Occurrence, distribution, and ecological risk assessment of heavy metals in Chao Phraya River, Thailand.

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.

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis.

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann-Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Association of cause-specific hospital admissions with high and low temperatures in Thailand: a nationwide time series study.

Background: Non-optimum temperatures are associated with a considerable mortality burden. However, evidence of temperature with all-cause and cause-specific hospital admissions in tropical countries like Thailand is still limited. Methods: Daily all-cause and cause-specific hospital admissions for outpatient and inpatient visits were collected from 77 provinces in Thailand from January 2013 to August 2019. A two-stage time-series approach was applied to assess the association between non-optimum temperatures and hospital admission. We first fitted the province-specific temperature-morbidity association and then obtained the national association in the second stage using a random-effects meta-analysis regression. The attributable fraction (AF) of hospital admissions with 95% empirical confidence interval (eCI) was calculated. Findings: A total of 878,513,460 all-cause outpatient admissions and 32,616,600 all-cause inpatient admissions were included in this study. We observed a J-shaped relationship with the risk of hospital admissions increasing for both cold and hot temperatures. The overall AFs of all-cause hospital admissions due to non-optimum temperatures were 7.57% (95% eCI: 6.47%, 8.39%) for outpatient visits and 6.17% (95% eCI: 4.88%, 7.20%) for inpatient visits. Hot temperatures were responsible for most of the AFs of hospital admissions, with 6.71% (95% eCI: 5.80%, 7.41%) for outpatient visits and 4.50% (95% eCI: 3.62%, 5.19%) for inpatient visits. The burden of hospital admissions was greater in females and in children and adolescents (0-19 years). The fractions of hospital admissions attributable to non-optimum temperatures exhibited variation among disease categories and geographical areas. Interpretation: The results indicate that low and high temperature has a significant impact on hospital admissions, especially among the females, and children and adolescents (0-19 years). The current investigation could provide evidence for policymakers to develop adaptation strategies and mitigate the adverse effects of climate change on public health in Thailand and other tropical countries. Funding: National Research Council of Thailand (NRCT): E-Asia Joint Research Program: Climate change impact on natural and human systems (N33A650979).

The association of meteorological parameters and AirQ+ health risk assessment of PM2.5 in Ratchaburi province, Thailand.

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 PM2.5 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 PM2.5 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 PM2.5 was highly variable, with the highest concentration being 136.42 µg/m3 and the lowest being 2.33 µg/m3. 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 PM2.5 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 PM2.5 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.

Biomarker-Determined Nonylphenol Exposure and Associated Risks in Children of Thailand, Indonesia, and Saudi Arabia.

Nonylphenol (NP) is an endocrine disruptor and environmental contaminant. Yet, data on individual body burdens and potential health risks in humans, especially among children, are scarce. We analyzed two specific urinary NP metabolites, hydroxy-NP (OH-NP) and oxo-NP. In contrast to parent NP, OH-NP has a much higher urinary excretion fraction (Fue), and both are insusceptible to external contamination. We investigated spot urine samples from school children of Thailand (n = 104), Indonesia (n = 89), and Saudi Arabia (n = 108) and could quantify OH-NP in 100% of Indonesian and Saudi children (median concentrations: 8.12 and 8.57 µg/L) and in 76% of Thai children (1.07 µg/L). Median oxo-NP concentrations were 0.95, 1.10, and <0.25 µg/L, respectively, in line with its lower Fue. Median daily NP intakes (DIs), back-calculated from urinary OH-NP concentrations, were significantly higher in Indonesia and Saudi Arabia [0.47 and 0.36 µg/(kg bw·d), respectively] than in Thailand [0.06 µg/(kg bw·d)]. Maximum DIs were close to the preliminary tolerable DI of 5 µg/(kg bw·d) from the Danish Environmental Protection Agency. Dominant sources of exposure or relevant exposure pathways could not be readily identified by questionnaire analyses and also potentially varied by region. The novel biomarkers provide long-needed support to the quantitative exposure and risk assessment of NP.

The Elemental Characteristics and Human Health Risk of PM2.5 during Haze Episode and Non-Haze Episode in Chiang Rai Province, Thailand.

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.

