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Standard spectral ratio (SSR) technique has limited applicability in the estimation of spectral amplification in the vast Indo-Gangetic plains (IGP). Here we take recourse to an alternative approach using the recordings of three largest aftershocks of the 2015 Gorkha earthquake (Mw 7.9). We separately compute geometric mean source spectrum of an event from the recordings at hard sites in India and from the target IGP site. The ratio of the source spectrum from the target IGP site to the geometric mean source spectrum from the hard sites (denoted here, for brevity, as RSS) provides the desired spectral amplification. At four soft IGP sites where a comparison is possible, the spectral amplifications from RSS and SSR methods show a reasonable resemblance but also significant differences, owing to the difference in the definition of the reference spectrum. RSS method may be preferable if the input motion can only be prescribed at a generic hard site. We document amplification at 28 IGP sites using the RSS technique. The fundamental frequency, f0, of the sites increase from 0.12 Hz near the foothills of Himalayas to 2.0 Hz at the southern edge of the basin and the amplification reaches about 10. At several sites, f0 is difficult to select and the amplification of ~5 is broadband in the range 0.12–0.7 Hz. Application of SSR technique to teleseismic S-wave data recorded in the IGP reveals that this approach may be useful in the estimation of amplification at low frequencies (f < 0.5 Hz).

This study presents a comprehensive 1 year (January 2007–March 2008) data set on the chemical composition of ambient aerosols collected from an urban location (Kanpur) in the Indo‐Gangetic Plain (IGP) and suggests that the varying strength of the regional emission sources, boundary layer dynamics, and formation of secondary aerosols all contribute significantly to the temporal variability in the mass concentrations of elemental carbon (EC), organic carbon (OC), and water‐soluble OC (WSOC). On average, carbonaceous aerosols contribute nearly one third of the PM10 mass during winter, whereas their fractional mass is only ∼10% during summer. A three‐ to four‐fold increase in the OC and K+ concentrations during winter and a significant linear relation between them suggest biomass burning (wood fuel and agricultural waste) emission as a dominant source. The relatively high OC/EC ratio (average: 7.4 ± 3.5 for n = 66) also supports that emissions from biomass burning are overwhelming for the particulate OC in the IGP. The WSOC/OC ratios vary from 0.21 to 0.70 over the annual seasonal cycle with relatively high ratios in the summer, suggesting the significance of secondary organic aerosols. The long‐range transport of mineral aerosols from Iran, Afghanistan, and the Thar Desert (western India) is pronounced during summer months. The temporal variability in the concentrations of selected inorganic constituents and neutralization of acidic species (SO42− and NO3−) by NH4+ (dominant during winter) and Ca2+ (in summer) reflect conspicuous changes in the source strength of anthropogenic emissions.

The Indo-Gangetic Plain (IGP), the source of grains for around 40% Indian population, is known as the breadbasket of India. The Indian Summer Monsoon Rainfall (ISMR) plays a vital role in the agricultural activities in this region. The rapid urbanization, land use and land cover change have significantly impacted the region’s agriculture, water resources, and socioeconomic facets. The present study has investigated the observed and regional modeling aspects of ISMR characteristics, associated extremes over the IGP, and future perspectives under the high-emission RCP8.5-scenario. Future projections suggest a 10–20% massive decrease during pre-monsoon (March–May) and earlier ISM season months (i.e., June and July). A significant 40–70% decline in mean monsoon rainfall during the June–July months in the near future (NF; 2041–2060) has been projected compared to the historical period (1986–2005). An abrupt increase of 80–170% in mean monsoon rainfall during the post-monsoon (October–December) in the far future (FF; 2080–2099) is also projected. The distribution of projected extreme rainfall events shows a decline in moderate or rather heavy events (5 or more) in NF and FF. Further, an increase in higher rainfall category events such as very heavy (5–10) and extremely heavy rainfall (5 or more) events in NF and FF under the warmer climate is found. However, the changes are less prominent during FF compared to the NF. The mean thresholds for extremely heavy rainfall may increase by 1.9–4.9% during NF and FF. Further, the evolution patterns of various quantities, such as tropospheric temperature gradient (TG), specific humidity, and mean sea level pressure, have been analyzed to understand the physical processes associated with rainfall extremes. The strengthening in TG and enhanced atmospheric moisture content in NF and FF support the intensification in projected rainfall extremes over IGP.

