Black Carbon Concentrations Research Articles

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM2.5), a significant portion of ambient PM2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM2.5, are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM2.5 both temporally and spatially. Monthly network-average BC was 3 × higher in winter than late spring, while PM2.5 was only 10% lower. In both seasons, average PM2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM2.5, and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM2.5. Thus, including BC in addition to PM2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

Pollution Characteristics and Source Apportionment of Black Carbon Aerosols during Spring in Beijing.

Black carbon (BC) aerosols are important for absorbing aerosols, affecting global climate change and regional air quality, and potentially harming human health. From March to May 2023, we investigated black carbon aerosol levels and air pollution in Beijing. Employing methods such as linear regression, Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT), we analyzed the characteristics and sources of black carbon aerosols in the region. Results indicate that the light absorption coefficients of BC and BrC decrease with increasing wavelength, with BrC accounting for less than 40% at 370 nm. Daily variations in BC and PM2.5 concentrations exhibit similar trends, peaking in March, and BC displays a distinct bimodal hourly concentration structure during this period. Aethalometer model results suggest that liquid fuel combustion contributes significantly to black carbon (1.08 ± 0.71 μg·m-3), surpassing the contribution from solid fuel combustion (0.31 ± 0.2 μg·m-3). Furthermore, the significant positive correlation between BC and CO suggests that BC emissions in Beijing predominantly result from liquid fuel combustion. Potential source area analysis indicates that air masses of spring in Beijing mainly originate from the northwest (40.93%), while potential source areas for BC are predominantly distributed in the Beijing-Tianjin-Hebei region, as well as parts of the Shandong, Shanxi and Henan provinces. Moreover, this study reveals that dust processes during spring in Beijing have a limited impact on black carbon concentrations. This study's findings support controlling pollution in Beijing and improving regional air quality.

Source-specified atmospheric age distribution of black carbon and its impact on optical properties over the Yangtze River Delta

Black carbon (BC) exerts a profound and intricate impact on both air quality and climate due to its high light absorption. However, the uncertainty in representing the absorption enhancement of BC in climate models leads to an increased range in the modeled aerosol climate effects. Changes in BC optical properties could result either from atmospheric aging processes or from variations in its sources. In this study, a source-age model for identifying emission sources and aging states presented by University of California at Davis/California Institute of Technology (UCD/CIT) was used to simulate the atmospheric age distribution of BC from different sources and to quantify its impact on the optical properties of BC-containing particles. The results indicate that regions with greater aged BC concentrations do not correspond to regions with higher BC emissions due to atmospheric transport. High concentrations of aged BC are found in northern Yangtze River Delta (YRD) regions during summer. The chemical compositions of particles from different sources and with different atmospheric ages differ significantly. BC and primary organic aerosols (POA) are dominating in Traffic-dominated source while other components dominate in Industry-dominated source. As the atmospheric age increases, the mass fraction of secondary inorganic aerosols rises. Compared to the original model, the simulated mass absorption cross section of BC particles in the source-age model decreases while the single scattering albedo increases. This compensates for ~11 % of the overestimation of the simulated BC direct radiative forcing. Our study highlights that incorporating atmospheric age and source information into models can greatly improve the estimation of optical properties of BC-containing particles and deepen our understanding of their climate effects.

Black Carbon along a Highway and in a Residential Neighborhood during Rush-Hour Traffic in a Cold Climate

Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold climate along an urban highway and 300 m into an adjacent residential neighborhood. BC was measured with an aethalometer (MA350, Aethlabs) along the main traffic artery in geothermally heated Reykjavík, the capital of Iceland (64.135° N–21.895° W, 230,000 inhabitants). Stationary monitoring confirmed that traffic was the dominant source of roadside BC in winter, averaging 1.0 ± 1.1 µg/m3 (0.6 and 1.1 µg/m3 median and interquartile range; 28,000 vehicles/day). Inter-day variations in BC were primarily correlated to the atmospheric lapse rate and wind speed, both during stationary and mobile campaigns. During winter stills, BC levels surpassed 10 µg/m3 at intersections and built up to 5 µg/m3 during the afternoon in the residential neighborhood (adjacent to the highway with 43,000 vehicles/day). The BC penetrated deeply into the neighborhood, where the lowest concentration was 1.8 µg/m3 within 300 m. BC concentration was highly correlated to nitrogen dioxide (r > 0.8) monitored at the local Urban Traffic Monitoring site.

