Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution.

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Downscaling of environmental indicators: A review.

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China.

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study.

The role of urban green space (UGS) in mitigating the urban heat island (UHI) effect has been demonstrated in a growing body of literature. However, the potential influence of the spatial equity of UGS distribution on the UHI effect has largely been overlooked. The present study aims to identify this potential influence using the spatial equity of UGS and the land surface temperature (LST) as measures of UGS spatial distribution and UHIs, respectively. A comprehensive spatial distribution map of UGS was generated by combining the UGS coverage fraction data within urban impervious pixels and the green cover data outside urban impervious pixels. Then, the spatial equity of UGS distribution across all urban impervious pixels was determined using the Gini coefficient. In addition, an LST map was derived using the thermal infrared spectral bands of Landsat 8 OLI/TIRS products. A case study of Dongguan, a highly urbanized city in China, showed that (1) the distribution of both UGS and LSTs were spatially aggregated in all the towns of the city, (2) the LST of urban impervious pixels was negatively correlated with the area of surrounding UGS, and (3) the Gini coefficient of UGS was positively correlated with the proportion of hot and cool areas, but negatively correlated with the proportion of medium-hot and medium-cool areas. These findings indicate that increasing the amount of UGS is beneficial to the reduction of urban average LSTs, while promoting the spatial equity of UGS distribution is conducive to reducing the spatial aggregation of LSTs within urban areas, thereby improving the overall urban thermal environment. Therefore, as a nature-based solution, promoting the spatial equity of UGS distribution could enhance the overall cooling effect of UGS more effectively at the city scale, and thus further underpin the sustainable development of the urban environment.

How to obtain industrial waste data at the county scale: Two downscaling models and their application in Dongguan, China.

The availability of environmental emission data is critical in evaluation of countries' ecological security and the implementation of environmental management. However, access to environmental emission data at the county level is not provided by statistical publications and bulletins. Therefore, in this paper, we develop two novel data downscaling models, an environmental Kuznets curve downscaling model (EKCDM) and a scale model (SM), to obtain county-level environmental emission data. The EKCDM relies on the EKC hypothesis and the assumption that the same model applies across scales, whereas the SM depends on the assumption that the share of a region's environmental pollution is equivalent to its share of economic output. Subsequently, environmental emission data above the county scale can be obtained through model transformation and simple calculations. By verifying and analyzing the official data with the one obtained through downscaling at municipal level and above, we verify the feasibility of the models, after which we apply the models to extrapolate information on the industrial waste of the counties of Dongguan city in Guangdong Province, China. We find that the EKCDM should be given priority in most cases, especially for the quadratic parameter model, and that the SM can be adopted when per capita gross domestic product differs greatly between adjacent levels of administrative units. In general, scholars can synthesize the characteristics of these two models, and obtain more accurate data by supplementing and verifying one with the other. Compared with other downscaling methods, our methods require far less data and the concepts are easily understood, which makes them more feasible and increases applicability. This study provides scholars with powerful tools to explore the relationship between industrial pollution and economic development in depth by obtaining industrial waste data at the county scale, thereby supporting scientific research and policy design.

Spatial Variation of the Effect of Multidimensional Urbanization on PM2.5 Concentration in the Beijing-Tianjin-Hebei (BTH) Urban Agglomeration.

Atmospheric PM2.5 pollution has become a prominent environmental problem in China, posing considerable threat to sustainable development. The primary driver of PM2.5 pollution in China is urbanization, and its relationship with PM2.5 concentration has attracted considerable recent academic interest. However, the spatial heterogeneity of the effect of urbanization on PM2.5 concentration has not been fully explored. This study sought to fill this knowledge gap by focusing on the Beijing-Tianjin-Hebei (BTH) urban agglomeration. Urbanization was decomposed into economic urbanization, population urbanization, and land urbanization, and four corresponding indicators were selected. A geographically weighted regression model revealed that the impact of multidimensional urbanization on PM2.5 concentration varies significantly. Economically, urbanization is correlated positively and negatively with PM2.5 concentration in northern and southern areas, respectively. Population size showed a positive correlation with PM2.5 concentration in northwestern and northeastern areas. A negative correlation was found between urban land size and PM2.5 concentration from central to southern regions. Urban compactness is the dominant influencing factor that is correlated positively with PM2.5 concentration in a major part of the BTH urban agglomeration. On the basis of these findings, BTH counties were categorized with regard to local policy recommendations intended to reduce PM2.5 concentrations.

Effects of chinese formula jueyin granules on psoriasis in an animal model.

Although Traditional Chinese medicine (TCM) is known to be effective for psoriasis patients, the responsible mechanisms still remain poorly understood. In this study, we aimed to evaluate the effect of one formula, named Jueyin granules (JYG) in the mouse model of the vaginal epithelium and tail epidermis. Additionally, we also determined the anti-inflammatory effects of JYG in an imiquimod- (IMQ-) induced psoriasis-like skin mouse model. Our results show that JYG can attenuate the IMQ-induced psoriasis-like inflammation, accompanied with increased epidermal hyperplasia. We also measured estrogenic stage mitosis of vaginal epithelial cells and the formation of granular cell layers in male mouse tails per 100 scales, as well as the tissue nitric oxide (NO) and malondialdehyde (MDA) levels using the ELISA method. The results suggest that JYG significantly inhibited mitosis in mouse vaginal epithelial cells, promoted the formation of the squamous epidermal granular layer in mice tails, and reduced the levels of NO and MDA in an imiquimod-induced psoriasis-like skin mouse model after 14 d (P < 0.05). These results demonstrate that JYG might be an effective clinical treatment for psoriasis and the effects may be related to inhibited keratinocytes proliferation, improved parakeratotic epidermal cells, and reduced expression of NO and MDA.