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

Gupta, Phalguni; Jangid, Ashok Kumar; Kumar, Ranjit

doi:10.1007/s10653-022-01430-6

Cited by 4 publications

(4 citation statements)

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“…To obtain an individual measurement of exposure, the individual internal black carbon load was quantified in whole blood using a specific and sensitive detection technique based on white light generation of carbonaceous particles under femtosecond pulsed illumination as previously reported (6)(7)(8). All images were collected at room temperature using a Zeiss LSM880 (Carl Zeiss, Germany) equipped with a femtosecond pulsed laser (810 nm, 120 fs, 80 MHz, MaiTai DeepSee, Spectra-Physics, USA) tuned to a central wavelength of 810 nm using a Plan-Aprochromat 20x/0.8 (Carl Zeiss, Germany).…”

Section: Blood Black Carbon Loadmentioning

confidence: 99%

“…[5] Ambient air pollution constitutes a serious risk factor not only for the emergence of respiratory and/or viral infections, but also for the development of reduced pulmonary function and/or aggravation of existing pulmonary diseases. [6,7] During the COVID-19 pandemic, air pollution concentrations were lower than before the pandemic, [8] due to the positive impact of several lockdown related effects such as less traffic and reduced industrial activities on air quality. During the pandemic, the attributable relative risk factor of black carbon exposure levels on human health were significantly lower than before the pandemic.…”

Section: Introductionmentioning

confidence: 99%

“…During the pandemic, the attributable relative risk factor of black carbon exposure levels on human health were significantly lower than before the pandemic. [8] Nonetheless, emerging data from epidemiological studies also suggest that both genetics but also air pollution may modulate the risk of disease by increasing patient susceptibility to infection, including COVID-19 Experimental data supports an important role of the ACE2 receptor, which COVID-19 viruses use to infiltrate target cells, in the pathophysiology of infection. [9] Indeed, studies showed that susceptibility to COVID-19 infection was correlated with ACE2 expression in cell lines.…”

Section: Introductionmentioning

confidence: 99%

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Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients

et al. 2023

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BackgroundAir pollutant exposure is one of the major risk factors for aggravation of respiratory diseases. We investigated whether exposure to air pollution and accumulated black carbon particles in blood were associated with COVID-19 disease severity, including the risk for intensive care and duration of hospitalisation.MethodsFrom May 2020 until March 2021, 328 hospitalised COVID-19 patients (29% at intensive care) were recruited from 2 hospitals in Belgium. Daily exposure levels (from 2016 to 2019) for particulate matter (PM2.5and PM10), nitrogen dioxide (NO2) and black carbon were modelled using a high-resolution spatiotemporal model. Blood black carbon particles (internal exposure to nano-sized particles) were quantified using pulsed laser illumination. Primary clinical parameters and outcomes included duration of hospitalisation, and risk of intensive care.ResultsIndependent of potential confounders, an interquartile range (IQR) increase in exposure in the week before admission was associated with increased duration of hospitalisation (PM2.5:+4.22 (95%CI:0.74-7.69) days;,NO2:+4.33 (1.30-7.37) days); Similar effects were observed for long-term NO2and BC exposure on hospitalisation duration. These effect-sizes for an IQR increase in air pollution on hospitalisation duration were equivalent to the effect of a 10-year increase in age on duration of hospitalisation. Furthermore, for an IQR higher blood black carbon load, the odds ratios for intensive care hospitalisation was 1.36 (1.11-1.70).ConclusionsIn hospitalised COVID-19 patients, higher pre-admission ambient air pollution and blood black carbon levels predicted adverse outcomes. Our findings imply that air pollution exposure influences on COVID-19 severity and therefor the burden on medical care systems during the COVID-19 pandemic.

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Section: Blood Black Carbon Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients

et al. 2023

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“…Vehicle exhaust pollution is an important factor threatening public health [ 1 ]. Dynamic estimation of mobile pollution sources changing over time on the road network is the fundamental data for air pollution control and public health promotion [ 2 ]. Pollution sources are required to be controlled in coordination in air quality management [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Zang

Jiao

Song

et al. 2022

IJERPH

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Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

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Assessment of equivalent black carbon variations and its source apportionment over Varanasi, Indo-Gangetic Basin

Chauhan,

Tiwari,

Gupta

et al. 2024

Atmospheric Pollution Research

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

Cited by 4 publications

References 50 publications

Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients

Pre-admission ambient air pollution and blood soot particles predict hospitalisation outcomes in COVID-19 patients

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Assessment of equivalent black carbon variations and its source apportionment over Varanasi, Indo-Gangetic Basin

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