Long range transport of Southeast Asia PM2.5 pollution to northern Thailand during high biomass burning episode

This paper aims to investigate the airflow that can transport emission sources of PM2.5 from neighboring countries to contribute to air pollution in northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurement from Pollution Control Department (PCD) to examine model performance. As a results of model evaluation, the meteorological variables fairly agreed well compared to observation with 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. Burma was a country that contributed much of hotpot locations by 37% of the entire hotspot locations of Southeast Asia in March. The influence of Asian Monsoon can bring pollutants from neighboring countries such as Burma and Laos toward northern Thailand in March that likely contribute to high concentrations of PM2.5 in northern Thailand.


Introduction
Air pollution is a widespread problem that affects human health and other atmospheric aspects in many parts of the world. It is released from a number of man-made and natural sources including fossil fuels in electricity, transportation, industry and households, agriculture, and waste processing. According to a 2014 WHO report the premature deaths of about 7 million people worldwide were caused by air pollution (WHO, 2014). It was estimated that in developing countries approximately 300,000 to 700,000 people can be prevented from premature death if aerosol levels are reduced to a safety level (Seinfeld and Pandis 2006). Aerosol also has a direct effect on radiative forcing through the dispersion and absorption of sunlight. It can also function as a cloud condensation nucleus to change the microphysics and cloud lives, thus indirectly changing the radiative force and hydrological circulation. The global biogeochemical balance on an unprecedented scale was markedly disrupted in recent decades by dramatic human activities caused by rapid industrialization, urbanization and motorization. The Effect Environment and climate change human activities have become increasingly important and aerosol research has become a major aspect of atmospheric science.
Southeast Asia is a region with air pollution problems frequently every year, particularly at the beginning of the year, from January to April. This is because of various reasons: a lack of rain means that dust particles can potentially irritate the air even in the most polluted areas and there is smoke from burning paddy fields. In northern Thailand, all these factors are combined. There's a lot to do with geography. Mostly cities in northern Thailand located in the mountainous area which are surrounded by paddy fields. Larger villages like Chiang Mai have increased problems with traffic congestion, but farmers are also burning stubbles ready for coming rain, rice planting, at this time of the year and these narrow valleys are making perfect bowls for this smog and smoke.
Long-range transport of pollutants in the mesoscale range generally means transport across spatial scales. With regard to the annual smoke haze issue in the dry season in Northern Thailand, the main cause is from open burning, including forest fire and agricultural waste. This problem of air pollution causes severe cultural, environmental, and health degradation. Poor air quality has a rise in the concentration of air pollutants both in the form of particulate substances and gaseous such as CO, NOx, and Particulate Matter (PM). Furthermore, secondary contaminants, including ozone (O3), which is harmful to human health and vegetation. The contributing pollutants in Northern Thailand, however, are not only from national sources but from transporting long distances of air pollutants (Kim Oanh and Leelasakultum, 2011). Trajectories are well known for being strong indicators of large flow and can be helpful in the study of possible regional sources. The term "fine particles" or 2.5 (PM2.5) refers to smaller particles or droplets in the air, two and a half microns or less. Unlike centimeters, meters, and miles, a micron is a unit of distance measurement. It's about 25.000 microns in an inch. The diameter of the larger PM2.5 particles will be around thirty times smaller than that of human hair. Particles in the PM2.5 range are able to 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. Fine particles are also formed by the reaction of gasses or droplets in the atmosphere from sources such as power plants. These chemical reactions may occur miles from the source of the original emission. Resulting of the high of PM2.5 in Chiang Mai which is one of Thailand's largest cities, reached 380 on the Air Quality Index (AQI) making the northern city is one of the most polluted cities in the world.
Since PM2.5 is the most important air pollutant and strong effects on human health, so there were previous studies about PM2.5 using both instrument and modeling in northern Thailand. . The modeled temperature and wind speed were compared to the dataset from the Pollution Control Department (PCD). To clarify the model capability, statistical metrics 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 meteorological conditions into the HYSPLIT model to find out the long-range transport of regional air pollutants from neighboring countries of Southeast Asia toward northern Thailand. and an air quality model called the HYSPLIT model to generate meteorological input into HYSPLIT. The results from HYSPLIT were then used to identify the PM2.5 pathway in Southeast Asia. The WRF model has been developed to study several atmospheric studies and also to be used for operational weather forecasting. It is a non-hydrostatic mesoscale model consisting of a number of physical schemes, including radiation, cumulus, and microphysics. While the HYSPLIT model is based on a Lagrangian calculation to address the air pollutant trajectory and concentration. It combines two computational approaches, i.e. 3D-particles and puffs, to calculate the concentrations of the pollutants. In this study, we are designing 1 WRF domain with a horizontal resolution of 20 km grid spacing. In addition, the model set 30 vertical levels up to 50 hPa. The outer domain covers entirely 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. Southeast Asia is influenced by East Asian monsoon, which carries air mass from high latitudes to this region. The transboundary emission from the western border, such as Myanmar and India, also affects the quality of the air in northern Thailand. While the inner domain is in northern Thailand. In order to solve the water vapor, cloud, and precipitation process, the model was configured using the WRF Single-Moment 3-class scheme followed by Hong et al . , 2004;Hong and Lim 2006. It predicts a simple-ice system with three types of hydrometers, i.e. vapor, cloud water, and rain. The calculation of these processes is based on the mass content of the diagnostic relationship. The Kain-Fritsch scheme (Kain, 2004) is the sub-grid scale process for 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 thermal gradient over the surface responsible for friction velocity and wind over the surface (Paulson, 1970;Dyer and Hicks, 1970;Webb, 1970;Beljaars, 1940;Zhang and Anthes, 1982). The model spin-up was conducted from 15 -28 February 2012 to reduce the effect from initial conditions. From March 1, 2012 -April 1, 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.   there are coupled station sites with a PM2.5 dataset. Here, we compared the modeled results to the PCD dataset at 3 locations, which is based on the complete PM2.5 dataset as shown in Table 3. We used the statistical indicator, that is. Index of Agreement (IOA) and Fractional Bias (FB) to examine the efficiency of the model.

