Next Article in Journal
Beyond Boundaries—Genetic Implications of Urbanisation and Isolation in Eastern Grey Kangaroos (Macropus giganteus)
Previous Article in Journal
An Automated Load Restoration Approach for Improving Load Serving Capabilities in Smart Urban Networks
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Urban Science
Volume 9
Issue 7
10.3390/urbansci9070256
urbansci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand

by
Titaporn Luangwilai
1,
Jadsada Kunno
1,
Basmon Manomaipiboon
2,
Witchakorn Ruamtawee
3 and
Parichat Ong-Artborirak
1,*
1
Department of Research and Medical Innovation, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok 10300, Thailand
2
Department of Urban Medicine, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok 10300, Thailand
3
Clinical Research Center, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok 10300, Thailand
*
Author to whom correspondence should be addressed.
Urban Sci. 2025, 9(7), 256; https://doi.org/10.3390/urbansci9070256
Submission received: 16 April 2025 / Revised: 20 June 2025 / Accepted: 30 June 2025 / Published: 3 July 2025
Versions Notes

Abstract

Exposure to fine particulate matter (PM2.5) has become an increasing public health concern, particularly in urban areas facing severe air pollution. In response, individuals are increasingly turning to real-time tracking systems and self-monitoring tools. This study aimed to examine the association between PM2.5 risk perception, self-monitoring behaviors, and anxiety levels in the general population of Thailand. A cross-sectional survey was conducted during the dry season using an online questionnaire, which included the 7-item Generalized Anxiety Disorder (GAD-7) scale. A total of 921 participants residing in Bangkok and Chiang Mai were included. Binary logistic regression analysis, adjusted for sex, age, marital status, monthly income, and years of residence, revealed a significant association between anxiety and perceived health risks of PM2.5 exposure (OR = 1.09; 95% CI: 1.06–1.13). Daily self-monitoring of air quality over the past two weeks was also significantly linked to higher anxiety levels compared to non-monitoring individuals: OR = 1.92 (95% CI: 1.11–3.33) for websites, OR = 1.65 (95% CI: 1.01–2.72) for mobile apps, OR = 1.72 (95% CI: 1.12–2.64) for air purifiers, and OR = 3.34 (95% CI: 1.77–6.31) for air quality detectors. Monitoring 4–6 days per week using apps and air detectors was similarly associated with increased anxiety (OR = 1.64 and 2.30, respectively). Heightened perception of PM2.5 health risks and frequent self-monitoring behaviors are associated with increased anxiety among urban residents in Thailand. Public health interventions should consider implementing targeted alert systems during high-pollution periods and prioritize strategies to reduce PM2.5 emissions to alleviate public anxiety.

1. Introduction

Air pollution from fine particulate matter (PM), particularly particles smaller than 2.5 microns (PM2.5), is a major global public health concern due to its detrimental effects on both physical and mental health [1]. This issue is especially acute in urban areas [2], where PM2.5 exposure is rising rapidly. Over 600 cities worldwide are currently affected [3]. PM2.5 is recognized as a key urban environmental factor contributing to adverse health outcomes [4]. By 2025, 82% of the population in more developed regions is expected to live in urban areas, with global urban populations projected to reach 6.0 billion by 2030 [5]. The increase in PM2.5 levels is due to rapid urbanization [6].
In Thailand, the urban population is expected to reach approximately 40 million by 2030, with over 50% of the national population living in urban areas by 2050. Bangkok and Chiang Mai, two of the country’s five most populous cities, are experiencing accelerated urban growth, with Bangkok projected to become a megacity by 2030 [5]. Between 1996 and 2016, the long-term average PM2.5 levels ranged from 20.5 to 37.4 μg/m3 across Thai provinces, with the highest concentrations recorded in Bangkok and the northern regions [7]. These urban centers face persistent air quality issues, regularly exceeding WHO standards, particularly during the dry season [8,9].
Globally, PM2.5 exposure is estimated to cause approximately 493,000 deaths annually, primarily due to ischemic heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer [10]. Long-term exposure has also been linked to increased risks of depression and anxiety [11]. In Thailand, poor air quality is associated with rising hospital admissions for respiratory and cardiovascular conditions, as well as a growing prevalence of mental health disorders [12].
Air quality monitoring in Thailand is managed by the Pollution Control Department; however, the current network of monitoring stations remains limited and lacks full national coverage. In contrast, some countries have adopted mobile monitoring technologies to enable broader and more adaptive data collection [13]. Emerging approaches, such as mobile-phone-based personal exposure monitoring, have also been explored [14]. In response to these challenges, Thailand has recently developed a real-time urban air quality prediction system [15] and introduced the Air Quality Health Index (AQHI), designed to improve environmental health risk communication at the provincial level [16]. However, these tools are not yet widely implemented.
Perceptions of air pollution and related health risks play a pivotal role in shaping individual health behaviors [17,18]. Individuals frequently exposed to severe haze events are more likely to adopt protective measures, such as wearing masks, staying indoors, or using air purifiers. This tendency is especially pronounced among urban residents, who demonstrate a significantly greater willingness to invest in protective strategies—primarily through mask purchases—compared to their rural counterparts. Urban populations also express lower trust in local authorities’ ability to manage haze pollution [19] and show heightened concern for the health of vulnerable groups, including infants, young children, the elderly, and individuals with pre-existing conditions [20].
Given that air pollution is not always directly perceptible, many individuals rely on real-time tracking systems and self-monitoring tools—such as websites, mobile apps, and portable air quality monitors—to stay informed and respond accordingly. In Thailand, commonly used applications include “AirVisual” and “Air4Thai” [21]. While these tools offer valuable data, constant exposure to air quality information and heightened risk awareness may also adversely affect psychological well-being. Emerging evidence suggests a link between poor environmental conditions and increased anxiety [22,23]. However, the psychological consequences of self-monitoring behaviors, particularly in relation to PM2.5 exposure and anxiety in urban settings, remain insufficiently explored.
In Thailand, anxiety has emerged as a growing mental health issue, with air pollution identified as a potential contributing factor. We hypothesize that during periods of elevated PM2.5 levels, individuals with heightened risk perception and frequent self-monitoring behaviors are more likely to experience increased anxiety. Given the country’s chronic exposure to air pollution, it is essential to understand how individual perceptions and behaviors interact with environmental stressors to shape mental health outcomes.
This study seeks to address this gap by investigating the relationship between PM2.5-related health perceptions, self-monitoring behaviors, and anxiety among urban populations in Thailand, focusing specifically on Bangkok and Chiang Mai. The findings aim to inform public health strategies and policy responses that account for both the physical and psychosocial impacts of air pollution in urban environments.

