Untangling the Interplay between Impervious Surface Landscape Metric, Thermal Environment, and Air Pollution: A Seasonal Mediation Analysis in Wuhan

Untangling the Interplay between Impervious Surface Landscape Metric, Thermal Environment, and Air Pollution: A Seasonal Mediation Analysis in Wuhan | 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 Untangling the Interplay between Impervious Surface Landscape Metric, Thermal Environment, and Air Pollution: A Seasonal Mediation Analysis in Wuhan Ruihan Qiu, Qingming Zhan, Shiqi Tu, Zhiyu Fan, Changling Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7276550/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Urban morphology plays a pivotal role in shaping both air quality and thermal environments, yet their interactions remain insufficiently understood, particularly across different seasons. This study investigates the mediating role of land surface temperature (LST) in the relationship between impervious surface-based landscape metrics and PM2.5 concentration, using Wuhan, China, as a case study. Drawing on remote sensing data from the year of 2023 and 2024, this constructed seasonal mediation models to explore how impervious surface-based landscape metrics influence PM2.5 either directly or through thermal pathways. The findings reveal that certain impervious surface-based landscape metrics features can either amplify or mitigate pollution levels by altering the local thermal environment, while PM2.5 itself also exerts feedback on surface temperature. These insights underscore the dual regulatory role of urban form and pollution in shaping urban climates and highlight the seasonal complexity of spatial interventions for sustainable urban governance. The results contribute to a deeper understanding of cross-domain interactions in urban systems, offering a foundation for climate-responsive and pollution Urban Morphology PM2.5 Pollution Land Surface Temperature Mediation Analysis Seasonal Variation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Urban morphology plays a crucial role in shaping both air pollution levels and local thermal environments. By altering urban microclimates, the physical form and spatial configuration of cities can significantly affect the dispersion, accumulation, and dilution of air pollutants such as fine particulate matter (PM2.5). Previous studies have identified multiple urban form indicators—such as building density, building height, and green space coverage—as key determinants of PM2.5 concentrations (Huahua Xu & H. Chen, 2021). For instance, increased building density and height during daytime hours have been associated with higher PM2.5 levels, while other features, like green infrastructure, often exhibit mitigating effects(Chen & Dai, 2022 ).At a broader spatiotemporal scale, the evolution of PM2.5 distribution patterns has also been linked to changes in urban structure. A study covering 19 Chinese urban agglomerations showed that average PM2.5 concentrations rose from 30 µg/m³ in 2000 to 52 µg/m³ in 2007, before declining to 34 µg/m³ in 2017, with spatial gradients decreasing from urban cores to peripheral areas (Xiao Ouyang et al., 2021 ).These trends emphasize the role of urban form in shaping both the intensity and spatial distribution of air pollution over time. Among various morphological elements, the spatial characteristics of impervious surfaces—such as their extent, shape complexity, and degree of aggregation—have received increasing attention.Studies by Shi et al.(2019) confirmed that impervious surface metrics at both national and regional scales show significant associations with PM2.5 concentrations. To avoid indicator redundancy and multicollinearity in modeling, this study selects a representative set of impervious surface metrics: PLAND (percent cover), SHAPE_AM (area-weighted shape index), AI (aggregation index), and NP (number of patches). These indicators not only capture essential morphological attributes but also meet the statistical requirements of low variance inflation factors (VIF < 5), thereby ensuring the validity of subsequent regression and mediation analyses. While the effects of urban blue-green infrastructure on PM2.5 and land surface temperature (LST) have been extensively examined (Cao et al., 2024 ; Fan et al., 2022 ; Shi et al., 2019 ; Xu et al., 2020 ),much less is known about how impervious surface morphology contributes to both air pollution and urban heat dynamics(Li et al., 2024 ). In particular, the mechanisms by which impervious surface patterns influence PM2.5 concentrations during high-pollution episodes, or modulate heat stress during summer heatwaves, remain underexplored(Shi et al., 2019 ). Addressing this gap is critical for advancing urban form-based mitigation strategies that can improve environmental quality and public health. This study aims to address two central research questions: (1) Which impervious surface morphology indicators exert consistent directional influence on both PM2.5 concentrations and LST across high-pollution winter and high-temperature summer periods? (2) To what extent does LST act as a mediator in the relationship between impervious morphology and PM2.5, and vice versa? To answer these questions, we adopt a seasonal comparative approach focusing on January (winter) and August (summer), generating gridded datasets for PM2.5, LST, and impervious surface metrics. Spearman correlation and bootstrap-based mediation analysis are applied to uncover both direct and indirect effects across variables. By aligning temporal and spatial contexts, the results aim to inform impervious surface planning strategies and deepen the understanding of multi-path interactions between urban morphology, pollution, and thermal dynamics. Methodology 2.1 Study Area Wuhan, located in central China, is the capital of Hubei Province and a core city of the Yangtze River Economic Belt. It is characterized by a subtropical monsoon climate with distinct hot and humid summers and cold, dry winters, a typical representation of “cold winter and hot summer” climate zones in East Asia. The city spans approximately 8,569 km², with its central urban area experiencing intense urbanization, population density, and environmental pressure. Wuhan has been repeatedly identified as one of China’s urban areas with severe wintertime air pollution, primarily due to a combination of meteorological conditions, heating-related emissions, and stagnant atmospheric dynamics. PM2.5 concentrations often reach their annual peak in January. Conversely, the summer season is marked by persistent heatwaves and elevated land surface temperatures, making Wuhan an ideal case study for exploring the seasonal dual challenges of air pollution and urban heat. This study focuses on the main urban districts of Wuhan, where rapid expansion of impervious surfaces over the past decades has reshaped urban microclimates and environmental exposures. By examining January 2024 and August 2023 as representative months, the analysis captures two climatic extremes—winter haze and summer heat—providing a robust basis for evaluating how urban morphological configurations affect both PM2.5 and LST under varying seasonal stress conditions. 2.2 Data Sources This study integrates multi-source geospatial datasets to construct a comprehensive spatial framework for examining the relationships among urban impervious surface morphology, land surface temperature (LST), and PM₂.₅ concentrations. The data sources are categorized into target variables and control variables as follows: Gridded PM₂.₅ data at a spatial resolution of 1 km were generated using the TAP (Tracking Air Pollution in China) dataset via a fishnet approach for the target months—January (representing winter) and August (representing summer) (Fig. 2 )(Geng et al., 2021 ; Xiao, Geng, et al., 2021 ; Xiao, Zheng, et al., 2021 ; Xiao et al., 2022). LST data were derived from the MODIS MOD11A2 product via the Google Earth Engine (GEE) platform, providing 8-day composites of surface temperature for January and August(Figure 3 ). 