วารสารทางวิชาการประกอบการขอพิจารณาตำแหน่งทางวิชาการระดับผู้ช่วยศาสตราจารย์

วารสารทางวชิ าการนานาชาติ ประกอบการขอกําหนดตําแหนงทางวิชาการระดับผูชว ยศาสตราจารย พลอากาศเอก ดร.ปรีชา ประดบั มขุ ผอู ํานวยการสถาบันเทคโนโลยปี องกันประเทศ อาจารยพเิ ศษวิชาการจดั การองคความรู หลักสูตรวทิ ยาศาสตรมหาบณั ฑิต ภาคเรยี นท่ี 3 ปการศกึ ษา 2562 มหาวิทยาลยั พะเยา



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VOL. 16, NO. 8, APRIL 2021 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com Figure-6. S factor analysis. Figure-7. C factor analysis. Result of C Factor Analysis Result of P Factor Analysis Plants as soil cover are key factors in preventing The conservation practice Factor is the factor that the soil erosion since they help to absorb and reduce shows the capability of controlling soil erosion. It was crashing force of rain, to slow down running water on soil calculated from the ratio of soil loss obtained from surface, to help soil better hold together, to increase soil experimented land plot where there was a kind of space so that water can flow down more, and to help to conservation and the soil loss obtained from the promote activities of living organisms in soil. In this study, experimented land plot where the soil was plowed down the soil group map at 1: 25,000 scale of Land the slope when other conditions stayed unchanged. In this Development Department was used and C factor was study, the soil group map at 1:25,000 scale was used. After input. The comparison was made with the data on the plant that, P factor was input. The comparison was made with management of the Land Development Department. That the data on factors related to the plant management of the created a map showing cropping management factors. Land Development Department. That created a map When the data was divided into 5 levels (Figure-7) for the showing the conservation practice factor. assessment of C factor, it was found that C factor in very low level of 0 covering an area of approximately 139.286 When data was divided into 2 levels (Figure-8) km2 or equal to 1.21%, C factor in low level of 0 - 0.02 for the assessment of P factor in the study area, it was covering an area of approximately 10,185.410 km2 or found at P factor at low level of 0 - 0.098 covering an area equal to 88.78%, C factor in moderate level of 0.02 - 0.048 of approximately 3.891 km2 or equal to 0.0339%, P factor covering an area of approximately 241.878 km2 or equal to at high level of 0.098 - 1 covering an area of 2.11%, C factor in high level of 0.048 - 0.280 covering an approximately 1,458.581 km2 or equal to 99.966%. area of approximately 503.209 km2 or equal to 4.39%, and C factor in very high level of 0.280 - 0.340 covering an area of approximately 402.289 km2 or equal to 3.51%. 827

VOL. 16, NO. 8, APRIL 2021 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com Figure-8. P factor analysis. Figure-9. Soil erosion analysis. Analysis Result of Areas Prone to Soil Erosion Table-1. Areas and risk levels. The analysis into the areas that were prone to soil No. Risk level Area erosion was performed by an overlay analysis of R factor, K factor, L and S factors, C factor, and P factor. Then the 1 Very low Km2 % results were divided according to the severity of soil 2 low erosion into 5 levels (Figure-9). The Figure-9 shows the 3 2,120.193 18.48 areas which are predominantly dark green and light green, 4 Moderate many areas are lower part and middle part of the 5 High 2,728.854 23.79 province, with the possibility of soil erosion in the very Total low and low level, covering the area of about 4,849.043 Very High 2,937.823 25.61 km2 equal to 42.27%; the area which is yellow is mostly the lower part at the eastern region and western region 2,133.648 18.60 with the possibility of soil erosion at the moderate level covering the area of about 2,937.822 km2 equal to 25.61%; 1,551.558 13.52 much of the orange area is middle region and some part of it in the northern part has the possibility of the occurrence 11,472.076 100 of soil erosion at the high level covering the area of about 2,133.648 km2 equal to 18.60%; and much of the red area CONCLUSIONS is northern part of the province with the possibility of soil The