Associations Between PM₂.₅ and PM₁₀ Exposure and Physical and Mental Health: A Comparative Study of Vegetated and Non-Vegetated Zones in Bangladesh

Associations Between PM₂.₅ and PM₁₀ Exposure and Physical and Mental Health: A Comparative Study of Vegetated and Non-Vegetated Zones in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations Between PM₂.₅ and PM₁₀ Exposure and Physical and Mental Health: A Comparative Study of Vegetated and Non-Vegetated Zones in Bangladesh Tamanna Tabassom, Md. Abu Sayem, Probal Talukder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7470759/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Air pollution has become a significant adverse contributor to health issues, especially in cities in low and middle-income countries (LMICs) like Bangladesh. Urban green spaces have emerged as potential buffers against particulate matter pollution-related health risks. This study investigates the impact of particulate matter on the physical and mental health of residents from green and non-green spaces. Methodology: A cross-sectional study was conducted with 384 participants (18–35 years), equally divided between green and non-green urban areas. PM values were collected using the NAX tool over seven days. Health outcomes were collected using DASS 10 and structured questionnaires. Spatial analysis was conducted using ArcGIS; statistical analysis involved logistic and ordinal regression. Results: Non-green areas exhibited significantly higher PM₂.₅ and PM₁₀ concentrations. Residents in these areas showed elevated rates of asthma, bronchitis, allergic rhinitis, skin disorders, and mental health symptoms. Bronchitis (OR=254.6) and asthma (OR=36.3) showed the strongest associations(p<0.001) with non-green residency. NDVI was inversely correlated with PM levels. Conclusion: Urban greening is a critical environmental health intervention. Expanding green infrastructure may reduce pollutant exposure and improve overall physical and mental health outcomes in urban LMIC settings. Particulate matter mental health physical health green space non-green space Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Air pollution is one of the most critical phenomena of the 21st century, which affects environmental health, contributing significantly to the global burden of disease, especially for low and middle-income countries(LMICs) (1). Several pollutants have a significant impact on health, though PM 2.5 and PM 10 contribute to the extensive association with adverse health impacts(2,3). They are associated with cardiovascular, neurological disorders, mental health, and many more(4,5). A 10 µg/m³ increase in PM₂.₅ exposure is linked to a 6% rise in non-accidental mortality, with lung cancer and cardiovascular deaths increasing by 15–21% and 12–14%, respectively, regardless of age, sex, or location(6). The World Health Organization reported that every year, about 4.2 million people face premature deaths, especially in LMICs (1,7,8). Anthropogenic activities such as rapid urbanization, unplanned industrial development, vehicular emissions and biomass burning have substantially deteriorated the air quality, especially in LIMCs like Bangladesh (3,9). 80%of urban settings are crossing the limit of air quality set by the WHO all over the world (6,9,10). In 2019, air pollution in urban areas of Bangladesh reported an estimation of 173,500 premature deaths (11). Recent studies revealed that green spaces or vegetated areas, such as parks, tree covers can eliminate air pollution through pollution deposition and dispersion mechanisms, while simultaneously serving psychological and physiological benefits (12–15). Urban green infrastructure is significantly reducing the ambient concentration of particulate matter and enhancing health outcomes, including lower rates of respiratory illness, stress, and depression. In contrast, non-green spaces reflected reverse results (15–17). Studies from the global to the regional level have revealed a significant association between particulate matter and serious health issues (11,18). Despite such empirical studies, only a few studies around the globe have been conducted to find out the association between particulate matter and mental health (19–21). Bangladesh is out of such approaches, as most of the studies focused on particulate matter and mental health(11,22,23). To address this gap, this study has focused on investigating the impact of PM 2.5 and PM 10 on the physical and mental health of residents living in green and non-green areas of an urban setting. By employing a comparative cross-sectional design, this study seeks to provide context-specific evidence that may inform targeted environmental health interventions, urban planning strategies, and policymaking in air quality management. Methodology Study Design and Study Participants A cross-sectional study was conducted to identify the association between particulate matter levels and the physical and mental health of residents from green and non-green urban settings throughout Mymensingh City. Areas were selected based on empirical approaches. A total of 384 young adults (aged 18–35) were recruited, equally divided between green (n = 192) and non-green (n = 192) areas, using stratified random sampling. This study was conducted between July and November 2024. Seven days' observation of particulate matter was taken to ensure the robustness of the data (Fig. 1 ). Sample Size The sample size was calculated using the following Eq. (24,25): $$\:n=\frac{{Z}^{2}\cdot\:p\cdot\:\left(1-p\right)}{{d}^{2}}$$ Where: n = required sample size Z = Z-score for desired confidence level (e.g., 1.96 for 95% CI) p = estimated proportion of the population with the characteristic of interest (commonly 0.5 for maximum variability) d = margin of error (e.g., 0.05 for ± 5%) $$\:n=\frac{{\left(1.96\right)}^{2}\cdot\:0.5\cdot\:\left(1-0.5\right)}{{\left(0.05\right)}^{2}}=\frac{3.8416\cdot\:0.25}{0.0025}=384.16$$ The sample size was determined using the standard formula for cross-sectional studies, assuming a 95% confidence level (Z = 1.96), 50% estimated prevalence, and a 5% margin of error. This yielded a minimum required sample of 384 participants, ensuring sufficient statistical power and precision. The chosen sample size accommodates variability across green and non-green urban settings, supporting reliable assessment of the physical and mental health impacts of PM₂.₅ and PM₁₀ exposure. Survey Instrument Adoption of Depression Anxiety Stress Scale (DASS 10) in the question tool is a shorter form of DASS 42 was used to conduct the mental health survey(26). Total scores ranged from 0 to 30, with higher values indicating greater distress, and subscale scores were calculated for Anxiety/Stress (Items 1, 4, 6, 7, 8, 9; range: 0–18) and Depression (Items 2, 3, 5, 10; range: 0–12). Additionally, some questions are customized to suit the study objective, especially the physical health data and demographic information such as sex, area type. The question tool was developed considering the sociodemographic factors in English and then translated into Bengali to ensure a robust outcome from the participants. The tool was verified by the Institutional Review Board of Jatiya Kabi Kazi Nazrul Islam University. Moreover, particulate matter was measured by using the NAX Formaldehyde Detector Air Quality Monitor, with a focus on particulate matter concentrations (PM₂.₅ and PM₁₀). The full English version of the developed tool is provided as a Supplementary File named ‘Developed Question Tool’ Data Analysis Data were collected using Google Forms and paper-based questionnaires and stored in Microsoft Excel for filtering outliers and missing values. Spatial Map of the distribution of PM and the NDVI was conducted using ArcGIS 10.8. The R programming language was used to conduct Exploratory Data Analysis and to illustrate the particulate matter distribution. Frequency distribution and regression analysis were done using SPSS. Result Demographic Characteristics of Participants The demographic profile of the study participants reveals a balanced representation in several key categories. Regarding gender, the sample consisted of 171 males (44.5%) and 213 females (55.5%), indicating a slightly higher proportion of female respondents. Participants were equally distributed across area types, with 192 individuals (50%) residing in green areas and the remaining 192 (50%) in non-green areas. The age range of all participants was between 18 and 35 years, ensuring a focus on young adults. Particulate matter variation with area type Table 1: Mean Concentrations of PM₁₀ and PM₂.