scholarly journals Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe

2021 ◽  
Vol 13 (15) ◽  
pp. 3027
Author(s):  
Saleem Ibrahim ◽  
Martin Landa ◽  
Ondřej Pešek ◽  
Karel Pavelka ◽  
Lena Halounova
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Missing Values ◽  
Space Time ◽  
Spatial Coverage ◽  
The Mean ◽  
Prediction Approach

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.

scholarly journals Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

2021 ◽  
Vol 13 (20) ◽  
pp. 4140
Author(s):  
Hao Lin ◽  
Siwei Li ◽  
Jia Xing ◽  
Jie Yang ◽  
Qingxin Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Beijing City ◽  
Spatial Coverage ◽  
Sentinel 2

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

scholarly journals Spatio-Temporal Variability of Aerosol Components, Their Optical and Microphysical Properties over North China during Winter Haze in 2012, as Derived from POLDER/PARASOL Satellite Observations

2021 ◽  
Vol 13 (14) ◽  
pp. 2682
Author(s):  
Yang Ou ◽  
Lei Li ◽  
Zhengqiang Li ◽  
Ying Zhang ◽  
Oleg Dubovik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mass Concentration ◽  
North China ◽  
Satellite Observations ◽  
Fine Mode ◽  
The Mean ◽  
Fine Mode Aerosol

Pollution haze is a frequent phenomenon in the North China Plain (NCP) appearing during winter when the aerosol is affected by various pollutant sources and has complex distribution of the aerosol properties, while different aerosol components may have various critical effects on air quality, human health and radiative balance. Therefore, large-scale and accurate aerosol components characterization is urgently and highly desirable but hardly achievable at the regional scale. In this respect, directional and polarimetric remote sensing observations have great potential for providing information about the aerosol components. In this study, a state-of-the-art GRASP/Component approach was employed for attempting to characterize aerosol components in the NCP using POLDER/PARASOL satellite observations. The analysis was done for January 2012 in Beijing (BJ) and Shanxi (SX). The results indicate a peak of the BC mass concentration in an atmospheric column of 82.8 mg/m2 in the SX region, with a mean of 29.2 mg/m2 that is about four times higher than one in BJ (8.9 mg/m2). The mean BrC mass concentrations are, however, higher in BJ (up to ca. 271 mg/m2) than that in SX, which can be attributed to a higher anthropogenic emission. The mean amount of fine ammonium sulfate-like particles observed in the BJ region was three times lower than in SX (131 mg/m2). The study also analyzes meteorological and air quality data for characterizing the pollution event in BJ. During the haze episode, the results suggest a rapid increase in the fine mode aerosol volume concentration associated with a decrease of a scale height of aerosol down to 1500 m. As expected, the values of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD) and fine mode aerosol optical depth (AODf) are much higher on hazy days. The mass fraction of ammonium sulfate-like aerosol increases from about 13% to 29% and mass concentration increases from 300 mg/m2 to 500 mg/m2. The daily mean PM2.5 concentration and RH independently measured during these reported pollution episodes reach up to 425 g/m3 and 80% correspondingly. The monthly mean mass concentrations of other aerosol components in the BJ are found to be in agreement with the results of previous research works. Finally, a preliminary comparison of these remote sensing derived results with literature and in situ PM2.5 measurements is also presented.

scholarly journals VALIDATION AND COMPARISON OF FINE-MODE AEROSOL OPTICAL DEPTH PRODUCTS BETWEEN MODIS AND POLDER

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 51-56
Author(s):  
B. Y. Ge ◽  
Z. Q. Li ◽  
W. Z. Hou ◽  
Y. Zhang ◽  
K. T. Li
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Large Population ◽  
Scale Up ◽  
Global Scale ◽  
Small Population ◽  
Spatial Coverage ◽  
Fine Mode ◽  
Fine Mode Aerosol

