Dissolved carbon in biochar: Exploring its chemistry, iron complexing capability, toxicity in natural redox environment.

Dissolved black carbon (DBC) plays a crucial role in the migration and bioavailability of iron in water. However, the properties of DBC releasing under diverse pyrolysis conditions and dissolving processes have not been systematically studied. Here, the compositions of DBC released from biochar through redox processes dominated by bacteria and light were thoroughly studied. It was found that the DBC released from straw biochar possess more oxygen-containing functional groups and aromatic substances. The content of phenolic and carboxylic groups in DBC was increased under influence of microorganisms and light, respectively. The concentration of phenolic hydroxyl groups increased from 10.0∼57.5 mmol/gC to 6.6 ∼65.2 mmol/gC, and the concentration of carboxyl groups increased from 49.7∼97.5 mmol/gC to 62.1 ∼113.3 mmol/gC. Then the impacts of DBC on pyrite dissolution and microalgae growth were also investigated. The complexing Fe3+ was proved to play a predominant role in the dissolution of ferrous mineral in DBC solution. Due to complexing between iron ion and DBC, the amount of dissolved Fe in aquatic water may rise as a result of elevated number of aromatic components with oxygen containing groups and low molecular weight generated under light conditions. Fe-DBC complexations in solution significantly promoted microalga growth, which might be attributed to the stimulating effect of dissolved Fe on the chlorophyll synthesis. The results of study will deepen our understanding of the behavior and ultimate destiny of DBC released into an iron-rich environment under redox conditions.

Airborne particulate matter and diesel engine exhaust on infrastructure construction sites in the Copenhagen metropolitan area.

Diesel engine exhaust (DEE) is carcinogenic and potentially hazardous for those working in close proximity to diesel-powered machines. This study characterizes workplace exposure to DEE and its associated particulate matter (PM) during outdoor construction activities. We sampled at 4 construction sites in the Copenhagen metropolitan area. We used portable constant-flow pumps and quartz-fiber filters to quantify personal exposure to elemental carbon (EC), and used real-time instruments to collect activity-based information about particle number and size distribution, as well as black carbon (BC) concentration. Full-shift measurements of EC concentration ranged from < 0.3 to 6.4 µg/m3. Geometric mean (GM) EC exposure was highest for ground workers (3.4 µg/m3 EC; geometric standard deviation, GSD = 1.3), followed by drilling rig operators (2.6 µg/m3 EC; GSD = 1.4). Exposure for non-drilling-rig machine operators (1.2 µg/m3 EC; GSD = 2.9) did not differ significantly from background (0.9 µg/m3 EC; GSD = 1.7). The maximum 15-min moving average concentration of BC was 17 µg/m3, and the highest recorded peak concentration was 44 µg/m3. In numbers, the particle size distributions were dominated by ultrafine particles ascribed to DEE and occasional welding activities at the sites. The average total particle number concentrations (PNCs) measured in near-field and far-field positions across all worksites were 10,600 (GSD = 3.0) and 6,000 (GSD = 2.8)/cm3, respectively. Sites with active drilling rigs saw significantly higher average total PNCs at their near-field stations (13,600, 32,000, and 9,700/cm3; GSD = 2.4, 3.4, and 2.4) than sites without (4,700/cm3; GSD = 1.6). Overall, the DEE exposures at these outdoor construction sites were below current occupational exposure limits for EC (10 µg/m3 in Denmark; 50 µg/m3 in the European Union), but extended durations of exposure to the observed DEE levels may still be a health risk.

Dissolved Black Carbon Facilitates the Photodegradation of Microplastics via Molecular Weight-Dependent Generation of Reactive Intermediates.

Photodegradation of microplastics (MPs) induced by sunlight plays a crucial role in determining their transport, fate, and impacts in aquatic environments. Dissolved black carbon (DBC), originating from pyrolyzed carbon, can potentially mediate the photodegradation of MPs owing to its potent photosensitization capacity. This study examined the impact of pyrolyzed wood derived DBC (5 mg C/L) on the photodegradation of polystyrene (PS) MPs in aquatic solutions under UV radiation. It revealed that the photodegradation of PS MPs primarily occurred at the benzene ring rather than the aliphatic segments due to the fast attack of hydroxyl radical (•OH) and singlet oxygen (1O2) on the benzene ring. The photosensitivity of DBC accelerated the degradation of PS MPs, primarily attributed to the increased production of •OH, 1O2, and triplet-excited state DBC (3DBC*). Notably, DBC-mediated photodegradation was related to its molecular weight (MW) and chemical properties. Low MW DBC (<3 kDa) containing more carbonyl groups generated more •OH and 1O2, accelerating the photodegradation of MPs. Nevertheless, higher aromatic phenols in high MW DBC (>30 kDa) scavenged •OH and generated more O2•-, inhibiting the photodegradation of MPs. Overall, this study offered valuable insights into UV-induced photodegradation of MPs and highlighted potential impacts of DBC on the transformation of MPs.

