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American Journal of   
Environment and Climate (AJEC)

Associations Between Daily PM2.5 Exposure and Respiratory Symptoms in Thai 
Adolescents: Evidence from Nonthaburi Province 

Patsorn Kiatsoongsong1*, Patraporn Ekvitayavetchanukul1 

Volume 4 Issue 3, Year 2025
ISSN: 2832-403X (Online) 

DOI: https://doi.org/10.54536/ajec.v4i3.5093
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: June 28, 2025

Accepted: August 02, 2025

Published: September 18, 2025

Air pollution remains a critical public health concern, especially for adolescents in densely 
populated urban areas. This cross-sectional study investigated the relationship between daily 
PM2.5 exposure and self-reported respiratory symptoms among 132 high school students in 
Nonthaburi Province, Thailand. Over a two-month period (January–February 2025), daily 
PM2.5 data were retrieved from government monitoring stations, while students completed 
daily online symptom surveys. During the study period, the mean PM2.5 concentration was 
36.2 µg/m³, more than double the World Health Organization’s recommended limit of  
15 µg/m³. A notable increase in symptom prevalence, 27.6% higher on days exceeding 40 
µg/m³ compared to days at or below 25 µg/m³, was observed. Logistic regression analysis 
further revealed that each 10 µg/m³ increase in PM2.5 was associated with a 52% higher 
likelihood of  respiratory symptoms (OR = 1.52, 95% CI: 1.26–1.79, p < 0.001).These 
findings highlight the acute impact of  short-term air pollution exposure on adolescent health 
and emphasize the need for school-based air quality monitoring, preventive education, and 
evidence-driven environmental policy to reduce exposure and safeguard student well-being 
in high-risk urban settings.

Keywords
Adolescent Health, Environmental 
Health Policy, PM2.5 Exposure, 
Respiratory Symptoms, Urban 
Pollution

1 Rittiyawannalai School, Thailand
* Corresponding author’s e-mail: prof.dr.pongkit@gmail.com

INTRODUCTION
Background and Rationale
Air pollution is a major public health problem, and 
fine particulate matter (PM2.5) particles less than 2.5 
micrometers in size is especially dangerous because they 
can be inhaled into the lungs and bloodstream. Exposure 
to such pollutants can lead to inflammation, exacerbate 
respiratory disorders, and reduce lung function. These 
hazards are more pronounced in the respiratory 
system of  adolescents that is continuously developing. 
PM2.5 above the safe WHO limits. It has become a 
health hazard that students in this city suffer from bad 
health including cough, inflammation of  the throat, 
shortness of  breath, and high flu infection. Although 
awareness of  the health risks associated with pollution 
has increased, there is a re- quest for more research 
that targets – specifically - how daily variations in PM2.5 
floors and adolescent respiratory health. 
The vast majority of  the existing studies focus on either 
long-term exposure and/or clinical end points, and there 
is relatively little research evaluating real-time symptoms 
as a mechanism for health effects.

Knowledge Gap and Study Significance
Despite increasing evidence of  the health risks posed 
by PM2.5, few studies have examined the short-term, 
day-to-day effects of  fluctuating PM2.5 levels on 
adolescent respiratory health, particularly in urban school 
environments. Most existing research focuses on long-
term exposure or relies on generalized regional data, 
which may not accurately reflect the real-time exposure 
experienced by students in specific school settings.

This study helps to bridge that gap by analyzing daily 
variations in PM2.5 concentrations and their association 
with self-reported respiratory symptoms among high 
school students in Nonthaburi Province. By using 24-
hour real-time air quality data collected at the school 
level alongside daily symptom reports from students, 
the study provides valuable, context-specific evidence. 
These findings offer practical insights for developing 
school-based health interventions and public health 
policies aimed at reducing exposure and improving 
respiratory outcomes for adolescents in high-risk 
environments.

Research Questions
1. How are daily PM2.5 concentrations associated with 

the frequency and severity of  respiratory symptoms in 
high school students?
2. Is there a specific PM2.5 threshold above which 

respiratory symptoms significantly increase?
3. To what extent do personal protective behaviors, such 

as mask-wearing and limiting outdoor activity, reduce 
symptom severity during high-pollution events?

Research Objectives and Hypotheses
Objectives
This study aims to:
• Evaluate the relationship between daily PM2.5 

exposure and respiratory symptoms in adolescents.
• Identify specific PM2.5 cut-points associated with 

above-average respiratory symptoms.
• Assess the effectiveness of  protective behaviors to 

reduce the severity of  respiratory symptoms. 



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Hypotheses
H1: Increased daily PM2.5 concentrations are linked to 

increased prevalence of  respiratory symptoms. 
H2: A clear PM2.5 There is a concentration level above 

which the incidence of  respiratory symptoms escalates 
substantially. 
H3: Participants that utilize protective behavior (e.g., 

mask-wearing, going outside less) will have fewer 
respiratory symptoms.

