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- Taylor & Francis
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- © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).
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Abstract
GEOLOGY, ECOLOGY, AND LANDSCAPES INWASCON https://doi.org/10.1080/24749508.2022.2132706 RESEARCH ARTICLE Spatio-temporal distribution of pollutant trace gases (CO, CH , O and NO ) in 4 3 2 India: an observational study a,c b b Komal Gupta , Arnab Saha and Bhaskar Sen Gupta a b Department of Earth Sciences, Banasthali University, Vanasthali, India; Institute of Infrastructure and Environment, Heriot-Watt University, Edinburgh, UK; Forest Research, Northern Research Station, Roslin, Midlothian, UK ABSTRACT ARTICLE HISTORY Received 16 June 2022 India is one of the largest contributors to anthropogenic emissions during the recent decade Accepted 30 September 2022 associated with its rapid economic growth in India. Trace gases are important components in the climate change process and due to that climate change, there will be a change in their KEYWORDS atmospheric concentrations as the climate is sensitive to Earth’s; therefore, proper assessment AIRS; OMI; seasonal variation; of trace gases is necessary for ongoing sudden changes in climate. In this study, we used air pollution; industrial remote-sensing datasets from the Atmospheric Infrared Sounder (AIRS) and Ozone Monitoring emissions Instrument (OMI) to analyze the spatio-temporal variations of four trace gases, like methane (CH ), ozone (O ), carbon monoxide (CO), and nitrogen dioxide (NO ) over India region during 4 3 2 2006–2015 and taken four seasons (i.e., winter, spring, summer, and winter) to interpret the seasonal variation. The project focuses on the temporal pattern of pollutant trace gases i.e., monthly, seasonal, and annual mean variations of trace gases, trend analysis of trace gases, and a comparison of the seasonal behavior of the trace gases by trend analysis was assessed. Higher concentrations of CO show east-to-west, CH show north-to-south, and O south-to-north 4 3 gradient, indicating the variations in trace gases due to the impact of emissions and local meteorology. On the other hand, due to immense population density, huge traffic emissions, tremendous, polluted air, and overgrown industrial activities, total NO concentrations shoot up over Delhi, Lucknow, and Kolkata. Now as a result of seasonal variation in the long-range transport of air parcels and biomass burning activities, all trace gases shown significant seasonal variations in the spring season and substantially reduced in the summer season. However, in the winter season, O concentration evaluates minimum due to less amount of heat on cold days which leads to the reduction of O formation. Due to trace gases, all are significant to get regional climate variability. In this study by taking 2006 as a base year and investigate the behaviors of gases for 2007–2015 years to exhibit the increment and decre- ments in four seasons of all trace gases by taking the most populated 11 different cities of India. 1. Introduction (1%) which are called trace gases, due to their low abundance (Petersen, 2009; Gupta and Saha, 2020b). India is one of the major origins of the anthropogenic GHGs are the prominent trace gases in the earth’s (man-made) pollutants that leads to the atmospheric atmosphere. The planet earth is sustained 33°C war- climate changes and global warming due to the recent mer than it would be without an atmosphere because economic extension and that accelerates the changes this GHGs (Holloway et al., 2000; King & Greenstone, in the reduction of atmospheric trace gases and green- 1999). As the GHGs’ key purpose in causing warming house gases (GHGs) both that amend the energy uni- is through the trapping of longwave radiation rather formity of the climate system (Nielsen, 2012; Khavrus than through the absorption of sunlight. In diminutive & Shelevytsky, 2010; Khavrus & Apple, 2012; WMO concentrations, the trace gas takes place or has low (World Meteorological Organization), 2007; Gupta elevation and numerously in part per billion (ppb) and Saha, 2020a). One of the demanding environmen- concentration. Trace gas interchanges between the tal issues in urban and industrial areas is atmospheric upper troposphere and the lower stratosphere occur trace gases (Aschbacher, 2002; Skidmore, 2002; due to large air mass movement (Monson & Holland, S. Wang & Hao, 2012). Earth is surrounded by layers 2001; Stull, 1988; Wada et al., 2011). Due to low of gases i.e., atmosphere and which are troposphere, abundance, it also has a significant impact on both stratosphere, mesosphere, and thermosphere climate change and air quality (Gupta et al., 2006). The (Houghton et al., 1992; Prather et al., 2001). Earth’s Diwali (India’s ritual festival) make a significant atmosphere is a counterbalance of nitrogen (78% by impact on the distribution of trace gases during post volume) and oxygen (21% by volume) with only monsoon season (Nanda et al., 2018). a minor contribution from other atmospheric gases CONTACT Komal Gupta gupta.komal.248@gmail.com Department of Earth Sciences, Banasthali University, Vanasthali 304022, Rajasthan, India © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 K. GUPTA ET AL. The Ozone (O ) is present in the troposphere and appearance including forest fire including (NO & SO ) 3 2 2 other parts of the atmosphere (not in uniform con- volcanic eruptions, and lighting strikes (Haq et al., 2014; centration; Herman et al., 1996; McPeters, 1998; Ramachandran et al., 2013). Through manmade pro- Pandey & Agrawal, 1992). The most important green- cesses during the 20th century, the ozone layer gets house/tropospheric gases are water vapour (H O), damaged by CFCs (chlorofluro carbons) which gave carbon dioxide (CO ; Barrett & Curtis, 1992; Karl & rise to new trace gases in the atmosphere Brasseur & Trenberth, 2003), methane (CH ; Jeong et al., 2012; Solomon, 1984; Hama et al., 2020 investigated the spa- C. Zhao et al., 2009), nitrogen oxides (NO ), sulphur tiotemporal features of trace gases (NO , O , SO , and x X 3 2 oxides (SO ), carbon monoxide (CO), NH CO) and particulate matter (PM10 and PM2.5) based on x 3 (Ammonia) and VOC (volcanic organic compounds), 12 air quality monitoring sites in Delhi-NCR over the like formaldehyde (HCHO). Earth’s energy balance is years 2014 to 2017. In the study of Bilal et al., 2021, influenced by the factors, called “Radiative climate a thorough assessment of air quality conditions across forcing (Aronoff, 2005; Caldeira, 2005; Elachi, 1987). Pakistan has ever been provided, which included Negative radiative forcing indicates cooling ground-based long-term remote sensing (2003–2020), whereas positive radiative forcing is associated with and model simulation datasets. In the previous study, warming. Revised national ambient air quality stan- using the OMI dataset from December 2004 to dards (NAAQS), pollutant time-weighted average November 2008, look over the spatio-temporal variabil- 3, annual for NO is 40µ ɡ/m O - 8 hours average is ity of monthly averaged Vertical Tropospheric Columns 2 3 3 3 100µ ɡ/m , and CO- 8 hours average is 02mɡ/m , are (VTCs) of NO over Pakistan (Levelt et al., 2006; Wahid, the safety limits set by Central Pollution Control 2006). Over the study region, the total column of NO Board (CPCB), India. The study of (Wang et al., values showed remarkable results of spatial and temporal 2021), investigated the long-term spatiotemporal dis- variability. For finding long-range transport of NO hot- tributions and changes of NO and SO pollution in spot regions air mass trajectories are used (Haq et al., 2 2 Jiangsu Province, as well as their proportions, pat- 2014; Levine et al., 1984). For the control of nitrogen terns, and sources. Significantly, NO and SO were oxides in East China, the previous comprehensive study 2 2 used to identify the substantial level of pollution examines the seasonal variations that have differences in throughout Jiangsu Province, and winter was the sea- Density-independent pixels (DP) and Scale-independent son with the highest intensity of NO and SO occur- pixels (SP; Zhang et al., 2007; F. Zheng et al., 2014). It has 2 2 rences. Due to the potential harmful impact on human been seen that due to winter monsoon discharge from health (such as cardiovascular and respiratory disor- South Asia induces to spreading of CO over the Arabian ders) and environmental concerns, air pollutants are Sea and Bay of Bengal (Ghude & Beig, 2008). From a major source of concern for both society and the June 2009 to May 2013 in northern India in the urban government (Wang et al., 2021). Gaseous contami- locations of Kanpur, they carried out the surviving small- nants (e.g., sulphur dioxide: SO ; nitrogen dioxide: scale features measurements of ozone (O ), sulphur diox- 2 3 NO ; and ozone: O ) draw concern owing to their ide (SO ), carbon monoxide (CO), and oxides of nitro- 2 3 2 major impacts on the atmospheric environment (e.g., gen (NO ). Over the entire period, the mean degrading flora and forests, and global warming) and concentrations of CO, O , NO , and SO were 721, 3 x 2 human health (e.g., asthma and cancer; Bilal et al., 27.9, 5.7, and 3.0 ppb, respectively. In the winter season, 2021; Tanvir et al., 2022). researchers found that NO , SO , and CO concentrations x 2 Aura satellite’s (launched in 2003) instrument Ozone were highest, whereas in summer O concentration was Monitoring Instrument (OMI) (13x24 km -column- highest in the Indian region (Tiwaria et al., 2008). Due to daily) and Aqua satellite’s (launched in 2002) sensor wet scavenging by precipitation, trace gases are measured Atmospheric Infrared Sounder (AIRS) (13.5 km dia- at a minimum rate during the monsoon season and in meter circle; 1d.f.), are two examples of sensors that addition to scavenging, the variation of planetary bound- monitor and measure GHGs (O , HCHO, NO , SO , ary layer height should be a very important contributing 3 2 2 O , CO, repetition