et al., 2009
8 Delhi (U) 2007-2008 3.8-28.3 AE-21 880 16.6 NM Virikulla 2007, 2015 Srivastava et al., 2018
9 Delhi (U) Feb-May 2010 14±6.3 AE-51 880 NM NM Kirchstettr and Novakov 2007 Apte et al., 2011
10 Delhi (U) Aug 2010-Jul 2011 2.44 ±0.58
AE-31 NM NM NM Weingartner 2003 Surendran et al., 2013
11 Delhi (U) 2011 6.7±5 AE-31 880 16.6 PM2.5 Weingartner 2003 Tiwari et al., 2013
12 Delhi (U) Jan 2011-May 2012 7.0±5 AE-31 880 16.6 PM2.5 Weingartner 2003 Srivastava et al., 2014
13 Delhi (U) Sep 2010-Aug 2012 7.0±5 AE-31 880 16.6 NM Weingartner 2003 Tiwari et al., 2014
14 Delhi (U) Jan-Dec 2012 7.9±5.1 AE-31 880 16.6 NM NM Bisht et al., 2015
15 Delhi (U) Dec 2013-Feb 2014 8.0±3.1 AE-31 880 16.6 PM2.5 NU Tyagi et al., 2017
16 Delhi (U) LBN Winter 2014-2015
Summer 2015
AE-51 NM NM NM NM Pant et al., 2017
17 Delhi (U) IIT Summer 2015 3.89±3.34 AE-51 NM NM NM NM Pant et al., 2017
18 Delhi (U) Dec 2015-Feb 2016 24.4±12.2 AE-33 880 NM NM Drinovec 2015 Dumka et al., 2018
19 Delhi (U) Sep-Dec 2015
Apr-Sep 2016
AE-42 880 16.6 NM Virikulla 2007 Sharma et al., 2018
20 Delhi (U) Dec 2015-Feb 2016 30.3±12 (370 nm)
24.1±5 (880 nm)
AE-33 370, 880 NM PM2.5 Drinovec 2015 Bisht et al., 2016
21 Delhi (U) 2016-2017 11.26 (2016)
14.20 (2017)
AE-33 880 NM NM NM Tyagi et al., 2020
22 Delhi (U) 2016-2018 13.57±8.4 AE-33 880 7.77 PM2.5 Drinovec 2015 Kumar et al., 2020
23 Guwahati (U) Jul 2013-Jun 2014 7.17±1.89 AE-31 880 16.6 NM Weingartner 2003
Virkkula 2007
Tiwari et al., 2016a
24 Hyderabad (U) 2010-2014 9.7±1.9 AE-31 880 NM NM NM Jose et al., 2016
25 Kolkata (U) Jun 2012-May 2013 5-27 AE-31 880 16.6 TSP Bodhaine 1995
Arnott 2005
Taludkar et al., 2015
26 Srinagar (U) 2013 6.0±4.8 AE-31 880 16.6 TSP Weingartner 2003 Bhat et al., 2017
27 Chennai (CU) Mar 2011-Aug 2012 5.39-11.19 AE-31 880 16.6 TSP Weingartner 2003
Arnott 2005
Aruna et al., 2013
28 Mumbai (CU) Jan 2009-Dec 2010 4.0 SSR NM 9.7 PM2.5 NM Sandeep et al., 2013
29 Visakhapatnam (CU) Dec 2005-Sep 2006 3.98 AE-47 880 NM NM NM Sreekant et al., 2007
30 Ahmednagar, Maharashtra (SU) Dec 2015-Dec 2016 13.8±10.4 AE-33 880 7.77 PM2.5 Drinovec 2015 Kolhe et al., 2018
31 Gwal Pahari (SU) Gurgaon Dec 2007-Jan 2010 12.3 MAAP 670 NM PM10 NM Hyvarinen et al., 2010
32 Patiala, Punjab (SU) Oct 2008-Sep 2010 6.5±3.2 AE-21 880 NM NM NM Sharma et al., 2017b
33 Anantapur, Andhra Pradesh (R) 2010 3.0±0.3 AE-21 880 16.6 PM2.5 NM Reddy et al., 2012
34 Ballia, Uttar Pradesh (R) Jun-Aug 2014 4.3±0.85 AE-51 880 NM PM1 NM Tiwari et al., 2016b
35 Kadapa (R) Andhra Pardesh Sep 2011-Nov 2012 2.2±0.8 AE-42 880 NM PM1 Weingartner 2003
Arnott 2005
Schmid 2006
Begam et al., 2016
36 Panchgaon (R) Delhi Outskirts Apr 2015-Mar 2016 7.2±0.3 AE-42 880 16.6 NM Weingartner 2003 Dumka et al., 2019
37 Hanle (Re) Ladakh Aug 2009-Jul 2012 0.07±0.03 AE-31 370
NM NM NM Gogoi et al., 2014
38 Kachchh (Re) Gujarat 2007-2010 0.2±0.1-1.4±0.7 AE-30 NM NM PM2.5 Weingartner 2003 Gogoi et al., 2013
39 Mukteshwar (Re) Nainital Sep 2005-Sep 2007 0.8 AE-31 880 NM PM2.5 Weingartner 2003 Hyvarinen et al., 2009
40 Manora Peak, Nainital (Re) Nov 2004-Dec 2007 1.0±0.7 AE-42 880 16.6 PM1 Weingartner 2003
Arnott 2005
Dumka et al., 2010
41 Ooty (Re) Apr 2010-May 2012 0.61±0.36 AE-31 880 16.6 NM Weingartner 2003 Udayasoorian et al., 2014
Abbreviations used are: U - Urban, R - Rural, NM - Not Mentioned, TSP - Total Suspended Particulate, CU - Coastal Urban, Re - Remote, NU - Not Used, IAP - Institute of Atmospheric Physics, SU - Sub-Urban, PM2.5,10 - Particulate Matter having particles of aerodynamic diameter less than 2.5 and 10 μm, LBN - Laxmi Bai Nagar, IIT - Indian Institute of Technology

As the number of studies in China (EBC studies published after 2005) is small as compared to studies in India, we have considered only those Indian studies which were reported for cities that are similar to Chinese cities either geographically or demographically. All the included studies from China are checked for references therein, and considered in this review. Only the studies published in English language are included, and studies published in national or regional languages are not included in this review. Based on the study sites, we have divided all the reported studies into five categories as: (i) Urban (U), (ii) Coastal-Urban (CU), (iii) Sub-Urban (SU), (iv) Rural (RU), and (v) Remote (Re).

This review further contains four sections. Section 2 gives a brief description of all the three prominent techniques (Light Absorption, Thermal and Laser-Induced Incandescence) used to measure and report BC measurements. As in this review, we have included studies which made use of optical properties of BC (EBC measurement studies) for measuring BC mass, a detailed comparative description of all optical (light absorption based) technique/ instruments is given in Section 3. Section 4 includes analysis and discussions on all the studies considered from both countries. It focuses on various critical parameters contributing to uncertainty and discrepancy arising in measurements carried out using different instruments.