soil erosion in Thailand frequently takes erosion at the very high level covering the area of about 1,551.558 km2 equal to 13.52%. The areas of the place in the Northern region of the country following mentioned risk levels are also summarized in Table-1. heavy rains over mountains that are sources of rivers. The severity of landslide depended upon the rainfall on the mountain, the steepness of the mountain, the abundance of the forest, and the geological characteristics of the mountain. This study embraced the integration of geo- informatics technology with the ULSE to analyze the areas which were prone to soil erosion where soil erosion took place every year. According to the study, it could be concluded that Nan Province had areas prone to soil erosion of about 3,685.206 km2 or equal to 57.73% due to its geography in general which was characterized by forest and mountain for almost 75 % and the plain area for 25 % or at forest and mountain areas to plain area ratio of 3:1. Therefore, 828

VOL. 16, NO. 8, APRIL 2021 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com many of the agricultural areas where plants were grown performance of Sheshegu community farmers in the was on the mountain at the steepness level of more than Eastern Cape of South Africa. Journal of Agricultural 5% with a total area of 6,975.325 km2 or equal to 60.80%. Science. 5(5): 140-147. In such area, land use should be changed from plants to farm plants and further to perennial plants. Also, there Mateos E., Edeso J. M. & Ormaetxea L. 2017. Soil should be measures in conserving soil and water deemed Erosion and Forests Biomass as Energy Resource in the suitable for conditions of the area in order to reduce soil Basin of the Oka River in Biscay, Northern Spain. Forests. erosion. 8(7): 258. ACKNOWLEDGEMENTS Musgrave G.W. 1947. The quantitative evaluation of This research was financially supported by factors. Journal of Soil and Water Conservation. 2(3): 133- 138. Defence Technology Institute (Grant year 2020). Help and support from disaster team management of the institute Nearing M., Xie Y., Liu B. & Ye Y. 2017. Natural and were highly appreciated and acknowledged herewith. anthropogenic rates of soil erosion. International Soil and Water Conservation Research. 5(2): 77-84. REFERENCES Ozsahin E., Duru U. & Eroglu I. 2018. Land Use and Land Baver L.D. 1933. Somes soil factors effecting erosion. Cover Changes (LULCC), a Key to Understand Soil Agricultural Engineering. 14(2): 51-52. Erosion Intensities in the Maritsa Basin. Wate. 10(3): 335. Belo D., Ernawati R., Cahyadi T., Nurkhamim. & Amri N. Ozsahin E. & Eroglu I. 2019. Soil Erosion Risk 2020. Analysis of Land Erosion Due to Mining of Clay Assessment due to Land Use/Land Cover Changes Material in Sidorejo Village, Sleman District, Yogyakarta. (LULCC) in Bulgaria from 1990 to 2015. Alinteri Journal Geographia Technica. 15: 33-41. of Agriculture Sciences. 34(1): 1-8. Browning G.W., Parish, C.L. & Glass J.A. 1947. A Panagos P., Meusburger K., Van Liedekerke M., Alewell method for determining the use and limit rotation of C., Hiederer R. & Montanarella L. 2014. Assessing soil rotation and conservation practices in control of soil erosion in Europe based on data collected through a erosion in Iowa. Soil Science Society of America, European Network. Soil Science and Plant Nutrition. 60 Proceedings. 23: 246-249. (1): 15-29. Burt T. & Weerasinghe K. 2014. Rainfall Distributions in Pholkerd R., Khunrattanasiri W. & Pattaratuma A. 2012. Sri Lanka in Time and Space: An Analysis Based on Application of Remote Sensing and Geographic Daily Rainfall Data. Climate. 2(4): 242-263. Information System for Soil Erosion Assessment in Huay Nam Rit Watershed, Uttaradit Province. Thai Journal of Conforti M. & Buttafuoco G. 2017. Assessing space–time Forestry. 31(2): 42-52. variations of denudation processes and related soil loss from 1955 to 2016 in southern Italy (Calabria region). Plakayrungrassamee S., Pantanahiran W. & Navanugraha Environmental Earth Sciences. 76: 457-475. C. 2011. Soil Erosion Analysis Using Universal Soil Loss Equation (USLE) to Estimate the Loss of Plant Nutrient in Cook H.L. 1936. The nature and controlling variables of Huaimaeprachan Watershed. Journal of Social Sciences the water erosion process. Soil Science Society of America Srinakharinwirot University. 