scholarly journals Evaluation of MODIS-Retrieved Aerosol Optical Depth in Alaska: Implications for Surface Air Quality Applications

2018 ◽  
Author(s):  
Alyson McPhetres ◽  
Srijan Aggarwal
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
National Parks ◽  
Urban Areas ◽  
Ground Level ◽  
Modis Data ◽  
Particulate Pollution ◽  
Modis Aod ◽  
Pollution Concentrations

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks leaving a large proportion of the state unmonitored. The use of MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world, and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from AERONET ground-based sunphotometers in Barrow and Bonanza Creek to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it could only be validated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10 km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. On the other hand, the individual Terra and Aqua MODIS collection 6 AOD products at 10 km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3 km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

scholarly journals An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska

2018 ◽  
Vol 10 (9) ◽  
pp. 1384 ◽  
Author(s):  
Alyson McPhetres ◽  
Srijan Aggarwal
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
National Parks ◽  
Urban Areas ◽  
Ground Level ◽  
Modis Data ◽  
Particulate Pollution ◽  
Modis Aod ◽  
Pollution Concentrations

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks, leaving a large proportion of the state unmonitored. The use of Moderate Resolution Imaging Spectroradiometer MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from two ground-based Aerosol Robotic Network (AERONET) sunphotometers in Alaska, located at Utqiagvik (previously known as Barrow) and Bonanza Creek, to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from years 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it was only evaluated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10-km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. Note that the individual Terra and Aqua MODIS collection 6 AOD products at 10-km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3-km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months.

scholarly journals Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

2021 ◽  
Vol 13 (16) ◽  
pp. 3102
Author(s):  
Johana M. Carmona ◽  
Pawan Gupta ◽  
Diego F. Lozano-García ◽  
Ana Y. Vanoye ◽  
Iván Y. Hernández-Paniagua ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Urban Areas ◽  
Model Performance ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Surface Data ◽  
Learning Techniques ◽  
Spatial Domains

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

scholarly journals Improving PM<sub>2.5</sub> retrievals in the San Joaquin Valley using A-Train Multi-Satellite Observations

2011 ◽  
Vol 11 (11) ◽  
pp. 30563-30598 ◽  
Author(s):  
A. W. Strawa ◽  
R. B. Chatfield ◽  
M. Legg ◽  
B. Scarnato ◽  
R. Esswein
Keyword(s):  
Linear Regression ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Ground Level ◽  
San Joaquin Valley ◽  
Satellite Observations ◽  
Target Area ◽  
Simple Linear Regression ◽  
Data Set ◽  
Modis Aod

Abstract. This paper demonstrates the use of a combination of multi-platform satellite observations and statistical data analysis to dramatically improve the correlation between satellite observed aerosol optical depth (AOD) and ground-level retrieved PM2.5. The target area is California's San Joaquin Valley which has a history of poor particulate air quality and where such correlations have not yielded good results. We have used MODIS AOD, OMI AOD, AAOD (absorption aerosol optical depth) and NO2 concentration, and a seasonal parameter in a generalized additive model (GAM) to improve retrieved/observed PM2.5 correlations (r2 at six individual sites and for a data set combining all sites. For the combined data set using the GAM, r2 improved to 0.69 compared with an r2 of 0.27 for a simple linear regression of MODIS AOD to surface PM. Parameter sensitivities and the effect of multi-platform data on the sample size are discussed. Particularly noteworthy is the fact that the PM retrieved using the GAM captures many of the PM exceedences that were not seen in the simple linear regression model.

scholarly journals An empirical relationship between PM2.5 and aerosol optical depth from MODIS satellite images for spatial simulation over Ho Chi Minh city

2021 ◽  
Vol 63 (4) ◽  
pp. 72-78
Author(s):  
Vo Quoc Bao ◽  
◽  
Tran Thi Van ◽  
◽  
◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particles ◽  
Empirical Relationship ◽  
Green Light ◽  
Monitoring Network ◽  
Ambient Air ◽  
Ho Chi Minh City ◽  
Modis Aod

Air quality in megacities has been a pressing concern of environmental managers and scientists for decades. Indeed, particulate matter (PM), especially PM2.5, is considered a dangerousparticle that is harmful to human health. The current sparse monitoring network in Ho Chi Minh city (HCMC) does not accurately reflect the spatial distribution of fine particles in ambient air. Therefore, this research examines the relationship between ground-based station data and aerosol optical depth (AOD) imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra/Aqua satellite to establish a PM2.5 distribution map of HCMC. PM2.5 concentration values monitored from two ground stations were collocated by time and space with Terra/MODIS AOD data from the period of 2016-2020. Pairs of values were checked for correlation and then fit to several regression functions. The most suitable function was chosen to simulate the quantified PM2.5distributions in the study area. A high correlation between PM2.5 concentrations and AOD at the wavelength of green light (R2=0.810) was found with a linear regression model. The results showed that the highest concentration of PM2.5 was in February, and the mean value was higher than QCVN 05:2013 (32.5 μg/m3compared with 25 μg/m3, annual mean). These results support the need for essential air quality monitoring in HCMC.