Equatorward shift of the boreal summer intertropical convergence zone in Maritime Continent and the impacts on surface black carbon concentration and public health

In Maritime Continent, the shift of intertropical convergence zone (ITCZ) location directly regulates the distribution of black carbon and hence affects public health in the region, but the mechanism and human health impacts have not yet been comprehensively revealed. Here we used multiple reanalysis datasets to investigate the long-term shift of seasonal-mean zonal-mean ITCZ location in this region from 1980 to 2014, and to assess the influences on black carbon distribution and the resultant health impact in terms of premature mortality. Results show that recent human-related equatorial warming contributed to an equatorward shift (∼2.1°) of ITCZ location in Maritime Continent. Spatially, the equatorward shift of ITCZ reduced surface black carbon concentration over the maritime area by enhancing updrafts and wet deposition, but raised the concentration in the continental area by inhibiting updrafts. Meanwhile, anomalous low-level northeasterlies weakened summer circulation and prevented black carbon from being transported to the Philippines. Our results also suggest that the equatorward shift decreased ∼13% of black carbon-associated monthly premature mortality in maritime countries, but increased ∼6% of that in continental countries based on the population and mortality rate in 2010. We therefore recommend considering climate change impacts in the design of adaptation strategies against regional air pollution.

Divergent features of the upper-tropospheric carbonaceous aerosol layer: Effects of atmospheric dynamics and pollution emissions in Asia, South America, and Africa

Abstract The upper-tropospheric carbonaceous aerosol layer (TCAL) represents the increase of aerosols in the upper-troposphere. It was first discovered over Asia but was found in this study to also occur over South America and Africa. The TCALs over three regions typically exist during the strong deep convection season, with the Asian, South American, and African TCALs showing peak intensity during July–August, October–December, and November–December, respectively. Over Asia, the TCAL has the highest altitude and widest spread due to strongest deep convection and upper-troposphere anticyclonic system. TCAL intensity is highest in South America maybe due to heaviest pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TCAL centers, whereas widespread wildfires result in single centers over South America and Africa. TCAL radiative effect at the top of the atmosphere has warming effects over Asia (+0.23 W m−2), whereas cooling effects perform over South America (−0.54 W m−2) and Africa (−0.20 W m−2) owing to its altitude and the divergent strengths of black-carbon absorption and organic-carbon scattering.

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.

Optical properties and simple forcing efficiency of the organic aerosols and black carbon emitted by residential wood burning in rural central Europe

Abstract. Recent years have seen an increase in the use of wood for energy production of over 30 %, and this trend is expected to continue due to the current energy crisis and geopolitical instability. At present, residential wood burning (RWB) is one of the most important sources of organic aerosols (OAs) and black carbon (BC), posing a significant risk to air quality and health. Simultaneously, as a substantial aerosol source, RWB also holds relevance in the context of aerosol radiative effects and climate. While BC is recognized for its large light absorption cross-section, the role of OAs in light absorption is still under evaluation due to their heterogeneous composition and source-dependent optical properties. Existing studies that characterize wood-burning aerosol emissions in Europe primarily concentrate on urban and background sites and focus on BC properties. Despite the significant RWB emissions in rural areas, these locations have received comparatively less attention. The present scenario underscores the imperative for an improved understanding of RWB pollution, aerosol optical properties, and their subsequent connection to climate impacts, particularly in rural areas. We have characterized atmospheric aerosol particles from a central European rural site during wintertime in the village of Retje in Loški Potok, Slovenia, from 1 December 2017 to 7 March 2018. The village experienced extremely high aerosol concentrations produced by RWB and near-ground temperature inversion. The isolated location of the site and the substantial local emissions made it an ideal laboratory-like place for characterizing RWB aerosols with low influence from non-RWB sources under ambient conditions. The mean mass concentrations of OA and BC were 35 µg m−3 (max⁡=270 µg m−3) and 3.1 µg m−3 (max⁡=24 µg m−3), respectively. The mean total particle number concentration (10–600 nm) was 9.9×103 particles cm−3 (max⁡=59×103 particles cm−3). The mean total light absorption coefficients at 370 and 880 nm measured by an AE33 Aethalometer were 120 and 22 Mm−1 and had maximum values of 1100 and 180 Mm−1, respectively. The aerosol concentrations and absorption coefficients measured during the campaign in Loški Potok were significantly larger than reported values for several urban areas in the region with larger populations and a larger extent of aerosol sources. Here, considerable contributions from brown carbon (BrC) to the total light absorption were identified, reaching up to 60 % and 48 % in the near-UV (370 nm) and blue (470 nm) wavelengths. These contributions are up to 3 times higher than values reported for other sites impacted by wood-burning emissions. The calculated mass absorption cross-section and the absorption Ångström exponent for RWB OA were MACOA,370nm=2.4 m2 g−1, and AAEBrC,370-590nm=3.9, respectively. Simple-forcing-efficiency (SFE) calculations were performed as a sensitivity analysis to evaluate the climate impact of the RWB aerosols produced at the study site by integrating the optical properties measured during the campaign. The SFE results show a considerable forcing capacity from the local RWB aerosols, with a high sensitivity to OA absorption properties and a more substantial impact over bright surfaces like snow, typical during the coldest season with higher OA emissions from RWB. Our study's results are highly significant regarding air pollution, optical properties, and climate impact. The findings suggest that there may be an underestimation of RWB emissions in rural Europe and that further investigation is necessary.

