1. Introduction
Air pollution is a widespread problem that affects human health and other atmospheric aspects, i.e., it can contribute to the warming of the atmosphere and can affect rain and cloud patterns. Air pollution is released from a number of man-made and natural sources including fossil fuels burning in electricity production, transportation, industry and households, agriculture, and waste processing. According to a 2014 World Health Organization (WHO) report the premature deaths of about 7 million people worldwide were caused by air pollution [
1]. It is estimated that in developing countries approximately 300,000 to 700,000 people can be saved from premature death if aerosol levels are reduced to a safe level (an Air Quality Index (AQI) number under 100 signifies good or acceptable air quality) [
2].
Southeast Asia is a region with frequent air pollution problems every year, particularly at the beginning of the year, from January to April. Biomass burning strongly dominates air pollution from the regional to local scale in Southeast Asia [
3]. Moreover, severe haze events in this region caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading the air quality in Southeast Asia [
4,
5]. In addition, appropriate meteorological and topographic effects are also favorable conditions that contribute to the air pollution problem in Southeast Asia. In northern Thailand, all these factors are combined. Most cities in northern Thailand are located in the mountainous area and surrounded by paddy fields. Larger villages like Chiang Mai face increasing problems due to traffic jams, but farmers also burn stubble in preparation for the coming rain and rice planting at this time of the year, and these narrow valleys provide perfect bowls for this smog and smoke.
The contributing pollutants in Northern Thailand, however, are not only from national sources but also from long-distance air pollutants introduced by meteorological factors, i.e., wind, temperature, and humidity [
6,
7,
8,
9]. The climate of northern Thailand is characterized by monsoons. The period from mid-February until the end of May is the transition period from Northeast monsoon (most prevalent in December–January) to Southwest monsoon (most prevalent in July–August) climates. The hottest weather observed during March–April, coinciding with the presence of intensive thermal lows in the area [
10]. During the transition period, winds can transport air pollutants from the surrounding area into northern Thailand [
11]. The northeastern monsoon transfers air pollutants from China to the southern Chinese sea from mid-December to mid-April and can then flow continuously to the mainland Southeast Asia.
Exposure to high levels of air pollution can cause a variety of adverse health outcomes. PM2.5 is the most important air pollutant and strongly affects human health. Recent calculations of global premature mortality rates on the basis of high-resolution global O3 and PM2.5 models show that Southeast Asia and the Western Pacific account for around 25% and 45% of world mortality [
12,
13]. PM2.5 can penetrate deeply into the respiratory tract and enter the lungs. Exposure to small particles can also impair the function of the lungs and exacerbate medical conditions such as asthma and heart disease [
14,
15,
16]. At the beginning of 2020, the PM2.5 measurements of air pollution in Chiang Mai, one of the largest cities in northern Thailand, reached a staggering level of 330 on the PM2.5 concentration over the weekend, making the northern city the most polluted city in the world, while the airborne pollution levels across Thailand’s northern region vary between 100 and 390 of the air pollutants concentrations, according to AirVisual data. There have been a few studies exploring the source contributions of PM2.5 in this region. For example, Chueinta et al. [
17] reported the characterization and source identification of fine and coarse particles collected in urban and suburban residential areas in Thailand and later performed an extended study on the Bangkok metropolitan curbside [
18]. Leenanupan et al. [
19] carried out similar work on the characterization of fine particulate pollution in the Mae Hong Son province in the north of Thailand. A few collaborative studies on fine and coarse particulate air pollution at the Asia Pacific regional scale were also reported, e.g., Oanh et al. [
6,
7]; Ebihara et al. [
20,
21]; Hopke et al. [
22]. Moreover, only a few long-term PM2.5 and PM
10–2.5 monitoring data are available for elsewhere in this region. However, it is unclear how much neighboring countries contribute to the air pollution problem in northern Thailand. In addition, Lee et al. [
5] found that nitrate aerosol was the major component of PM2.5 particles in Southeast Asia.
This work applied atmospheric model coupling with the air pollution model to investigate the potential contributions of biomass burning in air pollution in Northern Thailand. We used the Weather Research and Forecasting (WRF) model version 3.8.1 to simulate the meteorological conditions on March 2016. The model’s initial and boundary conditions were taken from the Final Analysis Data (FNL) [
23], and the modeled temperature and wind speed were compared to a dataset from the Pollution Control Department (PCD). To clarify the model’s capabilities, statistical analyses such as Index of Agreement (IOA) and Fractional Bias FB were used for the model evaluation. The output from the WRF model was used as the meteorological conditions to be inserted into the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to determine the long-range transport of regional air pollutants from the neighboring countries of Southeast Asia toward northern Thailand.
2. Materials and Methods
We use a coupled atmospheric model called the WRF model version 3.8 [
24] to generate the meteorological input for HYSPLIT, which is an air quality model. The results from HYSPLIT were then used to identify the PM2.5 pathway in Southeast Asia.