₅ (µg/m³) in Green and Non-Green Urban Spaces on Working and Non-Working Days with Relative Increases in Non-Green Areas (Table 1) The data provides a significant temporal variation of particulate matter between green and non-green spaces. Green spaces maintained a lower concentration both on working (48.7 ± 1.5 µg/m³) and non-working (59.3 ± 4.7 µg/m³) days. In contrast, non-green spaces exhibited substantially elevated PM10 levels, particularly on working days (109.9 ± 14.1 µg/m³), representing a 125.7% increase compared to green spaces. The non-working day difference remains pronounced (92.1 ± 4.7 µg/m³) which shows a 55.3% increase. A similar trend is noticeable in PM 2.5, where green spaces show lower concentration on working (43.8 ± 1.3 µg/m³) and non-working (51.3 ± 4.6 µg/m³) days. Non-green spaces recorded higher PM 2.5 on working (95.4 ± 12.0 µg/m³), which is a 117.8% increase from green space. The non-working day disparity persisted (83.7 ± 5.2 µg/m³), which is a 63.2% increase. The PM 2.5 limit is within the Bangladesh National Ambient Air Quality Standard of 24 hours in green spaces for both working and non-working days. It exceeds the limit in non-green spaces for both day types. PM 10 is within the limit but non-green spaces show higher concentration than green spaces for both day types (Figure 2). Relationship Between Particulate Matter Concentrations and Vegetation Density (NDVI) The integrated analysis of particulate matter (PM) distributions and normalized difference vegetation index (NDVI) reveals clear inverse relationships between vegetation density and air pollution levels across the study area. PM2.5 exhibited higher concentration (41-120 µg/m³) on working days compared to non-working days (44-91 µg/m³). Peak PM₂.₅ levels (89-120 µg/m³) consistently occurred in low-NDVI areas (0.4) maintained concentrations below 60 µg/m³ on both day types. PM 10 shows stronger spatial variability than PM 2.5 . Maximum concentrations (110-140 µg/m³) clustered in urban cores with NDVI 0.3 demonstrated 30-40% lower PM₁₀ levels than the adjacent built environments (Figure 3). Frequency of Visits of Green and Non-green Residents to Green Spaces Those residing near green spaces reported significantly higher visitation frequencies, with 24.5% visiting daily and 18.8% weekly, compared to only 4.9% daily and 10.2% weekly among non-green area residents (Figure 4). It suggests that proximity strongly influences usage, aligning with the "distance decay" principle in urban ecology. The near-absence of "never" responses among green-area residents underscores the accessibility advantage. Over 43.3% green space residents visit at least weekly (daily+weekly) to green areas compared to 15% of non-green residents. Rare or never visit ratio in green space residents is 2.6x lower than non-green residents. In contrast, 21.9% of non-green residents reported never visiting green spaces, compared to 0.5% of green-area residents. Monthly visits are similar (~10–11%), suggesting some moderate use regardless of proximity (Figure 4). Disease Prevalence by Area Type The descriptive analysis revealed notable differences in disease prevalence between residents of green and non-green urban areas (Figure 5). Non-green area residents exhibited higher percentages for most conditions, particularly respiratory and allergic disorders: asthma (23.44% vs 8.85%), bronchitis (46.35% vs 0.52%), and allergic rhinitis (18% vs 2.5%). Skin problems were more prevalent in non-green areas (31.77% vs 9.38%), while cardiovascular disease showed a moderate difference (5.73% vs 3.13%). Exceptions included pneumonia (4.17% vs 3.65%) and lung cancer (0.52% vs 1.56%), which showed inverse patterns. These unadjusted percentages suggest potential environmental health disparities warranting further investigation through controlled studies with proper confounder adjustment (Figure 5). Mental Health Status by Area Type The analysis of mental health status reveals distinct patterns between residents of green and non-green urban areas. Non-green space residents reported higher percentages of clinically significant mental health symptoms across all severity categories. Non-green and green residents have a subclinical status of 9.9% and 6.5% respectively. For moderate, severe, and extremely severe, the ratios are 26% and 19.8%, 26% and 7.6%, and 4.2% and 0% respectively. The most pronounced disparity appeared in the severe symptom category, where non-green area residents showed a 3.4-fold higher prevalence. Notably, no residents of green spaces reported extremely severe symptoms, while 4.2% of non-green area residents fell into this category (Figure 6). Multivariable Logistic Regression Analysis of Health Outcomes by Residential Area Type, Gender, and Protective Behavior Table 2: Binary Logistic Regression Analysis of Associations Between Health Outcomes and Gender, Area Type, and Mask Usage (Table 2) Binary Logistic Regression analysis examined the effects of area type (green vs. non-green), gender, and mask usage on the likelihood of developing specific health conditions. The findings highlight the significant impact of environmental factors and individual characteristics on health outcomes. For asthma, residing in non-green areas was a strong predictor (OR = 36.304, 95% CI [19.31, 68.36], p .05), suggesting limited influence or potential sample size constraints. For cardiovascular disease, the female gender was protective (OR = 0.173, 95% CI [0.048, 0.635], p = .008), while area type (OR = 1.827, p = .280) and mask usage (p = .486) were not significant. Allergic rhinitis was strongly associated with non-green areas (OR = 13.589, 95% CI [12.487, 26.118], p .05). However, skin problems were significantly associated with non-green environments (OR = 21.516, 95% CI [16.072, 27.881], p < .001), with gender (p = .160) and mask usage (p = .829) remaining non-significant. Bronchitis demonstrated the strongest environmental association, with non-green areas as a highly significant predictor (OR = 254.574, 95% CI [130.411, 397.084], p < .001), while gender (p = .230) and mask usage (p = .232) were not influential. Overall, the results emphasize the substantial health risks posed by non-green environments, particularly for asthma, allergic rhinitis, bronchitis, and skin problems. Gender differences were observed for asthma and cardiovascular disease, while mask usage showed limited protective effects. Predictors of Mental Health Severity: Ordinal Regression Analysis of Green Space Access, Frequency of Use, and Demographic Factors Table 3: Ordinal Logistic Regression Analysis of Predictors Influencing Mental Health Severity (DASS-10) Based on Area Type, Gender, and Frequency of Green Space Visitation (Table 3) An ordinal logistic regression was conducted to examine factors influencing mental health severity (DASS-10). Individuals with sub-clinical (β = -1.726, OR = 0.178, p < .001) and mild symptoms (β = -0.542, OR = 0.581, p = .029) were less likely to progress to higher severity, while those with moderate (β = 2.342, OR = 10.41, p < .001) and severe symptoms (β = 5.361, OR = 212.16, p < .001) had significantly higher odds. Predictor analysis showed that the frequency of visits had no significant effect (β = -0.008, OR = 0.99, p = .938). However, living in non-green areas (β = 2.536, OR = 12.63) and being female (β = 0.582, OR = 1.79, p = .004) were associated with greater mental health severity. These findings highlight strong associations between gender, environment, and