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Distinguish Potential Source Areas of PM2.5 and PM10 by Statistical Data Analysis

Power plant from biomass in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air. For air pollution, total suspended particulate (TSP) and particulate matter less than 10 micron (PM10) from rice husk energy process is quite large. Roi Et is a province located in Northeast of Thailand which obtains power plants using rice husk as fuel in the process. According to the land-use pattern of the community area of Roi Et, it revealed that there are many sources of air pollution. This study selected the statistical techniques to distinguish the potential source area influencing the high concentrations of particulate matter less than 2.5 micron (PM2.5), particulate matter 2.5 to 10 micron (PMcoarse), and PM10 which caused the health effects of people around the rice husk power plants. Hourly observed PM10 and PM2.5 data were analysed by time series and bivariate polar plot. Results showed that increasing the concentration of PM10 and PM2.5 were observed in the duration of low wind speed. However, daily average of PM10 was not more than National Ambient Air Quality Standard (NAAQS) which is 120 μg/m3. In contrast, results showed that daily average of PM2.5 performed over NAAQS of 50 μg/m3 in the 20th -22nd December 2018 that high pressure cover Thailand. While sources of these particulate matters could be distinguished into 3 potential areas based on the mobile air quality monitoring station location which were 1) east side and 2) north-west side which regarded as sub-urban and traffic areas, respectively and 3) north-east side which regarded as power plants area. Results revealed that PMcoarse was the major pollutant dispersed from the Northeast direction that the power plants were the major source. Whereas traffic and community were the main sources of PM2.5 coming from the East and the Northwest direction of the mobile air quality monitoring station point. Therefore, discrimination of each point source could be done first before implementing to control and manage the air pollution effectively.


Introduction
Today, air pollution problem resulted from expansion of energy sector; especially biomass power plant has been listed to be concerned. Because Thailand has various types of biomass that can be used to generate many alternative energies. Rice husk is the by-product from the rice mill, community rice mill and steamed rice mill. In each year, the 25 million tons of paddy products can produce 5.75 million ton of rice husks [1]. Rice husk is an optional fuel in direct combustion to produce an energy source for the large rice mills. Currently, rice husk has been used as fuel to generate electricity that the quantity of 9,800 ton of rice husks can be used for production 1 MW of electricity. There are rice husk biomass power plants has operated in Roi Et province that located in Northeast of Thailand. The rice husk biomass power plants located in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air [2]. For air pollution, total suspended particulate (TSP) including particulate matter from rice husk energy process of power generation is quite larger than emission from the process using coal fuel [3]. Although dust, which including PM10 and particulate matter less than 2.5 micron (PM2.5) at the community in the Nuea Mueang sub-district, Roi Et province were regarded to be mainly from power plants in the past. This was argued by the surrounding communities, and they believed that there is impact of dust emission affecting to them. The community has started to ask the provincial government and the enterprises to solve the environmental problems [2]. However, the land use of this area is revealed that there is spatial complex. Community and transportation in the area play the crucial sources of combustion and then result in the overall particulate matter concentration, as well. To implement the proper regulations and policies, potential source areas affecting air pollution in the community near the power plants would be identify.
Therefore, this study aimed to identify the potential source areas of PM10 and PM2.5 using integrated statistical techniques and bivariate polar plot. The statistical technique using bivariate polar plot is beneficial method to recognize source characterization in a complex emission area, and performs filtering air pollution data associated with wind speed and direction during the period of interest. Thus, they can prove the directional sources in a specific time period [4,5,6]. Results from this study are expected to be applied for the policymaker to reduce, control and management of PM10 and PM2.5 in the Roi Ed and the others, which have more complex emission area of air pollution.

