Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.
We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.
To purchase hard copies of this book, please contact the representative in India:
CBS Publishers & Distributors Pvt. Ltd.
www.cbspd.com
|
customercare@cbspd.com
The COVID-19 pandemic was declared by World Health Organization (WHO) on 11 March 2020 and advised countries to take immediate and concerted action. The governments of India and Himachal Pradesh carried out preventive and precautionary steps to minimize the spread of coronavirus disease. In this study, the impact of a sudden halt in human activity on air quality was investigated by looking at changes in satellite imagery using a remote sensing approach. The concentrations of the gaseous contaminants studied (CO, SO2, NO2, and C6H6) show a significant decrease during the lockdown. The average particulate matter concentrations (PM10 and PM2.5) differed significantly from gaseous emissions, meaning that particulate matter significantly affects anthropogenic activities. NO2 concentrations and NOx emission variations were tracked for rural/town areas around Himachal Pradesh and major urban cities of India. Daily top-down NOx emissions were measured using the Tropospheric Monitoring Instrument (TROPOMI), which assisted in retrieving NO2 from the steady-state continuity equation. The emissions of NOx from rural, urban, and power plants were compared before and after the lockdown. The research accounted for our studies on the levels of (NO2, Ozone (O3), and sulfur dioxide (SO2) were monitored using Sentinel-5P imagery using the GEE platform.
School of Interdisciplinary Research (SIRe), Indian Institute of Technology, India
Chander Prakash
Department of Civil Engineering, NIT, India
Devansh Raniwala
The Neerja Modi School, India
*Address all correspondence to: srz208737@sire.iitd.ac.in
1. Introduction
Dissolved gases in the air, especially (O2, O3, N2, and so on) are valuable and essential resources that help sustain life on earth. There is ever-increasing stress on openly accessible air resources due to the atmospheric pollution caused due to the advent of the use of fossil fuels, industrial discharges, and transport traffic, with only one factor responsible for it: the rise in population. An ever-increasing pressure mounting on them due to heavy dependence on fossil fuels leads to an unprecedented health crisis ranging from local to national levels [1, 2]. Air is vital to sustain and flourish the planet’s health and the environment [3, 4]. However, with rapid globalization, urbanization and industrialization, there has been an enormous crisis with unhealthy air quality levels leading to vulnerable ecosystems [5]. Poor air quality in developed Western countries leads to roughly 60,000 deaths annually. The economic costs and repercussions are massive staggerings at $150 – $ 200 billion in prices leading to high levels of air pollutants. Equally important information that supports life in an ecosystem is delivered by ambient air quality resources [6]. An increase in air pollution hampers and deteriorates ambient air quality and threatens human health, aerial ecosystem balance, economic development, and social well-being [7, 8].
According to the World Health Organization (WHO), 90% of the world population lives in harsh to dangerously polluted places, breathing high levels of air containing highly high levels/rates of pollutant concentrations, and 5–7 million deaths occur every year as a result of exposure to ambient air pollution and from the exposure to smoke from fuels such as wood and fossil fuels [5, 8]. As described earlier, the population’s health degradation occurs due to the onset and remission of harmful industrial pollutants. Nitrogen dioxide is one such greenhouse gas (GHG) that is reddish and results from NO conversion in the presence of volatile organic compounds (OVCs) [9]. NO2 monitoring is quintessential because of the potential threat they hold:
NO2 being a primary pollutant, is a significant cause of the creation of secondary pollutants such as peroxyacetyl nitrates (PAN), ozone (O3), and nitric acid (HNO3).
Visibility reduction in urban areas.
Negative impacts on human health.
The onset of the efforts that were carried out to limit the transmission of the SARS-CoV-2 virus that led to an unprecedented havoc and pandemic situation across the globe for roughly two years was minimized by carrying out strict lockdown implementations. These policies, in hindsight, had a more considerable extent impacting the day-to-day activities which confined the public within their homes, reducing human activities, especially in the industrial and transport sectors [10]. This led to a significant decrease in air pollution concentration levels. Like NO2, other greenhouse gases (GHGs) have a dampening and profound negative effect, impacting the overall health cycle of ambient air qualitative concentrations [4, 7].