Because EBC is not a regulatory parameter of National Ambient Air Quality Standards (NAAQS), in general EBC is not measured at air quality monitoring stations and thus EBC data are not available publicly in India (Rana et al., 2019) and China (Rohde and Muller, 2015). Also, the data of individual studies conducted in both countries are not sufficient to define the characteristics and trends over the years. Therefore, the NASA’s Modern- Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) satellite observation based modelled data (which are hereafter called “Surface Black Carbon, SBC”) are compared with ground observations (EBC) and are used to define the spatial distribution in both countries. Beijing and Delhi both were considered to be most polluted cities of the world till early 2000s (Gurjar et al., 2008). However, recent study (Ren, 2020) indicates that the situation has changed over the course of time and now pollution levels in Beijing are better than that of Delhi. Based on air quality report released by WHO for the years 2014 and 2018, Ren (2020) highlighted the better efficiency of pollution control measures in Beijing as compared to Delhi. Therefore, a qualitative analysis comparing temporal and seasonal variation of SBC and EBC concentrations of two national capitals is also included in Section 4.8.3 to realise the trends of BC levels over the years.

This review primarily focuses on biases in EBC measurement technique in India and China with a peripheral emphasis on comparison of spatial distribution of EBC and SBC levels in both the countries. Section 5 concludes the review and contains recommendations and suggestions which can be incorporated for future measurements. Summary of all studies are given in tabular form. This review can act as information source for further detailed quantitative comparison of EBC distribution in two major countries of Asia, which are facing severe air pollution problems in the world. Furthermore, this review summarizes important parameters and advantages of almost all the commercially available EBC measurement instruments, which can be helpful in choosing an instrument as per the specific needs.


As mentioned above, at present there is no overall agreed reference method available for measurement of BC. The main reason for the same is the complex and ambiguous definition of BC. The most convincing definition till date was provided by Bond et al. (2013) which can be stated as follows:

BC is a distinct type of carbonaceous material, which possess following properties:

1. It strongly absorbs visible light with a Mass Absorption Cross section (MAC) of at least 5 m-2 g-1 at a wavelength of 550 nm.

2. It is refractory; that is, it retains its basic form at very high temperatures, with a vaporization temperature near 4000 K.

3. It is insoluble in water, in organic solvents including methanol and acetone, and in other components of atmospheric aerosol.

4. It exists as an aggregate of small carbon spherules. Petzold et al. (2013) added a new property in order to include a distinct microstructural feature which is stated as

5. It contains a high fraction of graphite-like sp2 bonded carbon atoms.

At present there is no measurement technique or instrument available which can measure and confirm all these properties of atmospheric BC aerosol simultaneously, hence, we are deprived of a standard/reference method. Petzold et al. (2013) and Bond et al. (2013) have provided a very detailed description of all the BC measuring techniques and recommended terminology used to report BC. These methods are broadly classified into six categories, each confirming one or at most two properties mentioned above.

Out of these six methods, following is a brief description of three techniques (Light Absorption, Thermal/ Optical, and Laser Induced Incandescence) which have been widely used in Asia, especially in India and China. The other three techniques (Raman Spectroscopy, Electron Microscopy and Aerosol Mass Spectroscopy) to the best of our knowledge have not been used till now in India and China for reporting of long-term BC measurements. Detailed descriptions of these lesser-known techniques are given elsewhere (Nordmann et al., 2013; Onasch et al., 2012; Tumolva et al., 2010).

Fig. 1 shows a block diagram summarizing other three common measurement techniques used in India and China. Light absorption technique makes use of BC’s property of strongly absorbing visible light. Light rays of a selected wavelength in visible region are made to pass through aerosol particles which are either in-situ or are deposited on a filter. Light intensity before and after passing through deposited aerosols is measured and attenuation of light is converted into mass concentration of BC using appropriate formulae and conversion factors which are discussed in detail in Section 3. Measurements acquired through this technique are generally reported as concentration of Equivalent Black Carbon (EBC).

Fig. 1. 
Block diagram categorising three common technique used for BC measurement.

Thermal/optical technique makes use of two of the above-mentioned properties of BC, i.e. its (i) refractory nature and (ii) strong absorbance nature towards visible light. Measurements acquired through this technique are generally reported as concentration of Elemental Carbon (EC). The main advantage of this technique is that it can be helpful in source apportionment using correlation and ratio between Organic Carbon (OC) and EC, and also with other species (Wang et al., 2017a; Aggarwal and Kawamura, 2009, 2008). Charring and pyrolysis are the limitations which contribute to uncertainty in measurements. One of the disadvantages of this technique is that it cannot provide real-time data and process involved is more time consuming as compared to the light absorption technique. Detailed literature review on thermal/ optical method is given in Karanasiou et al. (2015).

Laser Induced Incandescence (LII) technique is newest of all the three techniques and uses refractory nature of BC in its working principle. A detailed informative review on LII is given by Michelsen et al. (2015). Two variations of LII techniques were described in above mentioned review. One is based on pulsed-laser excitation mainly used in combustion diagnostics and emissions measurements, another approach which uses continuous-wave lasers has become increasingly popular for BC measurements. Single Particle Soot Photometer (SP2) is an instrument based on second variation of LII which is used widely in these days. A detailed working of SP2 is provided throughout the literature (Laborde et al., 2012; Kondo et al., 2011; Schwarz et al., 2010, 2006; Snelling et al., 2005; Stephens et al., 2003). Mass concentration measured using LII technique is generally reported as Refractory Black Carbon (rBC) mass concentration (Lack et al., 2014; Petzold et al., 2013).


A very informative review on instruments used for EBC measurement was done by Moosmüller et al. (2009). However, several new instruments like COSMOS (Continuous Soot Monitoring System), new Aethalometer, Black Carbon Photometer (BCP) and Multi-Spectrum Carbon Photometer (MSCP) have been introduced since then and other instruments have undergone a fair share of modification, so we have presented a brief overview of all the currently available instruments/techniques for EBC measurement. Available EBC measurement techniques can be categorised as (i) filter-based and (ii) in-situ measurement technique.

3. 1 Filter-Based Instruments

Basic working principle of all the filter-based instruments is the same. Briefly, ambient air is drawn at a certain flow rate and Particulate Matter (PM) is collected on a filter. Simultaneously, light of certain wavelength is made to pass through the deposited PM spot and attenuation of light intensity is used to calculate the light attenuation coefficient (βabs) in units of m-1. In order to ensure that the observed change in filter transmittance is not due to change in the intensity of the LED light source, a second filter spot is used as a reference. The equation used to calculate βabs is derived from Beer-Lambert law and is written as


where A is the area of the filter sample spot in m2 and V is the volume of air drawn during the given time period in m3. I0 and I are the average filter transmittances during the prior time period and the current time period, respectively. Light attenuation coefficient can be converted into mass concentration of EBC mass using following equation



σBC= Mass Absorption Cross section (MAC) of EBC (m2 g-1)
MBC=Black Carbon Mass Concentration (μg m-3)

MAC is an experimentally determined calibration constant and has unit of area per unit mass. MAC value measures the ability of a material to absorb intensity of light at a particular wavelength per unit gram. Although units are in area, it does not refer to actual size and can be considered analogous to nuclear cross section (a term often used in nuclear physics) which is quantified physically in terms of characteristic area, where larger the area larger is the probability of nuclear interaction. MAC is an experimentally determined calibration constant and have been derived for BC throughout the literature (Nordmann et al., 2013; Kondo et al., 2009). Known mass of BC (measured using other technique, instrument) is taken and then corresponding light attenuation coefficient (βabs) is measured. Then the ratio of attenuation coefficient and known mass is used as MAC value.

