Black Carbon Research Articles

The politics of knowledge in black carbon mitigation: Policy entrepreneurship of Finnish actors and the Climate and Clean Air Coalition

Mitigation of black carbon (BC) is a potent short-term measure for climate and environmental policy due to the short lifetime of BC particles in the atmosphere. Fewer BC emissions would decelerate warming especially in the Arctic and bring significant co-benefits particularly in populous countries owing to BC’s negative public health, food security and socio-economic effects. However, scientific knowledge on BC remains uncertain regarding the measurement, monitoring and precise effects of BC while the respective global policy framework is fragmented. To address this situation, we scrutinize the science/policy nexus in BC mitigation on the regional level where some promising openings exist. For this end, our new analytical framework focuses on policy entrepreneurs, their interests and cognitive frames, and the structural environment. Utilising content analysis of expert interviews and document data, three cases are covered with significant policy entrepreneurship on BC. First, we examine Finland ‘s high-profile BC initiative, finding it to frame BC pollution as an Arctic climate threat. Second, Finland’s research, development and innovation cluster, which has substantial expertise on air pollution, frames BC as a public health hazard, and scopes prospects for policy frameworks helping to open markets for mitigation technologies and solutions. Third, the Climate and Clean Air Coalition approaches BC with a wide multipollutant frame, driven by broad socio-economic and developmental interests. The Finnish initiative’s potential is found to suffer from insufficient coordination with the RDI cluster. Both Finland’s BC specific and the CCAC’s wider multipollutant frame can in different ways contribute to abatement.

Accounting for black carbon refractive index in atmospheric radiation

AbstractBecause of the fractal aggregated structure of black carbon (BC), black carbon refractive index measurements are difficult. There are substantial differences among the over 40 existing measurement schemes and no two schemes are the same. Three typical BC refractive index schemes are chosen to explore the difference in black carbon optical properties and the consequences of the radiative effect. Two schemes are widely used in climate models, and the third is from a newer measurement in 2016. It is shown that black carbon optical properties are sensitive to different refractive indices. The relative differences in extinction coefficient and single scattering albedo can be over 100%. In addition, by using Maxwell–Garnett and Bruggeman mixing rules, it has been found that the effect of internal mixing on aerosol optical properties depends strongly on the choice of refractive index. Using a one‐dimensional radiative transfer model under clear‐sky conditions, we demonstrate that the choice of black carbon refractive index influences the inferred radiative effect. Using the more recent (2016) scheme for pure black carbon can increase the top‐of‐atmosphere radiative effect by 20% relative to the currently widely used lowest‐absorbing scheme. For internally mixed aerosol, the sign of the radiative effect can change depending on which refractive index is used.

Exposure to ultrafine particles, black carbon and particulate matter during commute in a suburb from the Yangtze River Delta, China

This study evaluated the exposure of commuters to particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP) concentrations in five different traffic microenvironments (bike, bus, taxi, electric motorbike, and subway) under various conditions, such as regular and dust days, weekdays and weekends, and peak and off-peak commute hours in Nanjing, China. In the morning peak hours, bicycle commuters were exposed to the highest PM concentration, those on buses to the highest BC concentration (3.4 ± 3.0 μg/m3), and those on buses and taxis to the highest UFP number concentration. Throughout the rides, the commuter on the subway had the lowest mean exposure concentration of PM, BC, and UFP number. Bicycle commuters (16.25 μg/km) had the highest total inhaled dose of PM2.5, followed by e-bikers (1.98 μg/km), bus riders (1.28 μg/km), taxi riders (0.69 μg/km), and subway users (0.45 μg/km). Weekday PM2.5 exposure levels (115 μg/m3) and BC exposure levels (1.8 μg/m3) were higher than weekend levels (170% and 158%, respectively). The mean exposure concentration of PM10 (1299 ± 1286 μg/m3) in the evening peak of dust days was 803% and 994% higher than the mean concentrations on weekdays and holidays, respectively. PM2.5, PM10, and BC concentrations on dust days were significantly higher than those on weekdays and holidays. The overall mean ratios of PM1/PM2.5 and PM2.5/PM10 were 0.47 and 0.36, respectively, and the five commuting microenvironments had higher proportions of 2.5–10 μm and 1–2.5 μm particles in PM10 and PM2.5, respectively. Reducing the sources of particulate matter in the corresponding particle size ranges could lower exposure levels.