2. Materials and Methods

2.1. Study Design

This study employed a cross-sectional design and was conducted over a 3 month period from March to May 2024, corresponding to the dry season when PM2.5 levels are substantially higher than in the wet season [24]. Ethical approval was granted by the Ethics Committee of the Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand (Approval no. COE: 057/2567).

2.2. Participants

Participants were residents of Bangkok and Chiang Mai, Thailand. The target sample size of 924 was calculated using an infinite population proportion estimate [25], assuming a 95% confidence level, 5% precision, 50% proportion for maximum variability, a design effect of 2, and an additional 20% to account for incomplete responses. A total of 921 participants provided sufficient data for analysis. Inclusion criteria were: age 18 years or older, residence in Bangkok or Chiang Mai for at least one year, and willingness to participate. Individuals with self-reported diagnoses of severe or chronic psychological disorders under treatment were excluded.

2.3. Questionnaire

The study employed a structured questionnaire to assess multiple variables, including: (1) demographic characteristics, health status, and PM2.5-related information; (2) self-monitoring behaviors; (3) perceptions of PM2.5 health effects; and (4) anxiety. To establish content validity, a panel of three experts in public and environmental health, health behavioral science, and psychology reviewed the questionnaire and assessed the Index of Item-Objective Congruence (IOC), with values for each perception item ranging from 0.67 to 1.00. Reliability was verified through a pilot test involving 25 participants from a population similar to the study’s target group. Cronbach’s alpha coefficients were calculated, yielding values of 0.824 for perception and 0.915 for anxiety, indicating high internal consistency.
The first section of the questionnaire captured demographic and contextual data, including sex, age, marital status, education, occupation, monthly income, duration of residence, family relationships, chronic diseases, history of PM2.5-induced symptoms, perceived severity of the PM2.5 issue in their area over the past two weeks, and level of concern about PM2.5. The second section focused on self-monitoring behaviors over the same period, assessing the use and frequency of tools such as websites, mobile applications, air purifiers, and portable air quality monitors (none, 1–3 days/week, 4–6 days/week, or every day). Participants reported the specific application used to check PM2.5 levels. Based on their responses, a self-monitoring variable was constructed to classify individuals into two groups: those who used at least one monitoring tool daily and those with no or only occasional use.
Perceptions of PM2.5-related health effects were measured using 13 items developed from a literature review and prior research [26]. Sample statements included: “Engaging in outdoor activities increases exposure to fine particulate matter,” “Fine particulate matter raises the risk of cardiovascular disease,” “Fine particulate matter affects mental health,” and “Fine particulate matter interferes with daily life.” Responses were rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Total scores, ranging from 13 to 65, were computed for data analysis.
To assess anxiety symptoms, the questionnaire incorporated the validated Thai version of the Generalized Anxiety Disorder-7 (GAD-7) scale. Respondents rated the frequency of symptoms over the past two weeks on a 4-point scale: 0 = not at all, 1 = several days, 2 = more than seven days, and 3 = nearly every day. Total scores were categorized into four severity levels: 0–4 (minimal or no anxiety), 5–9 (mild), 10–14 (moderate), and 15–21 (severe). A cut-off score of 10 was used to identify probable cases of GAD, with a sensitivity of 89% and specificity of 82% [27]. The scale has demonstrated strong reliability and validity for assessing anxiety in the general population [28].

2.4. Data Collection

Data were collected using online questionnaires developed with Google Forms. The survey link and accompanying QR code were distributed via social media platforms using snowball and convenience sampling techniques. The first page of the questionnaire included an invitation and comprehensive study information, outlining both inclusion and exclusion criteria. Informed consent was required on the following page; if consent was not provided, the form automatically terminated.

2.5. Statistical Analysis

Following data cleaning, statistical analyses were conducted using SPSS Version 28 (IBM Corp., Armonk, NY, USA). Descriptive statistics—including frequency (n), percentage (%), mean, and standard deviation (SD)—were reported. For inferential analyses, independent t-tests and one-way ANOVA were used to compare mean PM2.5 perception scores across study variables. Chi-square tests of independence assessed associations between categorical variables and self-monitoring behavior (none/occasional vs. daily) as well as anxiety levels (minimal/mild vs. moderate/severe). A p-value for trend was calculated to evaluate patterns in the frequency of self-monitoring and anxiety severity. To examine the relationship between key variables (PM2.5 perception score and self-monitoring) and anxiety, both univariable and multivariable binary logistic regression analyses were performed. Crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. The multivariable model controlled for sex, age, marital status, monthly income, and duration of residence [29,30]. Additionally, Spearman’s rank correlation coefficients were used to explore associations among the level of worry about PM2.5, PM2.5 perception score, self-monitoring frequency, and GAD score. Statistical significance was defined as p < 0.05.

3. Results

Table 1 summarizes participants’ demographic profiles, health status, and information related to PM2.5. Most participants were female (65.8%) and single (60.7%), with a mean age of 35.71 years (SD = 12.74). A substantial proportion held a bachelor’s degree (50.8%), were employed in government or the private sector (31.9%), earned more than 15,000 baht per month (65.0%), and had resided in the study area for 20 years or less (52.7%). Nearly half (48.5%) reported strong family relationships, and 34.4% had chronic illnesses, including dyslipidemia (n = 128), allergies (n = 112), hypertension (n = 98), and diabetes mellitus (n = 40). Regarding PM2.5-related experiences, 56.4% reported symptoms attributed to air pollution; 37.6% perceived a high level of pollution in their area; and only 13.1% expressed no concern about PM2.5.
Participants’ perceptions of PM2.5 yielded a mean score of 57.87 (SD = 6.07), ranging from 18 to 65. Perception scores varied significantly by sex, age group, education level, occupation, income, years of residence, family relationship quality, history of PM2.5-related symptoms, perceived severity of the pollution problem, and level of concern about PM2.5 (all p < 0.05).
Over the preceding two weeks, 31.3% of participants [95% CI: 28.3%–34.3%] engaged in daily self-monitoring of air quality using various platforms. The most frequent behavior was daily use of air purifiers (30.2%), followed by mobile apps (20.4%), websites (10.4%), and air quality detectors (5.4%). Among app users, usage rates were highest for AirVisual (44.3%, n = 408), followed by Air4Thai (21.6%, n = 199), Real-time Air Quality Index (9.2%, n = 85), and Plume Labs (2.6%, n = 24). Similar to perception patterns, self-monitoring frequency differed significantly by sex, age group, education, occupation, income, years of residence, family relationship, history of symptoms, perceived severity of the PM2.5 problem, and level of worry (all p < 0.05).
The prevalence of generalized anxiety disorder (GAD) among participants was 16.6% [95% CI: 14.2–19.0%], with a mean GAD score of 5.13 (SD = 5.12). Anxiety levels were categorized as minimal (56.9%), mild (26.5%), moderate (9.7%), and severe (6.9%). The prevalence of GAD varied significantly based on income, length of residence, history of PM2.5-related symptoms, perceived severity of air pollution, and level of concern (all p < 0.05). Mean GAD scores by level of PM2.5-related worry were as follows: no worry, 1.91 (SD = 2.65); low, 2.95 (SD = 3.87); moderate, 4.54 (SD = 4.79); high, 6.15 (SD = 4.97); and very high, 7.85 (SD = 5.75).
Table 2 summarizes the associations between PM2.5 perception, self-monitoring behaviors, and anxiety among participants. After adjusting for sex, age, marital status, monthly income, and years of residence, each unit increase in PM2.5 perception was significantly associated with higher odds of anxiety (adjusted OR = 1.09; 95% CI: 1.06–1.13). Participants who self-monitored PM2.5 levels daily had significantly greater odds of anxiety compared to those who monitored occasionally or not at all (adjusted OR = 1.76; 95% CI: 1.21–2.55). A dose–response relationship was observed between the frequency of self-monitoring via websites, apps, air purifiers, and air detectors and the likelihood of anxiety (all p-values for trend < 0.05). Specifically, daily users showed significantly increased odds of anxiety in the adjusted model: OR = 1.92 (95% CI: 1.11–3.33) for websites, OR = 1.65 (95% CI: 1.01–2.72) for apps, OR = 1.72 (95% CI: 1.12–2.64) for air purifiers, and OR = 3.34 (95% CI: 1.77–6.31) for air detectors. In addition, those who self-monitored 4–6 days per week using apps and air detectors also had elevated anxiety risk compared to non-monitors (adjusted OR = 1.64 and 2.30, respectively).
Table 3 presents Spearman’s rank correlation coefficients among GAD scores, worry about PM2.5, PM2.5 perception, and self-monitoring frequency. GAD scores were significantly correlated with worry about PM2.5 (rs = 0.416), PM2.5 perception (rs = 0.268), frequency of app use (rs = 0.109), air purifier use (rs = 0.089), and air detector use (rs = 0.117). According to the Chaddock scale, these frequencies of self-monitoring behaviors correspond to weak correlations. No significant correlation was found for monitoring frequency via websites.