mental health severity. Discussion This study reveals the environmental and health disparities associated with urban green and non-green residential settings, emphasizing the role of vegetation in mitigating air pollution and improving physical and mental health outcomes. It suggests that exposure to green settings is associated with reduced levels of particulate matter, lower prevalence of physical issues, and better mental health. PM 2.5 and PM 10 are both lower in concentration in green spaces compared to non-green spaces across both working and non-working days. A similar trend has been noticed in previous studies (27). The increase of PM 2.5 and PM 10 in non-vegetated areas, especially on working days strongly upholds the protective nature of trees. The inverse relationship between vegetation density (NDVI) and PM levels further confirms the air-filtering capacity of green spaces, aligning with satellite-based assessments(28). Health-related adverse outcomes such as breathing issues like asthma, bronchitis, and allergies, were much more common in people from non-green areas. It provides strong evidence that airborne exposure is triggering respiratory issues. Previous studies also justified this association (16,29). The adjusted odds ratios from logistic regression, particularly the extremely high values for bronchitis (OR = 254.57) and asthma (OR = 36.30), indicate robust environmental influences that merit urgent public health interventions. Interestingly, mask usage—although previously hypothesized to confer respiratory protection—did not emerge as a significant predictor in most models, suggesting limitations in either usage consistency or efficacy in high-pollution microenvironments. Gender showed a differential role as females were more sensitive to asthma and showed lower odds of cardiovascular diseases. Such gender-specific vulnerabilities and resilience have been reported in prior epidemiological studies(30,31). The observed association between non-green residency and dermatological conditions such as skin problems may reflect higher exposure to dust, allergens, and heat, compounded by reduced vegetative cooling and UV-buffering effects. Previously conducted studies revealed such phenomena (32). Mental health analysis reinforces the significance of green spaces. Residents of non-green areas are reported to have moderate and extremely severe mental health status. In contrast, green space residents are not reported to have such status. Previous studies also revealed the positive impact of green spaces on mental health(19,33). The ordinal regression findings additionally underscore the influence of both environmental (area type) and demographic (gender) factors, although frequency of green space use did not attain statistical significance, perhaps due to limited variation in visitation behaviors or psychosocial mediators not captured in the model. Behavioral insights from this study also support the "distance decay" principle in environmental psychology. Residents living near green spaces reported significantly higher visitation frequencies, suggesting that physical proximity enhances access and encourages regular touch. Several studies justified this practice(34,35). Overall, the results of this study offer multi-dimensional evidence that supports urban greening as a strategic public health intervention. However, limitations include the cross-sectional design, potential self-report bias in disease and mental health data, and the absence of biomarker-based health measurements. Moreover, while NDVI and PM data offer spatial granularity, longitudinal exposure assessments and finer temporal resolution could enhance causal inference. Future studies should integrate wearable sensors, longer-term health monitoring, and socioeconomic confounders to refine these associations. Conclusion This study underscores the critical role of urban green spaces in mitigating air pollution and enhancing public health outcomes. By integrating environmental exposure data with physical and mental health assessments, the findings reveal that residents of non-green urban areas are disproportionately burdened with higher levels of PM₂.₅ and PM₁₀, and suffer greater prevalence of respiratory, dermatological, and psychological disorders. Notably, the extraordinarily elevated odds for bronchitis and asthma among non-green residents point to an urgent need for targeted environmental health policies in rapidly urbanizing LMIC contexts like Bangladesh. Furthermore, the association between vegetation density (NDVI) and pollutant concentrations reaffirms the ecological utility of green infrastructure as a natural buffer against urban air pollution. Mental health disparities, particularly the absence of extremely severe symptoms among green space residents, also highlight the restorative potential of nature in densely populated settings. Although limited by its cross-sectional design and self-reported health metrics, this research provides robust, context-specific evidence that supports the expansion and equitable distribution of urban green spaces as a public health imperative. Policymakers and urban planners should consider integrating vegetative buffers in city design to foster cleaner air, healthier populations, and more resilient urban ecosystems Declarations Acknowledgement Thanks to Md. Monabbir Hossain Tonmoy, who helped us with some necessary logistics. We really appreciate his effort and support. Conflict of Interests The authors declare that there is no conflict. Ethics approval and consent to participate The study protocol was reviewed and approved by the Institutional Review Board of Jatiya Kabi Kazi Nazrul Islam University, Trishal-2220, Mymensingh, Bangladesh (Approval No.: Not Applicable). All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants prior to their inclusion in the study. Consent for publication Not applicable, as no identifiable personal data (such as names, images, or videos) are included in this manuscript. Human Ethics and Consent to Participate Declarations This study involved human participants. Ethical approval and informed consent procedures were carried out as described above. Availability of data and materials All data supporting the findings of this study are available from the corresponding author upon reasonable request. Funding The authors received no funding for this study. References Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet. 2017 May;389(10082):1907–18. Oujidi B, Benchrif A, Tahri M, Zahry F, Bounakhla M, Bazairi H, et al. 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Tables Table 1: Mean Concentrations of PM₁₀ and PM₂.₅ (µg/m³) in Green and Non-Green Urban Spaces on Working and Non-Working Days with Relative Increases in Non-Green Areas Mean Concentration (µg/m³) Pollutant Location Working Day Non-Working Day % Increase vs. Green PM10 PM10 Green Space 48.7 ± 1.5 59.3 ± 4.7 — PM10 Non-Green Space 109.9 ± 14.1 92.1 ± 4.7 125.7% (Work) 55.3% (Non-Work) PM2.5 PM2.5 Green Space 43.8 ± 1.3 51.3 ± 4.6 — PM2.5 Non-Green Space 95.4 ± 12.0 83.7 ± 5.2 117.8% (Work) 63.2% (Non-Work) Note: Data presented as Mean ± SD. Percentage increase calculated as [(Non-Green Mean)/(Green Mean) - 1] × 100. Table 2: Binary Logistic Regression Analysis of Associations Between Health Outcomes and Gender, Area Type, and Mask Usage Characteristics OR 95% CI P Value Asthma Gender Female 1.873 1.03, 3.41 .040 Area Type Non- green 36.304 19.31, 68.36 <0.001 Mask Usage 1.189 0.031, 0.119 .592 Lung Cancer Gender Female 0.846 0.11, 6.25 .869 Area Type Non- green 0.335 0.03, 3.41 .335 Mask Usage 1.022 0.13, 7.83 .983 Cardiovascular disease Gender Female 0.173 0.048, 0.635 .008 Area Type Non- green 1.827 0.509, 6.618 .280 Mask Usage 0.641 0.166, 2.483 .486 Allergic rhinitis Gender Female 1.000 0.605, 1.662 1.000 Area Type Non- green 13.589 12.487, 26.118 <0.001 Mask Usage 1.048 0.530, 2.470 .860 Pneumonia Gender Female 0.654 0.440, 0.954 .439 Area Type Non- green 0.932 0.361, 2.433 .896 Mask Usage 0.317 0.058, 1.776 .091 Skin problem Gender Female 0.670 0.383, 1.242 .160 Area Type Non- green 21.516 16.072, 27.881 <0.001 Mask Usage 0.940 0.474, 1.737 0.829 Bronchitis Gender Female 0.677 0.346, 1.018 0.230 Area Type Non- green 254.574 130.411, 397.084 <0.001 Mask Usage 1.496 0.776, 2.492 0.232 Table 3 : Ordinal Logistic Regression Analysis of Predictors Influencing Mental Health Severity (DASS-10) Based on Area Type, Gender, and Frequency of Green Space Visitation Parameter Estimate (β) OR= ) p- Value 95% CI Mental Health Status (DASS-10) Low High Sub-clinical -1.726 0.178 .000 -2.291 -1.161 Mild -.542 0.581 .029 -1.029 -.056 Moderate 2.342 10.41 .000 1.770 2.915 Severe 5.361 212.16 .000 4.581 6.141 Predictors' Effect on DASS-10 Score Frequency of Visit -.008 0.99 .938 -.219 .202 Area Type Non-green 2.536 12.63 .000 1.964 3.108 Gender Female .582 1.79 .004 .185 .978 Model Fit Final 0.000 Goodness-Of-Fit Pearson 0.857 Deviance 0.612 Pseudo R-Square Cox and Snell 0.307 Nagelkerke 0.334 Additional Declarations No competing interests reported. Supplementary Files DevelopedQuestionTool.