UNASSIGNED: Environmental contributors to kidney disease progression remain elusive. We explored how residential air pollution affects disease progression in patients with primary glomerulopathies.
UNASSIGNED: 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.
UNASSIGNED: 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-1β 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).
UNASSIGNED: Elevated exposure to select air pollutants is associated with increased risk of disease progression and systemic inflammation in patients with primary.

OBJECTIVE: To investigate the black carbon (BC) pollution in the indoor air of typical residential houses in urban areas of Beijing, and to explore the relationship between indoor and outdoor BC concentrations as well as the main influencing factors.
METHODS: The indoor and outdoor PM_(2.5) samples were collected simultaneously from 33 apartments in the urban areas of Beijing during both the heating season (January to March) and the non-heating season (June to August) in 2016. Subsequently, optical method were employed to analyze BC concentrations in PM_(2.5)samples. The Spearman correlation coefficient (r_s) and the indoor/outdoor (I/O) ratio of BC concentrations were both calculated to characterize the relationship between indoor and outdoor BC concentrations. The factors may influence indoor BC pollution was collected through a questionnaire, including the basic characteristics of the residential buildings and households, smoking, cooking, window opening behavior, the use of air conditioner or air purifier and so on. Additionally, a linear mixed-effects model or multiple linear regression model was applied to identify the main factors influencing the I/O ratio.
RESULTS: The(M(P25, P75)) concentrations of indoor and outdoor BC for season-pooled analysis were2.84 (2.59, 3.26)μg/m~3 and 3.08 (2.90, 3.63)μg/m~3, respectively. There were significant seasonal differences in both indoor and outdoor concentrations (P<0.05), with higher levels observed during the heating season compared to the non-heating season. There was a strong correlation between indoor and outdoor BC (r_s=0.74). The correlation during the heating season (r_s=0.78) was stronger than that during the non-heating season (r_s=0.44). The ■ of I/O ratio was 0.90±0.11, with 93.5%(29/31)and 86.7%(26/30) of I/O ratios being less than 1 during the heating season and non-heating season, respectively. Statistical analysis also showed that outdoor BCconcentrations were significantly higher than indoors (P<0.05). In season-pooled analysis, the result of the linear mixed-effects model showed that window opening duration was the most important factor affecting the I/O ratio, explaining 21.3%of the total variation. The I/O ratio increased with longer window opening duration. In season-specific analysis, the characteristics of residential buildings (including building age and floor level) and window opening duration were the main factors affecting the I/O ratio during the heating season and non-heating season, respectively in 2016.
CONCLUSIONS: Residents in the urban areas of Beijing experienced relatively high indoor levels of BCpollution, but lower than the outdoor concentration during the same period in 2016. The window opening and the characteristics of residential buildings were the most important factors affecting the I/O ratio of BC.

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.

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.

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.

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.

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.

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.

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.

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.