Special Issue on 2022 Asian Aerosol Conference (AAC 2022) (V)

Vivek Kumar1,2, Panuganti C.S. Devara This email address is being protected from spambots. You need JavaScript enabled to view it.2, Vijay K. Soni1 

1 Environmental Monitoring and Research Centre (EMRC), India Meteorological Department (IMD), Ministry of Earth Sciences, New Delhi 110003, India
2 Amity Centre of Excellence in Ocean-Atmospheric Science and Technology (ACOAST) & Environmental Science and Health (ACESH), Amity University Haryana (AUH), Gurugram 122413, India

Received: November 30, 2022
Revised: March 17, 2023
Accepted: March 30, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220435  

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Cite this article:

Kumar, V., Devara, P.C.S., Soni, V.K. (2023). Multisite Scenarios of Black Carbon and Biomass Burning Aerosol Characteristics in India. Aerosol Air Qual. Res. 23, 220435. https://doi.org/10.4209/aaqr.220435


  • BC mass concentration shows declining trend over study regions during 2016–2021.
  • Low BC mass concentration in monsoon and high in winter and post-monsoon seasons.
  • BB shows two peaks, one in April/May and another in September/October months. 
  • Stubble/BB emissions cause high BC concentration during post-monsoon months.
  • Multi-site BC and BB trends help air pollution assessment and model evaluation.
  • Results aid development of better policy matters for the reduction of BC emissions.


Black Carbon (BC) aerosols are not only substantial climate-forcing drivers but also impact human health. The spatial distribution of BC aerosols depends on the combination of anthropogenic activities and meteorological conditions. In this study, we used the India Meteorological Department (IMD) Black Carbon Observational Network datasets to assess the diurnal, seasonal, and long-term BC trends for the period, 2016–2021. The majority of the IMD’s BC monitoring stations show an overall declining trend in the BC mass concentration during the study period in India. Maximum BC concentrations are observed in the post-monsoon and winter seasons due to the stubble-burning activity and lower values of Atmospheric Boundary Layer Height (ABLH). Minimum concentrations are observed at all stations in the monsoon season due to the wet scavenging of aerosols by rain. There is a clear decrease in the BC mass concentration from winter to monsoon months and an increase in the post-monsoon months. Regional emissions from crop residue burning in the post-harvesting seasons are the main contributing factor for extremely high levels of BC mass concentration. Low wind speed and shallow mixed layer were found to be the main reasons for high levels of aerosol concentration during the winter season. There is an increasing trend in Biomass Burning (BB) at most of the stations except for Thiruvananthapuram, where a prominent decreasing trend in BC concentration is also noticed. In the present study, the impact of local meteorological parameters such as wind, temperature, rainfall and Atmospheric Boundary Layer Height on BC mass concentration is investigated. The results show a negative correlation with rainfall, relative humidity, wind speed, temperature and ABL height. Both local activity and long-range transport at each study site are also found to be responsible for the significant changes in BC mass concentration.

Keywords: Black carbon, Carbonaceous aerosols, Biomass burning, Trend analysis, Atmospheric boundary layer


