Long-term exposure to airborne fine particulate matter or PM2.5 is associated with an increase in the long-term risk of premature death that creates critical concerns for public health. This study uses twenty years (2002-2021) of daily remotely sensed data with multi-spatial resolution of 1 km to 3 km to examine the long-term spatiotemporal distribution of PM2.5 across Thailand. Good agreement is found between the in-situ measurements of PM2.5 and instantaneous estimates made from the satellite data with correlation coefficients of 0.51. Based on data analysis during the year 2002- 2021, the region with the highest yearly averaged concentration level of PM2.5 was a central region of Thailand (19.91 μg.m-3) followed by northern (19.11 μg.m-3), northeastern (18.92 μg.m-3), eastern (18.76 μg.m-3) and southern (16.16 μg.m-3) region, respectively. The period with the highest PM2.5 levels were during March and April with monthly averages 23.74 to 26.72 μg.m-3. For the 20-year record, monthly-mean PM2.5 concentration in northern Thailand showed statistically significant increase at the rate of 0.14 μg.m-3 month-1 in dry season, the same as in the northeastern (0.126 μg.m-3month-1), eastern (0.12 μg.m-3 month-1) and Central region (0.083 μg.m-3 month-1). While the southern region has a negative trend (-0.018 μg.m-3 month-1) which is different from other regions. The spatiotemporal variation and changing of PM2.5 concentrations were a result of both changing in meteorological factors and anthropogenic activities. Here, we discuss and present possible explanations for long-term spatiotemporal variation of PM2.5.
Coastal bathymetry data are great important environmental resources and disaster management. Today, there are many technologies used to explore the bathymetry, depending on the purpose of surveying. But almost technologies can be time consuming, complicated, and quite expensive. Remote sensing by satellite imagery is one of technology used to estimate the coastal bathymetry in term of accuracy, quality and up to datedness, timely availability and cost effectiveness. Especially the Coastal Blue Band of WorldView-2 for bathymetric measurements will improve both in depth and accuracy. From investigated the relation between surface reflectance and coastal bathymetry of the eight bands of WorldView-2 that band 1,2,3 and 4 are good reflection and when the depth are increase, the digital number (DN) will decrease. The objective of this research is to developing the classification of the coastal bathymetry estimation from satellite imagery. By utilized WorldView-2 satellite images along with regressive function and improving the accuracy result by comparing with in-situ truth depth to assess a coastal bathymetry.
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