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

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Project description:BackgroundExposure to air pollution is associated with increased blood pressure (BP) in adults and children. Some evidence suggests that air pollution exposure during the prenatal period may contribute to adverse cardiorenal health later in life. Here we apply a distributed lag model (DLM) approach to identify critical windows that may underlie the association between prenatal particulate matter ? 2.5 ?m in diameter (PM2.5) exposure and children's BP at ages 4-6 years.MethodsParticipants included 537 mother-child dyads enrolled in the Programming Research in Obesity, GRowth Environment, and Social Stress (PROGRESS) longitudinal birth cohort study based in Mexico City. Prenatal daily PM2.5 exposure was estimated using a validated satellite-based spatio-temporal model and BP was measured using the automated Spacelabs system with a sized cuff. We used distributed lag models (DLMs) to examine associations between daily PM2.5 exposure and systolic and diastolic BP (SBP and DBP), adjusting for child's age, sex and BMI, as well as maternal education, preeclampsia and indoor smoking report during the second and third trimester, seasonality and average postnatal year 1 PM2.5 exposure.ResultsWe found that PM2.5 exposure between weeks 11-32 of gestation (days 80-226) was significantly associated with children's increased SBP. Similarly, PM2.5 exposure between weeks 9-25 of gestation (days 63-176) was significantly associated with increased DBP. To place this into context, a constant 10 ?g/m3 increase in PM2.5 sustained throughout this critical window would predict a cumulative increase of 2.6 mmHg (CI: 0.5, 4.6) in SBP and 0.88 mmHg (CI: 0.1, 1.6) in DBP at ages 4-6 years. In a stratified analysis by sex, this association persisted in boys but not in girls.ConclusionsSecond and third trimester PM2.5 exposure may increase children's BP in early life. Further work investigating PM2.5 exposure with BP trajectories later in childhood will be important to understanding cardiorenal trajectories that may predict adult disease. Our results underscore the importance of reducing air pollution exposure among susceptible populations, including pregnant women.

Dataset Information

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

Background

Method

Results

Conclusions

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OmicsDI is part of the ELIXIR infrastructure

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