Assessing public health burden associated with exposure to ambient black carbon in the United States

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Source apportionment of fine atmospheric particles in Bloemfontein, South Africa, using positive matrix factorization

Air pollution is of major health and environmental concern globally and in South Africa. Studies on the sources of PM2.5 air pollution in low- and middle-income countries such as South Africa are limited. This study aimed to identify local and distant sources of PM2.5 pollution in Bloemfontein. PM2.5 samples were collected from June 16, 2020 to August 18, 2021. Trace element concentrations were determined by EDXRF spectroscopy. By use of the US EPA PMF 5.0 program, local sources were determined to be combustion/wood burning (49%), industry (22%), soil dust (10%), base metal/pyrometallurgical and traffic (9.6%) and water treatment/industry (9.4%). The HYSPLIT program was applied to determine distant PM2.5 source areas and the following clusters were identified: Mpumalanga province (52%), Northern Cape province (35%), Indian Ocean (8%) and Atlantic Ocean (6%). The majority of the air was found to come from the Mpumalanga province in the north-east, while the majority of local sources are ascribed to combustion/wood burning. Results from this study can be used to develop an Air Quality Management Plan for Bloemfontein.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

BACKGROUND

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA.

METHODS

In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health.

FINDINGS

Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke.

INTERPRETATION

Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades.

FUNDING

National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

New insights on black carbon in pelagic Atlantic sediments

Black carbon (BC) is ubiquitous in pelagic sediments and presumed to have an older radiocarbon age due to long ocean residence times and pre-aging in terrestrial soils. Here, we analyzed sediments from five regions in the subtropical Atlantic Ocean to quantify the black carbon fraction of the total organic carbon pool. Black carbon, derived from the chemothermal oxidation method, comprised between 17±6% of the sedimentary organic carbon in the Northwest Argentina Basin and 65±18% in the Amazon Delta. Black carbon sediment accumulation rates were six times greater in the Sierra Leone Rise (8.4±4.1 mg cm-2 kyr-1) compared to the remote Northwest Argentina Basin (1.3±0.4 mg cm-2 kyr-1), possibly due to enhanced regional atmospheric deposition from annual African grassland fires. The radiocarbon age for BC from subtropical Atlantic sediments were more modern compared to the bulk total organic carbon, and BC source was apportioned as biomass burning byproducts from their stable carbon isotopic signatures and characteristic ratios of polycyclic aromatic hydrocarbons. This study demonstrated that subtropical Atlantic Ocean sediments serve as an important sink for young BC.

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

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

Indoor (residential) and ambient particulate matter associations with urinary oxidative stress biomarkers in a COPD cohort

OBJECTIVES

Oxidative stress contributes to chronic obstructive pulmonary disease (COPD) pathophysiology. Associations between indoor (residential) exposure to particulate matter ≤2.5 μm in diameter (PM2.5) and one of its components, black carbon (BC), and oxidative stress are ill-defined.

METHODS

Between 2012 and 2017, 140 patients with COPD completed in-home air sampling over one week intervals, followed by collection of urine samples to measure oxidative stress biomarkers, malondialdehyde (MDA), a marker of lipid peroxidation, and 8-hydroxy-2' -deoxyguanosine (8-OHdG), a marker of oxidative DNA damage. Ambient (central site) BC and PM2.5 were measured, and the ratio of indoor/ambient sulfur in PM2.5, a surrogate for residential ventilation and particle infiltration, was used to estimate indoor BC and PM2.5 of outdoor origin. Mixed effects linear regression models with a participant-specific random intercept were used to assess associations with oxidative biomarkers, adjusting for personal characteristics.

RESULTS

There were positive associations (% increase per IQR; 95 % CI) of directly measured indoor BC with total MDA (6.96; 1.54, 12.69) and 8-OHdG (4.18; -0.67, 9.27), and similar associations with both indoor BC of outdoor origin and ambient BC. There were no associations with directly measured indoor PM2.5, but there were positive associations between indoor PM2.5 of outdoor origin and total MDA (5.40; -0.91, 12.11) and 8-OHdG (8.02; 2.14, 14.25).

CONCLUSIONS

In homes with few indoor combustion sources, directly measured indoor BC, estimates of indoor BC and PM2.5 of outdoor origin, and ambient BC, were positively associated with urinary biomarkers of oxidative stress. This suggests that the infiltration of particulate matter from outdoor sources, attributable to traffic and other sources of combustion, promotes oxidative stress in COPD patients.

Adverse Effects of Prenatal Exposure to Oxidized Black Carbon Particles on the Reproductive System of Male Mice

Ambient black carbon (BC), a main constituent of atmospheric particulate matter (PM), is a primary particle that is mainly generated by the incomplete combustion of fossil fuel and biomass burning. BC has been identified as a potential health risk via exposure. However, the adverse effects of exposure to BC on the male reproductive system remain unclear. In the present study, we explored the effects of maternal exposure to oxidized black carbon (OBC) during pregnancy on testicular development and steroid synthesis in male offspring. Pregnant mice were exposed to OBC (467 μg/kg BW) or nanopure water (as control) by intratracheal instillation from gestation day (GD) 4 to GD 16.5 (every other day). We examined the testicular histology, daily sperm production, serum testosterone, and mRNA expression of hormone synthesis process-related factors of male offspring at postnatal day (PND) 35 and PND 84. Histological examinations exhibited abnormal seminiferous tubules with degenerative changes and low cellular adhesion in testes of OBC-exposed mice at PND 35 and PND 84. Consistent with the decrease in daily sperm production, the serum testosterone level of male offspring of OBC-exposed mice also decreased significantly. Correspondingly, mRNA expression levels of hormone-synthesis-related genes (i.e., StAR, P450scc, P450c17, and 17β-HSD) were markedly down-regulated in male offspring of PND 35 and PND 84, respectively. In brief, these results suggest that prenatal exposure has detrimental effects on mouse spermatogenesis in adult offspring.

COVID-19-associated 2020 lockdown: a study on atmospheric black carbon fall impact on human health

The mean mass concentrations of black carbon (BC)_, biomass burning (BC)_bb, and fossil fuel combustion (BC)_ff have been estimated during March-May 2020 (during the COVID-19 outbreak) and March-May 2019 at a semiarid region of Agra over the Indo-Gangetic basin region. The daily mean mass concentration of BC in 2020 and 2019 was 3.9 and 6.9 µg m^-3, respectively. The high monthly mean mass concentration of BC was found to be 4.7, 3.4 and 3.3 µg m^-3 in Mar-2020, Apr-2020, and May-2020, respectively, whereas in Mar-2019, Apr-2019, and May-2019 was 7.7, 7.5 and 5.4 µg m^-3, respectively. The absorption coefficient (b_abs) and absorption angstrom exponent (AAE) of black carbon were calculated. The highest mean AAE was 1.6 in the year 2020 (Mar-May 2020) indicating the dominance of biomass burning. The mean mass concentration of fossil fuel (BC)_ff and biomass burning (BC)_bb is 3.4 and 0.51 µg m^-3, respectively, in 2020 whereas 6.4 and 0.73 µg m^-3, respectively, in 2019. The mean fraction contribution of BC with fossil fuel (BC)_ff was 82.1 ± 13.5% and biomass burning (BC)_bb was 17.9 ± 4.3% in 2020, while in 2019, fossil fuel (BC)_ff was 86.7 ± 13.5% and biomass burning (BC)_bb was 13.3 ± 6.7%. The population-weighted mean concentration of BC, fossil fuel (BC)_ff, and biomass burning (BC)_bb has been calculated. The health risk assessment of BC has been analyzed in the form of attributable relative risk factors and attributed relative risk during the COVID-19 outbreak using AirQ + v.2.0 model. The attributable relative risk factors of BC were 20.6% in 2020 and 29.4% in 2019. The mean attributed relative risk per 10,000,000 populations at 95% confidence interval (CI) due to BC was 184.06 (142.6-225.2) in 2020 and 609.06 (418.3-714.6) in 2019. The low attributed factor and attributed relative risk in 2020 may be attributed to improvements in air quality and a fall in the emission of BC. In 2020, due to the COVID-19 pandemic, the whole country faced the biggest lockdown, ban of the transportation of private vehicles, trains, aircraft, and construction activities, and shut down of the industry leading to a fall in the impact of BC on human health. Overall, this was like a blessing in disguise. This study will help in future planning of mitigation and emission control of air pollutants in large and BC in particular. It only needs a multipronged approach. This study may be like torch bearing to set path for mitigation of impacts of air pollution and improvement of air quality.

