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Understanding heavy metals in rivers is crucial, as their presence and distribution impact water quality, ecosystem health, and human well-being. This study examined the presence and levels of nine heavy metals (Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) in 16 surface water samples along the Chao Phraya River, identifying Fe, Mn, Zn, and Cr as predominant metals. Although average concentrations in both rainy and dry seasons generally adhered to WHO guidelines, Mn exceeded these limits yet remained within Thailand’s acceptable standards. Seasonal variations were observed in the Chao Phraya River, and Spearman’s correlation coefficient analysis established significant associations between season and concentrations of heavy metals. The water quality index (WQI) demonstrated varied water quality statuses at each sampling point along the Chao Phraya River, indicating poor conditions during the rainy season, further deteriorating to very poor conditions in the dry season. The hazard potential index (HPI) was employed to assess heavy metal contamination, revealing that during the dry season in the estuary area, the HPI value exceeded the critical threshold index, indicating the presence of heavy metal pollution in the water and unsuitable for consumption. Using the species sensitivity distribution model, an ecological risk assessment ranked the heavy metals’ HC5 values as Pb > Zn > Cr > Cu > Hg > Cd > Ni, identifying nickel as the most detrimental and lead as the least toxic. Despite Cr and Zn showing a moderate risk, and Cu and Ni posing a high risk to aquatic organisms, the main contributors to ecological risk were identified as Cu, Ni, and Zn, suggesting a significant potential ecological risk in the Chao Phraya River’s surface water. The results of this study provide fundamental insights that can direct future actions in preventing and managing heavy metal pollution in the river ecosystem.

Air quality is heavily influenced by rising pollution distribution levels which are a consequence of many artificial activities from numerous sources. This study aims to determine the relationship between meteorological data and air pollutants. The health effects of long-term PM 2.5 were estimated on expected life remaining (ELR) and years of life lost (YLL) indices in Ratchaburi province during the years 2015–2019 using AirQ+ software. Values obtained from the PM 2.5 averaging, and YLL data were processed for the whole population in the age range of 0–29, 30–60 and over 60. These values were entered into AirQ+ software. The mean annual concentration of PM 2.5 was highly variable, with the highest concentration being 136.42 μg/m 3 and the lowest being 2.33 μg/m 3 . The results estimated that the highest and lowest YLL in the next 10 years for all age groups would be 24,970.60 and 11,484.50 in 2017 and 2019, respectively. The number of deaths due to COPD, IHD, and stroke related to long-term exposure to ambient PM 2.5 were 125, 27 and 26, respectively. The results showed that older people (> 64) had a higher YLL index than the groups aged under 64 years. The highest and lowest values for all ages were 307.15 (2015) and 159 (2017). Thus, this study demonstrated that the PM 2.5 effect to all age groups, especially the the elderly people, which the policy level should be awared and fomulated the stratergies to protecting the sensitive group.

Understanding vegetation-climate interactions is essential amid escalating global climate change. This study investigates spatial-temporal and seasonal variations in Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) across six regions of Thailand (2007–2022). Results reveal distinct regional and seasonal characteristics, with significant negative correlations between LST and NDVI ( R  = 0.61 dry; 0.39 rainy; 0.72 winter). The strongest negative correlation occurred during the rainy season in 2017, highlighting complex interannual variations. Seasonal LST fluctuations (winter-summer: 1.24, winter-rainy: -1.54, summer-rainy: -2.78, p  < 0.001) and NDVI variations (winter-summer: 0.09, winter-rainy: 0.07, summer-rainy: -0.03, p  < 0.001) were statistically significant. These findings emphasize monitoring LST and NDVI as vital for understanding ecological impacts of climate change and urbanization. The study specifically explores whether increased vegetation consistently is associated with lower temperatures, underscoring the importance of strategies to mitigate heat and enhance climate resilience, particularly in rapidly urbanizing regions.

Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM 10 , accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM 10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM 10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM 10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM 10 was significantly different for both stations. CO was moderately related to PM 10 in the summer season. The PM 10 summer model was the best MLR model to predict PM 10 during haze episodes. In both stations, it revealed an R 2 of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM 10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM 10 in the winter season. Conclusions In conclusion, the MLR models are effective at estimating PM 10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R 2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Fine particle matter (PM2.5) was directly related to seasonal weather, and has become the influencing factor of air quality that is harmful for human health in Chiang Rai province. The aims were determining the elemental composition in PM2.5 and human health risk in haze (March 2021) and non-haze episodes (July–August 2021). Nine elements in PM2.5 were measured by using an Atomic Absorption Spectrophotometer, and an enrichment factor was used to identify the emission source. The results showed that the average concentration of PM2.5 was 63.07 μg/m3 in haze episodes, and 25.00 μg/m3 in a non-haze episode. The maximum concentration was 116.7 μg/m3 in March. The majority of elements originated from anthropogenic sources. In haze episodes, PM2.5 mean concentration was approximately 4.2 times that of the WHO guidelines (15 μg/m3 24 h), and 1.3 times that of the Thai Ambient Air Quality Standard (50 μg/m3). The analysis of backward air mass trajectory showed that transboundary and local sources significantly influenced PM2.5 at the monitoring site in the sampling period. In the health risk assessment, the non-carcinogenic risk of Cd was the highest, with a Hazard Quotient (HQ) of 0.048, and the cancer risk of Cr was classified as the highest cancer risk, with the values of 1.29 × 10−5, higher than the minimum acceptable level.

