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Dominance of the residential sector in Chinese black carbon emissions as identified from downwind atmospheric observations during the COVID-19 pandemic

Authors: Kanaya, Y.; Yamaji, K.; Miyakawa, T.; Taketani, F.; Zhu, C.; Choi, Y.; Ikeda, K.; +5 Authors

Dominance of the residential sector in Chinese black carbon emissions as identified from downwind atmospheric observations during the COVID-19 pandemic

Abstract

AbstractEmissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44–82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb–Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (− 18%) during Feb–Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (− 35%) in the period Feb–Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48–100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.

Country
Austria
Keywords

China, 550, 330, Science, Climate Change, 710, Wind, Article, Soot, Residence Characteristics, Air Pollution, Humans, Human Activities, Pandemics, Air Pollutants, Geography, Atmosphere, SARS-CoV-2, Q, R, COVID-19, Models, Theoretical, Medicine, Particulate Matter, Seasons, Algorithms, Environmental Monitoring

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
Green
gold