Scale dependence of urban green space cooling efficiency: A case study in Beijing metropolitan area

Surface and canopy urban heat island disparities across 2064 urban clusters in China

The vast majority of urban heat island (UHI) studies are now derived from surface temperatures, substituting for the original air temperature-based definition. The disparities in hourly surface-canopy UHI effects (SUHI, CUHI) and the contrasting mechanisms are currently poorly understood. Here, we use high-resolution hourly LST and air temperature data from 2064 urban clusters in China to estimate SUHI and CUHI intensities and their driving mechanisms during the summer and winter of 2022. Across all urban clusters, we find that SUHI is on average ten times higher than the CUHI in the summer (0.97 VS. 0.09 °C) yet nearly triple that in the winter (0.30 VS. 0.11 °C), with SUHI exceeding CUHI in 91.5 % and 65.7 % of urban clusters, respectively. Seasonal and hourly analyses on SUHI/CUHI confirm typically opposite hysteresis variations (magnitude, peak, and timing of occurrence) and more correlated surface-canopy UHIs patterns during the night. We further demonstrate that SUHI magnitude can be largely explained by biophysical factors, urban attributes, and climate contexts, whereas CUHI interferes with additional constraints linked to ground-air energy transfer and advective dissipation. The improvement of urban greenery aids summer cooling efficiently in equatorial and boreal regions, while albedo measures are relevant in mitigating nocturnal warming in arid regions. Our findings support multiple technologies as ideas for urban three-dimensional UHIs (surface, canopy and boundary) and energy mechanisms, and the urgent need for ambitious urban heat mitigation strategies to minimize future climate change impacts.

Association of Postpartum Temperature Exposure with Postpartum Depression: A Retrospective Cohort Study in Southern California

BACKGROUND

Postpartum depression (PPD) has been associated with biological, emotional, social, and environmental factors. However, evidence regarding the effect of temperature on PPD is extremely limited.

OBJECTIVES

We aimed to examine the associations between postpartum temperature exposure and PPD.

METHODS

We conducted a retrospective cohort study using data from Kaiser Permanente Southern California electronic health records from 1 January 2008 through 31 December 2018. PPD was first assessed using the Edinburgh Postnatal Depression Scale (score ≥ 10 ) during the first year of the postpartum period and further identified by using both diagnostic codes and prescription medications. Historical daily ambient temperatures were obtained from the 4 -km resolution gridMET dataset (https://www.climatologylab.org/gridmet.html) and linked to participants' residential addresses at delivery. Postpartum temperature exposures were measured by calculating various temperature metrics during the period from delivery to PPD diagnosis date. A time-to-event approach with a discrete-time logistic regression was applied to estimate the association between temperature exposure and time to PPD. Effect modification by maternal characteristics and other environmental factors was examined.

RESULTS

There were 46,114 (10.73%) PPD cases among 429,839 pregnancies (mean ± standard deviation age = 30.22 ± 5.75 y). Increased PPD risks were positively associated with exposure to higher mean temperature [adjusted odds ratio (aOR) per interquartile range increment: 1.07; 95% confidence interval (CI): 1.05, 1.09] and diurnal temperature range (aOR = 1.08 ; 95% CI: 1.06, 1.10); the associations were stronger for maximum temperature compared with minimum temperature. The temperature-related PPD risks were greater among African American, Asian, and Hispanic mothers and among mothers ≥ 25 years of age compared with their counterparts. We also observed higher effects of temperature on PPD among mothers exposed to higher air pollution or lower green space levels and among mothers with lower air conditioning penetration rates.

CONCLUSION

Maternal exposure to higher temperature and diurnal temperature variability during the postpartum period was associated with an increased risk of PPD. Effect modification by maternal age, race/ethnicity, air pollution, green space, and air conditioning penetration was identified. https://doi.org/10.1289/EHP14783.