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 02 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7470759","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512194916,"identity":"7397feb1-df0d-47b7-9d62-38918cc19947","order_by":0,"name":"Tamanna Tabassom","email":"","orcid":"","institution":"Jatiya Kabi Kazi Nazrul Islam University","correspondingAuthor":false,"prefix":"","firstName":"Tamanna","middleName":"","lastName":"Tabassom","suffix":""},{"id":512194917,"identity":"3526af55-735c-4a68-a283-f8e0b7156884","order_by":1,"name":"Md. Abu Sayem","email":"","orcid":"","institution":"Jatiya Kabi Kazi Nazrul Islam University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Abu","lastName":"Sayem","suffix":""},{"id":512194918,"identity":"3d6f2042-1f08-44e1-81c6-d53b9ee49f96","order_by":2,"name":"Probal Talukder","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYFAC5gbGBjYwC0gW2NT3g5gJBbg18DAwImsxSGOc2QDSYkC8lsOMGw6A2Hi02LMfbJOcUWaTby59+NiDHwbMzMbnVyd+eGDAIM8vdgC7LTyJbZIbzqVZ7uxLSzfsMWBjM7vxdrME0GGGM2cn4HAYUMvDtsMGBmd4zCR4DHh4zG6c3QDSkmBwG4cW/ocgLf/BWiT/GEhIGM84u/kHXi0SQFs2th0Aa5HmMQAC/t5t+G258bDZcsa5ZAPLHrZ0YxmDhASJG7zbLBIMJHD6hb0/+eDNnjI7A3Me5mMP31T8T+DvP7v55o8KG3l+aexa4AARERJglRL4laNq4T9AWPUoGAWjYBSMKAAAfItdIC49A/4AAAAASUVORK5CYII=","orcid":"","institution":"Jatiya Kabi Kazi Nazrul Islam University","correspondingAuthor":true,"prefix":"","firstName":"Probal","middleName":"","lastName":"Talukder","suffix":""}],"badges":[],"createdAt":"2025-08-27 10:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7470759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7470759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91074029,"identity":"8f55f5ab-c112-4832-a1f8-24fa13e0349f","added_by":"auto","created_at":"2025-09-11 11:02:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":790996,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area of Mymensingh City with green and non-green spaces points\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/fe0d2e4c8065508ca413cd55.png"},{"id":91072295,"identity":"1bb58cef-d1b2-43a8-a529-45c517d5670b","added_by":"auto","created_at":"2025-09-11 10:54:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":192471,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of (a) PM₂.₅ and (b) PM₁₀ Concentrations Across Green and Non-Green Urban Spaces by Day Type with Reference to BNAQS Standards\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/f6070ee5091dc7d14a445261.png"},{"id":91072294,"identity":"e03d5f04-be1c-4129-916f-cfa0d7bc5b59","added_by":"auto","created_at":"2025-09-11 10:54:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":505843,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of NDVI and PM2.5 and PM10, where (a) Spatial Distribution of PM2.5 on Working Days, (b) Spatial Distribution of PM2.5 on Non-working Days, (c) Spatial Distribution of PM10 on Non-working Days, (d) Spatial Distribution of PM10 on Working Days and (e) Distribution of vegetation all over the study area with NDVI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/c428a05f2273fa0b57fbace4.png"},{"id":91074028,"identity":"3c2acd48-8494-411c-bd2b-22a0522a1cbf","added_by":"auto","created_at":"2025-09-11 11:02:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143345,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of visits of residents to green spaces by area type\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/29f06c0ab72da24690b7e020.png"},{"id":91072301,"identity":"f1486195-2628-45bb-aace-e12b584f1a3a","added_by":"auto","created_at":"2025-09-11 10:54:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174928,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Disease Frequency in (a) Green Spaces and (b) Non-Green Spaces\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/3ccf23c21fbe63c0ed25d0e9.png"},{"id":91070701,"identity":"6fcc285f-71a8-4b34-8d74-056e5d1d17b6","added_by":"auto","created_at":"2025-09-11 10:46:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":85596,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of Mental Health Status (DASS-10) by Area Type: Comparison Between Green and Non-Green Spaces\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/a2753e2c31d5360a85462992.png"},{"id":91076437,"identity":"4b7401a0-b74d-423d-9a66-6daa759e759b","added_by":"auto","created_at":"2025-09-11 11:10:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2752492,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/adcccbf0-e54d-4b09-9904-d9af14829f41.pdf"},{"id":91072296,"identity":"bb9c3fad-833b-4de1-b5a5-2938ddd3d289","added_by":"auto","created_at":"2025-09-11 10:54:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":554072,"visible":true,"origin":"","legend":"","description":"","filename":"DevelopedQuestionTool.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7470759/v1/aa2d5166ddd4fcd5986e7870.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between PM₂.₅ and PM₁₀ Exposure and Physical and Mental Health: A Comparative Study of Vegetated and Non-Vegetated Zones in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollution is one of the most critical phenomena of the 21st century, which affects environmental health, contributing significantly to the global burden of disease, especially for low and middle-income countries(LMICs) (1). Several pollutants have a significant impact on health, though PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e contribute to the extensive association with adverse health impacts(2,3). They are associated with cardiovascular, neurological disorders, mental health, and many more(4,5). A 10 \u0026micro;g/m\u0026sup3; increase in PM₂.₅ exposure is linked to a 6% rise in non-accidental mortality, with lung cancer and cardiovascular deaths increasing by 15\u0026ndash;21% and 12\u0026ndash;14%, respectively, regardless of age, sex, or location(6). The World Health Organization reported that every year, about 4.2\u0026nbsp;million people face premature deaths, especially in LMICs (1,7,8). Anthropogenic activities such as rapid urbanization, unplanned industrial development, vehicular emissions and biomass burning have substantially deteriorated the air quality, especially in LIMCs like Bangladesh (3,9). 