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(searched for: doi:10.1016/j.atmosres.2021.105653)
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Published: 14 July 2022
by MDPI
Journal: Atmosphere
Atmosphere, Volume 13; https://doi.org/10.3390/atmos13071115

Abstract:
High concentrations of tropospheric ozone (O3) is a serious concern in India. The generation and atmospheric dynamics of this trace gas depend on the availability of its precursors and meteorological variables. Like other parts of the world, the COVID-19 imposed lockdown and restrictions on major anthropogenic activities executed a positive impact on the ambient air quality with reduced primary pollutants/precursors load. In spite of this, several reports pointed towards a higher O3 in major Indian cities during the lockdown. The present study designed with 30 pan-Indian mega-, class I-, and class II-cities revealed critical and contrasting aspects of the geographical location, source, precursor, and meteorological variable dependency of the spatial and temporal O3 formation. This unexpected O3 increase in the major cities might forecast the probable future risks for the National Air Quality policies, especially O3 pollution management, in the Indian sub-continent. The results also pointed towards the severity of the north Indian air quality, followed by the western and eastern parts. We believe these results will definitely pave the way for researchers and policy-makers for predicting/framing regional and/or national O3 management strategies in the future.
, , Kiran Sharma, Amit Sharma, Narendra Singh, Sachin S. Gunthe
Published: 18 November 2021
Scientific Reports, Volume 11, pp 1-7; https://doi.org/10.1038/s41598-021-01824-z

Abstract:
Machine learning (ML) has emerged as a powerful technique in the Earth system science, nevertheless, its potential to model complex atmospheric chemistry remains largely unexplored. Here, we applied ML to simulate the variability in urban ozone (O3) over Doon valley of the Himalaya. The ML model, trained with past variations in O3 and meteorological conditions, successfully reproduced the independent O3 data (r2 ~ 0.7). Model performance is found to be similar when the variation in major precursors (CO and NOx) were included in the model, instead of the meteorology. Further the inclusion of both precursors and meteorology improved the performance significantly (r2 = 0.86) and the model could also capture the outliers, which are crucial for air quality assessments. We suggest that in absence of high-resolution measurements, ML modeling has profound implications for unraveling the feedback between pollution and meteorology in the fragile Himalayan ecosystem.
Jianhua Lin, , Minghui Tao, Jun Ma, Liqin Fan, Ren-An Xu, Chunyu Fang
Published: 14 September 2021
ACS Earth and Space Chemistry, Volume 5, pp 2900-2909; https://doi.org/10.1021/acsearthspacechem.1c00251

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, Bhupendra Bahadur Singh, Rama Krishna Karumuri, Raju Attada, Vivek Seelanki, Kondapalli Niranjan Kumar
Published: 26 August 2021
Environmental Science and Pollution Research, Volume 29, pp 6219-6236; https://doi.org/10.1007/s11356-021-16011-w

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, , Qihou Hu, Ting Liu, Shuntian Wang, Meng Gao, Shiqi Xu, Chengxin Zhang, Wenjing Su
Published: 2 August 2021
Environmental Pollution, Volume 289, pp 117899-117899; https://doi.org/10.1016/j.envpol.2021.117899

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