Structure of  the Paper
The remainder of  this paper is organized as follows:
• Section 2 outlines the study methodology, including 

participant selection criteria, data collection procedures, 
and statistical analysis methods.
• Section 3 presents the key findings, emphasizing 

the relationship between daily PM2.5 levels and the 
prevalence of  self-reported respiratory symptoms.
• Section 4 discusses the broader public health 

implications of  the results, offers practical policy 
recommendations, and addresses the study’s limitations.
• Section 5 concludes with a summary of  the main 

findings and highlights their significance for future 
research and intervention planning.
By focusing on short-term variations in air pollution and 
their immediate impact on adolescent health, this study 
provides actionable insights for educators, policymakers, 
and public health professionals seeking to develop 
targeted strategies in urban school settings.

MATERIALS AND METHODS
Study Design  
This study employed a cross-sectional observational 
design to investigate the relationship between daily PM2.5 
exposure and self-reported respiratory symptoms among 
high school students in Nonthaburi Province, Thailand. 
Data collection was conducted over a two-month period 
(January–February 2025), capturing short-term air 
pollution effects during the dry season, when PM2.5 levels 
are typically elevated. Daily PM2.5 concentrations were 
obtained from official government air quality monitoring 
stations. Simultaneously, participating students completed 
an online self-report survey each evening, documenting 
respiratory symptoms and protective behaviors such as 
mask usage and outdoor activity levels. This real-time 
data collection approach reduced the risk of  recall bias 
and enabled a more accurate assessment of  day-to-
day symptom fluctuation in relation to pollution levels.
The study adhered to the STROBE (Strengthening the 

Reporting of  Observational Studies in Epidemiology) 
guidelines to ensure methodological transparency, 
scientific rigor, and reproducibility.

Study Population and Sampling Strategy 
A total of  132 high school students, aged 13 to 18, were 
recruited from three schools in Nonthaburi Province. 
These schools were chosen due to their proximity to high-
traffic, high-pollution zones, providing an ideal setting to 
study PM2.5 exposure effects in an urban context.

Sampling Method
A stratified random sampling technique was applied 
to ensure representation across key demographic and 
behavioral groups, including:
• Gender: Male and female students
• Outdoor exposure: High vs. low levels of  daily outdoor 

activity
• Residential environment: Urban vs. suburban living areas

Inclusion Criteria
• Students aged 13–18 years, enrolled in middle or high 

school
• Residing and attending school in Nonthaburi Province 

for at least one year
• Willingness to participate and complete daily symptom 

surveys

Exclusion Criteria
• Students with a prior diagnosis of  chronic respiratory 

illness (e.g., asthma, COPD) before the study period
• Students with other serious pre-existing medical 

conditions
• Students who completed less than 80% of  the daily 

surveys during the study period

Data Collection Procedures
PM2.5 Exposure Data 
PM2.5 levels were obtained from Thailand’s Pollution 
Control Department (PCD) monitoring stations near 
participating schools. Hourly PM2.5 readings were 
averaged into daily values (µg/m³). These daily averages 
were categorized according to Thailand’s Air Quality 
Index (AQI-THAI) for analytical purposes:
• 0–25 µg/m³: Good (low risk)
• 26–50 µg/m³: Moderate (increased risk for sensitive 
groups)
• Above 50 µg/m³: High (general health risk, requiring 
protective measures)

Table 1: PM2.5 Exposure Data
PM2.5 Concentration 
(µg/m³)

AQI-THAI Category Health Impact

0 – 25 Good (Green) No significant health effects; safe for all outdoor activities.
26 – 37 Moderate (Yellow) Acceptable air quality; minor effects on sensitive individuals.
38 – 50 Unhealthy for Sensitive 

Groups (Orange)
Respiratory irritation possible in sensitive individuals; reduced 
outdoor activities recommended.



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51 – 90 Unhealthy (Red) Increased risk of  respiratory issues for the general public; 
outdoor activities should be minimized.

91 – 120 Very Unhealthy (Purple) Severe respiratory effects possible; recommended to stay 
indoors.

> 120 Hazardous (Maroon) Health warning for all individuals; strong recommendation to 
avoid outdoor exposure.

For analytical purposes, this study classifies daily PM2.5 exposure levels into three categories:

Table 2: PM2.5 exposure levels
PM2.5 Concentration (µg/m³) Study Classification Exposure Category
≤ 25 µg/m³ Low Exposure Minimal health risk; no intervention needed.

26 – 50 µg/m³ Moderate Exposure Increased risk for sensitive individuals; precautionary 
measures recommended.

> 50 µg/m³ High Exposure General population at risk; outdoor activities should be 
reduced, and protective measures should be enforced.

