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<h2>1. Introduction</h2><p>Urbanization has transformed landscapes globally, leading to the emergence of urban heat islands (UHIs), where cities experience higher temperatures than their rural surroundings due to altered surface energy balances, reduced vegetation, and anthropogenic heat release (Oke, 1982). The UHI effect exacerbates heat stress, particularly during heatwaves, and has been linked to increased morbidity and mortality (Patz et al., 2005). With climate change projections indicating more frequent and intense heat events, understanding the dynamics of UHIs and their health impacts is of paramount importance (IPCC, 2021).</p><p>The npjis region, a rapidly growing metropolitan area with a population of approximately 1.2 million, has experienced significant urban expansion over the past decade. However, comprehensive assessments of its UHI characteristics and associated health burdens are lacking. This study aims to fill this gap by (1) characterizing the spatiotemporal patterns of UHII in the npjis region from 2013 to 2023, (2) quantifying the association between daily UHII and heat-related emergency department visits, and (3) identifying vulnerable population subgroups. Our findings will inform evidence-based urban planning and public health interventions.</p><h2>2. Methods</h2><h3>2.1 Study Area</h3><p>The npjis region (34.05°N, 118.25°W) encompasses a central urban core and surrounding suburban and rural zones. The climate is Mediterranean, with hot, dry summers and mild, wet winters. The region has a population density of 2,500 persons/km² in the urban core, with a mix of residential, commercial, and industrial land uses.</p><h3>2.2 Data Sources</h3><p>We acquired Landsat 8 Collection 2 Level-2 surface temperature products (30 m resolution) for the period 2013–2023 from the USGS EarthExplorer. A total of 120 cloud-free scenes were selected (approximately 10 per year, covering all seasons). Meteorological data, including air temperature, humidity, and wind speed, were obtained from three weather stations: one urban, one suburban, and one rural. Daily emergency department (ED) visit data for heat-related illnesses (ICD-10 codes: T67, X30, and related) were obtained from the regional health department for 2015–2023. Population demographic data were derived from the 2020 Census.</p><h3>2.3 Calculation of Land Surface Temperature and UHII</h3><p>Land surface temperature (LST) was extracted from the Landsat thermal band using the single-channel algorithm (Jiménez-Muñoz et al., 2014). The urban heat island intensity (UHII) was calculated as the difference between the mean LST of the urban area (defined as built-up areas with >50% impervious surface) and the mean LST of the rural reference area (agricultural land within 20 km of the urban boundary). For each scene, we computed the mean UHII. To obtain daily UHII estimates for health analysis, we developed a regression model relating satellite-derived UHII to meteorological variables (air temperature, wind speed, and cloud cover) and used this model to predict daily UHII for the entire study period (R² = 0.82).</p><h3>2.4 Statistical Analysis</h3><p>We used a distributed lag non-linear model (DLNM) to estimate the association between daily UHII and heat-related ED visits, controlling for daily mean air temperature, relative humidity, PM2.5, and ozone concentrations, as well as day of week and long-term trends (Gasparrini et al., 2010). The model included a cross-basis for UHII with a natural cubic spline for the exposure-response dimension (3 degrees of freedom) and a natural cubic spline for the lag dimension (up to 7 days). We also conducted stratified analyses by age group (<65, 65–74, ≥75 years) and by presence of pre-existing cardiovascular or respiratory conditions. Sensitivity analyses were performed using alternative lag structures and adjustment for air pollution. All analyses were conducted in R version 4.2.1 using the 'dlnm' package.