**3. Results**

#### *3.1. Daily Meteorological Factors at Observation Sites*

The WRF performance was evaluated by using the normalized mean bias (NMB) and the index of agreemen<sup>t</sup> (IA) for ground-level air temperature, relative humidity, and wind speed for Bangkok and Chiang Mai (Table 1). More statistics were shown in supplementary data (Table S1). IA is a statistical measure to present the model performance and varies between 0 and 1, where 1 means model performance is perfect [37]. IA values for air temperature, relative humidity, and wind speed were relatively high in both WRF simulations: the models applying both NCEP FNL and ERA-interim simulated spatial and temporal variations of these parameters in the same level.

**Table 1.** Model performance for daily mean meteorological factors in the two simulation cases for the periods of one year and two months (from March to April) in 2014.


Note: Mean—mean value, NMB—normalized mean bias, IA—index of agreement.

#### *3.2. Daily Concentrations of PM10 at Observation Sites*

The performance of daily mean PM10 concentration simulations in the four simulation cases with BB emissions was evaluated by using NMB and IA for the periods of both the entire year and the two month period from March to April 2014 (Table 2). More statistics were shown in supplementary data (Table S2). The highest PM10 concentrations in 2014 were observed on January 3 at Bangkok (123.9 μg m<sup>−</sup>3) and on March 21 at Chiang Mai (242.9 μg m<sup>−</sup>3), respectively. At Chiang Mai, maximum daily mean PM10 concentrations were simulated in all of the four simulation cases on the same day: March 21. The IA value for FNL + FINN was the highest for both the one-year and the two-month periods, indicating that FNL + FINN performed best throughout 2014, including the season when BB activities were intensified.


**Table 2.** Model performance for daily mean PM10 concentrations in the four simulation cases for the periods of one full year (2014) and two months (from March to April 2014).

Note: Mean—mean value, Maximum—maximum daily mean value, NMB—normalized mean bias, IA—index of agreement.

Figure 5 shows simulated and observed daily concentrations at Bangkok and Chiang Mai. All of the four simulation cases simulated the day-to-day variation patterns well at both sites for the year 2014. Cases applying FINN v1.5 for BB emissions (FNL + FINN; ERA + FINN) showed higher trends of simulated PM10 concentrations at both observation sites compared to those applying GFED v4.1s (FNL + GFED; ERA + GFED), regardless of the atmospheric reanalysis. In particular, the former two cases clearly presented higher trends of PM10 concentrations than the latter cases at Chiang Mai from the end of February to early April, when high PM10 concentrations were observed. Table 3 shows the normalized percentage difference for FNL + FINN and the other three cases for annual mean and maximum daily mean PM10 concentrations in 2014 at the two observation sites. The percentage difference at Bangkok (−9% to −4% for annual mean; −15% to −2% for maximum) was less than that at Chiang Mai (−19%–8% for annual mean; −53%–10% for maximum). At both of the two sites, the percentage difference between the FNL + FINN and ERA + FINN cases was the smallest for annual mean and maximum daily mean PM10 concentrations in 2014. In particular, the difference for maximum daily mean PM10 concentrations at Chiang Mai for FNL + FINN and the two cases applying GFED v4.1s for BB emissions (−53% to −27%) was much larger than that between the FNL + FINN and ERA + FINN cases (10%). Consequently, BB emission inventories more strongly impacted the PM10 simulation than atmospheric reanalyses at the two sites in Indochina.

**Figure 5.** Time series of simulated and observed daily mean concentrations of PM10 at (**a**) Bangkok and (**b**) Chiang Mai and estimated daily mean relative contributions of BB at (**c**) Bangkok and (**d**) Chiang Mai.


**Table 3.** Normalized percentage di fference for FNL + FINN and the other three cases for daily mean PM10 concentrations for the year 2014.

Note: Mean—annual mean value, Maximum—maximum daily mean value.

BB contributions were estimated by the di fference between cases with and without BB emissions (Figure 5). The BB contributions to PM10 concentrations of the four simulation cases were 2%–12% at Bangkok and 75%–89% at Chiang Mai for each day that the maximum concentrations were observed. Li et al. [12] reported that BB contributions to simulated PM2.5 was 70%–80% in the BB source regions duringMarch-April2013andthecontributionswerecloseto75%–89%atChiangMaion21March2014.

Figure 6 shows simulated and observed wind speed, planetary boundary layer (PBL) height, and daily concentrations at Bangkok and Chiang Mai from March to April. At Bangkok, these parameters were on the similar trends among the four simulation cases. At Chiang Mai, the maximum PM10 concentration was observed in weak-wind condition. On the other hand, there was little relationship between PBL height and PM10 concentrations. The simulated spatial distributions on January 3 and March 21 are analyzed in detail in the next subsection.

**Figure 6.** Time series of simulated and observed daily mean wind speed at (**a**) Bangkok and (**b**) Chiang Mai, and simulated daily mean planetary boundary layer (PBL) height at (**c**) Bangkok and (**d**) Chiang Mai. The simulated and observed daily mean concentrations of PM10 were also shown. The day when maximum PM10 concentration was observed from March to April 2014 at each site was highlighted.

#### *3.3. Spatial Distributions for Daily Concentrations of PM10*

Figure 7 shows the spatial distributions of daily PM10 concentrations and wind fields at ground level on January 3, when maximum PM10 concentration was observed in Bangkok. The large BB contribution area was spread out over Southern China and Cambodia. On January 3, ground-level winds were weak over Indochina. The area where simulated PM10 concentrations were around 80 μg m<sup>−</sup><sup>3</sup> was spread across Thailand, and the BB contributions were relatively small in the four cases; discrepancy among the four cases was small across Thailand.

**Figure 7.** Spatial distributions of daily mean of PM10 concentrations and wind fields at ground level in (**a**) FNL + FINN and (**b**) ERA + FINN cases, and BB contributions to PM10 concentrations in (**c**) FNL + FINN, (**d**) ERA + FINN, (**e**) FNL + GFED, and (**f**) ERA + GFED cases on 3 January 2014.

In March 2014, the largest number of hotspots were detected in Thailand and Myanmar from March 18 to 21 [5]. A large number of fire spots occurred in the mountainous areas of Indochina on March 21 (Figure 1). Figure 8 shows spatial distributions of daily PM10 concentrations and wind fields at ground level on March 21. Ground-level winds blew easterly in the eastern part of Indochina and converged at the Myanmar and Thailand border in all cases. Concentrations of 160 μg m<sup>−</sup><sup>3</sup> of PM10 were spread over the area along the Myanmar and Thailand border in the four cases with BB emissions; the two cases that FINN v1.5 selected for BB emissions were predicted to have higher PM10 concentrations in and around Northern Laos. In highly polluted areas caused by BB over Indochina, the discrepancy of simulated PM10 concentrations resulting from different BB emission inventories was larger than that resulting from different atmospheric reanalyses.

**Figure 8.** Spatial distributions of daily mean of PM10 concentrations and wind fields at ground level in (**a**) FNL + FINN and (**b**) ERA + FINN cases, and BB contributions to PM10 concentrations in (**c**) FNL + FINN, (**d**) ERA + FINN, (**e**) FNL + GFED, and (**f**) ERA + GFED cases on 21 March 2014.
