Investigation of Forest Fire Activity Changes Over the Central India Domain Using Satellite Observations During 2001–2020

Jain, Madhavi; Saxena, Pallavi; Sharma, Som; Sonwani, Saurabh

doi:10.1029/2021gh000528

Cited by 32 publications

(20 citation statements)

References 73 publications

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“…Jain et al ( 2020) identified Delhi's annual average levels of OC and EC as 15.73 ± 12.72 and 7.31 ± 6.17 µg m −3 , respectively, for PM 2.5 . Jain et al (2021) also noticed PM 2.5 bound OC and EC levels as 15.09 ± 8.72 and 6.54 ± 3.27 µg m −3 , respectively, in Varanasi. While in Kolkata, OC and EC levels were observed as 17.91 ± 9.72 and 9.43 ± 4.11 µg m −3 , respectively.…”

Section: Level Distribution Of Carbonaceous Componentsmentioning

confidence: 61%

Carbonaceous aerosol variability and SOA formation during foggy days in Delhi, India

Sonwani¹,

Saxena²,

Srivastava³

2022

Front. Sustain. Cities

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The variability of organic carbon (OC), elemental carbon (EC), and secondary organic aerosol (SOA) and their relationship with meteorological parameters have been studied during foggy and non-foggy days in the peak winter months (December–January) from 2015 to 2016 in Delhi, India. Different sectoral locations were chosen based on predominant industrial, traffic, and residential activities with a background location. The average level of OC, EC, and SOA was found to be 7.47 ± 7.74, 0.69 ± 0.7, and 10.46 ± 10.76 μg/m3, respectively, during the foggy period and 6.1 ± 6.8, 0.9 ± 1.1, and 9.1 ± 10.6 μg/m3, respectively, during the non-foggy period in Delhi. A relatively higher SOA level was observed at industrial and traffic intersection sites, which indicates the proximity of the dominant source of OC that play a significant role in SOA formation. It was also found that SOA production is associated with the OC/EC ratio and may vary from site to site. Correlation analysis has confirmed that OC is having a significant strong positive correlation with EC and SOA, while EC is showing a significant moderate positive correlation with SOA. Ambient temperature (AT) shows a significant negative moderate correlation with OC levels and SOA and formation. Due to hydrophilicity (hydrophobicity) of OC (EC), its average concentration was found high (less) due to its high (less) scavenging during foggy days in comparison to non-foggy days. The study further suggests the significant impact of source variability on SOA formation due to the different nature of sector-wise sites during foggy days in Delhi.

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Section: Level Distribution Of Carbonaceous Componentsmentioning

confidence: 61%

Carbonaceous aerosol variability and SOA formation during foggy days in Delhi, India

Sonwani¹,

Saxena²,

Srivastava³

2022

Front. Sustain. Cities

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“…Badarinath et al ( 2011 ) reported intense forest fire episodes in Kerala during the year 2004, which started in January and increased even more in February and March due to a decrease in rainfall and high temperatures than normal years. Jain et al ( 2021 ) observed the occurrence of increased forest fires associated with severe heatwave, drought and strong El Niño during 2015 July–January and 2018 February–June over central India. Chand et al ( 2006 ) found that the spatial variation in peak fire seasons was attributed to the varying forest types and climatic conditions over the Indian subcontinent.…”

Section: Resultsmentioning

confidence: 99%

Impact of Biomass Burning on Black Carbon and NO2 Over North Eastern Region of India Using Multi-satellite Observations

Borgohain

Gogoi

Barman

et al. 2023

J Indian Soc Remote Sens

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Large scale biomass burning like forest fires and crop residue burning can significantly impact the physical environment, including land cover, land use, ecology, habitats, and climate change. We investigated the effect of fire counts on surface Black Carbon mass concentration (BCC) and Tropospheric Columnar NO 2 (TCN) over the North Eastern Region (NER) of India in the domain: 20° N–30° N and 88° E–98° E for 15 years from 2006 to 2020 using MODIS, MERRA-2 and OMI data. Significant fire counts are recorded in January, February, March, and April. An average of 65,000 fire counts is recorded in March and April during the 15 years of study over the domain. TCN is high in Mizoram, Manipur, and Nagaland, followed by Assam, Tripura, and Meghalaya in March and April, which varies from 18.79 × 10 14 to 29.08 × 10 14 cm −2 in March, 10.76 × 10 14 –15.81 × 10 14 cm −2 in January and February, and 12.67 × 10 14 –14.2 × 10 14 cm −2 in April. Spatially averaged BC varies from 1.80 to 2.76 µg m −3 in January and February and 1.82–2.36 µg m −3 in March. BCC is high in Mizoram, Tripura, Manipur, Nagaland, and Brahmaputra valley of Assam than in the rest of the NER.

