Mapping of PM10 surface concentrations derived from satellite observations of aerosol optical thickness over South-Eastern France

Péré, Jean-Christophe; Pont, Véronique; Mallet, Marc; Bessagnet, Bertrand

doi:10.1016/j.atmosres.2008.05.001

Cited by 38 publications

(16 citation statements)

References 33 publications

(48 reference statements)

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“…Assuming that the vertical extent of the (well mixed) PP plume at some distance from the PP is 2 km, then 60 mgPM 10 /m 2 corresponds to a concentration of 30 µgPM 10 /m 3 . Using the linear relation between PM 10 (in µgPM 10 /m 3 ) and aerosol OD (at 440 nm) reported by Péré et al (2009) (i.e., PM 10 =54×AOT+13), it follows that 30 µgPM 10 /m 3 roughly corresponds to an increase of the aerosol OD of 0.5. This shows that the assumption of an aerosol OD enhancement of 0.5 per percent CO 2 column enhancement is quite reasonable.…”

Section: Figmentioning

confidence: 99%

A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

et al. 2010

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Abstract. Carbon dioxide (CO 2 ) is the most important anthropogenic greenhouse gas (GHG) causing global warming. The atmospheric CO 2 concentration increased by more than 30% since pre-industrial times -primarily due to burning of fossil fuels -and still continues to increase. Reporting of CO 2 emissions is required by the Kyoto protocol. Independent verification of reported emissions, which are typially not directly measured, by methods such as inverse modeling of measured atmospheric CO 2 concentrations is currently not possible globally due to lack of appropriate observations. Existing satellite instruments such as SCIAMACHY/ENVISAT and TANSO/GOSAT focus on advancing our understanding of natural CO 2 sources and sinks. The obvious next step for future generation satellites is to also constrain anthropogenic CO 2 emissions. Here we present a promising satellite remote sensing concept based on spectroscopic measurements of reflected solar radiation and show, using power plants as an example, that strong localized CO 2 point sources can be detected and their emissions quantified. This requires mapping the atmospheric CO 2 column distribution at a spatial resolution of 2×2 km 2 with a precision of 0.5% (2 ppm) or better. We indicate that this can be achieved with existing technology. For a single satellite in sun-synchronous orbit with a swath width of 500 km, each power plant (PP) is overflown every 6 days or more frequent. Based on the MODIS cloud mask data product we conservatively estimate that typically 20 sufficiently cloud free overpasses per PP can be achieved every year. We found that for typical wind speeds in the range of 2-6 m/s the statistical uncertainty of the retrieved Correspondence to: M. Buchwitz (michael.buchwitz@iup.physik.unibremen.de) PP CO 2 emission due to instrument noise is in the range 1.6-4.8 MtCO 2 /yr for single overpasses. This corresponds to 12-36% of the emission of a mid-size PP (13 MtCO 2 /yr). We have also determined the sensitivity to parameters which may result in systematic errors such as atmospheric transport and aerosol related parameters. We found that the emission error depends linearly on wind speed, i.e., a 10% wind speed error results in a 10% emission error, and that neglecting enhanced aerosol concentrations in the PP plume may result in errors in the range 0.2-2.5 MtCO 2 /yr, depending on PP aerosol emission. The discussed concept has the potential to contribute to an independent verification of reported anthropogenic CO 2 emissions and therefore could be an important component of a future global anthropogenic GHG emission monitoring system. This is of relevance in the context of Kyoto protocol follow-on agreements but also allows detection and monitoring of a variety of other strong natural and anthropogenic CO 2 and CH 4 emitters. The investigated instrument is not limited to these applications as it has been specified to also deliver the data needed for global regionalscale CO 2 and CH 4 surface flux inverse modeling.

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Section: Figmentioning

confidence: 99%

A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

et al. 2010

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“…MODIS Moderate Resolution Imaging Spectroradiometer) and MISR Multi-angle Imaging Spectroradiometer) images are analysed using algorithms designed to calculate how much direct sunlight is prevented from reaching the ground by aerosol particles the aerosol optical depth AOD). Several studies have used AOD to estimate PM 2.5 and PM 10 concentrations [31] and have shown that there is a high linear correlation between particulate mater concentrations and AOD [32]. One study published in 2014 used AOD along with meteorological variables to predict ground-level PM 10 but did not evaluate the degree to which including AOD inluenced the outcome of PM 10 predictions [32].…”

Section: Explanatory Variablesmentioning

confidence: 99%

Particulate Matter Sampling Techniques and Data Modelling Methods

Whalley¹,

Zandi²

2016

Air Quality - Measurement and Modeling

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Particulate mater with 10 μm or less in diameter PM 10 ) is known to have adverse efects on human health and the environment. For countries commited to reducing PM 10 emissions, it is essential to have models that accurately estimate and predict PM 10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM 10 is presented. This includes the instrumentation used to measure particulate mater, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM 10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM 10 trends in New Zealand a remote country of islands in the South Paciic Ocean are examined. In conclusion, the issues and challenges faced when modelling PM 10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM 10 prediction and estimation models are presented.

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“…Hence, the variety of spectral bands: 440 nm (eg. Péré et al 2009), 550 nm (e.g. Jung et al 2009), 555 nm (Kuśmierczyk-Michulec et.…”

Section: Use Of the åNgström Coefficient In Air Quality Estimation 5mentioning

confidence: 99%

“…Xia et al, 2006;Wang et al, 2010), in Amazonia (Artaxo et al, 2002), in the South-Eastern France (e.g. Péré et al, 2009), in the North France and Belgium (Pelletier et al, 2007) or in Italy (Barnaba et al, 2010). Similarly to beta-absorption technique, the sampled air is heated before collecting the aerosols on a filter, to avoid humidification of the filter.…”

Section: Tapered Element Oscillating Microbalance (Teom) Techniquementioning

confidence: 99%

Optical Measurements of Atmospheric Aerosols in Air Quality Monitoring

Kuśmierczyk-Michulec¹

2011

Air Quality-Models and Applications

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Mapping of PM10 surface concentrations derived from satellite observations of aerosol optical thickness over South-Eastern France

Cited by 38 publications

References 33 publications

A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

Particulate Matter Sampling Techniques and Data Modelling Methods

Optical Measurements of Atmospheric Aerosols in Air Quality Monitoring

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Mapping of PM10 surface concentrations derived from satellite observations of aerosol optical thickness over South-Eastern France

Cited by 38 publications

References 33 publications

A remote sensing technique for global monitoring of power plant CO&lt;sub&gt;2&lt;/sub&gt; emissions from space and related applications

A remote sensing technique for global monitoring of power plant CO&lt;sub&gt;2&lt;/sub&gt; emissions from space and related applications

Particulate Matter Sampling Techniques and Data Modelling Methods

Optical Measurements of Atmospheric Aerosols in Air Quality Monitoring

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A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications

A remote sensing technique for global monitoring of power plant CO<sub>2</sub> emissions from space and related applications