Elevated Exposure to Air Pollutants Accelerates Primary Glomerular Disease Progression.

Introduction: Environmental contributors to kidney disease progression remain elusive. We explored how residential air pollution affects disease progression in patients with primary glomerulopathies. Methods: Nephrotic Syndrome Study Network (NEPTUNE) and CureGlomerulonephropathy (CureGN) participants with residential census tract data and ≥2 years of follow-up were included. Using Cox proportional hazards models, the associations per doubling in annual average baseline concentrations of total particulate matter with diameter ≤2.5 µm (PM2.5) and its components, black carbon (BC), and sulfate, with time to ≥40% decline in estimated glomerular filtration rate (eGFR) or kidney failure were estimated. Serum tumour necrosis factor levels and kidney tissue transcriptomic inflammatory pathway activation scores were used as molecular markers of disease progression. Results: PM2.5, BC, and sulfate exposures were comparable in NEPTUNE (n = 228) and CureGN (n = 697). In both cohorts, participants from areas with higher levels of pollutants had lower eGFR, were older and more likely self-reported racial and ethnic minorities. In a fully adjusted model combining both cohorts, kidney disease progression was associated with PM2.5 (adjusted hazard ratio 1.55 [95% confidence interval: 1.00-2.38], P = 0.0489) and BC (adjusted hazard ratio 1.43 [95% confidence interval: 0.98-2.07], P = 0.0608) exposure. Sulfate and PM2.5 exposure were positively correlated with serum tumour necrosis factor (TNF) (P = 0.003) and interleukin-1ß levels (P = 0.03), respectively. Sulfate exposure was also directly associated with transcriptional activation of the TNF and JAK-STAT signaling pathways in kidneys (r = 0.55-0.67, P-value <0.01). Conclusion: Elevated exposure to select air pollutants is associated with increased risk of disease progression and systemic inflammation in patients with primary.

The impact of PM2.5 and its constituents on gestational diabetes mellitus: a retrospective cohort study.

BACKGROUND: There is increasing evidence that exposure to PM2.5 and its constituents is associated with an increased risk of gestational diabetes mellitus (GDM), but studies on the relationship between exposure to PM2.5 constituents and the risk of GDM are still limited. METHODS: A total of 17,855 pregnant women in Guangzhou were recruited for this retrospective cohort study, and the time-varying average concentration method was used to estimate individual exposure to PM2.5 and its constituents during pregnancy. Logistic regression was used to assess the relationship between exposure to PM2.5 and its constituents and the risk of GDM, and the expected inflection point between exposure to PM2.5 and its constituents and the risk of GDM was estimated using logistic regression combined with restricted cubic spline curves. Stratified analyses and interaction tests were performed. RESULTS: After adjustment for confounders, exposure to PM2.5 and its constituents (NO3-, NH4+, and OM) was positively associated with the risk of GDM during pregnancy, especially when exposure to NO3- and NH4+ occurred in the first to second trimester, with each interquartile range increase the risk of GDM by 20.2% (95% CI: 1.118-1.293) and 18.2% (95% CI. 1.107-1.263), respectively. The lowest inflection points between PM2.5, SO42-, NO3-, NH4+, OM, and BC concentrations and GDM risk throughout the gestation period were 18.96, 5.80, 3.22, 2.67, 4.77 and 0.97 µg/m3, respectively. In the first trimester, an age interaction effect between exposure to SO42-, OM, and BC and the risk of GDM was observed. CONCLUSIONS: This study demonstrates a positive association between exposure to PM2.5 and its constituents and the risk of GDM. Specifically, exposure to NO3-, NH4+, and OM was particularly associated with an increased risk of GDM. The present study contributes to a better understanding of the effects of exposure to PM2.5 and its constituents on the risk of GDM.

