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 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.

Relationship between PM10 and PM2.5 levels in high-traffic area determined using path analysis and linear regression.

The objective of this study was to determine the relationship between PM10 and PM2.5 levels as related to meteorological conditions and traffic flow using both a linear regression analysis and a path analysis. The Particulate matter (PM) samples were collected from Sukhumvit road, Bangkok, Thailand, at both open (104 samples) and covered (92 samples) areas along the road. Fifteen percent of all samples were separated before the statistical models were run and used for model validation. The results from the path analysis were more elaborate than those from the linear regression, thus indicating that meteorological conditions had a direct effect on the particulate levels and that the effects of traffic flow were more variable in open areas. The model also indicated that meteorological conditions had an indirect effect and that traffic flow had a direct effect on particulate levels in covered areas. The model validation results indicated that for open areas, the R2 values were not very different between the path analysis and the linear regression model, but that the path analysis was more accurate than the linear regression model at very low PM concentrations. At high PM concentrations, the path analysis model also had a better fit than did the linear regression, so the predictions from the path analysis model were more accurate than those from the linear regression.

AMBIENT PM10 AND PM2.5 CONCENTRATIONS AT DIFFERENT HIGH TRAFFIC-RELATED STREET CONFIGURATIONS IN BANGKOK, THAILAND.

This study investigated the potential exposure levels of PM10 and PM2.5 at two different road configuration sites in Bangkok, Thailand: covered and open roadside areas. One hundred samples were collected together with the meteorological data: temperature, relative humidity, wind speeds, and solar radiation. Spearman rank correlation was used to analyze the relationships between these factors, and PM10 and PM2.5 levels at different roadside areas. The PM10 and PM2.5 levels at the covered area were 1.72 and 1.60 times more than those levels at open area were. The mean levels were found to be 154.59 and 94.42 µg/m³ at the covered areas, and 89.43 and 58.69 µg/m³ at the open areas. These results suggested that a higher potential risk for workers, such as street vendors, was the exposure to particulate matters at the covered areas compared to open areas. Wind speeds and relative humidity were significantly negative-related influencing factors on PM10 and PM2.5 levels at the open area, but not significantly related for the covered areas.