16 research outputs found
āļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāđāļāļāļ·āđāļāļāļĩāđāļ āļēāļāđāļŦāļāļ·āļāļāļāļāļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒāđāļĨāļ°āļāļ·āđāļāļāļĩāđāđāļāļĒāļĢāļāļāļāđāļāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5: āļāļĢāļāļĩāļĻāļķāļāļĐāļēāļāđāļ§āļāļĪāļāļđāļŦāļĄāļāļāļāļ§āļąāļ āļāļĩ āļ.āļĻ. 2562 Relationship of Fire Hotspot, PM2.5 Concentrations, and Surrounding Areas in Upper Northern Thailand: A Case S
āļāļēāļĢāļ§āļīāļāļąāļĒāļāļĩāđāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļ·āđāļāļĻāļķāļāļĐāļēāļāļĨāļāļĢāļ°āļāļāļāļāļāļāļĢāļīāļĄāļēāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļāđāļāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āđāļāļāļ·āđāļāļāļĩāđāļ āļēāļāđāļŦāļāļ·āļāļāļāļāļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āļāđāļ§āļāļ§āļąāļāļāļĩāđ 1 āļĄāļāļĢāļēāļāļĄ â 31 āļāļĪāļĐāļ āļēāļāļĄ āļ.āļĻ. 2562 āđāļāļĒāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĢāļīāļĄāļēāļāđāļĨāļ°āļāļ§āļēāļĄāļŦāļāļēāđāļāđāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāđāļāļāļ·āđāļāļāļĩāđāļĻāļķāļāļĐāļēāđāļĨāļ°āļāļ·āđāļāļāļĩāđāđāļāļĒāļĢāļāļ āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļēāļĄāđāļ§āļĨāļēāđāļĨāļ°āļŠāļąāļĄāļāļĢāļ°āļŠāļīāļāļāļīāđāļŠāļŦāļŠāļąāļĄāļāļąāļāļāđ (r) āļāļāļāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āļāļąāļāļāļĢāļīāļĄāļēāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļāļēāļāļ āļēāļāļāđāļēāļĒāļāļēāļ§āđāļāļĩāļĒāļĄ āđāļĨāļ°āļāđāļāļĄāļđāļĨāļāļĢāļ§āļāļ§āļąāļāļāļąāļāļāļąāļĒāļāļēāļāļāļļāļāļļāļāļīāļĒāļĄāļ§āļīāļāļĒāļēāļāļēāļ 9 āļŠāļāļēāļāļĩ āļāļĨāļāļēāļĢāļ§āļīāļāļąāļĒāļāļāļ§āđāļē āļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāđāļāļāļ·āđāļāļāļĩāđāļĻāļķāļāļĐāļēāļĄāļĩāļāļĢāļīāļĄāļēāļāđāļāļīāđāļĄāļŠāļđāļāđāļāļāđāļ§āļāļāļĩāđāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āļāļĒāļđāđāđāļāđāļāļāļāđāļŠāđāļāļāļĨāļāļĢāļ°āļāļāļāđāļāļŠāļļāļāļ āļēāļ āđāļāļĒāļāļāļŦāļāļēāđāļāđāļāļŠāļđāļāļāļĢāļīāđāļ§āļāļĢāļāļĒāļāđāļāļĢāļ°āļŦāļ§āđāļēāļāļāļąāļāļŦāļ§āļąāļ āļāļ·āđāļāļāļĩāđāļāđāļēāđāļĨāļ°āļāļ·āđāļāļāļĩāđāđāļāļĐāļāļĢāđāļāļĨāđāđāļāļĩāļĒāļ āļŠāđāļ§āļāļāļ·āđāļāļāļĩāđāđāļāļĒāļĢāļāļāļāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļŦāļāļēāđāļāđāļāļŠāļđāļāļāļĢāļīāđāļ§āļāđāļāļĨāđāļāļąāļāļāļ·āđāļāļāļĩāđāļĻāļķāļāļĐāļēāđāļāļāļēāļāđāļŦāļāļ·āļ āļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āļāļāļāļŠāļāļēāļāļĩāļŠāđāļ§āļāđāļŦāļāđāļĄāļĩāļāđāļēāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļāļāļāļāļ·āđāļāļāļĩāđāđāļāļĒāļĢāļāļāļĄāļēāļāļāļ§āđāļēāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļāļāļāļāļąāļāļŦāļ§āļąāļ āļāļķāđāļāđāļŦāđāļāđāļāđāļāļēāļāļāđāļē r āļāļĩāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāđāļāđāļāļāļāđāļāļēāļāļāļĨāļēāļ-āļŠāļđāļ (r = 0.5 â 0.7) āļŠāđāļ§āļāļāļļāļāļāļ§āļēāļĄāļĢāđāļāļāļāļāļāļāļąāļāļŦāļ§āļąāļāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āđāļāđāļāļāļąāļ āļāđāļ§āļĒāļāđāļē r āļāļĩāđāļāđāļāļĒāļāļ§āđāļē āļāļķāđāļāđāļŠāļāļāđāļŦāđāđāļŦāđāļāļāļķāļāļāļīāļāļāļīāļāļĨāļāļāļāđāļŦāļĨāđāļāļāļģāđāļāļīāļāļāļēāļāļāļ·āđāļāļāļĩāđāđāļāļĒāļĢāļāļāļāļĩāđāļŠāđāļāļāļĨāļāđāļāļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļāđāļāļāļ·āđāļāļāļĩāđāļāļąāļāļŦāļ§āļąāļāļāļąāđāļ āđ āļāđāļēāļāļ§āļēāļĄāđāļāđāļĄāļāđāļ PM2.5 āđāļāļŠāļāļēāļāļĩāļŠāđāļ§āļāđāļŦāļāđāđāļāļĢāļāļāļāļąāļāļāļąāļāļāļąāļāļāļąāļĒāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāđāļĨāļ°āļāļ§āļēāļĄāđāļĢāđāļ§āļĨāļĄThe objective of this