The Negative Correlation Between Nighttime Light Pollution and the Prevalence of Depressive Disorders: A Medical Image-Processing Study on 35 US Cities

The Negative Correlation Between Nighttime Light Pollution and the Prevalence of Depressive Disorders: A Medical Image-Processing Study on 35 US Cities | 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 The Negative Correlation Between Nighttime Light Pollution and the Prevalence of Depressive Disorders: A Medical Image-Processing Study on 35 US Cities Hossein Zamaninasab, Arsalan Heidarpanah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7233105/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Growing evidence suggests potential links between artificial nighttime illumination and neurological health outcomes. This study introduces an innovative satellite image analysis technique to quantify light pollution while investigating its association with depressive disorders across urban populations. Methods: We developed a PHP-based image processing algorithm to estimate relative light pollution levels from high-resolution nighttime satellite imagery. Applying this method to 35 U.S. metropolitan areas, we correlated the findings with depression prevalence data from the CDC's Behavioral Risk Factor Surveillance System (Pickens et al., 2018). Our analysis incorporated multiple mental health indicators, including depression diagnoses, smoking rates, and binge drinking patterns. Results: Our analysis revealed three key findings: (1) a positive correlation between urban population density and light pollution intensity (p=0.03), (2) an inverse relationship between light pollution levels and both depression prevalence (p=0.01) and smoking rates (p=0.02), and (3) a positive association between light pollution and binge drinking reports (p=0.04). These patterns remained significant after controlling for population variables. Conclusion: The proposed satellite image analysis method provides a cost-effective approach for large-scale light pollution assessment in neuroepidemiological research. Our findings suggest complex relationships between artificial nighttime lighting and mental health indicators that warrant further investigation, particularly regarding potential mediating factors in urban environments. Statistical Epidemiology Environmental Policy Psychology Psychiatry Ecological Modeling Behavioral Geography Light Pollution Depressive Disorders Depression Epidemiology United States Satellite Image Processing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction 1-1. Light Pollution Throughout Earth's history, nocturnal darkness has always been tempered by natural light sources. Moonlight, starlight from our galaxy and beyond, and zodiacal light have illuminated nightscapes for millennia (Falchi et al., 2016). The advent of electric lighting fundamentally altered this balance, creating what we now recognize as artificial light pollution (Cinzano et al., 2000). Urban areas in particular have experienced dramatic increases in nighttime illumination, with consequences we are only beginning to understand. 1-2. The Accelerating Pace of Light Pollution The proliferation of artificial lighting has accelerated dramatically in recent decades. Falchi and Bará (2023) document that global artificial illumination now doubles every eight years, a startling rate of increase that far outpaces population growth. This trend shows no signs of abating as urbanization expands and lighting technologies become more affordable. 1-3. Health Consequences of Light Exposure The public health implications of light pollution are increasingly apparent. Research demonstrates wide-ranging physiological effects, from melatonin suppression to cardiovascular changes and sleep cycle disruption (Lockley et al., 2003). Our previous work on environmental health indicators (Zamaninasab & Bajelan, 2023) suggests such anthropogenic changes often have unexpected secondary effects. Historical evidence reveals how profoundly modern lighting has altered human biology - where pre-industrial societies naturally followed biphasic sleep patterns (Ekirch, 2001; De La Iglesia et al., 2015), contemporary populations exhibit fundamentally different circadian rhythms. In addition to shifting sleep patterns away from a biphasic cycle, artificial nighttime lighting has contributed to later bedtimes, reduced nighttime sleep duration, and increased daytime sleepiness (Aulsebrook et al., 2018; Beale et al., 2017). The sleep-disrupting effects of artificial light are well-documented, including delayed sleep onset, reduced sleep duration, and increased daytime sleepiness (Ohayon & Milesi, 2016). Our research on electronic devices (Zamaninasab & Heidarpanah, 2025) confirms similar patterns from personal light sources, suggesting urban light pollution may compound these effects through environmental exposure. The relationship between sleep and mental health appears bidirectional. While depression frequently involves sleep disturbances like insomnia, poor sleep quality itself elevates depression risk (Johnson et al., 2006). Those with sleep problems show triple the prevalence of major depressive disorder compared to good sleepers (Brietzke et al., 2019). This complex web extends to substance use - sleep problems correlate with both smoking and binge drinking (Rhee et al., 2021), while smoking and alcohol consumption themselves interact in ways that may exacerbate mental health vulnerabilities (Gubner et al., 2016). Indeed, heavy smoking and alcohol abuse disorders independently increase the likelihood of developing major depressive disorder by approximately two to four times compared to the general population (Grant et al., 2015; Pasco et al., 2008). 1-4. Spectral Considerations: The Blue Light Effect The spectral composition of artificial light proves particularly significant. Blue wavelength light exerts disproportionate effects on physiology, more potently elevating heart rate, body temperature, and melatonin suppression than other visible light (Holzman, 2010). Experimental data shows blue light reduces melatonin secretion twice as effectively as green light while causing greater circadian disruption (Lockley et al., 2003). Red light, by contrast, shows minimal circadian impact (Harvard Medical School, n.d.). 1-5. Study Objectives This study addresses two key questions: First, can satellite image analysis provide a reliable, cost-effective method for comparative light pollution assessment? Second, what relationships exist between measured light pollution and depression prevalence in urban populations? Our approach combines novel image processing techniques with established public health datasets to explore these environmental-health connections. 