MozartV1, PascalFrancis, Corpus, bibRecord, 000069

Comparison of satellite observed tropospheric NO2 over India with model simulations

Identifieur interne : 000069 ( PascalFrancis/Corpus ); précédent : 000068; suivant : 000070

Comparison of satellite observed tropospheric NO2 over India with model simulations

Auteurs : Varun Sheel ; Shyam Lal ; Andreas Richter ; John P. Burrows

Source :

Atmospheric environment : (1994) [ 1352-2310 ] ; 2010.

RBID : Pascal:10-0482923

Descripteurs français

Pascal (Inist)
- Observation par satellite, Troposphère, Dioxyde d'azote, Modélisation, Modèle chimique, Combustion, Croissance, Densité colonne, Transport chimique, Ozone, Surveillance, Incertitude, Inventaire émission, Analyse tendance, Zone urbaine, Trafic routier, Inde, Télédétection spatiale, Composé de l'azote, Pollution air, Véhicule à moteur.

English descriptors

KwdEn :
- Air pollution, Chemical model, Chemical transport, Column density, Combustion, Emission inventory, Growth, India, Modeling, Motor vehicle, Nitrogen compounds, Nitrogen dioxide, Ozone, Road traffic, Satellite observation, Space remote sensing, Surveillance, Trend analysis, Troposphere, Uncertainty, Urban area.

Abstract

Nitrogen dioxide (NO₂) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO₂ over this region. We thus take recourse to satellite data and compare tropospheric NO₂ column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO₂ compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A11	`04`	`1`		`@1 BURROWS (John P.)`
A14	`01`			`@1 Physical Research Laboratory, Ahmedabad 380009 @2 Gujarat @3 IND @Z 1 aut. @Z 2 aut.`
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C01	`01`		`ENG`	@0 Nitrogen dioxide (NO₂) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO₂ over this region. We thus take recourse to satellite data and compare tropospheric NO₂ column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO₂ compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.
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C03	`04`	`X`	`ENG`	`@0 Modeling @5 04`
C03	`04`	`X`	`SPA`	`@0 Modelización @5 04`
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C03	`07`	`X`	`SPA`	`@0 Crecimiento @5 07`
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C03	`08`	`X`	`SPA`	`@0 Densidad columna @5 08`
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C03	`18`	`X`	`SPA`	`@0 Teledetección espacial @5 35`
C03	`19`	`X`	`FRE`	`@0 Composé de l'azote @2 NK @5 36`
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Format Inist (serveur)

NO :	PASCAL 10-0482923 INIST
ET :	Comparison of satellite observed tropospheric NO₂ over India with model simulations
AU :	SHEEL (Varun); LAL (Shyam); RICHTER (Andreas); BURROWS (John P.)
AF :	Physical Research Laboratory, Ahmedabad 380009/Gujarat/Inde (1 aut., 2 aut.); Institute of Environmental Physics, University of Bremen/Bremen/Allemagne (3 aut., 4 aut.)
DT :	Publication en série; Niveau analytique
SO :	Atmospheric environment : (1994); ISSN 1352-2310; Royaume-Uni; Da. 2010; Vol. 44; No. 27; Pp. 3314-3321; Bibl. 3/4 p.
LA :	Anglais
EA :	Nitrogen dioxide (NO₂) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO₂ over this region. We thus take recourse to satellite data and compare tropospheric NO₂ column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO₂ compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.
CC :	001D16C; 001E02D
FD :	Observation par satellite; Troposphère; Dioxyde d'azote; Modélisation; Modèle chimique; Combustion; Croissance; Densité colonne; Transport chimique; Ozone; Surveillance; Incertitude; Inventaire émission; Analyse tendance; Zone urbaine; Trafic routier; Inde; Télédétection spatiale; Composé de l'azote; Pollution air; Véhicule à moteur
FG :	Asie
ED :	Satellite observation; Troposphere; Nitrogen dioxide; Modeling; Chemical model; Combustion; Growth; Column density; Chemical transport; Ozone; Surveillance; Uncertainty; Emission inventory; Trend analysis; Urban area; Road traffic; India; Space remote sensing; Nitrogen compounds; Air pollution; Motor vehicle
EG :	Asia
SD :	Observación por satélite; Troposfera; Nitrógeno dióxido; Modelización; Modelo químico; Combustión; Crecimiento; Densidad columna; Transporte químico; Ozono; Vigilancia; Incertidumbre; Inventario emisión; Análisis tendencia; Zona urbana; Tráfico carretera; India; Teledetección espacial; Compuesto nitrogenado; Contaminación aire; Vehículo de motor
LO :	INIST-8940B.354000193877460110
ID :	10-0482923

