Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China

Ma, Qingmiao; Li, Yingjie; Liu, Jane; Chen, Jing M.

doi:10.1109/jstars.2017.2650144

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Note that the climatological monthly AOD index is the highest in East Asia during AMJ (figure not shown). It was found that the AOD gradually increases until the mid-late 2000s, and then decreases until 2017, which is consistent with the findings of previous studies [51][52][53]. Therefore, a linear trend of AOD for 2000-2017 (-0.004/yr) was not statistically significant.…”

Section: Resultssupporting

confidence: 89%

The Lagged Effect of Anthropogenic Aerosol on East Asian Precipitation during the Summer Monsoon Season

Lee¹,

Jeong

Yeh

2020

Atmosphere

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The authors investigated the lagged effect of anthropogenic aerosols (AAs) during the premonsoon season (April–May–June) on the East Asian precipitation during the postmonsoon season (July–August) using the aerosol optical depth (AOD) from a satellite dataset and reanalysis datasets. When the AOD is high in Eastern China during the premonsoon season, the amount of precipitation increases in the western North Pacific, including the Korean Peninsula and Japan, during the postmonsoon season. The amount of cloud in the western-to-central North Pacific in the premonsoon season increases during the high-AOD period. Subsequently, it cools the sea surface temperature until the postmonsoon season, which strengthens the North Pacific High. The strengthened North Pacific High in the postmonsoon season expands to the western North Pacific, which leads to the enhancement of the moisture flows from the ocean. This results in the increase in precipitation in the western North Pacific, including the Korean Peninsula and Japan, during the postmonsoon season.

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Section: Resultssupporting

confidence: 89%

The Lagged Effect of Anthropogenic Aerosol on East Asian Precipitation during the Summer Monsoon Season

Lee¹,

Jeong

Yeh

2020

Atmosphere

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“…Generally, our RF model has a promising prediction accuracy. It is possible to use higher-spatialresolution materials to retrieve finer spatial patterns of PM 2.5 concentrations at regional and global scales, such as the MODIS Collection 6 aerosol products, which provide AOD retrievals with a spatial resolution of 3 km [57][58][59], and the multiangle implementation of the atmospheric correction algorithm (MAIAC), which infers the AOD retrievals at a spatial resolution of 1 km [60]. This strategy may also hold promise for multiyear applications, which could specifically provide further details regarding the spatial variation, to assess the acute and chronic epidemiological effects of PM 2.5 exposure, investigate the human exposure risk, and evaluate significant air quality events.…”

Section: Discussionmentioning

confidence: 99%

Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China

Chen

Zhang³

et al. 2018

Remote Sensing

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Exposure to fine particulate matter (PM 2.5 ) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM 2.5 concentrations. In this study, we developed a PM 2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM 2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing-Tianjin-Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 µg/m 3 ; Aqua: R = 0.85, RMSE = 33.90 µg/m 3 ). This study shows promise for predicting the spatiotemporal distribution of PM 2.5 using the RF model and Aqua aerosol product with the assistance of PM 2.5 site data.

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“…The AOD data used in this study were obtained from the Aqua MODIS data Level 2 Collection 6 (C6), with a spatial resolution of 3 km × 3 km, for the year 2015. The MODIS data were downloaded from NASA's Level 1 and Atmosphere Archive and Distribution System (LAADS) (available online: https://ladsweb.nascom.nasa.gov/data/search.html).Massive studies have been carried out to validate the accuracy of MODIS C6 aerosol product [21][22][23][24], which have proven that it is sufficiently accurate for use in a variety of applications [25]. The 3-km AOD data has also been widely utilized to estimate the air quality [26].…”

Section: Data Sourcesmentioning

confidence: 99%

Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity

Shi

Zhang

2018

Atmosphere

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A substantial number of studies have analyzed how driving factors impact aerosols, but they have been little concerned with the spatial heterogeneity of aerosols and the factors that impact aerosols. The spatial distributions of the aerosol optical depth (AOD) retrieved by Moderate Resolution Imaging Spectrometer (MODIS) data at 550-nm and 3-km resolution for three highly developed metropolises, the Beijing-Tianjin-Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), in China during 2015 were analyzed. Different degrees of spatial heterogeneity of the AOD were found, which were indexed by Moran's I index giving values of 0.940, 0.715, and 0.680 in BTH, YRD, and PRD, respectively. For the spatial heterogeneity, geographically weighted regression (GWR) was employed to carry out a spatial factor analysis, where terrain, climate condition, urban development, and vegetation coverage were taken as the potential driving factors. The results of the GWR imply varying relationships between the AOD and the factors. The results were generally consistent with existing studies, but the results suggest the following: (1) Elevation increase would more likely lead to a strong negative impact on aerosols (with most of the coefficients ranging from −1.5~0 in the BTH, −1.5~0 in the YRD, and −1~0 in the PRD) in the places with greater elevations where the R-squared values are always larger than 0.5. However, the variation of elevations cannot explain the variation of aerosols in the places with relatively low elevations (with R-squared values approximately 0.1, ranging from 0 to 0.3, and approximately 0.1 in the BTH, YRD, and PRD), such as urban areas in the BTH and YRD. (2) The density of the built-up areas made a strong and positive impact on aerosols in the urban areas of the BTH (R-squared larger than 0.5), while the R-squared dropped to 0.1 in the places far away from the urban areas. (3) The vegetation coverage led to a stronger relief on the AOD in parts of the YRD and PRD (with coefficients less than −0.6 and ranging from −0.4~−0.6, respectively) where there is greater vegetation coverage, and led to a weaker relief on the AOD in the urban area of the PRD with a coefficient of approximately −0.2~−0.4.Atmosphere 2018, 9, 156 2 of 14 images [2,5] and their variations can now be fully studied [6]. Moreover, a factor analysis for aerosols has been widely carried out. The Ordinary Least Square (OLS) based regression is usually used to identify the relationships between aerosols and their impact factors [7][8][9].However, OLS assumes that the relationship between the dependent and independent variables is consistent and cannot handle the spatial data with heterogeneity, which makes it difficult to meet the assumptions and requirements of OLS [10]. Heterogeneity here includes two aspects: spatial autocorrelation and spatially non-stationarity. Spatial autocorrelation means that the value of a variable at a location depends on the value of the same variable at nearby locations [11,12]. Spatial non-stati...

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Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China

Cited by 7 publications

References 29 publications

The Lagged Effect of Anthropogenic Aerosol on East Asian Precipitation during the Summer Monsoon Season

The Lagged Effect of Anthropogenic Aerosol on East Asian Precipitation during the Summer Monsoon Season

Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China

Spatial Factor Analysis for Aerosol Optical Depth in Metropolises in China with Regard to Spatial Heterogeneity

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