Ambient PM2.5 Chemical Composition and Cardiovascular Disease Hospitalizations in China.

Little is known about the impacts of specific chemical components on cardiovascular hospitalizations. We examined the relationships of PM2.5 chemical composition and daily hospitalizations for cardiovascular disease in 184 Chinese cities. Acute PM2.5 chemical composition exposures were linked to higher cardiovascular disease hospitalizations on the same day and the percentage change of cardiovascular admission was the highest at 1.76% (95% CI, 1.36-2.16%) per interquartile range increase in BC, followed by 1.07% (0.72-1.43%) for SO42-, 1.04% (0.63-1.46%) for NH4+, 0.99% (0.55-1.43%) for NO3-, 0.83% (0.50-1.17%) for OM, and 0.80% (0.34%-1.26%) for Cl-. Similar findings were observed for all cause-specific major cardiovascular diseases, except for heart rhythm disturbances. Short-term exposures to PM2.5 chemical composition were related to higher admissions and showed diverse impacts on major cardiovascular diseases.

Short-term exposure to ambient fine particulate matter constituents and myocardial infarction mortality.

Short-term ambient fine particulate matter (PM2.5) exposure has been related to an increased risk of myocardial infarction (MI) death, but which PM2.5 constituents are associated with MI death and to what extent remain unclear. We aimed to explore the associations of short-term exposure to PM2.5 constituents with MI death and evaluate excess mortality. We conducted a time-stratified case-crossover study on 237,492 MI decedents in Jiangsu province, China during 2015-2021. Utilizing a validated PM2.5 constituents grid dataset at 1 km spatial resolution, we estimated black carbon (BC), organic carbon (OC), sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) exposure by extracting daily concentrations grounding on the home address of each subject. We employed conditional logistic regression models to evaluate the exposure-response relationship between PM2.5 constituents and MI death. Overall, per interquartile range (IQR) increase of BC (lag 06-day; IQR: 1.75 µg/m3) and SO42- (lag 04-day; IQR: 5.06 µg/m3) exposures were significantly associated with a 3.91% and 2.94% increase in odds of MI death, respectively, and no significant departure from linearity was identified in the exposure-response curves for BC and SO42-. If BC and SO42- exposures were reduced to theoretical minimal risk exposure concentration (0.89 µg/m3 and 1.51 µg/m3), an estimate of 4.55% and 4.80% MI deaths would be avoided, respectively. We did not find robust associations of OC, NO3-, NH4+, and Cl- exposures with MI death. Individuals aged ≥80 years were more vulnerable to PM2.5 constituent exposures in MI death (p for difference <0.05). In conclusion, short-term exposure to PM2.5-bound BC and SO42- was significantly associated with increased odds of MI death and resulted in extensive excess mortality, notably in older adults. Our findings emphasized the necessity of reducing toxic PM2.5 constituent exposures to prevent deaths from MI and warranted further studies on the relative contribution of specific constituents.

Time-varying contributions of FeII and FeIII to AsV immobilization under anoxic/oxic conditions: The impacts of biochar and dissolved organic carbon.

Engineering black carbon (e.g. biochar) has been widely found in natural environments due to natural processes and extensive applications in engineering systems, and could influence the geochemical processes of coexisting arsenic (AsV) and FeII, especially when they are exposed to oxic conditions. Here, we studied time-varying kinetics and efficiencies of AsV immobilization by solid-phase FeII (FeIIsolid) and FeIII (FeIIIsolid) in FeII-AsV-biochar systems under both anoxic and oxic conditions at pH 7.0, with focuses on the effects of biochar surface and biochar-derived dissolved organic carbon (DOC). Under anoxic conditions, FeII could rapidly immobilize AsV via co-adsorption onto biochar surfaces, which also serves as the dominant pathway of AsV immobilization at the initial stage of reaction (0-5 min) under oxic conditions at high biochar concentrations. Subsequently, with increasing biochar concentrations, FeIIIsolid precipitation from aqueous FeII (FeIIaq) oxidation (5-60 min) starts to play an important role in AsV immobilization but in decreased efficiencies of AsV immobilization per unit iron. In the following stage (60-300 min), FeIIsolid oxidation is suppressed and leads to AsV release into solutions at >1.0 g·L-1 biochar. The decreasing efficiency of AsV immobilization over time is attributed to the gradual release of DOC into solution from biochar particles, which significantly inhibit AsV immobilization when FeIIIsolid is generated from FeIIsolid oxidation in the vicinity of biochar surfaces. Specifically, 4.06 mg·L of biochar-derived DOC can completely inhibit the immobilization of AsV in the 100 µM FeII system under oxic conditions. The findings are crucial to comprehensively understand and predict the behavior of FeII and AsV with coexisting engineering black carbon in natural environments.