4. Discussion

Screening with the GAD-7 scale (cut-off score ≥ 10) revealed a generalized anxiety disorder (GAD) prevalence of approximately 16.6% among urban Thai populations. This finding aligns with a nationwide online survey reporting a 15.4% prevalence of moderate to severe anxiety [31] and slightly exceeds the rate observed among Chinese adults, reported at 12.8% [32]. PM2.5 pollution in Thailand may partly explain elevated anxiety levels, as airborne concentrations are particularly high during the dry season. Between February and April 2024, the reported 24 h average PM2.5 levels ranged from 20 to 83 µg/m3 in Bangkok and from 15 to 164 µg/m3 in Chiang Mai [33], consistently exceeding the WHO’s recommended limit of 15 µg/m3 [34]. Exposure to PM2.5 may contribute to anxiety through mechanisms such as disruption of dopamine signaling, neuroinflammation, and oxidative stress, all of which impact the central nervous system and mental health [35,36,37]. A growing body of evidence links both acute and chronic PM2.5 exposure with increased anxiety risk [29,38,39,40,41]. Notably, Lan et al. [42] reported a positive association between GAD-7 scores and PM2.5 levels exceeding 17.2 µg/m3, reinforcing the direct mental health impacts of air pollution in the Thai context.
In Bangkok, traffic emissions and biomass burning are primary sources of PM2.5, with vehicular traffic contributing over 50% of total emissions [43]. In contrast, Chiang Mai’s pollution is driven mainly by forest fires, agricultural burning [43], and transboundary haze from neighboring countries [44]. During the dry season, forest fires dominate, resulting in the highest PM2.5 levels across northern Thailand [45]. Air pollution is also associated with increased non-accidental mortality, particularly during the summer and winter seasons [46]. PM2.5 accounts for 7.5% of the population-attributable fraction (PAF) of all-cause mortality in Thai adults—the highest among air pollutants—with a PAF of 16.8% for lung cancer and 14.6% for cardiovascular disease [47]. These findings underscore the significant physical health burden of PM2.5, particularly among vulnerable populations.
Increasing evidence of PM2.5’s adverse effects has heightened public awareness and risk perception in Thailand, potentially contributing to elevated anxiety levels. The current findings indicate a positive association between anxiety and self-reported symptoms attributed to PM2.5, perceived severity of pollution in one’s area, and beliefs about its health effects. Liu et al. [48] demonstrated that perceived health impacts of PM2.5 mediated the relationship between recalled physical symptoms and mental stress. Perceived pollution and health risk perception are also critical predictors of psychological annoyance [49]. Consistent with these findings, this study observed a link between heightened health risk perception and increased worry about PM2.5. Together, these results suggest that both the tangible effects of pollution and the subjective perception of its risks may indirectly exacerbate anxiety, particularly in urban Thai populations.
A higher perception of health risks is associated with more frequent self-monitoring of air quality. This relationship can be explained by the Health Belief Model, which posits that individuals are more likely to engage in protective behaviors when they perceive themselves to be susceptible to a threat and view its consequences as severe [50]. For example, individuals who recognize the adverse health effects of PM2.5 exposure—such as cardiovascular disease, respiratory problems, or heightened risk among vulnerable populations—are more likely to adopt preventive behaviors. These may include using air-quality-monitoring devices or digital platforms to track pollution levels and minimize exposure. As such, individuals who perceive a high level of risk and severity from PM2.5 are more inclined to take action, including increased use of air quality apps, air purifiers, and air detectors. Consistent with findings by Delmas and Kohli [51], the Theory of Planned Behavior [52] also offers a useful framework for understanding behavior change in this context. Awareness of the health impacts of air pollution plays a critical role in motivating action, while air quality apps—especially those providing real-time, localized data, alerts for pollution spikes, and forecasts—can enhance users’ sense of behavioral control [51].
Self-monitoring is viewed as an effective strategy, empowering individuals to check air quality in real time, assess personal risk, and make informed behavioral adjustments to reduce PM2.5 exposure. More than half of the respondents reported using websites, apps, or air purifiers, while fewer than 20% reported using air detectors. Mobile apps were used more frequently than websites, largely due to their convenience. Notably, a significant proportion of users favored international applications, such as AirVisual, over domestic ones like Air4Thai—potentially reflecting greater trust in the accuracy and reliability of the former. Nonetheless, both AirVisual and Air4Thai are commonly used for air quality monitoring in Thailand [21]. A study in China found that internet-based dissemination of real-time environmental pollution data significantly boosts public awareness, with nearly 70% of college students regularly checking the air pollution index [53]. Regarding monitoring devices, air purifiers—though relatively expensive—can reduce PM2.5 concentrations while enabling real-time air quality monitoring. Air detectors, on the other hand, are portable and allow users to assess pollution exposure on the go. The use of such devices suggests heightened concern, a strong perception of severity, and a willingness to invest in self-protection. Supporting this, a study found that anxiety levels, satisfaction with current air quality, and risk perception significantly influence individuals’ willingness to pay for health risk reductions related to PM2.5 exposure [54].
On the other hand, the findings revealed a significant association between self-monitoring and anxiety. Frequent use of air quality apps or detectors—specifically 4–7 days per week—was linked to an increased risk of generalized anxiety disorder (GAD), with the highest adjusted odds ratio (OR = 3.34) observed for daily use of air detectors. Notably, daily self-monitoring of PM2.5 levels may reflect a pattern of over-monitoring that contributes to heightened anxiety. These results suggest that the use of digital platforms and apps for air quality monitoring should ideally be limited to 1–3 days per week to mitigate mental health risks. The act of using an air purifier can be seen as a proactive and protective measure, as it serves both to clean the air and monitor PM2.5 concentrations. In contrast, air detectors are primarily designed for real-time tracking of air quality under any conditions, which may signal an elevated level of concern and, in turn, contribute to anxiety. Heightened public awareness and interest in PM2.5 often lead to increased searches on online portals, which may further amplify anxiety related to air pollution [55]. These findings suggest that PM2.5 pollution may influence mental health through multiple, indirect pathways. However, the association could be bidirectional, as increased levels of anxiety may also lead to increased self-monitoring.
Moreover, anxiety was also associated with worry about PM2.5 and the use of self-monitoring tools. Although 44% of participants reported no prior symptoms related to PM2.5 exposure, nearly half expressed high to very high levels of concern. This pattern suggests the presence of PM2.5-related anxiety, which may contribute to the development of GAD. This phenomenon parallels climate anxiety or eco-anxiety—emerging mental health concerns linked to environmental threats [56,57]. Even individuals who have not been directly affected by climate change may experience anxiety due to widespread exposure to information via communication technologies [58]. While a certain degree of concern is appropriate and may motivate adaptive coping behaviors, there is a threshold beyond which such anxiety becomes maladaptive and harmful to mental health [59]. However, there are potential tipping points at which climate anxiety no longer serves its adaptive function and instead becomes detrimental to mental health [57]. These insights underscore the potential mental health burden of PM2.5-related anxiety, particularly among Thai populations residing in areas with high pollution levels.
As a cross-sectional study, the research cannot establish causal relationships between PM2.5 perception, self-monitoring behaviors, and anxiety. Additionally, the use of non-probability sampling through an online survey limits the generalizability of the findings to the broader population due to possible gender imbalance and the likelihood of missing groups with low digital literacy or lower socio-economic status. Although the GAD-7 is a valid screening tool, formal clinical diagnosis is necessary to confirm anxiety cases. Further validity testing of the perception scale may be warranted to strengthen the accuracy of the measurement. The study also did not account for individual PM2.5 exposure levels, screen time, or daily frequency of device and platform use. Furthermore, other potential contributors to anxiety, such as exposure to additional air pollutants, were not considered. Future studies should adopt more rigorous methodologies, including longitudinal designs, to explore these associations in greater depth. Qualitative research may also provide valuable insights into the underlying mechanisms linking air pollution perception, self-monitoring, and anxiety. Despite these limitations, this study offers important evidence that increased exposure to air quality information via digital tools may elevate anxiety levels among urban populations in Thailand, an upper-middle-income country.