Self-Reported Health Symptoms (Google Form 
Surveys)
The participants reported on their respiratory 
symptoms and preventive behavior every day on an 
online questionnaire (conducted via Google Forms). 
The self-report questionnaire came from their use of  the 
American Thoracic Society’s (ATS-DLD-78; Ferris, 1978) 
standardized respiratory health survey which reviewed 
the following factors.

Assessment of  Respiratory Symptoms
• Cough (assessed as None, Mild, Moderate or Severe)

• Having the symptoms of  sore throat (Yes/No)
• Dyspnea (rated on the Likert scale from None to Severe)
• Runny nose or stuffy nose (Yes/No) 

Behaviors for Protection and Exposure Assessment 
• The type of  mask worn (N95, surgical or no mask) 
• Diary time spent outdoors on an average day (hours)
• Position of  windows in the home and school (open/

closed).
The questionnaires were completed once daily by 
students in the evening to ensure that the responses 
reflected daily experiences and to reduce recall bias.

Table 3: Self-Reported Health Symptoms
Variable Type Variable Name Measurement Approach
Independent Variable PM2.5 Exposure Daily µg/m³ (government air sensors)

Outdoor Exposure Self-reported hours spent outdoors
Mask-Wearing Frequency scale (always/sometimes/never)

Dependent Variables Respiratory Symptoms Likert scale (none/mild/moderate/severe)
Symptom Severity Score Aggregated symptom index score
Symptom Frequency Number of  days with symptoms per student

Variables and Measurement
Statistical Analysis
All statistical analyses were performed using SPSS version 
28 and R version 4.2 to ensure robust and accurate data 
analysis. The analytical methods included:

Descriptive Statistics
Calculation of  mean, standard deviation (SD), and 
frequency distributions for all collected variables.

Bivariate Analysis
Pearson correlation analyses to examine relationships 
between daily PM2.5 concentrations and respiratory 
symptom severity.

Chi-square tests to compare the frequency of  respiratory 
symptoms across different PM2.5 exposure levels.

Comparative Analysis
Independent t-tests to evaluate differences in respiratory 
symptoms between groups experiencing high versus low 
exposure to PM2.5.
ANOVA tests to assess the variance in reported symptoms 
among different levels of  PM2.5 exposure.

Multivariate Regression Analysis
Multiple linear regression analyses to predict respiratory 
symptom severity based on daily PM2.5 concentrations.
Adjustments were made for potential confounders such 



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as gender, socioeconomic status, and pre-existing health 
conditions.

Effect Size Interpretation
• r ≥ 0.50 indicates a strong correlation.
• r = 0.30–0.49 indicates a moderate correlation.
• r = 0.10–0.29 indicates a weak correlation.

Ethical Considerations 
This study was conducted in accordance with the ethical 
guidelines set by the Thai National Health Research 
Ethics Committee.
The following ethical principles were strictly upheld:

Informed Consent
Written informed consent was obtained from all 
participants, with parental consent required for minors 
under the age of  18.

Confidentiality
To ensure anonymity, each participant was assigned a unique 
identification number. No personal names or identifiable 
information were collected or used in data analysis.

Voluntary Participation
Participants were informed of  their right to withdraw 
from the study at any time, without penalty or negative 
consequences.

These ethical protocols were implemented to safeguard 
the rights, privacy, and well-being of  all student 
participants throughout the research process.

RESULTS AND DISCUSSION
Overview of  PM2.5 Exposure Levels
Daily PM2.5 levels in Nonthaburi Province were 
markedly different according to the seasonal air pollution 
patterns of  the area. The official monitoring stations 
found an average PM2.5 during this time frame, with a 
mean concentration of  36.2 µg/m³ (SD = 7.8) that is 
higher than the recommended daily limit (15µg/m³) by 
the WHO. According to the AQI-THAI classification, 
the study days can be classified in the following 
exposure classes: 
• Low exposure (≤25 µg/m³): 18 days (30.0% of  the 

study period)
• Moderate exposure (26–50 µg/m³): 29 days (48.3% of  

the study duration) 
• High exposure (>50 µg/m³): 13 days (21.7% of  the 

experimental period) 
As can be seen from Figure 1, in the five-year monitoring 
point in this study, the annual average concentration 
of  PM2.5 levels spiked at the end of  January and 
a second time in mid-February. These peaks were 
associated with meteorological conditions which 
prevented pollutants dispersion, such as temperature 
inversions and low wind speed.

Figure 1: Daily PM2.5 concentrations in Nonthaburi Province (January–February 2025).

Prevalence of  Respiratory Symptoms Among 
Students
A total of  3,960 self-reported symptom surveys were 
collected over the study period (132 students × 30 days). 
The prevalence of  common respiratory symptoms is 
summarized in Table 4.
Symptom prevalence varied significantly between high- 
and low-PM2.5 days, as shown in Figure 2.
• On high-exposure days (>50 µg/m³), the percentage 

of  students reporting symptoms increased by 27.6% 
compared to low-exposure days (≤25 µg/m³).

