Quantitative mapping of air pollution density using Earth observations: a new processing method and application to an urban area

Sifakis, N.

doi:10.1080/014311698213975

Cited by 87 publications

(44 citation statements)

References 11 publications

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“…Visible spectral bands of various remote sensing satellites have been exploited to estimate AOD over both water and land surfaces (e.g. Holben et al 1992, Sifakis et al 1998, King et al 1999, Christopher et al 2000, Chu et al 2003, Wang and Christopher 2003, Chu et al 2005, Petrakis et al 2005. In essence, AOD (τ) at a given site (s) is the log of the ratio of irradiance at the top of the atmosphere (I o ) to irradiance at the same location at the surface (I s ) (Hapke 1993).…”

Section: Aerosol Estimation-aerosolsmentioning

confidence: 99%

“…Recent literature suggests that the ground measurements of PM 2.5 and PM 10 correlate positively with the AOD at a coarser spatial resolution ≥ 10 km (e.g. Sifakis et al 1998, Chu et al 2002, Chu et al 2005, Petrakis et al 2005, Gupta et al 2006. Although data from various satellites can be used to compute the AOD, data from Moderate Resolution Imaging Spectroradiometers (MODIS) onboard the Terra and Aqua satellites are particularly useful, because these satellites have a daily repetitive global coverage, meaning that daily air quality surface, which is required to assess short-term exposure to air pollution, can be estimated.…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing of ambient particles in Delhi and its environs: estimation and validation

Kumar

Chu

Foster

2008

International Journal of Remote Sensing

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Recent advances in atmospheric remote sensing offer a unique opportunity to compute indirect estimates of air quality, particularly for developing countries that lack adequate spatial-temporal coverage of air pollution monitoring. The present research establishes an empirical relationship between satellite-based aerosol optical depth (AOD) and ambient particulate matter (PM) in Delhi and its environs. The PM data come from two different sources. Firstly, a field campaign was conducted to monitor airborne particles ≤ 2.5 μm and ≤10 μm in aerodynamic diameter (PM 2.5 and PM 10 respectively) at 113 spatially dispersed sites from July to December 2003 using photometric samplers. Secondly, data on eight hourly PM 10 and total suspended particulate (TSP) matter, collected using gravimetric samplers, from 2000 to 2005 were acquired from the Central Pollution Control Board (CPCB). The aerosol optical depths were estimated from MODIS data, acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center from 2000 to 2005. Both the PM and AOD data were collocated by time and space: PM mass ± 150 min of AOD time, and ± 2.5 and 5 km radius (separately) of the centroid of the AOD pixel for the 5 and 10 km AOD, respectively. The analysis here shows that PM correlates positively with the 5 km AOD; a 1% change in the AOD explains 0.52% ± 0.20% and 0.39% ± 0.15% changes in PM 2.5 within 45 and 150 min intervals (of AOD data) respectively. At a coarser spatial resolution, however, the relationship between AOD and PM is relatively weak. But, the relationship turns significantly stronger when monthly estimates are analysed over a span of six years (2000 to 2005), especially for the winter months, which have relatively stable meteorological conditions.

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Section: Aerosol Estimation-aerosolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote sensing of ambient particles in Delhi and its environs: estimation and validation

Kumar

Chu

Foster

2008

International Journal of Remote Sensing

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“…The high temporal resolution of the SEVIRI product presented in this paper is useful for the study of aerosol plumes at different time-scales (an hour, a half day) and could find applications for Earth radiation budget studies (De Paepe et al, 2008) and for air quality monitoring (Sifakis, 1998;Health Effects Institute, 2000) for instance through data assimilation in regional aerosol models.…”

Section: Discussionmentioning

confidence: 99%

“…Another important issue related to aerosols monitoring is the relation existing between aerosols and air quality, especially in urban areas (Health Effects Institute, 2000;Lim et al, 2004;Sifakis, 1998). The air quality domain is the study of gas (CO 2 ,O 3 , NO x , ...) and solid particles called particulate matter (PM).…”

mentioning

confidence: 99%

Description and validation of an AOT product over land at the 0.6 μm channel of the SEVIRI sensor onboard MSG

Bernard

Moulin

Ramon³

et al. 2011

Atmos. Meas. Tech.