Two decades of fire activity over the PEEX domain: a look from space, with contribution from models and ground-based measurements

ABSTRACT It has been suggested that forest fires will become more frequent/intense with changing climate, which would increase aerosol/gas emissions into the atmosphere. A better understanding of the relations between meteorological conditions, fires, and fire emissions will help estimate the climate response via forest fires. In this study, we use ERA5 meteorological products, including temperature, precipitation, and soil moisture, to explain the frequency of forest fires and the amount of radiant energy released per time unit by burning vegetation (fire radiative power, FRP). We explore the relationships between satellite-retrieved fire products and aerosol properties (aerosol optical depth, AOD), carbon monoxide (CO), formaldehyde (HCHO), and nitrogen dioxide (NO2) concentrations over the PEEX domain, which covers different vegetation zones (e.g. croplands/grasslands, forest, arctic tundra) of Pan-Eurasia and China. We analyse the concentrations of black carbon and absorbing organic carbon using ground-based AErosol RObotic NETwork. The analysis covers the months of May to August from 2002 to 2022. We show positive temperature trends in the Northern zone (>65°N) in June and August (1.56°C and 0.64°C, respectively); all statistically significant trends for precipitation and soil moisture are negative. This can explain increased fire activity in Siberia over the recent years (2019–2022). Over the whole PEEX domain, FC and FRP trends remain insignificant or negative; a decrease in AOD may address those negative trends. We show that intra-summer variations exist for cropland/grassland fires, which occur most often in May and August, while Siberian forest fires occur more often in July and August. We show that CO concentration has been gradually decreasing in the last two decades in May and June. CO trends are negative in May, June, and over summer for all regions, in July in Europe, China, the Southern zone (<55°N), and the PEEX domain. HCHO trends are not significant in all regions. NO2 trends are positive in May and negative in June in all zones. We calculated total column enhancement ratios for satellite observations influenced by wildfires. A common feature has been recognized with measurements and ratios utilized in SILAM (System for Integrated Modelling of Atmospheric Composition): AOD(or PM):CO and AOD(or PM):HCHO ratios for grass are clearly lower than for shrubs, opposite for AOD:NO2. We showed that emission ratios are increasing towards South and are 2–3 times higher for high (>0.5) AOD. Using a 21-year satellite record of the AOD and CO, an 18-year record of NO2, and a 16-year record of HCHO, we created background products of those variables over the PEEX domain. In the regions with low anthropogenic activity and conditions where long-range transport is not happening, anomalies in AOD, CO, and HCHO over biomass-burning areas may be assigned directly to the wildfire emissions.

Environmental impact assessment of the coal yard and ambient pollution.

