PM 2.5 sensitivity analysis

CMAQ simulations were conducted to investigate the response of PM2.5 concentrations in the KPAB to reductions in anthropogenic emissions of primary PM2.5 and PM2.5 precursors (i.e., NOX, SOX, VOCs, and NH3) in the KPAB and other regions, for 21 to 24 December 2010. We use DDM to calculate the sensitivities of PM2.5 to precursor emissions from KPAB and 4 upwind regions (i.e. North and Chu-Miao Air Basin (NCMAB), Central Air Basin (CAB), Yun-Chia-Nan Air Basin (YCNAB), and Yi-Lan and Hua-Dong Air Basin (YLHDAB) (Figure. 4.1b). Here, the anthropogenic emissions tracked by DDM are the point, area and mobile emissions defined by the Taiwan Emission Data System (TEDS 8.1). To prevent interference of initial conditions, 3 day spin-ups were used for this episode, beginning on 18 December. The modeled results were averaged to present 24-hour averages from the surface layer across the 4 day episodes at grid cells where the monitoring sites are located over the KPAB (Figure. 4.1c). In addition, we use the Air Resources Laboratory’s (ARL’s) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to locate the source of the high peak of PM2.5 occurrence by showing 72-hour

108 backward trajectories (Stein et al., 2015).

To assess the accuracy of DDM, we compared DDM and BFM simulations of the response of PM2.5 concentrations across the KPAB to 20% reductions in Taiwan domain-wide precursor emissions during 21–24 December 2010, using eq. 1 for BFM and eq. 2 for DDM. A 20% emission reduction was chosen to be large enough to prevent numerical noise but small enough to capture small variations from the emissions base case, following Koo et al., (2009) and Napelenok et al., (2006, 2008). This 20% emission perturbation is also applied during the spinup period to take into account the influence of emissions from before the focus period. We used three statistical modeling performance benchmarks, the normalized mean bias (NMB), normalized mean error (NME) and correlation coefficient (R), to compare the BFM and DDM techniques.

4.3 Results

4.3.1 CMAQ model simulation evaluation

Baseline simulation. Model performance metrics for the 24-hour average PM2.5 precursor, PM10 and PM2.5 mass and major PM2.5 species compared with 15 regular sites and 3 supersites within the KPAB domain during 1-31 December are shown in Table C3. Time series and scatter plots of 24-hour average observed and simulated PM2.5 and PM2.5 component concentrations are presented in Figure C2-C8. We use the model performance criteria recommended by Taiwan EPA (2015), indicating that criteria for PM2.5 and PM10 mass are met when MFB is less than or equal to ±35%, MFE is less than or equal to 55%, and R is greater than or equal to 0.5, while model performance criteria for PM2.5 precursor evaluation are met when MFB is less than or equal to ±65%, MFE is less than or equal to 85%, and R is greater than or equal to 0.45. For PM2.5 species, we use Boylan and Russell (2006)’s model performance criteria that the level of accuracy is

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considered acceptable for modeling applications if MFB ≤ ±60%, and MFE ≤ 75%.

In general, the 24-hour average PM10 and PM2.5 are underestimated by 39.2 μg/m3 and 10.0 μg/m3 _{with -52.5% and -28.7% for MFB and 60.8% and 47.7% for MFE, respectively (Table C3).} The model predicts 40-50% of the variability in both the daily PM10 and PM2.5. The overall the model performance for PM2.5 meets the criteria suggested by Taiwan EPA (2015), but violates for PM10. In addition, the model overpredicts the concentrations of NH4+, NO3- and EC by 0.7 μg/m3, 7.3 μg/m3 _{and 0.4 μg/m}3_{, respectively while underestimating the concentrations of SO}

42- and OC by 2.6 μg/m3 _{and 7.6 μg/m}3_{, respectively. The over-predicted NO}

3- may be explained by the underestimated SO42- which makes more NH3 available to react with NOX. The overestimate of NH4+ can be explained by over-predicted NO3- and underestimated SO42-, as NH4+ concentration was derived from these two species. The model significantly underestimates OC, -67.5% for MFB and 73.5% for MFE, which is expected given uncertainties in the atmospheric processing of primary and secondary OC. Except for OC, simulated PM2.5 species meet the model performance criteria of Boylan and Russell (2006). For PM2.5 precursors, the model overpredicted the concentrations of SO2, VOCs and NO2 by 0.7 ppb, 67.0 ppb and 6.9 ppb, respectively but the MFB and MFE meet the model performance criteria of the Taiwan EPA (2015). Overall, the model was able to duplicate the observed temporal variation in PM2.5 and major PM2.5 species, and the statistical comparison meets the model performance benchmarks, indicating acceptable modeling performance.