Evaluating the efficacy of targeted traffic management interventions: A novel methodology for determining the composition of particulate matter in urban air pollution hotspots.

Worldwide, there is an increasing uptake of traffic management interventions aimed at reducing the impact of traffic related air pollution on public health. However, the evidence base linking the proposed changes with the resulting improvements in air quality is lacking. In this paper we present data from a micro-network of low-cost PM10 samplers collected from an isolated urban centre (Auckland, New Zealand). The data was then analysed using a new combination of analytical methods aimed to identify the composition and hence, the source of pollution. Whilst across the three sites mass concentration of PM10 and black carbon were similar, Raman spectroscopy successfully identified variations in the soot composition at different sites, enabling some particulate matter to be linked to diesel vehicle emissions. A mass reconstruction approach proved useful in determining that the airshed is well-mixed and also highlighted the impacts of urban design on recorded concentrations. The results show that networks of low-cost sensors, combined with the range of analytical techniques used here can help policymakers test the efficacy of interventions and management strategies designed to combat the burden of air pollution on public health.

Joint effect of black carbon deriving from wheat straw burning and plastic mulch film debris on the soil biochemical properties, bacterial and fungal communities.

Black carbon (BC) formed after straw burning remains in farmland soil and coexists with plastic mulch film (PMF) debris. It is unclear how BC influences soil multifunctionality in the presence of PMF debris. In this study, we determined the joint effects of BC and PMF debris on soil biochemical properties and microbial communities. We conducted a soil microcosm experiment by adding BC formed by direct burning of wheat straw and PMF debris (polyethylene (PE) and biodegradable PMF (BP)) into soil at the dosages of 1 %, and soils were sampled on the 15th, 30th, and 100th day of soil incubation for high-throughput sequencing. The results showed that the degradation of PMF debris was accompanied by the release of microplastics (MPs). BC decreased NH4+-N (PE: 68.63 %; BP: 58.97 %) and NO3--N (PE: 12.83 %; BP: 51.37 %) and increased available phosphorus (AP) (PE: 79.12 %; BP: 26.09 %) in soil containing PMF debris. There were significant differences in enzyme activity among all the treatments. High-throughput sequencing indicated that BC reduced bacterial and fungal richness and fungal diversity in PMF debris-exposed soil, whereas PMF debris and BC resulted in significant changes in the proportion of dominant phyla and genera of bacteria and fungi, which were affected by incubation time. Furthermore, BC affected microorganisms by influencing soil properties, and pH and N content were the main influencing factors. In addition, FAPRPTAX analysis indicated that BC and PMF debris affected soil C and N cycling. These findings provide new insights into the response of soil multifunctionality to BC and PMF debris.

Characteristics and source apportionment of black carbon the urban and desert underlying surfaces.

The spatial distribution of black carbon (BC) concentrations was highly variable across different underlying surfaces. Differences in meteorological conditions and emission sources led to great temporal and spatial variations in BC characteristics. As the most important absorbing aerosol, BC can affect radiation, clouds, and surface snow. BC mass concentrations were measured using a seven channel aethalometer (AE-33) in the Urumqi and Taklimakan Deserts from January to December 2022. The aethalometer data, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models were used to analyse the variation characteristics, potential sources, and affected areas. Results showed that the BC concentrations in the Taklamakan Desert and Urumqi were in the range of 0-500 ng·m-3, accounting for 66.20 % and 59.50 % of the total, respectively. The backward trajectory simulation of BC mass concentration in the tower and Urumqi using the HYSPLIT model found that the airflow trajectories in the tower in summer corresponded to the BC concentration in the following order: trajectory 4 > trajectory 3 > trajectory 2 > trajectory 1, and trajectory 4 originated from the Turpan airflow accounting for 46.19 % of the total, which corresponded to the highest BC concentration of 621.73 ng·m-3. The trajectories of the airflow in Urumqi in summer corresponded to the BC concentration in the order of high to low, and the BC concentration in summer corresponded to the highest concentration in the Turpan airflow. BC concentrations arranged from high to low were trajectory 1 > trajectory 5 > trajectory 3 > trajectory 4 > trajectory 2. Trajectories 3, 4, and 5 airflow directions were the same, and the airflow trajectory accounted for 47.48 %, corresponding to BC concentrations of 599.82 ng·m-3, 579.99 ng·m-3, and 555.85 ng·m-3, respectively. Tower in the spring compared with other seasons of the CWT value of >400 ng·m-3 had the widest coverage, and Urumqi had more source areas contributing to moderate pollution concentration weights in winter (400 ng·m-3 < CWT < 800 ng·m-3). The conclusions of this study provide a scientific basis for regional environmental management and the formulation of air pollution measures in Xinjiang.

