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Journal ArticleDOI

Estimating spatial variability of ground-level PM2.5 based on a satellite-derived aerosol optical depth product: Fuzhou, China

Lijuan Yang, +2 more
- 01 Nov 2018 - 
- Vol. 9, Iss: 6, pp 1194-1203
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TLDR
Li et al. as mentioned in this paper developed a linear mixed effects model that integrates aerosol optical depth (AOD) measurements from MODIS and meteorological data from GEOS-FP meteorological fields as predictors to derive daily estimations of ground-level PM2.5 concentrations in Fuzhou (SE China).
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This article is published in Atmospheric Pollution Research.The article was published on 2018-11-01. It has received 12 citations till now. The article focuses on the topics: Spatial variability & Mixed model.

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Citations
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Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model

TL;DR: Li et al. as mentioned in this paper used the newly released MODIS aerosol optical depth (AOD) with higher resolution of 3.5 km incorporating meteorological fields from Goddard Earth Observing System-Forward Processing (GEOS-FP) and road density for ground-level PM2.5 estimation.
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Satellite AOD conversion into ground PM10, PM2.5 and PM1 over the Po valley (Milan, Italy) exploiting information on aerosol vertical profiles, chemistry, hygroscopicity and meteorology

TL;DR: In this article, a general methodological approach to overcome the aforementioned inconsistencies obtaining in any region the best retrieval was developed to select the best approach for the AOD to PM conversion starting from the knowledge of PM properties, meteorology and vertical behaviour.
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The polarization crossfire (PCF) sensor suite focusing on satellite remote sensing of fine particulate matter PM2.5 from space

TL;DR: In this article , a polarization crossfire (PCF) strategy was developed for satellite remote sensing of fine particulate matter PM2.5 from space, which includes the Particulate Observing Scanning Polarimeter (POSP) and the Directional Polarimetric Camera (DPC) together.
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Data Mining Paradigm in the Study of Air Quality

TL;DR: This study presents a systematic review of the literature from 2014 to 2018 on the use of data mining in the analysis of air pollutant measurements and recovers graphic design, air quality index development, heat mapping, and geographic information systems to provide inputs for air quality planning and management.

On the nature and origins of visibility-reducing aerosols in the Los Angeles air basin

TL;DR: In this article, the authors present the measurements of the light-scattering coefficient and chemical composition of ambient aerosols during smog periods in the Los Angeles air basin, which can estimate the contributions of reactive gases to reduced visibility.
References
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Journal ArticleDOI

The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol

TL;DR: A new technique for remote sensing of aerosol over the land and for atmospheric correction of Earth imagery is developed, based on detection of dark surface targets in the blue and red channels, but uses the 2.1-/spl mu/m channel, instead of the 3.75-/Spl mu/M channel, for their detection.
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Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality

TL;DR: In this article, the authors compared true color images and quantitative aerosol optical depth data from the MODIS sensor on the Terra satellite with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 April to 30 September, 2002.
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Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect

TL;DR: In this article, the authors summarized the science behind this change in remote sensing, and the sensitivity studies and applications of the new algorithms to data from present satellite and aircraft instruments, and concluded that the anticipated remote sensing of aerosol simultaneously from several space platforms with different observation strategies, together with continuous validations around the world, is expected to be of significant importance to test remote sensing approaches to characterize the complex and highly variable aerosol field.
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Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

TL;DR: A national-scale geographically weighted regression model was developed to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor and confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM 2.5 monitoring network in China.
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