Black carbon emissions and reduction potential in China: 2015-2050

Black carbon is a product of the incomplete combustion of carbonaceous fuels and has significant adverse effects on climate change, air quality, and human health. China has been a major contributor to global anthropogenic black carbon emissions. This study develops a black carbon inventory in China, using 2015 as the base year, and projects annual black carbon emissions in China for the period 2016-2050, under two scenarios: a Reference scenario and an Accelerated Reduction scenario. The study estimates that the total black carbon emissions in China in 2015 were 1100 thousand tons (kt), with residential use being the biggest contributor, accounting for more than half of the total black carbon emissions, followed by coke production, industry, agricultural waste burning, and transportation. This study then projects the total black carbon emissions in China in 2050 to be 278 kt in the Reference scenario and 86 kt in the Accelerated Reduction Scenario. Compared to the Reference scenario, the Accelerated Reduction scenario will achieve much faster and deeper black carbon reductions in all the sectors. The dramatic reductions can be attributed to the fuel switching in the residential sector, faster implementation of high-efficiency emission control measures in the industry, transportation, and coke production sectors, and faster phase-out of agricultural waste open burning. This analysis reveals the high potential of black carbon emission reductions across multiple sectors in China through the next thirty years.

Nearly five-year continuous atmospheric measurements of black carbon over a suburban area in central France

Atmospheric black carbon (BC) concentration over a nearly 5 year period (mid-2017-2021) was continuously monitored over a suburban area of Orléans city (France). Annual mean atmospheric BC concentration were 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48 ± 0.46 and 0.50 ± 0.72 μg m^-3, respectively, for the year of 2017, 2018, 2019, 2020 and 2021. Seasonal pattern was also observed with maximum concentration (0.70 ± 0.18 μg m^-3) in winter and minimum concentration (0.38 ± 0.04 μg m^-3) in summer. We found a different diurnal pattern between cold (winter and fall) and warm (spring and summer) seasons. Further, fossil fuel burning contributed >90 % of atmospheric BC in the summer and biomass burning had a contribution equivalent to that of the fossil fuel in the winter. Significant week days effect on BC concentrations was observed, indicating the important role of local emissions such as car exhaust in BC level at this site. The behavior of atmospheric BC level with COVID-19 lockdown was also analyzed. We found that during the lockdown in warm season (first lockdown: 27 March-10 May 2020 and third lockdown 17 March-3 May 2021) BC concentration were lower than in cold season (second lockdown: 29 October-15 December 2020), which could be mainly related to the BC emission from biomass burning for heating. This study provides a long-term BC measurement database input for air quality and climate models. The analysis of especially weekend and lockdown effect showed implications on future policymaking toward improving local and regional air quality as well.

Contextual and environmental factors that influence health: A within-subjects field experiment protocol

Background

Despite the growing research on environment-physical activity (PA) relationships, field experimental studies are limited. Such studies offer opportunities to focus on real-world environmental exposure and related PA and health outcomes, allowing researchers to better isolate the causal effect of exposures/interventions. Focusing on the street/pedestrian environment as a routine setting for people's daily activities, this research aims to develop and test a field experiment protocol that integrates instantaneous assessments of the environment, PA, and health outcomes. The protocol involves the use of state-of-the-art environmental monitoring and biosensing techniques and focuses on physically active road users (pedestrians and bicyclists) who are more directly exposed to their surrounding environment than others such as drivers.

Methods/Design

An interdisciplinary research team first identified the target measurement domains for the health outcomes (e.g., stress, thermal comfort, PA) and the street-level environmental exposures (e.g., land use, greenery, infrastructure conditions, air quality, weather) guided by the previous literature which was primarily observational. Portable or wearable measurement instruments (e.g., GPS, accelerometer, biosensor, mini camera, smartphone app, weather station, air quality sensor) were identified, pilot tested, and selected for the identified measures. We ensured that these measures are readily linkable using the time stamp and include eye-level exposures as they impact the users' experiences more directly yet missing in most prior studies relying on secondary, aerial-level measures. A 50-min experimental route was then determined to include typical everyday environments in park and mixed-use settings and to engage participants in three common modes of transportation (walking, bicycling, and driving). Finally, a detailed staff protocol was developed, pilot-tested, and used in a 36-participant within-subject field experiment in College Station, TX. The experiment was successfully executed, showing its potential to support future field experiments that can provide more accurate real-time, real-environment, and multi-dimensional information.

Discussion

Our study demonstrates the feasibility of capturing the multifold health benefits/harms related to walking and bicycling in varying urban environments by combining field experiments with environmental, behavioral, and physiological sensing. Our study protocol and reflections can be helpful for a broad spectrum of research addressing the complex and multi-level pathways between the environment, behavior, and health outcomes.

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

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

Prediction of black carbon in marine engines and correlation analysis of model characteristics based on multiple machine learning algorithms

Ship black carbon emissions have caused great harm to ecological environment. In order to estimate the black carbon emissions, thereby reducing the cost of black carbon experiments, here, we introduced four machine learning algorithms which are lasso regression, support vector machine, extreme gradient boosting, and artificial neural network to predict ship black carbon emissions. The prediction models were established with using the datasets acquired from similar marine engines under various steady-state conditions. The results show that SVM, XGB, and ANN have higher prediction accuracy than lasso regression, and the adjusted R² of each model is 0.9810, 0.9850, 0.9885, and 0.6088. Although ANN shows the best prediction performance, it is inferior to SVM and XGB in terms of model stability and training cost. Then, in order to simplify the optimization process of hyperparameters and improve the prediction accuracy of the model at the same time, we use three different swarm intelligence algorithms to automatically optimize the hyperparameters of SVM and XGB. In addition, we applied mutual information to measure the correlation between the characteristics of the prediction models and black carbon concentration and found that the characteristics which related to in-cylinder combustion have a strong correlation with the black carbon concentration. The findings in this paper prove the feasibility of machine learning in ship black carbon emission prediction and could provide references for reducing ship black carbon emissions and the formulation of emission regulations.

Long-term exposure to ambient black carbon is associated with sleep disturbance in college students

BACKGROUND

Evidence suggests an association of air pollution with sleep quality. However, there is limited knowledge regarding the effect of black carbon, a key component of ambient particulate matter, on sleep.

OBJECTIVES

To investigate the association of long-term exposure to black carbon and sleep quality in a group of college students.

METHODS

A retrospective cohort study was conducted in five universities in different regions of China. The concentrations of black carbon and other environment factors were defined as the averages during the 6 years prior to the recruitment. Averagely daily dose of black carbon exposure was estimated according to the respiratory rate. Sleep quality was measured by the Pittsburgh Sleep Quality Index (PSQI) with a cutoff >5 indicating sleep disturbance. Linear regression and logistic regression models were used to estimate the association. The sensitivity analyses were conducted to estimate the effects of 1-month, 6-month and 1-year mean levels of exposure to black carbon on sleep quality.

RESULTS

A total of 20,053 incoming college students were included. 29.3% reported impaired sleep quality, with a mean PSQI score of 4.3 ± 2.2. The logistic regression showed that the risk of impaired sleep quality was positively associated with black carbon exposure, especially in the highest quantile (OR: 1.26, 95% CI: 1.11-1.43) compared with the lowest quartile after adjusting for potential confounders. Subgroup analysis showed that the effect of black carbon on sleep quality was stronger in participants with higher BMI, lower household income, and lower parental educational level. The results of sensitivity analyses were similar with main analyses.

CONCLUSION

Long-term exposure to black carbon is associated with sleep disturbance in college students. Improvement of air quality may help improve sleep quality.

Black carbon health impacts in the Indo-Gangetic plain: Exposures, risks, and mitigation

A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC health impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including successfully predicted BC concentrations. Population exposure to BC is notable, with more than 60 million people identified as living in hotspots of BC concentration (wintertime mean, >20 μg m^-3). The attributable fraction of the total cardiovascular disease mortality (CVM) burden to BC exposures is 62% for the megacity. The semiurban area comprised about 49% of the total BC-attributable CVM burden over the IGP. More than 400,000 lives can potentially be saved from CVM annually by implementing prioritized emission reduction from the combustion of domestic biofuel in the semiurban area, diesel oil in transportation, and coal in thermal power plant and brick kiln industries in megacities.

