Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Kliengchuay W

PROVIDER: S-EPMC8611941 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Air pollution exposure, especially particulate matter ≤2.5 μm in diameter (PM2.5), is associated with poorer kidney function in adults and children. Perinatal exposure may occur during susceptible periods of nephron development. We used distributed lag nonlinear models (DLNMs) to examine time-varying associations between early life daily PM2.5 exposure (periconceptional through age 8 years) and kidney parameters in preadolescent children aged 8-10 years. Participants included 427 mother-child dyads enrolled in the PROGRESS birth cohort study based in Mexico City. Daily PM2.5 exposure was estimated at each participant's residence using a validated satellite-based spatio-temporal model. Kidney function parameters included estimated glomerular filtration rate (eGFR), serum cystatin C, and blood urea nitrogen (BUN). Models were adjusted for child's age, sex and body mass index (BMI) z-score, as well as maternal education, indoor smoking report and seasonality (prenatal models were additionally adjusted for average first year of life PM2.5 exposure). We also tested for sex-specific effects. Average perinatal PM2.5 was 22.7 μg/m3 and ranged 16.4-29.3 μg/m3. Early pregnancy PM2.5 exposures were associated with higher eGFR in preadolescence. Specifically, we found that PM2.5 exposure between weeks 1-18 of gestation was associated with increased preadolescent eGFR, whereas exposure in the first 14 months of life after birth were associated with decreased eGFR. Specifically, a 5 μg/m3 increase in PM2.5 during the detected prenatal window was associated with a cumulative increase in eGFR of 4.44 mL/min/1.732 (95%CI: 1.37, 7.52), and during the postnatal window we report a cumulative eGFR decrease of -10.36 mL/min/1.732 (95%CI: -17.68, -3.04). We identified perinatal windows of susceptibility to PM2.5 exposure with preadolescent kidney function parameters. Follow-up investigating PM2.5 exposure with peripubertal kidney function trajectories and risk of kidney disease in adulthood will be critical.

Dataset Information

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

Background

Method

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

Background

Method

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

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