Air quality resource management requires a continuous and accurate monitoring assessment to support a real-time network. Satellite remote sensing observations [1, 9] have provided real-time and continuous data that have been beneficial in tracking across several years [11] and have served in a time- and cost-effective manner for carrying out large-scale monitoring [2, 3]. Air pollution is an important environmental issue, which needs to be addressed with solid policy implementations with technical guidelines and administrative protocols. For measurements across space and time, remote sensing satellite observation can play a pivotal role in atmospheric measurements related to air quality. As a result of the European Space Agency’s close collaboration (ESA) with the Netherlands, the European Commission, EU industry, data users, and scientists came together for the Copernicus Sentinel-5 Precursor mission (Sentinel-5P). Sentinel-5P satellite was successfully launched from the Russian Federation (Plesetsk cosmodrome) on 13 October 2017. The main objective of the Copernicus Sentinel-5P’s mission is to perform atmospheric measurements with a high spatio-temporal resolution for air quality, ozone & UV radiation, and climate monitoring & forecasting. The task consists of one satellite carrying the Tropospheric Monitoring Instrument (TROPOMI). The TROPOMI instrument was co-funded by ESA and The Netherlands. The TROPOMI [10, 12] was on board with the 49 European Copernicus Sentinel-5 Precursor (S5P) satellite designed explicitly for 50 tropospheric monitoring on the global scale and has a daily revisit time. If we compare this to its predecessor OMI, TROPOMI’s spatial resolution (3.5 x 5.5 km2) is roughly 15 times better, and its signal-to-noise ratio (SNR ratio) per ground pixel is much more dominant. For relevant air quality products, including NO2, SO2, HCHO, and CHOCHO, this results in a spectacular improvement in measurement sensitivity, thus enabling the study of rapid emission changes for even smaller sources than existing options. The daily global coverage of TROPOMI for CO measurements is at a resolution of 7 x 5.5 km2, representing a massive improvement from its predecessor SCIAMACHY [13], especially with its spatial resolution.
The absorbent dissolved gases in the air and the estimations from Sentinel-5P provide an opportunity to observe the magnitude and timing of the changes in tropospheric trace gas constituents resulting from unprecedented COVID-19 lockdown measures. The initial TROPOMI observations offer a template that looks into the dramatic reduction and significant changes in the NO2 concentrations over regions with strictly enforced multiple lockdowns and mini-cluster zones in important cities for pan India. This triggered a high level of interest across the globe and initiated a massive scale of approximately 60 plus studies, initially starting at a global scale and then mainly aimed at a regional scale and finally, primarily focused on GHG emissions estimation [4]. However, the unparalleled capacity of TROPOMI to provide relevant information on COVID-19-driven emission reductions based on multiple measurements has not been exploited at a regional scale as far as was expected. The present research is to estimate the COVID-19-driven changes in the concentration of a few major trace gases (CHOCHO, NO2, SO2, CO, HCHO, etc.) from the regional scale for a region in the North of India, Himachal Pradesh, surrounded by the Himalayas from the North and bordered by China in the east with the impact of lockdown restrictions and using state-of-the-art TROPOMI operational and scientific data products. In doing so, we further try to expand on the view that the unique capabilities provided by the TROPOMI instrument are helpful inconsistently tracking the changes in ambient air quality and anthropogenic emissions across the region of interest [7].
These gases have significant human-induced anthropogenic effects with their relative contribution to energy variations, industrial standards, and transport sector emissions [6]. Each sector responded at a different scale to the COVID-19 lockdown. Several TROPOMI trace gas products contain additional metadata on emissions emitted at scale level and the COVID-19 lockdown-induced impacts on the atmospheric structure. We show that meaningful visualization graphical plots, trends, and source plots can be obtained using the high spatial resolution of TROPOMI data. By taking the median composite of the study area. Although this is primarily the result of the improved response or sensitivity of the instrument, we also introduce new engagements and developments in trace gas retrieval techniques and tweaking to enhance the sensor sensitivity of the TROPOMI datasets to even smaller trace determinations and more minor changes in emissions. To achieve these goals, we discuss the way forward and the limitations of each of the estimations or retrievals for monitoring regional changes [9].
In the present section, we have described TROPOMI data in general terms, followed by the study area, the methodology used to address the retrieval process, and a detailed discussion and analysis of how we handle each data result in this study [8]. The goal of this methods and data section is not only to explain how this study was conducted but also to provide guidance to remote sensing analysts, GIS enthusiasts, and research domain interest holders on how to best interpret, understand and analyze TROPOMI trace gas data not only for lockdown-driven emission changes but also for other event-driven changes. The below section describes the study area and the impacts of COVID-19 lockdown measures on all continents, using TROPOMI NO2 data. The following two areas will tell the effect of the lockdown measures on a regional scale by examining NO2, SO2, CO, HCHO, and CHOCHO for the North part of India. The last part of the book chapter will cater to the progressive outlook of future possibilities, the challenges, limitations, and a way forward for this type of analysis, followed by a conclusion and references [14].