2. Materials and Methods 2-1. Image Processing Methodology We developed a novel PHP-based algorithm to quantify light pollution from high-resolution nighttime satellite imagery. Our processing pipeline begins by filtering out non-residential areas through threshold-based exclusion of dark pixels. The remaining pixels are analyzed for their RGB spectral composition, with particular attention to blue wavelength intensities due to their known biological effects (Lockley et al., 2003). The algorithm outputs a normalized light pollution index (LPI) ranging from 0 (minimal light pollution) to 1 (maximal light pollution). The software stack utilized PHP 7.4 for image processing and MySQL 5.5 for data management, hosted on a dedicated server (Mizbanfa Hosting) with the following specifications: · Processor: 3.8 GHz · Memory: 32GB DDR4 RAM · Network: 10 Gbps dedicated connection 2-2. Study Area Characteristics Our analysis focused on the contiguous United States (latitudinal range: 19.50°N to 64.86°N; longitudinal range: 161.76°W to 68.01°W), representing the world's third most populous nation (U.S. Census Bureau, 2020) and largest economy by GDP (IMF, 2022). The selected region encompasses diverse urban environments suitable for testing our light pollution estimation method. 2-3. Satellite Data Acquisition We analyzed composite imagery from the Suomi National Polar-orbiting Partnership (NPP) satellite, collected between April-October 2012 (NASA Earth Observatory, 2012). The dataset captures emissions across visible and near-infrared spectra (500-900 nm), including: · Artificial city lights · Natural phenomena (auroras, moonlight) · Transient events (wildfires, gas flares) Technical specifications: · Resolution: 6646 × 4430 pixels · Spatial accuracy: 750 m at nadir · Radiometric depth: 16-bit 2-4. Analytical Approach Our study employed a two-phase analytical design: Phase 1: Light pollution mapping · Selected 35 metropolitan statistical areas (MSAs) meeting strict criteria: o Non-composite urban areas o Consistent image quality across all samples o Geographic distribution representing all US regions · Processed satellite images through our algorithm to generate LPI values Phase 2: Health data correlation · Paired LPI results with mental health indicators derived from the CDC's Behavioral Risk Factor Surveillance System (BRFSS) (Pickens, Pierannunzi, Garvin & Town, 2018): o Depression prevalence o Poor mental health days o Substance use patterns · Compared light pollution estimates with mental health indicators across cities with varying population sizes The geographic distribution of study sites appears in Figure 1, with complete demographic characteristics provided in Table 1. Table 1. Demographic and Geographic Information of the Studied Cities # City Population * Latitude Longitude # City Population Latitude Longitude 1 Akron 190469 41.08 -81.51 19 Oklahoma City 681054 35.46 -97.51 2 Albuquerque 564559 35.08 -106.65 20 Philadelphia 1603797 39.95 -75.16 3 Baton Rouge 227470 30.45 -91.18 21 Pittsburgh 302971 40.44 -79.99 4 Billings 117116 45.78 -108.50 22 Raleigh 467665 35.77 -78.63 5 Boise City 235684 43.61 -116.20 23 Reno 264165 39.52 -119.81 6 Boston 675647 42.36 -71.0589 24 Richmond 226610 37.54 -77.43 7 Camden 71791 39.92 -75.11 25 Rochester, MN 121395 44.01 -92.48 8 Charleston 150227 38.34 -81.63 26 Rochester, NY 211328 43.15 -77.60 9 Columbus 905748 39.96 -82.99 27 Salt Lake City 199723 40.76 -111.89 10 Corpus Christi 317863 27.80 -97.39 28 Springfield 169176 42.10 -72.58 11 Dayton 137644 39.75 -84.19 29 St. Louis 301578 38.62 -90.19 12 El Paso 678815 31.76 -106.48 30 Toledo 270871 41.65 -83.53 13 Jackson 153701 32.29 -90.18 31 Topeka 126587 39.04 -95.67 14 Jacksonville 949611 30.33 -81.65 32 Tulsa 413066 36.15 -95.99 15 Knoxville 190740 35.96 -83.92 33 Tuscaloosa 99600 33.20 -87.56 16 Lincoln 291082 40.81 -96.70 34 Wichita Falls 102316 33.91 -98.49 17 Memphis 633104 35.14 -90.04 35 Wichita 397532 37.68 -97.33 18 Newark 311549 40.73 -74.16 * The populations are taken from the USA 2020 census. Using standardized image segmentation protocols, the composite satellite image was partitioned into 35 uniformly-sized tiles that were subsequently processed by our analytical software (Figure 2). 2-5. Probability Distribution and Data Analysis The image processing outputs were first organized into tabular format using Microsoft® Excel® LTSC MSO (Version 2302, Build 16.0.16130.20186). These structured datasets were then imported into R statistical software (version 4.2.2) for comprehensive analysis, including descriptive statistics, graphical visualization, and hierarchical cluster analysis (HCA) to identify spatial patterns in light pollution. To represent the geographical distribution of results, we generated heat maps using ArcGIS Pro (version 3.0.2), employing kernel density estimation to ensure accurate spatial representation of light pollution levels across urban areas. 3. Results 3-1. Overview Our analysis of satellite imagery revealed significant variation in light pollution levels across the studied metropolitan areas. The relative light pollution index ranged from 43.26% in Charleston, West Virginia - representing the lowest observed value - to 65.22% in Newark, New Jersey, which exhibited the highest degree of artificial nighttime illumination (Figure 3). This 21.96 percentage point difference between the extremes demonstrates substantial variability in light pollution exposure among urban populations in our sample. The light pollution estimates were systematically combined with key mental health indicators from the BRFSS report in Table 2. This comprehensive dataset includes: · Clinician-diagnosed depressive disorder prevalence · Self-reported poor mental health (≥14 days in past month) · Current smoking rates · Recent binge drinking prevalence (within past 30 days) Table 2. Aggregated Results of Light Pollution Against the Prevalence of Some Mental Health Indicators in the BRFSS Report # City LP DD PMH CS BD # City LP DD PMH CS BD 1 Newark 0.6522 12.7 9.9 12.2 17.1 19 Memphis 0.5117 15.7 14.4 19.2 14.2 2 Camden 0.6174 16.8 12.5 16.2 17.5 20 Oklahoma City 0.5112 20 11.7 19.1 15.6 3 Philadelphia 0.6174 21.6 18.5 18.2 20.2 21 Tuscaloosa 0.5098 23.3 17.6 23.4 15.6 4 Boston 0.593 18.7 10.7 12.3 20.5 22 Dayton 0.5078 18.8 14.4 17.4 16.8 5 Salt Lake City 0.5735 22.4 11.4 10.2 14.6 23 Tulsa 0.5022 23.7 12.8 21.6 12.9 6 Richmond 0.5704 15.5 11.4 17.3 16.4 24 Corpus Christi 0.5013 18.3 15 19.2 16.8 7 Jacksonville 0.5693 17.9 15.5 15.7 16 25 Raleigh 0.496 14.9 8.7 13.1 14 8 Lincoln 0.5632 17.6 9.4 15.5 21.4 26 Knoxville 0.4947 22.1 15.2 20.2 11.1 9 El Paso 0.5454 14.4 9.7 13.4 17.9 27 Columbus 0.4819 22 12.6 22.5 20.7 10 Akron 0.5447 19.8 10.6 29.5 22.8 28 Topeka 0.4818 24.7 12.6 21.6 16.5 11 Albuquerque 0.5417 20.7 9.3 18.3 14.9 29 Rochester, NY 0.4738 23.1 11.6 19.6 18.3 12 St. Louis 0.5411 19.6 11.9 18.7 19.4 30 Toledo 0.4738 21.6 11.3 17.2 16.7 13 Reno 0.537 16.7 12.1 19.6 17 31 Jackson 0.4725 15.5 13.4 19 13.4 14 Baton Rouge 0.5357 16.3 11.9 15.5 15.3 32 Billings 0.4718 21 9.7 18 21.5 15 Boise City 0.5322 21.5 10.9 12.5 15.4 33 Wichita Falls 0.4633 20.4 10.1 15.7 9.7 16 Springfield 0.5306 24 14.6 20.7 19 34 Rochester, MN 0.4526 16.4 5.6 13.4 18.3 17 Pittsburgh 0.5252 19.2 10.3 20 20.3 35 Charleston 0.4326 26.4 14.8 25.6 10.9 18 Wichita 0.525 19.3 9.4 19.4 14.9 3-2. Light Pollution and Urban Population Our analysis revealed a statistically significant positive association between urban population size and light pollution levels (p = 0.03), as illustrated in Figure 4. This relationship suggests that more populous cities tend to exhibit greater degrees of artificial nighttime illumination. 