The above stated formula (Equation (1)) is based on the assumption that the absorption is independent of non-linearity arising due to filter loading and also does not take into account the scattering due to filter medium as well scattering due to accumulated particulate matter. Further two correction factors corresponding to two effects (i) loading and (ii) multiple scattering effect are needed to be incorporated in equation to make filterbased measurements more accurate (Arnott et al., 2005; Weingartner et al., 2003). Loading effect refers to shadowing of freshly collected particles by previously collected particles and as a result, the measured BC becomes underestimated as the loading of particles on filter increases (Song et al., 2013). Second effect is caused by multiple scattering of the light beam at the filter fibres in the unloaded filter leading to an enhancement of the optical path and thus to enhanced light absorption of the deposited particles and ultimately leading to overestimation.

Various empirical correction functions and algorithm have been derived to account for these artifacts arising due to use of filter (Virkkula et al., 2015, 2007; Park et al., 2010; Kirchstetter and Novakov, 2007; Bond and Bergstrom, 2006; Schmid et al., 2006; Arnott et al., 2005; Weingartner et al., 2003; Bodhaine, 1995). Equation (1) is further modified by adding two further coefficients called as correction factors. New equation used to calculate absorption coefficient taking above explained effects into account can be written as


where, C is correction factor corresponding to multiple scattering and R corresponds to shadowing effect. Value of these coefficients depends on correction method applied. Immersion of the particle loaded filters in oil with a refracting index similar to the filter fibres can minimise the multiple scattering effect (Ballach et al., 2001). However, this technique cannot be applied to online monitoring instruments. Recently introduced dual spot measurement technique provides a good alternative solution for loading effect (Drinovec et al., 2015).

Furthermore, MAC value of carbonaceous aerosol is calculated experimentally and is specific to wavelength of light intensity used. Its stability depends on coating of particles by volatile compounds and co-existence of lightscattering particles (Kondo et al., 2009), thus is also a source of uncertainty for optical instruments. Increase in relative humidity of ambient air can also lead to increase in MAC value. Wu et al. (2016) found the rate of increase in MAC value per 10% increase in RH to be 0.20-0.24 m2 g-1. In order to overcome all the limitations described above, a variety of optical absorption and filter-based realtime measuring instruments are available commercially each having their advantages as well as limitations. We have tried to explain features, parameters, advantages of each instrument in the Table 1. Detailed working of instruments can be understood from references given along in the Table 1.

3. 2 In-Situ Measurement

As mentioned above, filter-based techniques suffer from filter loading effects and also do not measure optical absorption by aerosol particles directly, none of the above methods can be considered as qualified to be a reference method. In-situ measurement of absorbance is a major advantage of the two techniques/instruments mentioned in this section, which are (i) Photoacoustic Spectroscopy (PAS), and (ii) Black Carbon Photometer (BCP).

(i) Photoacoustic Spectroscopy

PAS has been used widely for trace gas analysis as well as to study light absorption by aerosol and study the behaviour of materials (Harren and Cristescu, 2006; Sigrist, 2003). It is considered as a possible reference method for measurement of optical properties of aerosol in visible region (Petzold et al., 2013; Lack et al., 2006; Arnott et al., 2003). Use of difference between extinction and scattering coefficient as absorbance coefficient is another near contender (Singh et al., 2016; Sheridan et al., 2005) but it requires use of two separate instruments which can lead to significant errors due to potentially different measurement systems and conditions (Wang et al., 2017b). Accuracy of PAS system has been reported to be 5-10% depending on laboratory or field locations and methods of calibration (Lack et al., 2006). Also, in-situ measurement makes PAS a comparatively better choice. Detailed working of PAS as well as its use for measurement of optical absorbance of light by aerosol is given in various research articles published previously (Wang et al., 2017b; Lack et al., 2009, 2006; Arnott et al., 2006, 2003, 1999).

The working procedure for measuring aerosol optical properties is explained here briefly. Aerosol is passed through a chamber where a modulated light beam of certain wavelength is made to pass through the incoming aerosol, thus modulated light is absorbed by the particles. Light energy absorbed by the particles is transferred as heat energy to the surroundings, creating a pressure variation thus creating pressure/acoustic waves. These waves are further resonated in a cavity at characteristic radial and longitudinal frequencies. When laser beam power is modulated at the acoustic resonance frequency, the generated waves are further amplified and detected using microphones. As PAS offers various advantages (in-situ measurement and better accuracy) over other conventional methods, various photoacoustic sensors and analyser are developed individually with variable parameters (Wang et al., 2017b; Krämer et al., 2002). But despite its high relevance to EBC measurement, instruments based on this technique has not been fully explored and commercialised.

(ii) Black Carbon Photometer

This instrument is latest addition to the list of commercially available instruments which gives BC mass by measuring optical absorbance of aerosol. This instrument uses the technique of long-path photometry to quantify mass concentration of EBC by measuring extinction coefficient. It measures extinction coefficient of aerosol in-situ at two wavelengths 405 nm and 880 nm. Out of which, 880 nm channel is used to measure BC mass concentration. In situ-measurement of absorption coefficient using Beer-Lambert law approach (Equation 1) requires long path lengths of detection cells (where absorbance due to reference air and sample (ambient) air is measured in regular intervals, i.e. Io and I in Equation 1). However large detection cells are difficult to be rapidly flushed which leads to measurement error due to inherited variation in lamp intensity. The patented tubular design (Birks et al., 2020) of detection cell in BCP enables detection cell to be rapidly flushed, as a result sample (air containing aerosol) and reference measurement (clean air) can be switched quickly. Therefore, light attenuation through aerosol as well as clean air can be measured very rapidly. BCP’s tubular design is very similar to (extension of) the tubular design described in details in a study explaining folded tubular design for measurement of NO2 (Birks et al., 2018). This technique can prove to be a good option for in-situ measurements. However, to the best of our knowledge, there is no published data of EBC measurement or inter-comparison available presently using this instrument. Therefore, no concluding remarks can be made about field performance of this technique.


As mentioned earlier, eighty studies are considered for this review. All studies from both the countries (India and China) are analysed for techniques and various parameters used for the EBC measurement. Summary of parameters and technique involved as well as measurement period and reported BC concentration is given in Table 2 and Table 3. Main results and findings about various parameters involved are discussed in following sub-sections.

4. 1 Instrument

Aethalometer is the most widely used instrument for EBC measurement in both countries. 71 out of 80 studies (89%) considered in this review from both countries (33 out of 40 (82%) from China and 38 out of 40 (96%) from India) have made use of different models of Aethalometer (AE-16, AE-21, AE-31, AE-42, AE-51, AE-33) Different models of Aethalometer have different features. AE-16 is the classic single wavelength (880 nm) Aethalometer which was introduced in 1997. In 1999, upgraded versions of AE-16, dual wavelength aethalometer AE-21 (370 nm and 880 nm) and seven wavelength Aethalometer AE-31 were introduced to provide options to study spectral dependency of optical properties of aerosol. AE-42 and AE-51 are the portable Aethalometers which were designed to provide ease of operation at remote location and on-board vehicle measurement. AE- 42 uses the same aerosol collection, analysis and data systems as the ‘full-size’ standard models (AE-16, AE-21, AE-31) but is constructed in a smaller chassis and is equipped with an internal battery (Hansen and Schnell, 2005). AE-51 is pocket-size, light weight (230 g) and has a better resolution (1 ng m-3) than other models (micro-Aeth, 2016). AE-33 is the latest versions which can measure absorbance at 7 wavelengths and offers improved measurement by making use of a patented technology, i.e. Dual-Spot Measurement (Drinovec et al., 2015). Since AE-31 is most commonly used model of Aethalometer and uncertainties evaluation of this model has been done in few studies, detail about AE-31 and its parameters and uncertainties is given in Table 1 and detailed working procedure can be found in references given along.