The study area chosen lies in the northern part of India in the North belt of the Himalayas in Himachal Pradesh. Himachal Pradesh is a province of snow-laden mountains that constitute India’s north part. Situated in the Western Himalayas, it is one of the 13 mountain states and is characterized by an extreme landscape featuring several peaks and extensive river systems. Himachal Pradesh is the northernmost state of India and shares borders with the union territories of Jammu and Kashmir and Ladakh to the North, and the conditions of Punjab to the west, Haryana to the southwest, Uttarakhand to the southeast, and a very narrow border with Uttar Pradesh to the south. Himachal is in the western Himalayas, situated between 30°22′N and 33°12′N latitude and 75°47′E ́ and 79°04′E longitude. They cover an area of 55,673 square kilometers (21,495 sq. mi). The drainage system of Himachal is composed both of rivers and glaciers. Himalayan rivers crisscross the entire mountain chain. Himachal Pradesh provides water to both the Indus and Ganges basins. The drainage systems of the region are the Chandra Bhaga or the Chenab, the Ravi, the Beas, the Sutlej, and the Yamuna. These rivers are perennial and are fed by snow and rainfall. An extensive cover of natural vegetation protects them. Four Punjab rivers flow through the state, three originating here. The state of Himachal Pradesh is divided into 12 districts and three significant sub-divisions: Shimla, Kanga, and Mandi. The districts are divided into 73 subdivisions, 78 blocks, and 172 Tehsils. The predominantly mountainous region comprising the present-day Himachal Pradesh has been inhabited since prehistoric times, witnessing multiple waves of human migrations from other areas. Himachal Pradesh is spread across valleys with many perennial rivers flowing through them.
Roughly 90% of the state’s population lives in rural areas. Agriculture, horticulture, hydropower, and tourism are essential sectors contributing to the state’s economy. The hilly state is almost universally electrified, with 99.5% of the households having electricity as of 2016. The state was declared India’s second open-defecation-free state in 2016. According to a survey of CMS – India Corruption Study 2017, Himachal Pradesh is India’s least corrupt state.
The below figure gives a complete description of the study area, and the scope of interest, which is highlighted in orange color is depicted and shown on the map (Figure 1).
Figure 1.
Study area with the region highlighted on pan India map, Himachal Pradesh state location and boundary, the orange area shows a few of highlighted districts, for the part of interest (ROI), we have researched the entire state.
In this research study, our interpretation, analysis, and research are primarily based on TROPOMI data for regional understandings of the defined study area and compared with other major cities. The period of interest was initiated during the lockdown starting from mid of March 2020 and then carried out a comparison concerning the pre-lockdown periods of 2019. The results and analysis are presented in the broader context of the TROPOMI operational data, which started in April 2018. The sensor we use for carrying out the observations is from the TROPOMI instrument, which was onboarded onto the S5P and categorized as a push-broom imaging spectrometer [10]. This helps to measure the ultraviolet (UV), visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) spectral band combinations, which were selected keeping in mind to cover the absorption features, i.e., water absorption features, cloud features absorption and with a presence of a large number of gaseous traces having atmospheric constituents. Using the spectral radiance measurements available from TROPOMI, atmospheric concentrations of gaseous trails are retrieved, and cloud and aerosol properties are determined. We use the following TROPOMI data products for this work: NO2, SO2, CO, HCHO, and CHOCHO, summarized in Table 1. We have tried to accommodate as many dissolved trace gases as we can. The S5P satellite flies in a sun-synchronous orbit, with a local overpass time of 13:30. TROPOMI has a 2600 km wide swath, providing near-daily global coverage. The spatial sampling of TROPOMI varies over the spectral bands [11]. The nadir/top-view sampling was at the start of the operational period in the initial period of 2018, which was approximately 3.5 x 7 km2 (across- x along-track) for the ultraviolet (UV) and visible bands, and a 7 x 7 km2 cross-section area covered by the shortwave infrared band (SWIR). In the second half of 2019, implementing a modified co-adding scheme, the sampling for these bands was thus, improved to 3.5 x 5.5 km2 and 7 x 5.5 km2, respectively [18].
Summarizes the trace gases, their spectral range changes, atmospheric lifetime, and the primary emission sources for each trace gas addressed in this study.