Acute effects of black carbon on mortality in nine megacities of China, 2008-2016: a time-stratified case-crossover study

Black carbon (BC) may have more adverse effects on human health than other constituents of PM_2.5. The daily mean concentrations of BC in China are much higher than those in developed countries and are estimated to account for more than a quarter of global anthropogenic BC emissions. However, reports on the health effects of BC in China have been limited. Thus, a time-stratified case-crossover study was conducted to evaluate the impacts of BC on daily mortality risk in nine Chinese megacities from 2008-2016. Our results show that for all-cause mortality, when compared to the interquartile range (IQR) of BC concentration increased, odds ratios (ORs) were in the range of 1.01-1.06 (95% CIs: 0.99-1.10). For cardiovascular mortality, ORs were in the range of 1.02-1.07 (95% CIs: 1.003-1.12), and for respiratory mortality, ORs were in the range of 1.01-1.15 (95% CIs: 1.00-1.18). The effects of BC in the nine cities were robust after adjusting for PM_2.5, or even became more prominent. Furthermore, BC had stronger effects in spring and winter in northern cities, whereas in mid-latitude cities, BC had stronger effects in the warm seasons. In southern cities, BC had stronger effects in the cool and dry seasons. Our findings support an association between residential exposure to BC and mortality and thus provide further evidence that BC negatively impacts human health and is helpful for decision-making.

Impacts of Regional Speed Control Strategy Based on Macroscopic Fundamental Diagram on Energy Consumption and Traffic Emissions: A Case Study of Beijing

Numerous studies shown that particulate matter in the ambient environment has a significant impact on the health of the respiratory system. To understand the interrelationships between urban built environment, transportation operations and health, this study proposes an innovative approach that uses real-world GPS datasets to calculate energy consumption and emissions from transportation. The experiment used the traffic operation state in the Fourth Ring Road of Beijing as the research object and tested the impact of using the Regional speed optimization (RSO) strategy based on Macroscopic Fundamental Diagram (MFD) on energy consumption and emissions during peak hours. The impact of traffic emission on the health of roadside pedestrians is also considered. Changes in PM2.5 concentrations around four different built-up areas were calculated and compared. The computational experiments indicate that the PM2.5 pollutants exhausted by the traffic on the Ring Road during peak hours can reach up to 250 μg/m3, while the traffic emission on general roads near residential areas is only 50 μg/m3. Adopting Regional speed optimization can reduce the energy consumption of the road network by up to 18.8%. For roadside runners, the PM2.5 inhalation caused by night running in commercial and recreational areas is about 1.3-2.6 times that of night running in residential areas. Compared with morning or night running, the risk of respiratory disease caused by PM2.5 inhalation was about 10.3% higher than commuter running behavior. The research results provide a useful reference for energy conservation and emission reduction control strategies for different road types in cities and help existing cities to establish a traveler health evaluation system caused by traffic operation.

Modification of associations between indoor particulate matter and systemic inflammation in individuals with COPD

RATIONALE

Little is known about personal characteristics and systemic responses to particulate pollution in patients with COPD.

OBJECTIVES

Assess whether diabetes, obesity, statins and non-steroidal anti-inflammatory medications (NSAIDs) modify associations between indoor black carbon (BC) and fine particulate matter ≤2.5 μm in diameter (PM_2.5) on systemic inflammation and endothelial activation.

METHODS

144 individuals with COPD without current smoking and without major in-home combustion sources were recruited at Veterans Affairs Boston Healthcare System. PM_2.5 and BC were measured in each participant's home seasonally for a week (up to 4 times; 482 observations) and plasma biomarkers of systemic inflammation [C-reactive protein (CRP); interleukin-6 (IL-6)] and endothelial activation [soluble vascular adhesion molecule-1 (sVCAM-1)] measured. Linear mixed effects regression with a random intercept was used, and effect modification assessed with multiplicative interaction terms and stratum specific estimates.

RESULTS

Median (25%ile, 75%ile) indoor BC and PM_2.5 were 0.6 (0.5,0.7) μg/m³ and 6.8 (4.8,10.4) μg/m³, respectively. Although p-values for effect modification were not statistically significant, there were positive associations (%-increase/interquartile range; 95% CI) between CRP and BC greater among non-statin (18.8%; 3.6-36.3) than statin users (11.1%; 2.1-20.9). There were also positive associations greater among non-statin users between PM_2.5 and CRP. For IL-6, associations with BC and PM_2.5 were also greater among non-statin users. Associations between CRP and BC were greater (20.3%; 4.5-38.5) in persons with diabetes than without diabetes (10.3%; 0.92-20.6) with similar effects of PM_2.5. There were no consistent associations that differed based on obesity. Effect modification was not observed for NSAID use, or with any factor considered with sVCAM-1.

CONCLUSIONS

Associations between indoor BC and PM_2.5 and CRP were greater in patients with diabetes and those not taking statins, and with IL-6 if not taking statins. These results suggest that these characteristics may modify the systemic response to indoor BC and PM_2.5 in persons with COPD.

Measuring and predicting personal and household Black Carbon levels from 88 communities in eight countries

Black Carbon (BC) is an important component of household air pollution (HAP) in low- and middle- income countries (LMICs), but levels and drivers of exposure are poorly understood. As part of the Prospective Urban and Rural Epidemiological (PURE) study, we analyzed 48-hour BC measurements for 1187 individual and 2242 household samples from 88 communities in 8 LMICs (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Light absorbance (10^-5 m^-1) of collected PM_2.5 filters, a proxy for BC concentrations, was calculated via an image-based reflectance method. Surveys of household/personal characteristics and behaviors were collected after monitoring. The geometric mean (GM) of personal and household BC measures was 2.4 (3.3) and 3.5 (3.9)·10^-5 m^-1, respectively. The correlation between BC and PM_2.5 was r = 0.76 for personal and r = 0.82 for household measures. A gradient of increasing BC concentrations was observed for cooking fuels: BC increased 53% (95%CI: 30, 79) for coal, 142% (95%CI: 117, 169) for wood, and 190% (95%CI: 149, 238) for other biomass, compared to gas. Each hour of cooking was associated with an increase in household (5%, 95%CI: 3, 7) and personal (5%, 95%CI: 2, 8) BC; having a window in the kitchen was associated with a decrease in household (-38%, 95%CI: -45, -30) and personal (-31%, 95%CI: -44, -15) BC; and cooking on a mud stove, compared to a clean stove, was associated with an increase in household (125%, 95%CI: 96, 160) and personal (117%, 95%CI: 71, 117) BC. Male participants only had slightly lower personal BC (-0.6%, 95%CI: -1, 0.0) compared to females. In multivariate models, we were able to explain 46-60% of household BC variation and 33-54% of personal BC variation. These data and models provide new information on exposure to BC in LMICs, which can be incorporated into future exposure assessments, health research, and policy surrounding HAP and BC.

Effects of diluent gases on sooting transition process in ethylene counterflow diffusion flames

The impacts of adding diluent gases (nitrogen, carbon dioxide, and helium) either to the fuel side or the oxidizer side on the sooting transition process in ethylene counterflow diffusion flames are investigated experimentally and numerically. A series of ethylene flames ranging from non-sooting to heavy-sooting are studied by gradually increasing the oxygen concentration on the oxidizer side. The optical method is used to analyze flame images, determining the sooting transition process. It is found that whether CO₂ is added to the fuel side or the oxidizer side, the sooting transition process is delayed significantly. This process is slightly delayed when He is added to the fuel side, however, it is promoted when He is introduced to the oxidizer side. The numerical results show that in CO₂-diluted flames, the mole fraction of the main soot precursors C₂H₂, C₃H₃, and C₆H₆ are reduced, which leads to the delay of soot formation. In addition, the H radical decreases while the OH radical increases, both of them are important for soot formation. In He-diluted flames, the concentration of C₂H₂, C₃H₃, and C₆H₆ decreased, as well as H and OH radicals. Moreover, adding He obviously changes the distribution area of products.

This study analyzes the flame images by optical method to distinguish the sooting transition process under different diluent gases (CO₂, He, and N₂) and carries out chemical kinetic simulations during this transition process.