14: 1-12. (In Thai) Proceedings. 1: 487-494. Pradhan B., Chaudhari A., Adinarayana J. & Buchroithner Cruz D., María J., Benayas J., Ferreira G., Monteiro A. & M.F. 2012. Soil erosion assessment and its correlationwith Schwartz G. 2019. Evaluation of Soil Erosion Process and landslide events using remote sensing data and GIS: A Conservation Practices in the Paragominas-pa case study at Penang Island, Malaysia. Environmental Municipality (Brazil). Geographia Technica. 14(1): 14-35. Monitoring and Assessment. 184: 715-727. Ganasri B. P. & Ramesh H. 2016. Assessment of soil Senanayake S., Pradhan B., Huete A. & Brennan J. 2020. erosion by RUSLE model using remote sensing and GIS - Assessing Soil Erosion Hazards Using Land-Use Change A case study of Nethravathi Basin. Geoscience Frontiers. and Landslide Frequency Ratio Method: A Case Study of 7: 953-961. Sabaragamuwa Province, Sri Lanka. Remote Sensing. 12(9): 1483. Guzzetti F., Peruccacci S., Rossi M. & Stark C. 2008. The rainfall intensity-duration control of shallow landslides Smith D.D. 1941. Interpretation of soil conservation data and debris flows: An update. Landslides. 5(1): 3-17. for fiels use. Agricultural Engineering. 22: 173-175. Ighodaro I.D., Lategan F.S. & Yusuf, S.F. 2013. The impact of soil erosion on agricultural potential and 829

VOL. 16, NO. 8, APRIL 2021 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com Smith D.D. and Whitt D.M. 1947. Estimating soil losses form field areas and clay pan soil. Soil Science Society of America, Proceedings. 12: 485-490. Smith D.D. & Wichmeier W.H. 1957. Factor effecting sheet and rill erosion. Transactions of the American Geophysical Union. 38: 889-896. Suk-ueng K. & Chantima K. 2017. Application of Geographic Information System to Land Use Suitability Assessment in Ban Nanglae Nai, Muang District, Chiang Rai Province. Kasalongkham Research Journal. 11(3): 163-174. (In Thai) Van Doren C.A. & L.J. Bartelli. 1956. A method of forecasting soil loss. Agricultural Engineering. 37: 355 - 341. Vita P., Paola R., Bathurst J., Borga M., Crosta G., Crozier M., Glade T., Guzzetti F., Hansen A. & Wasowski J. 1998. Rainfall-triggered landslides: A reference list. Environmental Geology. 35(2): 219-233. Wischmeier W.H. & Smith D. D. 1965. Predicting rainfall erosion losses from cropland east of the Rocky Mountain: guide for selection of practices for soil and water conservation. Agr. Handbook No. 282.USDA, Washington, D.C. p. 47. Wischmeier W.H. & Smith D. D. 1978. Predicting rainfall erosion losses. A guide to conservation planning. Agr.Handbook No.537.USDA, Washington, D.C. p. 49. Zingg A.W. 1940. Degree and lenght of land slope as it effects soil loss in runoff. Agricultural Engineering. 21(2): 59-64. Zuazo V.H.D. & Pleguezuelo C.R.R. 2008. Soil-erosion and runoff prevention by plant covers. A review. Agronomy for Sustainable Development. 28: 65-86. 830

International Journal on ISSN 2077-3528 “Technical and Physical Problems of Engineering” IJTPE Journal (IJTPE) www.iotpe.com Published by International Organization of IOTPE [email protected] IJTPE Date: October 01, 2021 No: 2100100101 Attachment: - Dear Dr. Teerawong Laosuwan, Thanks for your interest for cooperation with International Journal on “Technical and Physical Problems of Engineering” (IJTPE). Your submitted paper sent to the journal secretariat has been processed as the following information. Paper ID: 1124-210912 Paper Title: ESTIMATION OF PM10 USING SPATIAL INTERPOLATION TECHNIQUES Author(s): P. Pradabmook, T. Laosuwan Your paper is accepted and will be published in IJTPE - Issue 49, Volume 13, Number 4, December 2021 (Serial No: 0049-1304-1221) of International Journal on “Technical and Physical Problems of Engineering” (IJTPE) (www.iotpe.com , www.iotpe.com/ijtpe.html) at the end of December 2021. International Journal of IJTPE is a multidisciplinary peer-reviewed journal published quarterly and electronically by International Organization on “Technical and Physical Problems of Engineering” (IOTPE) that is dedicated to promoting research in theoretical themes, physical subjects and technical problems of Science, Technology and Engineering. The issues of IJTPE Journal are registered, cited and indexed in some scientific organization and databases such as Elsevier-Scopus considering the following link: http://www.iotpe.com/IJTPE/JournalCitations.html Warmest regards, Prof. Naser MAHDAVI TABATABAEI Editor-in-Chief International Journal of IJTPE (ISSN 2077-3528) URL: www.iotpe.com/ijtpe.html Email: [email protected] , [email protected] Supported by: International Organization of IOTPE URL: www.iotpe.com Email: [email protected]

