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.

Black carbon (BC) aerosols play a very significant role in influencing air quality, climate, and human health. Large uncertainties still exist in BC emissions due to limited observations on the relative source contributions of fossil fuel (ff) combustion and biomass (wood fuel, wf) burning. Our understanding of long-term changes in BC emissions, especially their source apportionment, is sparse and limited. For the first time, BC characteristics, its source apportionment into ff and wf components, and their trends measured using a multi-wavelength aethalometer over an urban location (Ahmedabad) in India covering a 14 year period (2006-2019) are comprehensively investigated. The average contributions of eBCff and eBCwf concentrations to total eBC are 80 % and 20 %, respectively, which highlights the dominance of emissions from fossil fuel combustion processes. A statistically significant increasing trend in eBC and eBCff mass concentrations at the rate of 11 % and 29%yr-1, respectively, and a decreasing trend in eBCwf concentration at the rate of 36%yr-1 are detected. The study reveals a significant decrease in biomass (wood fuel) burning emissions over the past decade and an increase in emissions from fossil fuel combustion. However, the rates of increase and decrease in eBCff and eBCwf are different, which indicate that rapid urbanization led to an increase in anthropogenic emissions, whereas an increase in usage of non-polluting fuel led to a decreasing trend in wood burning contribution. During weekdays and weekends, eBC and eBCff mass concentrations did not exhibit any statistically significant trends. However, eBCwf concentration shows a statistically significant decreasing trend during weekdays 34%yr-1 and weekends 38%yr-1. Globally, several countries are adopting various strategies and mitigation policies to improve air quality; however, significant gaps exist in their implementation towards achieving cleaner air and less polluted environment. This comprehensive study is relevant for understanding the impact of urbanization and devising better BC emission control policies.

The study aimed to understand the optical properties of Black Carbon (BC) and radiative forcing over a data deficient Himalayan region focusing on critical zone observatory employing ground-based measurements by Aethalometer for BC and satellite retrieval techniques for optical properties during mid-May-June 2022 and January-May 2023. BC mass concentration ranged from 0.18 to 4.43 μgm-3, exhibit a mean of 1.47 ± 0.83 μgm-3 with higher summer concentration (1.51 ± 0.94 μgm-3) than winter (1.39 ± 0.61 μgm-3). The average Absorption Ångström Exponent observed to be significantly higher than unity (1.77 ± 0.31) over the studied high-altitude Himalayan region, suggesting the dominance of biomass-burning aerosol. Higher aethalometer derived compensation parameter (K) in winter suggesting locally originated BC while, lower K value in summer suggesting aged BC transported from Indo-Gangetic Plains. Optical properties calculated from \"Optical Properties of Aerosol and Cloud\" (OPAC) model are used in the \"Santa Barbara DISORT Atmospheric Radiative Transfer\" (SBDART) model to calculate the aerosol Direct Radiative Force (DRF). The entire studied period is characterized by the predominance of absorbing aerosols, particularly BC, increasing Aerosol Optical Depth, Asymmetric Parameters and decreasing Single Scattering Albedo, leading to a considerable increase in atmospheric radiative forcing (+0.9 Wm-2, top of atmosphere) and Heating Rate (0.36 KDay-1). The mean radiative forcing within atmosphere during summer was higher (+14.29 Wm-2) relative to the winter (+12.00 Wm-2), emphasizing the impact of absorbing aerosols on regional warming and potential glacier melting in the Himalayas at a faster rate. Urgent policy consideration for the reduction of absorbing aerosols is highlighted, recognizing the critical roles of Black Carbon in the changing behaviour of Critical Zone observatory. The study\'s data serve as a valuable resource to understanding and addressing uncertainties in climate models, aiding effective policy implementation for Black Carbon reduction.

Sub-Saharan Africa is a hotspot for biomass burning (BB)-derived carbonaceous aerosols, including light-absorbing organic (brown) carbon (BrC). However, the chemically complex nature of BrC in BB aerosols from this region is not fully understood. We generated smoke in a chamber through smoldering combustion of common sub-Saharan African biomass fuels (hardwoods, cow dung, savanna grass, and leaves). We quantified aethalometer-based, real-time light-absorption properties of BrC-containing organic-rich BB aerosols, accounting for variations in wavelength, fuel type, relative humidity, and photochemical aging conditions. In filter samples collected from the chamber and Botswana in the winter, we identified 182 BrC species, classified into lignin pyrolysis products, nitroaromatics, coumarins, stilbenes, and flavonoids. Using an extensive set of standards, we determined species-specific mass and emission factors. Our analysis revealed a linear relationship between the combined BrC species contribution to chamber-measured BB aerosol mass (0.4-14%) and the mass-absorption cross-section at 370 nm (0.2-2.2 m2 g-1). Hierarchical clustering resolved key molecular-level components from the BrC matrix, with photochemically aged emissions from leaf and cow-dung burning showing BrC fingerprints similar to those found in Botswana aerosols. These quantitative findings could potentially help refine climate model predictions, aid in source apportionment, and inform effective air quality management policies for human health and the global climate.