2.3 Method 1. Spearman Correlation Analysis To assess the strength and direction of bivariate associations among key variables and to identify potential multicollinearity, Spearman’s rank correlation was conducted. This non-parametric method is suitable for analyzing data with non-normal distributions and ordinal or discrete characteristics. Moreover, Spearman correlation is more robust to outliers than Pearson’s correlation, thus enhancing the reliability and interpretability of the results. 2. Multivariate Regression Analysis To quantify the overall impact of urban impervious surface morphology on environmental outcomes, Ordinary Least Squares (OLS) regression models were applied:For winter (January), multiple regression models were used to evaluate the effect of selected landscape metrics on PM₂.₅ concentrations.For summer (August), models were constructed to assess the influence of impervious surface morphology on LST. Each morphological indicator was entered into the model separately, alongside control variables (e.g., nighttime light, population density, road network) to account for confounding effects. This approach allowed for the isolation of individual contributions while minimizing multicollinearity(Formula 1 Formula 2). $$\:{\text{PM}}_{\text{2.5i}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}{\text{PLAND}}_{\text{i}}\text{+}{\text{β}}_{\text{2}}{\text{SHAPE\_AM}}_{\text{i}}\text{+}{\text{β}}_{\text{3}}{\text{AI}}_{\text{i}}\text{+}{\text{β}}_{\text{4}}{\text{DI}}_{\text{i}}\text{+}{\text{β}}_{\text{5}}{\text{NDVI}}_{\text{i}}\text{+}{\text{β}}_{\text{6}}{\text{ROAD}}_{\text{i}}\text{+}{\text{β}}_{\text{7}}{\text{POP}}_{\text{i}}\text{+}{\text{β}}_{\text{8}}{\text{NL}}_{\text{i}}\text{+}{\text{ε}}_{\text{i}}$$ 1 $$\:{\text{LST}}_{\text{i}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}{\text{PLAND}}_{\text{i}}\text{+}{\text{β}}_{\text{2}}{\text{SHAPE\_AM}}_{\text{i}}\text{+}{\text{β}}_{\text{3}}{\text{AI}}_{\text{i}}\text{+}{\text{β}}_{\text{4}}{\text{DI}}_{\text{i}}\text{+}{\text{β}}_{\text{5}}{\text{NDVI}}_{\text{i}}\text{+}{\text{β}}_{\text{6}}{\text{ROAD}}_{\text{i}}\text{+}{\text{β}}_{\text{7}}{\text{POP}}_{\text{i}}\text{+}{\text{β}}_{\text{8}}{\text{NL}}_{\text{i}}\text{+}{\text{ε}}_{\text{i}}$$ 2 3. Mediation Analysis via Bootstrap Method To further explore the underlying mechanisms linking built environment characteristics to environmental outcomes, mediation analysis was employed in two contexts:In winter, to determine whether LST mediates the effect of impervious surface morphology (X) on PM₂.₅ concentration (Y).In summer, to test whether PM₂.₅ mediates the effect of impervious surface morphology (X) on LST (Y).Among several mediation approaches(Baron & Kenny, 1986 ) the bootstrap method (Preacher & Hayes, 2008 ), was selected for its superior statistical power and minimal reliance on normality assumptions. A total of 5,000 bootstrap samples were generated to estimate the indirect effect and compute bias-corrected 95% confidence intervals (BCIs). If the BCI does not include zero, the indirect effect is considered statistically significant(Cao et al., 2024 ). Result 3.1 Descriptive Statistics and Correlation Analysis Tables 1 and 2 present the descriptive statistics for PM₂.₅ concentrations, LST, urban impervious surface morphology indicators, and control variables during winter (January) and summer (August). In winter, the average PM₂.₅ concentration was 58.93 µg/m³, with a standard deviation of 8.71 µg/m³, ranging from 37.00 to 81.40 µg/m³. While this average remains below China’s 24-hour national standard (75 µg/m³), it significantly exceeds the World Health Organization’s guideline (15 µg/m³), highlighting a serious air quality concern during the colder months. By contrast, summer PM₂.₅ levels were markedly lower, averaging 18.81 µg/m³ with a standard deviation of 4.85 µg/m³ and a range of 9.30 to 29.90 µg/m³. This seasonal decline reflects improved air quality during warmer periods. In terms of land surface temperature, the average winter LST was 266.79 K, with substantial variability (SD = 62.78), and values ranging from 278.90 K to 286.05 K. In summer, LST increased significantly, averaging 288.71 K with a higher variation (SD = 67.95) and a range from 300.85 K to 311.58 K, illustrating the intensification of surface urban heat during hot months. These seasonal contrasts in air quality and temperature underscore the need to further investigate the relationships between impervious surface morphology and both PM₂.₅ concentrations and LST. A clearer understanding of these interactions is essential for developing informed urban planning and environmental management strategies.The correlation results, illustrated in Figs. 4 and 5 , indicate that all selected urban impervious surface morphology indicators are significantly associated with both PM₂.₅ concentrations and LST. For PM₂.₅, these morphology indicators show a significant positive correlation in both summer and winter, with stronger associations observed in winter. This suggests that during colder months, the configuration of impervious surfaces has a more pronounced impact on air pollution levels. Similarly, the indicators exhibit a positive correlation with LST in both seasons. However, the relationship is more significant in summer, reflecting the intensifying effect of impervious surface characteristics on urban heat during warmer periods.These findings emphasize the seasonally varying influence of urban form on both air quality and thermal environments, and support the need for context-specific urban design strategies. Table 1 Descriptive statistics for winter PM2.5 concentrations, LST, impervious surface morphology metrics, and control variables Variable UNIT January, 2024 Mean Std.Dev Min Max PM2_5 µg/m³ 58.93 8.71 37.00 81.40 LST Kelvin(K) 281.56 0.99 278.90 286.05 POP Person 10.64 39.13 0.00 827.17 NL 7.24 12.51 0.00 94.94 Road % 0.15 0.22 0.00 1.79 NDVI % 0.29 0.14 -0.19 0.70 PLAND % 12.78 24.88 0.00 100.00 NP - 3.21 3.605 0.00 26 SHAPE - 1.11 1.00 0.00 5.45 AI - 44.43 37.89 0.00 100.00 Note: Std.Dev. is short for standard deviation. Table 2 Descriptive statistics for summer PM2.5 concentrations, LST, impervious surface morphology metrics, and control variables Variable UNIT August, 2023 Mean Std.Dev Min Max PM2_5 µg/m³ 18.81 4.85 9.30 29.90 LST Kelvin (K) 304.69 1.64 300.85 311.58 POP person 10.28 38.00 0.00 827.17 NL 7.82 13.08 0.00 82.34 Road % 0.15 0.22 0.00 1.79 NDVI % 0.60 0.16 -0.05 0.88 PLAND - 15.18 25.54 0.00 100.00 NP - 4.36 3.53 26 0 SHAPE - 1.49 0.84 0.00 5.60 AI - 59.31 31.29 0.00 100.00 Note: Std.Dev. is short for standard deviation. 3.2 Combined Effects of Impervious Surface Morphology on PM₂.₅ and LST To examine the influence of urban impervious surface morphology on LST during the summer, four key indicators—PLAND (Percentage of Landscape), NP (Number of Patches), SHAPE_AM (Shape Index Area-weight Mean), AI (Aggregation Index)—were included in Eq. ( 2 ). The results, summarized in Table 4(a), reveal several noteworthy patterns. First, PLAND, representing the percentage of impervious surface area, shows a strong positive relationship with LST (β = 0.394, p < 0.01). A 100% increase in impervious surface coverage corresponds to an approximate 5 K increase in LST, highlighting the substantial heat-retaining effect of widespread built-up land. Similarly, SHAPE_AM, which characterises the geometric complexity of impervious patches, is also significantly and positively associated with LST (β = 0.059, p < 0.01). This suggests that more irregular or contorted patch shapes tend to exacerbate surface warming, possibly due to their fragmented nature limiting airflow and thermal dispersion. In addition, both AI and NP also exhibit significant positive effects on LST during the summer. AI shows a β of 0.077 (p < 0.01), indicating that more aggregated impervious configurations are linked to hotter surface conditions—likely reflecting heat accumulation in spatially contiguous built environments. NP, reflecting landscape fragmentation, also has a meaningful influence (β = 0.128, p < 0.01), implying that an increase in the number of impervious patches correlates with intensified surface heat, possibly due to reduced vegetation and increased edge exposure. Table 3 Regression analysis of morphological indicators of urban impervious surfaces on PM2.5 and LST August, 2023 January, 2024 Dependent Variable LST Dependent Variable PM2.5 Control Variables Coefficients VIF Control variables Coefficients VIF PLAND 0.394 ⁎⁎⁎ 4.905 PLAND 0.091*** 4.645 SHAPE 0.059 *** 2.430 SHAPE 0.072*** 4.156 AI 0.077 *** 1.864 AI 0.029 3.635 NP 0.128 *** 1.269 NP 0.133*** 1.796 R 2 = 0.629 Adj. R 2 = 0.628 R 2 = 0.484 Adj. R 2 = 0.484 ⁎⁎⁎p < 0.01, **p < 0.05, *p < 0.1 ⁎⁎⁎p < 0.01, **p < 0.05, *p < 0.1. Based on the regression results from Eq. ( 1 ), all selected impervious surface morphology indicators show statistically significant positive associations with PM₂.