Xiaopei Wang

Ms. Xiaopei Wang


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 July 2023 in Forests
Verified
Journal Article
Open access
PDF file
XWXiaopei Wang
Xiaopei Wang
LMLingquan Meng
Lingquan Meng
YLYifeng Liu
Yifeng Liu
Hongxu WeiHongxu Wei
Hongxu Wei
Urban forests have important functions to alleviate air pollution, adjust the regional climate, and promote visitors’ mental health and well-being. Humans’ perceptions towards both atmospheric particulate matter (PM) and meteorological factors can be a gauge for assessing the functional services of urban forests. The geographical locations of host cities experiencing urbanization could take part in effects on emotional states of forest visitors. In this study, a total of 6309 facial photos of urban forest visitors were obtained from social networks in 42 cities of mainland China. Happy, sad, and neutral emotions were rated as percent scores in 2020, when the same-day air pollutants, meteorological factors, and socioeconomic indicators were recorded. The positive emotional index (PEI) was calculated as the difference between happy scores and sad scores. The results reveal that severe air pollutants (jointly PM2.5 > 75 μg/m3, PM10 > 150 μg/m3, and AQI > 150) were more frequently found in cities in the northeastern and northern areas of China. The forest visitors in the northeastern cities showed higher happiness scores compared to the visitors in other regions. The Quasi-Poisson regression suggested that high scores of happiness were frequently disclosed in weathers with low PM10. High scores of sadness were regressed on exposure to cities with a low GDP per capita and low total retail sales but with a high GDP at low-longitudinal and high-latitudinal locations with low levels of PM2.5 pollution, relative humidity, and wind velocity and a high temperature. The happiness score and PEI showed high-value aggregations in mega-sized cities (population over 10 million), such as Beijing and Zhengzhou, and in a metro-sized city (population of 5–10 million in Langfang) from climate regions of China.

27 October 2024 in Forests
Verified
Journal Article
Open access
PDF file
CCChanghong Chen
Changhong Chen
XWXiaopei Wang
Xiaopei Wang
Hongxu WeiHongxu Wei
Hongxu Wei
YLYifeng Liu
Yifeng Liu
ZCZhanhua Cao
Zhanhua Cao
PGPeng Guo
Peng Guo
Outdoor thermal comfort (OTC) is one of the most important issues for people exposed to urban green space (UGS). It is mainly evaluated by assessing equivalent meteorological factors that may combine and result in human responses of perceptions about comfortable vs. uncomfortable environments. The assessment of these responses is challenged by limits in a database that is powerful enough to support big-data analysis and an essential parameter that can evaluate health-related responses to OTC in urban forests. In this study, a meta-analysis was conducted by synthesizing studies of physiological and psychological health responses of green space (GS) visitors to meteorological factors in host cities on a global scale. A total of 15,000 numerical records about the psychological and physiological responses of UGS visitors were obtained from 105 papers across 68 global cities in 21 countries. A novel parameterization was developed to evaluate OTC by normalizing changes in health-related parameters and detecting their regressions against multiple meteorological factors. It was revealed that OTC resulted in more benefits in psychological health parameters than in physiological ones for UGS visitors worldwide. Globally, health-promoting OTC environments were more found in temperate countries than in countries subjected to other climates. People in Asian countries were indicated to live in environments with higher comfort that benefitted health states in cities at low latitudes. Thermal environments tended to be harsher and more challenging, mostly too chilling, in northern cities than in the south globally. A deep learning model using the ResNet algorithm was found to perform to an expected level with R2 as high as >70% and errors controlled generally lower than 0.01. Taking mainland China for instance, it was predicted that thermal environments in eastern cities of China highly challenged the psychological health of local UGS visitors across all seasons of the year. Northern cities of China were predicted to have satisfied thermal environments that benefited psychological and psychological health responses in spring. In summer, cities in West China were predicted to have comfortable thermal environments, and in winter, environments in the southwestern cities were more comfortable.