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Abstract. The Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) launched in 2003 byEUMETSAT is dedicated to the Nowcasting applications and Numerical Weather Prediction and to the provision of observations for climate monitoring and research. We use the data in visible and near infrared (NIR) channels to derive the aerosol optical thickness (AOT) over land. The algorithm is based on the assumption that the top of the atmosphere (TOA) reflectance increases with the aerosol load. This is a reasonable assumption except in case of absorbing aerosols above bright surfaces. We assume that the minimum in a 14-days time series of the TOA reflectance is, once corrected from gaseous scattering and absorption, representative of the surface reflectance. The AOT and the aerosol model (a set of 5 models is used), are retrieved by matching the simulated TOA reflectance with the TOA reflectances measured by SEVIRI in its visible and NIR spectral bands.The high temporal resolution of the data acquisition by SEVIRI allows to retrieve the AOT every 15 min with a spatial resolution of 3 km at sub-satellite point, over the entire SEVIRI disk covering Europe, Africa and part of South America. The resulting AOT, a level 2 product at the native temporal and spatial SEVIRI resolutions, is presented and evaluated in this paper.The AOT has been validated using ground based measurements from AErosol RObotic NETwork (AERONET), a sun-photometer network, focusing over Europe for 3 months Correspondence to: J. Riedi (jerome.riedi@univ-lille1.fr) in 2006. The SEVIRI estimates correlate well with the AERONET measurements, r = 0.64, with a slight overestimate, bias = −0.017. The sources of errors are mainly the cloud contamination and the bad estimation of the surface reflectance. The temporal evolutions exhibited by both datasets show very good agreement which allows to conclude that the AOT Level 2 product from SEVIRI can be used to quantify the aerosol content and to monitor its daily evolution with a high temporal frequency. The comparison with daily maps of Moderate Resolution Imaging Spectroradiometer (MODIS) AOT level 3 product shows qualitative good agreement in the retrieved geographic patterns of AOT.Given the high spatial and temporal resolutions obtained with this approach, our results have clear potential for applications ranging from air quality monitoring to climate studies. This paper presents a first evaluation and validation of the derived AOT over Europe in order to document the overall quality of a product that will be made publicly available to the users of the aforementioned research communities.

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“…Satellite imagery can be used to better assess the spatial structure of air pollution and interactions on global, regional, or local dispersion pattern (Tang et al 2005). The determination of aerosol optical thickness (AOT) from satellite image data can be used as a tool for assessing air pollution in any area of interest (Sifakis and Deschamps 1992;Sifakis et al 1998;Hadjimitsis and Clayton 2006;Leon et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

Hadjimitsis

2009

Air Qual Atmos Health

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The aim of this study is to present and apply the proposed algorithm on archived time series Landsat TM over an urban area in the vicinity of Heathrow Airport (UK) acquired in 1984-1986 and to two up-to-dated Landsat TM images in the vicinity of Paphos Airport in Cyprus acquired in July-August 2008. The monitoring of aerosol concentrations becomes a high environmental priority particularly in urban areas. The proposed algorithm has been developed to allow the quantification of the aerosol optical thickness (AOT) over land. The algorithm compares multitemporal satellite data sets and evaluates radiometric alterations due to the optical atmospheric effects of aerosols. Novel features of this algorithm which is based on the application of radiative transfer calculations are the inclusion of applying iteration procedures for selecting the suitable object for determining the aerosol optical thickness and the automatic division into working grid cells. AOT retrieval from Landsat TM band 1 images of Cyprus has been crossvalidated satisfactorily by comparing AOT values found from handheld MICROTOPS II sun photometer.

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Quantitative mapping of air pollution density using Earth observations: a new processing method and application to an urban area

Cited by 87 publications

References 11 publications

Remote sensing of ambient particles in Delhi and its environs: estimation and validation

Remote sensing of ambient particles in Delhi and its environs: estimation and validation

Description and validation of an AOT product over land at the 0.6 μm channel of the SEVIRI sensor onboard MSG

Aerosol optical thickness (AOT) retrieval over land using satellite image-based algorithm

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