Study area and methodology
The study area of this work focuses on the Nuea Mueang sub-district, Roi Et province that located in the northeastern part of Thailand. There are adjacent air pollution mobile, point, and residential sources, which affecting to surrounding community by their emissions. Major point sources are rice husk biomass power plants located in one plot of the area of 300 m 2 as shown in Figure 1. Their capacities are 9.95, 6.00, and 9.90 MW, respectively [2]. Whereas, the requirement of Thailand regulation to perform the environmental impact assessment (EIA) enforces for the power plant larger than 10 MW in capacity. Three of them were not assessed by the EIA. However, it does not mean that there is no pollution emitting from the small power plant (less than 10 MW) [2].
The data of this study was obtained by the mobile air quality unit of the Environmental Research and Training Center, Thailand during 18-21 December 2018. This period is winter season that is the affected period by the power plants to communities [2]. The mobile unit had been installed at the Nuea Mueang sub-district, Roi Et province to monitor PM10, PM2.5, wind direction, and wind speed. The unit equipped with the Thermo Scientific™ 1405-DF TEOM™ Continuous Dichotomous Ambient Air Monitor equipment and meteorological equipments. The location of the monitoring station located in the north of the Roi Et urban area, and in the southeast direction of the power plants area (Fig. 1). During the monitoring period, the dominant wind over Thailand is governed by the northeast monsoon [7]. Therefore, most of the wind blowing during this period would be coming from the northeast side to the station.
The hourly observed data obtained by the station were analyzed to present the variations of their parameters in order to understand the air pollution situation of the study area. To distinguish potential source areas affecting each observed particulate matters (PMs), the R program [8] with the bivariate polar plot function of the OpenAir package [9] was utilized. The bivariate polar plot is a plot of the interesting statistical metric on the polar coordinate. The coordinate systems are commonly used to visualize the data at the corresponding pair of axes on a plan as a spatial map. The polar coordinate system uses radial (r) and angles () axes. For the air quality analysis, the radial axis (r) can be applied to represent wind speed and the angular axis () be applied to represent wind direction. The interesting statistical index, an average of maximum concentration of the PMs for this study, in each corresponding ranges of wind speed and wind direction is plotted at the corresponding coordinate in the polar coordinate system. Also, the result can be compared with the aerial image to determine the consistency of the emission sources influencing the level of pollutant at the air quality monitoring station [5], but the plot in the polar coordinate system cannot be compared with the measurement value as the air quality dispersion model [10].

Result and discussion
The data obtained during the monitoring period (18-21 December 2018) were used for analysis. Firstly, wind circulation is an important factor used to present where the frequented wind direction that brings air mass to the monitoring station. Figure 2 presents the wind rose of the study period, which reveals the wind blows from the North side till the Northeast sides more than 50%, and especially coming from the Northeast 20%. The calm condition contributes 26% of the total, which results in less air mass transportation. The variation of wind speed shows in Figure 3, its range is between calm to 4 m/s. Changes in the hourly average concentrations of the PM10 and PM2.5 present in Figure 3. It is noteworthy that the variation of PM10 and PM2.5 occur at similar times. This would imply to the same forcing factor impacting the levels of both pollutants. While the variation of wind speed is low or in the calm condition during the pollutants become high concentrations. This coincident most occur during on the night time. Not only the signal of diurnal variation can be observed in the PM10, PM2.5  4 and wind speed variations, but also there is a signal of daily timescale variation envelops in their fluctuation. Therefore, the 24 hours rolling average was performed to the PM10, PM2.5 and wind speed data to remove the noise of diurnal variation. The advantage of using this technique does not only remove the noise, but the 24 hours rolling mean concentration can be compared to the NAAQs of PM10 and PM2.5 as shown in Figure 4.   From the analysis mentioned above, it was found that the main wind direction is the northeast wind blowing through the monitoring station. The concentration of PM10 and PM2.5 in the measurement area will increase during the low wind speed, but still does not know where the potential source areas are. There are mobile, residential, and point sources located around the community represented by the monitoring station. Impacts of them on the ambient air quality at the monitoring station area would be different. Therefore, analyzing the bivariate polar plot was applied for the observed data of PM 10 , particles of 2.5-10 microns (PMCoarse) and PM2.5.
The result of bivariate polar plot analysis presents in Figure 5. It was found that the source area (red shading) of PM10 originating from the east and north-west of the measurement point (Figure 5a). The east side is a community suburb area that having high traffic density and residential activities related to emit PM10. For the north-west side, the red shading similar to the line, and corresponds to the line of highway on the aerial image. This means that the mobile source plays an important role in releases PM10 affecting the community at the Nuea Mueang sub-district. For the red shading presents on the east side, it related to the suburb of Roi Et province that can be seen in the aerial image. It implies to the activities in the suburb area affecting the PM10 level as well. It is noteworthy that there is red shading as a point on the northeast side. Air pollution source in the northeast side is the power plants establishment, which collocated in the same place of the shading. While the source area of PM2.5 (Fig. 5b), which is a particle that is smaller than 2.5 microns and is part of the PM10, found that the source is from the east and the northwest above the measurement point. These imply that the potential sources related to activities in suburb area and traffic in the highway, respectively. The power plants are not the potential source contributing PM2.5 to the monitoring station. For particles having size from 2.5-10 microns (PMCoarse), The result reveals that the potential source of the PMCoarse comes from surrounding area near the monitoring station and the northeast area (red shading in Fig 5c). The light red shading close around the monitoring station corresponding to the residential sources that have activities such as burn weeds, cooking by firewood or charcoal stove, and light traffic. The red shading is in northeast side are the power plants and contributing more than the residential sources, which indicating by darker shading (Fig. 5c).