Episode simulation. During the winter episode of 21-24 December, a typical emanating high- pressure system moves from inland and northern China toward the East China Sea. The high hourly PM2.5 concentrations of 73 μg/m3 observed at the northeast background Wanli site, suggests a significant influence of transport from outside of Taiwan (Figure 4.2) (Chuang et al., 2018). At

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12am (LST) on the first day, a continental high pressure system (1022 hPa, 20N, 110E) forms moving toward the East China Sea, causing the northeasterly winds that prevailed in the vicinity of Taiwan (Figure 4.2a-b and Figure 4.3a-b). High PM2.5 concentrations (over 60 μg/m3 for 24- hour average PM2.5) are observed over almost all monitoring sites in the KPAB (except for Meinong, Pingtung and Hengchun) on the first day.

In the following two days, a high-pressure system (1056 hPa) developed over Siberia (100°E, 50°N) at 12am (LST) on 22 and 23 December, pushing a previous continental high pressure system (1022 hPa, 30N, 120E) that formed on 21 December away from mainland China(Figure 4.2c-f and Figure 4.3c-f). When the emanating high pressure system reaches Taiwan, the west of Taiwan is situated at the lee side of the Central Mountain Range and encounters a high PM2.5 concentration buildup. In particular, a vortex formed accompanied by a significant land–sea breeze over KPAB after 12 pm (LST) on 22 December, bringing significant concentrations of PM2.5 and precursors to KPAB areas, resulting in a high PM2.5 buildup over KPAB (Figure 4.2c-f and Figure 4.3c-f). Except for the Hengchun site, over 60 μg/m3 _{of 24-hour average PM}

2.5 was observed at all monitoring sites in KPAB, with the highest measured PM2.5 concentrations of 108 μg/m3 and 127 μg/m3 at Qiaotou on 22 and 23 December, respectively.

On 24 December the high-pressure system continues to impact Taiwan air quality, but the magnitude of effect has started to decrease due to the wind speed increase. Whereas the high PM2.5 concentration still can be observed during the daytime, on 24 December the PM2.5 concentration started to decrease after 12pm (LST) along with a decrease in long-range from outside of Taiwan, based on the Wanli site (Figure 4.2g-h and Figure 4.3h). On 25 December, the 24-hour average PM2.5 concentration at all monitoring sites was below 60 μg/m3.

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is 67.1 μg/m3 _{(Figure 4.4-4.5). The most abundant component is nitrate, accounting for 31.7%} (21.3 μg/m3_{) of PM}

2.5 mass, followed by others 17.5% (11.8 μg/m3), ammonium 16.5% (11.0 μg/m3_{), sulfate 14.7% (9.9 μg/m}3_{), Soil 7.4% (4.9 μg/m}3_{), OC 7.5% (5.0 μg/m}3_{), EC 3.2% (2.2} μg/m3_{) and sea salt 1.5% (1.0 μg/m}3_{). This proportion of simulated PM}

2.5 component is in good agreement with previous field experiments which indicate that SO42-, NO3-, NH4+ are the most important constituents of PM2.5 during winter (Chang et al., 2011; Chou et al., 2010; Hsu et al., 2008; Lu et al., 2016; Tsai et al., 2011; Tsai and Kuo, 2005; Tsai and Chen, 2006; TEPA, 2016a). Table 4.2 shows the model performance for the 24-hour average PM precursor, PM10 and PM2.5 mass and PM2.5 species at KPAB sites during this four-day episode. Compared with the model performance for 1-31 December (Table C3), these 4 episode days overall have lower bias for PM10 and PM2.5 mass but higher bias for PM2.5 precursors and components. The violation of R indicates that model has difficulty reproducing the variability during these 4 episode days (<40%). However, the simulated MFB and MFE for PM2.5/PM10 mass and PM2.5 precursor overall meet the model performance criteria (Taiwan EPA, 2015), except for MFB of PM10 (>±35%). For PM2.5 species, nitrate and OC violate the model performance criteria of Boylan and Russell (2006) but other PM2.5 species are within those criteria. During this four day episode, the meteorological parameters shows low wind speed, high relative humidity and high surface pressure, which have been associated with high PM2.5 by previous studies (Fang et al., 2007; Kuo et al,. 2011; Wang et al., 2014) (Figure 4.6).

4.3.2 CMAQ-DDM sensitivities to KPAB domain-wide emissions and evaluation with respect