International Journal on ISSN 2077-3528 “Technical and Physical Problems of Engineering” IJTPE Journal (IJTPE) www.iotpe.com Published by International Organization of IOTPE [email protected] March 2020 Issue 42 Volume 12 Number 1 Pages 1-6 ESTIMATION OF PM10 USING SPATIAL INTERPOLATION TECHNIQUES IN SOUTHERN REGION OF THAILAND Preecha Pradabmook1 and Teerawong Laosuwan2* 1. Defence Technology Institute, Office of the Permanent Secretary of Defence, Nonthaburi, Thailand 2. Department of Physics, Faculty of Science, Mahasarakham University, Kantarawichai District, Maha Sarakham, 44150, Thailand, [email protected] (*corresponding author) Abstract- Air pollution, especially the haze problem air pollution that was caused by the use of cars, motor boats, and airplanes, which were the cause of CO2, NO2, caused by the accumulation of smoke and dust in the air and hydrocarbons that directly affect human health [1,2]. is another important problem in Thailand. Especially at The burning of these fuels also caused more and more present, this issue has become increasingly serious pollution problems every year. Air pollution problems respectively. This study aimed to study the relationship arising from nature, such as volcanic eruptions, cause between Particulate Matter (PM10) content in the southern large amounts of smoke and ash to spread into the air region and the physical factors of the area and to assess [3,4]. Pollution caused by forest fires created haze that PM10 quantity by Spatial Interpolation Techniques, as was harmful to the respiratory system. Pollution caused well as to study the suitability of each method. The by decomposition of fossil plants, chemical reaction results of the study found that the 24-hour mean of the would produce CO2, CH4, and NH3 dispersed into the air three-year average PM10 intake between 2017 to 2019 [5]. Air pollution was caused by dust, which was caused was the highest in February, with the mean concentration by objects that were smashed, crushed, crushed to from all measuring stations of 36 µg/m³. It was followed shattered into small pieces, when it was exposed to the by March, July, and January. The smallest concentration airflow it disperses in the air. Although the pollution was in October. When analyzing the physical problem cannot be stopped, the results can be analyzed characteristics of the area with high levels of fine dust, it and monitored continuously in order to control the was found that the southern region was characterized by a increase in air pollution problems [6]. In each region of sharp topography or the land extending into the sea, Thailand there were identical and different causes of air which was influenced by the southwest monsoon at the pollution depending on the terrain and land use [7-10]. south into western Thailand was a major factor in getting dust from wildfires and burning in the open air that was For the southern region of Thailand, haze was mainly carried by winds from many places, including caused by forest fires and open burning in both the neighboring countries. Using mean PM10 data from six country and neighboring countries [11]. The nature of the Pollution Control Department (PCD) measuring stations, incineration of agricultural waste, incineration, these PM10 was assessed by Spatial Interpolation Techniques combustion resulted in small dust and various hazardous method using four different methods: Inverse Distance gases. This caused microscopic particles that affected Weighting (IDW), Kriging, Spline, and Trend, it was health, especially PM10 content [12-16]. When it entered found that Trend method was the most suitable