Practical Aspects of Using Satellite Data in Air Quality Modeling

Vijayaraghavan, Krish; Snell, Hilary E.; Seigneur, Christian

doi:10.1021/es7031339

Cited by 25 publications

(14 citation statements)

References 37 publications

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“…These results are included as an important component of annual emissions inventory processing used as input for the U.S. Environmental Protection Agency (EPA)'s air quality models such as the Community Multiscale Air Quality (CMAQ) model. 3 Sustained satellite observations support accountability analyses 4 addressing the relative efficacy of implemented management programs. Because of a lack of large-scale surface-based NO 2 measurements, satellite observations have been found to be very useful ambient indicators of progress in major initiatives such as the NO x State Implementation Plan (SIP) Call influencing power plant emissions across the eastern United States (Kim et al, 8 Figure 1).…”

Section: Examples Of Strengths and Weaknesses In Applicationsmentioning

confidence: 99%

“…Hoff and Christopher highlight 50 years of progress from the basic imagery of the early Television Infrared Observation Satellite (TIROS) to the recent satellite instrumentation capability to image trace gas and especially aerosol distributions in the atmosphere. The review serves as a bridge between more general discussions of satellite-based applications to air quality issues 2,3 and a large body of specialized research papers in the atmospheric science literature. The authors have done an admirable job of organizing material from current literature for the satellite retrieval of optical depth data and reliability of investigators' interpretation of AOD measurements compared with groundbased measurements of sun photometry and particle mass concentrations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

Hidy¹,

Brook

Chow

et al. 2009

Journal of the Air & Waste Management Association

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Section: Examples Of Strengths and Weaknesses In Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

Hidy¹,

Brook

Chow

et al. 2009

Journal of the Air & Waste Management Association

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“…In this context, the use of satellite data in combination with air pollution modelling opens a challenging perspective. An increasing interest to use satellite observations in air pollution modelling is mainly related with two important properties of the data in comparison with surface measurements: more complete spatial coverage and characterization of atmospheric components for vertical column Vijayaraghavan et al, 2008;Engel-Cox et al, 2004). Satellite data may be used to evaluate, initialize, constrain, and improve the performance of air pollution models (Vijayaraghavan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…An increasing interest to use satellite observations in air pollution modelling is mainly related with two important properties of the data in comparison with surface measurements: more complete spatial coverage and characterization of atmospheric components for vertical column Vijayaraghavan et al, 2008;Engel-Cox et al, 2004). Satellite data may be used to evaluate, initialize, constrain, and improve the performance of air pollution models (Vijayaraghavan et al, 2008). On the other side, chemical transport models provide essential information on aerosol composition, size distribution and vertical profiles that may be used to improve satellite aerosol retrievals (Hu et al, 2009;Randall, 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

Fernandes

Riffler

Ferreira

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx -Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

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“…Satellite measurements in combination with various types of models have been used to assess VOC emissions. However, such techniques are limited by the number of VOCs that can be measured via satellite-borne instruments (Vijayaraghavan et al, 2008) and to areas that are specific to a major source category, e.g. the use of formaldehyde (an oxidation product of isoprene) to estimate isoprene emissions in remote areas where biogenic emissions dominate (e.g.…”

Section: Introductionmentioning

confidence: 99%

Estimation of volatile organic compound emissions for Europe using data assimilation

et al. 2013

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The emissions of non-methane volatile organic compounds (VOCs) over western Europe for the year 2005 are estimated via inverse modelling by assimilation of in situ observations of concentration and then subsequently compared to a standard emission inventory. The study focuses on 15 VOC species: five aromatics, six alkanes, two alkenes, one alkyne and one biogenic diene. The inversion relies on a validated fast adjoint of the chemical transport model used to simulate the fate and transport of these VOCs. The assimilated ground-based measurements over Europe are provided by the European Monitoring and Evaluation Programme (EMEP) network. The background emission errors and the prior observational errors are estimated by maximum-likelihood approaches. The positivity assumption on the VOC emission fluxes is pivotal for a successful inversion, and this maximum-likelihood approach consistently accounts for the positivity of the fluxes. For most species, the retrieved emissions lead to a significant reduction of the bias, which underlines the misfit between the standard inventories and the observed concentrations. The results are validated through a forecast test and a cross-validation test. An estimation of the posterior uncertainty is also provided. It is shown that the statistically consistent non-Gaussian approach based on a reliable estimation of the errors offers the best performance. The efficiency in correcting the inventory depends on the lifetime of the VOCs and the accuracy of the boundary conditions. In particular, it is shown that the use of in situ observations using a sparse monitoring network to estimate emissions of isoprene is inadequate because its short chemical lifetime significantly limits the spatial radius of influence of the monitoring data. For species with a longer lifetime (a few days), successful, albeit partial, emission corrections can reach regions hundreds of kilometres away from the stations. Domain-wide corrections of the emission inventories of some VOCs are significant, with underestimations of the order of a factor of 2 for propane, ethane, ethylene and acetylene

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Practical Aspects of Using Satellite Data in Air Quality Modeling

Cited by 25 publications

References 37 publications

Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land?

Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

Estimation of volatile organic compound emissions for Europe using data assimilation

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