</p><h2>3. Results</h2><h3>3.1 Spatiotemporal Patterns of UHII</h3><p>Over the 2013–2023 period, the mean annual UHII in the npjis region was 2.8°C (SD = 0.6°C), with a statistically significant increasing trend of 0.45°C per decade (p < 0.001). The UHI footprint, defined as the area where LST exceeds the rural mean by at least 1°C, expanded from 45 km² in 2013 to 53 km² in 2023, an increase of 18%. Seasonally, UHII was highest in summer (mean = 3.5°C) and lowest in winter (mean = 1.9°C). Diurnally, the UHI effect was more pronounced at night, with a mean nocturnal UHII of 3.2°C compared to 2.4°C during the day. Spatial analysis revealed that the hottest areas were concentrated in the industrial and high-density residential zones, while parks and water bodies exhibited cooler temperatures.</p><h3>3.2 Association between UHII and Heat-Related Morbidity</h3><p>During 2015–2023, there were 1,847 heat-related ED visits in the npjis region, with a peak in the summer months. The DLNM analysis revealed a positive and statistically significant association between daily UHII and heat-related ED visits. The cumulative relative risk (RR) over a 7-day lag for a 1°C increase in UHII was 1.12 (95% CI: 1.05–1.20). The exposure-response curve was approximately linear, with no evidence of a threshold. The lag structure indicated that the effect was immediate (lag 0) and persisted for up to 3 days, with the strongest effect at lag 0 (RR = 1.08, 95% CI: 1.03–1.13).</p><h3>3.3 Vulnerable Populations</h3><p>Stratified analyses showed that the elderly (≥75 years) had a higher RR (1.18, 95% CI: 1.08–1.29) compared to younger age groups (RR = 1.08, 95% CI: 1.00–1.17 for <65 years). Individuals with pre-existing cardiovascular disease had an RR of 1.20 (95% CI: 1.10–1.31), while those with respiratory conditions had an RR of 1.15 (95% CI: 1.04–1.27). Sensitivity analyses using different lag structures and additional adjustment for air pollution did not materially change the estimates.</p><h2>4. Discussion</h2><p>This study provides the first comprehensive assessment of UHI dynamics and their health impacts in the npjis region. Our findings of a significant increasing trend in UHII (0.45°C per decade) are consistent with global observations of intensifying UHIs in rapidly urbanizing areas (Zhao et al., 2014). The expansion of the UHI footprint by 18% over a decade underscores the need for proactive urban planning to mitigate heat accumulation.</p><p>The observed association between UHII and heat-related ED visits (RR = 1.12 per 1°C) is comparable to previous studies in other cities. For example, a study in Phoenix, Arizona, reported a 6% increase in heat-related mortality per 1°C increase in UHII (Harlan et al., 2006). Our finding that the effect is strongest at lag 0 suggests that acute exposure to elevated UHII triggers immediate health responses, likely due to the inability of vulnerable individuals to thermoregulate effectively.</p><p>The heightened vulnerability of the elderly and those with cardiovascular conditions aligns with physiological mechanisms: older adults have reduced thermoregulatory capacity, and cardiovascular strain is exacerbated by heat stress (Kenney & Munce, 2003). These findings highlight the importance of targeted interventions, such as early warning systems and cooling centers, for high-risk populations.</p><p>Our study has several limitations. First, the use of satellite-derived UHII as a proxy for personal heat exposure may introduce measurement error, as individuals may spend time indoors or in shaded areas. Second, the regression model used to predict daily UHII may not fully capture microclimatic variations. Third, the ecological design limits causal inference. Nevertheless, the robustness of our results across sensitivity analyses lends confidence to the findings.</p><p>From a policy perspective, our results support the implementation of green infrastructure, such as urban parks, green roofs, and cool pavements, which have been shown to reduce UHII by 1–3°C (Bowler et al., 2010). Additionally, urban planning should prioritize the preservation of vegetation and water bodies, and the adoption of heat-resilient building designs. Public health agencies should integrate UHII forecasts into heat-health action plans, particularly for vulnerable neighborhoods.</p><h2>5. Conclusion</h2><p>This study demonstrates that the UHI effect in the npjis region has intensified over the past decade and is significantly associated with increased heat-related morbidity. The findings underscore the urgent need for integrated urban and public health policies to mitigate UHI effects and protect vulnerable populations. 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