Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in north western Indian Himalaya

Guleria, Raj Paul; Kuniyal, Jagdish Chandra; Rawat, P. S.; Thakur, Harinder Kumar; Sharma, Manum; Sharma, Nand Lal; Dhyani, P. P.; Singh, Mahavir

doi:10.1080/01431161.2012.657374

Cited by 30 publications

(22 citation statements)

References 70 publications

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“…Aerosol Optical Depth is recognized as the accumulated attenuation factor over a perpendicular column of unit cross section [44]. The Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products are well known in air pollution studies.…”

Section: Aerosol Optical Depth (Aod) Datamentioning

confidence: 99%

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

et al. 2019

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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

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Section: Aerosol Optical Depth (Aod) Datamentioning

confidence: 99%

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

et al. 2019

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“…Studies by Guleria et al . (, ) and Gautam et al . () showed the capability of satellites in characterizing AOD distribution over locations having a typical topography.…”

Section: Introductionmentioning

confidence: 99%

Spatial variations and trends in AOD climatology over East Africa during 2002–2016: a comparative study using three satellite data sets

Boiyo

Kumar

Zhao

2018

Intl Journal of Climatology

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Aerosol optical depth (AOD) has become one of the most crucial parameters for climate change assessment. This study presents long‐term (2002–2016) spatio‐temporal distributions and trends in AOD over East Africa (EA) retrieved from the moderate‐resolution imaging spectroradiometer (MODIS) Aqua [Dark Target (DT) and Deep Blue (DB)] and multi‐angle imaging spectroradiometer (MISR). An inter‐comparison of AODs retrieved from different algorithms noticed significant positive correlations (r = 0.72 − 0.87) with MISR underestimating MODIS. Moderate (>0.5–0.8) to high (≥0.8) correlations in AOD exhibited over EA, with a few regions representing low (0–0.5) positive correlations. The spatial patterns of annual mean AOD were generally characterized by low (<0.2), moderate (0.2–0.35) and high (>0.35) centres over EA. The seasonal mean AODs over EA were found high (low) during the local dry (wet) seasons, with annual mean (±σ) values of 0.20 ± 0.01, 0.18 ± 0.01 and 0.20 ± 0.02 as observed by DT, DB and MISR, respectively. A single peak distribution of frequencies in AOD was observed by the three sensors in the interval 0.1–0.2, signifying a generally less polluted environment dominated by particular aerosol type. Linear trend analysis revealed an increase in AOD by 0.52, 0.57 and 0.74% year−1 as detected by MISR, DT and DB, respectively, and were consistent with those noted in key meteorological parameters. Furthermore, annual and seasonal spatial trends and tendencies revealed a general increase in AOD over EA, being positive and significant over the northern part of EA. Later, classification of major aerosol types over major cities in EA revealed dominance of continental (74.47%) followed by the mixed (16.22%) and biomass‐burning/urban‐industrial (8.02%) aerosols, with minor contributions from desert dust (1.03%) and clean maritime (0.32%) type of aerosols.

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“…Nowadays, aerosol properties and distributions are investigated using ground-based sensors and satellitebased sensors and by conducting field-based campaigns (Holben, 1998;Kaufman et al, 1998;Remer et al, 2005;Guleria et al, 2012aGuleria et al, , 2012bWang et al, 2017). Ground-based measurements are generally high in precision and temporal resolution while low in spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

Spatial‐temporal characteristics of aerosol loading over the Yangtze River Basin during 2001–2015

Ming

Wang

et al. 2017

Intl Journal of Climatology

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The spatial‐temporal characteristics of aerosol loading over the Yangtze River Basin, China during 2001–2015 were investigated using moderate resolution imaging spectroradiometer (MODIS), multi‐angle imaging spectroradiometer (MISR), and ground‐level particulate matter (PM) data. Aerosol optical depth (AOD) >0.8 occurs in the Yangtze River Delta, central China, and the Sichuan Basin, while AOD <0.3 occurs over higher‐elevation areas in the western Sichuan Plateau and the source regions of the Yangtze River. The western Sichuan Plateau is characterized by fine‐mode natural aerosols, while the source region of the Yangtze River is more influenced by dust aerosols. The Sichuan Basin, central China, and the Yangtze River Delta are dominated by anthropogenic aerosols. The spatial distribution of ground‐level PM2.5 and its ratio to PM10 confirm the high aerosol loading and dominance of small particles over the middle and lower Yangtze Basin. Over the Yangtze River Delta, central China, and the Sichuan Basin, AOD varies seasonally from low in the cold months to high in the warm months, being suppressed by rainfall and wind during the Asian summer monsoon. Precipitation increases aerosol loading in the western Sichuan Plateau but reduces dust particles in the source region of the Yangtze River. There is no significant AOD trend over most areas of the Yangtze River Basin during 2001–2015, while strong decreasing trends are found over most of the middle and lower Yangtze Basin during 2011–2015. These decreasing trends may relate to changes in annual precipitation, wind speed, and air‐pollution control policies. The NO2 and SO2 emissions decreased by 16.51 and 23.40% over major provinces and cities of the Yangtze River Basin from 2011 to 2015. An increase in rainfall over the middle and lower Yangtze Basin and a better pollutant diffusion condition in the Sichuan Basin also favour the decreasing AOD during this period.

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Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in north western Indian Himalaya

Cited by 30 publications

References 70 publications

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

Spatial variations and trends in AOD climatology over East Africa during 2002–2016: a comparative study using three satellite data sets

Spatial‐temporal characteristics of aerosol loading over the Yangtze River Basin during 2001–2015

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