New positive feedback mechanism between boundary layer meteorology and secondary aerosol formation during severe haze events

Liu, Quan; Jia, Xingcan; Quan, Jiannong; Li, Jiayun; Li, Xia; Wu, Yongxue; Chen, Dan; Wang, Zifa; Liu, Yangang

doi:10.1038/s41598-018-24366-3

Cited by 101 publications

(95 citation statements)

References 54 publications

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“…Reduced surface downward shortwave radiation due to ARI shows a linear relationship with surface PM 2.5 concentrations ( figure S1(a)). This linear relationship is consistent with observations of surface PM 2.5 concentration and solar radiation in Beijing as shown by Liu et al (2018). Decreases in PBLH due to ARI follow a quadratic relationship with surface PM 2.5 concentrations ( figure S1(b)), reflecting a positive feedback between the two.…”

Section: Weakened Ari Effects Due To Emission Reductionssupporting

confidence: 90%

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The impact of aerosol–radiation interactions on the effectiveness of emission control measures

Zhou

Zhang

Chen³

et al. 2019

Environ. Res. Lett.

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Temporary emission control measures in Beijing and surrounding regions have become a prevailing practice to ensure good air quality for major events (e.g. the Asia-Pacific Economic Cooperation (APEC) Summit on 5-11 November 2014) and to mitigate the severity of coming pollution episodes. Since PM 2.5 affects meteorology via aerosol-meteorology interactions, a question arises how these interactions may impact the response of PM 2.5 to emission reductions and thus the effectiveness of emission control measures. Here we use the coupled meteorology-chemistry model WRF-Chem to investigate this issue with focus on aerosol-radiation interactions (ARI) for the APEC week and three more polluted episodes over North China. We find a quadratic relationship between PM 2.5 concentration changes due to emission reductions and PM 2.5 levels, instead of an approximately linear response in the absence of ARI. The ARI effects could only change the effectiveness of emission control by 6.7% during APEC in Beijing, but reach 21.9% under more polluted conditions. Our results reveal that ARI can strongly affect the attribution of PM 2.5 variability to emission changes and meteorology, and is thus important for assessing the effectiveness of emission control measures.

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Section: Weakened Ari Effects Due To Emission Reductionssupporting

confidence: 90%

“…Additional positive feedback can be caused by changes in other meteorological variables, such as RH. Decreases in downward solar radiation tend to decrease surface temperature and increase RH ( figure S1(c)), leading to conditions favorable for secondary aerosol formation (Liu et al 2018).…”

Section: Weakened Ari Effects Due To Emission Reductionsmentioning

confidence: 99%

The impact of aerosol–radiation interactions on the effectiveness of emission control measures

Zhou

Zhang

Chen³

et al. 2019

Environ. Res. Lett.

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“…For BLH values above

\sim

800 m, the PD remains constant, that is, the influence of the boundary layer on PM concentrations stagnates (Liu et al, ). Mean modeled PM10 concentrations increase slightly in conditions with very high BLH (>2,000 m).…”

Section: Resultsmentioning

confidence: 99%

“…This similarity is encouraging and proves the robustness of the modeling approach, since both studies use ERA-Interim BLH in a comparable geographic setting. For BLH values above ∼800 m, the PD remains constant, that is, the influence of the boundary layer on PM concentrations stagnates (Liu et al, 2018). Mean modeled PM10 concentrations increase slightly in conditions with very high BLH (>2,000 m).…”

Section: Blh and Capementioning

confidence: 95%

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM10) by combining satellite‐borne aerosol optical depth (AOD) with meteorological and land‐use parameters. The model is shown to accurately predict PM10 (overall R 2 = 0.77, RMSE = 7.44 μg/m 3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m 3 occur when BLH is below ∼500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m 3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.

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“…Aerosols change weather and climate via the following pathways: they absorb and scatter solar and thermal radiation to alter the radiative balance of the earth-atmosphere system (Gao et al, 2019b;Liu et al, 2011;Jia et al, 2018), which is referred to as direct effects, and they serve as cloud condensation nuclei (CCN) and/or ice nuclei (IN) to modify cloud properties, which is referred to as indirect effects (Haywood and Boucher, 2000). The suppression of cloud convection induced by direct effects of absorbing aerosols is known as the semidirect effect Lohmann and Feichter, 2005).…”

Section: Introductionmentioning

confidence: 99%

Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 2: aerosol radiative effects and aerosol feedbacks

et al. 2020

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Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating wintertime haze events in the North China Plain region and evaluates the importance of aerosol radiative feedbacks. This paper discusses the estimates of aerosol radiative forcing, aerosol feedbacks, and possible causes for the differences among the participating models. Over the Beijing-Tianjin-Hebei (BTH) region, the ensemble mean of estimated aerosol direct radiative forcing (ADRF) at the top of atmosphere, inside the atmo-sphere, and at the surface are − 1.1, 7.7, and −8.8 W m −2 during January 2010, respectively. Subdivisions of direct and indirect aerosol radiative forcing confirm the dominant role of direct forcing. During severe haze days (17-19 January 2010), the averaged reduction in near-surface temperature for the BTH region can reach 0.3-1.6 • C. The responses of wind speeds at 10 m (WS10) inferred from different models show consistent declines in eastern China. For the BTH region, aerosol-radiation feedback-induced daytime changes in PM 2.5 concentrations during severe haze days range fromPublished by Copernicus Publications on behalf of the European Geosciences Union. 1148M. Gao et al.: Air quality and climate change, Topic 3 -Part 2 6.0 to 12.9 µg m −3 (< 6 %). Sensitivity simulations indicate the important effect of aerosol mixing states on the estimates of ADRF and aerosol feedbacks. Besides, black carbon (BC) exhibits a large contribution to atmospheric heating and feedbacks although it accounts for a small share of mass concentration of PM 2.5 .

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New positive feedback mechanism between boundary layer meteorology and secondary aerosol formation during severe haze events

Cited by 101 publications

References 54 publications

The impact of aerosol–radiation interactions on the effectiveness of emission control measures

The impact of aerosol–radiation interactions on the effectiveness of emission control measures

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 2: aerosol radiative effects and aerosol feedbacks

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