Black Carbon (BC) aerosols are termed as short-lived climate drivers and play a significant role in the Earth’s climate system at regional and global scale. BC aerosols are generated from incomplete combustion of fossil fuels, biofuels, and biomass. BC aerosols exist as an aggregate of small carbon spherules (Bond et al., 2013; Jeong and Lee, 2017) and are insoluble in water and organic solvents. The temperature and carbon emission during the combustion process define the absorption properties of black carbon. The aging of BC aerosols and coating by other material through condensation and coagulation can enhance the light absorption capability of BC particles (Chan et al., 2011; Khalizov et al., 2009; Liu et al., 2015; Moffet and Prather, 2009; Sun et al., 2020; Xu et al., 2018; Zhang et al., 2018). These aerosols absorb solar radiation over a wide spectral band from UV to IR and contribute to the atmospheric warming (Bond et al., 2013Bond and Bergstrom, 2006; Jacobson, 2001; Ramanathan and Carmichael, 2008). BC aerosols affect distribution, lifetime, and microphysical properties of clouds through indirect effects (Hendricks et al., 2011; Twomey, 1974), the semi-direct effect and cloud absorption effects (Ackerman et al., 2000; Bond et al., 2013) and a feedback of clouds to the surface cooling induced by BC (Liepert et al., 2004) or through the dynamics and precipitation changes induced by BC absorption of solar radiation (Ming et al., 2010). The global mean radiative forcing of BC aerosol formed due to fossil fuel and biofuel burning has increased from +0.20 W m2 to +0.40 W m–2 (Myhre et al., 2013). BC aerosols act to increase lower tropospheric heating and reduce the amount of solar radiation reaching the surface. Reducing the black carbon emission concentration may provide a quick fix for the global warming problem (Ramanathan and Carmichael, 2008). BC aerosols can be transported long distances by atmospheric circulation and deposit on snow and/or ice surfaces by rainout, washout, and dry deposition thereby reducing the surface albedo. Reduced albedo of snow/ice results to increased absorption of solar radiation thus enhancing the melting of glaciers. Furthermore, the BC snow/ice forcing mechanism can cause the snow/ice albedo feedback through melting of snow, enhancing further surface warming (Flanner et al., 2009). Black carbon is a component of PM2.5 that affects human health (Safai et al., 2014; Beegum et al., 2009; EEA, 2013). It can cause respiratory and cardiovascular diseases. Carbonaceous emissions having concentrations in the range of 100,000–350,000 ng m–3 causes oxidative stress, inflammation, lipid peroxidation and atherosclerosis, change in heart rate variability, arrhythmias, ST-segment depression (heart function), and changes in vascular function (Grahame and Schlesinger, 2010).

Indo-Gangetic plain is a densely populated region of India and is termed a hotspot for black carbon emissions (Ramanathan and Carmichael, 2008). Improved understanding in characteristics and spatial heterogeneity of BC aerosols over India and in particular Indo-Gangetic Plain would provide valuable information for guiding measures to reduce emissions and to air pollution and climate change in the region. In the present study, we examined the interannual, seasonal and diurnal variation of BC over India using data from Black Carbon Monitoring Network of India Meteorological Department. The stations of this network are located in different geographical regions all over India. We also aim to quantify the role of meteorological parameters and anthropogenic and biomass burning emissions in the variation of mass concentration of BC aerosols. The seasonal changes in relation to the atmospheric boundary layer (ABL) height have been studied in detail. The emissions from stubble burning contributes to the episodic extreme pollution events in India (Paliwal et al., 2016). The aethalometer model proposed by Sandradewi et al. (2008) is used in the study to assess the biomass burning and fossil fuel contribution in black carbon aerosols. The impact of COVID-19 lockdown in BC concentration has also been considered while analyzing the data.


‘‘India Meteorological Department (IMD) Black Carbon (BC) Network’’, established in India, consists of a wide range of geographical locations associated with different environmental/meteorological conditions. Some salient details of the experimental locations are presented in Table 1. The BC datasets archived, employing a multi-spectral next-generation Aethalometer (AE-33) at the network stations during 2016–2021 are utilized to examine the morphological and long-term changes and trends. Albeit, many studies of BC aerosols have been carried out in India, most of them focus on specific locations. Comprehensive studies covering BC characteristics over different environments, comparison and trends are sparse. Such studies are possible only with systematic network observations. Such a national network with routinely calibrated AE-33 Model Magee Scientific seven-beam Aethalometer has been utilized in the present study. It works on light wavelength-dependence on absorption principle using suitable mass absorption cross-section values (Petzold et al., 2013). It uses seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) that allow spectral analysis for different purposes, such as mineral dust detection and source apportionment (Drinovec et al., 2015). It measures elemental carbon (EC) or black carbon (BC) mass concentration (in µg m3 or ng m3), and biomass burning (BB, in per cent). Traditionally, the equivalent black carbon concentration is calculated using the light attenuation at 880 nm along with an absorption cross-section value of 7.77 m2 g1 because absorption due to other aerosols is negligible at this wavelength (Sandradewi et al., 2008; Yang et al., 2009; Drinovec et al., 2015). The instrument uses Teflon coated glass fiber filter tape and an impactor (PM2.5) at inlet limiting the inlet particle size to 2.5 µm. Plus, the Aethalometer (Model AE-33) uses dual-spot technique, which enables near real time compensation for the spot loading effect. More details about the performance of the Aethalometer (AE-33) can be found in Drinovec et al. (2015) and recently by Sonbawne et al. (2021) and references therein.