Sentinel-5P observations are widely utilized within and beyond the remote sensing and GIS community. For the benefit of scientific users, it is crucial to provide information on how these observations can best be used, interpreted and analyzed [19]. The COVID-19 lockdown periods offer a unique use case for the Sentinel-5P lead model developers to highlight essential nuances in the individual atmospheric trace gases’ lifetime and the detectability of each trace gas and show how these characteristics are critical to the interpretation of the highlighted observations. It is not sufficient, for example, to illustrate the lockdown-driven changes that affect the emissions simply by selecting a single day or week of TROPOMI column data for a given region as measured during a lockdown period for the same day or week from the year(s) prior [17]. We further address the impending challenges that need to be addressed and which are taken into cognizance, especially the description of the meteorological and seasonal variability from lockdown-driven pre and post-changes in trace gas emissions.
A brief tabulation and the sources of trace gas emissions and their lockdown-driven changes are depicted in an evaluative format. Generally, primary production trace gases, like NO2 and SO2, have a short life span and exhibit emission changes of utmost precision. Although NO2 and SO2 are primary producers of anthropogenic gaseous pollutants, the source sectors differ in each case [12]. For instance, the impact of lockdown on the power industry and the transportation sector was projected to significantly have a more considerable effect on NO2 and SO2 levels, as this sector is responsible for approximately 30% of the global NOx emissions and about 1% of the global SO2 emissions [4]. On the other hand, SO2 emissions are most likely areas to be impacted by possible changes in power generation, as this sector accounts for 52% of the global SO2 emissions and approx—30% of the worldwide NOx emissions [4].
For CO, secondary production by methane oxidation and (biogenic) hydrocarbons accounts for at least 60% of the total atmospheric CO, followed by contributions from biomass burning and fossil fuel use [20]. Anthropogenic CO emissions originate from the industry, transportation, and residential sectors and account for about 30% of the global emissions [4]. Although local impacts of lockdown are likely for locations with anthropogenic solid CO emissions, overall, a much smaller lockdown-driven result is expected for CO based on its longer atmospheric lifetime and smaller contributions from lockdown-affected sources.
The trace gases of HCHO and CHOCHO are shortly living indicators of the non-methane volatile organic compound (NMVOC) categories resulting from an abiogenic process. The anthropogenic lead activity is significant to biomass exclusion and burning events [5]. They are primarily produced as secondary products from the oxidation of other NMVOCs but are also directly emitted from combustion and industrial processes, although to a lesser extent. In general, the relative production of CHOCHO from such combustion processes and the oxidation of aromatics, originating primarily from the industrial sector, is higher than for HCHO. Thus, the CHOCHO response to changes in anthropogenic emissions is expected to be more robust [17]. Retrievals provide information on the lower atmosphere/tropospheric level or total column that amounts to these gases because the spectra contain limited information on their vertical distribution in the atmosphere. TROPOMI observations thus provide a two-dimensional representation of the three-dimensional atmosphere [20]. The vertical profiles of each trace gas vary and significantly depend on the emissions’ height and the trace gases’ atmospheric lifetime (see Table 1). For example, NOx emissions at the surface result in NO2 vertical profiles that peak in the near-surface layer (lowest 1–2 km of the troposphere) due to the short lifetime of NO2. Similarly, SO2 has a vertical profile that generally peaks in the lower troposphere.
On the other hand, CO has a lifetime period typically of weeks - to months and can be transported over great distances, both horizontally and vertically. Therefore, even though CO is often co-emitted with NO2, it has a significantly higher background concentration throughout the troposphere than NO2. HCHO and CHOCHO have lifetimes of a few hours. Still, they are generally formed in the atmosphere via secondary production processes, leading to an intermediate profile shape compared to NO2 and CO [21].
In addition to vertical profiles that vary per trace gas type, the vertical sensitivity of the TROPOMI measurements also varies across the variety and nature of trace gases. The sensitivity decreases towards the surface for the trace gases favorable and sensitive to the UV and VIS ranges. The accuracy of the retrieved column depends on a well-characterized a priori knowledge of the vertical distribution lead across it. Due to scattering, the near-surface sensitivity is lower in the UV (SO2, HCHO) than in the VIS (NO2 and CHOCHO) [22]. In the SWIR range, the vertical sensitivity is more constant and reaches well into the saturation stage. As part of the retrieval process, a priori vertical profiles of each trace gas are scaled to match the measured tropospheric column. Uncertainty in the retrieved column amount or sheer column density (VCD) is associated with inherent differences between the true and the available vertical profiles. However, the averaging kernels reported in the data products can be used to replace the a priori profiles with existing custom profiles [6], reducing the corresponding uncertainty. This study mainly focuses on changes in VCDs and uses standard a priori profiles for each data product. Therefore, the uncertainty related to the vertical profile is relatively small [13]. Another contribution to this error is partially cloudy scenes for each retrieval which raises the amount of unusable data and changes the vertical sensitivity. The cloud fraction threshold for each trace gas is described. In future studies, the averaging kernels could be used for inversion modeling existing emissions, thus, eliminating the limitations [23].