• Coughing prevalence was highest on high-PM2.5 days 
(54.1%) and lowest on low-exposure days (22.8%).
• Shortness of  breath and sore throat showed statistically 

significant increases (p < 0.01) on days with higher 
pollution levels.

Association Between PM2.5 Levels and Symptom 
Severity
Correlation Analysis (Pearson’s r)
A Pearson correlation analysis was conducted to 
determine the strength of  association between daily 



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PM2.5 levels and self-reported symptom severity scores.
These findings confirm that higher daily PM2.5 

concentrations are significantly correlated with increased 
respiratory distress.

Table 4: Frequency of  Reported Symptoms by Students (N = 132)
Symptom Total Reports Exposure Category
 (N = 3,960) Average Daily Prevalence (%) Minimal health risk; no intervention needed.
Coughing (Mild-Severe) 1,478 37.3%
Sore Throat 1,132 28.6%
Shortness of  Breath 890 22.5%
Runny Nose/Nasal Congestion 1,629 41.1%

Respiratory Symptoms by PM2.5 Exposure Level

Figure 2: Distribution of  respiratory symptoms among students.   

Figure 3: The correlation between PM2.5 levels and symptom severity scores   

Table 5: Findings Comparison
Variable Comparison Pearson’s r p-value Interpretation
PM2.5 vs. Coughing Severity 0.62 <0.001 Strong Positive Correlation

PM2.5 vs. Sore Throat Frequency 0.51 <0.001 Moderate Positive Correlation
PM2.5 vs. Shortness of  Breath 0.57 <0.001 Strong Positive Correlation



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Comparative Analysis: Symptom Rates on High- vs. 
Low-Exposure Days
Chi-Square Test for Symptom Frequency

A Chi-square test (χ²) was performed to compare 
symptom occurrence between low- and high-exposure 
days.

Findings
• There is a statistically significant difference in symptom 

prevalence between low- and high-exposure days (p < 
0.001 for all symptoms).

• Shortness of  breath showed the largest increase 
(27.6% higher on high-PM2.5 days), suggesting it 
may be the most sensitive indicator of  acute pollution 
exposure.

Table 6: Chi-Square Test for Symptom Frequency
Symptom Low Exposure Days 

(≤25 µg/m³)
High Exposure Days 
(>50 µg/m³)

Chi-Square (χ²) 
Statistic

p-value

Coughing 22.8% 54.1% 15.92 <0.001

Sore Throat 17.3% 42.7% 12.88 <0.001
Shortness of  Breath 10.9% 38.5% 19.02 <0.001

Figure 4:  Comparing the prevalence of  respiratory symptoms on low (≤25 µg/m³) vs. high (>50 µg/m³) PM2.5 
exposure days.

Regression Analysis: Predicting Respiratory 
Symptoms Based on PM2.5 Exposure
A logistic regression model was applied to assess whether 
PM2.5 levels predict the likelihood of  experiencing 

respiratory symptoms.

Model
log (Odds of  Symptom) = β0 + β1 (PM2.5 level)

Table 7: Predicting Respiratory Symptoms Based on PM2.5 Exposure
Variable Odds Ratio (OR) 95% Confidence Interval (CI) p-value
PM2.5 (per 10 µg/m³ increase) 1.52 (1.26 – 1.79) <0.001

Mask-Wearing (Always) 0.64 (0.48 – 0.79) 0.002
Outdoor Exposure (≥3 hrs/day) 1.38 (1.12 – 1.61) <0.001

Key Interpretations
- For every 10 µg/m³ increase in PM2.5, students were 

1.52 times more likely to report respiratory symptoms (p 
< 0.001).
- Students who consistently wore masks had a 36% 

lower risk of  symptoms (p = 0.002).
- Outdoor exposure of  3+ hours per day increased 

symptom likelihood by 38% (p < 0.001).

Summary of  Key Findings
PM2.5 and Frequency of  Symptoms
Daily PM2.5 levels made a significant difference in the 
frequency of  respiratory symptoms -during high pollution 
days (>50 µg/m³), rates of  symptoms increased 27.6% 
compared with low pollution days (≤25 µg/m³).

Correlation Analysis of  Symptom Severity
An obvious positive correlation was observed between 



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Daily PM2.5 and severity of  respiratory symptoms (r = 
0.62, p < 0.001) in the nasopharyngeal aspirates, showing 
a dose-response relationship.

Low Versus High Exposure Days
As there were statistically significant differences between 
symptoms reported on low exposure days and high 
exposure days (Chi-square test p < 0.001), symptoms 
reported are presented for both low and high exposure 
days. Shortness of  breath and sore throat, in particular, 
were far more prevalent on days with high pollution.