Synergistic control of urban heat island and urban pollution island effects using green infrastructure

Urban heat island (UHI) and urban pollution island (UPI) effects are two major challenges that affect the liveability and sustainability of cities under the circumstance of climate change. However, existing studies mostly addressed them separately. Urban green infrastructure offers nature-based solutions to alleviate urban heat, enhance air quality and promote sustainability. This review paper provides a comprehensive synthesis of the roles of urban green spaces, street trees, street hedges, green roofs and vertical greenery in mitigating UHI and UPI effects. These types of green infrastructure can promote the thermal environment and air quality, but also potentially lead to conflicting impacts. Medium-sized urban green spaces are recommended for heat mitigation because they can provide a balance between cooling efficiency and magnitude. Conversely, street trees pose a complex challenge since they can provide cooling through shading and evapotranspiration while hindering pollutant dispersion due to reduced air ventilation. Integrated research that considers simultaneous UHI and UPI mitigation using green infrastructure, their interaction with building features, and the urban geographical environment is crucial to inform urban planning and maximize the benefits of green infrastructure installations.

Enhancing the city-level thermal environment through the strategic utilization of urban green spaces employing geospatial techniques

Smart urban planning needs to have a multicriteria-based approach to prevent the deteriorating local thermal climate. Maximizing the cooling potential using the available grey infrastructure would be the utmost priority of future smart cities. Remote sensing and GIS can be the appropriate tools to develop a climate-resilient urban planning framework. Studies are needed to include different features of vertical and horizontal landscaping to mitigate heat stress and enhance liveability at the city level. With this goal, the current work outlined a holistic approach to efficiently using green spaces with minimal reconstruction. The problem of regional climate threat was evaluated with urban heat island characterization. Moran's I clustering identified nearly 12% of the study area to be under considerable heat stress during summer days. Multiple techniques, such as mapping local climate zones, segment mean shift-based roof extraction, vegetation index computation, solar azimuth-based green wall site selection, etc., were applied to formulate solutions and provide an integrated method for city-level environment enhancement. A considerable area was identified as most suitable for green roof cover, and it was also computed that the transition towards green roof at only these locations may bring down the maximum heat island intensity by 0.74 °C. Additionally, solar zenith, illumination effect, and building height information were combined to create a distinct method where vertical plantation would flourish exceptionally. A rigorous assessment of more than 130 urban green spaces further quantified the relation between landscape geometry and cooling effect to provide optimum green space designs for future urban planning.

Construction of a cold island network for the urban heat island effect mitigation

The urban heat island (UHI) effect seriously challenges sustainable urban development strategies and livability. Numerous studies have explored the UHI problem from the perspective of isolated blue and green patches, ignoring the overall function of cold island networks. This study aims to explore the construction method of cold island network by integrating scattered cold island resources, rationally guiding urban planning and construction, and providing effective ideas and methods for improving the urban thermal environment. Taking the central city of Fuzhou as an example, the identification of the cold island core source (CICS) was optimized by applying relative land surface temperature (LST), morphological spatial pattern analysis, and landscape connectivity analysis. The combined resistance surface was constructed based on a spatial principal component analysis. Subsequently, the cold island network was constructed by applying circuit theory and identifying the key nodes. The results showed that the central and eastern parts of the study area experienced the most significant UHI effects and there was a tendency for them to cluster. Overall, 48 core sources, 104 corridors, 89 cooling nodes, and 34 heating nodes were identified. The average LST of the CICSs was 28.43 °C, significantly lower than the average LST of the entire study area (31.50 °C), and the 104 cold corridors were classified into three categories according to their importance. Different targeting measures should be adopted for the cooling and heating nodes to maintain the stability of the cold island network and prevent the formation of a heat network. Finally, we suggest a model for urban cold island network construction and explore methods for mitigating issues with UHI to achieve proactive and organized adaptation and mitigation of thermal environmental risks in urban areas, as well as to encourage sustainable urban development.

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human-nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m's great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.