Transforming air pollution management in India with AI and machine learning technologies

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

Nano-micro pore structure characteristics of carbon black and recycled carbon fiber reinforced alkali-activated materials

Microscopic scrutiny aids in alkali-activated materials’ (AAM) application in construction industry. This study delves into the pore structure and properties of one-part alkali-activated slag (AAS) mortar modified by carbon black (CB) and recycled carbon fiber (rCF). The additives enhanced flexural strength by 51.82% (12.16 MPa) with lower water absorption (10.24%). Refinement of pore size and reduction of connectivity are key factors in improving properties. The densification effect of CB and the strong interface between rCF and gel were observed. Furthermore, AAS mortars exhibited multifractal characteristics within the range of micropores and capillary pores. Despite altering fractal regions, the additives did not affect its size dependence. The backbone fractal dimension increases with the addition of CB and rCF, exhibiting strong correlations with various macro properties, thus serving as a comprehensive parameter to characterize pore shape and distribution. This study deepens understanding of AAM composites, facilitating their adoption of low-carbon building materials.

Quantum imaging-based nanobiosensors: Pioneering point-of-care approach for early diagnosis of environmental-linked breast cancer

Early detection is paramount for successful treatment outcomes in cancer diagnosis. Among women across the globe, breast cancer (BC) ranks as one of the deadliest forms of cancer. Prolonged exposure to numerous environmental pollutants has been linked to epigenetic reprogramming, which entails changes in the expression patterns of non-coding RNAs. These alterations have been strongly linked to an increased risk of developing BC. Women are confronted with hazardous smoke from polluting stoves and fuels for longer as they often perform home duties such as cooking. Inefficient combustion emits black carbon (sooty particles), which enters the bloodstream and is strongly connected to an elevated risk of BC. The use of several analytical methods, such as real-time polymerase chain reaction, microarray, and sequencing, has numerous disadvantages, such as high expenses, limitations in sensitivity, and lack of accuracy. However, the emergence of quantum dots (QDs), nanoscale semiconductor particles with unique optical properties, and the development of quantum imaging-based sensors offer a glimpse into the future of medical technology. These sensors have the potential to completely change the medical field by offering highly precise, non-invasive, and reliable techniques for early diagnosis. Our article delves into the intricacies of QDs imaging-based sensors, their applications in BC detection, and their transformative impact on improving patient care. In recent years, the confluence of quantum science and diagnostic imaging has opened new avenues for BC diagnosis. The present state of quantum imaging-based BC diagnosis sensors is examined in this article, along with potential future developments with the help of artificial intelligence.

Short-term effects of wildfire-specific fine particulate matter and its carbonaceous components on perinatal outcomes: A multicentre cohort study in New South Wales, Australia