80%of urban settings are crossing the limit of air quality set by the WHO all over the world (6,9,10). In 2019, air pollution in urban areas of Bangladesh reported an estimation of 173,500 premature deaths (11). Recent studies revealed that green spaces or vegetated areas, such as parks, tree covers can eliminate air pollution through pollution deposition and dispersion mechanisms, while simultaneously serving psychological and physiological benefits (12\u0026ndash;15). Urban green infrastructure is significantly reducing the ambient concentration of particulate matter and enhancing health outcomes, including lower rates of respiratory illness, stress, and depression. In contrast, non-green spaces reflected reverse results (15\u0026ndash;17). Studies from the global to the regional level have revealed a significant association between particulate matter and serious health issues (11,18). Despite such empirical studies, only a few studies around the globe have been conducted to find out the association between particulate matter and mental health (19\u0026ndash;21). Bangladesh is out of such approaches, as most of the studies focused on particulate matter and mental health(11,22,23). To address this gap, this study has focused on investigating the impact of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e on the physical and mental health of residents living in green and non-green areas of an urban setting. By employing a comparative cross-sectional design, this study seeks to provide context-specific evidence that may inform targeted environmental health interventions, urban planning strategies, and policymaking in air quality management.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design and Study Participants\u003c/h2\u003e\n \u003cp\u003eA cross-sectional study was conducted to identify the association between particulate matter levels and the physical and mental health of residents from green and non-green urban settings throughout Mymensingh City. Areas were selected based on empirical approaches. A total of 384 young adults (aged 18\u0026ndash;35) were recruited, equally divided between green (n\u0026thinsp;=\u0026thinsp;192) and non-green (n\u0026thinsp;=\u0026thinsp;192) areas, using stratified random sampling. This study was conducted between July and November 2024. Seven days\u0026apos; observation of particulate matter was taken to ensure the robustness of the data (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eSample Size\u003c/h2\u003e\n\u003cp\u003eThe sample size was calculated using the following Eq.\u0026nbsp;(24,25):\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:n=\\frac{{Z}^{2}\\cdot\\:p\\cdot\\:\\left(1-p\\right)}{{d}^{2}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;required sample size\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;Z-score for desired confidence level (e.g., 1.96 for 95% CI)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;estimated proportion of the population with the characteristic of interest (commonly 0.5 for maximum variability)\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ed\u0026thinsp;=\u0026thinsp;margin of error (e.g., 0.05 for \u0026plusmn;\u0026thinsp;5%)\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:n=\\frac{{\\left(1.96\\right)}^{2}\\cdot\\:0.5\\cdot\\:\\left(1-0.5\\right)}{{\\left(0.05\\right)}^{2}}=\\frac{3.8416\\cdot\\:0.25}{0.0025}=384.16$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe sample size was determined using the standard formula for cross-sectional studies, assuming a 95% confidence level (Z\u0026thinsp;=\u0026thinsp;1.96), 50% estimated prevalence, and a 5% margin of error. This yielded a minimum required sample of 384 participants, ensuring sufficient statistical power and precision. The chosen sample size accommodates variability across green and non-green urban settings, supporting reliable assessment of the physical and mental health impacts of PM₂.₅ and PM₁₀ exposure.\u003c/p\u003e\n\u003ch2\u003eSurvey Instrument\u003c/h2\u003e\n\u003cp\u003eAdoption of Depression Anxiety Stress Scale (DASS 10) in the question tool is a shorter form of DASS 42 was used to conduct the mental health survey(26). Total scores ranged from 0 to 30, with higher values indicating greater distress, and subscale scores were calculated for Anxiety/Stress (Items 1, 4, 6, 7, 8, 9; range: 0\u0026ndash;18) and Depression (Items 2, 3, 5, 10; range: 0\u0026ndash;12). Additionally, some questions are customized to suit the study objective, especially the physical health data and demographic information such as sex, area type. The question tool was developed considering the sociodemographic factors in English and then translated into Bengali to ensure a robust outcome from the participants. The tool was verified by the Institutional Review Board of Jatiya Kabi Kazi Nazrul Islam University. Moreover, particulate matter was measured by using the NAX Formaldehyde Detector Air Quality Monitor, with a focus on particulate matter concentrations (PM₂.₅ and PM₁₀). The full English version of the developed tool is provided as a Supplementary File named \u0026lsquo;Developed Question Tool\u0026rsquo;\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eData were collected using Google Forms and paper-based questionnaires and stored in Microsoft Excel for filtering outliers and missing values. Spatial Map of the distribution of PM and the NDVI was conducted using ArcGIS 10.8. The R programming language was used to conduct Exploratory Data Analysis and to illustrate the particulate matter distribution. Frequency distribution and regression analysis were done using SPSS.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eDemographic Characteristics of Participants\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe demographic profile of the study participants reveals a balanced representation in several key categories. Regarding gender, the sample consisted of 171 males (44.5%) and 213 females (55.5%), indicating a slightly higher proportion of female respondents. Participants were equally distributed across area types, with 192 individuals (50%) residing in green areas and the remaining 192 (50%) in non-green areas. The age range of all participants was between 18 and 35 years, ensuring a focus on young adults.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eParticulate matter variation with area type\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Mean Concentrations of PM₁₀ and PM₂.₅ (\u0026micro;g/m\u0026sup3;) in Green and Non-Green Urban Spaces on Working and Non-Working Days with Relative Increases in Non-Green Areas\u003c/p\u003e\n\u003cp\u003e(Table 1) The data provides a significant temporal variation of particulate matter between green and non-green spaces. Green spaces maintained a lower concentration both on working (48.7 \u0026plusmn; 1.5 \u0026micro;g/m\u0026sup3;) and non-working (59.3 \u0026plusmn; 4.7 \u0026micro;g/m\u0026sup3;) days. In contrast, non-green spaces exhibited substantially elevated PM10 levels, particularly on working days (109.9 \u0026plusmn; 14.1 \u0026micro;g/m\u0026sup3;), representing a 125.7% increase compared to green spaces. The non-working day difference remains pronounced (92.1 \u0026plusmn; 4.7 \u0026micro;g/m\u0026sup3;) which shows a 55.3% increase. A similar trend is noticeable in PM\u003csub\u003e2.5,\u0026nbsp;\u003c/sub\u003ewhere green spaces show lower concentration on working (43.8 \u0026plusmn; 1.3 \u0026micro;g/m\u0026sup3;) and non-working (51.3 \u0026plusmn; 4.6 \u0026micro;g/m\u0026sup3;) days. Non-green spaces recorded higher PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eon working (95.4 \u0026plusmn; 12.0 \u0026micro;g/m\u0026sup3;), which is a 117.8% increase from green space. The non-working day disparity persisted (83.7 \u0026plusmn; 5.2 \u0026micro;g/m\u0026sup3;), which is a 63.2% increase. The PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003elimit is within the Bangladesh National Ambient Air Quality Standard of 24 hours in green spaces for both working and non-working days. It exceeds the limit in non-green spaces for both day types. PM\u003csub\u003e10\u0026nbsp;\u003c/sub\u003eis within the limit but non-green spaces show higher concentration than green spaces for both day types (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between Particulate Matter Concentrations and Vegetation Density (NDVI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe integrated analysis of particulate matter (PM) distributions and normalized difference vegetation index (NDVI) reveals clear inverse relationships between vegetation density and air pollution levels across the study area. PM2.5 exhibited higher concentration (41-120 \u0026micro;g/m\u0026sup3;) on working days compared to non-working days (44-91 \u0026micro;g/m\u0026sup3;). Peak PM₂.