This study investigates the vertical distribution of pollutants emitted from coal yards using unmanned aerial vehicles (UAVs). Vertical concentration measurements of black carbon (BC) and particulate matter (PM) inarange of 1m to 100m above ground level (AGL) in the central coal yard showed clear spatial patterns and gradients of these pollutants. In addition, measurements were taken at specific heights (1 m, 30 m AGL, and60mAGL) at seven locations approximately 3 km from the yard. Thirteen measurements were carried out during the non-heating period under similar weather conditions. The measured BC concentrations decreased significantly with increasing altitude, with ground-level concentrations reaching 1.88 ± 0.61µg/m3 and decreasing by over 46% at 80 m AGL. Similarly, PM10 concentrations at 60 m AGL decreased by 21.7%, with values of25.99 ± 9.24µg/m3 measured near the ground level and 16.52 ± 8.31µg/m3 at 60 m AGL. The maximum coal particle pollution from the coal depot ranges from 500 to 1,000 m. The study showed a significant decrease in BC concentrations with height above the coal yard surface. Concentrations of PM10 and PM10-TSP showed a complex distribution influenced by local emissions and long-range particle transport. Meteorological factors, especially wind speed and direction, significantly influenced the pollutant dispersion. In addition, higher pollutant concentrations were measured during dry periods than after rainfall. The findings of this study contribute to abetter understanding of the dispersion patterns and potential impacts of coal dust, enabling the implementation of targeted mitigation strategies and improved pollution control measures.

Black carbon instrument responses to laboratory generated particles

Accurate measurement of black carbon (BC) particles is vital for climate models as well as air quality assessments. While the need for BC particle measurement has been recognized, standardization of instruments and procedures for ambient measurement is still underway. In this study, we used laboratory generated soot particles to assess nine instruments targeting BC mass concentration measurement. The measurement matrix included different BC concentrations (ranging from atmospheric levels to combustion emission levels), different particle coatings, two particle sources (gas burner and spark generator) and two dilution methods. The nine instruments included six different models: aethalometers AE33 and MA200, thermo-optical OC-EC analysis, multi-angle absorption photometer MAAP 5012, photoacoustic instrument MSS, and soot particle aerosol mass spectrometer SP-AMS. The main discrepancy we observed was that the SP-AMS results were systematically lower, approximately only half of the BC measured by other instruments. A portion of this is explained by particle losses in the aerodynamic lens of the SP-AMS and the parameters used in the data analysis. Some smaller discrepancies were identified for the other instruments, but overall, the median values from were within 25 % of each other. Instruments’ operation principles and covered concentration ranges need to be carefully considered especially in emission measurements where the aerosols can have high temporal variation as well as high BC concentrations. In general, the results can decrease the uncertainties in climate and air quality studies by providing tools for more accurate and comparable BC measurements and when the existing BC data is interpreted.

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery.

In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.

Dosage- and site-dependent retention of black carbon and polycyclic aromatic hydrocarbons in farmland soils via long-term biochar addition

Biochar, a soil conditioner containing significant amounts of polycyclic aromatic hydrocarbons (PAHs), has gained widespread popularity in agricultural practices due to its advantages in improving soil fertility and carbon sequestration. While biochar may increase soil black carbon (BC) and PAH contents, the quantitative accumulation of BC and PAHs in different soil environments under varying biochar addition dosages remains poorly understood. Here, we investigated the content and composition of black carbon (evaluated using benzene polycarboxylic acids, BPCAs) and PAHs in soils treated with different biochar addition dosages from two long-term experimental farmlands in Ningxia (5-year) and Shandong (7- and 11-year), China. Results showed that increasing cumulative biochar dosage caused elevated contents of black carbon and PAHs, accompanied by decreases in their retention efficiencies. Contrasting retention was observed between sites, with the Shandong site characterized by higher retention efficiencies of BPCAs and lower retention efficiencies of PAHs, possibly owing to its higher temperature, more sandy soil texture, less irrigation, and lower sunlight intensity. Despite both black carbon and PAHs originating from biochar and sharing similar condensed aromatic structures, there was no significant correlation between the contents of black carbon and PAHs, indicating distinct behaviors and fates of these compounds. These findings emphasize the importance of optimizing biochar addition dosages and considering site-specific environmental factors for effective soil black carbon sequestration through biochar application.Graphical