Table 1. IMD BC network stations and characteristics.

Atmospheric aerosols, along with their physical, chemical, optical and radiative properties, are highly variable in space and time. In this communication, we report the results of the analysis of a long series (2016–2021) of BC aerosol datasets, archived by the IMD network, covering over twelve locations, spread over the country. These locations include Ranichauri (RCH), Srinagar (SRN), Chandigarh (CHD), Varanasi (VRN), Jodhpur (JDP), New Delhi (NDL), Kolkata (KOL), Guwahati (GWH), Bhuj (BHJ), Pune (PUNE), Nagpur (NGP) and Thiruvananthapuram (TRV). Their locations are indicated on the India map in Fig. 1. In the present study, mainly three parameters i.e., biomass burning (BB), black carbon (BC) mass concentration, associated with wood-burning at wavelength 370 nm (BC370 nm), and fossil fuel mainly associated with vehicular exhaust at wavelength 880 nm (BC880 nm) in ng m3 are studied at multiple locations associated with varying environments, utilizing the above-shown IMD network. The BC monitoring stations used in this study are classified based on the population except for the background station, Ranichauri and a coastal station, Thiruvananthapuram. The stations that had a population of more than a hundred thousand but less than 2 million are classified as urban stations and those having more than 2 million are classified as mega city. The broad site characteristics of experimental locations are presented in Table 1.

Fig. 1. IMD black carbon network.Fig. 1. IMD black carbon network.

The basic one-minute interval data of BB, BC370 nm and BC880 nm are used to calculate the hourly means. They are further used to compute the daily, monthly, seasonal, annual means and to examine long-term changes and trends for future assessment.


3.1 Mean BC and BB Variations

The BC monitoring stations used in this study are classified based on the population except for the background station, Ranichauri and coastal station, Thiruvananthapuram. The mean mass concentrations of BC370 nm and BC880 nm, and corresponding biomass burning at all the study locations during 2016–2021 are compared in Figs. 2(a) and 2(b). The BC mass concentration at both 370 nm and 880 nm was found to be higher at New Delhi (15560 ± 592 ng m3) and Kolkata (12699 ± 6828 ng m3), while they were minimum at the background station, Ranichauri (2800 ± 1094 ng m3), which is ascribed to be due to the combined effects of the prevailing urbanization and meteorology. The BC concentration at the background station is found to be similar to the BC concentrations at other background site in Asia (Cha et al., 2019).

Fig. 2. (a) Variation in mean mass concentration of BC370 nm and BC880 nm with standard deviation. (b) Mean Biomass burning with standard deviation for all stations during 2016–2021.Fig. 2. (a) Variation in mean mass concentration of BC370 nm and BC880 nm with standard deviation. (b) Mean Biomass burning with standard deviation for all stations during 2016–2021.

The differences between these stations in respect of the sources of fossil fuel versus wood-burning, reveal relative strengths of activities (Fig. 2(a)). The higher BC values observed over megacity, Kolkata can be attributed to the emissions from transport and industrial sector. The mean BB percentage values is higher at background station Ranichauri (34 ± 9%) and lowest at Kolkata (12.4 ± 1.4%) (Fig. 2(b)). The higher BB component over Ranichauri is attributed mostly to the frequent forest fires during summer and increased emission from domestic heating during winter season while the lower BB values over Kolkata could be due to prevailing complex meteorology resulting from inhomogeneous terrain characteristics. The heterogeneity of the BC network has been studied for the period 2016–2018 by (Kumar et al., 2020). The results showed maximum mean BC880 nm concentrations at New Delhi but now Kolkata has surpassed New Delhi recording maximum mean BC880 nm concentration during 2016–2021 (Fig. 2(a)). This is attributed to the increase in the number of electric vehicles in New Delhi decreasing the vehicular exhaust of black carbon.

The long-term changes in BC mass concentration show a decreasing trend in most of the cities except Kolkata. Moreover, it is minimum in the monsoon months (June, July, August, September) and maximum in the post-monsoon months (October and November) during the year. The stations in the Indo-Gangetic plains show higher concentration than the peninsular stations (Beegum et al., 2009). The best practices behind the declining trend in BC concentrations over Indian stations help reducing the global warming (Takemura and Suzuki, 2019).