of CO, CH ; Chi et al., 2021; factor. In the summer season enhanced chemical pro- 3 4 C. Zheng et al., 2018). Concentrations of trace gases duction of O takes place, whereas in winter low mixing measured remotely by Light Detection and Ranging height and due to the mainly attributed near-surface (LIDAR) systems have been developed by both the anthropogenic sources (e.g., coal-burning power plants, Raman technique (which describes a ground-based agriculture hand-clearing, brick kilns, residential cook- water vapors system) or by the Differential Absorption ing, automobiles, and biomass burning; Srivastava & LIDAR (DIAL) technique (Grant, 1991; Islam et al., Sheel, 2013; Varotsos et al., 2003; Gupta and Saha, 2015). The trace gases CO , CH , NO , SO , HCHO, 2020a). Excluding the autumn season, the concentration 2 4 2 2 NH , and CO can be observed from space and are of O showed a definite undeviating relation with tem- 3 3 upsurge due to human activities (Chi et al., 2022; Liu perature in all seasons, and at the same time, O con- et al., 2009; Stull, 1988). Further, due to trace gases centrations showed a negative relative humidity relation natural phenomena in the atmosphere make an in all seasons. Likewise, averaged diurnal patterns GEOLOGY, ECOLOGY, AND LANDSCAPES 3 showed seasonal variation. Regardless of the seasons, micro-climates. Rabi (December-February), Kharif NO and CO showed peaks in morning and evening (July-October), and Zaid (March-June) are the three traffic hours and a valley in the afternoon, clearly linked significant yield seasons of India due to these climate to the boundary layer height evolution (Y. C. Zhao et al., behaviorisms. 2006; Herring, 2001). The atmospheric efficiency to dis- seminate the air pollution is the ventilation coefficient. 3. Data used and methodology The ventilation coefficient is an outcome of wind speed (i.e., horizontal ventilation) and mixing layer height (i.e., For the present and past work, satellite data are vertical dilution of pollutants; Goyal & Chalapati Rao, selected from the years 2006–2015 (in Table 1). 2007; Rama Krishna et al., 2004). O represents the low- In the present study, a combination of two different est concentrations in the morning hours and over satellites having different sensor retrievals is used to through the pattern with an inflated congregation in monitor the trace gases (i.e., CO, CH , O , NO ) over 4 3 2 the afternoon hours. During morning hours (08:00 to the Indian region. Over the Indian region, the exis- 11:00 hrs.) and evening hours (17:00 to 19:00 hrs.) in tence of trace gases is linked with the generally agri- Kanpur, the mean rate of change of O concentrations cultural land-clearing, biomass burning, residential −1 −1 (dO /dt) were noticed at 3.3 ppb h and −2.6 ppb h , cooking automobiles, brick kilns, power plants, biofuel respectively. Due to the evocative of tremendous pollu- burning, etc. OMI and AIRS retrievals are utilized for tion dispersion efficiency in the spring season, examined the detection of highly affected trace gases areas. GIS 2 −1 the excessive (15,622 m s ) and minimal ventilation Software’s used for this work with maps and geo- 2 −1 coefficient throughout the winter (2564 m s ) season. graphic information. It is widely used for multiple But high pollution potential occurs due to the low venti- purposes like generating maps; gathering geographic lation coefficient during winter and post-monsoon sea- information; sharing and data interpretation (mapped sons (Gaur et al., 2014; Y. Zheng et al., 1998). The GHGs information); using maps and their applications, and (NO , CO, CH , and O ) are considered to be derived in geographic information carried out in a data archive. 2 4 3 this study, and a dataset has been created to examine For the computation of data numerically, importing seasonal variation over a ten-year period in the Indian documents (files) from other platforms (computa- region. tional analysis), data interpretation, and envisaging of data high-level language is used. The monthly averaged data of resolutions 0.25 and 2. Study area 1 degree for the period 2006–2015 of AIRS and OMI In this study, we emphasize the Indian region (in are taken from the website http://giovanni.gsfc.nasa. Figure 1). India is a country in South Asia. It is the gov (NASA Earth data Giovanni, n.d.). In this study to seventh-largest country by geographical area of analyze monthly AIRS data of the total column of CO 3,287,263 km (1,269,219 sq.mi) of which 90.44% is and total column of CH used AIRX3STM.005 pro- land and 9.56% is water. India is situated in the north duct (AIRS Science Team/Moustafa Chahine, 2007). ° ° of the equator between 8 4´and 37 6´ north latitude And these data product (AIRX3STM.005) has day and ° ° and 68 7´ and 97 25´ east longitude (India Yearbook, night temporal resolution once per month with 1° 2007). The land of India is divided into seven regions. latitude resolution and 1° longitude resolution. From The northern Himalayan Mountain ranges, The Thar the level 