Verification of anthropogenic VOC emission inventory  through ambient measurements and satellite retrievals

Li, Jing; Hao, Yufang; Simayi, Maimaiti; Shi, Yuqi; Xi, Ziyan; Xie, Shaodong

doi:10.5194/acp-19-5905-2019

Cited by 65 publications

(25 citation statements)

References 70 publications

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“…There was an underestimation of C2-C3 alkanes in the emission inventory, probably because of the underestimation of natural gas usage (Borbon et al, 2013). This finding was consistent with the study by Li et al (2019b), which compared the NMHCs annual emissions derived from the ambient measurements and emission inventory in Beijing. These discrepancies revealed a need to validate the speciation of the current 255 emission inventories with more evidence at large spatial or temporal scales, and then evaluation of impact of NMHCs on air quality would be improved.…”

supporting

confidence: 88%

Long-term measurements (2009–2015) of non-methane hydrocarbons (NMHCs) in a megacity of China: implication for emission validation and source control

Peng

Wang

et al. 2020

Preprint

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Abstract. Long term measurements of air pollutants represented the footprints of emissions to some extent, which could provide useful and consecutive evolution of emissions. Both atmospheric concentrations and emissions of many air pollutants have been reported decreasing in the past decade due to the implement of various control measures in China, which were different for non-methane hydrocarbons (NMHCs) with increasing emissions as reported previously. The present study employed the long-term (2009–2015) NMHCs measurements as well as the related social and economic activities data in Shanghai, a megacity in eastern China, to explore the evolution of NMHCs emissions during the periods. The meteorology and photochemistry which might impact the NMHCs measurements were tested as negligible effects on an annual scale. As a result, NMHCs mixing ratio showed no statistic interannual changes, of which compositions varied significantly. This resulted a statistically decreasing trend of ozone formation potential by 3.8 % yr−1 (p

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supporting

confidence: 88%

Long-term measurements (2009–2015) of non-methane hydrocarbons (NMHCs) in a megacity of China: implication for emission validation and source control

Peng

Wang

et al. 2020

Preprint

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“…The Cp_R1+2 simulation largely underestimates the IVOC concentrations, especially in winter. The underestimated IVOC emissions are likely from the residential sector that has highly uncertain emission activity (Tao et al, 2018;Peng et al, 2019;J. Li et al, 2019a).…”

Section: Resultsmentioning

confidence: 99%

Process-based and Observation-constrained SOA Simulations in China: The Role of Semivolatile and Intermediate-Volatility Organic Compounds and OH Levels

Miao¹,

Chen²,

Shrivastava³

et al. 2021

Preprint

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Abstract. Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties to reproduce the variability of OA concentrations in polluted areas, hindering understanding of the OA budget. Herein, we applied both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model-observation comparisons. In the revised schemes, updates of the emissions, volatility distributions, and SOA yields of semivolatile and intermediate volatility organic compounds (S/IVOCs) were made. These updates are however insufficient to reproduce the SOA concentrations in observations. The addition of nitrous acid sources is an important model modification, which improves the simulation of surface concentrations of hydroxyl radical (OH) in winter in northern China. The increased surface OH concentrations enhance the SOA formation and lead to greater SOA mass concentrations by over 30 %, highlighting the importance of having good OH simulations in air quality models. There is a greater sensitivity of the SOA formation to the oxidant levels in winter than in summer in China. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic S/IVOCs are the dominant source of SOA in China with a contribution of over 50 %. The residential sector may be the predominant source of S/IVOCs in winter, despite large uncertainty remains in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More S/IVOC measurements are needed to constrain their emissions.

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“…The Cp_R1+2 simulation largely underestimates the IVOC concentrations, especially in winter. The underestimated IVOC emissions are likely from the residential sector, which has highly uncertain emission activity (Tao et al, 2018;Peng et al, 2019;J. Li et al, 2019a).…”

Section: Sensitivities Of Soa Simulation To the Oh Concentrations And Ivoc Emissionsmentioning

confidence: 99%

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

et al. 2021

View full text Add to dashboard Cite

Abstract. Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50 % in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30 % greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

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Verification of anthropogenic VOC emission inventory through ambient measurements and satellite retrievals

Cited by 65 publications

References 70 publications

Long-term measurements (2009–2015) of non-methane hydrocarbons (NMHCs) in a megacity of China: implication for emission validation and source control

Long-term measurements (2009–2015) of non-methane hydrocarbons (NMHCs) in a megacity of China: implication for emission validation and source control

Process-based and Observation-constrained SOA Simulations in China: The Role of Semivolatile and Intermediate-Volatility Organic Compounds and OH Levels

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

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