Evaluation of spatial and temporal heterogeneity of black carbon aerosol mass concentration over India using three year measurements from IMD BC observation network

Shipping-related air pollution at Busan Port: The unceasing threat of black carbon

Busan Port ranks among the top ten most air-polluted ports globally, yet the chemical characteristics of its air pollution, specifically the compositions and sources of aerosols, including black carbon (BC) have not been thoroughly studied. To assess the emission characteristics, four comprehensive air monitoring campaigns were conducted seasonally from fall 2020 to summer 2021. While mass concentrations of aerosol chemical species analyzed using the high-resolution time-of-flight aerosol mass spectrometer showed significant seasonal variations (3.6-11.4 µg·m-3), BC exhibited persistent concentrations throughout all seasons (1.9-2.2 µg·m-3). On average, BC constituted a staggering 26.5 % of the total non-refractory sub-micron aerosol mass concentrations. A positive matrix factorization model used to identify organic aerosol (OA) sources revealed six sources: two hydrocarbon-like OA, three oxygenated OA, and one biomass burning OA. Most of these sources were associated with shipping-related emissions at Busan Port. Spatial analysis of the elemental carbon revealed higher concentrations in port areas (1.2-1.5 µg·m-3) and surrounding areas (0.6-1.0 µg·m-3), compared to other urban cities (0.5-0.6 µg·m-3). This observation suggests that carbonaceous particles, along with persistent shipping-related OA emissions, are likely to disperse and impact air quality in adjacent urban areas, potentially posing health hazards.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

South and Southeast Asia controls black carbon characteristics of Meili Snow Mountains in southeast Tibetan Plateau

South and Southeast Asia (SSA) emitted black carbon (BC) exerts potential effects on glacier and snow melting and regional climate change in the Tibetan Plateau. In this study, online BC measurements were conducted for 1 year at a remote village located at the terminus of the Mingyong Glacier below the Meili Snow Mountains. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to investigate the contribution and potential effect of SSA-emitted BC. In addition, variations in the light absorption characteristics of BC and brown carbon (BrC) were examined. The results indicated that the annual mean concentration of BC was 415 ± 372 ngm-3, with the highest concentration observed in April (monthly mean: 930 ± 484 ngm-3). BC exhibited a similar diurnal variation throughout the year, with two peaks observed in the morning (from 8:00 to 9:00 AM) and in the afternoon (from 4:00 to 5:00 PM), with even lower values at nighttime. At a short wavelength of 370 nm, the absorption coefficient (babs) reached its maximum value, and the majority of babs values were < 20 Mm-1, indicating that the atmosphere was not overloaded with BC. At the same wavelength, BrC substantially contributed to babs, with an annual mean of 25.2 % ± 12.8 %. SSA was the largest contributor of BC (annual mean: 51.1 %) in the study area, particularly in spring (65.6 %). However, its contributions reached 20.2 % in summer, indicating non-negligible emissions from activities in other regions. In the atmosphere, the SSA BC-induced radiative forcing (RF) over the study region was positive. While at the near surface, the RF exhibited a significant seasonal variation, with the larger RF values occurring in winter and spring. Overall, our findings highlight the importance of controlling BC emissions from SSA to protect the Tibetan Plateau against pollution-related glacier and snow cover melting.

Utilising BC observations to estimate CO contributions from fossil fuel and biomass burning in the Central Himalayan region

