Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 µg/m3, 5.33 µg/m3, 5.63 µg/m3, 3.89 µg/m3, 9.14 µg/m3, and 0.52 µg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Evaluation of biological markers for the risk assessment of carbon black in epidemiological studies.

Background/objectives: Engineered nanomaterials (ENMs) have been suggested as being capable of promoting inflammation, a key component in the pathways associated with carcinogenesis, cardiovascular disease, and other conditions. As a result, the risk assessment of biological markers as early-stage indicators has the potential to improve translation from experimental toxicologic findings to identifying evidence in human studies. The study aims to review the possible early biological changes in workers exposed to carbon black (CB), followed by an evidentiary quality evaluation to determine the predictive value of the biological markers. Methods: We conducted a literature search to identify epidemiological studies that assessed biological markers that were involved in the inflammatory process at early stages among workers with exposure to CB. We reviewed the studies with specific reference to the study design, statistical analyses, findings, and limitations. Results: We identified five Chinese studies that investigated the potential impact of exposure to CB on inflammatory markers, bronchial wall thickening, genomic instability, and lung function impairment in CB production workers. Of the five Chinese studies, four were cross-sectional; another study reported results at two-time points over six years of follow-up. The authors of all five studies concluded positive relationships between exposure and the inflammatory cytokine profiles. The weak to very weak correlations between biomarkers and early-stage endpoints were reported. Conclusion: Most inflammatory markers failed to satisfy the proposed evidentiary quality criteria. The significance of the results of the reviewed studies is limited by the cross-sectional study design, inconsistency in results, uncertain clinical relevance, and high occupational exposures. Based on this review, the risk assessment relying on inflammatory markers does not seem appropriate at this time. Nevertheless, the novel research warrants further exploration in assessing exposure to ENMs and corresponding potential health risks in occupational settings.

Source apportionment, health risk assessment, and trajectory analysis of black carbon and light absorption properties of black and brown carbon in Delhi, India.

Black Carbon (BC) is an important atmospheric pollutant, well recognized for adverse health and climatic effects. The present work discusses the monthly and seasonal variations of BC sources, health risks, and light absorption properties. The measurement was done from January to December 2021 using a seven wavelength aethalometer. Annual average BC concentration during the study period was 12.2 ± 8.8 µg/m3 (ranged from 1.9 - 52.2 µg/m3). Results represent highest BC concentration during winter (W), followed by post-monsoon (P-M), summer (S), and monsoon (M) seasons where the fossil fuel (FF) combustion is the major source during W, S, and M seasons and biomass burning (BB) during the P-M season. The health risk assessment revealed that individuals in Delhi are exposed to BC levels equivalent to inhaling the smoke from 36 passively smoked cigarettes (PSC) everyday. The risk is highest during W reaching upto 71 PSC and minimum during M i.e., 9 PSC. The light absorption properties were calculated for BC (AbsBC) and Brown carbon (AbsBrC). AbsBC and varied from 229-89 Mm-1 between 370-950 nm and AbsBrC varied from 87-12 Mm-1 between 370-660 nm. AbsBC contributed substantially to total absorption at all wavelengths, while AbsBrC contribution is quite significant in the UV region only. Trajectory analysis confirmed significant influence of regional sources (e.g., biomass-burning aerosols from northwest and east direction) on air quality, health risks, and light absorption properties of BC over Delhi especially during the P-M season. The BB events of Punjab, Haryana, Uttar Pradesh, and eastern Pakistan seems to have significant influence on Delhi's air quality predominantly during P-M season.

Geochemical characteristics and socioeconomic associations of carbonaceous aerosols in coal-fueled cities with significant seasonal pollution pattern.