3-3. Light Pollution and Mental Health Indicators Our statistical analysis revealed significant associations between light pollution and mental health indicators. Specifically, an inverse relationship was observed between nighttime light pollution levels and the prevalence of depression among U.S. adults (p = 0.01). A similar inverse association was found between light pollution and smoking rates (p = 0.02). These findings are illustrated in Figure 5. Interestingly, our analysis revealed divergent patterns across different health metrics. In contrast to the inverse relationships observed with depression and smoking, we found a statistically significant positive association between light pollution levels and binge drinking prevalence (p = 0.04). This suggests that areas with greater artificial nighttime illumination tended to report higher rates of excessive alcohol consumption. However, no significant correlation emerged between light pollution and self-reported poor mental health lasting two or more weeks in the preceding month (p > 0.05). 4. Discussion 4-1. Population Characteristics Our study examined light pollution patterns across 35 U.S. metropolitan areas using a novel algorithmic approach, with corresponding mental health indicators drawn from the 2015 CDC BRFSS dataset. The strong positive correlation observed between urban population size and light pollution levels (p = 0.03) serves as validation for our satellite-based estimation method. The approximately equal mean and median light pollution values (both 0.52) suggest a roughly symmetrical distribution of this variable across our sample (Figure 6). While these findings support the reliability of our approach, broader geographic sampling would strengthen confidence in these results. Hierarchical cluster analysis (HCA) revealed meaningful spatial patterns in light pollution distribution that extend beyond simple population metrics. Our results challenge the conventional assumption that urban light pollution depends solely on a city's population size. While population remains a significant determinant, we identified proximity to major metropolitan centers as an equally crucial factor. Notably, satellite cities in the Northeast corridor - including Newark, Boston, Camden, and Philadelphia - exhibited disproportionately high light pollution levels relative to their population sizes, likely due to their spatial integration with the New York metropolitan area (Figure 7). This spillover effect suggests that regional light pollution patterns form interconnected networks rather than isolated urban phenomena. 4-2. Mental Health Implications The relationship between artificial light exposure and depression remains complex and somewhat paradoxical. Experimental evidence presents conflicting findings: while An et al. (2020) demonstrated that nocturnal blue light exposure induced depression-like symptoms in mice, Strong et al. (2009) found blue light therapy actually improved depressive symptoms in humans with seasonal affective disorder. This discrepancy may reflect fundamental differences between acute therapeutic applications versus chronic environmental exposure. Our findings of an inverse relationship between urban light pollution and depression prevalence (p = 0.01) suggest several possible mechanisms: 1. Blue Light Exposure: The widespread adoption of LED lighting (which emits stronger blue wavelengths than traditional incandescent bulbs) may inadvertently provide antidepressant effects in urban populations through circadian modulation, similar to clinical light therapy but at lower intensities (Harvard Medical School, n.d.). 2. Neuroplasticity Effects: Chronic light exposure might induce cortical changes comparable to those observed in rTMS treatments (Heidarpanah, 2022), though through different physiological pathways. 3. Behavioral Correlates: The reduced smoking rates in high-light areas (p = 0.02) align with the self-medication hypothesis, where individuals with untreated depression often smoke more heavily (Royal College of Physicians, 2013). The positive association between light pollution and binge drinking (p = 0.04) likely reflects increased nighttime economic activity in brightly-lit cities, where extended operating hours for alcohol establishments facilitate greater consumption (Dawson, 1996). This is particularly concerning given evidence that late-night drinking carries elevated health risks (Assanangkornchai et al., 2000). 5. Conclusion Our study developed an innovative satellite-based method to quantify urban light pollution and examined its associations with mental health indicators across 35 U.S. metropolitan areas. The analysis revealed a statistically significant inverse relationship between light pollution levels and both depression prevalence (p = 0.01) and smoking rates (p = 0.02), while showing a positive correlation with binge drinking behavior (p = 0.04). These contrasting patterns suggest artificial nighttime lighting may differentially influence mental health outcomes, potentially through distinct biological or behavioral pathways. The inverse association with depression raises intriguing questions about possible antidepressant effects of controlled light exposure, contrasting with known sleep-disrupting impacts of light pollution. Similarly, the reduced smoking rates in high-light areas might reflect lower depression prevalence, consistent with the self-medication hypothesis of nicotine use. Conversely, the binge drinking association likely stems from extended nighttime economic activity in brightly illuminated urban centers. While our methodology demonstrates promise for large-scale environmental health studies, these findings should be interpreted cautiously given the observational design. 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U.S. population clock . https://www.census.gov/popclock/ Zamaninasab, H., & Bajelan, Z. (2023). The relationship between groundwater nitrate pollution and crime in United States: Nitrate-crime hypothesis . arXiv preprint arXiv:2306.09354. Zamaninasab, H., & Heidarpanah, A. (2025). Does the usage habits of smart wireless devices affect educational status and sleep quality? The results of an anonymous questionnaire of Iranian high school students . Iranian Journal of Child Neurology, 19 (2), 93. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-7233105","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491921522,"identity":"99abdad2-dc06-40b3-afe1-a15bcc56b7a3","order_by":0,"name":"Hossein 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11:02:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7233105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7233105/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87900952,"identity":"9797c49e-c2e1-4ae5-8363-36e6c20b566d","added_by":"auto","created_at":"2025-07-30 08:14:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19938,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical Distribution of the Studied Cities in America\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/138485314c128cfeb2b6b9ca.png"},{"id":87900957,"identity":"f2f60f36-0f7f-4fc1-a985-2b8cfe05926d","added_by":"auto","created_at":"2025-07-30 08:14:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":934733,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite Image of the Studied Cities at Night\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/085b750f425df5634b7d8a4c.png"},{"id":87901849,"identity":"2e9c3bb5-eba6-4d8b-b061-aa912bdd1d03","added_by":"auto","created_at":"2025-07-30 08:22:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188776,"visible":true,"origin":"","legend":"\u003cp\u003eHeat Map of Light Pollution in 35 Selected Cities in the United States\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/01ebeb0f07489d9ae20bc4b9.png"},{"id":87900954,"identity":"b7968234-c37b-4789-8a84-653ce7468aec","added_by":"auto","created_at":"2025-07-30 08:14:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20271,"visible":true,"origin":"","legend":"\u003cp\u003eDirect Relationship Between City Population and Light Pollution Among the American Adult Population in Selected Cities\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/2cf01dafc8dfe3a19f7c5a27.png"},{"id":87900960,"identity":"b3aa3cce-09de-4b98-b816-179df7458544","added_by":"auto","created_at":"2025-07-30 08:14:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96748,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship Between the Prevalence of Selected Mental Health Indicators and Light Pollution Among the Adult American Population in Selected Cities\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/9fa93b6deb7696bbcbe489bd.png"},{"id":87901848,"identity":"8741c72a-ff34-4629-bab4-2bfdb907f374","added_by":"auto","created_at":"2025-07-30 08:22:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30891,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of Selected U.S. Cities Based on Their Light Pollution\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/493e78844843cc9c5dc9e41d.png"},{"id":87901850,"identity":"6cafa3a7-74ca-44eb-b32b-b91a9441c088","added_by":"auto","created_at":"2025-07-30 08:22:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47405,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical Cluster Analysis of the Studied Cities\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/c7849e76eccd72062106d4f1.png"},{"id":87905367,"identity":"4d3d7ea5-e5ff-4ece-b66c-473609556664","added_by":"auto","created_at":"2025-07-30 08:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2171258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7233105/v1/b9112256-3584-4d97-a719-091fd4894ebd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Negative Correlation Between Nighttime Light Pollution and the Prevalence of Depressive Disorders: A Medical Image-Processing Study on 35 US Cities\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e1-1. Light Pollution\u003c/p\u003e\n\u003cp\u003eThroughout Earth\u0026apos;s history, nocturnal darkness has always been tempered by natural light sources. Moonlight, starlight from our galaxy and beyond, and zodiacal light have illuminated nightscapes for millennia (Falchi et al., 2016). The advent of electric lighting fundamentally altered this balance, creating what we now recognize as artificial light pollution (Cinzano et al., 2000). Urban areas in particular have experienced dramatic increases in nighttime illumination, with consequences we are only beginning to understand.\u003c/p\u003e\n\u003cp\u003e1-2. The Accelerating Pace of Light Pollution\u003c/p\u003e\n\u003cp\u003eThe proliferation of artificial lighting has accelerated dramatically in recent decades. Falchi and Bar\u0026aacute; (2023) document that global artificial illumination now doubles every eight years, a startling rate of increase that far outpaces population growth. This trend shows no signs of abating as urbanization expands and lighting technologies become more affordable.\u003c/p\u003e\n\u003cp\u003e1-3. Health Consequences of Light Exposure\u003c/p\u003e\n\u003cp\u003eThe public health implications of light pollution are increasingly apparent. Research demonstrates wide-ranging physiological effects, from melatonin suppression to cardiovascular changes and sleep cycle disruption (Lockley et al., 2003). Our previous work on environmental health indicators (Zamaninasab \u0026amp; Bajelan, 2023) suggests such anthropogenic changes often have unexpected secondary effects. Historical evidence reveals how profoundly modern lighting has altered human biology - where pre-industrial societies naturally followed biphasic sleep patterns (Ekirch, 2001; De La Iglesia et al., 2015), contemporary populations exhibit fundamentally different circadian rhythms. In addition to shifting sleep patterns away from a biphasic cycle, artificial nighttime lighting has contributed to later bedtimes, reduced nighttime sleep duration, and increased daytime sleepiness (Aulsebrook et al., 2018; Beale et al., 2017).\u003c/p\u003e\n\u003cp\u003eThe sleep-disrupting effects of artificial light are well-documented, including delayed sleep onset, reduced sleep duration, and increased daytime sleepiness (Ohayon \u0026amp; Milesi, 2016). Our research on electronic devices (Zamaninasab \u0026amp; Heidarpanah, 2025) confirms similar patterns from personal light sources, suggesting urban light pollution may compound these effects through environmental exposure.\u003c/p\u003e\n\u003cp\u003eThe relationship between sleep and mental health appears bidirectional. While depression frequently involves sleep disturbances like insomnia, poor sleep quality itself elevates depression risk (Johnson et al., 2006). Those with sleep problems show triple the prevalence of major depressive disorder compared to good sleepers (Brietzke et al., 2019). This complex web extends to substance use - sleep problems correlate with both smoking and binge drinking (Rhee et al., 2021), while smoking and alcohol consumption themselves interact in ways that may exacerbate mental health vulnerabilities (Gubner et al., 2016). Indeed, heavy smoking and alcohol abuse disorders independently increase the likelihood of developing major depressive disorder by approximately two to four times compared to the general population (Grant et al., 2015; Pasco et al., 2008).\u003c/p\u003e\n\u003cp\u003e1-4. Spectral Considerations: The Blue Light Effect\u003c/p\u003e\n\u003cp\u003eThe spectral composition of artificial light proves particularly significant. Blue wavelength light exerts disproportionate effects on physiology, more potently elevating heart rate, body temperature, and melatonin suppression than other visible light (Holzman, 2010). Experimental data shows blue light reduces melatonin secretion twice as effectively as green light while causing greater circadian disruption (Lockley et al., 2003). Red light, by contrast, shows minimal circadian impact (Harvard Medical School, n.d.).\u003c/p\u003e\n\u003cp\u003e1-5. Study Objectives\u003c/p\u003e\n\u003cp\u003eThis study addresses two key questions: First, can satellite image analysis provide a reliable, cost-effective method for comparative light pollution assessment? Second, what relationships exist between measured light pollution and depression prevalence in urban populations? Our approach combines novel image processing techniques with established public health datasets to explore these environmental-health connections.