Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey

Zeydan, Özgür; Wang, Yuhang

doi:10.1016/j.apr.2019.05.005

Cited by 46 publications

(11 citation statements)

References 63 publications

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“…Although the population and daily COVID-19 cases were higher in İzmir and Bursa cities (populations of 4,321,000 and 2,995,000, respectively), there were no positive samples detected. Among the sampling locations, Tekirdağ and Zonguldak (populations of 1,030,000 and 213,544, respectively) are smaller cities compared to others but have worse air pollution (45). Tekirdağ is an industrialized city and Zonguldak is a coal mining area with coal-fired power plants.…”

Section: Discussionmentioning

confidence: 99%

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Kayalar

Arı

Babuççu

et al. 2021

Preprint

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Coronavirus disease 2019 (COVID-19) is caused by the SARS-CoV-2 virus and has been affecting the world since the end of 2019. Turkey is severely affected with the first case being reported on March 11th 2020. Ambient particulate matter (PM) samples in various size ranges were collected from 13 sites including urban and urban background locations and hospital gardens in 10 cities across Turkey between the 13th of May and the 14th of June, 2020 to investigate a possible presence of SARS-CoV-2 RNA on ambient PM. A total of 155 daily samples (TSP, n=80; PM2.5, n=33; PM2.5-10, n=23; PM10, n=19; and 6 size segregated, n=48) were collected using various samplers in each city. The N1 gene and RdRP gene expressions were analyzed for the presence of SARS-CoV-2 as suggested by the Centers for Disease Control and Prevention (CDC). According to RT-PCR and 3D-RT-PCR analysis, dual RdRP and N1 gene positivity were detected in 20 (9.8 %) of the samples. The highest percentage of virus detection on PM samples was from hospital gardens in Tekirdag, Zonguldak, and Istanbul, especially in PM2.5 mode. Samples collected from two urban sites were also positive. Findings of this study have suggested that SARS CoV2 may be transported by ambient particles especially at sites close to the infection hot-spots. However, whether this has an impact on the spread of the virus infection remains to be determined.

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Section: Discussionmentioning

confidence: 99%

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Kayalar

Arı

Babuççu

et al. 2021

Preprint

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“…Research on the relationship between AOD measures and ground-level particulate pollution is well-advanced. Recent work includes applications for the United States (Lee et al 2016), China (Xie et al 2015;Lv et al 2016;Hu et al 2019), Turkey (Özgür and Wang 2019) and India (Sathe et al 2019). The results are somewhat mixed, but generally support the utility of AOD as a proxy for ground-based particulate measures.…”

Section: Modismentioning

confidence: 95%

Global Technology for Local Monitoring of Air Pollution in Dhaka

Dasgupta¹,

Khaliquzzaman²,

Wheeler³

2020

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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“…Aerosol particulate matter with a diameter less than 2.5 µm (PM 2.5 ) is the main pollutant threatening health [3,4]. It was found that there is a strong positive relationship between ground-level PM 2.5 concentration and satellite-measured AOD [5][6][7]. Therefore, promoting the accuracy of satellite-retrieved AOD can make the performance of the PM 2.5 estimation better.…”

Section: Introductionmentioning

confidence: 99%

Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm

Chen

Fan

et al. 2022

Remote Sensing

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Due to the advantage of geostationary satellites, Himawari-8/AHI can provide near-real-time air quality monitoring over China with a high temporal resolution. Satellite-based aerosol optical depth (AOD) retrieval over land is a challenge because of the large surface contribution to the top of atmosphere (TOA) signal and the uncertainty of aerosol modes. Here, by combining satellite TOA reflectance, sun-sensor geometries, meteorological factors and vegetation information, we propose a data-driven AOD detection algorithm based on a deep neural network (DNN) model for Himawari-8/AHI. It is trained by sample data of 2018 and 2019 and is applied to derive hourly AODs over China in 2020. By comparison with ground-based AERONET measurements, R2 for DNN-estimated AOD is up to 0.8702, which is much higher than that for the AHI AOD product with R2 = 0.4869. The hourly AOD results indicate that the DNN model has a good potential in improving the performance of AOD retrieval in the early morning and in the late afternoon, and the spatial distribution is reliable for capturing the variation of aerosol pollution on the regional scale. By analyzing different DNN modeling strategies, it is found that seasonal modeling can hardly increase the accuracy of AOD retrieval to a certain extent, and R2 increases from 0.7394 to 0.8168 when meteorological features, especially air pressure, are involved in the model training.

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Using MODIS derived aerosol optical depth to estimate ground-level PM2.5 concentrations over Turkey

Cited by 46 publications

References 63 publications

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Global Technology for Local Monitoring of Air Pollution in Dhaka

Himawari-8/AHI Aerosol Optical Depth Detection Based on Machine Learning Algorithm

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