method the body, it caused symptoms that affected the body for map that showed the distribution of PM10 system, such as coughing, sneezing, and shortness of concentration data, especially from January to April with breath, which caused respiratory disease. Currently, the highest particulate matter. Geographic Information System (GIS) was used as part of the area analysis and air pollution distribution [17-22] Keywords: PM10, GIS, IDW, Spline, Trend due to field repositories, operators cannot store data anywhere of a large area. When mapping data, gaps in the 1. INTRODUCTION data may be found, so estimates for the missing sections must be estimated using Spatial Interpolation Techniques Air pollution is one of the major problems in such as topographic mapping, population density Thailand, specifically nowadays that the problem is determination, and climate estimate, continue to study becoming more and more serious. There are two main relationships in relation to other information [23-27]. sources of air pollution: the occurrence of air pollution This study was to study the relationship between PM10 caused by human actions, such as the need for energy for concentration in the southern region and physical factors domestic use, industrial use, and agriculture, as well as of the area and to assess PM10 quantity by Spatial Interpolation Techniques method. In addition, the study 1

International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 12, Vol. 4, No. 3, Sep. 2012 also studied the suitability of Spatial Interpolation sample point trying at least all of the curvature toward Techniques in various ways for the assessment of PM10 in those sample points as the surface. Spline method was a the southern region by using Geographic information mathematical equation suitable for gradual change system (GIS) as an operational tool. The results can be surfaces. useful in air pollution management and control and provide further support for further studies on air pollution Kriging: It was an advanced method of estimation by in the area. applying statistical processes and mathematical equations to the analysis. This method selects the appropriate 2. MATERIAL AND METHOD mathematical equation with the selected sample point within the specified radius to obtain results for each area. 2.1 Data collection Using Kriging, it was important to know the spatial This research collected data from various sources to correlation or direction bias in the Kriging data, which was different from other interpolation methods such as assess PM10 concentrations by Spatial Interpolation IDW or Spline since both were approximations directly Techniques in southern Thailand during 2017, 2018, and surrounding the sample point. 2019 as follows: Trend: This method selected an appropriate (1) PM10 concentration data from the Southern region mathematical equation by specifying a sequence of from Pollution Control Department (PCD) algebra (polynomial) to all sample points. (2) Air quality monitoring station location data from 3. RESULT AND DISCUSSION Pollution Control Department (PCD) 3.1 Results of the PM10 assessment and their (3) Southern region and provincial administrative relationship with the physical factors of the area boundaries in the south from Royal Thai Survey Department. From the 24-hour mean PM10 3-year mean, between 2017 to 2019 from the 6 Pollution Control Department 2.2 Operation tools (PCD) air quality monitoring stations can be shown as (1) Computer for processing data shown in Figure. 1. It was found that the three-year (2) Geographic information system (GIS) program average PM10 was the highest in February, with the (3) Dust data from the Air4Thai website average concentration from all monitoring stations equal to 36.07 µg/m³. It was followed by March, July, and (http://air4thai.pcd.go.th/webV2/) of the Pollution January with concentrations 33.95, 32.34, and 32.22 Control Department (PCD). µg/m³ respectively. The lowest concentration was in October, with the average concentration from all stations 2.3 Data analysis equal to 23.06 µg/m³. When considering the average (1) PM10 data were collected from air quality value for each measuring station, it was found that the Hat