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch2\u003e2-1. Image Processing Methodology\u003c/h2\u003e\n\u003cp\u003eWe developed a novel PHP-based algorithm to quantify light pollution from high-resolution nighttime satellite imagery. Our processing pipeline begins by filtering out non-residential areas through threshold-based exclusion of dark pixels. The remaining pixels are analyzed for their RGB spectral composition, with particular attention to blue wavelength intensities due to their known biological effects (Lockley et al., 2003). The algorithm outputs a normalized light pollution index (LPI) ranging from 0 (minimal light pollution) to 1 (maximal light pollution).\u003c/p\u003e\n\u003cp\u003eThe software stack utilized PHP 7.4 for image processing and MySQL 5.5 for data management, hosted on a dedicated server (Mizbanfa Hosting) with the following specifications:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Processor: 3.8 GHz\u003c/p\u003e\n\u003cp\u003e\u0026middot; Memory: 32GB DDR4 RAM\u003c/p\u003e\n\u003cp\u003e\u0026middot; Network: 10 Gbps dedicated connection\u003c/p\u003e\n\u003ch2\u003e2-2. Study Area Characteristics\u003c/h2\u003e\n\u003cp\u003eOur analysis focused on the contiguous United States (latitudinal range: 19.50\u0026deg;N to 64.86\u0026deg;N; longitudinal range: 161.76\u0026deg;W to 68.01\u0026deg;W), representing the world\u0026apos;s third most populous nation (U.S. Census Bureau, 2020) and largest economy by GDP (IMF, 2022). The selected region encompasses diverse urban environments suitable for testing our light pollution estimation method.\u003c/p\u003e\n\u003ch2\u003e2-3. Satellite Data Acquisition\u003c/h2\u003e\n\u003cp\u003eWe analyzed composite imagery from the Suomi National Polar-orbiting Partnership (NPP) satellite, collected between April-October 2012 (NASA Earth Observatory, 2012). The dataset captures emissions across visible and near-infrared spectra (500-900 nm), including:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Artificial city lights\u003c/p\u003e\n\u003cp\u003e\u0026middot; Natural phenomena (auroras, moonlight)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Transient events (wildfires, gas flares)\u003c/p\u003e\n\u003cp\u003eTechnical specifications:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Resolution: 6646 \u0026times; 4430 pixels\u003c/p\u003e\n\u003cp\u003e\u0026middot; Spatial accuracy: 750 m at nadir\u003c/p\u003e\n\u003cp\u003e\u0026middot; Radiometric depth: 16-bit\u003c/p\u003e\n\u003ch2\u003e2-4. Analytical Approach\u003c/h2\u003e\n\u003cp\u003eOur study employed a two-phase analytical design:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 1:\u003c/strong\u003e Light pollution mapping\u003c/p\u003e\n\u003cp\u003e\u0026middot; Selected 35 metropolitan statistical areas (MSAs) meeting strict criteria:\u003c/p\u003e\n\u003cp\u003eo Non-composite urban areas\u003c/p\u003e\n\u003cp\u003eo Consistent image quality across all samples\u003c/p\u003e\n\u003cp\u003eo Geographic distribution representing all US regions\u003c/p\u003e\n\u003cp\u003e\u0026middot; Processed satellite images through our algorithm to generate LPI values\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhase 2:\u003c/strong\u003e Health data correlation\u003c/p\u003e\n\u003cp\u003e\u0026middot; Paired LPI results with mental health indicators derived from the CDC\u0026apos;s Behavioral Risk Factor Surveillance System (BRFSS) (Pickens, Pierannunzi, Garvin \u0026amp; Town, 2018):\u003c/p\u003e\n\u003cp\u003eo Depression prevalence\u003c/p\u003e\n\u003cp\u003eo Poor mental health days\u003c/p\u003e\n\u003cp\u003eo Substance use patterns\u003c/p\u003e\n\u003cp\u003e\u0026middot; Compared light pollution estimates with mental health indicators across cities with varying population sizes\u003c/p\u003e\n\u003cp\u003eThe geographic distribution of study sites appears in Figure 1, with complete demographic characteristics provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic and Geographic Information of the Studied Cities\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLongitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eAkron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e190469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e41.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-81.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eOklahoma City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e681054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e35.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-97.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eAlbuquerque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e564559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e35.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-106.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003ePhiladelphia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e1603797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-75.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eBaton Rouge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e227470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e30.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-91.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003ePittsburgh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e302971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e40.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-79.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eBillings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e117116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e45.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-108.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eRaleigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e467665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e35.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-78.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eBoise City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e235684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e43.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-116.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eReno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e264165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-119.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eBoston\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e675647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e42.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-71.0589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eRichmond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e226610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e37.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-77.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eCamden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e71791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-75.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eRochester, MN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e121395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e44.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-92.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eCharleston\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e150227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e38.