Every year, Northern Thailand faces haze pollution during the haze episode. The particulate matter (PM), including fine fraction (PM2.5), a coarse fraction (PM2.5–10), and 16 polycyclic aromatic hydrocarbons (PAHs), was measured in six provinces in upper north Thailand during the haze and non‐haze episodes in 2018. Eighty‐three percent of the PM2.5 measurements (21.8–194.0 µg/m3) during the haze episode exceeded the national ambient air quality standard in Thailand. All 16 PAHs were detected in the study area in both periods. The average concentration of total PAHs (particle‐bound and gas‐phase) during the haze episode was 134.7 ± 80.4 ng/m3, which was about 26 times higher than those in the non‐haze (5.1 ± 9.7 µg/m3). Naphthalene and acenaphthene were the dominant PAHs in the gas phase; whereas, indeno[123‐cd] pyrene, benzo[a]pyrene, and Benzo[ghi]Perylene were dominant in the particle‐bound phase. The estimated inhalation excess cancer risk from PAHs exposure was 9.3 × 10−4 and 2.5 × 10−5 in the haze episode and non‐haze, respectively. Diagnostic ratios and principal component analysis revealed that PAHs were derived from mixed sources of vehicle emission and solid combustion in the haze episode and vehicle emission in the non‐haze period. High pollution levels of PM and large cancer risk attributable to the exposure of PAHs in the haze episode suggest urgent countermeasures to reduce the source emission, especially from the solid combustion in the area. Plain Language Summary Northern Thailand has been faced with air pollution every year, particularly in the dry season. The concerning pollutant is particulate matter (PM), namely: fine particle (PM2.5) and coarse particles (PM2.5–10). Some hazard substances were contaminated in the air and also bounded inside PMs like polycyclic aromatic hydrocarbon (PAH), some of them are classified as harmful to human health. This study investigated the PM2.5 concentrations and PAHs in the upper north of Thailand. PM2.5 concentrations was exceeded the national ambient air quality standard of Thailand in dry period so‐called haze period, moreover, PAHs concentration on haze period was 26 times higher than the non‐haze period. This research indicated that cancer risk due to PAHs exposure during the haze period was 9.3 × 10−4 whereas it was 2.5 × 10−5 in the non‐haze period. The findings of this research will be scientific information supporting the local government to provide intervention or mitigation measures to reduce air pollution or preparedness response plan. Key Points Eighty‐three percent of the PM2.5 measurements (21.8–194.0 μg/m3) during the haze episode exceeded the ambient air quality standard in Thailand The average concentration of total polycyclic aromatic hydrocarbons during the haze episode was about 26 times higher than those in the non‐haze Indeno[123‐cd] pyrene, benzo[a]pyrene, and Benzo[ghi]Perylene were dominant in the particle‐bound phase

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.

Fine particle matter (PM ) was directly related to seasonal weather, and has become the influencing factor of air quality that is harmful for human health in Chiang Rai province. The aims were determining the elemental composition in PM and human health risk in haze (March 2021) and non-haze episodes (July-August 2021). Nine elements in PM were measured by using an Atomic Absorption Spectrophotometer, and an enrichment factor was used to identify the emission source. The results showed that the average concentration of PM was 63.07 μg/m in haze episodes, and 25.00 μg/m in a non-haze episode. The maximum concentration was 116.7 μg/m in March. The majority of elements originated from anthropogenic sources. In haze episodes, PM mean concentration was approximately 4.2 times that of the WHO guidelines (15 μg/m 24 h), and 1.3 times that of the Thai Ambient Air Quality Standard (50 μg/m ). The analysis of backward air mass trajectory showed that transboundary and local sources significantly influenced PM at the monitoring site in the sampling period. In the health risk assessment, the non-carcinogenic risk of Cd was the highest, with a Hazard Quotient (HQ) of 0.048, and the cancer risk of Cr was classified as the highest cancer risk, with the values of 1.29 × 10 , higher than the minimum acceptable level.