Model configuration
The comparison between the hourly output of the model and the observation data is shown in Figure 2. In general, the model captures well in comparison to the observation. The modeled temperature at 2 m is slightly higher than the ground-based measurement by 2 -3 ° C, while the wind speed is overestimated by 1 m / s. In addition, the statistical analysis shown in Table 2 shows that the WRF and PCD data set agree well on hourly temperature and wind speed for most sites with        Since the atmosphere is a very complicated component, a linear and non-linear analysis may be necessary to analyze the relationship of each factor in the atmosphere. Data from PM2.5 and meteorology, such as temperature and wind, were used by the Pollution Control Department (PCD) to analyze the relationship between the weather factor and PM2.5 during 20-27 March 2016, which shows a high concentration of PM2.5 in northern Thailand. In this section, we used both linear and non-linear correlation analyses to see the relationship. The linear analysis of the correlations was (1) and analogously for , Spearman Equation;

Hot spot analysis
(2) di is the difference between the two ranks of dataset, n is the number of datasets.
In general, the linear correlation analysis shows a slightly positive wind speed coefficient and a negative temperature coefficient of 0.082 and (-0.46) ( Table 6). Non-linear correlation analysis shows a negative wind speed correlation with (-0.53) but a slightly positive temperature correlation with 0.18. Linear and non-linear correlation analyzes indicate that biomass burning is negative in relation to precipitation and carbonaceous aerosol by (-0.51) and (-0.67) ( Table 6). Look at Figure  At 500 m, the airflow from the north-east is over northern Thailand, which is likely to bring some pollutants from eastern China, northern Vietnam, and Laos to the north of Thailand. While at 3000 m it is clear that transport from Burma and India strongly dominates the emission sources of pollutants to the north of Thailand.