Links to Exploration step

Pascal:10-0482923

Le document en format XML

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<front><div type="abstract" xml:lang="en">Nitrogen dioxide (NO<sub>2</sub>
) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO<sub>2</sub>
 over this region. We thus take recourse to satellite data and compare tropospheric NO<sub>2</sub>
 column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO<sub>2</sub>
 compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.</div>
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<fC01 i1="01" l="ENG"><s0>Nitrogen dioxide (NO<sub>2</sub>
) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO<sub>2</sub>
 over this region. We thus take recourse to satellite data and compare tropospheric NO<sub>2</sub>
 column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO<sub>2</sub>
 compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.</s0>
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<s5>01</s5>
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<fC03 i1="02" i2="X" l="FRE"><s0>Troposphère</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Troposphere</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Troposfera</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Dioxyde d'azote</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Nitrogen dioxide</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Nitrógeno dióxido</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Modeling</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Modelización</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Modèle chimique</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Chemical model</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Modelo químico</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Combustion</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Combustion</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Combustión</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Croissance</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Growth</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Crecimiento</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Densité colonne</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Column density</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Densidad columna</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Transport chimique</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Chemical transport</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Transporte químico</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Ozone</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Ozone</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Ozono</s0>
<s2>NK</s2>
<s2>FX</s2>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Surveillance</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Surveillance</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Vigilancia</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Incertitude</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Uncertainty</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Incertidumbre</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Inventaire émission</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Emission inventory</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Inventario emisión</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Analyse tendance</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Trend analysis</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Análisis tendencia</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Zone urbaine</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Urban area</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Zona urbana</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Trafic routier</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Road traffic</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Tráfico carretera</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Inde</s0>
<s2>NG</s2>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>India</s0>
<s2>NG</s2>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>India</s0>
<s2>NG</s2>
<s5>31</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Télédétection spatiale</s0>
<s5>35</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Space remote sensing</s0>
<s5>35</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Teledetección espacial</s0>
<s5>35</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Composé de l'azote</s0>
<s2>NK</s2>
<s5>36</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Nitrogen compounds</s0>
<s2>NK</s2>
<s5>36</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Compuesto nitrogenado</s0>
<s2>NK</s2>
<s5>36</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Pollution air</s0>
<s5>37</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Air pollution</s0>
<s5>37</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Contaminación aire</s0>
<s5>37</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Véhicule à moteur</s0>
<s5>38</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Motor vehicle</s0>
<s5>38</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Vehículo de motor</s0>
<s5>38</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Asie</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Asia</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Asia</s0>
<s2>NG</s2>
</fC07>
<fN21><s1>319</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 10-0482923 INIST</NO>
<ET>Comparison of satellite observed tropospheric NO<sub>2</sub>
 over India with model simulations</ET>
<AU>SHEEL (Varun); LAL (Shyam); RICHTER (Andreas); BURROWS (John P.)</AU>
<AF>Physical Research Laboratory, Ahmedabad 380009/Gujarat/Inde (1 aut., 2 aut.); Institute of Environmental Physics, University of Bremen/Bremen/Allemagne (3 aut., 4 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Atmospheric environment : (1994); ISSN 1352-2310; Royaume-Uni; Da. 2010; Vol. 44; No. 27; Pp. 3314-3321; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>Nitrogen dioxide (NO<sub>2</sub>
) plays a key role in the chemistry of the atmosphere and is emitted mainly by combustion processes. These emissions have been increasing over India over the past few years due to rapid economic growth and yet there are very few systematic ground based observations of NO<sub>2</sub>
 over this region. We thus take recourse to satellite data and compare tropospheric NO<sub>2</sub>
 column abundances simulated by a chemical transport model, MOZART, with data from the Global Ozone Monitoring Experiment (GOME) for a few locations in India that have seen a rapid economic growth in the last decade. The model generally simulates higher columnar abundances of NO<sub>2</sub>
 compared to GOME observations and does not reproduce the features of the observed seasonal behaviour. The combined uncertainties of the emission inventory and retrieval of the satellite data could be contributing factors to the discrepancies. It may be thus worthwhile to develop emission inventories for India at a higher resolution to include local level activity data. The ten year data (1996-2006) from GOME and SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) show increasing trends for Indian cities where rapid industrial and vehicular traffic growth has been observed during this period.</EA>
<CC>001D16C; 001E02D</CC>
<FD>Observation par satellite; Troposphère; Dioxyde d'azote; Modélisation; Modèle chimique; Combustion; Croissance; Densité colonne; Transport chimique; Ozone; Surveillance; Incertitude; Inventaire émission; Analyse tendance; Zone urbaine; Trafic routier; Inde; Télédétection spatiale; Composé de l'azote; Pollution air; Véhicule à moteur</FD>
<FG>Asie</FG>
<ED>Satellite observation; Troposphere; Nitrogen dioxide; Modeling; Chemical model; Combustion; Growth; Column density; Chemical transport; Ozone; Surveillance; Uncertainty; Emission inventory; Trend analysis; Urban area; Road traffic; India; Space remote sensing; Nitrogen compounds; Air pollution; Motor vehicle</ED>
<EG>Asia</EG>
<SD>Observación por satélite; Troposfera; Nitrógeno dióxido; Modelización; Modelo químico; Combustión; Crecimiento; Densidad columna; Transporte químico; Ozono; Vigilancia; Incertidumbre; Inventario emisión; Análisis tendencia; Zona urbana; Tráfico carretera; India; Teledetección espacial; Compuesto nitrogenado; Contaminación aire; Vehículo de motor</SD>
<LO>INIST-8940B.354000193877460110</LO>
<ID>10-0482923</ID>
</server>
</inist>
</record>

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Comparison of satellite observed tropospheric NO2 over India with model simulations

Comparison of satellite observed tropospheric NO2 over India with model simulations

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