Carbonaceous aerosols, comprising organic carbon (OC) and elemental carbon (EC), are critical component of fine particulate matter (PM2.5), with diverse impacts on air quality and human health. This study investigated the concentrations and seasonal patterns of carbonaceous species in PM2.5 during both the heating season (January 2021) and non-heating season (July 2021) in three coal-fueled cities in northern China, as well as the differences in carbonaceous aerosols and their associations with socioeconomic parameters in cities situated on either side of the "Hu Line" in China. The results showed that, owing to intensified coal combustion and unfavorable meteorological conditions, levels of OC, EC, and OC/EC ratios were higher in winter compared to summer. Moreover, the presence of dust (DU) and light pollution (LP) days resulted in elevated OC levels but decreased EC levels. The Char-EC/Soot-EC ratios were highest during LP, followed by CL and DU. A source apportionment analysis demonstrated that coal burning, vehicle exhaust, road dust, and biomass burning were the primary contributors to carbonaceous aerosols, as confirmed by diagnostic ratios, Char-EC/Soot-EC ratios, and PCA analysis. Furthermore, our study found that carbonaceous aerosols concentrations and source apportionment primarily varied with diurnal and seasonal trends and different pollution types. Additionally, at the national scale, population density and urban green space exhibited a positive correlation with OC/EC ratios (p < 0.05), while energy consumption per unit of GDP showed a negative correlation (p < 0.05). The observation that OC/EC ratios were lower in coal-fueled cities than in economy-based cities suggests a more severe pollution scenario. These findings highlight the importance of comprehending of the seasonal variation and chemical characteristics of carbonaceous aerosol for understanding air pollution sources and characteristics, which is essential for both air quality management and human health.

Technical-economic and environmental protection performance evaluation of a novel hybrid solar-nuclear thermally coupled power and desalination plant.

Clean energy complementary system can reduce environmental pollution effectively and is considered as a future energy development direction. In this paper, an innovative solar-nuclear thermally coupled power and desalination plant for electricity and freshwater productions is proposed. As solar power and nuclear power are combined, this multi-energy system is a clean energy system and basically has no emissions of soot, sulfur oxides, carbon dioxide, and nitrogen oxides. The operating behavior assessment results of the multi-energy system show that the power generation and freshwater production systems can operate synergistically. The electric power and corresponding efficiency of the multi-energy system are 290.7 MW and 38.2%, in which the solar proportion is about 38.1%. The daily freshwater production of the multi-energy system is 3761.3 t. The economic assessment results reveal that the levelized costs of electricity and freshwater of the multi-energy system are 0.361 yuan/(kWh) and 1.645 yuan/t. The environmental protection analysis results show that in contrast with a coal-fired system, the annual emission reductions of soot, sulfur oxides, carbon dioxide, and nitrogen oxides of the multi-energy system are 7350.94 t, 12,634.42 t, 513,034.14 t, and 11,945.28 t, revealing a significant environmental protection performance.

Health risk assessment of indoor air quality, sociodemographic and kitchen characteristics on respiratory health among women responsible for cooking in urban settings of Oromia region, Ethiopia: a community-based cross-sectional study.

OBJECTIVES: In Ethiopia, where biomass fuel is used by the majority of the population, women who are primarily responsible for cooking are at a higher risk of having respiratory symptoms. However, there is limited information on the respiratory symptoms of exposed women. This study assessed the magnitude of respiratory disease symptoms and associated factors among women responsible for cooking in Mattu and Bedele towns, south-west Ethiopia. METHODS: A community-based cross-sectional study was conducted among 420 randomly selected women in urban settings in south-west Ethiopia. Data were collected through face-to-face interviews using a modified version of the American Thoracic Society Respiratory Questionnaire. The data were cleaned, coded and entered into EpiData V.3.1 and exported into SPSS V.22 for analysis. Bivariable and multivariable logistic regression analyses were used to identify factors associated with respiratory symptoms at a value of p<0.05. RESULTS: It is found that 34.9% of the study participants have respiratory symptoms (95% CI 30.6% to 39.4%). Unimproved floor (adjusted OR (AOR)=2.4 at 95% CI 1.42 to 4.15), presence of thick black soot in the ceiling (AOR=2.1 at 95% CI 1.2 to 3.6), using fuel wood (AOR=2.3 at 95% CI 1.1 to 4.7), using a traditional stove (AOR=3.37 at 95% CI 1.85 to 6.16), long duration of cooking (AOR=2.52 at 95% CI 1.4 to 4.5) and cooking room without a window (AOR=2.4 at 95% CI 1.5 to 3.9) were significantly associated with women's respiratory symptoms. CONCLUSION: More than two in six women who cook had respiratory symptoms. Floor, fuel and stove type, soot deposits in the ceiling, duration of cooking and cooking in a room without a window were the identified factors. Appropriate ventilation, improved floor and stove design and the switch to high-efficiency, low-emission fuels could help to lessen the effects of wood smoke on women's respiratory health.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses.