TROPOMI observes concentration changes of the emissions of the trace gases taken from the median or averaged out over a vertical column, which is not similar to the direct measurement of the near-surface emission. The column-averaged amount of a given trace gas measured at a specific location depends on emission, deposition, atmospheric changes, and photochemical reactions. Note that the background concentration is higher for trace gases with a longer atmospheric lifetime. In turn, enhanced background concentrations will increase the relative importance of atmospheric transport compared to local emissions. Local NO2 emissions significantly impact At the same time, for CO, the contribution of remote sources can, in some cases, be superimposed on local emissions, thus making the interpretation more difficult. The effects of atmospheric climatic changes and chemical impact must also be encountered to attribute a change in concentration to a corresponding shift in local emissions [16].
The major components of our research are collated into the following objectives, which are as listed below:
To estimate the concentration changes in (CO, SO2, NO2, and Aerosol (PM2.5, PM10) from the Sentinel-5 Precursor mission assessing the air quality. The TROPOMI instrument is a multispectral sensor with a spatial resolution of 0.01 arc degrees.
To compare the pollutant concentration changes from pre to post-lockdown conditions prevailing across the state.
To visualize the trace gas concentration changes as part of geospatial air quality theme maps and do a trend analysis where gas concentration changes are mapped as a time duration function.
To analyze the periodical changes in the absorbing aerosol index (AAI) across the one cycle period for the study area.
3.1 Methodology
During the 1st phase of COVID-19, India carried out intensive and strict national lockdown measures limiting activities across the country, which had started on 24 March 2020, for 21 days to tackle the spread of the SARS-CoV-2 virus and protect its 1.3 billion inhabitants [24]. Careful region-based relaxations followed the stringent initial phase 1 restriction in three subsequent steps carried out through the end of May, as shown inTable 2:
The country is split into 3 zones: (i) lockdown zone, (ii) zone with movement with private and hired vehicles, and (iii) normal movement zone.
India today
Phase 4
17th May – 31st May
Additional relaxations, more authority are given to local bodies.
The Economic Times
Table 2.
Lockdown phases in India.
Figure 2 gives an extent of TROPOMI observations of NO2, SO2, CO, HCHO, and CHOCHO, over India for April 2020, thus covering most phases 1 and 2 of the Indian lockdown compared to the same month in 2019. For NO2 and SO2, the concentrations are lower across the country in 2020 compared to 2019. Although less prominent, CO, HCHO, and CHOCHO appear to be lower in April 2020 over the Indo-Gangetic Plain (IGP) domain, which is one of the most densely populated areas of the world with roughly 900 million people [15].
Figure 2.
The atmospheric trace gas variations between April 2019 and April 2020 for pan India. Image source: https://acp.copernicus.org/preprints/acp-2021-534/.
NO2 trace gas production sources are mainly from the traffic transport and energy-power sectors, roughly contributing about 30% of total anthropogenic emissions in India [4]. During phase 1 of the lockdown, the traffic had dropped by 80% [22], and energy consumption dropped by 25% compared to 2019 [3]. We expect a substantial reduction in NO2, particularly in urban areas, due to significant decreases in transport sector activities. We also expect a weaker reduction near power plant corporations due to a smaller decrease in the energy demand. Indeed, as indicated by the maps of NO2 column concentrations in Figure 2, a notable reduction in NO2 can be seen in April 2020 compared to April 2019 [10]. An apparent reduction is observed in significant cities and the eastern part of India, where the most influential power plants are located. When both city centers and power plants are located within a 45 x 45 km2 box, this box is excluded from the averages to avoid the potential outflow of one source to the other. A sharp reduction of 42% can be seen in the amount of NO2 over cities during the first phase of the lockdown period starting at the end of March, compared to the same period in 2019. This initial drop in NO2 concentration is followed by a gradual increase in concentration back to pre-lockdown levels with the successive relaxation phase implementations [25].
