Molecular level insights into the direct health impacts of some organic aerosol components

Keshavarz, Fatemeh

doi:10.1039/d1nj00231g

Cited by 3 publications

(1 citation statement)

References 101 publications

(83 reference statements)

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“…For example, black carbon aerosols can heat the atmosphere, evaporate cloud droplets to reduce cloud cover [11], create stable atmospheric stratification [12], weaken convection, and reduce rain showers. High concentrations of aerosol particles seriously affect human health [13][14][15]. A number of studies have shown that the incidence of cardiopulmonary disease is closely related to the content of particulate matter in polluted air [16][17][18], especially the inhalable fine particles, such as PM 2.5 , which can be inhaled into the lungs, attached to the alveoli, or entered into the blood circulation through the alveoli, posing a great threat to human health [19,20].…”

Section: Introductionmentioning

confidence: 99%

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Jiang,

Liu,

Jiao

2023

Atmosphere

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Atmospheric aerosol significantly affects the climate environment and public health, and Aerosol Optical Depth (AOD) is a fundamental optical characteristic parameter of aerosols, so it is important to develop methods for obtaining AOD. In this work, a novel AOD retrieval algorithm based on a Convolutional Neural Network (CNN) method that could provide continuous and detailed aerosol distribution is proposed. The algorithm utilizes data from Sentinel-2 and Aerosol Robotic Network (AERONET) spanning from 2016 to 2022. The CNN AOD data are consistent with the AERONET measurements, with an R2 of 0.95 and RMSE of 0.049 on the test dataset. CNN demonstrates superior performance in retrieving AOD compared with other algorithms. CNN retrieves AOD well on high reflectance surfaces, such as urban and bare soil, with RMSEs of 0.051 and 0.042, respectively. CNN efficiently retrieves AOD in different seasons, but it performs better in summer and winter than in spring and autumn. In addition, to study the relationship between image size and model retrieval performance, image datasets of 32 × 32, 64 × 64 and 128 × 128 pixels were created to train and test the CNN model. The results show that the 128-size CNN performs better because large images contain rich aerosol information.

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Section: Introductionmentioning

confidence: 99%

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Jiang,

Liu,

Jiao

2023

Atmosphere

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Molecular-level understanding of highly selective heavy rare earth element uptake by organophosphorus modified MIL-101(Cr)

Keshavarz

Kavun

Veen

et al. 2022

Chemical Engineering Journal

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Application of molecular docking simulation to screening of metal–organic frameworks

Keshavarz

Barbiellini

2023

Computational Materials Science

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Molecular level insights into the direct health impacts of some organic aerosol components

Abstract: Organic aerosols (OAs) comprise 20-90 w% of atmospheric aerosols. While they have some positive impacts (e.g. acting as precipitation nuclei), they can pose some negative impacts on human health. The...

Cited by 3 publications

References 101 publications

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Molecular-level understanding of highly selective heavy rare earth element uptake by organophosphorus modified MIL-101(Cr)

Application of molecular docking simulation to screening of metal–organic frameworks

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