**1. Introduction**

Indochina, the peninsular region that includes Cambodia, Laos, Myanmar, Thailand, and Vietnam, has a monsoonal climate with a dry and wet season; the prevailing wind direction and the pattern of precipitation changes seasonally. During the dry season, particulate pollution degrades air quality over Indochina [1]. Outdoor exposure to particulate matter (PM) contributes to ill health, such as cardio- and cerebrovascular disease, respiratory illnesses, lung cancer, and possibly other diseases [2]. A major source of atmospheric particles over Indochina is biomass burning (BB), such as forest fires and agricultural burning. Thailand is surrounded by mountainous areas where BB frequently occurs in the dry season. Chiang Mai, the largest city in northern Thailand, has experienced severe air pollution caused by BB [3–5]. Furthermore, measurements of aerosol properties on Dongsha Island in the northeastern South China Sea revealed that smoke originating from BB in Indochina rises and is trapped within the free troposphere (3–4 km above the earth's surface) in some situations [5–7].

As part of NASA's 2006 Biomass Burning Aerosols in Southeast Asia: Smoke Impact Assessment (BASE-ASIA), regional modeling studies were conducted to assess the impacts of BB in Southeast Asia on atmospheric composition over Asia in 2006 by using the Community Multiscale Air Quality (CMAQ) model [8]. The CMAQ simulations revealed that during two intense episodes in 2006, BB contributed to ground-level CO, O3, and PM2.5 concentrations in the source region of Southeast Asia by as much as 400 ppbv, 20 ppbv, and 80 μg m<sup>−</sup>3, respectively [9]. The emissions from BB could also be spread over the southeastern parts of East Asia via strong eastward transport in the free troposphere from 2 to 8 km above the earth's surface [9,10]. It should be noted that the model performance was evaluated using only observed CO concentrations in Thailand, and that discrepancy existed between simulations and measurements due to uncertainty in emission inventories. Amnuaylojaroen et al. [11] reported that BB emissions create a substantial increase for simulated O3 and CO surface mixing ratios by up to 29% and 16%, respectively, for Southeast Asia in 2008. However, particulate matter was not evaluated in the simulations. Li et al. [12] reported that BB contributed 70%–80% of simulated PM2.5 in northern Myanmar, Laos, and Vietnam during March-April 2013.

Several BB emission inventories are available, such as the Fire INventory from NCAR (FINN) [13], the Global Fire Assimilation System (GFAS) [14], and the Global Fire Emissions Database (GFED) [15]. BB emission inventories have substantial uncertainties in their temporal and spatial variabilities because the data to estimate the emissions of BB, such as area, fuel loading, and emission factors, are limited [13,15]. Vongruang et al. [16] reported that PM10 was overestimated in air quality simulation with FINN, whereas PM10 was underestimated with GFAS in the source region of Northern Thailand in March 2012.

Indochina has convoluted meteorological scales, and regional meteorological conditions dominate the transport patterns of pollutants [1]. Atmospheric reanalyses, initial and boundary conditions for a mesoscale model, have grea<sup>t</sup> impacts on the simulated meteorological fields. Several atmospheric reanalyses have been developed by di fferent organizations, for example, the European Centre for Medium Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-interim) [17], the Japanese 55-year Reanalysis (JRA-55) [18] from the Japan Meteorological Agency (JMA), and the United States National Centers for Environmental Prediction Final (NCEP FNL) [19] Operational Global Analysis. These reanalyses employ various forecast models and data assimilation approaches. ERA-Interim has the highest ability to reproduce climate variables of the East Asian summer monsoon, especially precipitation [20,21].

To the best of our knowledge, there are few studies simulating particulate pollution in Indochina for a period of one year that consider the uncertainties of BB emissions and the complexities of the regional meteorology. In one study, two BB emission inventories were assessed by simulating particulate matter using CMAQ during a specific pollution event [16]. This study focused on the impacts of BB emission inventories and atmospheric reanalyses on the simulation of PM10 over Indochina in 2014. From the end of February to early April in 2014, agricultural burning and forest fires in northern Thailand caused severe particulate pollution over the region [5].

#### **2. Materials and Methods**

## *2.1. Simulation Design*

Air quality simulations for the year 2014 were conducted by CMAQ v5.0.2 with an initial spin up period of December 2013. The modeling domains covered regions from East Asia (D1) to Indochina (D2) (Figure 1). The horizontal resolutions were 72 km and 24 km, and the number of grid cells were 92 × 92 and 98 × 98 for D1 and D2, respectively. The domains consisted of 30 sigma-pressure coordinate vertical layers ranging from the surface to 100 hPa. Figure 2 shows the procedure of air quality simulation in this study.

**Figure 1.** Locations of observation sites for PM10 and meteorology, and fire spots (MCD14DL) provided by Fire Information for Resource Management System (FIRMS) on 21 March 2014 in the modeling domains covering (**a**) East Asia (D1) and (**b**) Indochina (D2). Elevation and dominant United States Geological Survey (USGS) 24-category land use are provided in (**a**) and (**b**), respectively.