A sharp reduction of roughly ~45% in the amounts of NO2 can be noticed for significant cities during the 1st phase of the lockdown period starting at the end of March, as compared to the same period in 2019. A slow but gradual increase follows this initial drop in NO2 in line with the successive relaxation phase. Power creation is a significant source of NO2 in India, mainly observed in coal-fired power plants. When examining the average amount of NO2 over the few largest coal-fired power plants, the observation was the significant decrease in NO2 during phase 1 of the lockdown period [26]. The fall in the observed coal-fired power plant sector is 23% compared to 2019 data, far less than the observed fall in NO2 over large cities. Sentinel- 5P also observed an overall reduction in NO2 with the shutting down of coal-power plants which were in line with the initial 25% decrease in the overload of electricity demand as reported by the National Load Dispatch Centre (NLDC) during phase 1 and lowering it to an 8% decrease during final steps of the lockdown (Fig. D1, [3]) (Figure 3).
Figure 3.
Average tropospheric NO2 concentrations for may 2018 (green), 2019 (black) until June 2020 (red) over the few largest Indian cities (top); and the few largest power plants in India (middle); and average SO2 concentrations over the largest SO2-emitting power plants in India. The different gray shading denotes the four different phases of the lockdown period. For each step, the reductions in NO2 (or SO2) concentrations are given relative to the definite periods in 2019. The dots are the daily means, and the solid lines represent the 7-day mean values [27]. Image source: https://acp.copernicus.org/preprints/acp-2021-534/.
According to the latest information from the emission inventory of 2019, the significant sources of SO2 emissions in India are power generation (65%) and industry (25%) [4]. Since India largely depends on coal to fulfill its energy production, it is now the world’s top emitter of anthropogenic SO2 [19]. So, most of the SO2 signal we observe in Sentinel- 5P data for this region is from coal-fired power plants, where contributions from conventional fossil fuels in India comprise a minor part of the response [3]. A reduction in SO2 is visible over most areas. It is especially noticeable for the central-eastern portion of India, India’s largest SO2-emitting region, with much more than the coal-fired power plants available at other locations [28].
The previous section dealt with changes in trace gas concentrations at the pan India scale. Still, this section will analyze the trace gas concentrations at the local and regional rankings for the study area region for the state of Himachal Pradesh. The work was carried out for the mentioned study area in the pre-lockdown and the post-lockdown steps, divided into 4 phases. The trace gas variability of CO (mol/m2), NO2 (mol/m2), and Absorbing Aerosol Index (AAI) (μg/m3) is depicted for both 2019 and 2020 (Figures 4 and 5) [29].
Figure 4.
The trace gas variability of CO (Mol/m2), NO2 (Mol/m2), and absorbing aerosol index (AAI) (μg/m3) is depicted for both 2019 and 2020.
Figure 5.
The NO2 concentration measured in (μmol/m2) is given in the above chart diagram for 2018–2022. Between march and April 2020, it’s visible in the graph with the plummeting of NO2 concentration values near the duration of 100 days and rising again with the lifting of lockdown that occurred phase-wise.
The UV Aerosol Index concentration measured as an index value in (−2.0 to 0.5) is given in the below chart diagram for 2018–2022. Between March and April 2020, it’s visible in the graph with the plummeting of UV Aerosol Index concentration values near the duration of 100 days and rising again with the lifting of lockdown that occurred phase-wise (Figure 6).
Figure 6.
The measured UV aerosol index concentration is given in the above chart diagram for 2018–2022.
The CO concentration measured in (mol/m2) is given in the chart diagram below for 2018–2022. Between March and April 2020, it’s visible in the graph with the plummeting of CO concentration values near the duration of 100 days and rising again with the lifting of lockdown that occurred phase-wise. The TROPOMI Explorer App provides a lucid visualization of different trace gas combinations; in this case, it’s for CO concentration which is depicted as a 9-day mean vertically integrated column (Figure 7).
Figure 7.
The CO concentration measured in the above chart diagram for 2018–2022.
The HCHO concentration measured in (μmol/m2) is given in the below chart diagram for 2018–2022. Between March and April 2020, it’s visible in the graph with the plummeting of HCHO concentration values near the duration of 100 days and rising again with the lifting of lockdown that occurred phase-wise (Figure 8).
Figure 8.
The HCHO concentration measured in the above chart diagram for 2018–2022.
The CH4 concentration measured in (ppmV) is given in the below chart diagram for 2018–2022. Between March and April 2020, it’s visible in the graph with the plummeting of CH4 concentration values near the duration of 100 days and rising again with the lifting of lockdown that occurred phase-wise (Figures 9 and 10).
Figure 9.
The CH4 concentration measured in (ppmV) is given in the above chart diagram for 2018–2022.
Figure 10.
The figure depicts the TROPOMI explorer (an application to visualize air pollutant time series data).