3.1.1 Ranichauri (background site)

Ranichauri is located in the Himalayas at an altitude of 1950 m AMSL with a very sparse population. It is considered to be a background station in the IMD BC network. Fig. 3(a) displays the time variation of mean BC mass concentration, BB, rainfall, and temperature over Ranichauri during the study period. A close association between these parameters, particularly a positive relationship between BC and BB, a negative relationship between BC mass concentration and precipitation (rain-scavenging effect) can be seen from the figure. The black carbon mass concentration shows a decreasing trend (see Table 2) in both the BC370 nm with coefficient of determination, R2 = 0.0011 in Fig. 3(c) and BC880 nm with R2 = 0.0055 in Fig. 3(d). While biomass burning in Fig. 3(b) shows a significant increase (R2 = 0.066 over the period 2016–2021). Higher biomass burning at Ranichauri is mainly because of domestic heating due to the colder climate of the region. The BC370 nm (Fig. 3(c)) and BC880 nm (Fig. 3(d)) show an increasing trend. Moreover, they show a peak in the post-monsoon or early winter and low in the monsoon season. As explained above, the increase in BC concentration and BB during this period is attributed to stubble-burning activity and domestic heating as well as lower boundary layer height. This aspect has been demonstrated in the monthly mean BC and BB variations, averaged over the study period of 2016–2021. Figs. 3(e) and 3(f) depict monthly mean variations in BC concentration at 370 nm, 880 nm, and BB at Ranichauri, respectively. As in the case of BC mass concentration at both 370 nm and 880 nm, BB also showed minimum during the monsoon months. The lowest values are observed during the month of September.

Fig. 3. (a) Relationship between time variations in BC, BB and meteorological parameters, (b) Biomass burning trend, (c) BC370 nm (wood burning) trend, (d) BC880 nm (vehicular) trend, (e) Monthly mean variations in BC370 nm and BC880 nm, and (f) Monthly mean variation in BB at Ranichauri during 2016–2021.Fig. 3. (a) Relationship between time variations in BC, BB and meteorological parameters, (b) Biomass burning trend, (c) BC370 nm (wood burning) trend, (d) BC880 nm (vehicular) trend, (e) Monthly mean variations in BC370 nm and BC880 nm, and (f) Monthly mean variation in BB at Ranichauri during 2016–2021.

Table 2. Slopes of the trend line and coefficient of determination (R2) for each station. Statistically significant coefficients above 0.05 (R2 > 0.05) are considered and are made bold. .

Both wind speed and wind direction play an important role in the accumulation and dispersion of BC aerosol pollution. To study the variation of BC concentration with wind, wind roses during different seasons for Ranichauri site have been presented in Fig. 4. The seasonal mean wind speed (Fig. 4(a)) also plays an important role in dispersing the carbonaceous aerosols in different seasons. The strong westerlies are observed during pre-monsoon and monsoon seasons as compared to the winter and post-monsson seasons, which contributes to lower concentration at Ranichauri. The strength of winds decreases relatively during the post-monsoon and winter seasons. The seasonal mean variation in BB is plotted in Fig. 4(b). The seasonal trend lines and extent of uncertainty or deviation of monthly mean from the 6-year mean are also shown in the figure as shaded bands.

Fig. 4. (a) Seasonal windrose, (b) Seasonal biomass burning variation at Ranichauri.Fig. 4. (a) Seasonal windrose, (b) Seasonal biomass burning variation at Ranichauri.

These features suggest that seasonal mean BB component decreases in the order as winter > post-Monsoon > pre-monsoon>monsoon (Fig. 4(b)). The higher concentration during colder season is because of increased emission from domestic heating in the region.