5. Conclusions

The findings revealed that heightened awareness of PM2.5-related health effects was associated with more frequent self-monitoring of air quality. Both elevated risk perception and excessive self-monitoring were linked to an increased likelihood of anxiety symptoms among Thai urban residents exposed to poor air quality, suggesting indirect impacts on mental health. These results highlight the critical need for timely, transparent, and effective communication strategies to convey accurate air quality information and prompt public health responses. Expanding the number of air monitoring stations in highly polluted urban areas could improve the accuracy of PM2.5 measurements, enhance the credibility of public information, and foster greater public trust. Long-term solutions require comprehensive policies and regulatory measures aimed at controlling emissions and reducing ambient PM2.5 concentrations.

Author Contributions

Project administration, T.L.; Conceptualization, T.L., J.K. and P.O.-A.; Methodology, T.L. and P.O.-A.; Data curation, T.L., J.K., B.M., W.R. and P.O.-A.; Formal analysis, T.L. and P.O.-A.; Investigation, T.L., J.K. and P.O.-A.; Validation, T.L., J.K. and P.O.-A.; Visualization, T.L., J.K. and P.O.-A.; Supervision, T.L.; Writing—original draft, T.L. and P.O.-A.; Writing—review and editing, T.L. and P.O.-A.; Submission, P.O.-A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Faculty of Medicine Vajira Hospital, Navamindradhiraj University (Approval no. COA 057/2567, date: 25 March 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data are available upon request from the corresponding author.

