Particulate matter (PM) is regarded a major problem worldwide because of the harm it causes to human health. Concentrations of PM with particle diameter less than 2.5 µm (PM_2.5) and with particle diameter less than 10 µm (PM₁₀) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM_2.5 and PM₁₀ monitoring stations are few, the ability to estimate concentrations at any location based on its environment will benefit healthcare policymakers. This research aimed to study the influence of land use, traffic load, and meteorological factors on the PM_2.5 and PM₁₀ concentrations in Bangkok using a land-use regression (LUR) approach. The backward stepwise selection method was applied to select the significant variables to be included in the resultant models. Results showed that the adjusted coefficient of determination of the PM_2.5 and PM₁₀ LUR models were 0.58 and 0.57, respectively, which are in the same range as reported in the previous studies. The meteorological variables included in both models were rainfall and air pressure; wind speed contributed to only the PM_2.5 LUR model. Further, the land-use types selected in the PM_2.5 LUR model were industrial and transportation areas. The PM₁₀ LUR model included residential, commercial, industrial, and agricultural areas. Traffic load was excluded from both models. The root mean squared error obtained by 10-fold cross validation was 9.77 and 16.95 for the PM_2.5 and PM₁₀ LUR models, respectively.

Krabi and Nakhon Si Thammarat are two coastal provinces in Thailand facing substantial threats from climate change induced hydrometeorological hazards, including enhanced coastal erosion and flooding. Human populations and livelihoods in these coastal provinces are at greater risk than those in inland provinces. However, little is known about the communities’ resilience and coping capacities regarding hydrometeorological hazards of varying magnitudes. The study conducted a quantitative socio-economic assessment of how people in Krabi and Nakhon Si Thammarat provinces manage and respond to hydrometeorological hazards, examining their resilience and coping capacities. This was a cross-sectional study based on secondary data collection on the social and economic dimensions of resilience, and a review of literature on coping mechanisms to hydrometeorological hazards within the study area. Measuring and mapping socio-economic resilience was based on the available data gathered from the social and economic dimensions, with existing or standard indicators on exposure and vulnerability applied uniformly across subdistricts. A combination of social and economic dimensions produced novel socio-economic resilience index scores by subdistrict, which were mapped accordingly for the two coastal provinces. The study also derived a coping capacity index scores by combining availability of skills or soft capacity and availability of structural resources or hard coping capacity. Socio-economic resilience index scores varied greatly amongst subdistricts. Combining the soft and hard coping capacities, the average score across districts in both provinces was 3 out of a possible 4, meaning that most of the districts were largely resilient. However, variations also existed by subdistrict. Few subdistricts in both Krabi and Nakhon Si Thammarat provinces had low coping capacity index scores between 1 and 2 out of 4. District averages of socio-economic resilience scores mask the variations at subdistrict level. More studies with rigorous methodologies at village or neighborhood level is needed to obtain a nuanced understanding of community resilience to hydrometeorological hazards.

The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind speed had the highest coefficients, i.e. β=0.17, β= –0.12 and β= –0.11 (P<0.001), respectively. The risk of dengue incidence occurring during the rainy season was almost twice as high as that during monsoon. Statistically significant spatial clusters of dengue cases were observed all through the province in different years. Nabon was identified as a hotspot, while Pak Phanang was a coldspot for dengue fever incidence, explained by the fact that the former is a rubber-plantation hub, while the agricultural plains of the latter lend themselves to the practice of pisciculture combined with rice farming. This information is imminently important for planning apt sustainable control measures for dengue epidemics.

Climate change is increasingly impacting both environments and human communities. Coastal regions in Thailand are experiencing more severe impacts, which vary based on the unique physical and socio-economic characteristics of each area. To assess the vulnerability of coastal regions in Thailand, this study focused on two provinces, Nakhon Si Thammarat (NST) and Krabi, each representing distinct coastal environments. NST, situated on the Gulf of Thailand’s east coast, has an agriculture-based economy, while Krabi, on the Andaman Sea’s west coast, relies heavily on tourism. The study utilized a multi-criteria decision analysis approach (MCDA) and GIS to analyze the Coastal Vulnerability Index at the sub-district level. The results revealed that, although NST was more vulnerable than Krabi to socio-economic factors such as the poverty rate and the number of fishery households, Krabi was much more vulnerable in the physical environment, including wave height, tidal level, coastal erosion, and slope. However, overall, Krabi exhibited high to the highest levels of coastal vulnerability, while NST displayed moderate to high levels. These findings provide valuable insights for policymakers and government agencies, aiding in the development of strategies to mitigate vulnerability and enhance the quality of life for local residents in both provinces.

Communities in coastal regions are affected by the impacts of extreme climatic events causing flooding and erosion. Reducing the impacts of flood and erosion in these areas by adopting coping strategies that fortify the resilience of individuals and their localities is desirable. This study used summative content analysis to explore the coping mechanisms of coastal communities before, during, and after various dangers relating to flooding and erosion. The findings from the study show that effective surveillance systems, disaster preparedness, risk mapping, early warning systems, availability of databases and functional command systems, as well as reliable funding are essential to efficiently cope with hazards of coastal flooding and erosion. As flooding and erosion have been predicted to be more severe due to climate change in the coming years, the adoption of effective natural and artificial mechanisms with modern technologies could help coastal regions to be more resilient in coping with the dangers associated with flooding and erosion. Pragmatic policies and programs to this end by actors are critical to averting crises induced by flooding and erosion in coastal areas.