A recent review on the basis of 142 studies conducted in India from 2002 to 2018 also found that 96% of the studies used the various version of Aethalometer (Rana et al., 2019). This might be due to provision of multi wavelength channels as a result the ratio of absorbance of light at 370 nm and 880 nm can be used for source apportionment. For an example, absorbance at 370 nm is largely due to organic compounds produced by biomass burning. Therefore, the ratio of absorbance of light at 370 nm and 880 nm can be useful to find the contribution of biomass/ wood burning in comparison to fossil fuels. Further details about “Aethalometer Model” used for source apportionment can be found in some of the studies included in this review (Dumka et al., 2018; Bisht et al., 2016). Another reason might be provision of measurement at 880 nm which is preferred wavelength for measurement of EBC. (Qin et al., 2019; Gupta et al., 2017; Srivastav et al., 2014; Tiwari et al., 2009). BC has been found to be primary absorber of light at 880 nm and absorbance due to other light absorbing aerosol is less significant at this wavelength (Bodhaine, 1995).

However, frequent use of Aethalometer is quite surprising considering the advantages of other filter-based instrument like COSMOS (provides stabilised MAC value due to removal of non-refractory light absorbing aerosol by using a heated inlet), Multi-Angle Absorption Photometer (MAAP) (provides correction for scattering due to aerosol and filter media) and Multi Spectrum Carbon Photometer (MSCP) (provides measurement at 10 wavelengths in comparison to 8 wavelength channels in Aethalometer). MAAP has been used in three studies (Zhang et al., 2020a; Xu et al., 2014; Hyvarinen et al., 2010) whereas COSMOS and MSCP have been used in only 1 study each (Xue et al., 2018; Wang et al., 2011). More surprisingly, in-situ measurement technique, i.e. of PAS has still not been used to report BC measurement. Some studies have used PAS technique as a reference method to check the performance of instrument used for measurement. Three unconventional instruments were used to report EBC concentrations. These are Spectrometer (Chen et al., 2016; Schleicher et al., 2013), Smoke Stain Reflectometer (SSR) (Sandeep et al., 2013) and Laser Integrating Plate Method (LIPM) (Genchen et al., 2005). All these instruments involve a common process of collecting particulate matter mass on a filter using a sampler and then using these samples to measure attenuation of light from different spots of filter using an instrument specific wavelength. These instruments give concentration of EBC in offline mode and hence are not much popular.

4. 2 Wavelength

Wavelength used is the key parameter of any measurement which is based on measuring optical response (absorption, scattering) of species. Although optical response of EBC is almost similar throughout the visible spectrum but response of other light absorbing aerosol (especially Brown Carbon) has been found to be highly wavelength dependent (Liu et al., 2018; Shamjad et al., 2016). Measuring BC mass at lower wavelengths (towards UV spectrum) can lead to overestimation due to absorption by other light absorbing and scattering aerosol. Therefore, selection of wavelength used for measurement of EBC mass plays an important role to remove interferences due to presence of other light absorbing and scattering aerosol. Overall, 69 out of 80 studies (86%) from both countries (87% from China and 85% from India) have mentioned the wavelength used for measurement. Most of the studies used attenuation at 880 nm wavelength to measure BC concentrations which is obvious given that Aethalometer is the most widely used instrument whose 880 nm channel is considered as standard channel for reporting BC concentrations. Other wavelengths at which measurements have been done are 370 nm (Bisht et al., 2016), 565 nm (Wang et al., 2011), 650 nm (Chen et al., 2016; Schleicher et al., 2013), and 670 nm (Hyvarinen et al., 2010).

Simultaneous measurements of BC concentrations at 370 nm and 880 nm were done in Delhi (Bisht et al., 2016) and BC mean concentrations reported from 880 nm wavelength channel were 22% lower than that of 370 nm wavelength channel which is quite significant suggesting obvious bias among optics-based instrument using different wavelengths. One study (Xue et al., 2018) measured EBC concentration for two months at three wavelength bands (i) near Ultra-Violet (ii) Visible and (iii) near Infra-Red. Mean EBC concentration at near-IR band was found to be 2.6 μg m-3 (ranging from 0.2 to 17.7 μgm-3) which is only 56% of the mean EBC concentration of 4.6 μg m-3 (ranging from 0.2 to 30 μg m-3) measured at near-UV region. As mentioned above, high absorbance at lower wavelengths (370 nm and near-UV region) is due to presence of other light absorbing aerosol which is significant at lower wavelength as compared to absorbance due to EBC. Thus, use of different wavelengths in different instruments may cause discrepancy in results of different instruments. So, standardisation of wavelength used for measurement and reporting of EBC concentrations is necessary. The 880 nm wavelength can be considered a possible candidate for standard measurement as BC has been found to be primary absorber at this wavelength. However, MAC value corresponding to wavelength 880 nm shows large variation showing influence of other light absorbing aerosol at 880 nm wavelength also.

4. 3 MAC Value

As per guidelines suggested by Petzold et al. (2013) for reporting BC measurements, MAC value corresponding to wavelength used should be mentioned when reporting EBC concentrations. Only 42 studies out of 80 (~52%) from both countries (52% from China and 52% from India) have mentioned the corresponding MAC value used for the conversion of attenuation coefficient to BC mass. About 93% of these MAC values used were manufacturer recommended. One study (Yan et al., 2008) conducted in rural area of China used MAC value which was derived by a linear regression of the BC concentrations at 880 nm from the Aethalometer against the light absorption coefficients at 532 nm simultaneously measured using PAS technique. The calculated value of MAC at 880 nm was 8.28 m2 g-1 which is almost half of the manufacturer recommended value of 16.6 m2 g-1 for 880 nm.

Similarly, other studies carried out around the world also found MAC value to be significantly different from that suggested by manufacturer for AE-31 corresponding to wavelength of 880 nm (Arnott et al., 2003; Bergin et al., 2001; Moosemuller et al., 1998). MAC value corresponding to 880 nm wavelength was found to be varying from 10 to 19 m2 g-1 (Lou et al., 2007). Manufacturer recommended value of MAC corresponding to 880 nm for new model of Aethalometer (AE-33) have been changed to 7.7 m2 g-1 (Kolhe et al., 2018; Liu et al., 2017) from previous value of 16.6 m2 g-1 used in its predecessors. These discrepancies in MAC value are due to different sources of EBC, aging and coating of primary particles over the time due to mixing states (Bond et al., 2013; Kondo et al., 2009; Lou et al., 2007).

MAC value of uncoated particles has been found to be highly stable having a value of 7.5±1.2 m2 g-1 at 550 nm (Bond and Bergstrom, 2006). MAC value of 10 m2 g-1 suggested for COSMOS remains stable due to use of heating inlet which removes coating of the particle (Kondo et al., 2009). As coating of particles is removed, MAC value becomes significantly independent of source and mixing states of the particles due to coating (Kondo et al., 2009). Stabilized MAC value provides a greater advantage to COSMOS over other filter-based instruments.