2 standard swath products, 5 level 3 gridded Desert, The Indo-Gangetic plain, Central Highlands products are derived. By first screening the level 2 data and the Deccan Plateau, the Mainland Mountain and then calculating the weighted average of the ranges, East Coast, Bordering seas and islands. India remaining data all the NO2 data fields are determined. is confounded by the coastal area of Bengal (the Bay of The worldwide grids 0.25° × 0.25° have averaged all Bengal) on the southeast, the Lakshadweep Sea to the good quality data provides by the OMI level 3 global south, and the Arabian Sea on the southwest. The land gridded data. The level 3 daily data products are cre- of India is divided into seven regions. The northern ated by using the files of level 2 HDF-EOS version 5 Himalayan mountain ranges, the Thar Desert, the (HE5). The data set are multiplied with the conversion Indo-Gangetic plain, Central Highlands and the factors to get the exact values of Trace gases. The Deccan Plateau, the Mainland Mountain ranges, East climatology map and yearly average map are being Coast, Bordering seas and islands. India has four sea- generated using the monthly mean products. In the sons: winter (December to February), spring (March present study, the areas containing valid values of to May), summer (June to August), and autumn trace gases are taken for analysis. The entire study is (September to November). The Himalayas and the divided into parts (covering each zone of India, most Thar Desert are vigorously prominent in the Indian populated) e.g., Selected randomly most crowded climate i.e., summer and winter monsoons that are Eleven different cities of India region covering each restraining the socially and economically essentials. zone (according to base year taken i.e., 2006, graphs Numerous Indian regions have inherently different manifesting the trace gases amount of increment and 4 K. GUPTA ET AL. Figure 1. Study area map of India. Table 1. Shows the descriptions of satellite data used. Satellite Images Sensor Resolutions Duration (Years) Nitrogen dioxide (NO ) AIRS 0.25 degree 2006–2015 Ozone (O ) OMI 0.25 degree 2006–2015 Carbon monoxide (CO) AIRS 1 degree 2006–2015 Methane (CH ) AIRS 1 degree 2006–2015 4 GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Satellite Derived Datasets AURA & AQUA – OMI & AIRS Level 3 datasets Extraction of Indian region from World dataset Spatial Distribution of Data Map Generation using GIS Software Preprocessing Averaging of Map Hot spot generation Generation of Trend Analysis of Trace gases Seasonal & Regional Variation of Trace gases Figure 2. Work flow of entire project. decrement), spatial variations in trace gases, monthly 2006–2015, due to biofuel burning, coal mines, gar- and seasonal mean variations of trace gases and sea- bage dumps, etc. in their region, whereas the lowest sonal climatological trends of trace gases. The salient amount of CH was seen in the northern part of India feature of the methodology is briefly explained in the region, shown below in (Figure 4). An analysis shows given flow chart (in Figure 2). that implication of trace gases an entwined with aero- sol loading due to the higher concentration on aerosol present in the western part of India (M. M. I. Mamun, 4. Results 2014b). Hence emission of trace gases intensifies due 4.1 Spatial seasonal variations of trace gases to indestructible concentrations of aerosol. The study over India of Ali et al., 2022, investigates the long-term spatio- temporal variations of aerosol optical depth (AOD) The spatial distribution of the total column of CO, and the relative contributions of aerosol species and column amount of NO , the total column of O , and 2 3 anthropogenic emissions to the total AOD as well as total column CH over India during the period of their trends. In many previous studies (Sahu & Lal, 2006–2015 are introduced below in (Figures 3–6). 2006; Sahu & Sheel, 2014; Sheel et al., 2014; Streets The features of spatial distributions of CH , CO, et al., 2003; Van der Werf et al., 2004, 2006) it was and O are quite perceptible. It can be seen that higher reported that emissions of various trace gases are also total columns of NO and CO were observed in the due to leading source of biomass burning (BB) emis- western region of India in all the seasons, whereas sions in South-East. higher total columns of CH are seen in the southern From Biomass burning sources many primary region of India, and O total columns are seen in the species are emitted are the precursors of O and Northern part of India region in all the seasons. The 3 secondary organic aerosol (SOA). In India’s north- highest column amount of CO was observed in the ern and eastern parts during the time frame, 2006– western part of the India region in all seasons during 2015 due to fuel ignition the excessive concentra- the period 2006–2015, and increased due to volcanoes tion of O was noticed in winter, and spring season erupting, smoke, and industrial process in the western 3 while in northeast and