Urban Atmospheric Aerosols: Sources, Analysis, and Effects

Duarte, Regina M.B.O.; Duarte, Armando C.

doi:10.3390/atmos11111221

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The release of such air toxics have negative effects on human respiratory system 41 as well as contaminate the air quality. 66 In order to abate the molecular structure, ionic wind has been applied to the aerosols captured by the filter (KN95 respirator). Irradiation exposure at 20 kV for 20 minutes has been provided as it is found (in the case of surgical mask) that the higher voltage is significant for the abatement of aerosols.…”

Section: Resultsmentioning

confidence: 99%

Abatement of Aerosols by Ionic Wind Extracted From Dielectric Barrier Discharge Plasma

Arshad,

Rafique,

Bashir

et al. 2024

Environ�Health�Insights

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Lahore (Pakistan), being an industrial city, has high emission of aerosols that affects and contaminates the air quality. Therefore, the abatement/inactivation of aerosols is necessary to restrict their infectious activities. In this project, ionic wind isolated from dielectric barrier discharge plasma (DBD plasma) has been utilized to abate the aerosols trapped in the Surgical Mask and KN95 Respirator. To infer the chemical and elemental detection of ambient aerosols, FTIR and LIBS have been employed. “From the results, it is noteworthy that abatement/removal of aerosols has been successfully carried out by the ionic wind irradiation and highlights the potential of DBD plasma technology in removing the aerosols pollution.”

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Section: Resultsmentioning

confidence: 99%

Abatement of Aerosols by Ionic Wind Extracted From Dielectric Barrier Discharge Plasma

Arshad,

Rafique,

Bashir

et al. 2024

Environ�Health�Insights

View full text Add to dashboard Cite

show abstract

“…To be comparable with the satellite and reanalysis data, the AERONET AOD data at 550 nm was interpolated from the two nearest wavelengths, i.e. 500 nm and 675 nm (Gupta et al, 2020;Duarte and Duarte, 2020).…”

Section: Aeronet Datamentioning

confidence: 99%

A Pan-European, High-Resolution, Daily Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset based on Quantile Machine Learning

Chen

Méndez

Pétetin

et al. 2023

Preprint

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Abstract. Ambient particulate matter (PM) is a widespread air pollutant, consisting of a mixture of different particle species suspended in the air that negatively affects human health. Given the generally sparse distribution of in-situ PM measurement networks, spatially-resolved PM estimates are typically derived from Aerosol Optical Depth (AOD) obtained from satellites. However, satellite AOD data over land is affected by several limitations (e.g., data gaps; coarser resolution; higher uncertainty; unavailable or unreliable size fraction information), which weakens the relationship between AOD and PM. We have developed a 0.1 degree resolution daily AOD data set over Europe over the period 2003–2020, based on new Quantile Machine Learning (QML) models. The dataset provides reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD), based on AERONET (AErosol RObotic NETwork) site observations and climate and air quality reanalyses. Our results show that the three QML AOD products guarantee better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23–92 %, 11–13 % and 115–132 % higher than the corresponding satellite or reanalysis products, respectively. Over 88.8 %, 80.5 % and 88.6 % of QML AOD, fAOD and cAOD predictions fall within ± 20 % Expected Error (EE) envelopes, respectively. Previous studies reported that Europe is one of the regions with the poorest satellite AOD-PM correlation (Pearson correlation coefficient (PCC) around 0.1). Our results show that the three QML products are more correlated with ground-level PMs, especially when they are paired with their corresponding PMs in terms of size: AOD with PM10, fAOD with PM2.5 and cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Our results show that different PM size fractions may be better predicted using different AOD size fractions, instead of total AOD. QML long-term aerosol dataset (and associated models) not only fix some problems of existing AOD data, but also provide better tools to monitor and analyse fine-mode and coarse-mode aerosols in spatial and temporal dimensions, and to further investigate their impacts on human health, climate, visibility, and biogeochemical cycling. The QML datasets can be downloaded from https://doi.org/10.5281/zenodo.7756570 (Chen et al., 2023).

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“…Elemental detection shows that the aerosols are composed of these heavy metals. The release of such air toxics have negative effects on human respiratory system [43] as well as contaminate the air quality [66]. In order to abate the molecular structure, ionic wind has been applied to the aerosols captured by the filter (KN95 respirator).…”

Section: Kn95 Respiratormentioning

confidence: 99%

Irradiation of Ionic Wind Extracted from Dielectric Barrier Discharge Plasma to Abate Aerosols Trapped in Surgical Mask and KN95 Respirator

Arshad

Rafique

Bashir

et al. 2023

Preprint

View full text Add to dashboard Cite

Urban Atmospheric Aerosols: Sources, Analysis, and Effects

Abstract: Atmospheric fine particulate matter (PM2 [...]

Cited by 10 publications

References 18 publications

Abatement of Aerosols by Ionic Wind Extracted From Dielectric Barrier Discharge Plasma

Abatement of Aerosols by Ionic Wind Extracted From Dielectric Barrier Discharge Plasma

A Pan-European, High-Resolution, Daily Total, Fine-Mode and Coarse-Mode Aerosol Optical Depth dataset based on Quantile Machine Learning

Irradiation of Ionic Wind Extracted from Dielectric Barrier Discharge Plasma to Abate Aerosols Trapped in Surgical Mask and KN95 Respirator

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