This paper discusses the results and discussions we showcase as a part of our study. In this paper, we have analyzed the impact of COVID-19 lockdown measures on air quality around the region of pan India, explicitly focusing on the state of Himachal Pradesh. These were based on the understanding and the observations of several trace gases from the Sentinel-5P. Sentinel-5P provides daily, global observations of multiple trace gases. The measured vertical column amounts are driven by emissions, including atmospheric, chemical source, and destination processes with multi-dimensional changes. We analyzed the time series of trace gas measurements from pan India to the regional level (state of Himachal Pradesh) and made a calculative comparison. We looked into the regional impacts of COVID-19 lockdown measures on the ambient air quality and anthropogenic emissions.
Furthermore, for the first time, we used a combination of five trace gases observed by Sentinel-5P, specifically NO2, SO2, CO, HCHO, and CH4, to assess the impact of COVID-19-related lockdown measures on the trace gas concentrations levels. TROPOMI data have been used to analyze the implications of COVID-19 lockdown measures on the ambient air quality and air pollution levels. These studies have been based on NO2 observations more than the other trace gases. We contemplate that the combined use of available trace gases from TROPOMI and the high spatial resolution of the sensor platform has massive potential for a significantly improved sector-specific analysis of the impact of the COVID-19 lockdown measures than previously encountered.
AG conceptualized, initiated, and managed this manuscript with contributions from CP. AG carried out a formal analysis. AG provided data curation and software support for Sentinel-5P data products with work on Google Earth Engine (GEE). AG prepared, edited, and co-managed the manuscript with minor changes done by CP.
1.Aljazeera: Coronavirus in India: What We know About World's most Extensive Lockdown. 2020a. pp. 912-13 https://www.aljazeera.com/news/2020/05/india-coronavirus-crisis-200519120521747.html. [Accessed: June 17, 2020]
2.Afshari R. Indoor air quality and severity of COVID-19: Where communicable and non-communicable preventive measures meet. Asia Pacific Journal of Medical Toxicology. 2020;9(1):1-2
3.Dattakiran J. Impact of Lockdown on India's Electricity Sector, Energy A. 2020 http://www.energy-a.eu/impact-of-ongoing lockdown-on-Indias-electricity-sector-an-overview/. [Accessed: June 9, 2020]
4.De Smedt I, Bai J, de Leeuw G, Theys N, Van Roozendael M, Sogacheva L, et al. Variations and photochemical transformations of atmospheric constituents in North China. Atmospheric Environment. 2018;189:213-226
5.BBC: India Extends Coronavirus Lockdown by Two Weeks. 2020a https://www.bbc.com/news/world-asia-india-52698828. [Accessed: June 17, 2020]
6.Fioletov V, McLinden CA, Griffin D, Theys N, Loyola DG, Hedelt P, et al. Anthropogenic and volcanic point source SO 2 emissions derived from TROPOMI on board Sentinel-5 precursor: First results. Atmospheric Chemistry and Physics. 2020;20(9):5591-5607
7.Granier C, Darras S, van der Gon HD, Jana D, Elguindi N, Bo G, et al. The Copernicus Atmosphere Monitoring Service global and regional emissions (April 2019 version). [Research Report] Copernicus Atmosphere Monitoring Service; 2019
8.Ialongo I, Virta H, Eskes H, Hovila J, Douros J. Comparison of TROPOMI/Sentinel-5 precursor NO 2 observations with ground-based measurements in Helsinki. Atmospheric Measurement Techniques. 2020;13(1):205-218
9.Jain S, Sharma T. Social and travel lockdown impact considering coronavirus disease (COVID-19) on air quality in megacities of India: Present benefits, future challenges, and ways forward. Aerosol and Air Quality Research. 2020;20:1222-1236. DOI: 10.4209/aaqr.2020.04.0171
10.Prabhjote G. The most congested cities in India lie vacant amid the nationwide lockdown. Business Insider India, https://www.businessinsider.in/india/news/most-congested-cities-in-india-low-lie-vacant-midst-thenationwide-lockdown/articleshow/75243376.cms, last access: 30 March 2021. 2020
11.Kharol SK, Fioletov V, McLinden CA, Shephard MW, Sioris CE, Li C, et al. Ceramic industry at Morbi as a significant source of SO2 emissions in India. Atmospheric Environment. 2019;1189:223. DOI: 10.1016/j.atmosenv.2019.117243