Abstract. Fine-mode aerosol usually comes from anthropogenic emissions. The fine-mode aerosol optical depth (AODf) is an important parameter for estimating the particulate matter with an aerodynamic diameter little than 2.5 μm (PM2.5). Compared to the ground-based observations, AODf products from satellite remote sensing have an advantage of high spatial coverage, which is suitable for monitoring the air quality at a regional or global scale. Up to now, AODf products have been released by several sensors, such as the single-angle multi-spectral intensity sensor MODIS and multi-angle multi-spectral polarization sensor POLDER, then what’re the different performances of AODf products from them? In this study, the different spatial resolution AODf products respectively from MODIS latest Collection 6.1 (C6.1, 3 and 10 km) and POLDER latest level 2 version 1.01 (L2, 18 km) were firstly compared with each other in Beijing-Tianjin-Hebei (BTH) domains. Then those products were validated against the ground-based AERosol RObotic NETwork (AERONET) measurements, where has been suffering the severe air pollution since decades ago. The comparison of yearly averaged AODf products between MODIS and POLDER shows a good consistency on the spatial distribution, the higher spatial resolution products of MODIS show more details, both low values of AODf appeared in the northwest area with small population and industry, high values appeared in the southeast area with lots of cities, industries, and large population. However, the whole yearly AODf average values of MODIS are higher than that of POLDER. The results of validation against AERONET show that the accuracy of AODf products at 865 nm from POLDER (R = 0.94, RMSE = 0.05) is high than that at 550 nm of MODIS (3 km: R = 0.69, RMSE = 0.32; 10 km: R = 0.76, RMSE = 0.3). In this study, the performance of different spatial resolutions AODf products retrieved from the intensity (MODIS 3 and 10 km) and polarized sensors (POLDER 18 km) were evaluated. Those results not only have a great significance to provide users amore appropriate choice of the AODf products in the BTH region but also display that the accuracy and spatial resolution of MODIS and POLDER AODf products need to be improved.

scholarly journals Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling

2012 ◽  
Vol 12 (9) ◽  
pp. 23219-23260 ◽  
Author(s):  
J. A. Ruiz-Arias ◽  
J. Dudhia ◽  
C. A. Gueymard ◽  
D. Pozo-Vázquez
Keyword(s):  
Solar Radiation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Surface Solar Radiation ◽  
Numerical Weather ◽  
Level 3 ◽  
Weather Modeling ◽  
The Mean ◽  
Level 2

Abstract. The Level-3 MODIS aerosol optical depth (AOD) product offers interesting features for surface solar radiation and numerical weather modeling applications. Remarkably, the Collection 5.1 dataset extends over more than a decade, and provides daily values of AOD over a global regular grid of 1°×1° spatial resolution. However, most of the validation efforts so far have focused on Level-2 products (10-km, at original resolution) and only rarely on Level-3 (at aggregated spatial resolution of 1°×1°). In this contribution, we compare the Level-3 Collection 5.1 MODIS AOD dataset available since 2000 against observed daily AOD values at 550 nm from more than 500 AERONET ground stations around the globe. One aim of this study is to check the advisability of this MODIS dataset for surface shortwave solar radiation calculations using numerical weather models. Overall, the mean error of the dataset is 0.03 (17%, relative to the mean ground-observed AOD), with a root mean square error of 0.14 (73%, relative to the same), albeit these values are found highly dependent on geographical region. For AOD values below about 0.3 the expected error is found very similar to that of the Level-2 product. However, for larger AOD values, higher errors are found. Consequently, we propose new functions for the expected error of the Level-3 AOD, as well as for both its mean error and its standard deviation. Additionally, we investigate the role of pixel count vis-à-vis the reliability of the AOD estimates. Our results show that a higher pixel count does not necessarily turn into a more reliable AOD estimate. Therefore, we recommend to verify this assumption in the dataset at hand if the pixel count is meant to be used. We also explore to what extent the spatial aggregation from Level-2 to Level-3 influences the total uncertainty in the Level-3 AOD. In particular, we found that, roughly, half of the error might be attributable to Level-3 AOD sub-pixel variability. Finally, we use a~radiative transfer model to investigate how the Level-3 AOD uncertainty propagates into the calculated direct normal (DNI) and global horizontal (GHI) irradiances. Overall, results indicate that, for Level-3 AODs smaller than 0.5, the induced uncertainty in DNI due to the AOD uncertainty alone is below 15% on average, and below 5% for GHI (for a solar zenith angle of 30°. However, the uncertainty in AOD is highly spatially variable, and so is that in irradiance.