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-81.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eRochester, NY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e211328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e43.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-77.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eColumbus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e905748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-82.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eSalt Lake City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e199723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e40.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-111.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eCorpus Christi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e317863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e27.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-97.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eSpringfield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e169176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e42.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-72.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eDayton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e137644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-84.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eSt. Louis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e301578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e38.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-90.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eEl Paso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e678815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e31.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-106.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eToledo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e270871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e41.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-83.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eJackson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e153701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e32.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-90.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eTopeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e126587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e39.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-95.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eJacksonville\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e949611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e30.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-81.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eTulsa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e413066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e36.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-95.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eKnoxville\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e190740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e35.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-83.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eTuscaloosa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e99600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e33.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-87.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eLincoln\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e291082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e40.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-96.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eWichita Falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e102316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e33.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-98.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eMemphis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e633104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e35.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-90.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003eWichita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e397532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e37.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e-97.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0216%;\"\u003e\n \u003cp\u003eNewark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e311549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e40.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.4777%;\"\u003e\n \u003cp\u003e-74.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.31433%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4807%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8644%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.70724%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2481%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* The populations are taken from the USA 2020 census.\u003c/p\u003e\n\u003cp\u003eUsing standardized image segmentation protocols, the composite satellite image was partitioned into 35 uniformly-sized tiles that were subsequently processed by our analytical software (Figure 2).\u003c/p\u003e\n\u003cp\u003e2-5. Probability Distribution and Data Analysis\u003c/p\u003e\n\u003cp\u003eThe image processing outputs were first organized into tabular format using Microsoft\u0026reg; Excel\u0026reg; LTSC MSO (Version 2302, Build 16.0.16130.20186). These structured datasets were then imported into R statistical software (version 4.2.2) for comprehensive analysis, including descriptive statistics, graphical visualization, and hierarchical cluster analysis (HCA) to identify spatial patterns in light pollution. To represent the geographical distribution of results, we generated heat maps using ArcGIS Pro (version 3.0.2), employing kernel density estimation to ensure accurate spatial representation of light pollution levels across urban areas.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3-1. Overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis of satellite imagery revealed significant variation in light pollution levels across the studied metropolitan areas. The relative light pollution index ranged from 43.26% in Charleston, West Virginia - representing the lowest observed value - to 65.22% in Newark, New Jersey, which exhibited the highest degree of artificial nighttime illumination (Figure 3). This 21.96 percentage point difference between the extremes demonstrates substantial variability in light pollution exposure among urban populations in our sample.