scholarly journals The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions

2017 ◽  
Vol 122 (16) ◽  
pp. 8801-8815 ◽  
Author(s):  
Yerong Wu ◽  
Martin de Graaf ◽  
Massimo Menenti
Keyword(s):  
Vertical Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Modis Data ◽  
The Impact

scholarly journals Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

2020 ◽  
Vol 12 (9) ◽  
pp. 1524 ◽  
Author(s):  
Chong Li ◽  
Jing Li ◽  
Oleg Dubovik ◽  
Zhao-Cheng Zeng ◽  
Yuk L. Yung
Keyword(s):  
Vertical Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Vertical Profile ◽  
Retrieval Algorithm ◽  
Retrieval Error ◽  
Satellite Sensors ◽  
Absorbing Aerosols ◽  
The Impact ◽  
The Vertical Distribution

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.

The Impact of Dust Storms on Aerosol Optical Depth and Radiative Forcing

2020 ◽  
Vol 16 (1) ◽  
pp. 1-14
Author(s):  
Monim Jiboori ◽  
Nadia Abed ◽  
Mohamed Abdel Wahab
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Dust Storms ◽  
The Impact

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.

The impact of Sahara dust on air quality and public health in European countries

2021 ◽  
Author(s):  
Qiaoqiao Wang ◽  
Jianwei Gu ◽  
Xurong Wang
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Health Effect ◽  
Population Attributable Fraction ◽  
European Countries ◽  
Dust Exposure ◽  
The Impact ◽  
Sahara Dust

<p>The frequent transport of Sahara dust toward Europe degrades the air quality and poses risk to human health. In this study we use GEOS-Chem (a global transport model) to examine the impact of Sahara dust on air quality and the consequent health effect in Europe for the year 2016–2017. The simualtion is conducted in a nested model with the native resolution of 0.25° × 0.3125° (Latitude × Logitude) over Europe (32.75°N–61.25°N, 15°W–40°E). The simulation on a global scale with a coarse horizontal resolution of 2° × 2.5° is also conducted to provide the boundary condition for the nested-grid simulation as well as aerosol optical depth (AOD) over the Sahara desert for model evaluation.</p><p>The model performance is evaluated by comparisons with surface observations including aerosol optical depth (AOD) from AERONET, and PM<sub>2.5</sub> and PM<sub>10</sub> concentrations from numerous air quality monitoring stations in European countries. Overall, the model well reproduces observed surface PM concentrations over most European countries with some underestimation in southern Europe. In addition, model AOD is highly correlated with AERONET data over both Sahara and European region.</p><p>The spatial distribution of dust concentrations, frequency of dust episodes, as well as the exposure and health effects are studied. The concentrations of Sahara dust decrease from 5–20 μg m<sup>-3</sup> in south to 0.5–1.0 μg m<sup>-3</sup> in north of Europe. Spain and Italy are most heavily influenced by Sahara dust in terms of both concentration levels and frequencies of occurrence. Strong dust episodes (>50 μg m<sup>-3</sup>) occur predominately in Southern Spain and Italy with frequency of 2–5%, while light dust episodes (>1 μg m<sup>-3</sup>) are often detected (5–30%) in Central and Western Europe.</p><p>The population-weighted dust concentrations are higher in Southern European countries (3.3–7.9 μg m<sup>-3</sup>) and lower in Western European countries (0.5–0.6 μg m<sup>-3</sup>). The health effects of exposure to dust is evaluated based on population attributable fraction (PAF). We use the relative risk (RR) value of 1.04 (95% confidence intervals: 1.00 – 1.09) per 10 µg m<sup>-3 </sup>of dust exposure based on the main model of Beelen et al. (2014). We estimate a total of 41884 (95% CI: 2110–81658) deaths per year attributed to the exposure to dust in the 13 European countries studied. Due to high contribution to PM<sub>10</sub> in Spain, Italy and Portugal, dust accounts for 44%, 27% and 22% of the total number of deaths linked to PM<sub>10</sub> exposure, respectively.</p>

scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

scholarly journals A Multi Linear Regression Model to Derive Dust PM10 in the Sahel Using AERONET Aerosol Optical Depth and CALIOP Aerosol Layer Products

2020 ◽  
Vol 12 (18) ◽  
pp. 3099
Author(s):  
Jean-François Léon ◽  
Nadège Martiny ◽  
Sébastien Merlet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Linear Regression Model ◽  
Population Exposure ◽  
Aerosol Layer ◽  
Multi Linear Regression ◽  
Sun Photometer ◽  
The Impact

Due to a limited number of monitoring stations in Western Africa, the impact of mineral dust on PM10 surface concentrations is still poorly known. We propose a new method to retrieve PM10 dust surface concentrations from sun photometer aerosol optical depth (AOD) and CALIPSO/CALIOP Level 2 aerosol layer products. The method is based on a multi linear regression model that is trained using co-located PM10, AERONET and CALIOP observations at 3 different locations in the Sahel. In addition to the sun photometer AOD, the regression model uses the CALIOP-derived base and top altitude of the lowermost dust layer, its AOD, the columnar total and columnar dust AOD. Due to the low revisit period of the CALIPSO satellite, the monthly mean annual cycles of the parameters are used as predictor variables rather than instantaneous observations. The regression model improves the correlation coefficient between monthly mean PM10 and AOD from 0.15 (AERONET AOD only) to 0.75 (AERONET AOD and CALIOP parameters). The respective high and low PM10 concentration during the winter dry season and summer season are well produced. Days with surface PM10 above 100 μg/m3 are better identified when using the CALIOP parameters in the multi linear regression model. The number of true positives (actual and predicted concentrations above the threshold) is increased and leads to an improvement in the classification sensitivity (recall) by a factor 1.8. Our methodology can be extrapolated to the whole Sahel area provided that satellite derived AOD maps are used in order to create a new dataset on population exposure to dust events in this area.

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