Yai Changwat Songkhla Municipality monitoring stations of the Pollution Control Department Meteorological Station had the highest average at 32.60 (PCD), air quality monitoring station location data, µg/m³. It was followed by the White Elephant School southern boundary data, and provincial regions in the Ceremony School Station in Yala Province, the south for import into Geographic information system Environment Office Region 14, Surat Thani Province, (GIS) and Narathiwat City Hall with a mean of 32.6, 30.87, and 30.27 µg/m³, respectively. In addition, the physical (2) Analyze the relationship between PM10 quantity analysis of the area with high levels of fine dust found and physical characteristics of the southern region, that the southern region was characterized by a sharp including location, range of dispersion of PM10, and topography or land extending into the sea, which was topography, etc. influenced by the southwest monsoon wind blowing from the south entering western Thailand was therefore an (3) Using data obtained from PM10 measurements important factor in getting dust from forest fires and from 6 stations of the Pollution Control Department burning in the open air that was carried by the wind from (PCD) including 1) Narathiwat City Hall, 2) Phuket many places, including neighboring countries. Municipality Public Health Service Center, 3) Elephant School Ceremony School Station in Yala Province, 4) Hat Yai Municipality Station (Songkhla), 5) the Environment Office Region 14, Surat Thani and 6) Satun City Hall, during 2017 to 2019, Spatial Interpolation Techniques was performed using 4 methods: Inverse Distance Weighting (IDW): This was an approximation by random sampling of each sample point from a location that can affect the cells that need to be estimated, which will have less of an impact over long distances. This method was suitable for variables referring to the computational distance because the closer it is, the more influence. Spline: It was a method of inserting values to fit at least the curved surface according to the imported sample data point. It was like twisting a rubber sheet through a 2

International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 12, Vol. 4, No. 3, Sep. 2012 Figure 1. PM10 3-year mean, between 2017 to 2019 b (Spline) c (Kriging) 3.2 The results of assessment of PM10 concentration by Spatial Interpolation Techniques 3.2.1 Results of the PM10 quantitative assessment by Spatial Interpolation Techniques from 6 measuring stations: The Spatial Interpolation Techniques for PM10 using the three-year mean data between 2017 and 2019 from six measuring stations used to validate the data was shown in Table 2, Figure 2 (a,b,c,d) and Figure 3. According to Table 2, Figure 2-3, the spatial estimation of the 3-year average PM10 concentrations based on the data of the 6 monitoring stations was the highest in February, followed by March and July as well. The mean obtained from the actual measurement was found to be the lowest in October. Figure 2. PM10 3-year mean, between 2017 and 2019 a (IDW) d (Trend) Figure 3. Illustration the Spatial Interpolation Techniques from 4 different methods 3.3.2 Difference of spatial quantity versus actual measurement When comparing the PM10 quantity data obtained from the six measurement stations with the data from the actual measurement, each station gave different values 3

International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 12, Vol. 4, No. 3, Sep. 2012 for different data. In February, it was the month with the method was the IDW and Spline methods, as they gave highest amount of PM10, which can differentiate each the greatest difference during this period. method as shown in Table 1. From Table 1, it was found that when using the PM10 quantity data from the 6 Hives During the cold season between November and Department monitoring stations in the South during December, the amount of PM10 increased, the IDW 2017-2019 in February to quantify the spatial values by method was found to be the most suitable method for various methods, it was found that Trend method spatial estimation during this period as the difference was provided the smallest difference and the IDW, Kriging, minimal. The improper method was the Kriging method, and Spline method gives the greatest difference. Each as it gave the most difference during this period. spatial estimation method gave different values as follows. 