Meteorological parameters play a crucial role in the ambient air quality of urban and rural environments. This study aims to investigate the relationship between meteorological parameters (including temperature, relative humidity, and wind speed) and the concentrations of PM2.5 and PM10 in the urban area and the rural area, northern Thailand during the haze period (January to April) from 2016 to 2020. Statistical analyses of the Spearman-Rank correlation coefficient and the multivariate gaussian regression were used to investigate the relationships. The secondary data of ambient PM2.5 and PM10 concentration and meteorological parameters were acquired from the Thai Pollution Control Department. The measurements are obtained using the Beta Ray attenuation method. The results showed that approximately 24% to 65% of daily average PM2.5 concentrations in the urban area over the study period exceeded Thailand’s National Ambient Air Quality Standards. The average PM2.5/PM10 ratios in the urban and the rural areas over the haze period were 0.69 and 0.66, respectively. Our analysis established a significant correlation between atmospheric temperature ( r = 0.624) and relative humidity ( r = −0.722) with the concentrations of PM2.5 and PM10. In both areas, PM2.5 and PM10 concentrations were also positively correlated with temperature. In contrast, relative humidity was significantly related with the decrease of PM2.5 and PM10 concentrations. However, wind speed does not affect PM2.5 and PM10 concentrations. Additionally, the daily backward trajectories using the hybrid-single particle Lagrangian integrated trajectory model also demonstrated air mass movement in March mostly came from the southwesterly direction, which moved through the highlands, the large biomass burned areas, upwind neighboring provinces, and transboundary transports before reaching the air monitoring stations. Our findings improve the understanding of particulate matter pollution and meteorological patterns during annual haze periods in the urban and rural areas. We expect the output of this study can help improve existing haze mitigation measures for improving the prediction accuracy of air pollution under variable meteorological parameters.

Correct emission factors are necessary for evaluating vehicle emissions and making proper decisions to manage air pollution in the transportation sector. In this study, using a chassis dynamometer at the Automotive Emission Laboratory, CO2 and CH4 emission factors of light-duty vehicles (LDVs) were developed by fuel types and driving speeds. The Bangkok driving cycle was used for the vehicle’s running and controlling under the standard procedure. Results present that the highest average CO2 and CH4 emission factors were emitted from LDG vehicles, at 232.25 g/km and 9.50 mg/km, respectively. The average CO2 emission factor of the LDD vehicles was higher than that of the LDG vehicles, at 182.53 g/km and 171.01 g/km, respectively. Nevertheless, the average CH4 emission factors of the LDD vehicles were lower than those of the LDG vehicles, at 2.21 mg/km and 3.02 mg/km, respectively. The result reveals that the lower driving speed emitted higher CO2 emission factors for LDVs. It reflects the higher fuel consumption rate (L/100 km) and the lower fuel economy rate (km/L). Moreover, the portion of CO2 emissions emitted from LDVs was 99.96% of total GHG emissions. The CO2 and CH4 emission factors developed through this study will be used to support the greenhouse gas reduction policies, especially concerning the CO2 and CH4 emitted from vehicles. Furthermore, it can be used as a database that encourages Thailand’s green transportation management system.

Background: Leptospirosis is an important health problem in Thailand. People infected with leptospirosis may not have any mild symptoms, whereas some people have acute and severe illnesses. It is crucial to strengthen critical patients’ diagnosis and treatment to prevent severe complications and reduce mortality. This study was performed to explore a set of parameters for the prediction of severe leptospirosis illness under routine clinical practice. Methods: A case-control study was conducted in eight general hospitals in Thailand. Retrospective collection data were used, and key information was retrieved from inpatient medical files. Patients were grouped into two severity categories, severe and non-severe infection. Backward elimination was used to reach the final multivariate model. Results: The six significant predictors identified in the study were hemoptysis (OR = 25.80, 95% CI 5.69, 116.92), hypotension (blood pressure < 90/60 mmHg) (OR = 17.33, 95% CI 6.89, 43.58), platelet count < 100,000/µL (OR = 8.37, 95% CI 4.65, 15.09), white blood cell count (WBC) > 14,000/µL (OR = 5.12, 95% CI 2.75, 9.51), hematocrit ≤ 30% (OR = 3.49, 95% CI 1.61, 7.57), and jaundice (OR = 3.11, 95% CI 1.71, 5.65). These predictors could correctly predict the severity of leptospirosis infection in 91.31% of the area under the receiver operation curve (AuROC). Conclusions: The results of this study showed that severe leptospirosis infections have identifiable predictors. The predictors may be used to develop a scoring system for predicting the level of severity.