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“…From a heat stress perspective, the triple whammy of unabated globally rising temperatures, increasing UHIIs (or SUHIIs), and increasingly frequent prolonged heatwaves of extreme severity during pre-monsoon (MAM) season is of utmost concern in cities of India (World Health Organization (WHO), 2020; India Meteorological Department (IMD), 2021a; Jain, 2021bJain, , 2022Jain et al, 2021). A number of factors such as urban area (e.g., impervious surface area, built-up density, building heights, etc.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…Studies have found a significant increase in frequency, severity and intensity of climate extremes e.g., droughts, heatwaves, wildfires, hurricanes, and floods; which likely will exacerbate life and property losses in the future, along with causing food shortages and an increase in disease causing pathogens (Robinson, 2001;Black et al, 2004;Li et al, 2009;Dai, 2011;Intergovernmental Panel on Climate Change (IPCC), 2012Mishra et al, 2017;Hawcroft et al, 2018;Bisht et al, 2019;Hasegawa et al, 2021;Jain, 2021aJain, , 2022Jain et al, 2021;Climate Transparency, 2022). Unlike the other climate extreme events, heatwave caused fatalities are not immediately obvious, but lead the weather-related cause of deaths globally (Hajat and Kosatky, 2010;Peterson et al, 2013;World Health Organization (WHO), 2020).…”

mentioning

confidence: 99%

Two decades of nighttime surface urban heat island intensity analysis over nine major populated cities of India and implications for heat stress

Jain

2023

Front. Sustain. Cities

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Warmer global climate and urban heat islands (UHIs) interact, by exacerbating heatwaves and increasing the extreme heat days in cities. The implications of added heat stress in urban environments due to intensifying surface UHIs (SUHIs) is of utmost concern. Seasonal, annual and decadal nighttime SUHI intensities (SUHIIs), from 2001 to 2020, for nine major populated cities of India are analyzed. This includes five megacities- Delhi, Mumbai, Kolkata, Bangalore, and Chennai, and four incipient megacities- Hyderabad, Ahmedabad, Surat, and Pune. The key role of increasing urbanization (pre- and post-2010) in expansion and intensification of nighttime SUHIs in India is highlighted. For all cities either pre-monsoon (MAM) or winter (December-February; DJF) seasons show the strongest SUHII development. During the 2001–2010, and the 2011–2020 decade, a nighttime SUHII maxima of respectively (i) 2.1°C and 2.5°C for Delhi, (ii) 1.3°C and 1.5°C for Mumbai, (iii) 1.3°C and 1.5°C for Kolkata, (iv) 0.6°C and 1.0°C Bangalore, (v) 1.7°C and 1.9°C for Chennai, (vi) 1.8°C and 2.3°C for Hyderabad, (vii) 2.8°C and 3.1°C for Ahmedabad, (viii) 1.9°C and 2.4°C for Surat, and (ix) 0.8°C and 1.3°C for Pune is noted. Further, all incipient megacities showed a mean annual growth rate of nighttime SUHII of over 0.007°C/year, substantially greater than in the megacities. High SUHII magnitudes, greater growth rates of SUHII, and huge populations, severely compounds the vulnerability of Indian cities to excessive heat exposure risk, especially during MAM heatwaves. Lastly, the implications of nighttime SUHII findings from the present study, on the increase in heat stress, the loss of labor productivity and the rise in heat-related mortality rate is emphasized. The study recommends implementation of city-specific action plans to mitigate the heat stressed urban environment. Targeted use of cooling strategies in localized hotspots within the urban areas where high intensity SUHIs are likely to form is also suggested.

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Investigation of Forest Fire Activity Changes Over the Central India Domain Using Satellite Observations During 2001–2020

Cited by 32 publications

References 73 publications

Carbonaceous aerosol variability and SOA formation during foggy days in Delhi, India

Carbonaceous aerosol variability and SOA formation during foggy days in Delhi, India

Impact of Biomass Burning on Black Carbon and NO2 Over North Eastern Region of India Using Multi-satellite Observations

Two decades of nighttime surface urban heat island intensity analysis over nine major populated cities of India and implications for heat stress

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