The Himalayan region is adversely affected by the increasing anthropogenic emissions from the adjacent Indo-Gangetic plain. However, source apportionment studies for the Himalayan region that are crucial for estimating CO concentration, are grossly insufficient, to say the least. It is in this context that our study reported here assumes significance. This study utilizes five years (2014-2018) of ground-based observations of eBC and multiple linear regression framework (MLR) to estimate CO and segregate its fossil fuel and biomass emission fractions at a high-altitude (1958 m) site in the Central Himalayas. The results show that MERRA2 always underestimates the observed CO; MOPITT has a high monthly difference ranging from -32% to +57% while WRF-Chem simulations underestimate CO from February to June and overestimate in other months. In contrast, CO estimated from MLR replicates diurnal and monthly variations and estimates CO with an r2 > 0.8 for 2014-2017. The CO predicted during 2018 closely follows the observed variations, and its mixing ratios lie within ±17% of the observed CO. The results reveal a unimodal diurnal variation of CO, COff (ff: fossil fuel) and CObb (bb: biomass burning) governed by the boundary layer evolution and upslope winds. COff has a higher diurnal amplitude (39.1-67.8 ppb) than CObb (5.7-33.5 ppb). Overall, COff is the major contributor (27%) in CO after its background fraction (58%). CObb fraction reaches a maximum (28%) during spring, a period of increased agricultural and forest fires in Northern India. In comparison, WRF-Chem tracer runs underestimate CObb (-38% to -98%) while they overestimate the anthropogenic CO during monsoon. This study thus attempts to address the lack of continuous CO monitoring and the need to segregate its fossil fuel and biomass sources, specifically over the Central Himalayas, by employing a methodology that utilizes the existing network of eBC observations.

Concentrations and light absorption properties of PM2.5 organic and black carbon based on online measurements in Lanzhou, China

To elucidate the variations in mass concentrations of organic carbon (OC) and black carbon (BC) in PM_2.5 and their light absorption characteristics in Lanzhou, we conducted one-year online measurements by using a newly developed total carbon analyzer (TCA08) coupled with an aethalometer (AE33) from July 2018 to July 2019. The mean OC and BC concentrations were 6.4 ± 4.4 and 2.0 ± 1.3 µg/m³, respectively. Clear seasonal variations were observed for both components, with winter having the highest concentrations, followed by autumn, spring, and summer. The diurnal variations of OC and BC concentrations were similar throughout the year, with daily two peaks occurring in the morning and evening, respectively. A relatively low OC/BC ratio (3.3 ± 1.2, n = 345) were observed, indicating that fossil fuel combustion was the primary source of the carbonaceous components. This is further substantiated by relatively low biomass burning contribution (f_biomass: 27.1% ± 11.3%) to BC using aethalometer based measurement though f_biomass value which increased significantly in winter (41.6% ± 5.7%). We estimated a considerable brown carbon (BrC) contribution to the total absorption coefficient (b_abs) at 370 nm (yearly average of 30.8% ± 11.1%), with a winter maximum of 44.2% ± 4.1% and a summer minimum of 19.2% ± 4.2%. Calculation of the wavelength dependence of total b_abs revealed an annual mean AAE_370-520 value of 4.2 ± 0.5, with slightly higher values in spring and winter. The mass absorption cross-section of BrC also exhibited higher values in winter, with an annual mean of 5.4 ± 1.9 m²/g, reflecting the impact of emissions from increased biomass burning on BrC concentrations.

Black carbon pollution in China from 2001 to 2019: Patterns, trends, and drivers

Based on a near real-time 10 km × 10 km resolution black carbon (BC) concentration dataset, this study investigated the spatial patterns, trend variations, and drivers of BC concentrations in China from 2001 to 2019 with spatial analysis, trend analysis, hotspot clustering, and multiscale geographically weighted regression (MGWR). The results indicate that Beijing-Tianjin-Hebei, the Chengdu-Chongqing agglomeration, Pearl River Delta, and East China Plain were the hotspot centers of BC concentration in China. From 2001 to 2019, the average rate of decline in BC concentrations across China was 0.36 μg/m³/year (p < 0.001), with BC concentrations peaking around 2006 and sustaining a decline for the next decade or so. The rate of BC decline was higher in Central, North, and East China than in other regions. The MGWR model revealed the spatial heterogeneity of the influences of different drivers. A number of enterprises had significant effects on BC in East, North, and Southwest China; coal production had strong effects on BC in Southwest and East China; electricity consumption had better effects on BC in Northeast, Northwest, and East China than in other regions; the ratio of secondary industries had the greatest effects on BC in North and Southwest China; and CO₂ emissions had the strongest effects on BC in East and North China. Meanwhile, the reduction of BC emissions from the industrial sector was the dominant factor in the decrease of BC concentration in China. These findings provide references and policy prescriptions for how cities in different regions can reduce BC emissions.