Source apportionment of fine atmospheric particles using positive matrix factorization in Pretoria, South Africa

In Pretoria South Africa, we looked into the origins of fine particulate matter (PM_2.5), based on 1-year sampling campaign carried out between April 18, 2017, and April 17, 2018. The average PM_2.5 concentration was 21.1 ± 15.0 µg/m³ (range 0.7-66.8 µg/m³), with winter being the highest and summer being the lowest. The XEPOS 5 energy dispersive X-ray fluorescence (EDXRF) spectroscopy was used for elemental analysis, and the US EPA PMF 5.0 program was used for source apportionment. The sources identified include fossil fuel combustion, soil dust, secondary sulphur, vehicle exhaust, road traffic, base metal/pyrometallurgical, and coal burning. Coal burning and secondary sulphur were significantly higher in winter and contributed more than 50% of PM_2.5 sources. The HYSPLIT model was used to calculate the air mass trajectories (version 4.9). During the 1-year research cycle, five transportation clusters were established: North Limpopo (NLP), Eastern Inland (EI), Short-Indian Ocean (SIO), Long-Indian Ocean (LIO), and South Westerly-Atlantic Ocean (SWA). Local and transboundary origin accounted for 85%, while 15% were long-range transport. Due to various anthropogenic activities such as biomass burning and coal mining, NLP clusters were the key source of emissions adding to the city's PM rate. In Pretoria, the main possible source regions of PM_2.5 were discovered to be NLP and EI. Effective control strategies designed at reducing secondary sulphur, coal burning, and fossil fuel combustion emissions at Southern African level and local combustion sources would be an important measure to combat the reduction of ambient PM_2.5 pollution in Pretoria.

Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images

Outdoor ultrafine particles (UFP, <0.1 μm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R² = 0.54) outperformed the CNN (R² = 0.47) and land use regression (LUR) models (R² = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R² = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset's feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries' collective response to PM2.5 pollution.

Direct and Inverse Reduced-Form Models for Reciprocal Calculation of BC Emissions and Atmospheric Concentrations

Atmospheric black carbon (BC) concentrations are governed by both emissions and meteorological conditions. Distinguishing these effects enables quantification of the effectiveness of emission mitigation actions by excluding meteorological effects. Here, we develop reduced-form models in both direct (RFDMs) and inverse (RFIMs) modes to estimate ambient BC concentrations. The models were developed based on outputs from multiyear simulations under three conditional scenarios with realistic or fixed emissions and meteorological conditions. We established a set of probabilistic functions (PFs) to quantify the meteorological influences. A significant two-way linear relationship between multiyear annual emissions and mean ambient BC concentrations was revealed at the grid cell scale. The correlation between them was more significant at grid cells with high emission densities. The concentrations and emissions at a given grid cell are also significantly correlated with emissions and concentrations of the surrounding areas, respectively, although to a lesser extent. These dependences are anisotropic depending on the prevailing winds and source regions. The meteorologically induced variation at the monthly scale was significantly higher than that at the annual scale. Of the major meteorological parameters, wind vectors, temperature, and relative humidity were found to most significantly affect variation in ambient BC concentrations.

Comparison of Aerosol Optical Depth from MODIS Product Collection 6.1 and AERONET in the Western United States

Recent observations reveal that dust storms are increasing in the western USA, posing imminent risks to public health, safety, and the economy. Much of the observational evidence has been obtained from ground-based platforms and the visual interpretation of satellite imagery from limited regions. Comprehensive satellite-based observations of long-term aerosol records are still lacking. In an effort to develop such a satellite aerosol dataset, we compared and evaluated the Aerosol Optical Depth (AOD) from Deep Blue (DB) and Dark Target (DT) product collection 6.1 with the Aerosol Robotic Network (AERONET) program in the western USA. We examined the seasonal and monthly average number of Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua DB AOD retrievals per 0.1∘×0.1∘ from January 2003 to December 2017 across the region’s different topographic, climatic, and land cover conditions. The number of retrievals in the southwest United States was on average greater than 37 days per 90 days for all seasons except summer. Springtime saw the highest number of AOD retrievals across the southwest, consistent with the peak season for synoptic-scale dust events. The majority of Arizona, New Mexico, and western Texas showed the lowest number of retrievals during the monsoon season. The majority of collocating domains of AOD from the Aqua sensor showed a better correlation with AERONET AOD than AOD from Terra, and the correlation coefficients exhibited large regional variability across the study area. The correlation coefficient between the couplings Aqua DB AOD-AERONET AOD and Terra DB AOD-AERONET AOD ranges from 0.1 to 0.94 and 0.001 to 0.94, respectively. In the majority of the sites that exhibited less than a 0.6 correlation coefficient and few matched data points at the nearest single pixel, the correlations gradually improved when the spatial domain increased to a 50 km × 50 km box averaging domain. In general, the majority of the stations revealed significant correlation between MODIS and AERONET AOD at all spatiotemporal aggregating domains, although MODIS generally overestimated AOD compared to AERONET. However, the correlation coefficient in the southwest United States was the lowest and in stations from a higher latitude was the highest. The difference in the brightness of the land surface and the latitudinal differences in the aerosol inputs from the forest fires and solar zenith angles are some of the factors that manifested the latitudinal correlation differences.

Assessing the Impact of Wildfires on the Use of Black Carbon as an Indicator of Traffic Exposures in Environmental Epidemiology Studies

Epidemiological studies frequently use black carbon (BC) as a proxy for traffic-related air pollution (TRAP). However, wildfire smoke (WFS) represents an important source of BC not often considered when using BC as a proxy for TRAP. Here, we examined the potential for WFS to bias TRAP exposure assessments based on BC measurements. Weekly integrated BC samples were collected across the Denver, CO region from May to November 2018. We collected 609 filters during our sampling campaigns, 35% of which were WFS-impacted. For each filter we calculated an average BC concentration. We assessed three GIS-based indicators of TRAP for each sampling location: annual average daily traffic within a 300 m buffer, the minimum distance to a highway, and the sum of the lengths of roadways within 300 m. Median BC concentrations were 9% higher for WFS-impacted filters (median = 1.14 μg/m3, IQR = 0.23 μg/m3) than nonimpacted filters (median = 1.04 μg/m3, IQR = 0.48 μg/m3). During WFS events, BC concentrations were elevated and expected spatial gradients in BC were reduced. We conducted a simulation study to estimate TRAP exposure misclassification as the result of regional WFS. Our results suggest that linear health effect estimates were biased away from the null when WFS was present. Thus, exposure assessments relying on BC as a proxy for TRAP may be biased by wildfire events. Alternative metrics that account for the influence of "brown" carbon associated with biomass burning may better isolate the effects of traffic emissions from those of other black carbon sources.

The impact of COVID-19 pandemic on nitrogen dioxide levels in Nigeria

The Coronavirus disease (COVID-19) has been transmitted worldwide over a very short time after it originated in China in December 2019. In an attempt to control its spread and reduce its health impacts, several countries including those in the African continent imposed restrictive measures that was termed "lockdown". The outcomes of this lockdown have been reported to be beneficial to air quality worldwide. The main objective of this study is to assess the impact of lockdown due to COVID-19 on nitrogen dioxide (NO2) levels over six major cities in Nigeria. Maps extracted from satellite (Sentinel-5P) were used to indicate the significant reduction in the level of NO2 in the selected cities in Nigeria during two time-intervals, pre-lockdown (December, 2019) and during lockdown (April, 2020). The results show a significant reduction in NO2 levels during the lockdown period compared with its levels during the pre-lockdown period in 2019. The reduction in NO2 concentration levels during lockdown is likely due to less traffic, social distancing and restrictions on business and human activities. There could be an element of uncertainty in the results due to seasonality, as the comparison is done with a different season. However, the magnitude of change due to lockdown is probably much higher than the seasonal variability. Although COVID-19 has negatively impacted the health and economic status of all regions worldwide, it has benefited some aspects of air quality in most countries including Nigeria. This indicates that anthropogenic activities may be managed to reduce air pollution and positively impact the health of human beings.