Studying the spatiotemporal variability of pollutants is necessary to identify the pollution hotspots with high health risk and enable the agencies to implement pollution abatement strategies in a targeted manner. Present study reports the spatio-temporal variability and health risk assessment (HRA) of PM 2.5 (Particulate matter with aerodynamic diameter <2.5μm) and NO 2 over IGP from 2019-2021. The HRA is expressed as passively smoked cigarettes (PSC) for four different health outcomes i.e., low birth weight (LBW), percentage decreased lung function (DLF) in school aged children, lung cancer (LC), and cardiovascular mortality (CM). The findings confirm very high PM 2.5 and NO 2 mass concentrations and high health risk over middle IGP and Delhi as compared to upper and lower IGP. Within Delhi, north Delhi region is the most polluted and at highest risk as compared to central and south Delhi. The health risk associated with PM 2.5 over IGP is highest for DLF, equivalent to 21.63 PSCs daily, followed by CM (11.69), LBW (8.27) and LC (6.94). For NO 2 , the health risk is highest for DLF (3.09 PSCs) and CM (2.95), followed by LC (1.47) and LBW (1.04). PM 2.5 and NO 2 concentrations, along with the associated health risks, are highest during the post-monsoon and winter seasons and lowest during the monsoon season.

Natural and anthropogenic aerosols over northern India play an important role in influencing the regional radiation budget, causing climate implications to the overall hydrological cycle of South Asia. In the context of regional climate change and air quality, we discuss aerosol loading variability and trends at Kanpur AERONET station located in the central part of the Indo-Gangetic plains (IGP), during the last decade (2001-10). Ground-based radiometric measurements show an overall increase in column-integrated aerosol optical depth (AOD) on a yearly basis. This upward trend is mainly due to a sustained increase in the seasonal/monthly averaged AOD during the winter (Dec-Feb) and post-monsoon (Oct-Nov) seasons (dominated by anthropogenic emissions). In contrast, a neutral to weak declining trend is observed during late pre-monsoon (Mar-May) and monsoon (Jun-Sep) months, mainly influenced by inter-annual variations of dust outbreaks. A general decrease in coarse-mode aerosols associated with variable dust activity is observed, whereas the statistically significant increasing post-monsoon/winter AOD is reflected in a shift of the columnar size distribution towards relatively larger particles in the accumulation mode. Overall, the present study provides an insight into the pronounced seasonal behavior in aerosol loading trends and, in general, is in agreement with that associating the findings with those recently reported by satellite observations (MODIS and MISR) over northern India. Our results further suggest that anthropogenic emissions (due mainly to fossil-fuel and biomass combustion) over the IGP have continued to increase in the last decade.

The outbreak of COVID-19 pandemic has impacted all the aspects of environment. The numbers of COVID-19 cases and deaths are increasing across the globe. In many countries lockdown has been imposed at local, regional as well as national level to combat with this global pandemic that caused the improvement of air quality. In India also lockdown was imposed on 25th March, 2020 and it was further extended in different phases. The lockdown due to outbreak of COVID-19 pandemic has showed substantial reduction of PM2.5 concentrations across the cities of India. The present study aims to assess concentration of PM2.5 across 12 cities located in different spatial segments Indo-Gangetic Plain (IGP). The result showed that there was substantial decrease of PM2.5 concentrations across the cities located in IGP after implementation of lockdown. Before 30 days of lockdown, average PM2.5 across cities was 65.77 µg/m3 that reached to 42.72 µg/m3 during lockdown periods (decreased by 35%). Maximum decrease of PM2.5 concentrations has been documented in Lower Gangetic Plain (LGP) cities (57%) followed by Middle Gangetic Plain (MGP) cities (34%) and Upper Gangetic Plain (UGP) cities (27%) respectively. Among all the cities of IGP, maximum decrease of PM2.5 concentrations was recorded in Kolkata (64%) (LGP) followed by Muzaffarpur (53%) (MGP), Asansol (51%) (LGP), Patna (43%) (MGP) and Varanasi (33%) (MGP).Therefore, this study has an immense potentiality to understand the impact of lockdown on a physical region (Ganga River Basin) and it may be also helpful for planners and policy makers to implement effective measures at regional level to control air pollution.

Stubble burning is an emerging environmental issue in Northern India, which has severe implications for the air quality of the region. Although stubble burning occurs twice during a year, first during April–May and again in October–November due to paddy burning, the effects are severe during October–November months. This is exacerbated by the role of meteorological parameters and presence of inversion conditions in the atmosphere. The deterioration in the atmospheric quality can be attributed to the emissions from stubble burning which can be perceived from the changes observed in land use land cover (LULC) pattern, fire events, and sources of aerosol and gaseous pollutants. In addition, wind speed and wind direction also play a role in changing the concentration of pollutants and particulate matter over a specified area. The present study has been carried out for the states of Punjab, Haryana, Delhi, and western Uttar Pradesh to study the influence of stubble burning on the aerosol load of this region of Indo-Gangetic Plains (IGP). In this study, the aerosol level, smoke plume characteristics, long-range transport of pollutants, and affected areas during October–November from year 2016 to 2020 were examined over the Indo-Gangetic Plains (Northern India) region by the satellite observations. By MODIS-FIRMS (Moderate Resolution Imaging Spectroradiometer-Fire Information for Resource Management System) observations, it was revealed that there was an increase in stubble burning events with the highest number of events being observed during the year 2016 and then a decrease in the number of events in subsequent years from 2017 to 2020. MODIS observations revealed a strong AOD gradient from west to east. The prevailing north-westerly winds assist the spread of smoke plumes over Northern India during the peak burning season of October to November. The findings of this study might be used to expand on the atmospheric processes that occur over northern India during the post-monsoon season. The pollutant, smoke plume features, and impacted regions of biomass-burning aerosols in this region are critical for weather and climate research, especially given the rising trend in agricultural burning over the previous two decades. Graphical Abstract