northern part of India region, whereas CO decreased in the northern region region showed up most in autumn and summer in all the seasons are shown below in (Figure 3). The season, however in southern winter and spring highest column of CH was seen in the southern, and season shown decrement of O concentration and south-west part of India region during the period 3 6 K. GUPTA ET AL. Figure 3. Spatial variations of CO over India region of (a) winter, (b) spring, (c) summer and (d) autumn seasons using AIRS data sets during the period 2006–2015. in north, northwest, and west part of India region observed that there is an increment in certain trace in summer and autumn season are shown above in gases due to anthropogenic activities, along with (Figure 5). The activities of BB in India, as well as meteorological conditions. in Myanmar, Indonesia, Laos, and Thailand, con- The highest column amount of NO loading was tributing to the annual budget of CO and NO observed in North-East and South-West of India emissions in the countries of south and south- region has the large industries, highest traffic, and eastern Asia (Sahu & Sheel, 2014). As a result, we highest population density: therefore, the highest GEOLOGY, ECOLOGY, AND LANDSCAPES 7 Figure 4. Spatial variations of CH over India region of (a) winter, (b) spring, (c) summer and (d) autumn seasons using AIRS data sets during the period 2006–2015. NO loading was observed in northeast and south- autumn, and winter seasons, whereas in the sum- west, due to main reasons of large anthropogenic mer season highest column in the west-northern emissions. Similarly, other than India NO emis- region of India region during the study period sions are also increased in China due to an increase 2006–2015, whereas the lowest column amount of in industries and traffic (Fan et al., 2020; X. Wang NO2 was also found in southern, eastern, and & Mauzerall, 2004). The highest column amount of northern part of India region are shown below in NO showed in the north-western region in spring, (Figure 6). 2 8 K. GUPTA ET AL. Figure 5. Spatial variations of O over India region of (a) winter, (b) spring, (c) summer and (d) autumn seasons using AIRS data sets during the period 2006–2015. 4.2 Seasonal yearly variations of trace gases over gases during the periods 2007–2015, the seasonal selected cities of India yearly analysis of trace gases of total column of CO, the total column of CH , the total column of O , 4 3 Figures (7 to 10) show the amount of trace gases and column amount of NO over selected cities of increases or decreases in all seasons of each gas India region. In this study, have taken randomly over the selected cities of the India region by con- eleven different cities in the India region covering sidering 2006 as a base year, from which we com- all zones Varanasi, Ahmedabad, Chennai, Mumbai, pared the bases of base year variations of trace GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Figure 6. Spatial variations of NO over India region of (a) winter, (b) spring, (c) summer, and (d) autumn seasons using AIRS data sets during the period 2006–2015. Bangalore, Dehradun, Delhi, Bhopal, Kolkata, period 2007–2015, in rest of the years of selected cities of total column of CO. In spring yearly var- Lucknow, and Hyderabad. iations of CO total column over selected cities seen In winter the yearly variations of CO total col- that there was a decrement in Delhi, Varanasi, umn over selected cities seen that there was Chennai, and Bangalore in the years 2007 to 2011 a decrement in Dehradun, Lucknow, and according to the base year 2006, whereas increment Ahmedabad in the years 2007 and 2010 according to the base year 2006, whereas an increment (due seen in total column of CO in rest of the selected to smoke and industrial process) seen during cities in rest of the years during period 2007–2015. 10 K. GUPTA ET AL. Yearly difference variation of CO total column over selected cities during winter season -1 Cities Yearly difference variation of CO total column over selected cities during spring season -1 Cities Yearly difference variation of CO total column over selected cities during summer season 1.5 0.5 Cities Yearly difference variation of CO total column over selected cities during autumn season 1.5 0.5 0 2013 Cities Figure 7. Yearly difference variations of CO total column over selected cities of India region during the period 2006–2015 in (a) winter, (b) spring, (c) summer, and (d) autumn seasons. In (Figure 7) showing, yearly variations increment cities seen that there was a decrement in Hyderabad during the period 2007–2015 over the India region and Bangalore in the year 2007 according to the in all the selected cities of total column of CO in base year 2006, whereas increment seen in total col- umn of CH in rest of the selected cities during period the summer and autumn seasons. 