Higher absorption enhancement of black carbon in summer shown by 2-year measurements at the high-altitude mountain site of Pic du Midi Observatory in the French Pyrenees

Abstract. Black-carbon-containing particles strongly absorb light, causing substantial radiative heating of the atmosphere. The climate-relevant properties of black carbon (BC) are poorly constrained in high-altitude mountain regions, where many complex interactions between BC, radiation, clouds and snow have important climate implications. This study presents 2-year measurements of BC microphysical and optical properties at the Pic du Midi (PDM) research station, a high-altitude observatory located at 2877 m above sea level in the French Pyrenees. Among the long-term monitoring sites in the world, PDM is subject to limited influence from the planetary boundary layer (PBL), making it a suitable site for characterizing the BC in the free troposphere (FT). The classification of the dominant aerosol type using aerosol spectral optical properties indicates that BC is the predominant aerosol absorption component at PDM and controls the variation in single-scattering albedo (SSA) throughout the 2 years. Single-particle soot photometer (SP2) measurements of refractory BC (rBC) show a mean mass concentration (MrBC) of 35 ng m−3 and a relatively constant rBC core mass-equivalent diameter of about 180 nm, which are typical values for remote mountain sites. Combining the MrBC with in situ absorption measurements, a rBC mass absorption cross-section (MACrBC) of 9.2 ± 3.7 m2 g−1 at λ=880 nm has been obtained, which corresponds to an absorption enhancement (Eabs) of ∼2.2 compared to that of bare rBC particles with equal rBC core size distribution. A significant reduction in the ΔMrBC/ΔCO ratio when precipitation occurred along the air mass transport suggests wet removal of rBC. However we found that the wet removal process did not affect the rBC size, resulting in unchanged Eabs. We observed a large seasonal contrast in rBC properties with higher MrBC and Eabs in summer than in winter. In winter a high diurnal variability in MrBC (Eabs) with higher (lower) values in the middle of the day was linked to the injection of rBC originating from the PBL. On the contrary, in summer, MrBC showed no diurnal variation despite more frequent PBL conditions, implying that MrBC fluctuations are rather dominated by regional and long-range transport in the FT. Combining the ΔMrBC/ΔCO ratio with air mass transport analysis, we observed additional sources from biomass burning in summer leading to an increase in MrBC and Eabs. The diurnal pattern of Eabs in summer was opposite to that observed in winter with maximum values of ∼2.9 observed at midday. We suggest that this daily variation may result from a photochemical process driving the rBC mixing state rather than a change in BC emission sources. Such direct 2-year observations of BC properties provide quantitative constraints for both regional and global climate models and have the potential to close the gap between model-predicted and observed effects of BC on the regional radiation budget and climate. The results demonstrate the complex influence of BC emission sources, transport pathways, atmospheric dynamics and chemical reactivity in driving the light absorption of BC.

Arachidonic Acid Metabolites in Self-collected Biospecimens Following Campfire Exposure: Exploring Non-invasive Biomarkers of Wildfire Health Effects.

Climate change has contributed to increased frequency and intensity of wildfire. Studying its acute effects is limited due to unpredictable nature of wildfire occurrence, which necessitates readily deployable techniques to collect biospecimens. To identify biomarkers of wildfire's acute effects, we conducted this exploratory study in eight healthy campers (four men and four women) who self-collected nasal fluid, urine, saliva, and skin wipes at different time points before, during, and after 4-hour exposure to wood smoke in a camping event. Concentrations of black carbon in the air and polycyclic aromatic hydrocarbons in participants' silicone wristbands were significantly elevated during the exposure session. Among 30 arachidonic acid metabolites measured, lipoxygenase metabolites were more abundant in nasal fluid and saliva, whereas cyclooxygenase and non-enzymatic metabolites were more abundant in urine. We observed drastic increases, at 8 hours following the exposure, in urinary levels of PGE2 (398%) and 15-keto-PGF2α (191%) (FDR<10%), with greater increases in men (FDR < 0.01%) than in women. No significant changes were observed for other metabolites in urine or the other biospecimens. Our results suggest urinary PGE2 and 15-keto-PGF2α as promising biomarkers reflecting pathophysiologic (likely sex-dependent) changes induced by short-term exposure to wildfire.

Effects of Diesel Emissions on Black Carbon and Particle Number Concentrations in the Eastern U.S.