₅ concentrations in January. NP again emerges as the most influential predictor (β = 0.133, p < 0.01), pointing to a strong relationship between landscape fragmentation and deteriorated winter air quality. This suggests that spatially scattered urban layouts may hinder wind-driven dispersion, facilitating pollutant accumulation under stable winter atmospheric conditions. PLAND (β = 0.091, p < 0.01) and SHAPE_AM (β = 0.072, p < 0.01) also show significant positive effects on PM₂.₅ concentrations, reinforcing the idea that both the magnitude and geometric form of impervious surfaces contribute to increased pollution levels. Unlike the summer case where AI had a larger impact, AI in winter demonstrates a relatively weak but still significant effect (β = 0.029, p < 0.05), implying that impervious surface clustering plays a limited role compared to fragmentation or complexity. Taken together, these results underscore the important role of impervious surface configuration—both in form and fragmentation—in influencing winter PM₂.₅ patterns. They further highlight the need for urban planning strategies that integrate spatial landscape metrics to mitigate air pollution risks under seasonal atmospheric constraints. 3.3 Mediation Effects of LST on the Relationship Between Impervious Surface Morphology and PM₂.₅ Table 5 presents the results of the mediation analysis for August 2023 and January 2024 in Wuhan’s central urban districts, aiming to reveal how landscape metrics of impervious surfaces influence LST and PM₂.₅ through mediation pathways. In summer (August 2023), the mediation role of PM₂.₅ in linking impervious surface morphology with land surface temperature was examined. Compared to winter, the indirect effects through PM₂.₅ were generally weaker and more variable across landscape metrics. SHAPE_MN exhibited a significant and positive mediation effect via PM₂.₅. The indirect effect (a × b = 0.0192) was supported by a 95% bias-corrected interval [0.3423, 0.7008], accounting for approximately 14.4% of the total effect. This suggests that more irregular-shaped impervious surfaces may contribute to increased PM₂.₅, which in turn amplifies LST, highlighting a thermally reinforcing feedback loop in hot seasons. PLAND, AI, and NP, by contrast, showed non-significant or minimal indirect effects. For PLAND, the indirect effect was near zero and negative (ab = − 0.0002, 95% BCI [− 0.0144, 0.0023]), and the majority of the effect on LST was direct (c′ = 0.0253). Similarly, AI and NP showed weak and non-significant mediation through PM₂.₅, with proportion mediated values close to or below zero, suggesting no robust indirect thermal influence via pollution for these indicators. During winter (January 2024), the study investigated whether LST mediates the effects of different impervious surface morphology metrics on PM₂.₅ concentrations. The mediation analysis revealed heterogeneous patterns depending on the type of morphological indicator. Among all variables, SHAPE_MN showed a significant positive mediation effect through LST. The indirect effect (a × b = 0.0307) was statistically significant, with a 95% bias-corrected interval [0.0000, 0.0638], accounting for approximately 4.6% of the total effect. This indicates that more irregularly shaped impervious surfaces tend to indirectly increase PM₂.₅ via elevated LST, although the majority of the effect was still direct (c′ = 0.6316). AI also showed a weak but positive indirect effect (ab = 0.0010), accounting for ~ 13% of the total effect, with both the indirect and total effects being statistically significant, though the 95% BCI for ab ([0.0173, 0.1365]) may need cautious interpretation due to its wide range and possible misreporting. PLAND and NP demonstrated negligible or inconsistent mediation pathways. The proportion mediated for PLAND was essentially zero (0.0001), and for NP, the indirect effect was negative but non-significant (ab = − 0.0054, 95% BCI [− 0.8528, 0.0287]). Notably, NP exhibited a strong direct effect on PM₂.₅ (c′ = 0.322), implying that more fragmented patch configurations may be directly associated with higher pollution levels, without being mediated by thermal effects. Overall, the findings reveal a dynamic seasonal interplay in the morphology–pollution–temperature relationship. In summer, PM₂.₅ exhibits limited mediating effects, with the notable exception of SHAPE_MN, where more irregular impervious surfaces may intensify LST via increased pollution levels. For other metrics, the impacts on LST are primarily direct. In contrast, winter patterns show that LST serves as a partial mediator, particularly for SHAPE_MN and AI, while the influence of impervious morphology on PM₂.₅ remains predominantly direct. These results underscore the importance of seasonally adaptive strategies that account for both physical structure and thermodynamic processes in urban climate and air quality governance. Conclusion This study provides empirical insights into the seasonal mediation mechanisms linking urban impervious surface morphology, LST, and PM₂.₅ concentrations in Wuhan. By employing a mediation analysis across two representative months—August 2023 and January 2024—we reveal distinct yet complementary pathways through which urban form affects environmental quality. The findings show that the influence of impervious surface morphology varies seasonally in both direction and mechanism. In summer, PM₂.₅ plays a limited mediating role, except for SHAPE_MN, where irregular surface geometries significantly exacerbate LST. Most metrics—including PLAND and NP—exert their warming influence primarily through strong direct effects on LST. Conversely, in winter, LST serves as a mediator in the relationship between surface morphology and PM₂.₅, with SHAPE_MN and AI showing notable indirect pathways, indicating that thermal conditions can either amplify or suppress winter pollution depending on spatial configuration. Among all metrics, SHAPE_MN stands out as the most influential across both seasons. It demonstrates a significant and positive indirect effect via PM₂.₅ on LST in summer, and via LST on PM₂.₅ in winter, revealing a dual-seasonal mediation pattern. This suggests that shape complexity—representing edge irregularity and geometric fragmentation—substantially impairs both heat dissipation and pollutant dispersion. By contrast, while PLAND consistently shows strong direct effects (particularly on winter PM₂.