High‐Resolution Post‐Process Corrected Satellite AOD

Taskinen, Henri; Väisänen, Arttu; Hatakka, Lauri; Virtanen, Timo; Lähivaara, Timo; Arola, Antti; Kolehmainen, Ville; Lipponen, Antti

doi:10.1029/2022gl099733

Cited by 2 publications

(1 citation statement)

References 15 publications

(20 reference statements)

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“…The uncertainty of MODIS AOD over the land has been reported as ±(0.05 + AOD AERONET × 0.15) [68] and ±(0.05 + AOD AERONET × 0.20) [69]. In this study, MODIS AOD data were obtained from NASA's Level-1 and the Atmosphere Archive and Distribution System's Distributed Active Archive Center (LAADS DAAC) online archive [70][71][72][73].…”

Section: Aerosol Optical Depth (Aod) Indexmentioning

confidence: 99%

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

et al. 2023

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This study investigates the impact of the COVID-19 pandemic and the Ever Given ship blockage on the air quality in Suez Canal region. Nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and aerosol optical depth (AOD) were studied, and data were obtained from satellite instruments. The study compared monthly average data for 2020, 2021, and 2022 with a baseline period of 2017–2019 to investigate the pandemic’s effect. The study also analyzed the corresponding period of the canal blockage to identify its impact on air pollution levels. The pandemic had a significant role in decreasing NO2 by 2.5 × 1014 molecule/cm2 and SO2 by 0.05 DU due to reduced car traffic and industrial activities. A reduction in AOD by 20% and CO concentration in the range from 3.5% to 4.7% was reported in early 2020. During the blockage, NO2 and SO2 levels decreased by 14.4% and 66.0%, respectively, while CO and AOD index increased by 12.68% and 51.0%, respectively. The study concludes that the containment measures during the pandemic had a positive impact on the environment, which shows how the reduction in the anthropogenic activities, especially industrial and transportation activities, have improved the air quality. Thus, stricter actions are needed to protect the environment; for example, the transition towards the using of electric vehicle is necessary, which is part of Egypt’s strategy to transition towards a green economy. The government should also adopt a policy to trade carbon emissions reduction certificates to help reduce air pollution.

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Section: Aerosol Optical Depth (Aod) Indexmentioning

confidence: 99%

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

et al. 2023

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Post-process correction improves the accuracy of satellite PM_2.5 retrievals

Porcheddu,

Kolehmainen,

Lähivaara

et al. 2024

Atmos. Meas. Tech.

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Abstract. Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of aerosol optical depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD-to-PM2.5 conversion, we employ the machine-learning-based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process-correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high-resolution geographical indicators, meteorological data, and ground station observations for learning a predictor for the approximation error in the AOD-to-PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. The region of study is central Europe during the year 2019. Our model produces PM2.5 estimates with a spatial resolution of 100 m at satellite overpass times with R2 = 0.55 and RMSE = 6.2 µg m−3. The corresponding metrics for monthly averages are R2 = 0.72 and RMSE = 3.7 µg m−3. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine-learning-related uncertainty in the PM2.5 estimates. The proposed approach can produce accurate high-resolution PM2.5 data that can be very useful for air quality monitoring, emission regulation, and epidemiological studies.

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High‐Resolution Post‐Process Corrected Satellite AOD

Cited by 2 publications

References 15 publications

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

Post-process correction improves the accuracy of satellite PM_2.5 retrievals

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High‐Resolution Post‐Process Corrected Satellite AOD

Cited by 2 publications

References 15 publications

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

Remote Sensing Surveillance of NO2, SO2, CO, and AOD along the Suez Canal Pre- and Post-COVID-19 Lockdown Periods and during the Blockage

Post-process correction improves the accuracy of satellite PM2.5 retrievals

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Post-process correction improves the accuracy of satellite PM_2.5 retrievals