research is to study the effects of thermal hotspots on PM2.5 concentrations in the upper northern of Thailand during 1 Januaryâ31 May 2019. The number and the density of fire hotspots of the examined and adjacent areas was investigated. The time-series relationships between PM2.5 concentrations, the number of satellite-based fire hotspots, and meteorological factors derived from 9 stations were analyzed. As results, the greater number of hotspots was correlated with increased levels of PM2.5 concentrations. Such conditions exhibit considerable impacts on health. High PM2.5 concentrations were specifically found around provincial boundaries, in forests, agricultural areas, as well as in Thailandâs neighboring countries. As for the surrounding areas, the areas that have high density of fire hotspots were found near investigated areas in the north region. Provincial fire hotspots were correlated to high PM2.5 concentration, with a lower r-value. The thermal hotspot locations from the surrounding areas have effects on provincial PM2.5 concentrations. Finally, the effect of meteorological factors on PM2.5 concentrations was analyzed. As a result, precipitation and wind speed have inverse effects on PM2.5 concentrations
Projection of the Near-Future PM<sub>2.5</sub> in Northern Peninsular Southeast Asia under RCP8.5
Throughout the year, particularly during the dry season, the northern peninsula of Southeast Asia struggles with air pollution from PM2.5. In this study, we used the Nested Regional Climate and Chemistry Model (NRCM-Chem) to predict the PM2.5 concentrations over Southeast Asiaâs northern peninsula during the years 2020â2029 under the Representative Concentration Pathway (RCP)8.5. In general, the model reasonably shows a good result, including temperature, precipitation, and PM2.5 concentration, compared to the observation with an Index of Agreement (IOA) in the range of 0.63 to 0.80. However, there were some underestimations for modeled precipitation and temperature and an overestimation for modeled PM2.5 concentration. As a response to changes in climatic parameters and the emission of PM2.5âs precursors, PM2.5 concentrations tend to increase across the region in the range of (+1) to (+35) Âĩg/m3 during the dry season (November to April) and decline in the range of (â3) to (â30) Âĩg/m3 during the wet season (May to October). The maximum increase in PM2.5 concentrations were found in March by >40 Âĩg/m3
Effect of the Near-Future Climate Change under RCP8.5 on the Heat Stress and Associated Work Performance in Thailand
Increased heat stress affects well-being, comfort, and economic activities across the world. It also causes a significant decrease in work performance, as well as heat-related mortality. This study aims to investigate the impacts of the projected climate change scenario under RCP8.5 on heat stress and associated work performance in Thailand during the years 2020â2029. The model evaluation shows exceptional performance in the present-day simulation (1990â1999) of temperature and relative humidity, with R2 values ranging from 0.79 to 0.87; however, the modeled temperature and relative humidity are all underestimated when compared to observation data by â0.9 °C and â27%, respectively. The model results show that the temperature change will tend to increase by 0.62 °C per decade in the future. This could lead to an increase in the heat index by 2.57 °C if the temperature increases by up to 1.5 °C in Thailand. The effect of climate change is predicted to increase heat stress by 0.1 °C to 4 °C and to reduce work performance in the range of 4% to >10% across Thailand during the years 2020 and 2029