₅), its lack of mediation and smaller summer effect size indicate that its impact, though stable, may be less responsive to design-based interventions. NP and AI show weaker or inconsistent pathways, acting more as modifiers than primary drivers. Taken together, these results highlight SHAPE_MN as a priority metric for urban morphological optimization under the dual challenge of heat and air pollution. Its consistent and mediating influence across seasons suggests that improving urban shape regularity—through compact design, smoother perimeters, or reduced edge complexity—could yield co-benefits for both thermal comfort and air quality. Such interventions are critical for advancing integrated governance of the urban environment. However, several limitations should be acknowledged. First, the analysis was based on single-month data from two seasons, which may not capture interannual variability or anomalous climate events. Future research should incorporate multi-year, multi-season datasets to improve the robustness and generalisability of results. Second, the spatial resolution of both LST and PM₂.₅ data was limited to 1 km, potentially overlooking intra-urban heterogeneity. Where feasible, higher-resolution datasets should be adopted to enhance understanding of microclimatic and pollution dynamics at finer spatial scales. Despite these limitations, the study contributes to a growing body of evidence that highlights the value of morphological planning for environmental resilience. In particular, prioritising the control and restructuring of impervious surface area is crucial for advancing synergistic strategies to optimise both urban heat environments and air quality management. Declarations Author Contributions Statement Ruihan Qiu conceived the original idea, conducted the main experiments, and drafted the manuscript. Shiqi Tu contributed to the validation of the results and assisted with revisions. Qinmin Zhan supervised the research, provided guidance throughout the study, and performed the final review. All authors read and approved the final version of the manuscript. Funding This research received no external funding. Competing Interests The authors declare no competing interests. Data Availability The data supporting the findings of this study are not publicly available but may be obtained from the corresponding author upon reasonable request. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. References Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. 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Ecological Indicators , 110 , 105889. https://doi.org/10.1016/j.ecolind.2019.105889 Table Table 5 is available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files Table5.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 30 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 02 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7276550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504807000,"identity":"bf2be533-e56f-444c-af91-72f69a31b795","order_by":0,"name":"Ruihan Qiu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Ruihan","middleName":"","lastName":"Qiu","suffix":""},{"id":504807001,"identity":"66a6ffb3-f734-4ad3-aa59-102bd94b7ab1","order_by":1,"name":"Qingming Zhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYHACNiC2YTAAMXlI0JJGupbDJGjhu3b82YOfO84nbpdIYHzwto1B3pyQFsnbCemGvWduJ+6ckcBsOLeNwXBnAwEtBrcTjknwtt3O3XAjgU2at40hweAAQS2JbZJ/286BtLD/JlJLMsjwA2BbmInSInk7jU1ati25fsOZh82Sc85JGG4gpIXvdvozybdtdsYGx5MPfnhTZiNP0BYGhALGBiAhQUg9ipZRMApGwSgYBTgAAMXoQzN3EcY4AAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Qingming","middleName":"","lastName":"Zhan","suffix":""},{"id":504807002,"identity":"aa63cf47-b18f-4ccb-9f58-2f1578453d4d","order_by":2,"name":"Shiqi Tu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Tu","suffix":""},{"id":504807005,"identity":"04e9a437-6a2b-4417-8dbc-e338875c3600","order_by":3,"name":"Zhiyu Fan","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyu","middleName":"","lastName":"Fan","suffix":""},{"id":504807007,"identity":"3d53991c-397c-4a57-aaa0-3d7a9327b11f","order_by":4,"name":"Changling Li","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Changling","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-02 08:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7276550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7276550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90305816,"identity":"5c314995-988f-423e-b7bb-271aceddfc4a","added_by":"auto","created_at":"2025-09-01 09:24:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":510740,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/c210c91dd453d125807b4304.png"},{"id":90307542,"identity":"45b37e1b-fe15-4f7e-8d3b-c065288a59ed","added_by":"auto","created_at":"2025-09-01 09:32:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151637,"visible":true,"origin":"","legend":"\u003cp\u003ePM2.5 Concentration distribution\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/6807b3fa8e55b5fc06275cf6.png"},{"id":90305814,"identity":"c3e28213-d3a4-4344-9422-8995a21c8551","added_by":"auto","created_at":"2025-09-01 09:24:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132768,"visible":true,"origin":"","legend":"\u003cp\u003eLST Distribution\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/65be25bde0ae339cbe86f9c2.png"},{"id":90307544,"identity":"b80764a8-5633-492a-90fd-bbbfce19afd8","added_by":"auto","created_at":"2025-09-01 09:32:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92701,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of PM2.5 and LST with other influencing factors in Wuhan in August summer 2023\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/da156a43ea27d01c59d72afb.png"},{"id":90305826,"identity":"bc85bce1-36b9-4d02-aeff-be8327b4115a","added_by":"auto","created_at":"2025-09-01 09:24:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92163,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of PM2.5 and LST with other influencing factors in Wuhan in January winter 2024\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/3cd27f85d0b20eb4d3864677.png"},{"id":90309895,"identity":"0bc70650-3a3c-4fc7-9bac-3556a17f0fbc","added_by":"auto","created_at":"2025-09-01 09:40:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1717100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/7b621893-516b-413d-ad20-16080b37ff83.pdf"},{"id":90305817,"identity":"ade40675-3c91-4b1d-b51a-a796a1ce5183","added_by":"auto","created_at":"2025-09-01 09:24:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24229,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7276550/v1/79697396183602f885172897.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Untangling the Interplay between Impervious Surface Landscape Metric, Thermal Environment, and Air Pollution: A Seasonal Mediation Analysis in Wuhan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban morphology plays a crucial role in shaping both air pollution levels and local thermal environments. By altering urban microclimates, the physical form and spatial configuration of cities can significantly affect the dispersion, accumulation, and dilution of air pollutants such as fine particulate matter (PM2.5). Previous studies have identified multiple urban form indicators\u0026mdash;such as building density, building height, and green space coverage\u0026mdash;as key determinants of PM2.5 concentrations (Huahua Xu \u0026amp; H. Chen, 2021). For instance, increased building density and height during daytime hours have been associated with higher PM2.5 levels, while other features, like green infrastructure, often exhibit mitigating effects(Chen \u0026amp; Dai, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).At a broader spatiotemporal scale, the evolution of PM2.5 distribution patterns has also been linked to changes in urban structure. A study covering 19 Chinese urban agglomerations showed that average PM2.5 concentrations rose from 30 \u0026micro;g/m\u0026sup3; in 2000 to 52 \u0026micro;g/m\u0026sup3; in 2007, before declining to 34 \u0026micro;g/m\u0026sup3; in 2017, with spatial gradients decreasing from urban cores to peripheral areas (Xiao Ouyang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).These trends emphasize the role of urban form in shaping both the intensity and spatial distribution of air pollution over time.