Effects of PM10 and Weather on Respiratory and Cardiovascular Diseases in the Ciuc Basin (Romanian Carpathians)

This study presents the PM10 concentration, respiratory and cardiovascular disease hospital admissions evolution in the Ciuc basin for a period of 9 years (2008–2016), taking into consideration different meteorological conditions: boundary layer, lifting condensation level, temperature-humidity index, and wind chill equivalent chart index. The PM10 and hospital admissions evolution showed a very fluctuated hourly, weekly, monthly, yearly tendency. The PM10 concentration in winter (34.72 μg/m3) was 82% higher than the multiannual average (19.00 μg/m3), and almost three times higher than in summer (11.71 μg/m3). During the winter, PM10 concentration increased by an average of 9.36 μg/m3 due to the increased household heating. Climatological parameters have a demonstrable effect on the PM10 concentration variation. Children, the elderly and men are more sensitive to air pollution, the calculated relative risk for men was (RR = 1.45), and for women (RR = 1.37), respectively. A moderate correlation (0.51) was found between PM10 and pneumonia (P), while a relatively weak correlation (0.39) was demonstrated in the case of PM10 and upper respiratory tract infections (URTI). Furthermore, except thermal humidity index (THI), strong negative correlations were observed between the multiannual monthly mean PM10 and the meteorological data. The PM10 followed a moderate negative correlation with the boundary layer (−0.61). In the case of URTI and P, the highest number of hospital admissions occurred with a 5 to 7-day lag, while the 10 μg/m3 PM10 increase resulted in a 2.04% and 8.28% morbidity increase. For lung cancer (LC) and cardiovascular diseases (AMI, IHD, CCP), a maximum delay of 5–6 months was found. Three-month delay and an average growth of 1.51% was observed in the case of chronic obstructive pulmonary disease (COPD). Overall, these findings revealed that PM10 was and it is responsible for one-third of the diseases.

Preliminary study of black carbon content in airborne particulate matters from an open site in the city of Yaoundé, Cameroon

A light absorbing reflectometer was used to calculate black carbon (BC) mass concentrations contained in both fine and coarse particulate matters (PM_2.5 and PM_2.5-10) collected in an urban area of Yaoundé from 1st March 2018 to 26th February 2019 to highlight BC fluctuation and determine its potential predominant sources. The highest BC concentrations were obtained on 14/02/2019 (5.37 µg/m³) and 17/01/2019 (3.90 µg/m³) whereas the cleanest days were 28/06/2018 (0.01 µg/m³) and 30/06/2018 (0.21 µg/m³) for BC content of PM_2.5 and PM_2.5-10, respectively. While BC values for BC_2.5-10 were only obtained from November 2018 to February 2019 and in the long dry season, those of BC_2.5 were obtained for all the seasons. Months, which were most polluted with BC, were January with an average value of 2.34 ± 1.71 µg/m³ and December with 1.65 ± 0.82 µg/m³ whilst the least polluted were September with 0.54 ± 0.33 µg/m³ and August with 0.21 ± 0.10 µg/m³ for BC_2.5 and BC_2.5-10, respectively. The BC_2.5 and BC_2.5-10 annual average values were 1.18 ± 0.98 µg/m³ and 0.66 ± 0.39 µg/m³. The annual mean BC contributions were 8.27 ± 5.00% and 6.63 ± 4.49%, respectively, in PM_2.5 and PM_2.5-10. The BC_2.5/PM_2.5 percentages varied widely; thus, a complete identification of potential sources of BC was very complex. However, by analogy with other studies, predominant sources were agricultural burn, light-duty gasoline, household wood and charcoal burning sources.

The impact of urbanization and consumption patterns on China's black carbon emissions based on input-output analysis and structural decomposition analysis

Urbanization in China has dramatically increased from 39.10 in 2002 to 58.52% in 2017. Studies have discussed the impacts of urbanization and its corresponding changes in consumption patterns on carbon dioxide emissions; however, little is known about their impacts on black carbon (BC). Therefore, we collected data on the BC emissions of various sectors to calculate the consumption-based BC emissions in China, and we used an input-output analysis (IOA) and structural decomposition analysis (SDA) to explore the impacts of urbanization and changes in consumption patterns on BC emissions from 2002 to 2017, focusing on sectoral BC emissions. The total BC emissions of various sectors first increased and then decreased. BC emissions increased from 1083.47 in 2002 to 2550.83 Gg in 2012. They were then reduced to 2478.63 Gg in 2017. Additionally, with the rise in the urbanization rate, household consumption BC emissions increased from 446.18 in 2002 to 1080.12 Gg in 2017. Urban consumption, rural consumption, and BC emission intensity were the three main contributing factors to household consumption BC emission changes. Transport, storage, postal, and telecommunications services (TSP); farming, forestry, animal husbandry, and fishery (FFA); and residential and other industries (RES) contributed the most to the urbanization-related BC emission increase. In particular, the TSP sector contributed the most to the BC emission increase because of the increasing TSP needs related to urbanization. Therefore, it is necessary to formulate mitigation policies for the TSP sector.

Source Apportionment of Ambient Black Carbon During the COVID-19 Lockdown

Black carbon (BC) particles being emitted from mobile and stationary emission sources as a result of combustion activities have significant impacts on human health and climate change. A lot of social activities have been halted during the COVID-19 lockdowns, which has evidently enhanced the ambient and indoor air quality. This paper investigates the possible emission sources and evaluates the meteorological conditions that may affect the dispersion and transport of BC locally and regionally. Ground-level equivalent BC (eBC) measurements were performed between January 2020 and July 2020 at a university campus located in Dammam city of the Kingdom of Saudi Arabia (KSA). The fossil fuel (eBCff) and biomass burning (eBCbb) fractions of total eBC (eBCt) concentrations were estimated as 84% and 16%, respectively, during the entire study period. The mean eBCbb, eBCff, and eBCt concentrations during the lockdown reduced by 14%, 24%, and 23%, respectively. The results of statistical analyses indicated that local fossil fuel burning emissions and atmospheric conditions apparently affected the observed eBC levels. Long-range potential source locations, including Iraq, Kuwait, Iran, distributed zones in the Arabian Gulf, and United Arab Emirates and regional source areas, such as the Arabian Gulf coastline of the KSA, Bahrain, and Qatar, were associated with moderate to high concentrations observed at the receptor site as a result of cluster analysis and concentration-weighted trajectory analysis methods.

A study of cardiorespiratory related mortality as a result of exposure to black carbon

INTRODUCTION

Air pollution is a global phenomenon which invariably leads to a serious environmental and health related sequalae. "Black carbon" (BC), a subset of fine particulate matter ≤2.5 μm (PM_2.5), is a fossil fuel emission by-product and has more recently been recognized as a major health hazard. The objective of this study is to statistically analyze the BC concentration and its correlation with cardiorespiratory related mortality and to estimate the benefits of BC reduction on the health of the population in the capital city of Tehran.

METHODS

We analyzed the ambient air BC concentration and its correlation with cardiorespiratory related mortality and conducted health impact assessment of BC in Tehran (Jan 2018-Jan 2019). The data pertaining to BC concentration was obtained from Tehran's four major pollution monitoring stations. The mortality data was obtained from Tehran's cemetery registry. We calculated and analyzed BC concentration statistics including the mean, standard deviation, coefficient of variation, skewness, and kurtosis. We then assessed the cross-correlation and temporal relationship (0-7 days) between the daily mean concentration of BC for the entire city and cardiorespiratory related mortality. The BenMAP software was utilized to estimate the potential reduction in cardiorespiratory related mortality rates if BC concentration is reduced. Three hypothetical scenarios were employed in the analysis, utilizing the BenMAP software: (I) BC concentration was completely removed from the ambient air; (II) BC concentration was eliminated, and the remaining (non-BC portion of) PM_2.5 concentration was reverted to the United States Environmental Protection Agency (EPA)'s standard level (i.e., 35 μg/m³); and (III) The BC emission during the night (22:00 h-6:00 h, when heavy-duty vehicles (HDVs) are allowed to commute in the city) was distributed throughout the whole day. Since the planetary boundary layer during daytime is much higher than that of nighttime, with the same rate of emission, lower concentrations are spread during the whole day.

RESULTS

The trend of BC concentration variation revealed a persistently higher emission of BC during the nighttime, which is consistent with the large-scale operation of HDVs during these hours in the city of Tehran. We observed a direct correlation between BC concentration and cardiorespiratory related mortality. Analysis also showed a 1.4-day lag period from the time of exposure to BC polluted air and respiratory related deaths, and 2 days for cardiovascular related deaths. As a result, the reduction in BC has significant beneficial effects in reducing potentially preventable cardiorespiratory related mortality. The aforementioned three scenarios for age groups of 30 and above yielded the following results: (I) 11,369 (126 per 100,000 population), (II) 15,386 (171 per 100,000 population), and (III) 2552 (28 per 100,000 population) potentially preventable all-cause (including cardiorespiratory) related deaths annually.