scholarly journals A Study of Air Quality in the Southeastern Hampton–Norfolk–Virginia Beach Region with Airborne Lidar Measurements and MODIS Aerosol Optical Depth Retrievals

2010 ◽  
Vol 49 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Jasper Lewis ◽  
Russell De Young ◽  
D. Allen Chu
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Airborne Lidar ◽  
Eastern United States ◽  
Modis Aod ◽  
Virginia Beach ◽  
Lidar Measurements ◽  
Langley Research Center ◽  
Linear Regressions

Abstract A study of air quality was performed using a compact, aircraft aerosol lidar designed in the Science Directorate at NASA Langley Research Center and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals. Five flights of lidar measurements conducted in the Hampton–Norfolk–Virginia Beach, Virginia, region showed complex regional aerosol distributions. Comparisons with MODIS AOD at 10 km × 10 km and 5 km × 5 km resolutions show good agreement, with correlation R2 values of 0.82 and 0.88, respectively. Linear regressions of particulate matter with a diameter of less than 2.5 μm (PM2.5) and AOD within the ranges of 5–40 μg m−3 and 0.05–0.7, respectively, result in R2 values of ∼0.64 and ∼0.82 for MODIS and the Compact Aerosol Lidar, respectively. The linear regressions reflect approximately 51 μg m−3 to 1 AOD. These relationships are in agreement with previous findings for air pollution aerosols in the eastern United States and in northern Italy. However, large vertical variation is seen case by case, with planetary boundary layer heights ranging between 0.7 and 2 km and uncertainties ranging between 0.1 and 0.4 km. The results of the case studies suggest that AOD can be used as an indicator of surface measurements of PM2.5 but with larger uncertainties associated with small aerosol loading (AOD &lt; 0.3).

scholarly journals Relationship Between MODIS-based Aerosol Optical Depth and PM10 over Sumatra to Overcome the Limitations of Air Quality Monitoring Data Availability

2018 ◽  
Vol 34 (4) ◽  
pp. 2163-2169 ◽  
Author(s):  
Syafrijon Syafrijon ◽  
Marzuki Marzuki ◽  
Emriadi Emriadi ◽  
Ridho Pratama
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Forest Fire ◽  
Correlation Coefficients ◽  
Data Availability ◽  
Near Surface ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The present study uses the aerosol optical depth (AOD) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite as a proxy to estimate the surface particulate matter (PM) concentrations over Sumatra. The daily average PM10 data collected during 2015 from three air quality stations across Sumatra, i.e., Kototabang, Jambi and Pekanbaru, were analyzed. The 2015 Indonesian forest fire significantly increased the PM10 concentrations and MODIS AOD values. The ratios of the mean PM10 concentrations and AOD values during the peak forest fire period to those during the period of normal conditions varied from 6 to 9. MODIS AOD may be a good indicator of the near-surface PM10 concentrations over Sumatra, as the correlation coefficients of the linear regressions were 0.86 (Kototabang), 0.80 (Jambi), and 0.81 (Pekanbaru). The linear regression functions of PM10 and satellite-observed AOD can be used to estimate the surface PM10 concentrations, and the correlation coefficient is 0.84.

scholarly journals USING MODIS AEROSOL OPTICAL DEPTH TO PREDICT PM10 OVER AL AIN REGION, UAE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 419-423
Author(s):  
N. Saleous ◽  
S. Issa ◽  
M. Alsuwaidi
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Remote Sensing Data ◽  
Regression Equation ◽  
Continuous Coverage ◽  
Al Ain ◽  
Modis Aod ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

Abstract. PM10 concentrations are essential for assessing air quality in arid areas. They are usually measured at air quality monitoring stations. The limited number of monitoring stations can make difficult to study significantly the spatial variability of PM10 over relatively large areas. This study aimed at evaluating the use of Aerosol Optical Depth derived from satellite data to estimate PM10 concentrations. The continuous coverage offered by remote sensing data helps to address the limitation encountered with the spatial distribution of relevant monitoring stations. In the current study we compared MODIS AOD at 550 nm included in MCD19A2 and we established a regression equation between AOD and PM10. The use of daily AOD at 1 km resolution helped establish regression with acceptable correlation coefficient. The regression equation is then used to create daily maps of estimated PM10 concentrations over the study area and helped assessing their variability.

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