Predictive Value of  PM2.5 Exposure
A logistic regression model indicated that PM2.5 exposure 
is a strong predictor of  symptom development. Higher 
all cause and cardiovascular mortality was associated with 
a 10 µg/m³ increase in daily PM2.5 increased the risk 
1.52 (95% CI: 1.26–1.79, p < 0.001).

Protective Effects for Mask Wearing
Regular mask wearing was deemed a protective factor, 
associated with a 36% lower risk of  respiratory symptoms 
(p = 0.002) on high-pollution days

Discussion
Key Findings and Interpretation
This study presents a strong evidence that daily variation 
of  PM2.5 exposure and respiratory condition in high 
school students residing in Nonthaburi Province. 
Using self-reported symptom logs and official PM2.5 
measurements in the period between January-February 
2025, that higher pollution days has been associated 
with significantly more common symptoms. For instance, 
on PM2.5 µg/m³ that exceeded 50 µg/m³, symptom 
rates (cough, sore throat, dyspnea and runny nose) were 
27.6% higher than on cleaner days (≤ 25 µg/m³). A 
strong positive association was also seen between daily 
PM2.5 levels and a symptom severity score of  students 
(r = 0.62, p < 0.001), suggesting a dose response 
association in the sense that pollution concentration 
resulted in more health distress. These associations are 
also documented by statistical tests. Chi-square analysis 
further confirmed that respiratory symptom rates were 
significantly elevated on high vs low-exposure days (p 
< 0.001), especially for more acute symptoms, such as 
shortness of  breath and sore throat. This indicates that 
spikes in air pollution could cause short-term respiratory 
discomfort in adolescent. Furthermore, we find that our 
logistic regression results suggest that even small levels of  
increases in pollution can result in much increased health 
risks - for every 10 μg/m³ increase in daily PM2.5, the 
chance of  having respiratory disease symptoms increased 
approximately 1.52-fold (95% CI: 1.26–1.79, p < 0.001). 
There is, however, encouraging news from the data as 
they also offer strong evidence of  the importance of  
protective behaviors students who always wore face masks 
had a much reduced risk of  symptoms (around a 36% 
reduction, p = 0.002), indicating that individual measures 

can at least ameliorate some of  the negative impacts of  
air pollution. These results provide novel insight into the 
acute effect of  daily air quality on adolescent physiological 
and emotive well-being and emphasize the urgency of  
developing mitigation strategies to protect students’ 
respiratory health in urban environments.

Comparison with Existing Literature
Our findings are potentially consistent with an expanding 
literature implicating PM2.5 exposure to paediatric-
type respiratory outcomes as a young person. For 
instance, Xu et al. (2021) found that daily PM2.5 levels 
was associated with increased occurrence of  chronic 
cough and wheezing in schoolchildren, similar to the 
high symptom rates we found on high pollution days. 
Similarly, Liu et al. (2023) reported a linear dose-response 
association of  particulate exposure with respiratory 
distress with every additional 10 µg/m³ PM2.5 led to an 
estimate of  approximately 20–30% increase in symptom 
morbidity, a reasonable correspondence with our fnding 
of  a 1.52 (≈ 52%) increase in odds of  symptoms per 
10 µg/m³. Wu et al. (2023) also illustrate the benefit of  
behavioral interventions  such as consistent mask-wearing 
and minimizing outdoor activity on  high pollution days – 
thought to reduce severity of  respiratory symptoms. This 
supports our interpretation that protective actions are 
able to reduce symptomatic days during pollution peaks. 
It is important to highlight that, as opposed to most 
previous studies that were based on clinical diagnoses or 
lung function tests, the data of  our study were collected 
using daily self-reported symptoms as an estimate of  
health effects. It offers a near-term, practical overview of  
how pollution affects students on a daily basis in a high-
exposure urban environment, to supplement current 
literature, which tends to focus on long-term or clinically 
recorded results. By examining short-term variation of  
pollution, our study takes a novel approach to identify 
the immediate impact of  daily PM2.5 heterogeneity, while 
the majority of  current studies have focused on chronic 
exposure.

Implications for Public Health and Policy
School-Based Air Pollution Mitigation Strategies
Considering the substantial contribution of  high PM2.5 
reported in this article impact on student health recorded 
in this study, schools should consider implementing 
evidence-based interventions to reduce their students’ 
exposure. School-level strategies that are crucial for such 
initiatives:

Air Quality Monitoring
Implement real-time PM2.5 sensors, and daily air quality alerts 
will be shared with the campus. This information can help 
in making decisions, such as relocating outdoor activities 
indoors when the level of  pollution is high.

Air Filtration Systems
Install HEPA air purifiers (i.e., high-efficiency particulate 



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air purifiers) in classrooms and indoor spaces, particularly 
during peak-pollution or wildfire seasons, to reduce 
indoor levels of  particulates.   