scholarly journals Deep Neural Networks for Aerosol Optical Depth Retrieval

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 101
Author(s):  
Renee Zbizika ◽  
Paulina Pakszys ◽  
Tymon Zielinski
Keyword(s):  
Machine Learning ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Absolute Error ◽  
Spatial Coverage ◽  
Minimal Data ◽  
Time Of Year ◽  
Validation Set ◽  
Viable Method

Aerosol Optical Depth (AOD) is a measure of the extinction of solar radiation by aerosols in the atmosphere. Understanding the variations of global AOD is necessary for precisely determining the role of aerosols. Arctic warming is partially caused by aerosols transported from vast distances, including those released during biomass burning events (BBEs). However, measuring AODs is challenging, typically requiring active LIDAR systems or passive sun photometers. Both are limited to cloud-free conditions; sun photometers provide only point measurements, thus requiring more spatial coverage. A more viable method to obtain accurate AOD may be found through machine learning. This study uses DNNs to estimate Svalbard’s AODs using a minimal set of meteorological parameters (temperature, air mass, water vapor, wind speed, latitude, longitude, and time of year). The mean absolute error (MAE) between predicted and true data was 0.00401 for the entire set and 0.0079 for the validation set. It was then shown that the inclusion of BBE data improves predictions by 42.167%. It was demonstrated that AODs may be accurately estimated without the use of expensive instrumentation, using machine learning and minimal data. Similar models may be developed for other regions, allowing immediate improvement of current meteorological models.

scholarly journals Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning

2021 ◽  
Vol 18 (3) ◽  
pp. 1132
Author(s):  
Qijiao Xie ◽  
Qi Sun
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Urban Planning ◽  
Land Cover ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Water Bodies ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
The Impact

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

scholarly journals Airborne observation during KORUS-AQ show aerosol optical depth are more spatially self-consistent than aerosol intensive properties

2022 ◽  
Author(s):  
Samuel E. LeBlanc ◽  
Michal Segal-Rozenhaimer ◽  
Jens Redemann ◽  
Connor J. Flynn ◽  
Roy R. Johnson ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Variability ◽  
Spatial Scales ◽  
Aerosol Emission ◽  
Fine Mode ◽  
Predominant Factor ◽  
Spatio Temporal ◽  
Microphysical Processes

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (Aerosol Optical Depth), and aerosol size (using Angstrom Exponent; AE, and Fine-Mode-Fraction; FMF) over Korea during the 2016 KORUS-AQ (KORea-US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8, geostationary satellite (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval (YAER) version 2) and reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 500 m, show an average AOD at 501 nm of 0.43 and an average AE of 1.15 with large standard deviation (0.32 and 0.26 for AOD and AE respectively) likely due to mixing of different aerosol types (fine and coarse mode). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories matches the south-Korean regional average derived from GOCI. We also observed that, contrary to prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and model for the entire KORUS-AQ period, a reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial-temporal homogeneity is the meteorological period. High spatio-temporal variability occur during the dynamic period (25–31 May), and low spatio-temporal variability occur during blocking Rex pattern (01–07 June). The changes in spatial variability scales between AOD and FMF/AE, while inter-related, indicate that microphysical processes that impact mostly the dominant aerosol size, like aerosol particle formation, growth, and coagulation, vary at shorter scales than the aerosol concentration processes that mostly impact AOD, like aerosol emission, transport, and removal.

Sign in / Sign up

Export Citation Format

Share Document