Current methods for measuring black carbon aerosol (BC) by optical methods apportion BC to fossil fuel and wood combustion. However, these results are aggregated: local and non-local combustion sources are lumped together. The spatial apportioning of carbonaceous aerosol sources is challenging in remote or suburban areas because non-local sources may be significant. Air quality modeling would require highly accurate emission inventories and unbiased dispersion models to quantify such apportionment. We propose FUSTA (FUzzy SpatioTemporal Apportionment) methodology for analyzing aethalometer results for equivalent black carbon coming from fossil fuel (eBCff) and wood combustion (eBCwb). We applied this methodology to ambient measurements at three suburban sites around Santiago, Chile, in the winter season 2021. FUSTA results showed that local sources contributed ∼80% to eBCff and eBCwb in all sites. By using PM2.5 - eBCff and PM2.5 - eBCwb scatterplots for each fuzzy cluster (or source) found by FUSTA, the estimated lower edge lines showed distinctive slopes in each measurement site. These slopes were larger for non-local sources (aged aerosols) than for local ones (fresh emissions) and were used to apportion combustion PM2.5 in each site. In sites Colina, Melipilla and San Jose de Maipo, fossil fuel combustion contributions to PM2.5 were 26 % (15.9 μg m-3), 22 % (9.9 μg m-3), and 22 % (7.8 μg m-3), respectively. Wood burning contributions to PM2.5 were 22 % (13.4 μg m-3), 19 % (8.9 μg m-3) and 22% (7.3 μg m-3), respectively. This methodology generates a joint source apportionment of eBC and PM2.5, which is consistent with available chemical speciation data for PM2.5 in Santiago.

Black carbon (BC) or soot contains ultrafine combustion particles that are associated with a wide range of health impacts, leading to respiratory and cardiovascular diseases. Both long-term and short-term health impacts of BC have been documented, with even low-level exposures to BC resulting in negative health outcomes for vulnerable groups. Two aethalometers-AethLabs MA350 and Aerosol Magee Scientific AE33-were co-located at a Utah Division of Air Quality site in Bountiful, Utah for just under a year. The aethalometer comparison showed a close relationship between instruments for IR BC, Blue BC, and fossil fuel source-specific BC estimates. The biomass source-specific BC estimates were markedly different between instruments at the minute and hour scale but became more similar and perhaps less-affected by high-leverage outliers at the daily time scale. The greater inter-device difference for biomass BC may have been confounded by very low biomass-specific BC concentrations during the study period. These findings at a mountainous, high-elevation, Greater Salt Lake City Area site support previous study results and broaden the body of evidence validating the performance of the MA350.

The three-year Black Carbon (BC) aerosol measurements made during 2020, 2021, and 2022 over a rural location, namely, Panchgaon, surrounded by Aravali hillocks (elevation of about 400-600 m) have been analyzed with an aim to determine their optical and radiative characteristics, seasonal and long-term variations in mass concentration. The affinity between these parameters and associated pollutants and planetary boundary layer height (PBLH), affected by the orography, to delineate their role in mass concentration changes with time have been investigated. The coincident OPAC (Optical Properties of Aerosols and Clouds) Model-derived aerosol optical depth (AOD), and single scattering albedo (SSA) have been compared with the observed BC mass concentration, and also with synchronous satellite measurements. The year-to-year variability analysis of the data reveals that the rate of increase of BC concentration is high. The variability was low due to the reasons explained. It implies that the year-to-year variability in BC concentration at the study site depends on the source strength modulated by the valley-driven meteorology. Added, the percentage departures of BC concentration show positive values (higher concentration) during morning and evening hours, which could be due to more anthropogenic activities while it shows negative values during afternoon hours and lower boundary layer heights. The force exerted by the radiation due to BC aerosols at the bottom of the atmosphere (BOA), and in the atmosphere (ATM) are almost equal in magnitude and negative, while that at the top-of-the-atmosphere (TOA) is smaller and positive, indicating that BC aerosols in the study region cools the atmosphere at the BOA and warms the ATM and TOA, which indirectly reveals the dominant role of long-range transport phenomenon at higher levels as compared to the surface level.