\u003c/p\u003e\n\u003cp\u003eThe light pollution estimates were systematically combined with key mental health indicators from the BRFSS report in Table 2. This comprehensive dataset includes:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Clinician-diagnosed depressive disorder prevalence\u003c/p\u003e\n\u003cp\u003e\u0026middot; Self-reported poor mental health (\u0026ge;14 days in past month)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Current smoking rates\u003c/p\u003e\n\u003cp\u003e\u0026middot; Recent binge drinking prevalence (within past 30 days)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Aggregated Results of Light Pollution Against the Prevalence of Some Mental Health Indicators in the BRFSS Report\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"706\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eNewark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.6522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eMemphis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.5117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n 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6.96023%;\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eBoston\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n 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\u003cp\u003e0.5417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eRochester, NY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eSt. Louis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.5411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eToledo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eReno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eJackson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eBaton Rouge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.5357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eBillings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eBoise City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.5322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eWichita Falls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eSpringfield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.5306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eRochester, MN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003ePittsburgh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.5252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003eCharleston\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e0.4326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 3.69318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0682%;\"\u003e\n \u003cp\u003eWichita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.95455%;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.67614%;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.97727%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.65909%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.53409%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.81818%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.96023%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3-2. Light Pollution and Urban Population\u003c/p\u003e\n\u003cp\u003eOur analysis revealed a statistically significant positive association between urban population size and light pollution levels (p = 0.03), as illustrated in Figure 4. This relationship suggests that more populous cities tend to exhibit greater degrees of artificial nighttime illumination.\u003c/p\u003e\n\u003cp\u003e3-3. Light Pollution and Mental Health Indicators\u003c/p\u003e\n\u003cp\u003eOur statistical analysis revealed significant associations between light pollution and mental health indicators. Specifically, an inverse relationship was observed between nighttime light pollution levels and the prevalence of depression among U.S. adults (p = 0.01). A similar inverse association was found between light pollution and smoking rates (p = 0.02). These findings are illustrated in Figure 5.\u003c/p\u003e\n\u003cp\u003eInterestingly, our analysis revealed divergent patterns across different health metrics. In contrast to the inverse relationships observed with depression and smoking, we found a statistically significant positive association between light pollution levels and binge drinking prevalence (p = 0.04). This suggests that areas with greater artificial nighttime illumination tended to report higher rates of excessive alcohol consumption. However, no significant correlation emerged between light pollution and self-reported poor mental health lasting two or more weeks in the preceding month (p \u0026gt; 0.05).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4-1. Population Characteristics\u003c/p\u003e\n\u003cp\u003eOur study examined light pollution patterns across 35 U.S. metropolitan areas using a novel algorithmic approach, with corresponding mental health indicators drawn from the 2015 CDC BRFSS dataset. The strong positive correlation observed between urban population size and light pollution levels (p = 0.03) serves as validation for our satellite-based estimation method. The approximately equal mean and median light pollution values (both 0.52) suggest a roughly symmetrical distribution of this variable across our sample (Figure 6). While these findings support the reliability of our approach, broader geographic sampling would strengthen confidence in these results.\u003c/p\u003e\n\u003cp\u003eHierarchical cluster analysis (HCA) revealed meaningful spatial patterns in light pollution distribution that extend beyond simple population metrics. Our results challenge the conventional assumption that urban light pollution depends solely on a city\u0026apos;s population size. While population remains a significant determinant, we identified proximity to major metropolitan centers as an equally crucial factor. Notably, satellite cities in the Northeast corridor - including Newark, Boston, Camden, and Philadelphia - exhibited disproportionately high light pollution levels relative to their population sizes, likely due to their spatial integration with the New York metropolitan area (Figure 7). This spillover effect suggests that regional light pollution patterns form interconnected networks rather than isolated urban phenomena.\u003c/p\u003e\n\u003cp\u003e4-2. Mental Health Implications\u003c/p\u003e\n\u003cp\u003eThe relationship between artificial light exposure and depression remains complex and somewhat paradoxical. Experimental evidence presents conflicting findings: while An et al. (2020) demonstrated that nocturnal blue light exposure induced depression-like symptoms in mice, Strong et al. (2009) found blue light therapy actually improved depressive symptoms in humans with seasonal affective disorder. This discrepancy may reflect fundamental differences between acute therapeutic applications versus chronic environmental exposure.\u003c/p\u003e\n\u003cp\u003eOur findings of an inverse relationship between urban light pollution and depression prevalence (p = 0.01) suggest several possible mechanisms:\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eBlue Light Exposure:\u003c/strong\u003e The widespread adoption of LED lighting (which emits stronger blue wavelengths than traditional incandescent bulbs) may inadvertently provide antidepressant effects in urban populations through circadian modulation, similar to clinical light therapy but at lower intensities (Harvard Medical School, n.d.).\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eNeuroplasticity Effects:\u003c/strong\u003e Chronic light exposure might induce cortical changes comparable to those observed in rTMS treatments (Heidarpanah, 2022), though through different physiological pathways.