\u003c/p\u003e\u003cp\u003eAmong various morphological elements, the spatial characteristics of impervious surfaces\u0026mdash;such as their extent, shape complexity, and degree of aggregation\u0026mdash;have received increasing attention.Studies by Shi et al.(2019) confirmed that impervious surface metrics at both national and regional scales show significant associations with PM2.5 concentrations. To avoid indicator redundancy and multicollinearity in modeling, this study selects a representative set of impervious surface metrics: PLAND (percent cover), SHAPE_AM (area-weighted shape index), AI (aggregation index), and NP (number of patches). These indicators not only capture essential morphological attributes but also meet the statistical requirements of low variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5), thereby ensuring the validity of subsequent regression and mediation analyses. While the effects of urban blue-green infrastructure on PM2.5 and land surface temperature (LST) have been extensively examined (Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e),much less is known about how impervious surface morphology contributes to both air pollution and urban heat dynamics(Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, the mechanisms by which impervious surface patterns influence PM2.5 concentrations during high-pollution episodes, or modulate heat stress during summer heatwaves, remain underexplored(Shi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Addressing this gap is critical for advancing urban form-based mitigation strategies that can improve environmental quality and public health.\u003c/p\u003e\u003cp\u003eThis study aims to address two central research questions:\u003c/p\u003e\u003cp\u003e(1) Which impervious surface morphology indicators exert consistent directional influence on both PM2.5 concentrations and LST across high-pollution winter and high-temperature summer periods?\u003c/p\u003e\u003cp\u003e(2) To what extent does LST act as a mediator in the relationship between impervious morphology and PM2.5, and vice versa?\u003c/p\u003e\u003cp\u003eTo answer these questions, we adopt a seasonal comparative approach focusing on January (winter) and August (summer), generating gridded datasets for PM2.5, LST, and impervious surface metrics. Spearman correlation and bootstrap-based mediation analysis are applied to uncover both direct and indirect effects across variables. By aligning temporal and spatial contexts, the results aim to inform impervious surface planning strategies and deepen the understanding of multi-path interactions between urban morphology, pollution, and thermal dynamics.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n \u003cp\u003eWuhan, located in central China, is the capital of Hubei Province and a core city of the Yangtze River Economic Belt. It is characterized by a subtropical monsoon climate with distinct hot and humid summers and cold, dry winters, a typical representation of \u0026ldquo;cold winter and hot summer\u0026rdquo; climate zones in East Asia. The city spans approximately 8,569 km\u0026sup2;, with its central urban area experiencing intense urbanization, population density, and environmental pressure.\u003c/p\u003e\n \u003cp\u003eWuhan has been repeatedly identified as one of China\u0026rsquo;s urban areas with severe wintertime air pollution, primarily due to a combination of meteorological conditions, heating-related emissions, and stagnant atmospheric dynamics. PM2.5 concentrations often reach their annual peak in January. Conversely, the summer season is marked by persistent heatwaves and elevated land surface temperatures, making Wuhan an ideal case study for exploring the seasonal dual challenges of air pollution and urban heat.\u003c/p\u003e\n \u003cp\u003eThis study focuses on the main urban districts of Wuhan, where rapid expansion of impervious surfaces over the past decades has reshaped urban microclimates and environmental exposures. By examining January 2024 and August 2023 as representative months, the analysis captures two climatic extremes\u0026mdash;winter haze and summer heat\u0026mdash;providing a robust basis for evaluating how urban morphological configurations affect both PM2.5 and LST under varying seasonal stress conditions.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e\n \u003cp\u003eThis study integrates multi-source geospatial datasets to construct a comprehensive spatial framework for examining the relationships among urban impervious surface morphology, land surface temperature (LST), and PM₂.₅ concentrations. The data sources are categorized into target variables and control variables as follows:\u003c/p\u003e\n \u003cp\u003eGridded PM₂.₅ data at a spatial resolution of 1 km were generated using the TAP (Tracking Air Pollution in China) dataset via a fishnet approach for the target months\u0026mdash;January (representing winter) and August (representing summer) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)(Geng et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiao, Geng, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiao, Zheng, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiao et al., 2022). LST data were derived from the MODIS MOD11A2 product via the Google Earth Engine (GEE) platform, providing 8-day composites of surface temperature for January and August(Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Method\u003c/h2\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1. Spearman Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eTo assess the strength and direction of bivariate associations among key variables and to identify potential multicollinearity, Spearman\u0026rsquo;s rank correlation was conducted. This non-parametric method is suitable for analyzing data with non-normal distributions and ordinal or discrete characteristics. Moreover, Spearman correlation is more robust to outliers than Pearson\u0026rsquo;s correlation, thus enhancing the reliability and interpretability of the results.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2. Multivariate Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eTo quantify the overall impact of urban impervious surface morphology on environmental outcomes, Ordinary Least Squares (OLS) regression models were applied:For winter (January), multiple regression models were used to evaluate the effect of selected landscape metrics on PM₂.₅ concentrations.For summer (August), models were constructed to assess the influence of impervious surface morphology on LST. Each morphological indicator was entered into the model separately, alongside control variables (e.g., nighttime light, population density, road network) to account for confounding effects. This approach allowed for the isolation of individual contributions while minimizing multicollinearity(Formula 1 Formula 2).\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\text{PM}}_{\\text{2.5i}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}{\\text{\u0026beta;}}_{\\text{1}}{\\text{PLAND}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}{\\text{SHAPE\\_AM}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{3}}{\\text{AI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{4}}{\\text{DI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{5}}{\\text{NDVI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{6}}{\\text{ROAD}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{7}}{\\text{POP}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{8}}{\\text{NL}}_{\\text{i}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{i}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{\\text{LST}}_{\\text{i}}\\text{=}{\\text{\u0026beta;}}_{\\text{0}}\\text{+}{\\text{\u0026beta;}}_{\\text{1}}{\\text{PLAND}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{2}}{\\text{SHAPE\\_AM}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{3}}{\\text{AI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{4}}{\\text{DI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{5}}{\\text{NDVI}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{6}}{\\text{ROAD}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{7}}{\\text{POP}}_{\\text{i}}\\text{+}{\\text{\u0026beta;}}_{\\text{8}}{\\text{NL}}_{\\text{i}}\\text{+}{\\text{\u0026epsilon;}}_{\\text{i}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3. Mediation Analysis via Bootstrap Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eTo further explore the underlying mechanisms linking built environment characteristics to environmental outcomes, mediation analysis was employed in two contexts:In winter, to determine whether LST mediates the effect of impervious surface morphology (X) on PM₂.