BackgroundEpidemiological evidence on the association between wildfire-specific fine particulate matter (PM2.5) and its carbonaceous components with perinatal outcomes is limited.We aimed to examine the short-term effects of wildfire-specific PM2.5 and its carbonaceous components on perinatal outcomes. MethodsA multicentre cohort of 9743 singleton births during the wildfire seasons from 1 September 2009 to 31 December 2015 across six cities in New South Wales, Australia were linked with daily wildfire-specific PM2.5 and carbonaceous components (organic carbon and black carbon). Adjusted distributed lag Cox regression models with spatial clustering were performed to estimate daily and cumulative adjusted hazard ratios (aHRs) during the last four gestational weeks for preterm birth, stillbirth, nonvertex presentation, low 5-min Apgar score, special care nursery/neonatal intensive care unit (SCN/NICU) admission, and caesarean section. ResultsDaily aHRs per 10 µg/m3 PM2.5 showed nearly inverted ‘U’-shaped positive associations and daily cumulative aHRs that increased with increasing duration of the exposures. The aHRs for lag 0–6 days were 1.17 (95 % CI: 1.04, 1.32) for preterm birth, 1.40 (95 % CI: 1.11, 1.78) for stillbirth, 1.20 (95 % CI: 1.08, 1.33) for nonvertex presentation, 1.12 (95 % CI: 0.93, 1.35) for low 5-min Apgar score, 0.99 (95 % CI: 0.83, 1.19) for SNC/NICU admission, and 1.01 (95 % CI: 0.94, 1.08) for caesarean section. Organic carbon and black carbon components for lag 0–6 days showed positive associations. The highest component-specific aHRs were 1.09 (95 % CI: 1.03, 1.15) and 4.57 (95 % CI: 1.96, 10.68) for stillbirth per 1 µg/m3 organic carbon and black carbon, respectively. The subgroups identified as most vulnerable were female births, births to mothers with low socioeconomic status, and births to mothers with high biothermal exposure. ConclusionsPositive associations of short-term wildfire-specific PM2.5 exposure and its carbonaceous components with adverse perinatal outcomes suggest that policies to reduce exposure would benefit public health.

Impacts of fossil fuel thermophoretic convective heat transfer on climate change with variable viscosity and thermal conductivity

The main aim of the current study is to analyze the impacts of fossil fuel thermophoretic convective heat transfer on climate change with variable viscosity and thermal conductivity. Furthermore, the purpose of the proposed problem is to develop a mathematical model based on three regions: source region (in terms of rectangular coordinates), plume region (in terms of cylindrical coordinates), and atmospheric region (in terms of spherical coordinates). The fossil fuels release thermophoretic particles, such as carbon dioxide, methane, black carbon, and many others, during burning process in the source region, and then release through the plume region. These particles are then distributed into the atmosphere, where the impact of thermophoretic particles on climate change is analyzed. The modeled nonlinear partial differential equations are transformed into a dimensionless form using suitable non-dimensional scaling variables. The proposed model is solved using finite difference approach in order to analyze the impacts of fossil fuel thermophoretic particles in the atmosphere in terms of climate change. In this regard, the effect of dimensionless parameters, viscosity variation parameter γ, Schimdt number Sc, thermal conductivity variation parameter ε, coefficient of thermophoretic process k, and thermophoresis parameter Nt on the velocity, temperature, and thermophoretic concentration fields are discussed. The main novelty of current work is that three models in three regions are coupled via trans-boundaries in term of temperature differences. It is very interesting to note that the concentration of thermophoretic particles, along with temperature profile, is maximum at α=π rad and minimum at α=1.5 rad in the atmospheric region.

Genomic and metabolomic analysis of Latilactobacillus sakei DCF0720 for black soybean yogurt fermentation