12.Lerot C, Theys N, Volkamer R, Müller JF, Zarzana KJ, Kille N, et al. Wildfires enhance global nitrous acid emissions and levels of regional oxidants. Nature Geoscience. 2020;13(10):681-686
13.Lokhandwala S, Gautam P. The indirect impact of COVID-19 on the environment: A brief study in the Indian context. Environmental Research. 2020;188:109807
14.Mahato S, Pal S, Ghosh KG. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Science in the Total Environment. 2020;730:139086. DOI: 10.1016/j.scitotenv.2020.139086
15.Van Geffen JH, Lorente A, Boersma KF, Eskes HJ, Veefkind JP, De Zeeuw MB, et al. Quantification of nitrogen oxide emissions from the build-up of pollution over Paris with TROPOMI. Scientific Reports. 2019;9(1):1-10
16.Theys K, Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, et al. Computational strategies to combat COVID-19: Practical tools to accelerate SARS-CoV-2 and coronavirus research. Briefings in Bioinformatics. 2021;22(2):642-663
17.Landgraf J, Fu D, Bowman KW, Worden HM, Natraj V, Worden JR, et al. High-resolution tropospheric carbon monoxide profiles were retrieved from CrIS and TROPOMI. Atmospheric Measurement Techniques. 2016;9(6):2567-2579
18.Sun K, Zhu L, Cady-Pereira K, Chan Miller C, Chance K, Clarisse L, et al. A physics-based approach to oversample multi-satellite, multispecies observations to a standard grid. Atmospheric Measurement Techniques. 2018;11(12):6679-6701
19.Kumari P, Toshniwal D. Impact of lockdown measures during COVID-19 on air quality–a case study of India. International Journal of Environmental Health Research. 2020;32(3):503-510. DOI: 10.1080/09603123.2020.1778646
20.Levelt PF, Stein Zweers DC, Aben I, Bauwens M, Borsdorff T, De Smedt I, et al. Air quality impacts of COVID-19 lockdown measures were detected from space using high spatial resolution observations of multiple trace gases from sentinel-5P/TROPOMI. Atmospheric Chemistry and Physics Discussions, 1-53. 2021;10(7):356-368. DOI: 10.5194/acp-2021-534 in review, 2021
21.Li C, McLinden C, Fioletov V, Krotkov N, Carn S, Joiner J, et al. India is overtaking China as the World's largest emitter of anthropogenic sulfur dioxide. Scientific Reports. 2017;7:14304. DOI: 10.1038/s41598-017-14639-8
22.Li X, Jónsson S, Cao Y. Interseismic deformation from Sentinel-1 burst-overlap interferometry: Application to the southern Dead Sea fault. Geophysical Research Letters. 2021;48(16):e2021GL093481
23.Ludewig A, Kleipool Q , Bartstra R, Landzaat R, Leloux J, Loots E, et al. In-flight calibration results of the TROPOMI payload on board the Sentinel-5 precursor satellite. Atmospheric Measurement Techniques. 2020;13(7):3561-3580
24.Singh KD, Goel V, Kumar H, Gettleman J. India, Day 1: World's largest coronavirus lockdown begins. The New York Times, https://www.nytimes.com/2020/03/25/world/asia/india-lockdown-coronavirus.html, last access: 30 March 2021. 2020;9(1):312-321
25.Verhoelst T, Compernolle S, Pinardi G, Lambert JC, Eskes HJ, Eichmann KU, et al. Ground-based validation of the Copernicus sentinel-5p TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS, and Pandonia global networks. Atmospheric Measurement Techniques. 2021;14(1):481-510
26.Sharma M, Jain S, Lamba BY. Epigrammatic study on the effect of lockdown amid Covid-19 pandemic on air quality of most polluted cities of Rajasthan (India). Air Quality, Atmosphere & Health. 2020;13(10):197-1165
27.Veefkind JP, Aben I, McMullan K, Förster H, De Vries J, Otter G, et al. TROPOMI on the ESA Sentinel-5 precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality, and ozone layer applications. Remote Sensing of Environment. 2012;120:70-83
28.Schneising O, Buchwitz M, Reuter M, Bovensmann H, Burrows JP, Borsdorff T, et al. A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 precursor. Atmospheric Measurement Techniques. 2019;12(12):6771-6802
29.Qiu Z, Ali MA, Nichol JE, Bilal M, Tiwari P, Habtemicheal BA, et al. Spatiotemporal investigations of multi-sensor air pollution data over Bangladesh during COVID-19 lockdown. Remote Sensing. 2021;13(5):877
Written By
Abhinav Galodha, Chander Prakash and Devansh Raniwala
Submitted: 20 April 2022Reviewed: 02 May 2022Published: 11 October 2022
Open access peer-reviewed chapter