The WRF model was developed to study several atmospheric factors and also to be used for operational weather forecasting. It is a non-hydrostatic mesoscale model consisting of several physical schemes, including radiation, cumulus, and microphysics. In this study, we designed 1 WRF domain with a horizontal resolution of 20 km grid spacing. In addition, the model sets 30 vertical levels up to 50 hPa. The outer domain entirely covers the upper mainland of Southeast Asia and some areas of East and South Asia, such as the South of China and East of India, as shown in
Figure 1. The model configuration was listed in
Table 1. Southeast Asia is influenced by East Asian monsoons, which carry the air mass from high latitudes to this region. The transboundary transport from the western border, such as Myanmar and India, also affect the quality of the air in northern Thailand, while the inner domain is located in northern Thailand. To solve the water vapor, cloud, and precipitation process, the model was configured using the WRF Single-Moment 3-class scheme according to Hong et al. [
25]; Hong and Lim [
26]. This method predicts a simple-ice system with three types of hydrometers: vapor, cloud water, and rain. The calculation of these processes is based on the mass content of the diagnostic relationship. The Kain–Fritsch scheme [
27] is the sub-grid scale process used for the convective resolution. It has the potential to use a cloud model with updrafts and downdrafts, as well as to consider the effects of detrainment and training on cloud formation. The similarity theory scheme is used to emulate the thermal gradient over the surface responsible for friction velocity and wind over the surface [
28,
29,
30,
31,
32]. The model spin-up was conducted from 15 to 28 February 2012 to reduce the effects from the initial conditions. From 1 March 2012 to 1 April 2012, the WRF model was designed to simulate the weather conditions for the HYSPLIT model. The main meteorological variables of the WRF model, i.e., wind (U, V, W), temperature (T), surface pressure (Psfc), and relative humidity (RH), were used as input data for the HYSPLIT model.
The HYSPLIT model [
33] is designed to calculate both simple air plot pathways and complex dispersal and deposition simulations. This model is used to determine air pollutants in source–receptor relationships through trajectory analyses and predict dispersion for a number of events, such as volcanic eruptions, the transportation of wildfire smoke, and episodes of dust storms (
http:/ready.arl.noaa.gov/index.php). HYSPLIT is a Lagrangian model, meaning that its scatter calculations follow the vector of transport, and only the computing point meteorological fields are required. HYSPLIT has the same horizontal coordination and map projections as the meteorological input. The meteorological profiles for the vertical grid are interpolated linearly onto an internal model following the field coordination. Particles released from a source are advected by a mid-wind field in a particle dispersion simulation and dispersed according to the random components caused by atmospheric turbulence. The meteorological data fields of each meteorological input source are then converted from (Gridded Binary (GRIB1 and GRIB2), netCDF, MM5, etc.) to a common format required for the running of HYSPLIT. Typically, 1 or 3 h weather data are the most commonly used for the calculation of transport and dispersion and are then temporarily interpolated linearly between the input data times to the HYSPLIT integration time.
The HYSPLIT model was driven to produce back trajectories of parcels originating from an observational site via the above-described WRF simulation atmospheric fields. The backward trajectory provides the Lagrangian paths of the air parcels within the timeframe chosen (24 h in the current study). This path is useful for identifying the source locations of the pollutants that fall within the backward trajectory. Based on the PM2.5 concentrations in 8 observation sites in northern Thailand in March, the maximum PM2.5 concentration for the back-trajectory analysis was found on 27 March 2012. Once the sources were identified, the HYSPLIT model was applied to each of the identified sites for a 24-h period. The computer domain in HYSPLIT was designed to have a horizontal grid of 300 to 300 cells, each with a resolution of 0.01° and eight vertical levels (500, 1000, 1500, 2000, 2500, and 3000 m above ground level). A full 3D dispersion particulate model with a total of 5000 particles released for each emission cycle was used. The speed of the horizontal and vertical turbulence was calculated using the Kantha and Clayson method [
34]. The stability of the boundary layer was estimated by heat and momentum, and the mixed depth of the layer was derived from the meteorological model.
4. Conclusions
This paper aimed to understand the potential contributions of biomass burning to PM2.5 pollution in Northern Thailand. We developed a coupled atmospheric and air pollution modeling system based on the Weather Research and Forecasting Model and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The WRF model was used with 1 domain of 20 km grid spacing covering Southeast Asia, some parts of India, and China. The final analysis data were used as a meteorological initial and boundary condition. The model results, including temperature and wind speed, were compared with the ground-based measurements from the Pollution Control Department (PCD) in northern Thailand. The model’s capability was shown to be acceptable when compared to observations, as indicated by the Index of Agreement (IOA) in ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional bias of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. The meteorological and trajectory analysis found that the influence of the Asian Winter Monsoon can carry air pollutants to northern Thailand through two major channels. The first channel is characterized by winds blowing from eastern Asia towards Laos into northern Thailand, while the second channel is characterized by northwesterly winds blowing from Burma and entering northern Thailand. Additionally, the low temperature and wind speed during this time in northern Thailand provide favorable conditions that contribute to the air pollution problem. The analysis of Near Real Time (NRT) Moderate Imaging Spectroradiometer (MODIS) hotspot data indicated that the biomass burning from Burma has greater potential to contribute to the air pollution problem in Thailand compared to national emissions.