The effects of emissions of diesel engines on black carbon and particle number concentrations, as well as climate-relevant aerosol properties, are explored for a summertime period in the Eastern U.S. using the chemical transport model PMCAMx-UF. A 50% reduction in diesel particulate emissions results in lower (23%) black carbon mass concentrations, as expected, and similar changes both in magnitude (27–30%) and spatial pattern for the absorption coefficient. However, an average 2% increase in the total particle number concentrations is predicted due to a decrease in the coagulation and condensation sinks and, at the same time, a 2% decrease in N100 (particles larger than 100 nm) concentrations. The diesel reduction results suggest that mitigation of large diesel particles and/or particle mass emissions can reduce climate-relevant properties related to the absorption of black carbon and provide health benefits; however, the changes could also have the unintended effect of increased ultrafine particle number concentrations. Changes in cloud condensation nuclei are predicted to be significantly less than expected, assuming a proportional reduction during this photochemically active period. Doubling the diesel emissions results in a domain-averaged 3% decrease in total particle number concentrations and a 3% increase in N100 concentrations. PM2.5 BC concentrations increase on average by 46%, and similar changes (52–60%) are predicted for the absorption coefficient. Extinction coefficients for both perturbation simulations changed by only a few percent due to the dominance of scattering aerosols in the Eastern U.S. during this period characterized by high photochemical activity.

A Long-Term Comparison between the AethLabs MA350 and Aerosol Magee Scientific AE33 Black Carbon Monitors in the Greater Salt Lake City Metropolitan Area.

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

Carbonaceous particles and PM2.5 optical properties in Mexico City during the ACU15 campaign

We measured the optical properties of aerosols with two photoacoustic spectrometers operating at 532 and 870 nm wavelengths and sampled PM2.5 to analyze the organic carbon (OC) and elemental carbon (EC) content. The measuring site was in the southwest corner of Mexico City. We sorted the data by OC/EC ratios and calculated four mass absorption efficiencies (MAEs) for each wavelength with linear regressions. The MAEs ranged from 2.27 to 19.75 and 2.03 to 15.26 m2 g–1 at 532 and 870 nm, respectively, with determination coefficients above 0.88, showing that the amount of OC modifies the absorption properties of particles, sometimes underestimating or overestimating the black carbon concentration. It is possible to choose the MAE based on the daily median O3 concentration when there is no information about the EC and OC composition.

Constructing transferable and interpretable machine learning models for black carbon concentrations

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73–0.85) and nitrogen dioxide (NO2, r = 0.68–0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80–0.86; mean absolute error MAE = 3.90–4.73 %) and at the urban background site in Dresden (R2 = 0.79–0.84; MAE = 4.23–4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Levels of Dry Deposition Submicron Black Carbon on Plant Leaves and the Associated Oxidative Potential

There is a need for monitoring air pollution associated with black carbon (BC) using a passive monitor is required in remote areas where the measurements are absent. In this pilot study, we developed a quantitative method to determine dry deposition submicron BC using dual-wavelength ultraviolet–visible spectroscopy. Furthermore, we measured the levels of dry deposition BC on plant leaves from 30 plant species located in urban Nanjing using the established method. The oxidative potential of BC on plant leaves as passive bio-monitoring samplers was assessed. The concentrations of black carbon (BC) on tree leaves varied from 0.01 to 1.6 mg m−2. Significant differences in levels of BC across leaves from different tree types were observed. The values of oxidative potential in deposited particles of leaf samples were observed to be in the range of 33–46 nmol min−1 mg−1 using the dithiothreitol (DTT) assay and 18–32 nmol min−1 mg−1 using the ascorbic acid (AA) assay, respectively. In comparison, the oxidative potential of BC-dominated mass in water extracts of leaf samples was in the range of 5–35 nmol min−1 mg−1 measured using the DTT assay and 2 to 12 nmol min−1 mg−1 using the AA assay, respectively. We found variations in the levels of OP across the leaves of different tree types were not large, while the levels of OP in terms of BC-dominated mass varied greatly. These results indicate that the established method with dual-wavelength ultraviolet–visible spectroscopy could provide a simple tool to determine submicron BC in plant leaves of the passive monitor.