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Variation of multivariate ENSO index from year 2010 to 2019 (Top panel). Variation of relative humidity in surface air over Uttarakhand from April 2010 to April 2020 (Bottom panel)
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A widespread forest fire episode occurred over Uttarakhand during April 24–May 2, 2016. This event released large amount of carbon monoxide (CO), nitrogen dioxide (NO2) and aerosols in the pristine environment of Uttarakhand. AIRS observations showed 60–125 ppbv higher CO during fire-impacted period with respect to background CO at 925, 850 and 700...
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... Satellite observations during such events showed increased CO concentration (60-124 ppbv) in the upper troposphere. Extremely high CO levels up to 19 mg m −3 (~8700 ppbv) were also observed in November 2017 over the IGP (including the cities of Delhi, Agra, Kanpur, Lucknow, Patna and Kolkata) [40]. ...
Crop residue burning emits a variety of air pollutants that drastically affect air quality, both locally and regionally. To study the impact of crop residue burning, in the present study, concentrations of particulate matter (PM2.5), trace gases (tropospheric ozone (O3), nitrogen oxides (NOx), carbon monoxide (CO), and volatile organic compounds (VOCs)) were recorded in Agra, a suburban downwind site. The study was conducted during the pre-harvest (15 September to 5 October 2021) and post-harvest periods (6 October to 10 November 2021). During the post-harvest period, PM2.5 concentrations were recorded to be three to four times higher than the NAAQ Standards (35 µg/m3), while O3 and VOC concentrations showed an increment of 16% and 30.4%, respectively. NOx and CO concentrations also showed higher levels (19.7 ± 7.5 ppb and 1498.5 ± 1077.5 ppb) during this period. Moderate resolution imaging spectroradiometer (MODIS), along with air mass backward trajectory analysis (HYSPLIT Model), were used to detect fire hotspots that suggested that the enhanced pollutant levels may be due to the burning of crop residue in agricultural fields over the northwest Indo-Gangetic Plain (NW-IGP). Field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FESEM-EDX) analysis showed high K concentrations during the post-harvest period, which may be attributed to crop residue burning or biomass combustion.
... This is followed by the transport of heat to adjacent fuel causing spread of fire. [13,14,15] Fire is one of the major causes for the decrease in forest land in India, and has significant environmental, monetary and social effects. In Uttarakhand, Chir Pine woods spread over approximately 16% of the entire woodland of the region lying at an altitude of 1000 AMSL to 1800 AMSL, that are fire prone because of gum rich leaf litter collection on the backwoods floor during summer. ...
... Additionally, wildfires have significant longterm impacts on both the environment and people, such as changing the structure of the ecosystems, soil erosion, destructing wildlife habitats, and increasing potential of flooding [4]. Moreover, wildfires emit a considerable amount of greenhouse gases (e.g., carbon dioxide and methane) which results in global warming [5]. Therefore, reliable, timely, and detailed information on burned areas in a wildfire event is necessary. ...
... Most studies conducted on burned area mapping have focused on binary burned area mapping [5,[11][12][13][14][15][16][17][18]21,22,33,52,57,58]. However, in most cases, it is very important to know the land cover types of burned areas. ...
Wildfires are major natural disasters negatively affecting human safety, natural ecosystems , and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differenc-ing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.
The provinces of Punjab, Haryana and western Uttar Pradesh in northwest Indo-Gangetic Plain (IGP) produce two-thirds of India’s food grains, and are widely known as the “breadbasket” of India. This “breadbasket” accounts for roughly a quarter of the total annual fire events observed over India, and hence is a regional hotspot for biomass burning. The enormous amount of aerosols and trace gases, emitted largely from extensive post-harvest crop residue burning in autumn (October–November) and spring (April–May), along with events of wildfires, and domestic consumption of biofuels, unfurls its influence along the length of the IGP under the prevailing meteorological conditions. The elevated levels of pollutants, primarily generated from intense post-monsoon biomass burning over northwest IGP, under the influence of unfavourable meteorological conditions, contribute significantly to the episodes of extreme pollution over the regions downwind of northwest IGP. The downwind region of intense biomass burning experiences high values of particulate matter, including black carbon, organic carbon, ozone, volatile organic compounds and other ozone precursors, resulting in an intense photochemistry and an adverse air quality. Furthermore, excessive smoke aerosols in the atmosphere exert a strong surface cooling, but an overall atmospheric warming, thereby changing the regional radiative balance.KeywordsBiomass burningNorth IndiaExtreme eventsAerosolsTrace gasesHazeRadiative forcingAir pollutionAir qualityTropospheric chemistry
Darkening of surface snow caused by deposition of the Light absorbing impurities (LAIs) can reduce its albedo and further enhance its melting. Early melting of surface snow leads to exposing the underneath ice mass to sunlight for longer duration and consequent retreat of glaciers. This retreat of glaciers, further cause several hydrological and climatic implications. In this study, efforts were made to perform source apportionment of the LAIs found in the surface snow samples, collected from the three glaciers located in the Indian Western Himalayas (IWHs). Ionic composition of the snow samples derived from ion chromatography was further analyzed using two receptor models viz., principal component analysis (PCA) and positive matrix factorization (PMF). It was observed that biomass burning, vehicular emissions, dust emissions and coal combustion were the major sources of the LAIs in the IWHs. The PMF analysis, estimated that biomass burning and vehicular emissions contributed ∼30% each in the emission of the LAIs. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)-based back trajectories for 7-days were plotted to identify locations of the sources of the LAIs. Back trajectories were observed to pass through agricultural, forest-covered and urban areas. The observations made, during this study, would help local authorities in identifying the hot spots of the LAIs’-emissions, on the basis of which region-specific policies could be framed to mitigate the anthropogenic emissions of the LAIs.