Urinary Concentrations of Major Phthalate and Alternative Plasticizer Metabolites in Children of Thailand, Indonesia, and Saudi Arabia, and Associated Risks.

Phthalates are widely used in consumer products and are well-known for adverse endocrine outcomes. Di-(2-ethylhexyl) phthalate (DEHP), one of the most extensively used phthalates, has been rapidly substituted with alternative plasticizers in many consumer products. The aim of this study was to assess urinary phthalate and alternative plasticizer exposure and associated risks in children of three Asian countries with different geographical, climate, and cultural characteristics. Children were recruited from elementary schools of Saudi Arabia (n = 109), Thailand (n = 104), and Indonesia (n = 89) in 2017-2018, and their urine samples were collected. Metabolites of major phthalates and alternative plasticizers were measured in the urine samples by HPLC-MS/MS. Urinary metabolite levels differed substantially between the three countries. Metabolite levels of diisononyl phthalate (DiNP), diisodecyl phthalate (DiDP), di(2-ethylhexyl) terephthalate (DEHTP), and 1,2-cyclohexane dicarboxylic acid diisononyl ester (DINCH) were the highest in Saudi children: Median urinary concentrations of oxo-MiNP, OH-MiDP, 5cx-MEPTP, and OH-MINCH were 8.3, 8.4, 128.0, and 2.9 ng/mL, respectively. Urinary DEHP metabolite concentrations were the highest in the Indonesian children. The hazard index (HI) derived for the plasticizers with antiandrogenicity based reference doses (RfDAA) was >1 in 86%, 80%, and 49% of the Saudi, Indonesian, and Thai children, respectively. DEHP was identified as a common major risk driver for the children of all three countries, followed by DnBP and DiBP depending on the country. Among alternative plasticizers, urinary DEHTP metabolites were detected at levels comparable to those of DEHP metabolites or higher among the Saudi children, and about 4% of the Saudi children exceeded the health based human biomonitoring (HBM)-I value. Priority plasticizers that were identified among the children of three countries warrant refined exposure assessment for source identification and relevant exposure reduction measures.

Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand.

BACKGROUND: The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, 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 PM10 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 PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. RESULTS: The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM10 was significantly different for both stations. CO was moderately related to PM10 in the summer season. The PM10 summer model was the best MLR model to predict PM10 during haze episodes. In both stations, it revealed an R2 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 PM10 concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM10 in the winter season. CONCLUSIONS: In conclusion, the MLR models are effective at estimating PM10 concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R2 of 0.61 and 0.52 for stations 65 and 73, respectively.

Influence of the meteorological conditions and some pollutants on PM10 concentrations in Lamphun, Thailand.

Particulate matter (PM) has been occurring regularly during the dry season in the upper north of Thailand including Lamphun Province that might be influenced by various factors including climatologic and other pollutants. This paper aims to investigate the climatologic and gaseous factors influencing the occurrence of PM10 concentration using Pollution Control Department (PCD) data. The secondary data of 2009 to 2017 obtained from the PCD was used for analysis. We used descriptive statistics, Pearson's correlation coefficient, multiple regression and graphic presentation using R program (R packages of 'open air' and 'ncdf4') and Microsoft Excel Spreadsheet®. In addition, the periodic measurement of PM2.5 and PM10 were investigated to determine the ratio of PM2.5/PM10. The results indicated that haze episodes (daily PM10 concentration always over the PCD standard) normally occur during the dry season from February to April. The maximum concentration was always found in March. The PM10 concentration was negatively associated with relative humidity and temperature while the PM10 concentration showed a strongly positive association with CO and NO2 concentration with correlation values of 0.70 and 0.57, respectively. Furthermore, we found CO and PM10 concentration was associated with ozone concentration. This finding will benefit local communities and the public health sector to provide a warning system for preparation and response plans to react to PM10 episodes in their responsible areas.

The Health Risks of Airborne Polycyclic Aromatic Hydrocarbons (PAHs): Upper North Thailand.

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.

Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand.

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM10) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM10, carbon monoxide (CO), ozone (O3), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009⁻2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM10 is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM10 concentrations were significantly related to CO and O3 concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and -0.37, respectively (p-value < 0.001). Additionally, the hourly PM10 concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM10 concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.