Lactic acid bacteria are commonly used in plant-based fermentation to reduce off-flavor and improve sensory characteristics. However, there have been few studies on Latilactobacillus sakei for plant-based yogurt fermentation and, particularly, its metabolic features at the genomic level remain unclear. This study aims to analyze the fermentation characteristics of the L. sakei DCF0720 strain and compare genetics and metabolic relations. For this, DCF0720 was used to ferment the black soybean milk and conduct the physicochemical analysis and sensory test. The genomic and metabolic analyses were performed by complete genome sequencing and 500 MHz 1H NMR, respectively. As a result, DCF0720 exhibited enhanced fermentation performance and sensory evaluations at 37 °C compared to 30 °C, which is generally recognized as the optimal growth temperature for most L. sakei strains. It also produced flavor enhancing volatile compounds such as acetoin and hydroxyacetone, possessing all three key genes for acetoin biosynthesis. DCF0720 lacks 2,3-butanediol dehydrogenase, which leads to the inhibition of acetoin production. DCF0720 possesses a complete pathway to utilize primary black soybean carbon sources such as sucrose, raffinose, and stachyose. DCF0720 also possesses genes for the GH28 family, including the key enzymes in the hydrolysis of pectin substances, which means eliminating the main soybean nonstarch polysaccharides. This study demonstrates that DCF0720 is a suitable starter for plant-based yogurt fermentation, providing a better understanding of fermentation conditions with genetic and metabolic features for black soybean yogurt. Various carbon source utilization abilities with depth metabolic pathway analysis provide that DCF0720 can be employed to develop enhanced starter cultures for black soybean yogurt and diverse plant-based yogurts.

P21-30 Effects of black carbon particles on human monocyte-derived macrophages in vitro

P21-30 Effects of black carbon particles on human monocyte-derived macrophages in vitro

Insights into chemical aging of urban aerosols over Delhi, India

Atmospheric particles can undergo aging as they are transported over long distances and mix with particles from other sources. This can lead to the accumulation of pollutants and the formation of complex aerosol mixtures with diverse chemical and physical properties. To investigate the process of aging in ambient atmosphere, 24h sampling of PM2.5 aerosol particles on Quartz microfiber filter with a tin substrate was carried out from November 2020 to March 2021 at CSIR-National Physical Laboratory, New Delhi (28°38'10″ N and 77°10′17" E), using fine particle sampler. Based on the observations of weather and meteorological parameters, a few episodic cases have been selected, and samples were analyzed at bulk and individual particle level. The objective of the present study is to investigate the aging characteristics of aerosols, enabling us to understand the mixing of aerosols (at both bulk and individual particle levels) and the variation in fresh and deformed (aged with other species) graphitic content in the episodic cases. The Raman Spectroscopy technique employed measures the intensity of graphitic (G band; around 1580 cm−1) and disordered graphitic (D band; around 1320 cm−1) content of aerosols. Individual particle microscopic observations reveal the occurrence of open chain fractals of black carbon in variable monomer sizes, sometimes agglomerated with metals like Cu, Cr, Ca etc., along with the presence of S- rich and organic aerosols while the Raman Spectrum (bulk sample analysis) highlights graphitic and disordered (when graphite interacts with other chemical species) graphitic intensities. Comparing the intensities of heavy haze and moderate haze with non-haze days (for comparison purpose, March 23, 2021 with the lowest PM2.5 concentration ∼ 62 μg/m3, has been considered as a non-haze day), it was observed that the intensities recorded on haze days were 45 to 200 times higher for the G band and 43 to 93 times higher for the D band; while for moderate haze days, the intensities were 4 to 61 times higher for the G band and 2 to 29 times higher for the D band. These findings suggest chemical processing of BC during haze days.

Interaction and band structure-determined inhibition of negative Cr (VI) and positive Fe (III) for antibiotic photodegradation by nitrogen-doped dissolved black carbon

The influences of the positive Fe3+ and the negative Cr2O72− on the tetracycline (TC) photodegradation by N-doped dissolved black carbon (NDBC) have been investigated in this work. A series of samples (NDBC300, NDBC400 and NDBC500) have been extracted from the corresponding biochar. NDBC400 has the best photodegradation performance (79%) for TC under visible light irradiation. Adding Cr2O72− and Fe3+ can reduces TC photodegradation efficiency into 37% and 53%, respectively. This maybe from that Cr2O72− has stronger interaction with NDBC400 than Fe3+ since it can quench more fluorescence intensity of NDBC400 than Fe3+. Furthermore, Cr2O72− can reduce the steady-state concentration of 3NDBC400*, 1O2 and •OH, whereas Fe3+can just reduce the steady-state concentration of 3NDBC400* and increase the concentration of •OH. This may explain why Cr2O72− has stronger inhibit performance of TC photodegradation by NDBC400 than Fe3+. The band structures of NDBC400, NDBC400-Fe3+ and NDBC400-Cr2O72− are constructed. And the VB of NDBC400-Fe3+ has a stronger ability to produce •OH than NDBC400. In summary, coupling interaction and band structure characterization of NDBC400, NDBC400-Fe3+ and NDBC400-Cr2O72− can explain well why Cr2O72 has stronger inhibition effect than Fe3+ and Fe3+ can increase the concentration of •OH. This work provides a deep insight for the photochemical behavior of dissolved black carbon and the transformation behavior of the co-existed metal ions and antibiotics.