3.2.5 Statistical analysis results by correlation analysis and simple linear regression The spatial estimation using IDW method had the lowest margin at Narathiwat City Hall Station, Regional For statistical analysis results using simple linear Environment Office 14 (Surat Thani) and had the highest regression and correlation analysis from the monthly margin at Hat Yai Municipality Station (Songkhla). mean of PM10 quantity from actual measuring stations in all three years (2017-2019) and PM10 concentrations from Spatial estimation by Kriging method had the lowest Spatial Interpolation Techniques with IDW, it was found margin at Narathiwat City Hall Station, Regional that the temporal shift of PM10 concentrations from actual Environment Office 14 (Surat Thani) and had the highest measuring stations was the most consistent with the PM10 margin at Hat Yai Municipality Station (Songkhla). concentrations from Spatial Interpolation Techniques Spline estimation was the lowest at Narathiwat City Hall with IDW. The relationship between the quantity from Station, Regional Environment Office 14 (Surat Thani) the IDW estimation between the actual measuring station and had the highest difference at Hat Yai Municipality and the quantity from the IDW method can be shown in Station (Songkhla). Figure 4. The spatial estimation using Trend method had the lowest margin at the Environmental Office Region 14 Station (Surat Thani) and the highest margin at Hat Yai Municipality Station (Songkhla). 3.2.3 The results of the difference analysis of the four (a) IDW Spatial Interpolation Techniques (monthly) (b) Kriging The results of the analysis of the differences of the four Spatial Interpolation Techniques from the total 12- month mean between 2017 and 2019 were shown in Table 2. From Table 2, it was found that when considering the suitability of Spatial Interpolation Techniques from the 12 months of 2017-2019, the four methods were IDW, Kriging, Spline, and Trend, and Trend was an ideal method to map the distribution of PM10. Due to the least difference between the spatial estimation and the actual measurement of 9 months from 12 months, followed by the Spline method of 8 months from 12 months. 3.2.4 The results of the analysis of the differences of the five spatial estimation methods (seasonally) The difference in the amount of dust obtained from the estimation to the values obtained from the actual measurements for the whole 12 months when analyzed seasonal with climate difference between 2017 and 2019. During the dry season between January, February, March, and April, there was a large amount of PM10. The study had shown that all methods were most suitable for spatial estimation during this period because the difference was minimal and there was no improper method. During the rainy season between May, June, July, August, September, and October, where PM10 was low, the study found that Kriging method was the most suitable method for spatial estimation in This range, because the difference was minimal. The unsuitable 4

International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 12, Vol. 4, No. 3, Sep. 2012 (c) Spline and (R²=0.9911), respectively. The remaining 4.58%, 9.09%, 6.96%, and 0.89% were due to other causes. d) Trend 4. CONCLUSIONS Figure 4. Illustration correlation analysis and simple linear The study results of the relationship between PM10 regression from 4 different methods concentration in the southern region and the physical factors of the area in three-year average during the From Figure 4, the relationship between PM10 summer of 2017-2019, from the end of February to concentration from the actual measuring station and PM10 March and April, with large amounts of PM10 dust. The concentration from Spatial Interpolation Techniques with reason was that the topography was a cape or land IDW, Kriging, Spline, and Trend can be described as extending into the sea, which had been influenced by the follows. These two sets of data were analyzed