₅ levels (89-120 \u0026micro;g/m\u0026sup3;) consistently occurred in low-NDVI areas (\u0026lt;0.2). Green spaces (NDVI \u0026gt;0.4) maintained concentrations below 60 \u0026micro;g/m\u0026sup3; on both day types. PM\u003csub\u003e10\u0026nbsp;\u003c/sub\u003eshows stronger spatial variability than PM\u003csub\u003e2.5\u003c/sub\u003e. Maximum concentrations (110-140 \u0026micro;g/m\u0026sup3;) clustered in urban cores with NDVI \u0026lt;0.1. Areas with NDVI \u0026gt;0.3 demonstrated 30-40% lower PM₁₀ levels than the adjacent built environments (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFrequency of Visits of Green and Non-green Residents to Green Spaces\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;Those residing near green spaces reported significantly higher visitation frequencies, with 24.5% visiting daily and 18.8% weekly, compared to only 4.9% daily and 10.2% weekly among non-green area residents (Figure 4). It suggests that proximity strongly influences usage, aligning with the \u0026quot;distance decay\u0026quot; principle in urban ecology. The near-absence of \u0026quot;never\u0026quot; responses among green-area residents underscores the accessibility advantage. Over 43.3% green space residents visit at least weekly (daily+weekly) to green areas compared to 15% of non-green residents. Rare or never visit ratio in green space residents is 2.6x lower than non-green residents. In contrast, 21.9% of non-green residents reported never visiting green spaces, compared to 0.5% of green-area residents. Monthly visits are similar (~10\u0026ndash;11%), suggesting some moderate use regardless of proximity (Figure 4).\u003c/p\u003e\n\u003ch2\u003eDisease Prevalence by Area Type\u003c/h2\u003e\n\u003cp\u003eThe descriptive analysis revealed notable differences in disease prevalence between residents of green and non-green urban areas (Figure 5). Non-green area residents exhibited higher percentages for most conditions, particularly respiratory and allergic disorders: asthma (23.44% vs 8.85%), bronchitis (46.35% vs 0.52%), and allergic rhinitis (18% vs 2.5%). Skin problems were more prevalent in non-green areas (31.77% vs 9.38%), while cardiovascular disease showed a moderate difference (5.73% vs 3.13%). Exceptions included pneumonia (4.17% vs 3.65%) and lung cancer (0.52% vs 1.56%), which showed inverse patterns. These unadjusted percentages suggest potential environmental health disparities warranting further investigation through controlled studies with proper confounder adjustment (Figure 5).\u003c/p\u003e\n\u003ch2\u003eMental Health Status by Area Type\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe analysis of mental health status reveals distinct patterns between residents of green and non-green urban areas. Non-green space residents reported higher percentages of clinically significant mental health symptoms across all severity categories. Non-green and green residents have a subclinical status of 9.9% and 6.5% respectively. For moderate, severe, and extremely severe, the ratios are 26% and 19.8%, 26% and 7.6%, and 4.2% and 0% respectively. The most pronounced disparity appeared in the severe symptom category, where non-green area residents showed a 3.4-fold higher prevalence. Notably, no residents of green spaces reported extremely severe symptoms, while 4.2% of non-green area residents fell into this category (Figure 6).\u003c/p\u003e\n\u003ch2\u003eMultivariable Logistic Regression Analysis of Health Outcomes by Residential Area Type, Gender, and Protective Behavior\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Binary Logistic Regression Analysis of Associations Between Health Outcomes and Gender, Area Type, and Mask Usage\u003c/p\u003e\n\u003cp\u003e(Table 2) Binary Logistic Regression analysis examined the effects of area type (green vs. non-green), gender, and mask usage on the likelihood of developing specific health conditions. The findings highlight the significant impact of environmental factors and individual characteristics on health outcomes. For asthma, residing in non-green areas was a strong predictor (OR = 36.304, 95% CI [19.31, 68.36], p \u0026lt; .001), with females also at higher risk (OR = 1.873, 95% CI [1.03, 3.41], p = .040). Mask usage was not significant (p = .592). In the case of lung cancer, none of the predictors were significant (p \u0026gt; .05), suggesting limited influence or potential sample size constraints. For cardiovascular disease, the female gender was protective (OR = 0.173, 95% CI [0.048, 0.635], p = .008), while area type (OR = 1.827, p = .280) and mask usage (p = .486) were not significant. Allergic rhinitis was strongly associated with non-green areas (OR = 13.589, 95% CI [12.487, 26.118], p \u0026lt; .001), while gender (p = 1.000) and mask usage (p = .860) showed no effects. No significant predictors emerged for pneumonia (p \u0026gt; .05). However, skin problems were significantly associated with non-green environments (OR = 21.516, 95% CI [16.072, 27.881], p \u0026lt; .001), with gender (p = .160) and mask usage (p = .829) remaining non-significant. Bronchitis demonstrated the strongest environmental association, with non-green areas as a highly significant predictor (OR = 254.574, 95% CI [130.411, 397.084], p \u0026lt; .001), while gender (p = .230) and mask usage (p = .232) were not influential. Overall, the results emphasize the substantial health risks posed by non-green environments, particularly for asthma, allergic rhinitis, bronchitis, and skin problems. Gender differences were observed for asthma and cardiovascular disease, while mask usage showed limited protective effects.\u003c/p\u003e\n\u003ch2\u003ePredictors of Mental Health Severity: Ordinal Regression Analysis of Green Space Access, Frequency of Use, and Demographic Factors\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Ordinal Logistic Regression Analysis of Predictors Influencing Mental Health Severity (DASS-10) Based on Area Type, Gender, and Frequency of Green Space Visitation\u003c/p\u003e\n\u003cp\u003e(Table 3) An ordinal logistic regression was conducted to examine factors influencing mental health severity (DASS-10). Individuals with sub-clinical (\u0026beta; = -1.726, OR = 0.178, p \u0026lt; .001) and mild symptoms (\u0026beta; = -0.542, OR = 0.581, p = .029) were less likely to progress to higher severity, while those with moderate (\u0026beta; = 2.342, OR = 10.41, p \u0026lt; .001) and severe symptoms (\u0026beta; = 5.361, OR = 212.16, p \u0026lt; .001) had significantly higher odds. Predictor analysis showed that the frequency of visits had no significant effect (\u0026beta; = -0.008, OR = 0.99, p = .938). However, living in non-green areas (\u0026beta; = 2.536, OR = 12.63) and being female (\u0026beta; = 0.582, OR = 1.79, p = .004) were associated with greater mental health severity. \u0026nbsp;These findings highlight strong associations between gender, environment, and mental health severity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reveals the environmental and health disparities associated with urban green and non-green residential settings, emphasizing the role of vegetation in mitigating air pollution and improving physical and mental health outcomes. It suggests that exposure to green settings is associated with reduced levels of particulate matter, lower prevalence of physical issues, and better mental health. PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e are both lower in concentration in green spaces compared to non-green spaces across both working and non-working days. A similar trend has been noticed in previous studies (27). The increase of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in non-vegetated areas, especially on working days strongly upholds the protective nature of trees. The inverse relationship between vegetation density (NDVI) and PM levels further confirms the air-filtering capacity of green spaces, aligning with satellite-based assessments(28). Health-related adverse outcomes such as breathing issues like asthma, bronchitis, and allergies, were much more common in people from non-green