Analysis of oceanic suspended particulate matter in the western North Pacific using the complex amplitude sensor

Oceanic suspended particulate matter (SPM) plays important roles in the coupling of climate and biogeochemical cycles via ocean–atmosphere interactions. However, methods for quantifying the properties of SPM in seawater have not yet been well established. Here we present the application of the recently developed complex amplitude sensor (CAS) for analyzing the complex forward-scattering amplitude of individual SPM (0.2–5.0 µm in diameter) obtained at depths of 0–100 m during a research cruise in the western North Pacific. The measured distribution of the complex amplitude indicated that the CAS-derived SPM data could be roughly classified into five major types. Comparison with reference sample’s complex amplitude data and scanning electron microscopy analysis suggested that these types could be attributed mainly to diatom fragments, carbonaceous materials (likely organic matter), mineral dusts, iron oxides, or black carbon. Depth profiles revealed that relatively high concentrations of SPM, presumably dominated by diatom fragments and carbonaceous materials with peak diameters of 0.7–1.0 µm, were typically associated with elevated turbidities and chlorophyll a concentrations. Based on this case study, we discuss the practical advantages and limitations of using the CAS to measure size-resolved concentrations of SPM in seawater and to characterize its composition.

Dynamic evolution of particulate and gaseous emissions for typical residential coal combustion

Residential coal combustion still accounts for half of the heating energy consumption in many developing countries. The dynamic variation during the combustion process importantly determines the combustion facility design and appropriate air quality assessment, which was omitted in conventional studies. This study investigated the emissions of particulate and gaseous pollutants during the combustion process for typical coal types using online monitoring. During the first pyrolysis stage with temperature climbing, the organic aerosols (OA) and gases reached peak concentration. The second fierce combustion stage had the highest temperature and produced the highest cumulative emissions, particularly a substantial amount of black carbon for coals with higher volatile content. Using higher-quality coals will undoubtedly reduce PM emissions, by a factor of 10 from bituminous to anthracite coal. However, more ultrafine particles (d < 0.1 μm) from cleaner coal may pose additional health risks. Anthracite and honeycomb coal had approximately twice the energy content and emitted more CO2 per unit mass of fuel and had more persistent SO2 emissions throughout the burnout stage. The oxygenation of OA and organic gases remained increased during combustion, suggesting the pyrolysis products underwent oxidation before being emitted. The investigation of the coal combustion process suggests the importance of reducing volatiles to control PM emissions, but the potential negative synergistic effects between PM reduction and increased carbon emissions should also be considered.

Effects of Air Pollution on Respiratory Events and Pain Crises among Children with Sickle Cell Disease in New York City.