Acknowledgments

Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand, covered the article publishing charges as well as the English language editing service for the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Orellano, P.; Reynoso, J.; Quaranta, N.; Bardach, A.; Ciapponi, A. Short-term exposure to particulate matter (PM(10) and PM(2.5)), nitrogen dioxide (NO(2)), and ozone (O(3)) and all-cause and cause-specific mortality: Systematic review and meta-analysis. Environ. Int. 2020, 142, 105876. [Google Scholar] [CrossRef] [PubMed]
  2. Flies, E.J.; Mavoa, S.; Zosky, G.R.; Mantzioris, E.; Williams, C.; Eri, R.; Brook, B.W.; Buettel, J.C. Urban-associated diseases: Candidate diseases, environmental risk factors, and a path forward. Environ. Int. 2019, 133 Pt A, 105187. [Google Scholar] [CrossRef]
  3. Liu, C.; Chen, R.; Sera, F.; Vicedo-Cabrera, A.M.; Guo, Y.; Tong, S.; Coelho, M.; Saldiva, P.H.N.; Lavigne, E.; Matus, P.; et al. Ambient Particulate Air Pollution and Daily Mortality in 652 Cities. N. Engl. J. Med. 2019, 381, 705–715. [Google Scholar] [CrossRef] [PubMed]
  4. Kunno, J.; Luangwilai, T.; Manomaipiboon, B.; Onklin, I.; Ong-artborirak, P.; Krainara, P.; Chaichana, T.; Robson, M.G. The Urban Health Themes and Urban Factors Associated with Health: A Brief Review: Status Quo in Urban Health and Its Factors Associated with Health. Vajira. Med. J. 2024, 68, e269743. [Google Scholar] [CrossRef]
  5. Brockerhoff, M. An urbanizing world. Popul. Bull. 2000, 55, 1–44. [Google Scholar]
  6. Han, L.; Zhou, W.; Li, W. Fine particulate (PM2.5) dynamics during rapid urbanization in Beijing, 1973–2013. Sci. Rep. 2016, 6, 23604. [Google Scholar] [CrossRef]
  7. Mueller, W.; Vardoulakis, S.; Steinle, S.; Loh, M.; Johnston, H.J.; Precha, N.; Kliengchuay, W.; Sahanavin, N.; Nakhapakorn, K.; Sillaparassamee, R. A health impact assessment of long-term exposure to particulate air pollution in Thailand. Environ. Res. Lett 2021, 16, 055018. [Google Scholar] [CrossRef]
  8. Bran, S.H.; Macatangay, R.; Chotamonsak, C.; Chantara, S.; Surapipith, V. Understanding the seasonal dynamics of surface PM2. 5 mass distribution and source contributions over Thailand. Atmos. Environ. 2024, 331, 120613. [Google Scholar] [CrossRef]
  9. Ahmad, M.; Thaveevong, P.; Aman, N.; Ngamsritrakul, T.; Panyametheekul, S. The PM2. 5-bound metals in the metropolitan area of Bangkok, Thailand: Temporal trends, sources, and human health challenges. Environ. Chall. 2025, 18, 101092. [Google Scholar] [CrossRef]
  10. Silva, R.A.; Adelman, Z.; Fry, M.M.; West, J.J. The impact of individual anthropogenic emissions sectors on the global burden of human mortality due to ambient air pollution. Environ. Health Perspect. 2016, 124, 1776–1784. [Google Scholar] [CrossRef]
  11. Yang, T.; Wang, J.; Huang, J.; Kelly, F.J.; Li, G. Long-term Exposure to Multiple Ambient Air Pollutants and Association With Incident Depression and Anxiety. JAMA Psychiatry 2023, 80, 305–313. [Google Scholar] [CrossRef]
  12. Jarernwong, K.; Gheewala, S.H.; Sampattagul, S. Health impact related to ambient particulate matter exposure as a spatial health risk map case study in Chiang Mai, Thailand. Atmosphere 2023, 14, 261. [Google Scholar] [CrossRef]
  13. Kumar, P.; Morawska, L.; Martani, C.; Biskos, G.; Neophytou, M.; Di Sabatino, S.; Bell, M.; Norford, L.; Britter, R. The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 2015, 75, 199–205. [Google Scholar] [CrossRef] [PubMed]
  14. Nyarku, M.; Mazaheri, M.; Jayaratne, R.; Dunbabin, M.; Rahman, M.M.; Uhde, E.; Morawska, L. Mobile phones as monitors of personal exposure to air pollution: Is this the future? PLoS ONE 2018, 13, e0193150. [Google Scholar] [CrossRef]
  15. Otanasap, N.; Tadsuan, S.; Chalermsuk, C. An AIoT-based Air Quality Monitoring System for Real-time PM2. 5 Prediction in Urban Environments. ASEAN J. Sci. Tech. Rep. 2025, 28, e255168. [Google Scholar] [CrossRef]
  16. Punnasiri, K.; Tawatsupa, B.; Pumipan, T.; Kruon, N.; Sritong-Aon, C.; Phosri, A. Development of the national Air Quality Health Index based on short-term effects of ambient air pollution on mortality in Thailand. Chemosphere 2025, 371, 144049. [Google Scholar] [CrossRef]
  17. Ban, J.; Zhou, L.; Zhang, Y.; Brooke Anderson, G.; Li, T. The health policy implications of individual adaptive behavior responses to smog pollution in urban China. Environ. Int. 2017, 106, 144–152. [Google Scholar] [CrossRef] [PubMed]
  18. Xu, Z.; Li, J.; Shan, J.; Zhang, W. Extending the Theory of Planned Behavior to understand residents’ coping behaviors for reducing the health risks posed by haze pollution. Environ. Dev. Sustain. 2021, 23, 2122–2142. [Google Scholar] [CrossRef]
  19. Sereenonchai, S.; Arunrat, N.; Kamnoonwatana, D. Risk perception on haze pollution and willingness to pay for self-protection and haze management in Chiang Mai Province, Northern Thailand. Atmosphere 2020, 11, 600. [Google Scholar] [CrossRef]
  20. Romarate, R.A.; Madarcos, J.R.V.; Pacilan, C.J.M.; Bacosa, H.P.; Torres, A.G. When air quality matters: Awareness, perception, and attitude of the residents in Metro Manila, Philippines. Environ. Dev. Sustain. 2024. [Google Scholar] [CrossRef]
  21. Ong, A.K.S.; Prasetyo, Y.T.; Kusonwattana, P.; Marinas, K.A.; Yuduang, N.; Chuenyindee, T.; Robas, K.P.E.; Persada, S.F.; Nadlifatin, R. Determining factors affecting the perceived usability of air pollution detection mobile application “AirVisual” in Thailand: A structural equation model forest classifier approach. Heliyon 2022, 8, e12538. [Google Scholar] [CrossRef] [PubMed]