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NOx), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NOx, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NOx to CO and BC to NOx emission ratios, which were between 0.21-0.32 µg m-3 NOx (µg m-3 CO)-1 and 0.013-0.018 µg m-3 BC (µg m-3 NOx)-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana.

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 µg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

A low-cost carbon-based electrochemical platform for determining 2,3-dihydroxyphenol: applications in natural water and biodiesel samples.

2,3-Dihydroxyphenol (DHP) is a phenolic compound that has been used as an additive in biodiesel to avoid the auto-oxidation of biofuels and also in the production of cosmetic products. However, this substance can be released into the environment during its manufacture, transport, disposal and industrial use and can be harmful to health due to its toxicity, and hence, monitoring its presence in different samples is very important. Therefore, this work describes an electroanalytical study of DHP using different carbon-based pastes prepared to evaluate which one would be more promising to be used as an electrochemical platform for DHP quantification. The materials studied (graphite, carbon black and carbon nanotubes) in this work were characterized by Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and the Boehm method. Voltammetric studies showed that pure carbon black presented a higher current density for detecting DHP than the other materials tested (graphite, carbon black + graphite, carbon nanotubes, carbon nanotubes + graphite). In studying the medium's pH, the highest currents occurred in acid media and acetate buffer solutions. After optimizing the experimental parameters, it was possible to obtain a wide range of linear responses from 0.1 to 10 000 µmol L-1 for DHP and a good limit of detection (LOD) of 0.03 µmol L-1. The selectivity of the electrode was tested for different species that may be present in samples containing DHP. Finally, the electrode was applied to determine DHP in natural water and biodiesel samples, showing recovery values between 98 and 102%, indicating good accuracy.

Reliable assessment of carbon black nanomaterial of a variety of cell culture media for in vitro toxicity assays by asymmetrical flow field-flow fractionation.

Carbon black nanomaterial (CB-NM), as an industrial product with a large number of applications, poses a high risk of exposure, and its impact on health needs to be assessed. The most common testing platform for engineered (E)NMs is in vitro toxicity assessment, which requires prior ENM dispersion, stabilization, and characterization in cell culture media. Here, asymmetric flow field-flow fractionation (AF4) coupled to UV-Vis and dynamic light scattering (DLS) detectors in series was used for the study of CB dispersions in cell culture media, optimizing instrumental variables and working conditions. It was possible to disperse CB in a non-ionic surfactant aqueous solution due to the steric effect provided by surfactant molecules attached on the CB surface which prevented agglomeration. The protection provided by the surfactant or by culture media alone was insufficient to ensure good dispersion stability needed for carrying out in vitro toxicity studies. On the other hand, cell culture media in combination with the surfactant improved dispersion stability considerably, enabling the generation of shorter particles and a more favourable zeta potential magnitude, leading to greater stability due to electrostatic repulsion. It was demonstrated that the presence of amino acids in the culture media improved the monodisperse nature and stability of the CB dispersions, and resulted in a turn towards more negative zeta potential values when the pH was above the amino acid isoelectric point (IEP). Culture media used in real cell culture scenarios were also tested, and in vitro toxicity assays were developed optimizing the compatible amount of surfactant.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Application of machine learning for the low-cost prediction of soot concentration in a turbulent flame.

The second most potent forcer of climate change, soot, has severe harmful effects on both human health and the environment. Accurate numerical modeling of soot formation is extremely complex and has a high computational cost due to its dependence on many physical and chemical interactions, specifically in turbulent flames. The high computational cost of coupling chemistry, fluid dynamics, thermodynamics, and heat transfer raise the need for a novel, precise, and computationally cost-effective numerical technique for predicting soot concentrations. This study applies machine learning (ML) to predict soot formation in a turbulent flame. It has been discovered that the local soot volume fraction is correlated to the histories of gas properties strongly correlative to soot formation and oxidation. A library with the Lagrangian temporal histories of soot-containing fluid parcels is created from turbulent diffusion flame data computed using direct numerical simulation (DNS). This library is then used to train an ML algorithm to predict soot volume fraction along randomly selected trajectories (pathlines) in the domain. The prediction capability is tested over 10% of the entire dataset, and it is seen that soot volume fraction can be predicted well along the selected pathlines with low error and computational cost. To describe quantitative results, the calculated R2 in the current work is equal to 0.92, which shows good accuracy of the predictions.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany.