Tailored Outdoor Activity Policies
Modify school schedules and policies so that outdoor 
athletics and physical education are restricted or 
rescheduled on days when PM2.5 exceeds unsafe levels 
(such as >50 µg/m³). This can reduce during peak times 
the exposure of  students outdoors to pollution.

Community and Government Action
Wider community and policy-level interventions are also 
needed to address wider air quality problems, which can 
impact on schools. Recommended measures include: 

Strengthen PM2.5 Regulations
Government agencies need to impose more stringent 
emissions standards on large sources of  pollution 
(e.g., vehicle, industry, and agriculture burning) in order 
to lower ambient PM2.5 levels in urban areas. More 
stringent regulations, and regular monitoring, can limit 
the number of  days with significant pollution. 

Raise Awareness for Public Health
Public health education at the community level is 
necessary to inform students, parents, and school 
personnel to understand the health effects of  PM2.5. 
Best practices during pollution episodes, such as correct 
mask wearing and symptom monitoring, that encourage 
proactive health protection should also be included in 
these programmes.

Enhance Health Service Accessibility
Schools and local health departments may work together 
to offer respiratory health checks or medical examinations 
for students under heavy pollution situations. Better 
access to healthcare and early intervention (for example, 
asthma inhalers, medical advice) can reduce the impact of  
pollution on symptoms of  disease.

Promotion of  Individual Protective Measures
At the individual level, activating students and their 
families to engage in protective behaviors can decrease 
the health effects on the most polluted days: 

Mask-Wearing
Recommend or require use of  high-efficiency filtration 
face masks (e.g., N95 masks) by students on days with 
unhealthy air quality. Our results indicate that sustained 
mask wearing could drive down reported symptom 
prevalence by approximately 0.3, a straightforward and 
effective measure. 

Behavioral Modifications
Recommend that students minimize time spent outside 
during periods of  high pollution and discover clean 
indoor areas, ideally with air filtration, when air pollution 

is high. The sum of  these individual reductions (which a 
nudging approach could attempt to balance and integrate) 
could, from nudge-analysis point of  view, represent a 
reduction in the amount of  pollution anyone is left to 
inhale, in the course of  a day. Ultimately, a combined 
strategy targeting all intervention levels – such as school-
based projects, government policies, and personal 
protective measures – can help reduce the respiratory 
health impacts of  air pollution among adolescents in 
Nonthaburi and other urban locations. An integrated 
multilevel implementation strategy would maximize the 
shielding of  students from harmful PM2. 5 exposure.

Study Limitations and Future Research Directions
Although providing important clues, the present study 
had several limitations that need to be taken into account: 

Self-Reported Data Bias 
The self-report of  symptoms (daily Google Form) is 
the source of  some risk of  reporting bias or error. 
Symptoms might be forgotten or reported inconstantly by 
students, potentially resulting in misclassification. Future 
studies need to collect an objective health indicator, 
such as clinical lung function tests or clinical respiratory 
examination, concurrent with the self-report data to 
confirm such associations. 

Confounding Factors 
We did not extensively consider others possible 
confounders for respiratory symptoms. Symptom 
reporting on some days may have been influenced by 
indoor air pollution and previous health (e.g. asthma) 
and concurrent seasonal respiratory infections (e.g. 
common colds or flu). These confounders might partially 
account for differences in health effects regardless of  
outdoor PM2.5. It would be necessary in future research 
to adjust for or stratify for such variables possibly by 
adopting longitudinal study or multivariate analysis to 
extract the impact of  daily PM2.5 more clearly.

Short Study Duration 
This analysis focused on a rather narrow exposure window 
(the months of  January–February 2025). This short-
term design may miss seasonality, or longer term health 
effects of  prolonged pollution exposure. For example, 
pollution levels and their respiratory health effects may be 
different in other seasons (e.g., months of  crop-burning 
or rainy season). Longer term observation over several 
seasons or a year would lead to a better understanding 
of  the relative influence of  chronic and seasonal PM2.5 
exposure affects respiratory health in adolescents. 

Exposure Data 
At the Community Level Daily PM2.5 level 
were extrapolated from community monitoring stations 
near the schools and not from personal exposure 
monitors. This is useful as a general rule of  thumb 
but the actual exposure for individual students can 



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sometimes differ by micro-environments (home, transit 
etc.). The lack of  individual exposure data suggests the 
potential for measurement error if  a student’s immediate 
area experienced different pollution levels than those 
reported by the nearest station. Personal air quality sensors 
are essential, or wearable PM2.5 studies for quantifying 
individual exposure in a more precise way. 
Nevertheless, despite these limitations, the present study 
presents an important first step toward investigating the 
acute impact of  daily variation in air pollution on adolescent 
health. It highlights the importance of  studying short-
term exposure effects, and offers preliminary data to guide 
larger, better-controlled future studies.