₅ concentration (Y).In summer, to test whether PM₂.₅ mediates the effect of impervious surface morphology (X) on LST (Y).Among several mediation approaches(Baron \u0026amp; Kenny, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) the bootstrap method (Preacher \u0026amp; Hayes, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), was selected for its superior statistical power and minimal reliance on normality assumptions. A total of 5,000 bootstrap samples were generated to estimate the indirect effect and compute bias-corrected 95% confidence intervals (BCIs). If the BCI does not include zero, the indirect effect is considered statistically significant(Cao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Descriptive Statistics and Correlation Analysis\u003c/h2\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the descriptive statistics for PM₂.₅ concentrations, LST, urban impervious surface morphology indicators, and control variables during winter (January) and summer (August). In winter, the average PM₂.₅ concentration was 58.93 \u0026micro;g/m\u0026sup3;, with a standard deviation of 8.71 \u0026micro;g/m\u0026sup3;, ranging from 37.00 to 81.40 \u0026micro;g/m\u0026sup3;. While this average remains below China\u0026rsquo;s 24-hour national standard (75 \u0026micro;g/m\u0026sup3;), it significantly exceeds the World Health Organization\u0026rsquo;s guideline (15 \u0026micro;g/m\u0026sup3;), highlighting a serious air quality concern during the colder months. By contrast, summer PM₂.₅ levels were markedly lower, averaging 18.81 \u0026micro;g/m\u0026sup3; with a standard deviation of 4.85 \u0026micro;g/m\u0026sup3; and a range of 9.30 to 29.90 \u0026micro;g/m\u0026sup3;. This seasonal decline reflects improved air quality during warmer periods.\u003c/p\u003e\u003cp\u003eIn terms of land surface temperature, the average winter LST was 266.79 K, with substantial variability (SD\u0026thinsp;=\u0026thinsp;62.78), and values ranging from 278.90 K to 286.05 K. In summer, LST increased significantly, averaging 288.71 K with a higher variation (SD\u0026thinsp;=\u0026thinsp;67.95) and a range from 300.85 K to 311.58 K, illustrating the intensification of surface urban heat during hot months.\u003c/p\u003e\u003cp\u003eThese seasonal contrasts in air quality and temperature underscore the need to further investigate the relationships between impervious surface morphology and both PM₂.₅ concentrations and LST. A clearer understanding of these interactions is essential for developing informed urban planning and environmental management strategies.The correlation results, illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, indicate that all selected urban impervious surface morphology indicators are significantly associated with both PM₂.₅ concentrations and LST. For PM₂.₅, these morphology indicators show a significant positive correlation in both summer and winter, with stronger associations observed in winter. This suggests that during colder months, the configuration of impervious surfaces has a more pronounced impact on air pollution levels.\u003c/p\u003e\u003cp\u003eSimilarly, the indicators exhibit a positive correlation with LST in both seasons. However, the relationship is more significant in summer, reflecting the intensifying effect of impervious surface characteristics on urban heat during warmer periods.These findings emphasize the seasonally varying influence of urban form on both air quality and thermal environments, and support the need for context-specific urban design strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for winter PM2.5 concentrations, LST, impervious surface morphology metrics, and control variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUNIT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eJanuary, 2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd.Dev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM2_5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026micro;g/m\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLST\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKelvin(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e278.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e286.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePOP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e827.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRoad\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLAND\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSHAPE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: Std.Dev. is short for standard deviation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for summer PM2.5 concentrations, LST, impervious surface morphology metrics, and control variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUNIT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eAugust, 2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd.Dev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM2_5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026micro;g/m\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLST\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKelvin\u003c/p\u003e\u003cp\u003e(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e311.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePOP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eperson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e827.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRoad\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLAND\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSHAPE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: Std.Dev. is short for standard deviation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Combined Effects of Impervious Surface Morphology on PM₂.₅ and LST\u003c/h2\u003e\u003cp\u003eTo examine the influence of urban impervious surface morphology on LST during the summer, four key indicators\u0026mdash;PLAND (Percentage of Landscape), NP (Number of Patches), SHAPE_AM (Shape Index Area-weight Mean), AI (Aggregation Index)\u0026mdash;were included in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results, summarized in Table\u0026nbsp;4(a), reveal several noteworthy patterns.\u003c/p\u003e\u003cp\u003eFirst, PLAND, representing the percentage of impervious surface area, shows a strong positive relationship with LST (β\u0026thinsp;=\u0026thinsp;0.394, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A 100% increase in impervious surface coverage corresponds to an approximate 5 K increase in LST, highlighting the substantial heat-retaining effect of widespread built-up land. Similarly, SHAPE_AM, which characterises the geometric complexity of impervious patches, is also significantly and positively associated with LST (β\u0026thinsp;=\u0026thinsp;0.059, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests that more irregular or contorted patch shapes tend to exacerbate surface warming, possibly due to their fragmented nature limiting airflow and thermal dispersion.