2007–2015 in rest of the years are presented below in In winter and spring, the yearly variations of CH (Figure 8). total column over selected cities seen that there was an According to the base year 2006, over India region increment (due to an increase in biomass burning in all the selected cities in winter season during the activities) seen in all the selected cities of India region period 2007–2015 seen a yearly increment of O total during the period 2007–2015. But in summer the column, and decrement in yearly variations of O in yearly variations of CH total column over selected 3 spring season in the years 2007–2013, considering that cities seen that there was a decrement (due to less in during period 2007–2015 according to the base year BB activities) in Dehradun, Delhi, Lucknow, Varanasi, 2006, the total column of O seen increases in rest of Mumbai, Chennai, and Bangalore in the year 2007 the years for selected cities. In summer the yearly according to the base year 2006 and found the highest variations of O total column seen that there was an peak in Dehradun, whereas increment seen in total increment in the selected cities Delhi, Dehradun, column of CH in rest of the selected cities in rest of Lucknow, Ahmedabad, and Mumbai in the years the years during period 2007–2015. In autumn the 2009 to 2013 and 2015, whereas decrement seen in yearly variations of CH total column over selected Conc. of CO (molec/cm ) Conc. of CO (molec/cm ) 2 Conc. of CO (molec/cm ) Conc. of CO (molec/cm ) GEOLOGY, ECOLOGY, AND LANDSCAPES 11 Yearly difference variation of CH total column over selected cities during winter season 1.5 1 2009 0.5 -0.5 Cities Yearly difference variation of CH total column over selected cities during spring season 2007 1.5 0.5 0 2013 -0.5 Cities Yearly difference variation of CH total column over selected cities during summer season 0.5 0 2013 -0.5 Cities Yearly difference variation of CH total column over selected cities during autumn season 1.5 0.5 -0.5 2014 Cities Figure 8. Yearly difference variations of CH total column over selected cities of India region during the period 2006–2015 in (a) winter, (b) spring, (c) summer, and (d) autumn seasons. the years 2007, 2008, 2011, and 2014 in selected cities the selected cities are Lucknow, Bhopal, Ahmedabad, all through the period 2007–2015. Figure (9) shows Mumbai, Chennai, Bangalore, and Kolkata according yearly variations in all selected cities in the year 2015 to the base year 2006, whereas increment seen in rest according to the base year 2006, rise in the total of the selected cities during the period 2007–2015. In column of O in the autumn season, while on the summer the yearly variations of NO column amount 3 2 other hand over India region during the period over selected cities seen that there was decrement in 2007–2015 in all the selected cities, it shows decrement the selected cities are Lucknow, Hyderabad, in all the years excluding the 2015 year. Varanasi, and Bangalore in the years 2007–2010 and In winter the yearly variations of NO column 2015 according to the base year 2006, whereas incre- amount over selected cities seen that there was ment seen in rest of the selected cities during the a decrement in the selected cities are Bhopal, period 2007–2015. In autumn the yearly variations of Kolkata, Mumbai, Hyderabad, Chennai, and NO column amount over selected cities seen that Bangalore in the years 2007–2011 and 2013–2015 there was a decrement in the selected cities are according to the base year 2006, whereas increment Ahmedabad, Mumbai, Bangalore, Chennai, and seen during period 2007–2015 in rest of the years In Bhopal according to the base year 2006, whereas spring the yearly variations of NO column amount increment seen in rest of the selected cities during over selected cities seen that there was a decrement in the period 2007–2015 due to increase in traffic load 2 Conc. of CH4 (molec/cm ) Conc. of CH4 (molec/cm ) Conc. of CH4 (molec/cm ) Conc. of CH4 (molec/cm ) 12 K. GUPTA ET AL. Yearly difference variation O total column over selected cities during winter season 1 2012 Cities Yearly difference variation of O total column over selected cities during spring season -1 -2 Cities Yearly difference variation of O total column over selected cities during 1 summer season 0.5 -0.5 -1 -1.5 Cities Yearly difference variation of O total column over selected cities during autumn season -1 -2 Cities Figure 9. Yearly difference variations of O total column over selected cities of India region during the period 2006–2015 in (a) winter, (b) spring, (c) summer, and (d) autumn seasons. and vehicular emissions (during all seasons) are September. In March, found that the monthly varia- shown below in (Figure 10). tions of CO and NO values start increasing continu- ously manner until the end of July and September, and then again in March start increases till they reach the 4.3 Monthly and seasonal mean variations of maximum values shown below (Figure 11 (a,b)). trace gases Figure 12(a) showing that the least concentrations of O values were recorded in December, November, and During the periods 2006–2015 over the India region January respectively, and again shoot up uninter- the