3.1.2 Jodhpur (urban site)

Jodhpur is an urban desert location that exhibits high temperatures and frequent dust storms during the pre-monsoon months. Fig. 5(a) displays the time variation of mean BC mass concentration, BB, rainfall, and temperature over Jodhpur during the study period. A close association between these parameters, particularly a positive relationship between BC and BB, a negative relationship between BC mass concentration and precipitation (rain-scavenging effect) can be seen from the figure. The BC mass concentration at Jodhpur shows a decreasing trend (Table 2) in both the BC370 nm with coefficient of determination, R2 = 0.056 in Fig. 5(c) and BC880 nm with R2 = 0.07 in Fig. 5(d). While BB in Fig. 5(b) shows a significant increase (R2 = 0.13 over the period 2016–2021). The BC concentration at 370 nm (Fig. 5(c)) and at 880 nm (Fig. 5(d)) shows an increasing trend. Jodhpur shows peak in the post-monsoon or early winter and low in the monsoon season. The increase in BC concentration and BB during this period is attributed to both stubble-burning activity and lower boundary layer height.

Fig. 5. Same as Fig. 3, but for Jodhpur.Fig. 5. Same as Fig. 3, but for Jodhpur.

The BC concentrations show a peak in winter season and minima in the monsoon season (Figs. 5(e) and 5(f)). Fig. 5(a) shows the time series of average temperature, rainfall, BC370 nm, BC880 nm and biomass burning at Jodhpur. The BC is negatively correlated with temperature. The correlation coefficient between temperature and BC370 nm, BC880 nm, and BB is –0.48, –0.50 and –0.24 respectively. The BC-Rainfall correlation is also negative (BC370 nm = –0.39, BC880 nm = –0.38 and BB = –0.38). The BB shows bimodal monthly variation at Jodhpur (Fig. 5(f)), showing one peak in May and other in November/December due to crop residue burning of Rabi and Kharif crops. During monsoon and pre-monsoon seasons, south-westerly wind (Fig. 6(a)) prevails at Jodhpur, having negative BC-wind speed correlation (BC370 nm = –0.48, BC880 nm = –0.50 and BB = –0.24) for the study period. Seasonal trend in BB at Jodhpur is seen in decreasing order as post-monsoon > winter > pre-monsoon > monsoon (Fig. 6(b)).

Fig. 6. Same as Fig. 4, but for Jodhpur.Fig. 6. Same as Fig. 4, but for Jodhpur.

3.1.3 New Delhi

New Delhi is an urban mega city and the capital of India. It is a densely populated location. The agricultural residues are burnt mainly in April–May, which is the wheat crop and is not the dominant pollutant source (Brooks et al., 2019) in India but its contribution is very much seen in the peaks of mean yearly plots in Fig. 7(f). The meteorological conditions such as higher atmospheric boundary layer and strong winds are favorable for effective dispersion of pollutants during summer season. Agricultural residue burning also appears in the post-monsoon season. It is a large-scale agricultural residual burning or stubble burning phase that prevails in October and November (post-monsoon season) every year. The emissions from stubble burning together with unfavorable meteorological conditions such as lower atmospheric boundary layer height and light winds during post-monsoon season favor accumulation of pollutants and cause several extreme air pollution events in entire Indo-Gangetic Plain.

Fig. 7. Same as Fig. 3, but for New Delhi.
Fig. 7. Same as Fig. 3, but for New Delhi.

Fig. 7(a) shows the time series of average temperature, rainfall, BC370 nm, BC880 nm and BB at New Delhi. The biomass burning component of BC (Fig. 7(b)) shows an increasing trend (R2 = 0.033) over the study period. The BC mass concentration shows a decreasing trend at New Delhi (Table 2) in both the BC370 nm (wood burning) with coefficient of determination, R2 = 0.026 in Fig. 7(c), and more prominent in BC880 nm with R2 = 0.06 (Fig. 7(d)). The BC concentrations show a peak in the winter and minima in the monsoon season (Figs. 7(e) and 7(f)). Strong north-westerly wind blows during the pre-monsoon season which brings BC aerosol concentration down. Seasonal BB concentration is in order as post-monsoon > winter > pre-monsoon > monsoon. This seasonal variation shows the impact of stubble burning during post-monsoon at the station. The BC-Temperature correlation (BC370 = –0.70,BC880 = –0.71 and BB = –0.53) is also negative which is depicted in the Fig. 7(a). BC-Rainfall negative correlation coefficients (BC370 = –0.52, BC880 = –0.53 and BB = –0.38) indicates the rainfall effectively scavenge BC aerosols. Correlation analysis shows that BC is negatively correlated with wind speed with coefficients (BC370 = –0.64, BC880 = –0.61 and BB = –0.59). Maximum occurrence of BC is observed associated with low wind speed categories. The BC mass concentrations show declining trend for both BC370 nm and BC880 nm. The seasonal variation of wind and BB can be seen from Fig. 8(a) and Fig. 8(b), respectively.

