This study aims to examine the relationship between meteorological factors, specifically temperature, solar radiation, and ozone concentration levels. Levels of surface ozone were monitored (O₃) in Chonburi, Thailand (located at 3.2017° N, 101.2524° E), from January 2010 to December 2020. Thailand’s coastal tropical environment provided a unique setting for the study. The study revealed a distinctive seasonal trend in ozone levels, with the highest concentrations occurring during the winter and the lowest in the rainy season, on average. The increase of O₃ in the summer was primarily attributed to intense ground-level solar radiation and higher temperatures of around 30–35 °C, enhancing O₃ concentrations ranging from 200 to 1400. During the winter, there is an increased elimination of the O₃ concentration by higher levels of NO₂. The study also examined the relationship between ozone levels and various meteorological factors to identify which had the most significant impact on ozone formation. The analysis showed that the ozone concentration has a strong negative correlation with relative humidity but is positively correlated with solar radiation, temperature, and wind speed.

Land use data can be used to understand patterns of economic behavior, such as the relationship between land use and property values or the impact of land use on environmental factors like air and water quality. The combination of land use data with other data sources and analysis methods can yield significant insights into economic growth and behavior. In this study, the land use and land cover (LULC) were classified using multi-temporal Sentinel-2 imagery (2019 and 2021) and random forest through the Google Earth Engine platform (GGE) with an overall accuracy of more than 89.79%. According to the results of the change detection analysis, there was a 16.96% increase in miscellaneous surface areas and a 15.50% increase in artificial surface areas. These disclose confirm that the sea salt farm, which are the traditional economic function, are losing 37.40%. Furthermore, the CA-Markov model was utilized to predict alterations in land use patterns in the year 2023 through the extrapolation of existing trends. The predicted LULC map of 2023 publicizes the trend of the sea salt farm decreasing, contrasty the artificial surface areas are increasing. In summary, this research reveals the evidence that LULC is strongly related to traditional living changes, and spatial analysis techniques are reasonable and committing tools for study.

Water use of human depends on daily life activities. Agriculture is the main activity of Thailand. In the present, there are the extension of agriculture, industry, residence, and community which increase water demand. It can cause a problem of water use among urban, industrial and agricultural. Most agricultural in Samut Songkhram Province, Thailand were planting crops such as pomelo, coconut and lychee, which are famous and generate a lot of income for farmers. The quality and quantity of water is an indicator for measure of the quality and yield of agricultural products. The aim of research is to estimate water use through geographic information system, and remote sensing from Landsat8 satellite. This research considers the main parameters which were; estimation of spatial rainfall runoff and quantity of evaporation, infiltration rate of the soil. To evaluate the amount of soil moisture in order to create soil moisture map for planning the utilization and management of water for agriculture. The classification land use was to investigate from Landsat8 which shown currently agricultural area. The result showed the soil moisture which distributes in each area especially the agricultural area. This information can be used by agriculturists and related organizations to plan and make the decision of growing appropriate plants according to the soil moisture for the sustainability of the agriculture.