monthly and seasonal mean variations of the total rupted until May and then again showed a drop in column of CH , the total column of O , the total 4 3 June month. In June, July, and August respectively, the column of CO, and the column amount of NO are total amount of CH values were estimated as shown in (Figure 11–12 and Figure 13–14). Now for a minimum and they start increasing continually mean variations we have taken averaged monthly data from January to May and then decreasing and then sets of trace gases for each year to obtain it. In again increasing from August to December shown in (Figure 11 (a) and (b)) show the maximum monthly (Figure 12(b)). values of CO and NO in March, whereas (Figure 12 In India due to biomass burning activities in (a) and (b)) show the maximum monthly O values in the month from January to May the northern May and maximum monthly CH values in January hemisphere parts of South-East Asia play an indis- and December. During the last decade over the India pensable role (Sahu & Sheel, 2014). However, from region, we found minimum monthly CO, NO , and O 2 3 October – December lowest human action of fuel in July, September, December, and January, whereas CH was found minimum in June, July, and ignition was over South and South-East Asia (Sahu Conc. of Ozone (DU) Conc. of ozone (DU) Conc.of ozone (DU) Conc. of ozone (DU) GEOLOGY, ECOLOGY, AND LANDSCAPES 13 Figure 10. Yearly difference variations of NO total column over selected cities of India region during the period 2006–2015 in (a) winter, (b) spring, (c) summer, and (d) autumn seasons. & Sheel, 2014). For these monthly variations of months, and again decreases (M. M. I. Mamun, trace gases CO and NO over India during the 2014b). periods 2006–2015, therefore BB activities are the Intense seasonal variations are shown by the main factor for them to raise. Now wind velocity trace gases. Over the India region during the per- and rainfall with BB also play a vital role in low- iods 2006–2015 respectively, we inspect seasonal variations in the total column of CH , the total ering the CO column amount in July in S-SE (Sahu et al., 2013). But in August higher value of column of CO, the total column of O , and the CO is again seen as it shows random variation in total column of NO shown in (Figures 13 and 14). this month. In the month from January to May, To obtain climatology for each season viz. i.e., winter, spring, summer, and autumn, during the monthly variations of CH increase due to biofuel burning, coal mines, garbage dumps, etc. each year by averaging the monthly data set of and decreases then again increases from August to trace gases. In the spring and summer seasons, the highest sea- December during the period from 2006–2015 of monthly averaged data sets. Trends in monthly sonal average values were found of all four trace gases. variations of CO and NO show good similarities. On the other hand, in the summer season, the lowest Aerosol optical depth (AOD) is also found similar seasonal averages of the total column of CO and total to the monthly variation of trace gases as in June column of CH were found, whereas a total column of month from each year AOD start to rise and O and NO were found in the winter and spring 3 2 reaches its peak values during the summer seasons, respectively. Similar variations of CO and 14 K. GUPTA ET AL. 2.3 Total column of CO 2.2 2.1 1.9 1.8 1.7 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) (a) Monthly mean variations in total column of CO over India observed using AIRS data sets during 2006-2015 5.6 5.4 Total column amount of NO 5.2 4.8 4.6 4.4 4.2 3.8 3.6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) (b) Monthly mean variations in total column of NO over India observed using AIRS data sets during 2006-2015 Figure 11. Monthly mean variations in total column of (a) CO and (b) NO over India observed using AIRS data sets during 2006– CH values of seasonal averages; both showing Figure 14). And there are some other factors for varia- a decreasing trend in the autumn season and then tions in the total column of O which may be caused by (for example) large-scale air circulation in the stra- increasing in the spring which then relatively increases tosphere, O production by UV, etc. (Srivastava & until the next spring shown in (Figure 13). 3 Sheel, 2013) Overall, the main factors for the seasonal Contrastingly, the similar average seasonal variations of variation of trace gases are long-range transportation O and NO show an increasing trend in the spring that 3 2 (Prabhu et al., 2020), BB activities, and the local begins uninterruptedly until the end of the autumn and meteorology. then further increases from the winter season. Over S-SE Asia the BB emissions activities and long-range transportation have seasonal variations (Sahu et al., 4.4 Seasonal climatological trends of trace gases 2013). BB activities again increase in the autumn sea- son. After the spring season and then winter season Monthly data sets of trace gases (CO, NO , O , and 2 3 CH ) were analyzed to make climatological trends for higher concentrations of trace gases, due to the highly each season, viz. winter (December-February), spring contaminated air transported from the Indo-Gangetic (March-May), summer (June-August), and autumn Plain (IGP). The higher surface temperature causes (September-November), during each year. From higher NO concentration (NO soil emissions) during 2 x 2006–2015 over the India region to get all seasons, the spring season (Van Der et al., 2006). seasonal mean values drive decadal linear climatologi- Due to tropospheric O will be less distinct from the cal drifts by averaging each year. The climatological lower mixing ratio of anthropogenic air pollutants trends figure of total column CO, total column NO , (VOCs and NO ), and therefore in cold days in the total column CH , and total column of O are shown 4 3 winter season, O concentration was minimum. In the in (Figures 15 and 16). winter season therefore O formation is reduced (in Total column of CO (mol/cm ) Total column of NO (mol/cm ) 2 GEOLOGY, ECOLOGY, AND LANDSCAPES 15 Total column of O 280 3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) (a) Monthly mean variations in total column of O3 over India observed using OMI data sets during 2006-2015 3.6 Total column of CH 3.58 3.56 3.54 3.52 3.5 3.48 3.46 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time (month) (b) Monthly mean variations in total column of CH4 over India observed using AIRS data sets during 2006-2015 Figure 12. Monthly mean variations in total column of (a) O and (b) CH over India observed using OMI and AIRS data sets during 3 4 2006–2015. 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Winter Spring Summer Autumn Average total column amount of CO Average total column amount of CH4 Average total column amount of NO2 Figure 13. Seasonal mean of total column of CO, total column of NO , and total column of CH over India derived by remote 2 4 sensing satellite data sets during 2006–2015. Total column amount of CO, NO and Total column of CH (mol/cm ) 2 4 Total column of O (DU) CH (mol/cm ) 4 16 K. GUPTA ET AL. Total column of O Winter Spring Summer Autumn Figure 14. Seasonal mean of total column of O over India using OMI data during 2006–2015. 2.6 2.5 Total column of CO 2.4 2.3 2.2 2.1 1.9 1.8 1.7 1.6 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Time (month) Winter column amount of CO Spring column amount of CO Summer column amount of CO Autumn column amount of CO (a) Seasonal climatological trend of total column of CO 5.5 Total column amount of NO 5.3 2 5.1 4.9 4.7 4.5 4.3 4.1 3.9 3.7 3.5 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Time(year) Winter average total column amount of NO2 Spring average total column amount of NO2 Summer average total column amount of NO2 Autumn average total column amount of NO2 (b) Seasonal climatological trend of total column of NO Figure 15. Seasonal Climatological trends of total column of (a) CO and total column of (b) NO over India using remote-sensing data set during time periods 2006–2015. Average total column amount of 2 Total column of CO (mol/cm ) NO (mol/cm ) Total column of O (DU) 3 GEOLOGY, ECOLOGY, AND LANDSCAPES 17 Total column of O 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Time (Year) linear (winter total column of O3) Linear(spring total column of O3) Linear (summer total column of O3) Linear (autumn total column of O3) (a) Seasonal climatological trends of O 3.7 Total column of CH 3.65 3.6 3.55 3.5 3.45 3.4 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Time (year) Winter total column amount of CH4 Spring total column amount of CH4 Summer total column amount of CH4 Autumn total column amount of CH4 (b) Seasonal climatological trend of CH Figure 16. Seasonal Climatological trends of total column of (a) O and total column of (b) CH over India using remote-sensing 3 4 data set during periods 2006–2015. NO , CH , and O values graphs show increas- 5. Conclusion 2 4 3 ing trends during all seasons, but CO showed To study the variability of trace gases over the Indian a decreasing trend during winter, spring, summer, region during the periods 2006–2015, used satellite and autumn seasons in (Figure 15 (a)). Except for dataset of OMI and AIRS which shows the spatial, summer, AOD is also increasing in all seasons. In monthly, seasonal, and annual mean differences of CO, all seasons CH was rapidly increased as shown in CH , NO , and O gases. NO , CH , and O loadings 4 2 3 2 4 3 (Figure 16 (b)). are increasing, whereas CO is decreasing. CO, O , NO , 3 2 During the last decade in India, the results show and CH are showing higher concentrations in the a clear increase in trace gases loading with aerosols. Indo-Gangetic Plains i.e., from the western to eastern During the spring season, aerosol loading and all the part of India. Population density, transportation, local trace gases (M. I.P. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Oct 14, 2022
Keywords: AIRS; OMI; seasonal variation; air pollution; industrial emissions