A study of waterside microenvironmental factors and their effects on summer outdoor thermal comfort in a Cfa-climate campus.

Outdoor thermal comfort (OTC) studies explore outdoor subjects' responses to their thermal environment, usually evaluated using the neutral temperature (NT). This study investigated the influences of microenvironmental factors around a waterbody on thermal perceptions, using questionnaires and meteorological measurements at the Central Lake of Southwest University of Science and Technology (SWUST) in Mianyang. Microenvironmental factors included sky view factor (SVF) and distance from the lake (DFL). It was found that people felt most comfortable in the shade of trees although some volunteers voted artificial canopy as their preferred thermal adaptation element. In addition, a linear regression yielded an NT of 28.44 °C in Mianyang during the summer of 2022. There were NT variations among different measurement sites (e.g., on the east shore, it was 28.18 °C on the waterside, 27.11 °C away from the lake, and 25.53 °C far from the lake; while it was 27.57 °C under the tree crown, 25.11 °C on the lawn, and 29.13 °C in the square). This variation may be due to human adaptation towards microenvironmental factors and their effects on microclimate. The variation in thermal responses owing to microenvironmental differences (different NTs at various types of sites) might be a novel finding in the field of OTC. This study provides important directions for microenvironment design in the future for OTC improvement.

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the different thresholds of different indicators of PLES" plays an indispensable role in the studies of the multi-scenario simulation of land space changes and needs to be tackled in an appropriate way, given that the process simulation of key elements that affect the evolution of urban systems is yet to achieve complete coupling with PLES utilization configuration schemes. In this paper, we developed a scenario simulation framework combining the dynamic coupling model of Bagging-Cellular Automata (Bagging-CA) to generate various environmental element configuration patterns for urban PLES development. The key merit of our analytical approach is that the weights of different key driving factors under different scenarios are obtained through the automatic parameterized adjustment process, and we enrich the study cases for the vast southwest region in China, which is beneficial for balanced development between eastern and western regions in the country. Finally, we simulate the PLES with the data of finer land use classification, combining a machine learning and multi-objective scenario. Automatic parameterization of environmental elements can help planners and stakeholders understand more comprehensively the complex land space changes caused by the uncertainty of space resources and environment changes, so as to formulate appropriate policies and effectively guide the implementation of land space planning. The multi-scenario simulation method developed in this study has offered new insights and high applicability to other regions for modeling PLES.

Spatio-temporal distribution and classification of utilization of urban bare lots in low-slope hilly regions.

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.