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017-2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m^-3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m^-3) in winter and minimum concentration (0.38 ± 0.04 μg m^-3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March-10 May 2020 and third lockdown 17 March-3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October-15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well.

Characteristics, source apportionment and long-range transport of black carbon at a high-altitude urban centre in the Kashmir valley, North-western Himalaya

Six years of data (2012-2017) at an urban site-Srinagar in the Northwest Himalaya were used to investigate temporal variability, meteorological influences, source apportionment and potential source regions of BC. The daily BC concentration varies from 0.56 to 40.16 μg/m³ with an inter-annual variation of 4.20-7.04 μg/m³ and is higher than majority of the Himalayan urban locations. High mean annual BC concentration (6.06 μg/m³) is attributed to the high BC observations during winter (8.60 μg/m³) and autumn (8.31 μg/m³) with a major contribution from Nov (13.88 μg/m³) to Dec (13.4 μg/m³). A considerable inter-month and inter-seasonal BC variability was observed owing to the large changes in synoptic meteorology. Low BC concentrations were observed in spring and summer (3.14 μg/m³ and 3.21 μg/m³), corresponding to high minimum temperatures (6.6 °C and 15.7 °C), wind speed (2.4 and 1.6 m/s), ventilation coefficient (2262 and 2616 m²/s), precipitation (316.7 mm and 173.3 mm) and low relative humidity (68% and 62%). However, during late autumn and winter, frequent temperature inversions, shallow PBL (173-1042 m), stagnant and dry weather conditions cause BC to accumulate in the valley. Through the observation period, two predominant diurnal BC peaks were observed at ⁓9:00 h (7.75 μg/m³) and ⁓21:00 h (6.67 μg/m³). Morning peak concentration in autumn (11.28 μg/m³) is ⁓2-2.5 times greater than spring (4.32 μg/m³) and summer (5.23 μg/m³), owing to the emission source peaks and diurnal boundary layer height. Diurnal BC concentration during autumn and winter is 65% and 60% higher than spring and summer respectively. During autumn and winter, biomass burning contributes approximately 50% of the BC concentration compared to only 10% during the summer. Air masses transport considerable BC from the Middle East and northern portions of South Asia, especially the Indo-Gangetic Plains, to Srinagar, with serious consequences for climate, human health, and the environment.

Comparison of INSAT-3D retrieved total column ozone with ground-based and AIRS observations over India

Ozone plays an important role in the thermal structure and chemical composition of the atmosphere. The present study compares the temporal and spatial distributions of Total Column Ozone (TCO) over the Indian sub-continent retrieved from a geostationary Indian National Satellite (INSAT-3D) and Atmospheric Infrared Sounder (AIRS). The INSAT-3D TCO values are also evaluated against the Dobson spectrophotometer observations at two locations. The inter-comparison results reveal a good correlation of 0.8, the bias of -5 DU, and Root Mean Square Error (RMSE) of 15 DU approximately between the TCO retrieved from INSAT-3D and AIRS. The lowest RMSE and highest correlation coefficient were found in the pre-monsoon season. The INSAT-3D and AIRS show reasonable agreement with the RMSE varying between 10 and 30 DU. On the other hand, evaluation of the INSAT-3D TCO with the ground-based observations from Dobson spectrophotometers located at New Delhi and Varanasi showed fair agreement with a maximum monthly mean correlation coefficient of 0.68 and 0.76, respectively, and RMSE varying from 11 to 16 DU for both the stations. The seasonal distribution of TCO and its variation over the Indian region has also been studied using INSAT-3D and AIRS data. The analysis exhibits strong seasonal variations, with higher values in pre-monsoon season and minimum values in winter season. The noticeable seasonal variability of TCO can be attributed to complex combination of photochemical and dynamical processes in the troposphere and stratosphere. The main objectives of the study are to compare the INSAT-3D TCO with two independent ground-based Dobson spectrophotometer observations and Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite.