āļāļēāļĢāļāļĢāļąāļāđāļāđāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļāļēāļāđāļāļāļāļģāļĨāļāļ WRF-CFSR āđāļāļĒāļ§āļīāļāļĩ EOF āļ āļēāļāđāļŦāļāļ·āļāļāļāļāļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒPrecipitation Bias Correction of WRF-CFSR Model by EOF Method Over Upper Northern Thailand
āļĢāļ°āļāļāđāļāļāļāļģāļĨāļāļāļ āļđāļĄāļīāļāļēāļāļēāļĻāđāļāđāļāļāļēāļāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāđāļēāļāļēāļĒāđāļĨāļ°āļĄāļĩāļāļ§āļēāļĄāļĒāļēāļ āđāļāļ·āđāļāļāļāļēāļāļāļēāļĢāļāļĢāļ°āļĄāļ§āļĨāļāļĨāļāļēāļāđāļāļāļāļģāļĨāļāļāļĄāļĩāļāļ§āļēāļĄāđāļĄāđāđāļāđāļāļāļāļāļķāđāļāđāļāļīāļāļāļēāļāļŦāļĨāļēāļĒāļāļąāļāļāļąāļĒāļŠāđāļāļāļĨāļāđāļāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļāļāļāļĨāļāļēāļĢāļāļģāļĨāļāļāļāļąāđāļāđāļāđāļāļīāļāļāļ·āđāļāļāļĩāđāđāļĨāļ°āđāļ§āļĨāļē āļāļ°āļāļąāđāļāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļĢāļąāđāļāļāļĩāđāļāļķāļāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļāļāļēāļĢāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāļ§āļīāļāļĩāļāļēāļĢāļŦāļĢāļ·āļāđāļāļāļāļīāļāļāļēāļĢāļāļĢāļąāļāđāļāđāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļŠāļģāļŦāļĢāļąāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļāļēāļāđāļāļāļāļģāļĨāļāļāļŠāļ āļēāļāļāļēāļāļēāļĻāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ WRF-CFSR āđāļĨāļ°āļāļĢāļ°āđāļĄāļīāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļ§āļīāļāļĩāļāļēāļĢāļāļĢāļąāļāđāļāđāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļāļēāļāđāļāļāļāļģāļĨāļāļāļŠāļ āļēāļāļāļēāļāļēāļĻāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ WRF-CFSR āđāļāļĒāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāđāļāđāđāļĨāļ·āļāļāđāļāđāļ§āļīāļāļĩāļāļēāļĢ Empirical Orthogonal Function (EOF) āđāļāļāļēāļĢāļāļĢāļąāļāđāļāđāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāđāļāļāļĢāļēāļĒāđāļāļ·āļāļ āđāļāļĒāļĻāļķāļāļĐāļēāđāļāļāļ·āđāļāļāļĩāđāļ āļēāļāđāļŦāļāļ·āļāļāļāļāļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒāļāļąāđāļāļŦāļĄāļ 18 āļŠāļāļēāļāļĩ āļāļĢāļāļāļāļĨāļļāļĄāļāļąāđāļāđāļāđāļāļĩ āļ.āļĻ. 1980-2010 (31āļāļĩ) āđāļĨāļ°āđāļāđāļāđāļāļĄāļđāļĨāļāļĢāļ§āļāļ§āļąāļāđāļāļāļāļĢāļīāļ (APHRODITE CRU āđāļĨāļ°GPCP) āđāļāļāļēāļĢāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļāļĨāļĢāđāļ§āļĄāļāļąāļāļāđāļāļĄāļđāļĨāđāļāļāļāļģāļĨāļāļāļŠāļ āļēāļāļāļēāļāļēāļĻāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ WRF-CFSR āļāļēāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļāļ§āđāļēāļ§āļīāļāļĩāļāļĢāļąāļāđāļāđ EOF āļŠāļēāļĄāļēāļĢāļāļĨāļāļāđāļēāļāļ§āļēāļĄāđāļāļāļāđāļēāļāļĢāļ°āļŦāļ§āđāļēāļāļāđāļēāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļāļīāļāļāļāļāļīāđāļĨāļ°āļāđāļēāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļāļāļāļīāđāļāļĨāļĩāđāļĒāđāļŦāđāļĄāļĩāļāļ§āļēāļĄāđāļāļĨāđāđāļāļĩāļĒāļāļāļąāļāļāđāļēāļāļ§āļēāļĄāđāļāļāļāđāļēāļāļāļāļāļāđāļāļĄāļđāļĨāļāļĢāļ§āļāļ§āļąāļ āđāļĨāļ°āđāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļ§āļēāļĄāļāļđāļāļāđāļāļāļāđāļ§āļĒāļāđāļēāļĢāļēāļāļāļĩāđāļŠāļāļāļāļāļāļāđāļēāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļģāļĨāļąāļāļŠāļāļāđāļāļĨāļĩāđāļĒ (RMSE) āļāļāļ§āđāļēāļ§āļīāļāļĩāļāļēāļĢāļāļĢāļąāļāđāļāđāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļ EOF āļĒāļąāļāđāļĄāđāļŠāļēāļĄāļēāļĢāļāļĨāļāļāđāļēāļāļ§āļēāļĄāļāļĨāļēāļāđāļāļĨāļ·āđāļāļāļāļāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāđāļāđ āđāļāđāļāļĒāđāļēāļāđāļĢāļāđāļāļēāļĄāļāļēāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļ§āļēāļĄāļāļđāļāļāđāļāļāļāđāļ§āļĒāļāđāļēāļŠāļąāļĄāļāļĢāļ°āļŠāļīāļāļāļīāđāļŠāļŦāļŠāļąāļĄāļāļąāļāļāđ (r) āļāļāļ§āđāļēāļ§āļīāļāļĩ EOF āļŠāļēāļĄāļēāļĢāļāļĢāļąāļāļĐāļēāļāļ§āļēāļĄāļāđāļāđāļāļ·āđāļāļāđāļāļīāļāļāļ·āđāļāļāļĩāđāļāļāļāļāļĢāļīāļĄāļēāļāļāđāļģāļāļāļĢāļēāļĒāđāļāļ·āļāļāđāļāđ āđāļāļĒāđāļāļāļēāļ°āļāļēāļĢāļāļĢāļąāļāđāļāđāļāđāļāļĄāļđāļĨāđāļāļāļāļģāļĨāļāļāļŠāļ āļēāļāļāļēāļāļēāļĻāļĢāļ°āļāļąāļāļ āļđāļĄāļīāļ āļēāļ WRF-CFSR āđāļĨāļ°āļāđāļāļĄāļđāļĨāļāļĢāļ§āļāļ§āļąāļāļāļĢāļīāļ GPCP āļĄāļĩāļāđāļē r āļāļĒāļđāđāđāļāļāđāļ§āļ 0.52 āļāļķāļ 0.97 āļāļķāđāļāđāļāđāļāļāđāļēāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļŦāļĨāļąāļāļāļĢāļąāļāđāļāđāļāļĩāđāļāļĩāļāļĩāđāļŠāļļāļClimate modeling system is a challenging and difficult task. Because uncertainty of the model processing is caused by many factors that influence the discrepancy of model output in both spatial and time. Therefore, in this study, the objective of this study was to apply methods or techniques for precipitation bias correction method from the WRF-CFSR regional climate model and to evaluate the efficiency of precipitation bias correction methods from the WRF-CFSR regional climate model. This study was selected the Empirical Orthogonal Function (EOF) for the monthly precipitation bias correction method in the upper northern region of Thailand, all 18 stations covering from 1980-2010 (31 years) and use observation grids data (APHRODITE CRU and GPCP) to compare the results with the WRF-CFSR regional climate model data. The result that the EOF correction method can reduce the difference between the precipitation anomaly and mean precipitation to be closer to the difference of the observation data. For validation with the Root Mean Square Error (RMSE) was found that the EOF bias correction method was unable to reduce the precipitation error. However, the validation with correlation coefficient values, the EOF method can maintain the spatial continuity of monthly precipitation. In particular, the correction of the WRF-CFSR regional climate model data and the GPCP grid observation data had r values 0.52 to 0.97 which is the best correction correlation
Effect of the Near-Future Climate Change under RCP8.5 on the Heat Stress and Associated Work Performance in Thailand
Increased heat stress affects well-being, comfort, and economic activities across the world. It also causes a significant decrease in work performance, as well as heat-related mortality. This study aims to investigate the impacts of the projected climate change scenario under RCP8.5 on heat stress and associated work performance in Thailand during the years 2020–2029. The model evaluation shows exceptional performance in the present-day simulation (1990–1999) of temperature and relative humidity, with R2 values ranging from 0.79 to 0.87; however, the modeled temperature and relative humidity are all underestimated when compared to observation data by −0.9 °C and −27%, respectively. The model results show that the temperature change will tend to increase by 0.62 °C per decade in the future. This could lead to an increase in the heat index by 2.57 °C if the temperature increases by up to 1.5 °C in Thailand. The effect of climate change is predicted to increase heat stress by 0.1 °C to 4 °C and to reduce work performance in the range of 4% to >10% across Thailand during the years 2020 and 2029