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.

Enhanced Aging of Black Carbon under Recent Clean Air Actions and Future Carbon Neutrality Scenario in China.

China has implemented strict emission control measures, but it is unclear how they affect black carbon (BC) aging and light absorption. Here, we use the Community Atmosphere Model version 6 (CAM6) with the four-mode version of the Modal Aerosol Module coupled with machine learning (MAM4-ML) to simulate BC aging during 2011-2018 and 2050/2100 following a carbon neutrality scenario (SSP1-2.6), respectively. During 2011-2018, the mass ratio of coatings to BC (RBC) widely increased (5.4% yr-1) over the east of China. The increased secondary organic aerosol (SOA) coatings dominate (88%) the increased RBC, while the sulfate coatings decrease. The drivers of BC coating changes come from the different magnitudes of emission reductions in secondary aerosol precursors (i.e., volatile organic compounds (VOCs) and SO2) and BC. During 2011-2018, the increased RBC enhances the BC mass absorption cross section (MAC, 0.7% yr-1). In 2050/2100 for SSP1-2.6, emission control leads to further increased RBC (95/145%) and BC MAC (12/17%). For both 2011-2018 and 2050/2100, the enhanced BC MAC partly offsets the declining direct radiative effect (DRE) of BC due to direct emission reduction. As a result, the full impact of direct emission reductions of BC on BC DRE is only 75% for 2011-2018 and 90/94% for 2050/2100.

Air pollution exposure in active versus passive travel modes across five continents: A Bayesian random-effects meta-analysis.

Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10, PM2.5) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO2). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM10 and PM2.5, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM10 and pedestrians for UFP) and higher exposures (compared to cyclist for PM2.5 and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.

The effect of quarantine policy on pollution emission and the usage of private transportation in urban areas.

Governmental policies, regulations, and responses to the pandemic can benefit from a better understanding of people's resulting behaviours before, during, and after COVID-19. To avoid the inelasticity and subjectivity of survey datasets, several studies have already used some objective variables like air pollutants to estimate the potential impacts of COVID-19 on the urban transportation system. However, the usage of reactant gases and a narrow time scale might weaken the results somehow. Here, both the objective passenger volume of public transport and the concentration of private traffic emitted black carbon (BC) from 2018 to 2023 were collected/calculated to decipher the potential relationship between public and private traffic during the COVID-19 period. Our results indicated that the commuting patterns of citizens show significant (p < 0.01) different patterns before, during, and after the pandemic. To be specific, public transportation showed a significant (p < 0.01) positive correlation with private transportation before the pandemic. This public transportation was significantly (p < 0.01) affected by the outbreaks of COVID-19, showing a significant (p < 0.01) negative correlation with private transportation. Such impacts of the virus and governmental policy would affect the long-term behaviour of individuals and even affect public transportation usage after the pandemic. Our results also indicated that such behaviour was mainly linked to the governmental restriction policy and would soon be neglected after the cancellation of the restriction policy in China.

Associations between fine particulate matter, gene expression, and promoter methylation in human bronchial epithelial cells exposed within a classroom under air-liquid interface.