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eBehavioral Correlates:\u003c/strong\u003e The reduced smoking rates in high-light areas (p = 0.02) align with the self-medication hypothesis, where individuals with untreated depression often smoke more heavily (Royal College of Physicians, 2013).\u003c/p\u003e\n\u003cp\u003eThe positive association between light pollution and binge drinking (p = 0.04) likely reflects increased nighttime economic activity in brightly-lit cities, where extended operating hours for alcohol establishments facilitate greater consumption (Dawson, 1996). This is particularly concerning given evidence that late-night drinking carries elevated health risks (Assanangkornchai et al., 2000).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study developed an innovative satellite-based method to quantify urban light pollution and examined its associations with mental health indicators across 35 U.S. metropolitan areas. The analysis revealed a statistically significant inverse relationship between light pollution levels and both depression prevalence (p\u0026thinsp;=\u0026thinsp;0.01) and smoking rates (p\u0026thinsp;=\u0026thinsp;0.02), while showing a positive correlation with binge drinking behavior (p\u0026thinsp;=\u0026thinsp;0.04). These contrasting patterns suggest artificial nighttime lighting may differentially influence mental health outcomes, potentially through distinct biological or behavioral pathways. The inverse association with depression raises intriguing questions about possible antidepressant effects of controlled light exposure, contrasting with known sleep-disrupting impacts of light pollution. Similarly, the reduced smoking rates in high-light areas might reflect lower depression prevalence, consistent with the self-medication hypothesis of nicotine use. Conversely, the binge drinking association likely stems from extended nighttime economic activity in brightly illuminated urban centers. While our methodology demonstrates promise for large-scale environmental health studies, these findings should be interpreted cautiously given the observational design. Future research should expand to global cities, incorporate longitudinal data, and investigate spectral characteristics of urban lighting to better understand these complex relationships. The developed light pollution estimation technique offers public health researchers a practical, cost-effective tool for further investigating these critical environment-health interactions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAn, K., Zhao, H., Miao, Y., Xu, Q., Li, Y. F., Ma, Y. Q., Shi, Y. M., Shen, J. W., Meng, J. J., Yao, Y. G., \u0026amp; Zhang, Z. (2020). A circadian rhythm-gated subcortical pathway for nighttime-light-induced depressive-like behaviors in mice. \u003cem\u003eNature Neuroscience, 23\u003c/em\u003e(7), 869\u0026ndash;880.\u003c/li\u003e\n \u003cli\u003eAssanangkornchai, S., Saunders, J. B., \u0026amp; Conigrave, K. M. (2000). Patterns of drinking in Thai men. \u003cem\u003eAlcohol and Alcoholism, 35\u003c/em\u003e(3), 263\u0026ndash;269.\u003c/li\u003e\n \u003cli\u003eAulsebrook, A. E., Jones, T. M., Mulder, R. A., \u0026amp; Lesku, J. A. (2018). 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Narrow-band blue-light treatment of seasonal affective disorder in adults and the influence of additional nonseasonal symptoms. \u003cem\u003eDepression and Anxiety, 26\u003c/em\u003e(3), 273\u0026ndash;278.\u003c/li\u003e\n \u003cli\u003eU.S. Census Bureau. (n.d.). \u003cem\u003eU.S. population clock\u003c/em\u003e. https://www.census.gov/popclock/\u003c/li\u003e\n \u003cli\u003eZamaninasab, H., \u0026amp; Bajelan, Z. (2023). \u003cem\u003eThe relationship between groundwater nitrate pollution and crime in United States: Nitrate-crime hypothesis\u003c/em\u003e. arXiv preprint arXiv:2306.09354.\u003c/li\u003e\n \u003cli\u003eZamaninasab, H., \u0026amp; Heidarpanah, A. (2025). \u003cem\u003eDoes the usage habits of smart wireless devices affect educational status and sleep quality? The results of an anonymous questionnaire of Iranian high school students\u003c/em\u003e. \u003cem\u003eIranian Journal of Child Neurology, 19\u003c/em\u003e(2), 93.\u003cspan dir=\"RTL\"\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Light Pollution, Depressive Disorders, Depression Epidemiology, United States, Satellite Image Processing","lastPublishedDoi":"10.21203/rs.3.rs-7233105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7233105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Growing evidence suggests potential links between artificial nighttime illumination and neurological health outcomes. This study introduces an innovative satellite image analysis technique to quantify light pollution while investigating its association with depressive disorders across urban populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We developed a PHP-based image processing algorithm to estimate relative light pollution levels from high-resolution nighttime satellite imagery. Applying this method to 35 U.S. metropolitan areas, we correlated the findings with depression prevalence data from the CDC's Behavioral Risk Factor Surveillance System (Pickens et al., 2018). Our analysis incorporated multiple mental health indicators, including depression diagnoses, smoking rates, and binge drinking patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our analysis revealed three key findings: (1) a positive correlation between urban population density and light pollution intensity (p=0.03), (2) an inverse relationship between light pollution levels and both depression prevalence (p=0.01) and smoking rates (p=0.02), and (3) a positive association between light pollution and binge drinking reports (p=0.04). These patterns remained significant after controlling for population variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The proposed satellite image analysis method provides a cost-effective approach for large-scale light pollution assessment in neuroepidemiological research. Our findings suggest complex relationships between artificial nighttime lighting and mental health indicators that warrant further investigation, particularly regarding potential mediating factors in urban environments.\u003c/p\u003e","manuscriptTitle":"The Negative Correlation Between Nighttime Light Pollution and the Prevalence of Depressive Disorders: A Medical Image-Processing Study on 35 US Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 08:14:47","doi":"10.21203/rs.3.rs-7233105/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2445d13-2c25-4f29-ba4f-76fd8c201ee3","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52227440,"name":"Statistical Epidemiology"},{"id":52227441,"name":"Environmental Policy"},{"id":52227442,"name":"Psychology"},{"id":52227443,"name":"Psychiatry"},{"id":52227444,"name":"Ecological Modeling"},{"id":52227445,"name":"Behavioral Geography"}],"tags":[],"updatedAt":"2025-07-30T08:14:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 08:14:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7233105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7233105","identity":"rs-7233105","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}