  22. Boluda-Verdu, I.; Senent-Valero, M.; Casas-Escolano, M.; Matijasevich, A.; Pastor-Valero, M. Fear for the future: Eco-anxiety and health implications, a systematic review. J. Environ. Psychol. 2022, 84, 101904. [Google Scholar] [CrossRef]
  23. Albrecht, G. Chronic Environmental Change: Emerging ‘Psychoterratic’ Syndromes. In Climate Change and Human Well-Being: Global Challenges and Opportunities; Weissbecker, I., Ed.; Springer Science+Business Media: New York, NY, USA, 2011. [Google Scholar]
  24. Narita, D.; Oanh, N.T.K.; Sato, K.; Huo, M.; Permadi, D.A.; Chi, N.N.H.; Ratanajaratroj, T.; Pawarmart, I. Pollution characteristics and policy actions on fine particulate matter in a growing Asian economy: The case of Bangkok Metropolitan Region. Atmosphere 2019, 10, 227. [Google Scholar] [CrossRef]
  25. Wayne, W. Biostatistics: A Foundation of Analysis in the Health Sciences; John Wiley & Sons: New York, NY, USA, 1995. [Google Scholar]
  26. Phungkat, Y.; Bumprom, P.; Thonkhaimook, B.; Sangsiriwut, N.; Sirijaroonwong, U. Perception and preventive bahavior towards particulate matter among personel: A case study of an organization in Bangkok. HCU J. 2022, 26, 98–107. [Google Scholar]
  27. Spitzer, R.L.; Kroenke, K.; Williams, J.B.; Lowe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 2006, 166, 1092–1097. [Google Scholar] [CrossRef] [PubMed]
  28. Lowe, B.; Decker, O.; Muller, S.; Brahler, E.; Schellberg, D.; Herzog, W.; Herzberg, P.Y. Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med. Care 2008, 46, 266–274. [Google Scholar] [CrossRef]
  29. Acharya, S.R.; Bhatta, J.; Timilsina, D.; Ray, N.; Pahari, S. Long-term exposure to air pollutants, meteorological factors, and mental health status: A nationwide population-based study with multilevel regression analysis. Arch. Public Health 2025, 83, 81. [Google Scholar] [CrossRef]
  30. Moreno-Peral, P.; Conejo-Ceron, S.; Motrico, E.; Rodriguez-Morejon, A.; Fernandez, A.; Garcia-Campayo, J.; Roca, M.; Serrano-Blanco, A.; Rubio-Valera, M.; Bellon, J.A. Risk factors for the onset of panic and generalised anxiety disorders in the general adult population: A systematic review of cohort studies. J. Affect. Disord. 2014, 168, 337–348. [Google Scholar] [CrossRef]
  31. Mongkhon, P.; Ruengorn, C.; Awiphan, R.; Thavorn, K.; Hutton, B.; Wongpakaran, N.; Wongpakaran, T.; Nochaiwong, S. Exposure to COVID-19-Related Information and its Association with Mental Health Problems in Thailand: Nationwide, Cross-sectional Survey Study. J. Med. Internet Res. 2021, 23, e25363. [Google Scholar] [CrossRef]
  32. Wang, X.; Wu, Y.; Chen, Y.; Xu, J.; Gao, Q.; Zang, S. Traffic-related pollution and symptoms of depression and anxiety among Chinese adults: A population-based study. J. Affect. Disord. 2024, 352, 101–109. [Google Scholar] [CrossRef]
  33. Archived Data PM2.5. Available online: http://air4thai.pcd.go.th/webV3/#/Home (accessed on 14 February 2025).
  34. WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. 2021. Available online: https://www.who.int/publications/i/item/9789240034228 (accessed on 14 February 2025).
  35. Ji, S.; Guo, Y.; Yan, W.; Wei, F.; Ding, J.; Hong, W.; Wu, X.; Ku, T.; Yue, H.; Sang, N. PM(2.5) exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor. Proc. Natl. Acad. Sci. USA 2024, 121, e2319595121. [Google Scholar] [CrossRef]
  36. Zundel, C.G.; Ryan, P.; Brokamp, C.; Heeter, A.; Huang, Y.; Strawn, J.R.; Marusak, H.A. Air pollution, depressive and anxiety disorders, and brain effects: A systematic review. Neurotoxicology 2022, 93, 272–300. [Google Scholar] [CrossRef] [PubMed]
  37. Braithwaite, I.; Zhang, S.; Kirkbride, J.B.; Osborn, D.P.J.; Hayes, J.F. Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis. Environ. Health Perspect. 2019, 127, 126002. [Google Scholar] [CrossRef] [PubMed]
  38. Lyons, S.; Nolan, A.; Carthy, P.; Griffin, M.; O’Connell, B. Long-term exposure to PM(2.5) air pollution and mental health: A retrospective cohort study in Ireland. Environ. Health 2024, 23, 54. [Google Scholar] [CrossRef]
  39. Power, M.C.; Kioumourtzoglou, M.-A.; Hart, J.E.; Okereke, O.I.; Laden, F.; Weisskopf, M.G. The relation between past exposure to fine particulate air pollution and prevalent anxiety: Observational cohort study. BMJ 2015. [Google Scholar] [CrossRef]
  40. Pun, V.C.; Manjourides, J.; Suh, H. Association of Ambient Air Pollution with Depressive and Anxiety Symptoms in Older Adults: Results from the NSHAP Study. Environ. Health Perspect. 2017, 125, 342–348. [Google Scholar] [CrossRef] [PubMed]
  41. Gao, X.; Jiang, M.; Huang, N.; Guo, X.; Huang, T. Long-Term Air Pollution, Genetic Susceptibility, and the Risk of Depression and Anxiety: A Prospective Study in the UK Biobank Cohort. Environ. Health Perspect. 2023, 131, 17002. [Google Scholar] [CrossRef]
  42. Lan, Y.; Roberts, H.; Kwan, M.P.; Helbich, M. Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study. Sci. Total Environ. 2022, 834, 155276. [Google Scholar] [CrossRef]
  43. Suriyawong, P.; Chuetor, S.; Samae, H.; Piriyakarnsakul, S.; Amin, M.; Furuuchi, M.; Hata, M.; Inerb, M.; Phairuang, W. Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options. Heliyon 2023, 9, e14261. [Google Scholar] [CrossRef]
  44. Vichit-Vadakan, N.; Vajanapoom, N. Health impact from air pollution in Thailand: Current and future challenges. Environ. Health Perspect. 2011, 119, A197–A198. [Google Scholar] [CrossRef]