Quantifying the contributions of local emissions and regional transport to elemental carbon in Thailand
We used the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) to simulate elemental carbon (EC) concentrations in Thailand in 2017. The goals were to quantify the respective contributions of local emissions and regional transport outside Thailand to EC pollution in Thailand, and to identify the most effective emission control strategy for decreasing EC pollution. The simulated EC concentrations in Chiang Mai, Bangkok, and Phuket were comparable with the observation data. The correlation coefficient between the simulated and observed EC concentrations was 0.84, providing a good basis for evaluating EC sources in Thailand. The simulated mean EC concentration over the whole country was the highest (1.38 mg m(-3)) in spring, and the lowest (0.51 mg m(-3)) in summer. We conducted several sensitivity simulations to evaluate EC sources. Local emissions (including anthropogenic and biomass burning emissions) and regional transport outside Thailand contributed 81.2% and 18.8% to the annual mean EC concentrations, respectively, indicating that local sources played the dominant role for EC pollution in Thailand. Among the local sources, anthropogenic emissions (including the industry, power plant, residential, and transportation sectors) and biomass burning contributed 75.1% and 6.1% to the annual mean EC concentrations, respectively. As the anthropogenic emissions dominated the EC pollution, we performed four sensitivity simulations by reducing 30% of the emissions from each of the industry, power plant, residential, and transportation sectors in Thailand. The results indicated that controlling transportation emissions in Thailand was the most effective way in reducing the EC pollution. The 30% reduction of transportation emissions decreased the annual mean EC concentrations by 12.1%. In contrast, 30% reductions of the residential, industry, and power plant emissions caused 8.4%, 6.4%, and 4.0% decreases in the annual mean EC concentrations, respectively. The model results could potentially provide useful information for air pollution control strategies in Thailand. (C) 2020 Elsevier Ltd. All rights reserved
Long-range Transboundary Atmospheric Transport of Polycyclic Aromatic Hydrocarbons, Carbonaceous Compositions, and Water-soluble Ionic Species in Southern Thailand
This study investigated atmospheric particulate matter (PM) with an aerodynamic diameter of < 2.5 mu m (PM2.5) observed at the Prince of Songkla University (Phuket Campus) in southern Thailand. All samples (n = 75) were collected using MiniVolT portable air samplers from March 2017 to February 2018. Carbonaceous aerosol compositions, i.e., organic carbon (OC) and elemental carbon (EC), water-soluble ionic species (WSIS), and polycyclic aromatic hydrocarbons (PAHs) in the PM2.5 samples were identified and quantified. We found that the average PM2.5 concentration was 42.26 +/- 13.45 mu g m(-3), while the average concentrations of OC and EC were 3.05 +/- 1.70 and 0.63 +/- 0.58 mu g m(-3), respectively. The OC/EC ratio was in the range of 2.69-16.9 (mean: 