areas. It provides strong evidence that airborne exposure is triggering respiratory issues. Previous studies also justified this association (16,29). The adjusted odds ratios from logistic regression, particularly the extremely high values for bronchitis (OR\u0026thinsp;=\u0026thinsp;254.57) and asthma (OR\u0026thinsp;=\u0026thinsp;36.30), indicate robust environmental influences that merit urgent public health interventions. Interestingly, mask usage\u0026mdash;although previously hypothesized to confer respiratory protection\u0026mdash;did not emerge as a significant predictor in most models, suggesting limitations in either usage consistency or efficacy in high-pollution microenvironments. Gender showed a differential role as females were more sensitive to asthma and showed lower odds of cardiovascular diseases. Such gender-specific vulnerabilities and resilience have been reported in prior epidemiological studies(30,31). The observed association between non-green residency and dermatological conditions such as skin problems may reflect higher exposure to dust, allergens, and heat, compounded by reduced vegetative cooling and UV-buffering effects. Previously conducted studies revealed such phenomena (32). Mental health analysis reinforces the significance of green spaces. Residents of non-green areas are reported to have moderate and extremely severe mental health status. In contrast, green space residents are not reported to have such status. Previous studies also revealed the positive impact of green spaces on mental health(19,33). The ordinal regression findings additionally underscore the influence of both environmental (area type) and demographic (gender) factors, although frequency of green space use did not attain statistical significance, perhaps due to limited variation in visitation behaviors or psychosocial mediators not captured in the model. Behavioral insights from this study also support the \"distance decay\" principle in environmental psychology. Residents living near green spaces reported significantly higher visitation frequencies, suggesting that physical proximity enhances access and encourages regular touch. Several studies justified this practice(34,35). Overall, the results of this study offer multi-dimensional evidence that supports urban greening as a strategic public health intervention. However, limitations include the cross-sectional design, potential self-report bias in disease and mental health data, and the absence of biomarker-based health measurements. Moreover, while NDVI and PM data offer spatial granularity, longitudinal exposure assessments and finer temporal resolution could enhance causal inference. Future studies should integrate wearable sensors, longer-term health monitoring, and socioeconomic confounders to refine these associations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the critical role of urban green spaces in mitigating air pollution and enhancing public health outcomes. By integrating environmental exposure data with physical and mental health assessments, the findings reveal that residents of non-green urban areas are disproportionately burdened with higher levels of PM₂.₅ and PM₁₀, and suffer greater prevalence of respiratory, dermatological, and psychological disorders. Notably, the extraordinarily elevated odds for bronchitis and asthma among non-green residents point to an urgent need for targeted environmental health policies in rapidly urbanizing LMIC contexts like Bangladesh. Furthermore, the association between vegetation density (NDVI) and pollutant concentrations reaffirms the ecological utility of green infrastructure as a natural buffer against urban air pollution. Mental health disparities, particularly the absence of extremely severe symptoms among green space residents, also highlight the restorative potential of nature in densely populated settings. Although limited by its cross-sectional design and self-reported health metrics, this research provides robust, context-specific evidence that supports the expansion and equitable distribution of urban green spaces as a public health imperative. Policymakers and urban planners should consider integrating vegetative buffers in city design to foster cleaner air, healthier populations, and more resilient urban ecosystems\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Md. Monabbir Hossain Tonmoy, who helped us with some necessary logistics. We really appreciate his effort and support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Review Board of Jatiya Kabi Kazi Nazrul Islam University, Trishal-2220, Mymensingh, Bangladesh (Approval No.: Not Applicable). All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as no identifiable personal data (such as names, images, or videos) are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved human participants. Ethical approval and informed consent procedures were carried out as described above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet. 2017 May;389(10082):1907\u0026ndash;18. \u003c/li\u003e\n\u003cli\u003eOujidi B, Benchrif A, Tahri M, Zahry F, Bounakhla M, Bazairi H, et al. 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Proc Natl Acad Sci USA. 2015 Jun 30;112(26):7937\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eSalman MA, Haque A, Rahman M, Rabby MMJ, Hossen MS, Halder P, et al. NDVI-based Analysis of Green Space Decline and Air Quality in Dhaka: Implications for Sustainable Development Goals. EESRJ. 2023 Jun 30;10(2):73\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eTseng E, Ho WC, Lin MH, Cheng TJ, Chen PC, Lin HH. Chronic exposure to particulate matter and risk of cardiovascular mortality: cohort study from Taiwan. BMC Public Health [Internet]. 2015 Dec [cited 2025 Jul 14];15(1). Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-015-2272-6\u003c/li\u003e\n\u003cli\u003eChen LH, Knutsen SF, Shavlik D, Beeson WL, Petersen F, Ghamsary M, et al. The Association between Fatal Coronary Heart Disease and Ambient Particulate Air Pollution: Are Females at Greater Risk? Environ Health Perspect. 2005 Dec;113(12):1723\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eBrook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart Association. Circulation. 2010 Jun;121(21):2331\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eJenerette GD, Harlan SL, Buyantuev A, Stefanov WL, Declet-Barreto J, Ruddell BL, et al. Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landscape Ecol. 2016 May;31(4):745\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eChen K, Zhang T, Liu F, Zhang Y, Song Y. How Does Urban Green Space Impact Residents\u0026rsquo; Mental Health: A Literature Review of Mediators. IJERPH. 2021 Nov 9;18(22):11746. \u003c/li\u003e\n\u003cli\u003eDadvand P, De Nazelle A, Triguero-Mas M, Schembari A, Cirach M, Amoly E, et al. Surrounding Greenness and Exposure to Air Pollution During Pregnancy: An Analysis of Personal Monitoring Data. Environ Health Perspect. 2012 Sep;120(9):1286\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eRossi SD, Byrne JA, Pickering CM. The role of distance in peri-urban national park use: Who visits them and how far do they travel? Applied Geography. 2015 Sep;63:77\u0026ndash;88. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Mean Concentrations of PM₁₀ and PM₂.