CONCLUSION
This study confirms that daily PM2.5 fluctuations directly 
affect adolescent respiratory health. In our high school 
cohort, high-pollution days (>50 µg/m³) were associated 
with a 27.6% increase in respiratory symptom prevalence 
compared to cleaner days. We also found a significant 
correlation between rising PM2.5 levels and worsening 
respiratory distress (r = 0.62, p < 0.001), reinforcing 
evidence that air pollution poses a serious health risk for 
students. On a positive note, proactive behaviors like mask-
wearing proved effective – students who consistently wore 
masks had markedly lower odds of  symptoms (about a 
36% risk reduction, OR = 0.64, p = 0.002), highlighting 
the value of  personal preventive measures.
Overall, our findings illustrate the acute influence of  air 
quality on youth health and signal that immediate action 
is needed. Efforts to improve urban air quality – through 
stricter pollution control policies, school-based air quality 
management, and community education – are critical for 
protecting young people in Nonthaburi and other polluted 
regions. By combining policy interventions with on-the-
ground protective strategies, we can better safeguard 
adolescent health against the day-to-day challenges posed 
by PM2.5 air pollution.

Recommendations
This study confirms that daily PM2.5 fluctuations 
significantly affect adolescent respiratory health. Among 
132 students in Nonthaburi, symptom prevalence rose by 
27.6% on days with PM2.5 >50 µg/m³ compared to low-
exposure days (≤25 µg/m³). A strong positive correlation 
was found between PM2.5 levels and symptom severity 
(r = 0.62, p < 0.001). Logistic regression revealed a 
52% increase in symptom risk per 10 µg/m³ increase in 
PM2.5 (OR = 1.52, 95% CI: 1.26–1.79, p < 0.001). Mask-
wearing reduced symptom likelihood by 36% (p = 0.002), 
highlighting the importance of  protective behaviors.
To address these risks, we recommend:
1. Policy Reform – Implement stricter emissions controls 

and real-time pollution alerts.
2. School-Based Measures – Install air filtration systems 

and adjust outdoor activities during high-pollution days.
3. Public Awareness – Promote N95 mask use and 

pollution avoidance behaviors.

4. Further Research – Conduct longitudinal studies with 
personal exposure tracking and clinical assessments.
These integrated strategies are essential for safeguarding 
adolescent health in Nonthaburi and similar urban 
environments facing persistent air pollution challenges.

REFERENCES
Balakrishnan, K., Steenland, K., & Clasen, T. (2023). 

Exposure–response relationships for personal exposure 
to fine particulate matter (PM2.5), carbon monoxide, 
and black carbon and birthweight: An observational 
study. The Lancet Planetary Health, 7(2), 125-134. https://
doi.org/10.1016/S2542-5196(23)00052-9

Brauer, M., Southerland, V. A., & Mohegh, A. (2022). 
Global urban temporal trends in fine particulate 
matter (PM2.5) and attributable health burdens: 
Estimates from global datasets. The Lancet Planetary 
Health, 6(10), 543-555. https://doi.org/10.1016/
S2542-5196(21)00350-8

Chen, X., Zhao, J., Liu, M., & Li, Y. (2020). The effects 
of  PM2.5 exposure on lung function and respiratory 
symptoms in children: A systematic review and meta-
analysis. Environmental Research, 190, 110032. https://
doi.org/10.1016/j.envres.2020.110032

D. A. (1984). Experientiallearning: Experience as the source 
of  learning and development. Prentice-Hall. https://doi.
org/10.4324/9781003449020

Ekvitayavetchanukul, P., & Ekvitayavetchanukul, P. 
(2023). Comparing the effectiveness of  distance 
learning and onsite learning in pre-medical courses. 
Recent Educational Research, 1(2), 141-147. https://doi.
org/10.59762/rer904105361220231220143511

Ekvitayavetchanukul, P., Bhavani, C., Nath, N., Sharma, 
L., Aggarwal, G., Singh, R. (2024). Revolutionizing 
Healthcare: Telemedicine and Remote Diagnostics 
in the Era of  Digital Health. In: Kumar, P., Singh, 
P., Diwakar, M., Garg, D. (eds) Healthcare Industry 
Assessment: Analyzing Risks, Security, and Reliability. 
Engineering Cyber-Physical Systems and Critical 
Infrastructures, vol 11. Springer, Cham. https://doi.
org/10.1007/978-3-031-65434-3_11Kolb, 

Gauderman, W. J., Urman, R., Avol, E., Berhane, K., 
McConnell, R., Rappaport, E. B., Chang, R., Lurmann, 
F., & Gilliland, F. (2015). Association of  improved 
air quality with lung development in children. New 
England Journal of  Medicine, 372(10), 905–913. https://
doi.org/10.1056/NEJMoa1414123