  45. Phairuang, W.; Hata, M.; Furuuchi, M. Influence of agricultural activities, forest fires and agro-industries on air quality in Thailand. J. Environ. Sci. 2017, 52, 85–97. [Google Scholar] [CrossRef] [PubMed]
  46. Guo, Y.; Li, S.; Tawatsupa, B.; Punnasiri, K.; Jaakkola, J.J.; Williams, G. The association between air pollution and mortality in Thailand. Sci. Rep. 2014, 4, 5509. [Google Scholar] [CrossRef] [PubMed]
  47. Pinichka, C.; Makka, N.; Sukkumnoed, D.; Chariyalertsak, S.; Inchai, P.; Bundhamcharoen, K. Burden of disease attributed to ambient air pollution in Thailand: A GIS-based approach. PLoS ONE 2017, 12, e0189909. [Google Scholar] [CrossRef]
  48. Liu, L.; Yan, Y.; Nazhalati, N.; Kuerban, A.; Li, J.; Huang, L. The effect of PM(2.5) exposure and risk perception on the mental stress of Nanjing citizens in China. Chemosphere 2020, 254, 126797. [Google Scholar] [CrossRef]
  49. Claeson, A.S.; Liden, E.; Nordin, M.; Nordin, S. The role of perceived pollution and health risk perception in annoyance and health symptoms: A population-based study of odorous air pollution. Int. Arch. Occup. Environ. Health 2013, 86, 367–374. [Google Scholar] [CrossRef]
  50. Rosenstock, I.M.; Strecher, V.J.; Becker, M.H. Social learning theory and the Health Belief Model. Health Educ. Q. 1988, 15, 175–183. [Google Scholar] [CrossRef]
  51. Delmas, M.A.; Kohli, A. Can apps make air pollution visible? Learning about health impacts through engagement with air quality information. J. Bus. Ethics 2020, 161, 279–302. [Google Scholar] [CrossRef]
  52. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process 1991, 50, 179–211. [Google Scholar] [CrossRef]
  53. Lan, G.-l.; Yuan, Z.-k.; Maddock, J.E.; Cook, A.; Chu, Y.-y.; Pan, B.-b.; Tu, H.; Fan, S.; Liao, X.; Lu, Y. Public perception of air pollution and health effects in Nanchang, China. Air Qual. Atmos. Health 2016, 9, 951–959. [Google Scholar] [CrossRef]
  54. Xu, Z.; Shan, J. The effect of risk perception on willingness to pay for reductions in the health risks posed by particulate matter 2.5: A case study of Beijing, China. Energy Environ. 2018, 29, 1319–1337. [Google Scholar] [CrossRef]
  55. Lee, K.-H.; Bae, M.-S. Discrepancy between scientific measurement and public anxiety about particulate matter concentrations. Sci. Total Environ. 2021, 760, 143980. [Google Scholar] [CrossRef] [PubMed]
  56. Stanley, S.K.; Hogg, T.L.; Leviston, Z.; Walker, I. From anger to action: Differential impacts of eco-anxiety, eco-depression, and eco-anger on climate action and wellbeing. J. Clim. Change Health 2021, 1, 100003. [Google Scholar] [CrossRef]
  57. Heeren, A.; Asmundson, G.J. Understanding climate anxiety: What decision-makers, health care providers, and the mental health community need to know to promote adaptative coping. J. Anxiety Disord. 2023, 93, 102654. [Google Scholar] [CrossRef] [PubMed]
  58. Clayton, S. Climate anxiety: Psychological responses to climate change. J. Anxiety Disord. 2020, 74, 102263. [Google Scholar] [CrossRef]
  59. Clayton, S.; Karazsia, B.T. Development and validation of a measure of climate change anxiety. J. Environ. Psychol. 2020, 69, 101434. [Google Scholar] [CrossRef]
Table 1. Personal characteristics and information related to PM2.5 among participants, classified by PM2.5 perception score, frequency of self-monitoring, and anxiety levels.
Table 1. Personal characteristics and information related to PM2.5 among participants, classified by PM2.5 perception score, frequency of self-monitoring, and anxiety levels.
Variablesn (%)Perception
[Mean ± SD]		pSelf-Monitoring [n (%)]pAnxiety Level [n (%)]p
No/OccasionalEverydayMinimal/MildModerate/Severe
Total921 (100%)57.87 ± 6.07-633 (68.7%)288 (31.3%)-768 (83.4%)153 (16.6%)-
Sex <0.001 0.006 0.320
 Male315 (34.2%)56.79 ± 6.24 235 (74.6%)80 (25.4%) 268 (85.1%)47 (14.9%)
 Female 606 (65.8%)58.43 ± 5.90 398 (65.7%)208 (34.3%) 500 (82.5%)106 (17.5%)
Age (years) 0.002 <0.001 0.301
 18–24 years226 (24.5%)57.29 ± 6.70 174 (77.0%)52 (23.0%) 181 (80.1%)45 (19.9%)
 25–44 years481 (52.2%)58.11 ± 5.65 306 (63.6%)175 (36.4%) 404 (84.0%)77 (16.0%)
 45–59 years163 (17.7%)58.91 ± 5.43 107 (65.6%)56 (34.4%) 137 (84.0%)26 (16.0%)
 ≥60 years51 (5.5%)54.88 ± 7.64 46 (90.2%)5 (9.8%) 46 (90.2%)5 (9.8%)
Marital status 0.440 0.076 0.856
 Single559 (60.7%)57.78 ± 6.19 388 (69.4%)171 (30.6%) 466 (83.4%)93 (16.6%)
 Married310 (33.7%)58.15 ± 5.91 203 (65.5%)107 (34.5%) 260 (83.9%)50 (16.1%)
 Divorced/widowed/separated52 (5.6%)57.10 ± 5.62 42 (80.8%)10 (19.2%) 42 (80.8%)10 (19.2%)
Education <0.001 <0.001 0.079
 Secondary education212 (23.0%)55.64 ± 6.09 173 (81.6%)39 (18.4%) 175 (82.5%)37 (17.5%)
 Bachelor’s degree468 (50.8%)58.06 ± 6.29 317 (67.7%)151 (32.3%) 381 (81.4%)87 (18.6%)
 Graduate level241 (26.2%)59.46 ± 4.95 143 (59.3%)98 (40.7%) 212 (88.0%)29 (12.0%)
Occupation <0.001 0.003 0.068
 Unemployment45 (4.9%)57.84 ± 5.68 33 (73.3%)12 (26.7%) 33 (73.3%)12 (26.7%)
 Students184 (20.0%)57.61 ± 6.36 143 (77.7%)41 (22.3%) 152 (82.6%)32 (17.4%)
 Civil servant/state enterprise234 (25.4%)59.33 ± 5.43 149 (63.7%)85 (36.3%) 207 (88.5%)27 (11.5%)
 Government or private sector294 (31.9%)57.33 ± 5.91 187 (63.6%)107 (36.4%) 244 (83.0%)50 (17.0%)
 Self-employed/merchant/freelance164 (17.8%)57.05 ± 6.65 121 (73.8%)43 (26.2%) 132 (80.5%)32 (19.5%)
Monthly income <0.001 <0.001 0.002
 ≤15,000 THB322 (35.0%)56.81 ± 6.51 247 (76.7%)75 (23.3%) 252 (78.3%)70 (21.7%)
 >15,000 THB599 (65.0%)58.44 ± 5.74 386 (64.4%)213 (35.6%) 516 (86.1%)83 (13.9%)