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00-8:00 LT) and night (19:00-22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect" was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health.

Assessing the Impact of Green Transformation on Ecological Well-Being Performance: A Case Study of 78 Cities in Western China.

The contradiction between the endless pursuit of material possessions and finite natural resources hampers ecological well-being performance (EWP) improvement. Green transformation, recognized as an emerging strategy in sustainable development, can help to coordinate ecological, social, and economic growth by optimizing resource usage, with the ultimate objective of enhancing EWP. This research quantifies how green transformation influences EWP by using panel data from 78 prefecture-level cities in western China from 2012 to 2019. Using the super-SBM and entropy weight models, we assess the EWP and green transformation index (GTI) of 78 prefecture-level cities in western China. On this basis, we quantify the spatial characteristics of EWP by an analysis of the Theil index and spatial autocorrelation. Finally, we examine how GTI affects EWP using the Spatial Durbin model. The results demonstrate that the GTI can raise the EWP of local and nearby cities in western China. According to a GTI analysis of internal indicators, the industrial solid waste usage, harm-less treatment rate of domestic waste, savings level, and R&D expenditure significantly affect EWP. In contrast, the soot emission and consumption levels impede EWP advancement. The analysis of effect decomposition indicates that the sewage treatment rate, expenditure on science and technology, and green patents have a significant spatial spillover effect on the improvement of EWP.

Assessing public health and economic loss associated with black carbon exposure using monitoring and MERRA-2 data.

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R2 = 0.74 and RMSE = 1.76 µg/m3) and long-term (R2 = 0.76 and RMSE = 1.3 µg/m3) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration-response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900-124,500) and 538,400 (95% CI: 495,000-581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%-0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 104 billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%-0.8% of China's total GDP in 2017, respectively.

Assessment of primary and inflammation-driven genotoxicity of carbon black nanoparticles in vitro and in vivo.

Carbon black nanoparticles (CBNPs) have a large surface area/volume ratio and are known to generate oxidative stress and inflammation that may result in genotoxicity and cancer. Here, we evaluated the primary and inflammatory response-driven (i.e. secondary) genotoxicity of two CBNPs, Flammruss101 (FL101) and PrintexXE2B (XE2B) that differ in size and specific surface area (SSA), and cause different amounts of reactive oxygen species. Three doses (low, medium and high) of FL101 and XE2B were assessed in vitro in the lung epithelial (A549) and activated THP-1 (THP-1a) monocytic cells exposed in submerged conditions for 6 and 24 h, and in C57BL/6 mice at day 1, 28 and 90 following intratracheal instillation. In vitro, we assessed pro-inflammatory response as IL-8 and IL-1ß gene expression, and in vivo, inflammation was determined as inflammatory cell infiltrates in bronchial lavage (BAL) fluid and as histological changes in lung tissue. DNA damage was quantified in vitro and in vivo as DNA strand breaks levels by the alkaline comet assay. Inflammatory responses in vitro and in vivo correlated with dosed CBNPs SSA. Both materials induced DNA damage in THP-1a (correlated with dosed mass), and only XE2B in A549 cells. Non-statistically significant increase in DNA damage in vivo was observed in BAL cells. In conclusion, this study shows dosed SSA predicted inflammation both in vivo and in vitro, whereas dosed mass predicted genotoxicity in vitro in THP-1a cells. The observed lack of correlation between CBNP surface area and genotoxicity provides little evidence of inflammation-driven genotoxicity in vivo and in vitro.

PyCoCa:A quantifying tool of carbon content in airway macrophage for assessment the internal dose of particles.