\u003c/p\u003e\u003cp\u003eIn addition, both AI and NP also exhibit significant positive effects on LST during the summer. AI shows a β of 0.077 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that more aggregated impervious configurations are linked to hotter surface conditions\u0026mdash;likely reflecting heat accumulation in spatially contiguous built environments. NP, reflecting landscape fragmentation, also has a meaningful influence (β\u0026thinsp;=\u0026thinsp;0.128, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), implying that an increase in the number of impervious patches correlates with intensified surface heat, possibly due to reduced vegetation and increased edge exposure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression analysis of morphological indicators of urban impervious surfaces on PM2.5 and LST\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAugust, 2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eJanuary, 2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDependent Variable LST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eDependent Variable PM2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLAND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.394\u003csup\u003e⁎⁎⁎\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePLAND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHAPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSHAPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.072***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.077\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.629 Adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.484 Adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e⁎⁎⁎p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e⁎⁎⁎p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the regression results from Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), all selected impervious surface morphology indicators show statistically significant positive associations with PM₂.₅ concentrations in January. NP again emerges as the most influential predictor (β\u0026thinsp;=\u0026thinsp;0.133, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), pointing to a strong relationship between landscape fragmentation and deteriorated winter air quality. This suggests that spatially scattered urban layouts may hinder wind-driven dispersion, facilitating pollutant accumulation under stable winter atmospheric conditions.\u003c/p\u003e\u003cp\u003ePLAND (β\u0026thinsp;=\u0026thinsp;0.091, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and SHAPE_AM (β\u0026thinsp;=\u0026thinsp;0.072, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) also show significant positive effects on PM₂.₅ concentrations, reinforcing the idea that both the magnitude and geometric form of impervious surfaces contribute to increased pollution levels. Unlike the summer case where AI had a larger impact, AI in winter demonstrates a relatively weak but still significant effect (β\u0026thinsp;=\u0026thinsp;0.029, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), implying that impervious surface clustering plays a limited role compared to fragmentation or complexity.\u003c/p\u003e\u003cp\u003eTaken together, these results underscore the important role of impervious surface configuration\u0026mdash;both in form and fragmentation\u0026mdash;in influencing winter PM₂.₅ patterns. They further highlight the need for urban planning strategies that integrate spatial landscape metrics to mitigate air pollution risks under seasonal atmospheric constraints.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Mediation Effects of LST on the Relationship Between Impervious Surface Morphology and PM₂.₅\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the mediation analysis for August 2023 and January 2024 in Wuhan\u0026rsquo;s central urban districts, aiming to reveal how landscape metrics of impervious surfaces influence LST and PM₂.₅ through mediation pathways. In summer (August 2023), the mediation role of PM₂.₅ in linking impervious surface morphology with land surface temperature was examined. Compared to winter, the indirect effects through PM₂.₅ were generally weaker and more variable across landscape metrics. SHAPE_MN exhibited a significant and positive mediation effect via PM₂.₅. The indirect effect (a \u0026times; b\u0026thinsp;=\u0026thinsp;0.0192) was supported by a 95% bias-corrected interval [0.3423, 0.7008], accounting for approximately 14.4% of the total effect. This suggests that more irregular-shaped impervious surfaces may contribute to increased PM₂.₅, which in turn amplifies LST, highlighting a thermally reinforcing feedback loop in hot seasons.\u003c/p\u003e\u003cp\u003ePLAND, AI, and NP, by contrast, showed non-significant or minimal indirect effects. For PLAND, the indirect effect was near zero and negative (ab\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0002, 95% BCI [\u0026minus;\u0026thinsp;0.0144, 0.0023]), and the majority of the effect on LST was direct (c\u0026prime; = 0.0253). Similarly, AI and NP showed weak and non-significant mediation through PM₂.₅, with proportion mediated values close to or below zero, suggesting no robust indirect thermal influence via pollution for these indicators.\u003c/p\u003e\u003cp\u003eDuring winter (January 2024), the study investigated whether LST mediates the effects of different impervious surface morphology metrics on PM₂.₅ concentrations. The mediation analysis revealed heterogeneous patterns depending on the type of morphological indicator.\u003c/p\u003e\u003cp\u003eAmong all variables, SHAPE_MN showed a significant positive mediation effect through LST. The indirect effect (a \u0026times; b\u0026thinsp;=\u0026thinsp;0.0307) was statistically significant, with a 95% bias-corrected interval [0.0000, 0.0638], accounting for approximately 4.6% of the total effect. This indicates that more irregularly shaped impervious surfaces tend to indirectly increase PM₂.₅ via elevated LST, although the majority of the effect was still direct (c\u0026prime; = 0.6316). AI also showed a weak but positive indirect effect (ab\u0026thinsp;=\u0026thinsp;0.0010), accounting for ~\u0026thinsp;13% of the total effect, with both the indirect and total effects being statistically significant, though the 95% BCI for ab ([0.0173, 0.1365]) may need cautious interpretation due to its wide range and possible misreporting. PLAND and NP demonstrated negligible or inconsistent mediation pathways. The proportion mediated for PLAND was essentially zero (0.0001), and for NP, the indirect effect was negative but non-significant (ab\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.0054, 95% BCI [\u0026minus;\u0026thinsp;0.8528, 0.0287]). Notably, NP exhibited a strong direct effect on PM₂.₅ (c\u0026prime; = 0.322), implying that more fragmented patch configurations may be directly associated with higher pollution levels, without being mediated by thermal effects.\u003c/p\u003e\u003cp\u003eOverall, the findings reveal a dynamic seasonal interplay in the morphology\u0026ndash;pollution\u0026ndash;temperature relationship. In summer, PM₂.₅ exhibits limited mediating effects, with the notable exception of SHAPE_MN, where more irregular impervious surfaces may intensify LST via increased pollution levels. For other metrics, the impacts on LST are primarily direct. In contrast, winter patterns show that LST serves as a partial mediator, particularly for SHAPE_MN and AI, while the influence of impervious morphology on PM₂.₅ remains predominantly direct. These results underscore the importance of seasonally adaptive strategies that account for both physical structure and thermodynamic processes in urban climate and air quality governance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides empirical insights into the seasonal mediation mechanisms linking urban impervious surface morphology, LST, and PM₂.₅ concentrations in Wuhan. By employing a mediation analysis across two representative months\u0026mdash;August 2023 and January 2024\u0026mdash;we reveal distinct yet complementary pathways through which urban form affects environmental quality.