CONCLUSIONS

The BC concentration is relatively high in Tehran and HDVs have a major role in emission of this pollutant. A direct correlation between BC concentration and cardiorespiratory related mortality is observed. There are considerable health benefits in reducing BC concentration in this city. Our findings highlight the urgent need to actively curtail emissions of this harmful pollutant. This can be achieved through utilizing control mechanisms such as particulate filters or amending traffic laws.

Impact of Polycyclic Aromatic Hydrocarbons (PAHs) from an Asphalt Mix Plant in a Suburban Residential Area

Polycyclic aromatic hydrocarbons (PAHs), an important class of hazardous airborne pollutants, are mutagenic and carcinogenic substances known to be released during the paving of asphalt. In this study, PAHs emitted from an asphalt mix plant were analyzed to investigate the effects on a suburban residential area. Black carbon, organic carbon, elemental carbon, and PAHs in fine particulate matter (PM2.5) were analyzed in a village near the asphalt mix plant. The results of wind direction analysis revealed that the village was meteorologically affected by emissions from the asphalt mix plant. PAHs in PM2.5 ranged from 0.51 to 60.73 ng/m3, with an average of 11.54 ng/m3. Seasonal PAHs were highest in winter, followed in order by spring, autumn, and summer. The diagnostic ratios between PAHs indicate that the source of PAHs could be incomplete combustion of petrogenic origin. The maximum black carbon concentration in the intensive periods reaches up to 14.17 μg/m3 during mix plant operation periods. Seasonal ∑BaPTEF values based on Toxic Equivalence Factor were: winter (2.284 ng/m3), spring (0.575 ng/m3), autumn (0.550 ng/m3), and summer (0.176 ng/m3). The values are about 6.5 times higher than the concentration in another background area and more than three times higher than those in the capital city, Seoul, in the Republic of Korea. In conclusion, primary emissions from the point source can be considered the major contributor to pollution in the residential area.

Radiocarbon analysis of carbonaceous aerosols in Bratislava, Slovakia

Aerosols dispersed in the atmosphere represent important factors influencing not only the environment, but also human health. Carbonaceous aerosols are one of the main components of total atmospheric aerosols, and their sources are of great interest. Radiocarbon analysis provides an excellent way to determine the fraction of fossil and non-fossil aerosols in the atmosphere. Over the period of one year (June 2017-June 2018), we sampled atmospheric aerosols with size greater than 0.3 μm in Bratislava, Slovakia and used the exposed quartz filters for radiocarbon analysis of the elemental carbon (EC) aerosol fraction. The results show that on average the fossil fuel combustion is the dominant source of EC aerosol particles in Bratislava. In summer months, they represent more than half (65-80%) of the total EC aerosols. The relative amount of EC particles derived from biomass burning was 20-35% in summer, which increased to 40-55% in winter months. The dominance of fossil fraction is caused by high degree of industrialization and urbanization of the city. The increase of biomass fraction in winter is probably caused by domestic wood burning in areas surrounding the Bratislava city.

Black carbon over an urban atmosphere in northern peninsular Southeast Asia: Characteristics, source apportionment, and associated health risks

Black carbon (BC) has been demonstrated to pose significant negative impacts on climate and human health. Equivalent BC (EBC) measurements were conducted using a 7-wavelength aethalometer, from March to May 2016, over an urban atmosphere, viz., Chiang Mai (98.957°E, 18.795°N, 373 m above sea level), Thailand in northern peninsular Southeast Asia. Daily variations in aerosol light absorption were mainly governed by open fire activities in the region. The mean mass-specific absorption cross-section (MAC) value of EBC at 880 nm was estimated to be 9.3 m² g^-1. The median EBC mass concentration was the highest in March (3.3 μg m^-3) due to biomass-burning (comprised of forest fire and agricultural burning) emissions accompanied by urban air pollution within the planetary boundary layer under favorable meteorological conditions. Daily mean absorption Ångström exponent (AAE_470/950) varied between 1.3 and 1.7 and could be due to variations in EBC emission sources and atmospheric mixing processes. EBC source apportionment results revealed that biomass-burning contributed significantly more to total EBC concentrations (34-92%) as compared to fossil-fuel (traffic emissions). Health risk estimates of EBC in relation to different health outcomes were assessed in terms of passive cigarette equivalence, highlighting the considerable health effects associated with exposure to EBC levels. As a necessary action, the reduction of EBC emissions would promote considerable climate and health co-benefits.

Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China

An unmanned aerial vehicle (UAV) equipped with miniature monitors was used to study the vertical profiles of PM_2.5 (particulate matter with a ≤2.5-μm diameter) and black carbon (BC) in Macau, China, from the surface to 500 m above ground level (AGL). Twelve- and 11-day measurements were conducted during February and March 2018, respectively. In total, 46 flights were conducted between 05:00 and 06:00 AM Local Time (LT). The average concentrations of PM_2.5 and BC were significantly lower in March (40.1 ± 17.9 and 2.3 ± 2.0 μg m^-3, respectively) when easterly winds prevailed, compared with those in February (69.8 ± 35.7 and 3.6 ± 2.0 μg m^-3, respectively) when northerly winds dominated. In general, PM_2.5 concentrations decreased with height, with a vertical decrement of 0.2 μg m^-3 per 10 m. BC concentrations exhibited diverse vertical profiles with an overall vertical decrement of 0.1 μg m^-3 per 10 m. Meteorological analyses including back-trajectory analysis and atmospheric stability categorization revealed that both advection and convection transports may have notable influences on the vertical profiles of PM pollutants. The concentration of PM pollutants above the boundary layer was lower than that within the layer, thus exhibiting a sigmoid profile in some cases. In addition, the lighting of firecrackers and fireworks on February 16 (first day of the Chinese New Year) resulted in the elevated concentrations of PM_2.5 and BC within 150 m AGL. The takeoff of a civil flight on February 10 may have resulted in a substantial increase in the PM_2.5 concentrations from 80.8 (±2.1) μg m^-3 at the ground level to 119.2 (±9.3) μg m^-3 at a height of 330 m. Although the results are confined to a height of 500 m AGL, the current study provides a useful dataset for PM vertical distributions, complementing the spatiotemporal variations by ground-based measurements.

Indoor measurements of air pollutants in residential houses in urban and suburban areas: Indoor versus ambient concentrations

Indoor exposure to air pollutants was assessed through 99 visits to 51 homes located in downtown high-rise buildings and detached houses in suburban and rural areas. The ambient concentrations of ultrafine particles (UFP), black carbon (BC), particulate matter smaller than 2.5 μm in diameter (PM2.5), and trace elements were concurrently measured at a central monitoring site in downtown Toronto. Median hourly indoor concentrations for all measurements were 4700 particles/cm³ for UFP, 270 ng/m³ for BC, and 4 μg/m³ for PM2.5, which were lower than ambient outdoor levels by a factor of 2-3. Much higher variability was observed for indoor UFP and BC across the homes compared to ambient levels, mostly due to the influence of indoor cooking emissions. Traffic emissions appeared to have a strong influence on the indoor background (i.e., outdoor-originated) concentrations of BC, UFP, and some trace elements. Specifically, 85% and 34% of the indoor concentrations of BC and UFP were predominantly from outdoor sources, respectively. Moreover, a positive correlation was observed between indoor concentrations of BC and UFP and total road length within a 300 m buffer zone. There was no significant decrease in indoor air pollution with increasing floor level among high-rise residences. In addition to the influence of outdoor sources on indoor air quality, indoor sources contributed to elevated concentrations of K, Ca, Cr, and Cu. A factor analysis was performed on trace elements, UFP, and BC in homes to further resolve possible sources. Local traffic emissions, soil dust, biomass burning, and regional coal combustion were identified as outdoor-originated sources, while cooking emissions was a dominant indoor source. This study highlights how outdoor sources can contribute to chronic exposure in indoor environments and how indoor activities can be associated with acute exposure to temporally varying indoor-originated air pollutants.