George, P. E., Thakkar, N., & Yasobant, S. (2024). Impact 
of  ambient air pollution and socio-environmental 
factors on the health of  children younger than 5 
years in India: A population-based analysis. The Lancet 
Regional Health - Southeast Asia, 8, 125-139. https://
doi.org/10.1016/j.lansea.2023.00188

International Agency for Research on Cancer (IARC). 
(2016). Outdoor air pollution: A leading environmental 
cause of  cancer deaths. World Health Organization 
Report. https://www.iarc.who.int/wp-content/
uploads/2018/07/pr221_E.pdf



Pa
ge

 
68

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 4(3) 59-68, 2025

Kawintra, T., Kraikittiwut, R., Ekvitayavetchanukul, P., 
Muangsiri, K., & Ekvitayavetchanukul, P. (2024). 
Relationship between sugar-sweetened beverage 
intake and the risk of  dental caries among primary 
school children: A cross-sectional study in Nonthaburi 
Province, Thailand. Frontiers in Health Informatics, 13(3), 
1716-1723. 

Lee, J. Y., & Kim, H. (2018). Ambient air pollution-
induced health risk for children worldwide. The 
Lancet Planetary Health, 2(7), 297-308. https://doi.
org/10.1016/S2542-5196(18)30149-9

Liu, C., Yin, P., Chen, R., Meng, X., Wang, L., & 
Niu, Y. (2020). Ambient carbon monoxide and 
cardiovascular mortality: A nationwide time-series 
study in 272 Chinese cities. The Lancet Planetary Health, 
4(11), e512–e521. https://doi.org/10.1016/S2542-
5196(20)30207-3

Liu, Q., Xu, C., Ji, G., Shao, W., & Liu, H. (2017). Effect of  
exposure to ambient PM2.5 pollution on the risk of  
respiratory tract diseases: A meta-analysis of  cohort 
studies. Journal of  Biomedical Research, 31(2), 130-144. 
https://doi.org/10.7555/JBR.31.02.130

Mohajeri, N., Hsu, S. C., Milner, J., & Taylor, J. (2023). 
Urban–rural disparity in global estimation of  PM2.5 
household air pollution and its attributable health 
burden. The Lancet Planetary Health, 7(1), 245-259. 
https://doi.org/10.1016/S2542-5196(23)00133-X

Mathasuriyapong, P., Korcharlermsonthi, N., 
Ekvitayavetchanukul, P., & Ekvitayavetchanukul, 
P. (2025). Modeling the health burden of  PM₂.₅ 
Forecasting hospital admissions and medical 
demand in Bangkok and neighboring regions. 
Journal of  Posthumanism, 5(6), 862–873. https://doi.
org/10.63332/joph.v5i6.2155

Ni, R., Su, H., Burnett, R. T., Guo, Y., & Cheng, Y. 
(2024). Long-term exposure to PM2.5 has significant 
adverse effects on childhood and adult asthma: A 

global meta-analysis and health impact assessment. 
One Earth, 10(2), 234-249. https://doi.org/10.1016/j.
oneear.2024.04.087

Pongthong, S., Chantara, S., & Wiriya, W. (2022). Sources 
and seasonal variations of  PM2.5-bound heavy metals 
and their health risks in Bangkok. Atmospheric Pollution 
Research, 13(2), 101333. https://doi.org/10.1016/j.
apr.2021.101333

Singh, J., Kumar, V., Sinduja, K., Ekvitayavetchanukul, 
P., Agnihotri, A. K., & Imran, H. (2024). Enhancing 
heart disease diagnosis through particle swarm 
optimization and ensemble deep learning models. In 
Nature-Inspired Optimization Algorithms for Cyber-Physical 
Systems. IGI Global. https://doi.org/10.1016/j.
envres.2022.02961

Tham, R., Ziou, M., Wheeler, A. J., & Zosky, G. R. 
(2022). Outdoor particulate matter exposure and 
upper respiratory tract infections in children and 
adolescents: A systematic review and meta-analysis. 
Environmental Research, 15(3), 219-232. https://doi.
org/10.1016/j.envres.2022.02961

World Health Organization (WHO). (2021). WHO 
global air quality guidelines: Particulate matter (PM2.5 
and PM10), ozone, nitrogen dioxide, sulfur dioxide, and 
carbon monoxide. World Health Organization Report. 
Retrieved from https://www.who.int/publications/i/
item/9789240034228

Wu, T., Fang, Y., Zhou, Y., & Li, J. (2022). Protective 
effects of  personal air filtration on reducing PM2.5 
exposure and associated health risks in school 
children. Environmental Science & Technology, 56(5), 
3241–3250. https://doi.org/10.1021/acs.est.1c08079

Zhang, Y., Guo, Z., Li, Q., & Zhao, Y. (2023). Effect of  
acute PM2.5 exposure on lung function in children: A 
systematic review and meta-analysis. Journal of  Asthma 
& Allergy, 20(4), 115-130. https://doi.org/10.2147/
JAA.S405929