Fig. 8. Same as Fig. 4, but for New Delhi.Fig. 8. Same as Fig. 4, but for New Delhi.

3.1.4 Thiruvananthapuram (coastal site)

Thiruvananthapuram is a coastal city with very prominent decreasing trend in BC370 nm, BC880 nm and BB for the period 2016–2021. The BC mass concentration shows a significant decreasing trend (Table 2) in both the BC370 nm with coefficient of determination, R2 = 0.17 in Fig. 9(c) and BC880 nm with R2 = 0.11 in Fig. 9(d). While biomass burning component in Fig. 9(b) also shows a significant decreasing trend with coefficient of determination, R2 = 0.48 over the period 2016–2021.

Fig. 9. Same as Fig. 3, but for Thiruvananthapuram.Fig. 9. Same as Fig. 3, but for Thiruvananthapuram.

The BC-Temperature correlation (BC370 nm = 0.10, BC880 nm = 0.11 and BB = 0.12) is positive which is depicted in Fig. 9(a), BC concentration remains same during peaks in temperature curve, BC-Rainfall correlation (BC370 nm = –0.51, BC880 nm = –0.50 and BB = –0.37) is negative reducing the concentration due to scavenging (Figs. 9(a), 9(e) and 9(f)) shown in the mean monthly variation of BC370 nm, BC880 nm and BB at Thiruvananthapuram for the study period.

Strong north-westerly wind prevails at the station during monsoon season (Fig. 10(a)), having negative BC-wind speed correlation (BC370 nm = –0.59, BC880 nm = –0.63 and BB = –0.12) for the study period. Seasonal mean BB concentration values at Thiruvananthapuram are in decreasing order as winter > pre-monsoon > post-monsoon > monsoon (Fig. 10(b)).

Fig. 10. Same as Fig. 4, but for Thiruvananthapuram.Fig. 10. Same as Fig. 4, but for Thiruvananthapuram.

3.1.5 Year-to-year variation of impact of ABLH on environmental pollution

Atmospheric Boundary Layer Height (ABLH) plays a crucial role in the formation and development of air pollution events (Devara, 2017; Yan et al., 2019). During post-monsoon and winter season, the variations in ABL height, driven by a strong radiative thermal inversion affect the regional air pollution due to weak turbulence in the bulk of the atmosphere because of stable stratification between different layers. Besides the source strength and dynamics, ABL height regulates the pollution levels at any location. The ABL height is more during daytime due to convective activity and vice-versa during nighttime.

The ERA-5 Re-analysis data are used for retrieving information on ABLH over the study sites during 2016–2021. The monthly mean variation of ABLH over the study locations (Ranichauri, Jodhpur, New Delhi, and Thiruvananthapuram) during the observation period (2016–2021) are shown in Fig. 11. High humidity, low wind and inversion are unfavorable to the development of boundary layer height. An increase in ABL height is observed after sunrise, corresponding to the rise in temperature and drop in relative humidity. The convective mixing after sunrise leads to the breaking of the nocturnal stable atmospheric layer, ensuing an increase in ABL height. The ABL height remains elevated until evening. After sunset, the ground surface cools faster than the air above surface, resulting radiation inversion and consequently the ABL height comes close to the ground surface. At Ranichauri, the diurnal ABLH varies from 20 m at winter night to 2000 m during pre-monsoon afternoon. The seasonal diurnal variation shows minimum ABL height during winter and maximum during pre-monsoon due to higher atmospheric temperature. The ABL height varies over Jodhpur from 500 m to 3000 m. Over New Delhi, the BLH was found to vary from 200 m to 2500 m. Thiruvananthapuram, being a coastal station, the variation in ABL height is small as compared to other stations because of the moisture and moderate temperature. The BLH over this location varies from 400 m to 800 m. A strong influence of the low nocturnal ABL height on the measured BC mass concentration was observed, with an inverse relationship between concentrations and ABL height.