The application of Raman spectroscopy combined with multivariable analysis on source apportionment of atmospheric black carbon aerosols

Source apportionment studies become increasingly crucial for black carbon (BC) in atmospheric particulate matters given its linkage with adverse public health and climate impacts. In this work, a facile and rapid method using Raman spectra combined with stepwise discriminant analysis (SDA) was proposed to identify and quantify the contributions of atmospheric BC sources. Four BC samples from biomass burning, coal combustion, gasoline and diesel vehicle emission were characterized by Raman spectra. The SDA model was established based on 10 parameters with significant differences (p < 0.05), giving an accuracy of 83% with a cross-validation rate of 80%. Utilizing four suggested discriminant variables from SDA model, vehicle emission was predicted as the dominant contributor to ambient BC particles, among which gasoline contributed much higher than diesel at an urban road intersection in Shanghai, China. This new method shows great potential to classify and investigate the sources of atmospheric BC aerosols and provide more effective information on air pollution control measures.

The air quality and health impacts of projected long-haul truck and rail freight transportation in the United States in 2050

Diesel emissions from freight transportation activities are a key threat to public health. This study examined the air quality and public health impacts of projected freight-related emissions in 2050 over the continental United States. Three emission scenarios were considered: (1) a projected business-as-usual socioeconomic growth with freight fleet turnover and stringent emission control (CTR); (2) the application of a carbon pricing climate policy (PO); and (3) further technology improvements to eliminate high-emitting conditions in the truck fleet (NS). The PO and NS cases are superimposed on the CTR case. Using a WRF-SMOKE-CMAQ-BenMAP modeling framework, we quantified the impacts of diesel fine particulate matter (PM_2.5) emissions change on air quality, health, and economic benefits. In the CTR case, we simulate a widespread reduction of PM_2.5 concentrations, between 0.5 and 1.5 μg m^-3, comparing to a base year of 2011. This translates into health benefits of 3600 (95% CI: 2400-4800) prevented premature deaths, corresponding to $38 (95% CI: $3.5-$100) billion. Compared to CTR case, the PO case can obtain ~9% more health benefits nationally, however, climate policy also affects the health outcomes regionally due to transition of demand from truck to rail; regions with fewer trucks could gain in health benefits, while regions with added rail freight may potentially experience a loss in health benefits due to air quality degradation. The NS case provides substantial additional benefits (~20%). These results support that a combination of continuous adoption of stringent emission standards and strong improvements in vehicle technology are necessary, as well as rewarding, to meet the sustainable freight and community health goals. States and metropolitan areas with high population density and usually high freight demand and emissions can take more immediate actions, such as accelerating vehicle technology improvements and removing high-emitting trucks, to improve air quality and health benefits.

Respiratory tract deposition of inhaled roadside ultrafine refractory particles in a polluted megacity of South-East Asia

Recent studies demonstrate that Black Carbon (BC) pollution in economically developing megacities remain higher than the values, which the World Health Organization considers to be safe. Despite the scientific evidence of the degrees of BC exposure, there is still a lack of understanding on how the severe levels of BC pollution affect human health in these regions. We consider information on the respiratory tract deposition dose (DD) of BC to be essential in understanding the link between personal exposure to air pollutants and corresponding health effects. In this work, we combine data on fine and ultrafine refractory particle number concentrations (BC proxy), and activity patterns to derive the respiratory tract deposited amounts of BC particles for the population of the highly polluted metropolitan area of Manila, Philippines. We calculated the total DD of refractory particles based on three metrics: refractory particle number, surface area, and mass concentrations. The calculated DD of total refractory particle number in Metro Manila was found to be 1.6 to 17 times higher than average values reported from Europe and the U.S. In the case of Manila, ultrafine particles smaller than 100 nm accounted for more than 90% of the total deposited refractory particle dose in terms of particle number. This work is a first attempt to quantitatively evaluate the DD of refractory particles and raise awareness in assessing pollution-related health effects in developing megacities. We demonstrate that the majority of the population may be highly affected by BC pollution, which is known to have negative health outcomes if no actions are taken to mitigate its emission. For the governments of such metropolitan areas, we suggest to revise currently existing environmental legislation, raise public awareness, and to establish supplementary monitoring of black carbon in parallel to already existing PM₁₀ and PM_2.5 measures.

Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in China and the role of urbanization in aggravating the health burden

In this study, the trend of PM_2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM_2.5 data. PM_2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM_2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM_2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM_2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM_2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM_2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM_2.5 concentration and mortality are analyzed. The PM_2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM_2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.

Short-Term Association between Black Carbon Exposure and Cardiovascular Diseases in Pakistan’s Largest Megacity

This study investigated the association between black carbon (BC) exposure and hospital admissions (HAs) and outpatient department/emergency room (OPD/ER) visits for cardiovascular diseases (CVD) among residents of Karachi, the largest city in Pakistan. We measured daily concentrations of BC in fine particulate matter (PM2.5) and collected records of HAs and OPD/ER visits for CVD from 2 major tertiary care hospitals serving Karachi for 6 weeks continuously during each quarter over 1 year (August 2008–August 2009). We subsequently analyzed daily counts of hospital and BC data over 0–3 lag days. Daily mean BC concentrations varied from 1 to 32 µg/m3. Results suggest that BC concentrations are associated with CVD HAs and OPD/ER visits. However, associations were generally only observed when modeled with BC from Tibet Center, the commercial-residential site, as compared to Korangi, the industrial-residential site. Overall, low statistical significance suggests that while BC may be a valuable indicator for CVD health risks from combustion-derived particles, further evaluation of the constituents of PM2.5 and their relative contributions to CVD health impacts is necessary.

Projecting future climate change impacts on heat-related mortality in large urban areas in China

Global climate change is anticipated to raise overall temperatures and has the potential to increase future mortality attributable to heat. Urban areas are particularly vulnerable to heat because of high concentrations of susceptible people. As the world's largest developing country, China has experienced noticeable changes in climate, partially evidenced by frequent occurrence of extreme heat in urban areas, which could expose millions of residents to summer heat stress that may result in increased health risk, including mortality. While there is a growing literature on future impacts of extreme temperatures on public health, projecting changes in future health outcomes associated with climate warming remains challenging and underexplored, particularly in developing countries. This is an exploratory study aimed at projecting future heat-related mortality risk in major urban areas in China. We focus on the 51 largest Chinese cities that include about one third of the total population in China, and project the potential changes in heat-related mortality based on 19 different global-scale climate models and three Representative Concentration Pathways (RCPs). City-specific risk estimates for high temperature and all-cause mortality were used to estimate annual heat-related mortality over two future twenty-year time periods. We estimated that for the 20-year period in Mid-21st century (2041-2060) relative to 1970-2000, incidence of excess heat-related mortality in the 51 cities to be approximately 37,800 (95% CI: 31,300-43,500), 31,700 (95% CI: 26,200-36,600) and 25,800 (95% CI: 21,300-29,800) deaths per year under RCP8.5, RCP4.5 and RCP2.6, respectively. Slowing climate change through the most stringent emission control scenario RCP2.6, relative to RCP8.5, was estimated to avoid 12,900 (95% CI: 10,800-14,800) deaths per year in the 51 cities in the 2050s, and 35,100 (95% CI: 29,200-40,100) deaths per year in the 2070s. The highest mortality risk is primarily in cities located in the North, East and Central regions of China. Population adaptation to heat is likely to reduce excess heat mortality, but the extent of adaptation is still unclear. Future heat mortality risk attributable to exposure to elevated warm season temperature is likely to be considerable in China's urban centers, with substantial geographic variations. Climate mitigation and heat risk management are needed to reduce such risk and produce substantial public health benefits.

