Long-term trends and variations in haze-related weather conditions in north China during 1980–2018 based on emission-weighted stagnation intensity

Feng, Jin; Liao, Hong; Li, Yanjie; Zhang, Ziyin; Tang, Yu

doi:10.1016/j.atmosenv.2020.117830

Cited by 15 publications

(11 citation statements)

References 41 publications

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“…The regional mean winter CBD3 is decreasing at a rate of −0.11 d/y in China, with minimum centers located in northwestern China, southwestern China, and eastern China. These results are consistent with the increase in wintertime air pollution and an obvious increase in winter haze in China (Feng et al 2020 ; Lu et al 2020 ; Zhang et al 2020b ). In the following section, we focus on analyzing the reasons for the long-term change of wintertime CBD3 in China.…”

Section: Resultssupporting

confidence: 88%

The deep blue day is decreasing in China

Wang

Huang

et al. 2022

Theor Appl Climatol

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The deep blue sky is an indicator of a lower concentration of aerosols and a cloudless sky. With increasing human emissions, a trend towards days with fewer deep blue skies might indicate a decline in a good living environment for humans. This study investigates the long-term changes of the deep blue sky in China from 1980 to 2018. Due to a lack of direct measurements, we use atmospheric visibility and low cloud cover to classify blue sky days into three grades: light blue day, medium blue day, and deep blue day. Climatologically, annual deep blue days increase from southeast China to northwest China, with the maximum number in Xinjiang and eastern Inner Mongolia and the minimum number in western Qinghai and southern Hebei. From 1980 to 2018, annual deep blue days show a prominent decreasing trend in most of China, with area-mean annual deep blue days decreasing by −0.48 days per year (d/y) in China, and the variation becomes more obvious after 2013. The maximum decreasing trend is observed in eastern China. The most prominent decreases of deep blue days are seen in winter. Both air pollution and the change in meteorological conditions contribute to the decrease of wintertime deep blue days in China. Specifically, the decrease in surface wind speed hinders the cleaning of air by winds, the increase in surface air temperature, and decrease in relative humidity is favorable for low cloud increase, and the increasing emission of pollution reduces atmospheric visibility. Supplementary Information The online version contains supplementary material available at 10.1007/s00704-021-03898-1.

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Section: Resultssupporting

confidence: 88%

The deep blue day is decreasing in China

Wang

Huang

et al. 2022

Theor Appl Climatol

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“…The interannual variations in the meteorology-associate PM2.5 assessed in this study were consistent with those reported in previous studies (Zhang et al, 2018). For example, Feng et al (2020) presented the long-term variations in air stagnation in north China that characterized the circulation and diffusion in the boundary layer with fixed emissions to describe the temporal trend of haze-related weather conditions. The temporal pattern of the air stagnation index from 2000-2018 was closely resembled the temporal trend of the estimated meteorological-associate PM2.5 in this study.…”

Section: Interannual and Seasonal Trends Of Meteorology-associate Pm25supporting

confidence: 90%

Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Xiao¹,

Zheng²,

Geng³

et al. 2021

Preprint

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Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used a combination of machine learning model, statistical model and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods, a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals as well as the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modelling system, to examine the meteorological contribution to PM2.5. We found good agreements between the GAM model estimations and the CMAQ model estimations of meteorological contribution to PM2.5 on monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual trends in meteorology-associate PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution control across the North China Plain and central China, but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over East China peaked at 2006 and 2011, mainly driven by the emission peaks in PM2.5 and PM2.5 precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in East China estimated from the GAM model. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fen-wei Plain, −4.8 % to 4.3 % in the Yangtze River Delta and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the worsening meteorological conditions in the northern part of China where air pollution was severe and population was clustered, stricter clean air actions are needed to avoid haze events in the future.

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“…The model was trained and ten-fold CV applied using ground-based PM2. (Yang et al, 2016), ASI (Feng et al, 2018(Feng et al, , 2020b, and deepWIA models.…”

Section: Discussionmentioning

confidence: 99%

“…A data-based model requires negligible computational resources compared with CTMs. In China, two semi-empirical meteorological indices are used for daily variations in aerosol concentrations, the Parameter Linking Air quality to Meteorological conditions (PLAM) (Yang et al, 2016) and Air Stagnation Index (ASI) (Feng et al, 2018(Feng et al, , 2020b. Both indices include an extra "background factor" describing the effects of slowly changing emissions and regional differences.…”

mentioning

confidence: 99%

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Feng

Li²,

Qiu³

et al. 2022

Preprint

Self Cite

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Abstract. The estimation of daily variations in aerosol concentrations using meteorological data is meaningful and challenging, given the need for accurate air quality forecasts and assessments. In this study, a 3×50-layer spatiotemporal deep learning (DL) model is proposed to link synoptic variations in aerosol concentrations and meteorology, thereby building a “deep” Weather Index for Aerosols (deepWIA). The model was trained and validated using seven years of data and tested in Jan–Apr 2022. The index successfully reproduced the variation in daily PM2.5 observations in China. The coefficient of determination between PM2.5 concentrations calculated from the index and observation was 0.72, with a root-mean-square error of 16.5 µg m−3. DeepWIA performed better than Weather Forecast and Research (WRF)-Chem simulations for eight aerosol-polluted cities in China. The predictive power of the DL model also outperformed reported semi-empirical meteorological indices and machine learning-based PM2.5 concentration retrievals based on aerosol optical depth and visibility observations. The index and the DL model can be used as robust tools for estimating daily variations in aerosol concentrations.

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Long-term trends and variations in haze-related weather conditions in north China during 1980–2018 based on emission-weighted stagnation intensity

Cited by 15 publications

References 41 publications

The deep blue day is decreasing in China

The deep blue day is decreasing in China

Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

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Long-term trends and variations in haze-related weather conditions in north China during 1980–2018 based on emission-weighted stagnation intensity

Cited by 15 publications

References 41 publications

The deep blue day is decreasing in China

The deep blue day is decreasing in China

Separating emission and meteorological contribution to PM&lt;sub&gt;2.5&lt;/sub&gt; trends over East China during 2000–2018

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

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Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018