Light-absorbing aerosols heat the atmosphere; an accurate quantification of their absorption coefficient is mandatory. However, standard reference instruments (CAPS, MAAP, PAX, PTAAM) are not always available at each measuring site around the world. By integrating all previous published studies concerning the Aethalometers, the AE33 filter loading parameter, provided by the dual-spot algorithm, were used to determine the multiple scattering enhancement factor from the Aethalometer itself (hereinafter CAE) on an yearly and a monthly basis. The method was developed in Milan, where Aethalometer measurements were compared with MAAP data; the comparison showed a good agreement in terms of equivalent black carbon (R2 = 0.93; slope = 1.02 and a negligible intercept = 0.12 μg m-3) leading to a yearly experimental multiple scattering enhancement factor of 2.51 ± 0.04 (hereinafter CMAAP). On a yearly time base the CAE values obtained using the new approach was 2.52 ± 0.01, corresponding to the experimental one (CMAAP). Considering the seasonal behavior, higher experimental CMAAP and computed CAE values were found in summer (2.83 ± 0.12) whereas, the lower ones in winter/early-spring (2.37 ± 0.03), in agreement with the single scattering albedo behavior in the Po Valley. Overall, the agreement between the experimental CMAAP and CAE showed a root mean squared error (RMSE) of just 0.038 on the CMAAP prediction, characterized by a slope close to 1 (1.001 ± 0.178), a negligible intercept (-0.002 ± 0.455) and a high degree of correlation (R2 = 0.955). From an environmental point of view, the application of a dynamic (space/time) determination of CAE increases the accuracy of the aerosol heating rate (compared to applying a fixed C value) up to 16 % solely in Milan, and to 114 % when applied in the Arctic at 80°N.

Black carbon (BC) is associated with adverse human health and climate change. Mapping BC spatial distribution imperatively requires low-cost and portable devices. Several portable BC monitors are commercially available, but their accuracy and reliability are not always satisfactory during continuous field observation. This study evaluated three models of portable black carbon monitors, C12, MA350 and DST, and investigates the factors that affect their performance. The monitors were tested in urban Beijing, where portable devices running for one month alongside a regular-size reference aethalometer AE33. The study considers several factors that could influence the monitors\' performance, including ambient weather, aerosol composition, loading artifacts, and built-in algorithms. The results show that MA350 and DST present considerable discrepancies to the reference instrument, mainly occurring at lower concentrations (0-500 ng/m3) and higher concentrations (2500-8000 ng/m3), respectively. These discrepancies were likely caused by the anomalous noise of MA350 and the loading artifacts of DST. The study also suggests that the ambient environment has limited influence on the monitors\' performance, but loading artifacts and accompanying compensation algorithms can result in unrealistic data. Based on the evaluation, the study suggests that C12 is the best choice for unsupervised field measurement, DST should be used in scenarios where frequent maintenance is available, and MA350 is suitable for research purposes with post-processing applicable. The study highlights the importance of assigning portable BC monitors to appropriate applications and the need for optimized real-time compensation algorithms.

Emissions from biomass burning challenge efforts to curb air pollution in cities downwind of fire-prone regions, as they contribute large amounts of brown carbon (BrC) and black carbon (BC) particles. We investigated the patterns of BrC and BC concentrations using Aethalometer data (at λ = 370 and 880 nm, respectively) spanning four years at a site impacted by the outflow of smoke. The data required to be post processed for the shadowing effect since, without correction, concentrations would be between 29% and 35% underestimated. The BrC concentrations were consistently higher than the BC concentrations, indicating the prevalence of aerosols from biomass burning. The results were supported by the Ångström coefficient (Å370/880), with values predominantly larger than 1 (mean ± standard deviation: 1.25 ± 0.31). Å370/880 values below 1 were more prevalent during the wet season, which suggests a contribution from fossil fuel combustion. We observed sharp BrC and BC seasonal signals, with mean minimum concentrations of 0.40 µg/m3 and 0.36 µg/m3, respectively, in the wet season, and mean maximum concentrations of 2.05 µg/m3 and 1.53 µg/m3 in the dry season. The largest concentrations were observed when northerly air masses moved over regions with a high density of fire spots. Local burning of residential solid waste and industrial combustion caused extreme BrC and BC concentrations under favourable wind directions. Although neither pollutant is included in any ambient air quality standards, our results suggest that transboundary smoke may hamper efforts to meet the World Health Organization guidelines for fine particles.