Long-term trends in the ambient PM2.5- and O3-related mortality burdens in the United States under emission reductions from 1990 to 2010

Concentrations of both fine particulate matter (PM2.5) and ozone (O3) in the United States (US) have decreased significantly since 1990, mainly because of air quality regulations. Exposure to these air pollutants is associated with premature death. Here we quantify the annual mortality burdens from PM2.5 and O3 in the US from 1990 to 2010, estimate trends and inter-annual variability, and evaluate the contributions to those trends from changes in pollutant concentrations, population, and baseline mortality rates. We use a fine-resolution (36 km) self-consistent 21-year simulation of air pollutant concentrations in the US from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates. From 1990 to 2010, the modeled population-weighted annual PM2.5 decreased by 39 %, and summertime (April to September) 1 h average daily maximum O3 decreased by 9 % from 1990 to 2010. The PM2.5-related mortality burden from ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and stroke steadily decreased by 54% from 123 700 deaths year-1 (95% confidence interval, 70 800-178 100) in 1990 to 58 600 deaths year-1 (24 900-98 500) in 2010. The PM2.5-related mortality burden would have decreased by only 24% from 1990 to 2010 if the PM2.5 concentrations had stayed at the 1990 level, due to decreases in baseline mortality rates for major diseases affected by PM2.5. The mortality burden associated with O3 from chronic respiratory disease increased by 13% from 10 900 deaths year-1 (3700-17 500) in 1990 to 12 300 deaths year-1 (4100-19 800) in 2010, mainly caused by increases in the baseline mortality rates and population, despite decreases in O3 concentration. The O3-related mortality burden would have increased by 55% from 1990 to 2010 if the O3 concentrations had stayed at the 1990 level. The detrended annual O3 mortality burden has larger inter-annual variability (coefficient of variation of 12%) than the PM2.5-related burden (4%), mainly from the inter-annual variation of O3 concentration. We conclude that air quality improvements have significantly decreased the mortality burden, avoiding roughly 35 800 (38%) PM2.5-related deaths and 4600 (27%) O3-related deaths in 2010, compared to the case if air quality had stayed at 1990 levels (at 2010 baseline mortality rates and population).

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States

Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R² of the model was good in both cold (10-fold CV R² = 0.87) and warm seasons (CV R² = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.

Co-benefits of global, domestic, and sectoral greenhouse gas mitigation for US air quality and human health in 2050

Reductions in greenhouse gas (GHG) emissions can bring ancillary benefits of improved air quality and reduced premature mortality, in addition to slowing climate change. Here we study the co-benefits of global and domestic GHG mitigation on US air quality and human health in 2050 at fine resolution using dynamical downscaling of meteorology and air quality from global simulations to the continental US, and quantify for the first time the co-benefits from foreign GHG mitigation. Relative to the reference scenario from which RCP4.5 was created, global GHG reductions in RCP4.5 avoid 16000 PM_2.5-related all-cause deaths yr^-1 (90% confidence interval, 11700-20300), and 8000 (3600-12400) O₃-related respiratory deaths yr^-1 in the US in 2050. Foreign GHG mitigation avoids 15% and 62% of PM_2.5- and O₃-related total avoided deaths, highlighting the importance of foreign mitigation for US health. GHG mitigation in the US residential sector brings the largest co-benefits for PM_2.5-related deaths (21% of total domestic co-benefits), and industry for O₃ (17%). Monetized benefits for avoided deaths from ozone and PM_2.5 are $137 ($87-187) per ton CO₂ at high valuation and $45 ($29-62) at low valuation, of which 31% are from foreign GHG reductions. These benefits likely exceed the marginal cost of GHG reductions in 2050. The US gains significantly greater air quality and health co-benefits when its GHG emission reductions are concurrent with reductions in other nations. Similarly, previous studies estimating co-benefits locally or regionally may greatly underestimate the full co-benefits of coordinated global actions.

A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon

BACKGROUND

Black carbon (BC) is a ubiquitous component of particulate matter (PM) emitted from combustion-related sources and is associated with a number of health outcomes.

OBJECTIVES

We conducted a systematic review to evaluate the potential for cardiovascular morbidity and mortality following exposure to ambient BC, or the related component elemental carbon (EC), in the context of what is already known about the associations between exposure to fine particulate matter (PM_2.5) and cardiovascular health outcomes.

DATA SOURCES

We conducted a stepwise systematic literature search of the PubMed database and employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting our results.

STUDY ELIGIBILITY CRITERIA

Studies meeting inclusion criteria (i.e., include a quantitative measurement of BC or EC used to characterize exposure and an effect estimate of the association of the exposure metric with ED visits, hospital admissions, or mortality due to cardiovascular disease) were evaluated for risk of bias in study design and results.

STUDY APPRAISAL AND SYNTHESIS METHODS

Risk of bias evaluations assess some aspects of internal validity of study findings based on study design, conduct, and reporting and identify potential issues related to confounding or other biases.

RESULTS

The results of our systematic review demonstrate similar results for BC or EC and PM_2.5; that is, a generally modest, positive association of each pollutant measurement with cardiovascular emergency department visits, hospital admissions, and mortality. There is no clear evidence that health risks are greater for either BC or EC when compared to one another, or when either is compared to PM_2.5.

LIMITATIONS

We were unable to adequately evaluate the role of copollutant confounding or differential spatial heterogeneity for BC or EC compared to PM_2.5.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Overall, the evidence at present indicates that BC or EC is consistently associated with cardiovascular morbidity and mortality but is not sufficient to conclude that BC or EC is independently associated with these effects rather than being an indicator for PM_2.5 mass.

SYSTEMATIC REVIEW REGISTRATION NUMBER

Not available.

Effects of air pollution on respiratory hospital admissions in İstanbul, Turkey, 2013 to 2015

We examined the associations between the daily variations of air pollutants and hospital admissions for respiratory diseases in İstanbul, the largest city of Turkey. A time series analysis of counts of daily hospital admissions and outdoor air pollutants was performed using single-pollutant Poisson generalized linear model (GLM) while controlling for time trends and meteorological factors over a 3-year period (2013-2015) at different time lags (0-9 days). Effects of the pollutants (Excess Risk, ER) on current-day (lag 0) hospital admissions to the first ten days (lag 9) were determined. Data on hospital admissions, daily mean concentrations of air pollutants of PM₁₀, PM_2.5 and NO₂ and daily mean concentrations of temperature and humidity of İstanbul were used in the study. The analysis was conducted among people of all ages, but also focused on different sexes and different age groups including children (0-14 years), adults (35-44 years) and elderly (≥65 years). We found significant associations between air pollution and respiratory related hospital admissions in the city. Our findings showed that the relative magnitude of risks for an association of the pollutants with the total respiratory hospital admissions was in the order of: PM_2.5, NO₂, and PM₁₀. The highest association of each pollutant with total hospital admission was observed with PM_2.5 at lag 4 (ER = 1.50; 95% CI = 1.09-1.99), NO₂ at lag 4 (ER = 1.27; 95% CI = 1.02-1.53) and PM₁₀ at lag 0 (ER = 0.61; 95% CI = 0.33-0.89) for an increase of 10 μg/m3 in concentrations of the pollutants. In conclusion, our study showed that short-term exposure to air pollution was positively associated with increased respiratory hospital admissions in İstanbul during 2013-2015. As the first air pollution hospital admission study using GLM in İstanbul, these findings may have implications for local environmental and social policies.

Columnar aerosol characteristics and radiative forcing over the Doon Valley in the Shivalik range of northwestern Himalayas

Spectral aerosol optical depth (AOD) measurements obtained from multi-wavelength radiometer under cloudless conditions over Doon Valley, in the foothills of the western Himalayas, are analysed during the period January 2007 to December 2012. High AOD values of 0.46 ± 0.08 and 0.52 ± 0.1 at 500 nm, along with low values of Ångström exponent (0.49 ± 0.01 and 0.44 ± 0.03) during spring (March-May) and summer (June-August), respectively, suggest a flat AOD spectrum indicative of coarse-mode aerosol abundance compared with winter (December-February) and autumn (September-November), which are mostly dominated by fine aerosols from urban/industrial emissions and biomass burning. The columnar size distributions (CSD) retrieved from the King's inversion of spectral AOD exhibit bimodal size patterns during spring and autumn, while combinations of the power-law and unimodal distributions better simulate the retrieved CSDs during winter and summer. High values of extinction coefficient near the surface (∼0.8-1.0 km^-1 at 532 nm) and a steep decreasing gradient above are observed via CALIPSO profiles in autumn and winter, while spring and summer exhibit elevated aerosol layers between ∼1.5 and 3.5 km due to the presence of dust. The particle depolarisation ratio shows a slight increasing trend with altitude, with higher values in spring and summer indicative of non-spherical particles of dust origin. The aerosol-climate implications are evaluated via the aerosol radiative forcing (ARF), which is estimated via the synergy of OPAC and SBDART models. On the monthly basis, the ARF values range from ∼ -30 to -90 W m^-2 at the surface, while aerosols cause an overall cooling effect at the top of atmosphere (approx. -5 to -15 W m^-2). The atmospheric heating via aerosol absorption results in heating rates of 1.2-1.6 K day^-1 during March-June, which may contribute to changes in monsoon circulation over northern India and the Himalayas.