Inter-annual variations in satellite observations of nitrogen dioxide and formaldehyde over India

Mahajan, Anoop S.; Smedt, Isabelle De; Biswas, Mriganka Sekhar; Ghude, Sachin D.; Fadnavis, Suvarna; Roy, Chaitri; Roozendaël, Michel Van

doi:10.1016/j.atmosenv.2015.06.004

Cited by 68 publications

(76 citation statements)

References 51 publications

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“…Combined with the increase in O 3 , this result gives an indication of the presence of a VOC-sensitive regime. This contrasts with the NO x -sensitive regime otherwise prevailing in India as calculated by and observed by Mahajan et al (2015). It is however interesting to note that the presence of a VOC-limited regime over region (A) during the pre-monsoon period and over region (B) in winter, was already observed by satellite measurements (Mahajan et al, 2015).…”

Section: Ocontrasting

confidence: 45%

Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India

et al. 2018

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Abstract. Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O 3 ) and fine particulate matter (PM 2.5 ) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surfacebased measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O 3 (r = 0.9) and high spatial correlation for PM 2.5 (r = 0.5 and r = 0.8 depending on the data set) between the model results and observations. While the overall bias in PM 2.5 is small (lower than 6 %), the model overestimates O 3 by 35 %. The underestimation in NO x titration is probably the main reason for the O 3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data.For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O 3 pollution, with a region in the north characterized by a statistically significant increase by up to 4 % (2 ppb) and one in the south by a decrease up to −3 % (−1.4 ppb). This variation in O 3 is assumed to be partly related to changes in O 3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds.Our calculations suggest that PM 2.5 will increase by up to 6.5 % over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed.The large increase in anthropogenic emissions has a larger impact than climate change, causing O 3 and PM 2.5 levels to increase by 13 and 67 % on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM 2.5 in India, and the largest in the 2050s, exceeding OM and dust.

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Section: Ocontrasting

confidence: 45%

Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India

et al. 2018

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“…The annual total NO x emission used in this study is 1.6 TgN in 2010 (Sharma et al, ). It may be underestimated if growth of economic activities during 2005–2010 is taken into account (Mahajan et al, ). The annual total PM 10 emission used in this study is 11 Tg.…”

Section: Resultsmentioning

confidence: 99%

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Chatani

Sharma

2018

JGR Atmospheres

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Many places in India suffer from severe air pollution. Regional air quality simulations are essential to develop effective strategies for improving air quality, considering the nonlinear relationships between ambient pollutants and their precursor emissions. Meteorological fields used in simulations are derived from regional meteorological models with analysis data sets as inputs. This study reveals that two major analysis data sets provided by the National Centers for Environmental Prediction and the European Centre for Medium‐Range Weather Forecasts (ECMWF) cause significant differences in simulated meteorological fields over India. Especially, relative humidity values simulated using the ECMWF data set are much higher and closer to the observed values than those simulated using the National Centers for Environmental Prediction data set. Results simulated using the ECMWF data set show better model performance for most meteorological parameters over India. Differences in relative humidity originate in the data contained in the analysis data sets through grid nudging. It is not possible to avoid this underestimation by simply turning off grid nudging. The meteorological fields simulated with two major analysis data sets also lead to differences in pollutant concentrations simulated by regional chemical transport models through various physical and chemical processes. Differences originating in the two analysis data sets could be comparable with the uncertainties originating in various emission inventories in some regions and seasons in India. However, discrepancies between observed and simulated pollutant concentrations cannot be explained only by differences of the meteorological fields. Other aspects need to be explored for better performance required to develop effective strategies for India, based on accurate regional air quality simulations.

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“…The ambient ozone level strongly depends on both the relative and absolute amounts of VOCs and NO x (Geng et al, 2007;Tang et al, 2009Tang et al, , 2012. Moreover, there are many studies that use HCHO / NO 2 column ratio as an indicator to determine surface O 3 -NO x -VOC sensitivity in previous studies (Martin et al, 2004;Tang et al, 2012;Mahajan et al, 2015). However, there is a lack of observation for NO x and HCHO vertical distribution in order to investigate the O 3 formation and atmospheric chemistry in the lower troposphere.…”

Section: Introductionmentioning

confidence: 99%

Observations of the vertical distributions of summertime atmospheric pollutants and the corresponding ozone production in Shanghai, China

et al. 2017

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Abstract. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and lidar measurements were performed in Shanghai, China, during May 2016 to investigate the vertical distribution of summertime atmospheric pollutants. In this study, vertical profiles of aerosol extinction coefficient, nitrogen dioxide (NO 2 ) and formaldehyde (HCHO) concentrations were retrieved from MAX-DOAS measurements using the Heidelberg Profile (HEIPRO) algorithm, while vertical distribution of ozone (O 3 ) was obtained from an ozone lidar. Sensitivity study of the MAX-DOAS aerosol profile retrieval shows that the a priori aerosol profile shape has significant influences on the aerosol profile retrieval. Aerosol profiles retrieved from MAX-DOAS measurements with Gaussian a priori profile demonstrate the best agreements with simultaneous lidar measurements and vehicle-based tethered-balloon observations among all a priori aerosol profiles. Tropospheric NO 2 vertical column densities (VCDs) measured with MAX-DOAS show a good agreement with OMI satellite observations with a Pearson correlation coefficient (R) of 0.95. In addition, measurements of the O 3 vertical distribution indicate that the ozone productions do not only occur at surface level but also at higher altitudes (about 1.1 km). Planetary boundary layer (PBL) height and horizontal and vertical wind field information were integrated to discuss the ozone formation at upper altitudes. The results reveal that enhanced ozone concentrations at ground level and upper altitudes are not directly related to horizontal and vertical transportation. Similar patterns of O 3 and HCHO vertical distributions were observed during this campaign, which implies that the ozone productions near the surface and at higher alPublished by Copernicus Publications on behalf of the European Geosciences Union.

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Inter-annual variations in satellite observations of nitrogen dioxide and formaldehyde over India

Cited by 68 publications

References 51 publications

Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Observations of the vertical distributions of summertime atmospheric pollutants and the corresponding ozone production in Shanghai, China

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Inter-annual variations in satellite observations of nitrogen dioxide and formaldehyde over India

Cited by 68 publications

References 51 publications

Impact of regional climate change and future emission scenarios on surface O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; over India

Impact of regional climate change and future emission scenarios on surface O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; over India

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Observations of the vertical distributions of summertime atmospheric pollutants and the corresponding ozone production in Shanghai, China

Contact Info

Product

Resources

About

Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India