Thailand has experienced rapid urbanization and industrialization, resulting in increased air pollution. Urban air pollution is a major environmental issue contributing to respiratory diseases. The purpose of this study was to develop an air pollution platform, namely “Life Dee”, for near real-time monitoring of the fine particulate matter (PM2.5) in Chonburi province at microclimate conditions. This was achieved using a combination of remote sensing data, ground based stations, and microclimate modeling. The Weather Research and Forecasting with Chemistry (WRF-CHEM) model was used to simulate PM2.5 concentrations with 1 km × 1 km spatial resolution. A High Definition (HD) map was created using ArcGIS CityEngine, which utilizing for visualization of PM2.5 concentrations in urban areas. The user-friendly platform was developed to make the data accessible to the public via mobile applications. This platform can serve as a prototype model for other urban areas dealing with similar air pollution challenges. Users can utilize the platform to monitor air quality and receive information on appropriate action plans to protect their health. In addition, the platform provides valuable information to government agencies, allowing them to take proactive measures to mitigate air pollution and improve quality of life for citizens.
The primary objective of this research is to investigate the common characteristics and causes of PM2.5 pollution in Chonburi province, Thailand. The study specifically focused on examining the relationship between PM2.5 pollutants and time. The study utilizes the CAMS global reanalysis (EAC4) dataset provided by the European Centre for Medium Range Weather Forecasts (ECMWF). The data present variations in the total amount of PM2.5 over three distinct periods: Period I (2006 to 2010), Period II (2011 to 2015), and Period III (2016 to 2020). The study employs various statistical analyses, including assessments of PM2.5 levels annually and monthly, trend regression analysis of monthly PM2.5 data, identification of the highest annual mean PM2.5 concentrations, examination of the standard deviation associated with the highest annual PM2.5 mean, and exploration of potential spatial patterns of PM2.5 trends across Chonburi province using CAMS products. Overall, this research contributes to a better understanding of the PM2.5 pollution problem in the area, which is essential for formulating effective environmental policies and public health strategies to mitigate the adverse impacts of air pollution on the local population. The spatial patterns and trend analyses generated from CAMS products enable evidence-based decision-making and can inform policymakers on appropriate measures to improve air quality and safeguard public health in Chonburi province.
Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC
classification applying remotely sensed data is a technique popularly employed on a global and local dimension
particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and
ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification
using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand
Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a
comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector
machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased
classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN)
classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping
generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with
THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data
fusion provides higher land cover classification accuracy than per-pixel data fusion.
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