The disease burden of sickle cell disease (SCD) is highest among U.S. Black and Hispanic populations, who are often disproportionately represented in communities with poor air quality. There is limited data on the effects of air pollution exposure and social environmental factors on health outcomes in children with SCD. The objective of our study was to examine the associations between air pollution exposure and acute respiratory and vaso-occlusive pain crises (VOCs), and further study the associations stratifying by asthma status and neighborhood disadvantages. We conducted a retrospective study collecting data on outpatient sick and ED visits, and hospital admissions for respiratory events (i.e., respiratory tract infections, asthma exacerbation, acute chest syndrome), and hospitalizations for VOCs among children with SCD in a tertiary care center in New York City (NYC) from 2015-2018. Modeled data from the NYC Community Air Survey data using home addresses estimated street-level, annual-average exposure to air pollution (i.e., black carbon (BC), fine particulate matter (PM2.5), and nitrogen dioxide (NO2)). The area deprivation index (ADI): continuous national ranking percentile (1-100) was used, representing a composite index for neighborhood-level social disadvantage. We further dichotomized study participants at the upper tertile (high vs. low ADI). Multivariable Poisson regression in generalized estimating equation (GEE) models were used to estimate relative risks (RR), after adjusting for potential covariates. A total of 114 children with SCD were included in this study and had between 1-4 annual repeated measures of annual average air pollutants over a total of 425 visits. Overall, there were no significant associations between air pollution levels and acute respiratory and VOCs among children with SCD and when stratified by asthma status. We found significant interactions between air pollution levels and the continuous ADI variable on respiratory outpatient and frequent respiratory outpatient/ED visits (p<0.1). When stratified by high ADI, increased exposure to PM2.5 was significantly associated with more frequent respiratory outpatient/ED visits among children residing in higher ADI neighborhoods (RR (95% CI)= 1.13 (1.01, 1.27), p<0.05), but not among those in lower ADI neighborhoods. Increased exposure to NO2 was associated with more outpatient respiratory events for children in high ADI neighborhoods (RR (95%CI= 2.74 (1.24, 6.08), p<0.05), compared with low ADI neighborhoods. Air pollution exposures increased respiratory complications among children with SCD living in deprived neighborhoods.

Black carbon and particle lung-deposited surface area in residential wood combustion emissions: Effects of an electrostatic precipitator and photochemical aging

Residential wood combustion (RWC) remains a significant global source of particulate matter (PM) emissions with adverse impacts on regional air quality, climate, and human health. The lung-deposited surface area (LDSA) and equivalent black carbon (eBC) concentrations have emerged as important metrics to assess particulate pollution. In this study we estimated combustion phase-dependent emission factors of LDSA for alveolar, tracheobronchial, and head-airway regions of human lungs and explored the relationships between eBC and LDSA in fresh and photochemically aged RWC emissions. Photochemical aging was simulated in an oxidative flow reactor at OH• exposures equivalent to 1.4 or 3.4 days in the atmosphere. Further, the efficiency of a small-scale electrostatic precipitator (ESP) for reducing LDSA and eBC from the wood stove was determined. For fresh emission eBC correlated extremely well with LDSA, but the correlation decreased after aging. Soot-dominated flaming phase showed the highest eBC dependency of LDSA whereas for ignition and char burning phases non-BC particles contributed strongly the LDSA. Deposition to the alveolar region contributed around 60 % of the total lung-deposition. The ESP was found as an effective method to mitigate particulate mass, LDSA, as well as eBC emissions from wood stoves, as they were reduced on average by 72%, 71%, and 69%, respectively. The reduction efficiencies, however, consistently dropped over the span of an experiment, especially for eBC. Further, the ESP was found to increase the sub-30 nm ultrafine particle number emissions, with implications for LDSA. The results of this study can be used for assessing the contribution of RWC to LDSA concentrations in ambient air.

Dark brown carbon from wildfires: a potent snow radiative forcing agent?

Deposition of wildfire smoke on snow contributes to its darkening and accelerated snowmelt. Recent field studies have identified dark brown carbon (d-BrC) to contribute 50–75% of shortwave absorption in wildfire smoke. d-BrC is a distinct class of water-insoluble, light-absorbing organic carbon that co-exists in abundance with black carbon (BC) in snow across the world. However, the importance of d-BrC as a snow warming agent relative to BC remains unexplored. We address this gap using aerosol-snow radiative transfer calculations on datasets from laboratory and field measurement. We show d-BrC increases the annual mean snow radiative forcing between 0.6 and 17.9 W m−2, corresponding to different wildfire smoke deposition scenarios. This is a 1.6 to 2.1-fold enhancement when compared with BC-only deposition on snow. This study suggests d-BrC is an important contributor to snowmelt in midlatitude glaciers, where ~40% of the world’s glacier surface area resides.