Fig. 11. Multi-site mean ABLH variations during 2016–2021.Fig. 11. Multi-site mean ABLH variations during 2016–2021.

3.2 Impact of COVID-19 Lockdowns

The COVID-19 lockdown resulted in a significant reduction in industrial activities and transportation throughout India, leading to lower levels of BC mass concentrations. Table 3 presents the mean BC mass concentration BC880 nm, along with their standard deviation. The mean BC mass concentration data from January 2016 to December 2019, and from January 2020 to August 2021 are analyzed separately to study the impact of pre-COVID-19 and post-COVID-19, respectively. With the exception of Ranichauri (background), all other prominent locations showed a reduction in mean BC mass concentration after COVID-19, ranging from 20% to 70%. The background station showed an increase of 8% in BC mass concentration after COVID-19, likely due to the sparse population, minimal transport activity but higher wood burning in the area. Jodhpur showed a significant reduction of 46% from the pre-COVID-19 mean concentration (as shown in Fig. 12). New Delhi and Thiruvananthapuram also showed a decline in BC mass concentration of 29% and 34%, respectively, from pre-COVID-19 levels.

Table 3. Impact of COVID-19 on mean BC mass concentration with their standard deviation. Before COVID-19 (mean of 2016 to 2019) and After COVID-19 (mean of 2020 and 2021).

Fig. 12. Effect of COVID-19 on BC mass concentration for discussed stations. Before COVID-19 (2016–2019) and After COVID-19 (2020–2021).Fig. 12. Effect of COVID-19 on BC mass concentration for discussed stations. Before COVID-19 (2016–2019) and After COVID-19 (2020–2021).

The impact of lockdown on BC mass concentration is not straightforward, as it can be influenced by various factors such as the type of city, atmospheric conditions, the stringency of lockdown measures, and the prevailing economic activities during the lockdown period.


The BC mass concentration for both BC370 nm and BC880 nm shows a declining trend for the period of our study at most of the stations. The relative dominance between BC mass concentration and biomass burning at different experimental sites and causative reasons are explained. The BC concentrations are lowest in the monsoon and highest in the post-monsoon and winter seasons. There are two peaks at most of the stations in respect of biomass burning, i.e., in April/May and September/October. The recent COVID-19 lockdowns have also impacted the BC mass concentration. Biomass burning component of BC shows an increasing trend at most of the stations except Thiruvananthapuram. Emissions from stubble burning cause extreme high BC concentration events. High BC mass concentration during winter and post-monsoon seasons is also attributed to the atmospheric dynamics played by the ABL height which controls the surface concentration of pollutants.

The BC emission reduction during lockdowns of COVID-19 suggests a potential mitigation strategy that could reduce global climate forcing from anthropogenic activities in the short-term and shows the associated rate of climate change. The results of the present study of black carbon and biomass burning trends at different geographical locations in India would help not only in the seamless model evaluation and assessment of air pollution but also in the evaluation and development of policy matters for the reduction of black carbon emissions. In this regard, continuous monitoring, and evaluation of source strength vis-à-vis local as well as long-range meteorological patterns are highly essential. Such large-scale, National/International cooperative research programs are in progress. We intend to report such investigations in our further communication. Black Carbon aerosols have a very short lifetime compared to GHGs. Reducing black carbon as it has a shorter lifetime will eventually reduce global warming and can give us a quick fix to limit mean global temperature.


We express our sincere gratitude to the Secretary, Ministry of Earth Sciences (MoES), Director General of Meteorology (DGM), India Meteorological Department (IMD), Government of India (GoI); Founder President, Chancellor, and Vice-Chancellor of Amity University Haryana (AUH), Gurugram, India for their continued motivation and support. The authors are thankful to the personnel involved in the maintenance of the BC Network, IMD, India. Thanks to the Climate Data Centre, we acknowledge the ERA ECMWF Reanalysis Datasets (DOI: 10.24381/cds.adbb2d47) used in the study. Thanks, are also due to the Organizers of the 12th Asian Aerosol Conference (AAC 2022) held during 12–16 June 2022 in Taipei, Taiwan, where the preliminary results of this study have been presented, discussed, and improved. The critical comments and valuable suggestions provided by the Editor and anonymous Reviewer, which have helped to improve the scientific content of the original manuscript.


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