for a southwest monsoon that winds from the bottom to the simple correlation analysis, which studied the relationship western part of Thailand. Therefore, this was an between x and y, how much they were related, and how important factor in the exposure to dust from forest fires they were directed when x was an independent variable and open burning caused by winds from many places and y was a dependent variable. In this study, the including neighboring countries. The 24-hour mean of the independent variable (x) was assigned the amount of three-year average PM10 concentration between 2017 PM10 from the actual measurement station obtained from and 2019 was the highest in February and the average the ground monitoring station. Variables following (y) concentration from all monitoring stations was 36 µg/m³. PM10 from Spatial Interpolation Techniques with IDW, It was followed by March and January with Kriging, Spline, and Trend. concentrations 34 and 32 µg/m³, respectively. The lowest concentration was in October, with the average The results of the aforementioned correlation analysis concentration from all stations of 23 µg/m³. When using showed that PM10 concentrations from actual the mean PM10 data through the Spatial Interpolation measurement stations obtained from ground measurement Techniques process, it was found that the values obtained stations were correlated with PM10 from Spatial from Spatial Interpolation Techniques by various Interpolation Techniques with IDW, Kriging, Spline, and methods for each month were different. When Trend in the same direction 97.78% (R=0.9778), 95.34% (R=0.9534), 96.45% (R=0.9645), and 99.55% considering the suitability of the four methods - IDW, (R=0.9955), respectively. When these two sets of data Spline, Kriging, and Trend, it was found that spatial were analyzed using simple linear regression, which estimation by IDW, Kriging, and Trend was considered analyzes estimates (predictor, x) and response (response, the appropriate method (Trend was the most appropriate) y), it was found that the change in PM10 concentration to map showing the distribution of PM10 concentration from Spatial Interpolation Techniques by IDW, Kriging, data due to the slightest difference in the measured values Spline, and Trend. It was caused by a change in PM10 from the actual measurements for 9 months out of 12 quantity from actual measurement values obtained from months. ground measurement stations approximately 95.62% (R²=0.956), 90.91% (R²=0.9091), 93.04% (R²=0.9304), ACKNOWLEDGEMENTS This research project is financially supported by Defence Technology Institute. 5

International Journal on “Technical and Physical Problems of Engineering” (IJTPE), Iss. 12, Vol. 4, No. 3, Sep. 2012 Table 1. The difference of spatial quantity versus actual measurement Spatial Interpolation Techniques Measurement PM10 IDW Kriging Spline Trend stations from stations Amount Difference Amount Difference Amount Difference Amount Difference 1. Narathiwat City Hall 30 of PM10 (+/-) of PM10 (+/-) of PM10 (+/-) of PM10 (+/-) 2. Phuket 28 Municipality 40 -10 40 -10 40 -10 37 -7 Public Health 31 Service Center 37 -9 37 -9 37 -9 33 -5 3. Elephant School 33 Ceremony School 38 -7 38 -7 38 -7 36 -5 Station 31 4. Hat Yai 24 32 1 32 1 32 1 36 -3 Municipality Station 41 -10 41 -10 41 -10 42 -11 5. Environment 29 -5 29 -5 29 -5 33 -9 Office Region 14, Surat Thani 42 42 42 40 6. Satun City Hall Total Table 2. The difference analysis of the four Spatial Interpolation Techniques Month Spatial Interpolation PM10 Least different Most different Techniques From approach approach January Station February IDW Krigin Splin Tren all method without March g ed 32 all method without 36 all method without April 32 32 32 32 34 all method without May 36 36 36 36 31 all method without June 34 34 34 34 26 IDW/Spline/Tren 31 31 31 31 28 Kriging July 26 26 26 26 d August 29 28 29 29 32 Kriging/Trend IDW/Spline 29 Spline IDW/Kriging/Tre September 33 32 33 32 October 29 29 27 29 28 all method nd 23 IDW/Kriging/Tre without November 28 28 28 28 Spline 24 24 25 24 25 nd December IDW/Spline/Tren Kriging 25 29 25 25 29 d Kriging 27 30 28 29 IDW 6

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