From Bulk to Nano: Formation, Features, and Functions of Nano-Black Carbon in Biogeochemical Processes.

Globally increasing wildfires and widespread applications of biochar have led to a growing amount of black carbon (BC) entering terrestrial ecosystems. The significance of BC in carbon sequestration, environmental remediation, and the agricultural industry has long been recognized. However, the formation, features, and environmental functions of nanosized BC, which is one of the most active fractions in the BC continuum during global climate change, are poorly understood. This review highlights the formation, surface reactivity (sorption, redox, and heteroaggregation), biotic, and abiotic transformations of nano-BC, and its major differences compared to other fractions of BC and engineered carbon nanomaterials. Potential applications of nano-BC including suspending agent, soil amendment, and nanofertilizer are elucidated based on its unique properties and functions. Future studies are suggested to develop more reliable detection techniques to provide multidimensional information on nano-BC in environmental samples, explore the critical role of nano-BC in promoting soil and planetary health from a one health perspective, and extend the multifield applications of nano-BC with a lower environmental footprint but higher efficiency.

Critical Issues in Assessing Occupational Exposure to Diesel Dust Exhaust.

The Italian Interministerial Decree of February 11, 2021, introduces the diesel engine exhaust (DDE) among the carcinogenic occupational compounds, also establishing an occupational exposure limit. Elemental carbon (EC), improperly called black carbon, has been proposed as a tracer of DDE exposure; EC is the carbon that is quantified in the ambient matrixes after all the organic carbon has been removed; traditionally, EC is measured with a thermo-optical analytical technique. EC determination and relative interpretation are challenging for the following reasons: (i) the scarce availability of equipped laboratories hampers EC analysis, (ii) EC interpretation is not easy due to the lack of reference values. Finally, (iii) the limit value of 0.050 mg/m3 of EC in the workplace appears too high compared to recently published exposure data. All these aspects stimulate a reflection on the significance of EC data in the context of both occupational hygiene and occupational medicine.

High emission of black carbon in heavy-duty diesel vehicles: Insights from microscopic operating conditions

The in-depth investigation of the high black carbon (BC) emission scenarios of heavy-duty diesel vehicles (HDDVs) is a crucial step toward developing effective control strategies. Chassis dynamometer tests were conducted for three in-use HDDVs, namely, vehicle #1, #2, and #3, focusing on the instantaneous BC characterizations during multiple driving conditions, i.e., speed phases and acceleration intervals. BC emission was found to increase with positive acceleration, and high acceleration could result in instantaneous BC spikes. The total BC emissions during velocity-acceleration interval 15–60 km h−1 and 0.1–0.9 m s−2 contributed to 43.4 ± 10.2 % of the whole-cycle emissions, while the proportions of time spent in the velocity-acceleration interval to the whole cycle were 23.1 ± 7.6 %. The cold-start microscopic operating condition was assessed by the cold-start extra emissions (CSEEs). Under various pre-defined cold-start durations, the proportions of CSEEs in the total cycle emissions were 9.4–21.0 %, 0–9.1 %, and 6.8–39.4 % for vehicles #1, #2, and #3, respectively. The CSEEs exhibited an initial rise, followed by a plateau as the assumed cold-start durations extended. A uniform cold-start duration of 600 s was established based on the criterion that the relative standard deviation (RSD) of CSEEs during the plateau period was <10 %. We proposed that the updated cold-start duration can enhance the accuracy and consistency of cold-start corrections in emission inventory models.