4. 4 Correction Methods

To reduce underestimation due to loading/shadowing effects various correction methods have been implemented in some of the studies which are included here. Virkkula (Virkkula et al., 2015, 2007) and Weingartnar (Weingartnar et al., 2003) method are the most common methods used to correct shadowing effect for Aethalometer. In these methods, empirically derived correction factor (R in Equation (3)) is used which is the function of instantaneous and threshold light attenuation through filter media. Recently introduced dual spot measurement technique (Drinovec et al., 2015) provides a good alternative to these conventionally used correction methods. Correction methods used to account for shadowing effect in COSMOS and MAAP are not mentioned in this review but they are explained in details in some other studies conducted worldwide (Miyazaki et al., 2008; Petzold and Schlonirrer, 2004).

Correction for multiple scattering effect have been done in very few studies (Begam et al., 2016; Ran et al., 2016; Dumka et al., 2010). Schmid method (Schmid et al., 2006) and Arnott method (Arnott et al., 2005) have been used in these studies to treat multiple scattering effects of filter media. A wavelength dependent correction factor (C in Equation (3)) derived by modelling analysis of basic scattering theory is used to account for multiple scattering in filter media. This correction factor can be further modified to account for scattering due to aerosol by adding another term which is a function of aerosol single scattering albedo.

Using MAAP to measure EBC concentration do not require corrections for scattering as it measures transmittance and reflectance at multiple angles and uses twostream approximation in the radiative transfer budget. Inter-comparison of data with MAAP can be used to correct for the multiple scattering effect of the filter and scattering due to aerosols. This method has been implemented in two studies included in this review (Xue et al., 2018; Ran et al., 2016).

4. 5 Inlet Size Segregation

Study carried out in Beijing (Lou et al., 2007) analysed the contribution of EBC mass in TSP, PM10 and PM2.5. EBC mass detected using PM10 inlet was found to be 90% of EBC mass detected simultaneously without using any inlet (i.e. TSP). Similarly, EBC mass detected using PM2.5 inlet was found to be 82.6% of EBC mass detected simultaneously without using any inlet (i.e. TSP). Other studies conducted around world (Gatari and Boman, 2003; Moloi et al., 2002; Hitzenberger and Tohno, 2001) also found that most of the EBC is present in fine mode (PM2.5) with a little mass existing in coarse mode (above PM2.5). BC particles from combustion sources are always emitted in fine mode. However, internal mixing of BC with hygroscopic materials (sulphates and nitrates) can lead to particle growth at high humidity levels (Hitzenberger and Tohno, 2001).

Furthermore, EBC from non-exhaust emission and resuspension (Lugon et al., 2020) can also be present in coarse mode. Thus, using different source (TSP, PM10, PM2.5, PM1) for EBC measurement can give slightly different results and thus should be mentioned while reporting EBC concentrations. 51 studies out of 80 (64%) from both countries (58% from India and 70% from China) have mentioned the inlet cutoff size. Out of these, 51% used PM2.5 size segregation device (cyclone or impactor) and 25% percent measured EBC concentration in TSP. PM10 and PM1 inlets are used in 12% of studies reviewed here. Furthermore, cutoff points for different segregation device used can get altered due to flow rate of sampling. Two studies (Reddy et al., 2012; Lou et al., 2007) have clearly mentioned the changed cutoff size of cyclones used due to use of fixed flow rate of instrument of 3 lpm (Litre Per Minute).

4. 6 Terminology Used

Use of simplified term “Black Carbon” has become a common practice to describe various type of carbonaceous particles. On the basis of its chemical and optical properties, measurement technique used, the more general term Black Carbon has been dissected into three specific terms as Elemental Carbon (EC), Equivalent Black Carbon (EBC) and Refractory Black Carbon (rBC) (Lack et al., 2014; Petzold et al., 2013). As mentioned earlier, it is recommended to use term EBC for BC mass measuring optical absorbance of aerosol. Only 4 out of 47 studies published after 2013 (Kumar et al., 2020; Wang et al., 2020; Zhang et al., 2017; Liu et al., 2016) have made use of this terminology (EBC) to report measurement. Out of these, 3 studies were from China whereas there is only one study from India which has used EBC term instead of BC.

4. 7 Uncertainty

Uncertainty of a measurement is an important aspect which is needed to be mentioned in order to validate the measurement. Only a few studies (~30%) from both the countries have reported uncertainty quoting/citing the previous research or inter-comparisons performed as well as manufacturer specifications. Undefined contribution from above mentioned sources of uncertainty (shadowing, multiple scattering effects, and coating of EBC particles contributing to uncertainty in βabs and MAC value, ultimately leading to uncertainty in MBC) makes it difficult to evaluate uncertainty of EBC measuring instruments in a rigorous way. Several studies carried around the world have tried to in-corporate errors occurring due to these sources (Backman et al., 2017; Singh et al., 2010; Schmid et al., 2006; Arnott et al. 2005; Weingartener et al., 2003). As mentioned earlier, various correction methods are suggested to reduce these errors but magnitude of errors and the uncertainty arising from these effects are still unknown and are site and source specific. Apart from these sources, various other sources of uncertainty which can be defined and quantified are flow rate, area of the filter spot and drift in intensity of light source used.

Instrument specific uncertainty/accuracy based on the studies reviewed here and references therein are included in Table 1. Petzold and Schonlinner (2004) determined uncertainty in estimation of βabs (12%) for MAAP by performing error analysis of scattering and absorbance due to particle loaded and particle free filter matrices. They also estimated uncertainty in MBC calculation by MAAP to be 25% based on comparison with thermal technique (German reference method, Schmid et al., 2001). COSMOS accuracy (in MBC) was determined to be 10% on the basis of results of COSMOS vs SP2 comparison summary given in table 2 of Ohata et al. (2019). However, later on Ohata et al. (2021) estimated the absolute accuracy of COSMOS to be 15% taking into account the 10% uncertainty of reference instrument (SP2) as well. Total root mean squared systematic error in absorption coefficient (βabs) calculation due to quantifiable sources (flow rate, drift in reference and absorbance intensity, filter spot area) and correction factor has been shown to be 12% for COSMOS (Miyazaki et al., 2008). Using comparison with thermal method, Corrigan et al. (2006) reported uncertainty for Aethalometer (AE-31) to be ranging from 5-40%. Backman et al. (2017) evaluated relative uncertainty of Aethalometer (AE-31) to be 36% using equation of uncertainty propagation approach (Sharma et al., 2017a). However, manufacturer specified uncertainty of Aethalometer is mentioned to be 5% and 10% in few studies reviewed in current study (Zhou et al., 2018; Ji et al., 2017; Jose et al., 2016; Tiwari et al., 2013).

As mentioned in discussion above, some of the references given in included studies were based on inter-comparison of instruments (Ohata et al., 2019; Kondo et al., 2009; Corrigen et al., 2006; Arnott et al., 2005) and some were based on using error analysis and equation of uncertainty propagation (Backman et al., 2017; Miyazaki et al., 2008; Petzold and Schonlinner, 2004). However, instruments/ techniques used as reference for inter-comparison are different for different studies. Inter-comparison technique can be much more effective for defining accuracy once a standard reference method for measurement of EBC is declared by the regulatory bodies. Furthermore, there is a considerable negligence in reporting uncertainty of the instruments as only very few studies have specified uncertainty of the instrument and most of them were based on manufacturer recommended value. Error analysis and equation of propagation of uncertainty approach can be applied whenever possible to further strengthen the reporting of EBC concentrations.