Years of residence 0.007 0.012 0.010
 ≤20 years485 (52.7%)57.35 ± 6.26 351 (72.4%)134 (27.6%) 419 (86.4%)66 (13.6%)
 >20 years436 (47.3%)58.44 ± 5.80 282 (64.7%)154 (35.3%) 349 (80.0%)87 (20.0%)
Family relationship (n = 916) † <0.001 <0.001 0.680
 Fair472 (51.5%)56.19 ± 5.84 359 (76.1%)113 (23.9%) 396 (83.9%)76 (16.1%)
 Very good444 (48.5%)59.70 ± 5.46 272 (61.3%)172 (38.7% 368 (82.9%)76 (17.1%)
Chronic disease 0.735 0.380 0.620
 No604 (65.6%)57.82 ± 6.36 421 (69.7%)183 (30.3%) 501 (82.9%)103 (17.1%)
 Yes317 (34.4%)57.96 ± 5.47 212 (66.9%)105 (33.1%) 267 (84.2%)50 (15.8%)
History of symptoms caused by PM2.5 <0.001 <0.001 <0.001
 No prior experience402 (43.6%)57.18 ± 6.80 299 (74.4%)103 (25.6%) 344 (85.6%)58 (14.4%)
 1–3 symptoms307 (33.3%)57.09 ± 5.62 222 (72.3%)85 (27.7%) 271 (88.3%)36 (11.7%)
 >3 symptoms212 (23.0%)60.31 ± 4.37 112 (52.8%)100 (47.2%) 153 (72.2%)59 (27.8%)
Perceived level of the PM2.5 problem <0.001 <0.001 0.002
 Unknown127 (13.8%)53.58 ± 6.84 121 (95.3%)6 (4.7%) 114 (89.8%)13 (10.2%)
 Mild448 (48.6%)57.20 ± 5.90 324 (72.3%)124 (27.7%) 384 (85.7%)64 (14.3%)
 High346 (37.6%)60.32 ± 4.75 188 (54.3%)158 (45.7%) 270 (78.0%)76 (22.0%)
Level of worry about PM2.5 <0.001 <0.001 ‡ <0.001 ‡
 No121 (13.1%)52.15 ± 6.16 110 (90.9%)11 (9.1%) 118 (97.5%)3 (2.5%)
 Low103 (11.2%)54.91 ± 6.69 92 (89.3%)11 (10.7%) 97 (94.2%)6 (5.8%)
 Moderate259 (28.1%)56.65 ± 5.63 184 (71.0%)75 (29.0%) 224 (86.5%)35 (13.5%)
 High246 (26.7%)60.00 ± 4.54 166 (67.5%)80 (32.5%) 201 (81.7%)45 (18.3%)
 Very high192 (20.8%)61.98 ± 3.14 81 (42.2%)111 (57.8%) 128 (66.7%)64 (33.3%)
† Missing data (n = 5), ‡ p for trend.
Table 2. PM2.5 perception and self-monitoring associated with GAD among participants.
Table 2. PM2.5 perception and self-monitoring associated with GAD among participants.
Variablesn (%)Anxiety LevelUnivariable AnalysisMultivariable Analysis
Minimal/MildModerate/SevereCrude OR
(95%CI)		pAdjusted OR
(95%CI) *		p
Perception score 57.44 ± 6.06 †60.03 ± 5.63 †1.09 (1.05, 1.13)<0.0011.09 (1.06, 1.13)<0.001
Self-monitoring
 No/occasional633 (68.7%)543 (85.8%)90 (14.2%)1 1
 Everyday288 (31.3%)225 (78.1%)63 (21.9%)1.69 (1.18, 2.42)0.0041.76 (1.21, 2.55)0.003
Using website
 No373 (40.5%)316 (84.7%)57 (15.3%)10.10510.078
 1–3 days/week339 (36.8%)288 (85.0%)51 (15.0%)0.98 (0.65, 1.48)0.9300.95 (0.62, 1.44)0.794
 4–6 days/week113 (12.3%)92 (81.4%)21 (18.6%)1.27 (0.73, 2.20)0.4031.19 (0.68, 2.09)0.538
 Everyday96 (10.4%)72 (75.0%)24 (25.0%)1.85 (1.08, 3.18)0.0261.92 (1.11, 3.33)0.021
 p for trend 0.033
Using app
 No307 (33.3%)264 (86.0%)43 (14.0%)10.03710.022
 1–3 days/week230 (25.0%)200 (87.0%)30 (13.0%)0.92 (0.56, 1.52)0.7470.87 (0.52, 1.46)0.594
 4–6 days/week196 (21.3%)156 (79.6%)40 (20.4%)1.57 (0.98, 2.53)0.0611.64 (1.01, 2.68)0.048
 Everyday188 (20.4%)148 (78.7%)40 (21.3%)1.66 (1.03, 2.67)0.0371.65 (1.01, 2.72)0.047
 p for trend 0.010
Using air purifier
 No374 (40.6%)322 (86.1%)52 (13.9%)10.20610.103
 1–3 days/week137 (14.9%)113 (82.5%)24 (17.5%)1.32 (0.77, 2.23)0.3101.24 (0.73, 2.13)0.430
 4–6 days/week132 (14.3%)111 (84.1%)21 (15.9%)1.17 (0.68, 2.03)0.5731.30 (0.74, 2.29)0.356
 Everyday278 (30.2%)222 (79.9%)56 (20.1%)1.56 (1.03, 2.36)0.0351.72 (1.12, 2.64)0.013
 p for trend 0.046
Using air detector
 No762 (82.7%)653 (85.7%)109 (14.3%)1<0.0011<0.001
 1–3 days/week74 (8.0%)57 (77.0%)17 (23.0%)1.79 (1.00, 3.19)0.0491.67 (0.93, 3.00)0.089
 4–6 days/week35 (3.8%)25 (71.4%)10 (28.6%)2.40 (1.12, 5.13)0.0242.30 (1.06, 4.99)0.035
 Everyday50 (5.4%)33 (66.0%)17 (34.0%)3.09 (1.66, 5.73)<0.0013.34 (1.77, 6.31)<0.001
 p for trend <0.001
† Mean ± SD * Adjusted for sex, age (years), marital status, monthly income, and years of residence.
Table 3. Spearman’s rank correlation coefficients among GAD score, level of worry about PM2.5, perception score, and frequency of monitoring using air quality devices and platforms.
Table 3. Spearman’s rank correlation coefficients among GAD score, level of worry about PM2.5, perception score, and frequency of monitoring using air quality devices and platforms.
VariableSpearman’s Rank Correlation Coefficient (rs)
1234567
1. GAD score1.000
2. Level of worry about PM2.50.416 *1.000
3. PM2.5 perception score0.268 *0.521 *1.000
4. Frequency of monitoring via website0.0250.111 *0.0111.000
5. Frequency of monitoring via app0.109 *0.283 *0.153 *0.294 *1.000
6. Frequency of monitoring via air purifier0.089 *0.156 *0.156 *0.245 *0.244 *1.000
7. Frequency of monitoring via air detector0.117 *0.140 *0.131 *0.250 *0.230 *0.212 *1.000
* Significance at the 0.01 level.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Luangwilai, T.; Kunno, J.; Manomaipiboon, B.; Ruamtawee, W.; Ong-Artborirak, P. Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand. Urban Sci. 2025, 9, 256. https://doi.org/10.3390/urbansci9070256

AMA Style

Luangwilai T, Kunno J, Manomaipiboon B, Ruamtawee W, Ong-Artborirak P. Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand. Urban Science. 2025; 9(7):256. https://doi.org/10.3390/urbansci9070256

Chicago/Turabian Style

Luangwilai, Titaporn, Jadsada Kunno, Basmon Manomaipiboon, Witchakorn Ruamtawee, and Parichat Ong-Artborirak. 2025. "Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand" Urban Science 9, no. 7: 256. https://doi.org/10.3390/urbansci9070256

APA Style

Luangwilai, T., Kunno, J., Manomaipiboon, B., Ruamtawee, W., & Ong-Artborirak, P. (2025). Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand. Urban Science, 9(7), 256. https://doi.org/10.3390/urbansci9070256

Article Metrics

Back to TopTop