Earthquakes in the Himalayas pose a significant hazard to the densely populated Indo-Gangetic Plains (IGP). Due to liquefaction of soils, ground settlements and structural tilting are prevalent during earthquakes. This study aims to identify liquefaction potential zones in the North-Bihar region of the East Ganga Plains by performing morphotectonic analysis over six drainage basins and liquefaction susceptibility mapping using Analytical Hierarchy Process (AHP) while taking site-specific parameters into account. The Index of Relative Active Tectonic (IRAT) value for Gandak and Mahananda was determined to be 1, indicating that the basins are extremely active, whereas Burhi Gandak, Bagmati, and Kosi were moderately active with a 2 IRAT value. The Kamla basin is the least active with an IRAT value of 4. The Liquefaction Susceptibility Map (LSM) was divided into three categories: liquefaction not likely (24%), liquefaction possible (45%), and liquefaction likely (31%); the estimated RMSE values were 0.0084, 0.00048, and 0.00031, respectively. The integrated analysis, which employs both techniques, demonstrates how the individual basins will be affected by liquefaction during an earthquake. The proposed methodology would be beneficial to decision-makers when designing strategies for urban planning projects, as well as structural engineers when selecting sites for field-based surveys.

The increasing frequency of heat waves (HW) in many parts of the world is emerging as one of the climatic vulnerabilities across the world resulting in elevated thermal stress and high mortality. With increase in HW intensity, frequency and duration at global level, India has seen several major HW events in the last decade. HW conditions have mostly been studied by analysing ground-based observations; however, this approach lacks information on spatial variability at the local scale, which is not adequate to identify HW risk and vulnerability hotspots. In this study, gridded analysis of spatio-temporal variability of HW indices has been carried out by utilising freely available Moderate Resolution Imaging Spectrometer (MODIS) Land Surface Temperature (LST) data on Google Earth Engine (GEE) platform in the Indo-Gangetic Plains of India. HW indices to analyse duration, frequency and intensity of HW have been identified and further computed on a grid size of 10 km*10 km area. HW risk and vulnerability hotspot in the study region have been identified by spatial modelling of HW indices, LULC change and population density. The HW risk and vulnerability hotspot layer identified NCT Delhi and its surrounding region at the highest risk of HW with high vulnerability. A strong positive correlation of variability of HW indicators with increasing built-up shows that built-up surfaces affect strongly the HW conditions.

Various agroforestry systems are prevalent in different agro-climatic regions of India and occupy sizeable area. Populus deltoides (poplar) based agroforestry systems are very predominant in Indo-Gangetic Plains of northern India. These systems are not only meeting wood demand of wood-based industries but also contributing significantly in generating employment, reducing atmospheric CO2 vis-à-vis mitigating climate change. The present study aimed at estimating area under poplar plantations and assessment of biomass production and carbon stock at district level for selected districts of Punjab and Haryana states. High resolution multispectral remote sensing data (LISS-IV, spatial resolution- 5.8 m) have been used for mapping poplar area in these districts. Growth data (tree density, diameter at breast height) from farmers’ fields were collected for assessment of aboveground and total biomass. Poplar area in Hoshiarpur, Rupnagar, Ludhiana, Shahid Bhagat Singh Nagar, Yamunanagar, Kurukshetra and Karnal districts accounted for 3.19, 4.09, 1.02, 0.43, 7.25, 1.58 and 0.97%, respectively, of their geographical area. As percentage of agroforestry area, poplar area occupied 43.8% in four districts of Punjab and 53.4% in three districts of Haryana. Estimated carbon stock in total biomass of poplar plantations was 0.931, 0.481, 0.287, 0.040, 0.949, 0.256 and 0.229 million tonnes, respectively. CO2 equivalent C in aboveground and total biomass was estimated to be 7.854 and 9.946 million t, respectively, in all these seven districts. The proposed methodology based on object-oriented image classification showed promising results as far as mapping of agroforestry systems/species is concerned.