4. 8 Comparison Between MERRA-2 Reanalysis SBC and Ground-Based Observation EBC Data
4. 8. 1 MERRA-2 Data

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest long-term global atmospheric reanalysis to assimilate space-based observations of aerosols and represent their interactions with other physical processes in the climate system initiated by NASA’s Global Modeling and Assimilation Office (GMAO). Details about MERRA-2 dataset can be accessed from previous studies (Bali et al., 2017; Kuo, 2017; Pfenninger and Stafell, 2016; Bosilovich et al., 2016, 2015; Buchard et al., 2015). The bias-corrected aerosol observations from satellites, Multi-Angle Image Spectro Radiometer and Advance Very High-Resolution Radiometer are used as input to the atmospheric model Goddard Earth Observing System Data 153 Assimilation System (GEOS-5) version 5.12.4 (Bosilovich et al., 2015; Buchard et al., 2015). Different type of aerosols (including BC) are simulated using the Goddard Chemistry Aerosol Radiation and Transport module in GEOS-5 (Bali et al., 2017; Buchard et al., 2015).

MERRA-2 reanalysis Surface Black Carbon (SBC) data have been used and validated in several previous studies in China (Xu et al., 2020a; Qin et al., 2019), India (Rana et al., 2019; Bali et al., 2017) and other part on the globe (Evangeliou et al., 2021; Sitnov et al., 2020). Qin et al. (2019) found that the 12-hourly resolved MERRA-2 SBC data were highly correlated with ground-based EBC data throughout the years in 2015 (Spearman’s rank correlation coefficient=0.70) and 2016 (0.78) in Beijing. Similarly, Xu et al. (2020a) found correlation coefficient (r) ranging from 0.55 to 0.96 between MERRA-2 data and ground observations at nine sites in China. In India, Aerosol Optical Depth (AOD, an important parameter used to derive BC column and surface concentrations) of AERONET and MERRA-2 observation were found to be highly correlated at Kanpur (r=0.79) and Pokhra (r=0.70) (Bali et al., 2017).

4. 8. 2 Comparison between Spatial Distribution of SBC and EBC Levels in China and India

Decade long (2011-2020) time averaged heat map of Surface Black Carbon (SBC) concentrations using MERRA-2 reanalysis data over both countries is given in Fig. 2. Monthly averaged and spatially resolved gridded (0.50×0.6250) dataset of SBC levels from MERRA-2 is used to plot the heatmap. Indo-Gangetic Plane (IGP) has the highest SBC levels in India whereas Sichuan Basin (SB) and Northern China Plane (NCP) are the most polluted region in China in terms of SBC levels. More broadly, it can be said that northern India has large SBC emissions as compared to southern peninsula region. Similarly, it can be concluded that eastern China has high SBC density than the western China. Previous emission inventory (Wang et al., 2012) focussed on estimating BC emissions in China from 1949-2050 also found average emission densities (for the year 2007) in eastern China to be more than those in western China. Similar distribution of BC emission in India has been reported in previous inventory (Paliwal et al., 2016). Moreover, SBC levels in highly polluted regions in China (NCP and SB) (7-8 μg m-3) are significantly greater (double) than SBC levels in polluted region of India (IGP) (3-4 μg m-3). This result is similar to previous emission inventory of SO2 and carbonaceous aerosol (Lu et al., 2011) where BC emission estimates from China were almost doubled of BC emission estimates of India for the year 2010. Other inventories summarized in table 10 of Bond et al. (2013) also reported similar scale of differences between emissions from both countries.

Fig. 2. 
Heat map showing spatial distribution of SBC levels in China and India from 2011-2020.

Ground observation of EBC in both countries present a rather different picture from above results when EBC levels of both countries are compared. Annual average (reported in all studies conducted for at least 1 year between 2011 and 2020) EBC levels at highly polluted regions (IGP in India and NCP in China) ranges from 3.4 μg m-3 to 5.1 μg m-3 (Chen et al., 2020; Xia et al., 2020; Ji et al., 2017; Chen et al., 2016; Liu et al., 2016; Ran et al., 2016) in China, and ranges from 7 μg m-3 to 13.57 μg m-3 in India (Kumar et al., 2020; Tyagi et al., 2020; Gupta et al., 2017; Tiwari et al., 2016a, b, 2014, 2013; Talukdar et al., 2015). Therefore, ground observations data of EBC suggests that BC levels in IGP are significantly higher (double) than those of NCP. This result is contrasting to results of MERRA-2 and previous BC inventories (Bond et al., 2013; Lu et al., 2011) where SBC levels and BC emissions in China are found to be significantly higher than India. This anomaly can be further explained by country wise comparison maps (comparing EBC and SBC levels) of both countries given as Figs. 3 and 4.

Fig. 3. 
In China, distribution of average SBC levels (2011-2020) and location of measurement sites along with corresponding measurement period, EBC and SBC levels of studies conducted in time period 2011-2020. Abbreviations used are: CUGB-China University of Geosciences, BAST - Beijing Academy of Science and Technology, BNU - Beijing Normal University, IAP - Institute of Atmospheric Physics, AIO - Atmospheric Integrated Observatory.

Fig. 4. 
In India, distribution of average SBC levels (2011-2020) and location of measurement sites along with corresponding measurement period, EBC and SBC levels of studies conducted in time period 2011-2020. Abbreviations used are: IIT - Indian Institute of Technology, IMD - Indian Meteorological Department, IITM - Indian Institute of Tropical Management, LBN - Laxmi Bai Nagar, JNU - Jawaharlal Nehru University.

Gridded (0.50×0.6250) raw datapoints of averaged SBC levels from 2010-2020 are plotted in respective country maps of China (Fig. 3) and India (Fig. 4). Location of all measurement sites of studies conducted in time period 2011-2020 is also denoted by circular markers. Measurement period, EBC levels and SBC levels (in μg m-3)corresponding to same time period are also given in pointed rectangular/square boxes. Grid value closest to location of EBC measurement site is used to find the corresponding SBC levels for same period.

MERRA-2 data (SBC) correlated very well with the ground observations (EBC) in both countries. The Pearson correlation coefficient (r) value for comparison between mean SBC levels and mean EBC levels for 24 sites in China (Fig. 3) is 0.76. Similarly, the calculated r value for all 24 sites in India (Fig. 4) is 0.80. However, this correlation vanishes when all the observations from both countries are considered simultaneously to find correlation co-efficient (r=0.02, n=48). This is due to opposite biases between MERRA-2 data and ground observations data in both countries. Relative errors for each site are calculated as:


SBC levels in China consistently showed a positive bias in comparison to all ground observations studies included in current study with mean(±SD) relative error of 104±91%. Qin et al. (2019) also found a positive bias in temporal variation of MERRA-2 data during 2015-2016 in Beijing (ratio of the MERRA-2 SBC to ground-based EBC concentration ranged between 1.53 and 2.77 throughout the years of 2015 and 2016). SBC levels in India showed a negative bias in comparison with all groundbased observations (mean±SD=-60±27%) except for the two remote sites (Hanle and Ooty). A previous study (Navinya et al., 2020) found the negative mean bias of 38% between PM2.5 levels derived using MERRA-2 data (SBC, sea-salt, dust, SO4, OC) and PM2.5 data of 20 ground monitoring stations in India.