Variations in Black Carbon concentration and sources during COVID-19 lockdown in Delhi

A nationwide lockdown was imposed in India due to COVID-19 pandemic in five phases from 25th March to May 31, 2020. The lockdown restricted major anthropogenic activities, primarily vehicular and industrial, thereby reducing the particulate matter concentration. This work investigates the variation in Black Carbon (BC) concentration and its sources (primarily Fossil Fuel (ff) burning and Biomass Burning (bb)) over Delhi from 18th February to July 31, 2020, covering one month of pre-lockdown phase, all the lockdown phases, and two months of successive lockdown relaxations. The daily average BC concentration varied from 0.22 to 16.92 μg/m³, with a mean value of 3.62 ± 2.93 μg/m³. During Pre-Lockdown (PL, 18th Feb-24th March 2020), Lockdown-1 (L1, 25th March-14th April 2020), Lockdown-2 (L2, 15th April-3rd May 2020), Lockdown-3 (L3, 4th-17th May 2020), Lockdown-4 (L4, 18th-31st May 2020), Unlock-1 (UN1, June 2020), and Unlock-2 (UN2, July 2020) the average BC concentrations were 7.93, 1.73, 2.59, 3.76, 3.26, 2.07, and 2.70 μg/m³, respectively. During the lockdown and unlock phases, BC decreased up to 78% compared to the PL period. The BC source apportionment studies show that fossil fuel burning was the dominant BC source during the entire sampling period. From L1 to UN2 an increasing trend in BC_ff contribution was observed (except L3) due to the successive relaxations given to anthropogenic activities. BC_ff contribution dipped briefly during L3 due to the intensive crop residue burning events in neighboring states. CWT analysis showed that local emission sources were the dominant contributors to BC concentration over Delhi.

Identification of key aerosol types and mixing states in the central Indian Himalayas during the GVAX campaign: the role of particle size in aerosol classification

Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM₁ and PM₁₀). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. absorption Ångström exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named "large/BC mix" dominates in both PM₁ (45.4%) and PM₁₀ (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE < 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM₁, while in PM₁₀, a large fraction (39%) corresponds to "large/low-absorbing" aerosols and only 3.9% is characterized as "BC-dominated". The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole.

Source Apportionment of Ambient Black Carbon During the COVID-19 Lockdown

Black carbon (BC) particles being emitted from mobile and stationary emission sources as a result of combustion activities have significant impacts on human health and climate change. A lot of social activities have been halted during the COVID-19 lockdowns, which has evidently enhanced the ambient and indoor air quality. This paper investigates the possible emission sources and evaluates the meteorological conditions that may affect the dispersion and transport of BC locally and regionally. Ground-level equivalent BC (eBC) measurements were performed between January 2020 and July 2020 at a university campus located in Dammam city of the Kingdom of Saudi Arabia (KSA). The fossil fuel (eBCff) and biomass burning (eBCbb) fractions of total eBC (eBCt) concentrations were estimated as 84% and 16%, respectively, during the entire study period. The mean eBCbb, eBCff, and eBCt concentrations during the lockdown reduced by 14%, 24%, and 23%, respectively. The results of statistical analyses indicated that local fossil fuel burning emissions and atmospheric conditions apparently affected the observed eBC levels. Long-range potential source locations, including Iraq, Kuwait, Iran, distributed zones in the Arabian Gulf, and United Arab Emirates and regional source areas, such as the Arabian Gulf coastline of the KSA, Bahrain, and Qatar, were associated with moderate to high concentrations observed at the receptor site as a result of cluster analysis and concentration-weighted trajectory analysis methods.