\u003c/p\u003e\n\u003cp\u003eThe findings show that the influence of impervious surface morphology varies seasonally in both direction and mechanism. In summer, PM₂.₅ plays a limited mediating role, except for SHAPE_MN, where irregular surface geometries significantly exacerbate LST. Most metrics\u0026mdash;including PLAND and NP\u0026mdash;exert their warming influence primarily through strong direct effects on LST. Conversely, in winter, LST serves as a mediator in the relationship between surface morphology and PM₂.₅, with SHAPE_MN and AI showing notable indirect pathways, indicating that thermal conditions can either amplify or suppress winter pollution depending on spatial configuration.\u003c/p\u003e\n\u003cp\u003eAmong all metrics, SHAPE_MN stands out as the most influential across both seasons. It demonstrates a significant and positive indirect effect via PM₂.₅ on LST in summer, and via LST on PM₂.₅ in winter, revealing a dual-seasonal mediation pattern. This suggests that shape complexity\u0026mdash;representing edge irregularity and geometric fragmentation\u0026mdash;substantially impairs both heat dissipation and pollutant dispersion. By contrast, while PLAND consistently shows strong direct effects (particularly on winter PM₂.₅), its lack of mediation and smaller summer effect size indicate that its impact, though stable, may be less responsive to design-based interventions. NP and AI show weaker or inconsistent pathways, acting more as modifiers than primary drivers.\u003c/p\u003e\n\u003cp\u003eTaken together, these results highlight SHAPE_MN as a priority metric for urban morphological optimization under the dual challenge of heat and air pollution. Its consistent and mediating influence across seasons suggests that improving urban shape regularity\u0026mdash;through compact design, smoother perimeters, or reduced edge complexity\u0026mdash;could yield co-benefits for both thermal comfort and air quality. Such interventions are critical for advancing integrated governance of the urban environment.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations should be acknowledged. First, the analysis was based on single-month data from two seasons, which may not capture interannual variability or anomalous climate events. Future research should incorporate multi-year, multi-season datasets to improve the robustness and generalisability of results. Second, the spatial resolution of both LST and PM₂.₅ data was limited to 1 km, potentially overlooking intra-urban heterogeneity. Where feasible, higher-resolution datasets should be adopted to enhance understanding of microclimatic and pollution dynamics at finer spatial scales.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the study contributes to a growing body of evidence that highlights the value of morphological planning for environmental resilience. In particular, prioritising the control and restructuring of impervious surface area is crucial for advancing synergistic strategies to optimise both urban heat environments and air quality management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ruihan Qiu conceived the original idea, conducted the main experiments, and drafted the manuscript. Shiqi Tu contributed to the validation of the results and assisted with revisions. Qinmin Zhan supervised the research, provided guidance throughout the study, and performed the final review. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The data supporting the findings of this study are not publicly available but may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBaron, R. M., \u0026amp; Kenny, D. A. (1986). 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Separating emission and meteorological contributions to long-term PM\u003csub\u003e2.5\u003c/sub\u003e trends over eastern China during 2000\u0026ndash;2018. \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(12), 9475\u0026ndash;9496. https://doi.org/10.5194/acp-21-9475-2021\u003c/li\u003e\n \u003cli\u003eXu, G., Ren, X., Xiong, K., Li, L., Bi, X., \u0026amp; Wu, Q. (2020). Analysis of the driving factors of PM2.5 concentration in the air: A case study of the Yangtze River Delta, China. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e, 105889. https://doi.org/10.1016/j.ecolind.2019.105889\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urban-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Urban Informatics](https://link.springer.com/journal/44212)","snPcode":"4212","submissionUrl":"https://submission.springernature.com/new-submission/44212/3","title":"Urban Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban Morphology, PM2.5 Pollution, Land Surface Temperature, Mediation Analysis, Seasonal Variation","lastPublishedDoi":"10.21203/rs.3.rs-7276550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7276550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban morphology plays a pivotal role in shaping both air quality and thermal environments, yet their interactions remain insufficiently understood, particularly across different seasons. This study investigates the mediating role of land surface temperature (LST) in the relationship between impervious surface-based landscape \u0026nbsp;metrics and PM2.5 concentration, using Wuhan, China, as a case study. Drawing on remote sensing data from the year of 2023 and 2024, this constructed seasonal mediation models to explore how impervious surface-based landscape metrics influence PM2.5 either directly or through thermal pathways. The findings reveal that certain impervious surface-based landscape metrics features can either amplify or mitigate pollution levels by altering the local thermal environment, while PM2.5 itself also exerts feedback on surface temperature. These insights underscore the dual regulatory role of urban form and pollution in shaping urban climates and highlight the seasonal complexity of spatial interventions for sustainable urban governance. The results contribute to a deeper understanding of cross-domain interactions in urban systems, offering a foundation for climate-responsive and pollution\u003c/p\u003e","manuscriptTitle":"Untangling the Interplay between Impervious Surface Landscape Metric, Thermal Environment, and Air Pollution: A Seasonal Mediation Analysis in Wuhan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:24:50","doi":"10.21203/rs.3.rs-7276550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T05:49:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-31T00:38:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T12:38:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41898342843544631668463164373647745110","date":"2025-08-22T15:11:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T01:59:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169236897469907734142150336391337249135","date":"2025-08-20T06:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9448370072620596354671548463273952262","date":"2025-08-20T03:33:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T03:28:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T00:43:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T02:12:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urban Informatics","date":"2025-08-02T07:57:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urban-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Urban Informatics](https://link.springer.com/journal/44212)","snPcode":"4212","submissionUrl":"https://submission.springernature.com/new-submission/44212/3","title":"Urban Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7090b991-17ac-4107-9e77-9fa33ba37e97","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T01:09:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 09:24:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7276550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7276550","identity":"rs-7276550","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}