Given the lack of a comprehensive understanding of the complex metabolism and variable exposure environment, carbon particles in macrophages have become a potentially valuable biomarker to assess the exposure level of atmospheric particles, such as black carbon. However, the tedious and subjective quantification method limits the application of carbon particles as a valid biomarker. Aiming to obtain an accurate carbon particles quantification method, the deep learning and binarization algorithm were implemented to develop a quantitative tool for carbon content in airway macrophage (CCAM), named PyCoCa. Two types of macrophages, normal and foamy appearance, were applied for the development of PyCoCa. In comparison with the traditional methods, PyCoCa significantly improves the identification efficiency for over 100 times. Consistency assessment with the gold standard revealed that PyCoCa exhibits outstanding prediction ability with the Interclass Correlation Coefficient (ICC) values of over 0.80. And a proper fresh dye will enhance the performance of PyCoCa (ICC = 0.89). Subsequent sensitivity analysis confirmed an excellent performance regarding accuracy and robustness of PyCoCa under high/low exposure environments (sensitivity > 0.80). Furthermore, a successful application of our quantitative tool in cohort studies indicates that carbon particles induce macrophage foaming and the foaming decrease the carbon particles internalization in reverse. Our present study provides a robust and efficient tool to accurately quantify the carbon particles loading in macrophage for exposure assessment.

Carcinogenicity of Poorly Soluble Low Toxicity Particles: Commentary on Epidemiology as a Risk Assessment "Reality Check".

Inhaled particles that are poorly soluble or insoluble and of low toxicity ("poorly soluble low toxicity" or "PSLT" particles), can accumulate in the lung and at lung overload levels induce lung cancers in rats. The question of whether PSLT particles increase lung cancer risk in humans is complicated by large differences between rats and humans and the relatively large particle doses administered in animal studies even when compared with heavy human occupational exposures. We review the findings of epidemiological studies on occupational exposure to each of three different PSLT particles (carbon black, talc and taconite). The epidemiological evidence indicates that at even very high occupational exposure levels at which non-malignant respiratory diseases including pneumoconiosis and even talcosis are observed, lung cancer risks appear not to be elevated. Although positive human cancer risks might be predicted based on extrapolation from overload doses in rats to relevant exposures in humans, the epidemiological "reality check" based on the three examples indicates that these PSLT particles are unlikely to increase lung cancer risk in humans even at high occupational levels of exposure. Therefore, we propose that careful evaluation of the epidemiological evidence can serve as a "reality check" for human risk assessment and help balance the risk evaluation process.

Turning lignin into treasure: An innovative filler comparable to commercial carbon black for the green development of the rubber industry.

Driven by the global carbon neutrality action, biomass-derived functional materials have been applied in many fields to alleviate the pressure brought by the depletion of fossil energy. However, due to the complex structure, lignin faces many difficulties in its high-value utilization. The second largest biomass in the world has become the largest "natural waste". In this paper, the lignin-based biochar-silica (LB-S) hybrid nanoparticles were prepared via a combination of two-step acid precipitation and carbonization using lignin black liquor extracted from xylose residue and sodium silicate as raw materials. The effects of carbonization temperature and lignin-based biochar (LB) content on the reinforcing properties of LB-S were studied. The results show that the particle size, specific surface area, pore characteristics, and surface polarity of LB-S all affect the mechanical properties of the final vulcanizates. The reinforcement performance of the best sample (LMB500-S) with "high structure" characteristics can be comparable to that of commercial carbon black (CB) N550. This study shows that LMB500-S hybrid nanoparticles with economic benefits possess the potential to completely replace commercial CB, which can turn lignin waste into treasure and promote the green development of traditional rubber industry in the context of carbon neutrality.

Thermo-soil weathering and life cycle assessment of carbon black, silica and cellulose nanocrystal filled rubber nanocomposites.

Carbon black (CB) and silica (Sil) as rubber reinforcement have raised environmental concerns for being high resources consumptive and less susceptible towards biodegradability. Cellulose nanocrystal (CNC) has demonstrated great potentials for use as biodegradable nanofillers in rubber nanocomposites while evaluation of its environmental impacts with optimal end-of-life (EOL) choices is not carried out. To simulate realistic EOL, thermo-oxidative aging and soil burial aging behaviors of rubber nanocomposites with 33.3% filler were performed. The environmental weathering performance modeled with the help of life cycle assessment (LCA) illustrates increased biodegradation susceptibility with partial replacement of CB or Sil with CNC in the nanocomposites, hence promoting the environmental solutions for waste minimalization by enhancing the biodegradability potentials. In terms of LCA, the CNC incorporation contributes more to the environmental impacts in manufacturing but greatly lowers the EOL choices, by reducing the global warming potential values.