6.05 +/- 2.70), and the average concentration of 10 selected ions was 6.91 +/- 3.54 mu g m(-3). The average concentration of SO42- was the highest throughout the entire study period (2.33 +/- 1.73 mu g m(-3)); the average contribution of SO42- to the major ionic components was 34%. Surprisingly, the average concentrations of NO3- and NH4+ were relatively low. The mean ratio of [NO3-]/[SO42-] was 0.33 +/- 0.24. Strong positive correlation was found between K+ and both OC and EC (r = 0.90 and r = 0.93, respectively). It is also precious to highlight that biomass burning (BB) is the major source of OC, EC and K+, which multiple studies have confirmed that the role of K+ as a biomass marker. Results showed that BB episodes might play a major role in producing the observed high levels of OC. The relatively high abundance of both B[g,h,i]P and Ind suggests that motor vehicles, petroleum/oil combustion, and industrial waste burning are the primary emission sources of PAHs in the ambient air of Phuket. Interestingly, principal component analysis (PCA) indicated that vehicular exhausts are the main source of carbonaceous aerosol compositions found in the ambient air of Phuket, whereas the contributions of biomass burning, diesel emissions, sea salt aerosols and industrial emissions were also important
Effects of Agricultural Waste Burning on PM2.5-Bound Polycyclic Aromatic Hydrocarbons, Carbonaceous Compositions, and Water-Soluble Ionic Species in the Ambient Air of Chiang-Mai, Thailand
PM2.5 is widely regarded as a major air pollutant due to its adverse health impacts and intimate relationship with the climate system. This study aims to characterize the chemical components (e.g., organic carbon (OC), elemental carbon (EC), water soluble ionic species (WSIS) and polycyclic aromatic hydrocarbons (PAHs) in PM2.5 collected at Doi-Inthanon in Chiang-Mai, Thailand, the highest mountain in Thailand. All samples (n = 50) were collected by MiniVolTM portable air samplers from March 2017 to March 2018. In this study we found the average PM2.5 concentration was 100 +/- 48.6 mu g m(-3). The OC/EC ratio was 6.8 +/- 3.0, and the decreasing order of the WSIS concentrations was SO42->Na+>Ca2+>NH4+>NO3->K+>Cl->NO2->Mg2+> F-. The total concentrations of nineteen PAHs were defined as the sum of Ace, Fl, Phe, Ant, Fluo, Pyr, B[a]A, Chry, B[b]F, B[k]F, B[a]F, B[e]P, B[a]P, Per, Ind, B[g,h,i]P, D[a,h]A, Cor, and D[a,e]P. The concentration of total PAHs was 2.361 +/- 2.154 mu g m(-3). Principal component analysis (PCA) highlights the importance of vehicular exhaust, biomass burning, diesel emissions, sea-salt aerosols and volatilization from fertilizers as the five dominant potential sources that accounted for 51.6%, 16.2%, 10.6%, 5.20% and 3.70% of the total variance, respectively. The rest of the 12.7% variance probably is associated with unidentified local and regional sources such as incinerators, joss paper/incense burning, and domestic cooking. Interestingly, the results from the source estimations from the PCA underlined the importance of vehicular exhaust as the major contributor to the PM2.5 concentrations in the ambient air of Don-Inthanon, Chiang-Mai province. However, it is crucial to emphasize that the impacts of agricultural waste burning, fossil fuel combustion, coal combustion and forest fires on the variations of OC, EC and WSIS contents were not negligible