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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.

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.

Fine particle matter (PM

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.

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., 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 ThailandThe average concentration of total polycyclic aromatic hydrocarbons during the haze episode was about 26 times higher than those in the non‐hazeIndeno[123‐cd] pyrene, benzo[a]pyrene, and Benzo[ghi]Perylene were dominant in the particle‐bound phase

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. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-12217-2.