Research advances on Regional Heat Islands in the Guangdong-Hong Kong-Macao Greater Bay Area urban Agglomeration
Rapid urbanization and increasing population have widely caused the urban heat island effect. As the most prominent feature of global urbanization, the urban agglomeration has become an inevitable outcome of regional economic concentration and an advanced spatial organization of highly developed industrialization and urbanization. The decreasing and disappearing distance between cities in an urban agglomeration region can considerably alter the regional thermal environment. Thus, there is an urgent need to reevaluate regional heat island intensity (RHII) in an urban agglomeration scale by considering all cities together instead of from conventional single city perspective. Additionally, most previous studies emphasize the influence of drivers on UHI effect, but how these drivers affect UHI intensity, which is more valuable to guide actionable strategies for UHI mitigation, is poorly understood.
The Ecosystem Observation and Restoration Team of the Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration Ecosystem Observation and Research Station used cropland land surface temperature as the reference temperature to assess the diurnal and seasonal RHII variations for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration over the period 2003-2017. The boosted regression trees (BRT) method was then used to analyze the relative influence and marginal effect of possible drivers to disentangle their underlying driving mechanisms.
Results demonstrated that urbanization had an obvious warming effect and the daytime RHII spatial patterns illustrated higher intensity and greater heterogeneity than their nighttime counterparts (Fig. 1). Seasonal dynamics of daytime RHII displayed a generally descending trend from summer to winter, but the opposite for night, indicating that a relatively higher intensity was observed in the day for summer and at night for winter.
Fig. 1. Spatial patterns of annual and seasonal RHII grades averaged over the period 2003-2017 in GBA. SCI, strong cold island; SSCI, sub-strong cold island; WCI, weak cold island; NHI, no heat island; WHI, weak heat island; SSHI, sub-strong heat island; SHI, strong heat island.
Results of BRT analyses indicated that at both annual and seasonal scales, vegetation fraction and background temperature had a dominant influence on RHII in daytime and nighttime, respectively (Fig. 2). RHII variations were also considerably attributed to other drivers for different seasons. For daytime RHII, the other influential drivers included anthropogenic heat emissions and precipitation in summer, anthropogenic heat emissions and terrain in the transition season, and temperature and albedo in winter. For nighttime RHII, anthropogenic heat emissions for all seasons, vegetation activities for summer and the transition season, and precipitation for winter also had important contributions.
Fig. 2. Relative influence of potential drivers on RHII at annual mean scales (Mean ± SD, n = 100). VCF, vegetation continuous fields; EVI, enhanced vegetation index; TEM, temperature; PRE, precipitation; DEM, digital elevation model; WSA, white sky albedo; NL, night light; BI, built-up intensity.
In addition, the marginal effects detected the different nonlinear responses of diurnal and seasonal RHII to potential drivers, suggesting contrasting driving mechanisms (Fig. 3).
Fig. 3. Partial dependency plots for potential drivers on the annual daytime and nighttime RHII. VCF, vegetation continuous fields; EVI, enhanced vegetation index; TEM, temperature; PRE, precipitation; DEM, digital elevation model; WSA, white sky albedo; NL, night light; BI, built-up intensity.
This study quantified the spatiotemporal RHII variations and revealed the underlying driving mechanisms in the GBA, which highlighted more targeted and informed strategies for RHII mitigation and provided helpful insights into livability improvements of urban agglomerations. Related results have been published in Science of The Total Environment.
This work was supported by the National Natural Science Foundation of China and the GDAS’ Project of Science and Technology Development.
Reference:
Geng S, Yang L*, Sun Z, Wang Z, Qian J, Jiang C, Wen M. Spatiotemporal patterns and driving forces of remotely sensed urban agglomeration heat islands in South China. Science of The Total Environment, 2021, 800: 149499. https://doi.org/10.1016/j.scitotenv.2021.149499
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