ESTIMATION OF PARTICULATE MATTER LESS THAN 10 MICRONS VOLUME THROUGH VARIOUS FORMATS OF SPATIAL INTERPOLATION METHODS

Jumpol ITSARAWISUT , Teerawong LAOSUWAN

DOI: 10.21163/GT_2022.172.03

ABSTRACT: The problem on air pollution, especially the problem on smoke caused by accumulation of smoke and dust in the air, is considered as one of important problems in Thailand and this problem is currently more serious increasingly. This study aims to study on relationship between Particulate Matter Less Than 10 Microns (PM₁₀) volume in northern area and physical factors of the area as well as estimate PM10 through various formats of Spatial Interpolation Methods and study on appropriateness of each method. The result revealed that mean of 24 hours of PM10 volume throughout 5 years from 2017 – 2021 was in the highest level in March with average volume from all weather stations at 101µg/m³ followed by February and April. Month with the lowest PM₁₀ volume was July. When analyzing on physical characteristics of areas with high PM₁₀ volume, it was found that northern area had landscape of intermontane plateau with small area. Since it was surrounded by mountain ranges, distribution of dust was poor. In addition, since it was located near some neighboring countries, it was affected by dust from wildfire and open burning that was blown from many areas as well as those neighboring countries. When using data on mean of PM₁₀ volume from 9 weather stations of Pollution Control Department (PCD) to evaluate PM₁₀ volume through various formats of Spatial Interpolation Methods, including Inverse Distance Weight (IDW), Kriging, Spline, and Trend, it was found that IDW was the most suitable method for making map showing distribution of PM₁₀ volume, especially from February to April with the highest volume of dust due to the lowest level of difference between estimated value and measured value. For spatial interpolation by using Spline method, it was found to be improper due to the highest level of difference between estimated value and measured value.

Keywords: Air pollution, PM₁₀, Spatial Interpolation, Geographic Information System

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