Associations between indoor air pollution from fine particulate matter (PM with aerodynamic diameter dp < 2.5 µm) and human health are poorly understood. Here, we analyse the concentration-response curves for fine and ultrafine PM, the gene expression, and the methylation patterns in human bronchial epithelial cells (BEAS-2B) exposed at the air-liquid interface (ALI) within a classroom in downtown Rome. Our results document the upregulation of aryl hydrocarbon receptor (AhR) and genes associated with xenobiotic metabolism (CYP1A1 and CYP1B1) in response to single exposure of cells to fresh urban aerosols at low fine PM mass concentrations within the classroom. This is evidenced by concentrations of ultrafine particles (UFPs, dp < 0.1 µm), polycyclic aromatic hydrocarbons (PAH), and ratios of black carbon (BC) to organic aerosol (OA). Additionally, an interleukin 18 (IL-18) down-regulation was found during periods of high human occupancy. Despite the observed gene expression dysregulation, no changes were detected in the methylation levels of the promoter regions of these genes, indicating that the altered gene expression is not linked to changes in DNA methylation and suggesting the involvement of another epigenetic mechanism in the gene regulation. Gene expression changes at low exposure doses have been previously reported. Here, we add the possibility that lung epithelial cells, when singly exposed to real environmental concentrations of fine PM that translate into ultra-low doses of treatment, may undergo epigenetic alteration in the expression of genes related to xenobiotic metabolism. Our findings provide a perspective for future indoor air quality regulations. We underscore the potential role of indoor UFPs as carriers of toxic molecules with low-pressure weather conditions, when rainfall and strong winds may favour low levels of fine PM.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

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.

Evolution of black carbon and brown carbon during summertime in Southwestern China: An assessment of control measures during the 2023 Chengdu Summer World University Games.

The 31st FISU Summer World University Games (SWUG) was held in Chengdu, southwestern China, from July 22 to August 8, 2023. A series of control measures were carried out to ensure good air quality during the SWUG, providing an opportunity to investigate the atmospheric behaviors of light-absorbing aerosols under such a substantial disturbance caused by the control measures. To assess the impacts of emission controls on primary pollutants, a field campaign was conducted at a rural site in Chengdu to investigate the characterization of equivalent black carbon (eBC). The changes of eBC concentrations before, during, and after the SWUG were characterized. The sources of eBC were resolved, and the impacts of atmospheric processes on the absorption capacity were also investigated. During the SWUG, the eBC concentration decreased by 12.1 % and 25.3 % compared with those before and after the SWUG. A fossil fuel combustion (eBCff) and a biomass burning (eBCbb) originated eBC were resolved using the aethalometer model. Both eBCff and eBCbb decreased during the SWUG, indicating the effectiveness of control measures. After the SWUG, the influence of biomass burning emissions became more and more significant, and the contribution of brown carbon (BrC) to light absorption at 370-660 nm increased by 52, 19, 7, 6, and 17 % compared to those during the SWUG. As the biomass burning emitted aerosols aged, the absorption Ångström exponent and babs(BrC370nm) decreased gradually, which was mainly due to the photobleaching of the chromophores during the daytime. eBCff was mainly affected by strong wind, while high eBCbb concentration was mainly attributed to the gradual accumulation of biomass-burning emissions near the observation site. The results show the significant reduction of eBC with the implementation of the air pollution mitigation campaign, and provide insights on the impacts of atmospheric processes on BC optical properties during summertime.

Characterization of on-road nitrogen oxides and black carbon emissions from high emitters of heavy-duty diesel vehicles in China.

Heavy-duty diesel vehicles (HDDVs) significantly contribute to atmospheric nitrogen oxides (NOX) and black carbon (BC), with high emitters within the HDDV fleet impacting the total emissions. However, emission patterns and contributions of high emitters are rarely explored from a fleet-perspective. We investigated NOX and BC emission factors (EFs) from 1925 HDDVs in Shenzhen by the plume-chasing method, and found that the fleet-average EFs decreased with stricter emission standards. Unexpectedly, the average NOX EF for the China IV fleet was comparable with that for the China III fleet due to possible ineffective aftertreatment in high-emitter sectors of China IV HDDVs. Decreasing trend in average NOX EF since 2017 reflected the effective emission controls by the implementation of China V standard. Besides, semi-trailer tractors exhibited a higher incidence of NOX over-emissions, whereas BC high emitters were more pronounced in box trucks. Total NOX and BC emissions from HDDVs in Shenzhen were revisited, reaching 54.0 and 1.1 Gg·yr-1, with updated NOX EF correcting a 26.2 % underestimation in national guidelines. Notably, eliminating high emitters yields greater emission reduction benefits than merely retiring old HDDVs, with BC reduction outpacing NOX. This study provides new insights into the implementation of targeted emission reduction measures for HDDVs.