Chemical transport model

**Figure 2.** Procedure of air quality simulation by meteorological model and chemical transport model.

## *2.2. Meteorological Model*

Meteorological fields to input to CMAQ were produced by using the Weather Research and Forecasting (WRF) model [22] v3.4. The WRF simulations were conducted using the high-resolution, real-time, global sea surface temperature analysis (RTG\_SST\_HR) of NCEP, and two atmospheric reanalyses: NCEP FNL or ERA-Interim. The physics options were as follows: Rapid Radiative Transfer Model for General Circulation Models Shortwave and Longwave Schemes [23], planetary boundary layer physics of Asymmetric Convection Model 2 Scheme [24], cumulus parameterization of the Kain–Fritsch Scheme [25], micro physics of Morrison 2-moment Scheme [26], and Pleim–Xiu Land Surface Model [27]. Grid nudging was applied to horizontal wind components at all the vertical layers with a coefficient of 3.0 × 10−<sup>4</sup> s<sup>−</sup><sup>1</sup> in D1 and 1.0 × 10−<sup>4</sup> s<sup>−</sup><sup>1</sup> in D2 for the entire period.

#### *2.3. Chemical Transport Model*

CMAQ was configured with the Carbon Bond chemical mechanism (CB05) [28] for gas-phase chemistry and the sixth generation CMAQ aerosol module for the aerosol process. The initial and lateral boundary conditions for D1 were obtained from the Model for Ozone and Related Chemical Tracers v4 (MOZART-4) [29]. Several datasets were used to produce emission data. In particular, the Fire INventory from NCAR (FINN) [11] v1.5 or the Global Fire Emissions Database including small fires (GFED v4.1s) [30] was selected for BB emissions. FINN v1.5 provided 1 km-resolution BB emissions estimated from the active fire observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellite. GFED v4.1s provided 0.25◦-resolution BB emissions estimated from the 500 m Collection 5.1 MODIS direct broadcast burned area product plus additional burned area from small fires based on a revised version of the Randerson et al. [31] small-fire estimation approach. Anthropogenic emissions were obtained from the Task Force Hemispheric Transport of Air Pollution (HTAP) v2.2 inventory [32]. The other natural emissions were derived from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) [33] v2.04 for biogenic emissions and the Aerosol Comparisons between Observations and Models (AEROCOM) data [34] for baseline volcanic SO2 emissions. There were di fferences between the periods of simulations and the reference years in the anthropogenic emission data available for the simulations, and the uncertainties associated with the di fferences may have a ffected model performance.

In order to evaluate the impacts of BB emission inventories and atmospheric reanalyses on simulated PM10 concentrations, four cases of simulations (FNL + FINN; FNL + GFED; ERA + FINN; and ERA + GFED) were executed. The first parts of cases' names indicate the applied reanalysis: FNL and ERA stand for NCEP FNL and ERA-Interim, respectively. The second parts indicate the selected BB emission inventories. Furthermore, simulations without BB emission inventories (FNL + exBB; ERA + exBB) were conducted. The di fference between cases with BB emissions and those without BB emissions was calculated to estimate BB contributions to PM10 concentrations in the four simulation cases with BB emissions.

Figure 3 shows spatial distributions of PM10 emissions from FINN v1.5 and GFED v4.1s in March and April 2014. During these two months, PM10 emissions in both BB emission inventories were concentrated in Indochina, especially the mountainous areas in Laos, Myanmar, and Thailand. FINN v1.5 estimated larger emissions in a wider area than GFED v4.1s. Figure 4 shows the variation of the amount of PM10 emissions over D2 and the relative contributions from BB. PM10 emissions from BB produced by FINN v1.5 and GFED v4.1s accounted for 244 mg s<sup>−</sup><sup>1</sup> km−<sup>2</sup> and 91 mg s<sup>−</sup><sup>1</sup> km−2, respectively, which contributed 93% and 83%, respectively, of the total amount of PM10 emissions in March when burning activities were intensified.

The WRF performance was evaluated through comparison with ground-level air temperature, relative humidity, and wind speed data in Bangkok and Chiang Mai distributed by the University of Wyoming [35]. The CMAQ performance was evaluated through comparison with ground-level PM10 concentration data in Bangkok and Chiang Mai distributed by the Acid Deposition Monitoring Network in East Asia (EANET) [36].

**Figure 3.** Spatial distributions of mean PM10 emissions from biomass burning: (**a**) FINN v1.5 and (**b**) GFED v4.1s in March, and (**c**) FINN v1.5 and (**d**) GFED v4.1s in April.

**Figure 4.** Variation of mean PM10 emissions over D2 and the relative contributions from biomass burning (BB): (**a**) FINN v1.5 and (**b**) GFED v4.1s.