The probable reason for these biases between two datasets is the spatial resolution of MERRA-2. One grid of MERRA-2 data covers a large area (about 50×50 km2) and is more appropriate to define the averaged SBC levels of entire city. Mode relative errors for all studies conducted at single site in Delhi are more than 47% and goes up to 77%, whereas mode relative error is only 31% for a study conducted in Delhi (Tyagi et al., 2017), in which mean EBC value of simultaneous observations of 8 monitoring stations spanning over Delhi is reported. Furthermore, MERRA-2 SBC levels represent the average SBC levels of vertical region up to the Planetary Boundary Layer (PBL) height in the gridded area, whereas ground observations represent EBC mass very near to surface (usually measurements are done at building rooftops, i.e. at <30 meters height). Surface level BC concentrations in a long-term study (Zhang et al., 2020b) in Beijing were calculated by measuring BC column density derived by Aerosol Robotic Network (AERONET) data and subsequently dividing it by PBL height. Surface level BC reported in Zhang et al. (2020b) are very similar (relative error less than 10% for 6-year observation data) to SBC levels.

Overall, it can be concluded that spatial distribution of SBC correlates well with spatial distribution of EBC levels in each country individually but the relation between SBC and EBC is exactly the opposite between China and India (positive bias in China and negative bias in India). Even though various possible reasons (difference in methodology, different horizontal and vertical resolution of both data set) are discussed above, a rigorous scientific assessment using highly resolved ground measurements is further required for better assessment. Therefore, currently available SBC and EBC datasets can only be used to define the relative spatial distribution of BC in both countries individually and it is not possible to compare the concentrations of BC in China and India on an equal footing.

4. 8. 3 Comparison Between Temporal Variation and Seasonal Characteristics of SBC and EBC Levels in Beijing and Delhi

Yearly averaged EBC levels (reported from studies conducted for at least 1 year) and MERRA-2 SBC levels of Delhi and Beijing spanning time period 2006-2020 are plotted in Fig. 5(a) and 5(b), respectively. There exists a moderate correlation (r=0.46) between temporal variation of yearly averaged SBC and EBC levels in Beijing, but there is no correlation (r=0.06) among yearly averaged SBC and EBC levels in Delhi. Since both Beijing and Delhi are megacities, the concentration variation greatly depends on the environment/surrounding of the measurement sites. Furthermore, as discussed in current study, biases due to use of different instruments can also contribute to variation in EBC levels.

Fig. 5. 
Annually averaged temporal variation of EBC and SBC levels in (a) Beijing and (b) Delhi spanning time period 2006-2020.

Mean EBC levels reported by three studies (Bisht et al., 2015; Tiwari et al., 2014; Tiwari et al., 2013) in Delhi spanning time period 2011-2012 varied slightly ranging from 6.7 μg m-3 in 2011 (Tiwari et al., 2013) to 7.9 μg m-3 in 2012 (Bisht et al., 2015). All these studies were conducted at the same site (IITM, Delhi) and used same instrument (AE-31). EBC levels reported in these studies are about 50% of EBC levels reported by two recent studies (Kumar et al., 2020; Tyagi et al., 2020) in which measurements were done by a different instrument (AE- 33 in both studies). Mean EBC level reported by measurement at 7 locations using AE-31 in Delhi by Tyagi et al. (2017) during December 2013-February 2014 is 8.0±3.1 μg m-3. These EBC levels reported are merely 33% of the EBC levels (24 μg m-3) reported by two studies (Bisht et al., 2016; Dumka et al., 2018) conducted for the time period December 2015-February 2016 using different instrument (AE-33) in Delhi. It is worth mentioning that even though site of EBC measurement was different in later studies (IITM in Bisht et al. (2016) and IMD in Dumka et al. (2018), Fig. 4) but EBC levels reported are very similar (24.1±5 μg m-3 and 24.4± 12.2 μg m-3) showing the importance of using same parameters and instruments to bring comparability/uniformity in measurements.

In Beijing, there are 4 studies spanning over a time period of 12 years (2006-2017) compared to that of 8 studies in Delhi covering 8 years in the time period 2006-2018. Therefore, based on above discussions it can be deduced that lack of long-term studies (long temporal observation at the same site using same instrument) in Delhi is one of the major reasons for less to no temporal correlation between MERRA-2 data and ground observations data. SBC levels over the years in both cities show negligible variation from 2006 to 2020 with mean value(±SD) of 7.9±0.27 μg m-3 in Beijing and 3.2±0.23 μg m-3 in Delhi. EBC levels in Beijing showed less variation, Fig. 5(a) (mean±SD=4.16±0.9 μg m-3) as compared to Delhi, Fig. 5(b) (11.3±3.2 μg m-3). However, these trends are contradictory to studies based on PM data (Ren, 2020) in which it was pointed out that pollution levels have decreased in Beijing over the last few years (2014-2018). Furthermore, as mentioned above, less to no correlation exists between temporal variation of SBC and EBC levels of studies included from Delhi and Beijing. Also available EBC data cannot be considered reliable to define trend over the years in both cities due to less spatial coverage and use of different instrument and different sites in studies. Therefore, no concluding remarks can be made using either EBC or SBC data about trend of BC aerosol over the years in both cities.

Seasonal characteristics of EBC levels and SBC levels in Beijing and Delhi for the time period (2006-2020) is shown in Fig. 6(a) and 6(b), respectively. Season wise mean SBC and EBC levels are highly correlated (r=0.90 in Beijing and r=0.97 in Delhi) in both the cities. Therefore, relative seasonal characteristics in both cities can be explained on the basis of SBC levels.

Fig. 6. 
Seasonal characteristics of EBC and SBC levels from 2006 to 2020 in (a) Beijing and (b) Delhi.

SBC levels in Beijing show little seasonal variation with SBC levels being slightly higher in winter and autumn as compared to that of spring and summer. Mean(±SD) SBC levels (during time period 2006-2020) are 9.00± 0.84 μg m-3 in winter, 8.9±0.46 μg m-3 in autumn, 7.27±0.38 μg m-3 in summer, and 6.61±0.40 μg m-3 in spring. Residential coal burning during periods with low temperature and relatively stable meteorological conditions could be the possible reasons for slight increment in EBC levels in winter (December-February) and partly (in November) in autumn (Chen et al., 2016; Liu et al., 2016). Biomass burning in harvest season was found be another reason for comparatively high pollution in autumn (Ji et al., 2017; Yu et al., 2013). Wet scavenging, increased dispersion and low emissions in summer are the possible reasons for less EBC levels in summer (Ji et al., 2017).

