Carbon monoxide over Indian region as observed by MOPITT

Girach, Imran A.; Nair, Prabha R.

doi:10.1016/j.atmosenv.2014.10.019

Cited by 57 publications

(31 citation statements)

References 41 publications

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“…Some recent studies on CO trends over larger regions overcame the uneven sampling problem by deseasonalizing the data before studying trends (Strode et al, 2016;Girach and Nair, 2014). In our method using the WRF model (see below), the problem of uneven sampling is largely solved as we sample our model according to the availability of satellite data.…”

Section: Sampling Differences and Averaging Periodmentioning

confidence: 99%

“…To reduce the random noise and to increase the signal from relatively small sources, it is required to average MOPITT data over longer time periods, as in the earlier studies already mentioned (e.g., Clerbaux et al, 2008;Girach and Nair, 2014;Deeter et al, 2014). Averaging times ranged in these studies from 1 month for the second study to 7 years for the first study; it should be noted, however, that these studies used a coarser spatial resolution: 1 • × 1 • .…”

Section: Simulation Periodmentioning

confidence: 99%

“…Close to cities, this seasonal variation reflects both the change in emissions over the year, with higher emissions in winter and low emissions in summer, and the seasonal cycle of the OH sink, which varies with season and peaks in summer (e.g., Girach and Nair, 2014;Lal et al, 2000;Novelli et al, 1998), leading also to low CO mixing ratios in summer. Because of this, seasonal variations in measurement coverage may bias annual averages.…”

Section: Sampling Differences and Averaging Periodmentioning

confidence: 99%

“…So far, satellite retrievals of CO have been used mainly in global-scale analyses, quantifying large-scale CO emissions (e.g., Hooghiemstra et al, 2012a, b;van Leeuwen et al, 2013;Girach and Nair, 2014;Yin et al, 2015;Jiang et al, 2017) with a primary interest in biomass burning. Furthermore, the first attempts have been made to use MOPITT CO retrievals to estimate emission changes over cities (Pommier et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

et al. 2017

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Abstract. The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multiyear retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-to-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRFChem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R 2 = 0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48

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Section: Sampling Differences and Averaging Periodmentioning

confidence: 99%

Section: Simulation Periodmentioning

confidence: 99%

Section: Sampling Differences and Averaging Periodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

et al. 2017

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“…They argued that the main reason could be due to the rise in personal vehicles as well as two-wheeler segments in recent time. Girach and Nair (2014) have also shown the increasing trend in total CO emission from India over 2000Á2008 that were estimated from EDGAR version-4.2 inventory and REAS (Regional Emission inventory in ASia) version-2.1 inventory (Kurokawa et al, 2013).…”

Section: Seasonal and Interannual Variabilitymentioning

confidence: 99%

Spatio-temporal variability of CO and O<sub>3</sub> in Hyderabad (17°N, 78°E), central India, based on MOZAIC and TES observations and WRF-Chem and MOZART-4 models

Sheel¹,

Bisht²,

Sahu³

et al. 2016

Tellus B: Chemical and Physical Meteorology

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What controls the seasonal cycle of black carbon aerosols in India?

et al. 2015

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The seasonal variability of black carbon (BC) aerosols in India is studied using high resolution (10 km) BC simulations conducted using the Weather Research and Forecasting Model coupled with Chemistry. The model reproduces the observed seasonality of surface BC fairly well over most parts of India but fails to capture the seasonality in the Himalayas and deviates from the observed BC magnitude at several sites. The errors in modeled BC are attributed to uncertainties in BC emissions and their diurnal cycle, planetary boundary layer height underestimation, and aerosol processes. Model results show distinct but opposite seasonality of BC in the lower (LT) and free troposphere (FT) with BC showing winter maximum and summer minimum in the LT and vice versa in the FT. Our analysis shows that BC seasonality is not driven by seasonality of the anthropogenic emissions but by changes in the regional meteorology through weakening of the horizontal transport and strengthening of the vertical transport of BC during summertime compared to winter. BC in both the LT and FT comes mostly from anthropogenic emissions followed by biomass burning emissions except during winter when long-distant sources become more important in the FT. BC in the FT is significantly affected by anthropogenic emissions from all parts of India. The source-receptor relationship changes seasonally, but the regional transport remains a significant contributor to BC loadings in the LT of India, highlighting the necessity of considering nonlocal sources along with local emissions when designing strategies for mitigating BC impacts on air quality.

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Carbon monoxide over Indian region as observed by MOPITT

Cited by 57 publications

References 41 publications

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

Spatio-temporal variability of CO and O<sub>3</sub> in Hyderabad (17°N, 78°E), central India, based on MOZAIC and TES observations and WRF-Chem and MOZART-4 models

What controls the seasonal cycle of black carbon aerosols in India?

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