₅ (\u0026micro;g/m\u0026sup3;) in Green and Non-Green Urban Spaces on Working and Non-Working Days with Relative Increases in Non-Green Areas\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Concentration (\u0026micro;g/m\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePollutant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking Day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Working Day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Increase vs. Green\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGreen Space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e48.7 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e59.3 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNon-Green Space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e109.9 \u0026plusmn; 14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e92.1 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e125.7% (Work)\u003cbr\u003e\u0026nbsp;55.3% (Non-Work)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGreen Space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e43.8 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e51.3 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNon-Green Space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e95.4 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e83.7 \u0026plusmn; 5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e117.8% (Work)\u003cbr\u003e\u0026nbsp;63.2% (Non-Work)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Data presented as Mean \u0026plusmn; SD. Percentage increase calculated as [(Non-Green Mean)/(Green Mean) - 1] \u0026times; 100.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Binary Logistic Regression Analysis of Associations Between Health Outcomes and Gender, Area Type, and Mask Usage\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.03, 3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e36.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e19.31, 68.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.031, 0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eLung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.11, 6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.03, 3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.13, 7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.048, 0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.509, 6.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.166, 2.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eAllergic rhinitis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.605, 1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e13.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e12.487, 26.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.530, 2.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.440, 0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.361, 2.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.058, 1.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eSkin problem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.383, 1.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e21.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e16.072, 27.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.474, 1.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eBronchitis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.346, 1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003eArea Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eNon- green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e254.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e130.411, 397.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMask Usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.776, 2.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Ordinal Logistic Regression Analysis of Predictors Influencing Mental Health Severity (DASS-10) Based on Area Type, Gender, and Frequency of Green Space Visitation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR=\u003c/strong\u003e\u003cimg width=\"22\" height=\"20\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep- Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Health Status (DASS-10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSub-clinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-1.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e-2.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-1.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e-1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e-.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e2.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e212.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e4.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e6.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u0026apos; Effect on DASS-10 Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFrequency of Visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e-.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNon-green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e3.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoodness-Of-Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.857\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePseudo R-Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eCox and Snell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 500px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNagelkerke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 500px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Particulate matter, mental health, physical health, green space, non-green space","lastPublishedDoi":"10.21203/rs.3.rs-7470759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7470759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Air pollution has become a significant adverse contributor to health issues, especially in cities in low and middle-income countries (LMICs) like Bangladesh. Urban green spaces have emerged as potential buffers against particulate matter pollution-related health risks. This study investigates the impact of particulate matter on the physical and mental health of residents from green and non-green spaces.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eA cross-sectional study was conducted with 384 participants (18–35 years), equally divided between green and non-green urban areas. PM values were collected using the NAX tool over seven days. Health outcomes were collected using DASS 10 and structured questionnaires. Spatial analysis was conducted using ArcGIS; statistical analysis involved logistic and ordinal regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Non-green areas exhibited significantly higher PM₂.₅ and PM₁₀ concentrations. Residents in these areas showed elevated rates of asthma, bronchitis, allergic rhinitis, skin disorders, and mental health symptoms. Bronchitis (OR=254.6) and asthma (OR=36.3) showed the strongest associations(p\u0026lt;0.001) with non-green residency. NDVI was inversely correlated with PM levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Urban greening is a critical environmental health intervention. Expanding green infrastructure may reduce pollutant exposure and improve overall physical and mental health outcomes in urban LMIC settings.\u003c/p\u003e","manuscriptTitle":"Associations Between PM₂.₅ and PM₁₀ Exposure and Physical and Mental Health: A Comparative Study of Vegetated and Non-Vegetated Zones in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:45:56","doi":"10.21203/rs.3.rs-7470759/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-11T07:16:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T14:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55881070180185159260885853931477869612","date":"2025-10-18T09:22:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274330796250331822430226131335347321612","date":"2025-10-18T08:37:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T06:13:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186810468439952147352679110375757638567","date":"2025-09-09T02:03:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-03T14:32:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T23:10:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T15:20:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T09:17:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-02T09:13:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41bbd30b-7c7d-4bd3-877b-8ea441166b93","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T17:23:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 10:45:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7470759","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7470759","identity":"rs-7470759","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}