Tracking a Severe Pollution Event in Beijing in December 2016 with the GRAPES–CUACE Adjoint Model

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  • We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE (Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System). The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 Dece-mber 2016 as the objective time when PM2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m–3 just 1 h before the objective time, suggesting that PM2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period. The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%, and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m–3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.
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  • Fig. 1.  Schematic diagram showing the coupling of GRAPES–CUACE atmospheric chemical model with its adjoint model.

    Fig. 2.  Sea-level pressure fields at 0800 Beijing Time (BJT) on (a) 2, (b) 3, (c) 4, and (d) 5 December 2016.

    Fig. 3.  Variation curves and scatter graphs of the observed and simulated hourly PM2.5 concentration at (a) Nanjiao (NJ) station and (b) Shangdianzi (SDZ) station from 0800 BJT 1 December to 1900 BJT 5 December 2016.

    Fig. 4.  Time-accumulated sensitivity coefficient distributions at (a) 1, (b) 7, (c) 13, (d) 19, (e) 25, (f) 37, (g) 47, and (h) 72 h ahead of the objective time (2000 BT 3 December 2016).

    Fig. 5.  Variations of the (a) hourly and (b) time-accumulated sensitivity coefficient (SC) of the local and surrounding emissions.

    Fig. 6.  Percentage variations of the hourly and time-accumulated contribution of local and surrounding emissions to the PM2.5 peak concentration at the objective time (2000 BJT 3 December 2016).

    Fig. 7.  Variations in the (a) hourly and (b) time-accumulated sensitivity coefficients for the Beijing (BJ), Tianjin (TJ), Hebei (HB), and Shanxi (SX) emissions.

    Fig. 8.  Percentage variations of (a) hourly and (b) time-accumulated contribution of the Beijing (BJ), Tianjin (TJ), Hebei (HB), and Shanxi (SX) emissions to the PM2.5 peak concentration at the objective time (2000 BJT 3 December 2016).

    Table 1.  Statistical summaries of the simulation and observation results

    Station No. of observations Simulated
    maximum
    (μg m–3)
    Observed
    maximum
    (μg m–3)
    Simulated
    mean
    (μg m–3)
    Observed
    mean
    (μg m–3)
    Mean bias
    (μg m–3)
    Normalized
    mean bias
    (%)
    RMSE
    (μg m–3)
    R Index of agreement
    NJ 108 538.73 552.50 224.43 216.91 7.52 3.47 121.60 0.65 0.80
    SDZ 107 291.71 228.00 75.43 51.10 24.33 47.61 62.53 0.59 0.73
    Notes: Mi is simulated concentration; Oi is observed concentration; mean bias $ {\rm{MB}} =\displaystyle \frac{1}{N}\sum {({M_i} - {O_i})} $; normalized mean bias $ {\rm{NMB}} = 100\% \times \displaystyle \frac{{\sum {({M_i} - {O_i})} }}{{\sum {{O_i}} }} $; root-mean-square error RMSE $= \displaystyle \sqrt {\frac{{\sum {{{({M_i} - {O_i})}^2}} }}{N}} $; index of agreement IA $ = 1 - \displaystyle\frac{{\sum {{{({M_i} - {O_i})}^2}} }}{{\sum {{{\left( {\left| {{M_i} - \bar O} \right| + \left| {{O_i} - \bar O} \right|} \right)}^2}} }} $.
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    Table 2.  Regional contribution period and rate (%) of PM2.5 pollution over Beijing by the GRAPRS–CUACE adjoint model

    Pollution event Surface weather conditions Main contribution period
    (hours before objective time)
    Contribution rate in the 72-h period
    before objective time (%)
    BJ TJ HB SX BJ TJ HB SX
    27 November to 2 December 2015 (Wang et al., 2017) At the rear of a surface high 1–13 1–33 1–57 17–33 31.0 9.0 56.0 4.0
    2–5 December 2016 Under a surface low 1–30 1–26 1–54 14–53 34.2 3.0 49.4 13.4