SBC levels in Delhi show distinctive seasonal variation and are significantly higher (3 times) in post-monsoon and winter in comparison to summer and monsoon. Mean(±SD) SBC levels (during time period 2006- 2020) in winter and post-monsoon are 5.26±0.59 μg m-3 and 5.66±0.93 μg m-3, respectively. Whereas mean SBC level in summer and monsoon are 1.87±0.12 μg m-3 and 1.72±0.11 μg m-3. Unfavorable meteorological conditions (low relative humidity and low solar heating of land accompanied by low ventilation coefficients that result in less dispersion of aerosols) (Tyagi et al., 2020; Tiwari et al., 2013) along with increased local emissions (open waste burning by people to fight cold) (Dumka et al., 2018) are the main reasons for high EBC levels in winter in Delhi. Bisht et al. (2015) found the strong influence of agricultural biomass burning on PM2.5 and carbonaceous aerosol during post-monsoon in 2012. Other studies have also found the impact of agricultural burning in nearby states on PM2.5 levels in post-monsoon season in Delhi. They used fire spots (NASA’s Fire Information for Resource Management’s Archives) data and air mass back trajectories to analyse the effect. However, study relating to the effect of fire-spots primarily on EBC levels in Delhi is still missing.


EBC over the years or to realise the distribution of carbonaceous aerosol throughout the country. In India, the number of studies is comparatively higher but adequate longterm data are limitedly available to set a determinate trend over the years of EBC. The data presently available dampens the possibility of a meta-analysis being carried out because of the inconsistency in methodologies and variegated parameters adopted for measurement in different studies. The incoherency in methodologies is the result of a need of standard guideline and establishment of a reference method. In this review we have highlighted several parameters that need to be standardised through a systematic and scientific assessment. It is found that variability in parameters like wavelength, MAC, and particle size can lead to variation in EBC mass reported by an instrument. Measuring EBC at near UV region can lead to 20-50% over estimation, using a size segregation device at inlet of aerosol flow line can result to underestimation of 10-15% as compared to when no size segregation device is used. Using manufacturer recommended value of MAC can lead to a large uncertainty (i.e. up to 100% over- as well as under-estimation of measurement results).

In an attempt to reduce discrepancy and improve the quality of reporting of EBC concentrations, following recommendations are made based on studies included in this review:

(i) Other instruments used for EBC measurement (COSMOS, MAAP, MSCP) should be explored and more studies based on these instruments are needed in both countries to check their performance as they provide better advantage over conventionally used instrument Aethalometer. Furthermore, a filter-based instrument which has all the advantages/features (like heated inlet as provided in COSMOS to get stable MAC value, multiwavelength channel and dual spot measurement technique as provided in AE-33 to reduce filter loading effect, multiangle reflectance measurement as provided in MAAP to remove multiple scattering error) can provide better measurement results with minimum uncertainty. Manufacturers can collaborate to come up with such an instrument.

(ii) In-situ measurement instruments (PAS and BCP) need to be promoted in both countries as these instruments provide a direct approach to measure optical properties of carbonaceous aerosol due to consumable (filter) free operation. These methods do not suffer from measurement errors arising due to filter loading and filter multiple scattering. Furthermore, inter-comparison of absolute value of BC mass concentration measured by SP2 should be performed as it can selectively detect BC particles and can be helpful in studying various properties (effective density, mixing states) of BC.

(iii) Standardization of wavelength used to report EBC measurement is necessary as measurements at different wavelengths can produce inconsistent results. Measurement at 880 nm wavelength channel (standard channel in Aethalometer measurements) is a good approach as other aerosol has less absorbance at this wavelength, and most of the previous measurements have been performed at this wavelength. However, high instability of MAC value at 880 nm wavelength is a major issue related to these measurements. Stabilised MAC value at 565 nm due to use of heated inlet makes measurement at this wavelength another alternative.

(iv) Use of a stable and correct MAC value is very essential to reduce uncertainty in measurement of EBC. All sources of uncertainty should be reduced by applying all correction measures simultaneously which are (a) use of a heated inlet to remove coating and get a stable MAC value (b) inter-comparison with MAAP to reduce effects of scattering due to aerosol.

(v) More studies are needed to realise the fraction of EBC in various type of aerosol based upon size (TSP, PM10, PM2.5, PM1). It is recommended to measure EBC concentration in PM1 to realise its climate effects. If a study is carried out to realise health effects of EBC then use of size-segregation device to measure EBC is recommended to get concentration of inhalable, respirable EBC only. Furthermore, EBC in TSP (or at least PM10) should be considered to characterise the distribution of EBC at given place. Use of cutoff can lead to 10 to 15% underestimation of total suspended EBC.

(vi) Use of specific term “EBC” is recommended to report measurements done by making use of light absorption technique to avoid confusion with EC or rBC. Use of simplified term BC should be avoided.

segregation device as well as flow rate and other important parameters. If flowrate of instrument is different from the one as per guidelines suitable measures like implementation of an extra pump to draw ambient air from air can be taken. Further, isokinetic sampling must be insured if extra pump is used to achieve desired particle concentration.

(viii) Combined uncertainty of measurement derived from sources which can be quantified should be calculated and mentioned. Inter-comparison technique with the reference method can be used to report accuracy of instruments and get an idea due to all factors (quantifiable as well as unquantifiable) once a reference method is widely accepted throughout the world.

(ix) International and national level regulatory bodies like International Bureau of Weights and Measure (BIPM) and National Metrological Institutes (NMIs) can come together to develop a standard traceable calibration procedure for BC instruments. Certified Reference Material (CRM) can be developed by further investigating the properties of possible candidates summarized in Baumgardner et al. (2012).

(x) Due to high significance of BC measurements, it should be included as one of the parameters in National Air Quality Standards (NAAQS) of nations, especially in India and China which are considered largest emitters of carbonaceous aerosol globally. Furthermore, efforts should be made towards developing and establishing a globally accepted standard protocol/ method for BC measurements to bring consistency in independent measurements.

MERRA-2 SBC data correlates very well with EBC data of spatially distributed ground-based observations reviewed in this study from each country. However, distinctively opposite biases (EBC greater than SBC in India and EBC less than SBC in China) between MERRA-2 SBC and EBC distribution levels were observed. Therefore, it is not possible to compare the concentrations of China and India on an equal footing. Heat map of SBC levels shows that Sichuan Basin and Northern Plane in China, and Indo-Gangetic Plane in India experience significantly higher concentrations of carbonaceous aerosol as compared to other regions in the respective countries. Temporal variation of SBC data does not show any significant change in SBC levels over the years in Beijing and Delhi in contrast to previous studies comparing pollution in Beijing and Delhi based on PM2.5 levels. There is moderate correlation between temporally varying EBC levels and SBC levels in Beijing and no correlation exists in Delhi. Therefore, no concluding remarks can be made about trend of BC aerosol down the years in both cities. Seasonally, mean EBC levels and SBC levels correlate very well and show similar variations. BC levels in Delhi show large variation seasonally as post-monsoon and winter seasons accounted for most of the carbonaceous aerosol in Delhi. BC levels in Beijing do not vary much seasonally and concentrations in winter and autumn were slightly higher than spring and summer. Absence of publicly available ground observation based reliable data and studies spanning large area and time period makes it difficult to fully validate modelled SBC data. Therefore, it is recommended to have long term observations taking into account above mentioned measures especially for studying health impact and making better control planning.


AM is thankful to University Grant Commission (UGC) for providing the fellowship under UGC-SRF scheme (Project No. P90802). The authors thank Director, CSIR-National Physical Laboratory for facilitating support. All the members of the Gas Metrology group and Head of the ESBM division are acknowledged for their help and support. Authors are also thankful to three anonymous reviewers for their valuable suggestions. Authors acknowledge the Global Modelling and Assimilation Office of the Goddard Space Flight Centre, NASA for providing the MERRA-2 plaform.

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