Comparisons of the Urbanization Effect on Heat Stress Changes in Guangdong during Different Periods

Abstract

While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities.

An effort to distinguish the effects of cloud cover and aerosols on the decadal variations of surface solar radiation in the Northern Hemisphere

“…Commonly utilized methods for separating driving factors include the random forest method [55] and partial least squares regression (PLSR) [56,57], among others. Likewise, the possible driving factors of the SSR all-sky in this paper exhibit multi-correlated (collinearity) with each other (see table S1 in the supporting information).…”

“…Consequently, the ordinary multiple linear regression (MLR) equation is no longer the best linear unbiased estimator when using methods such as multiple regression directly [58,59]. This paper employed the PLSR method, which has demonstrated strong performance in distinguishing the independent contributions of multiple factors that are significantly correlated with each other [56,57,[60][61][62][63]. PLSR is a multivariate statistical data analysis method that was first proposed by Wold and Esbensen [62] in 1983.…”

“…The advantages of PLSR analysis are as follows: (1) it can create regression models for multiple sets of dependent variables and independent variables. (2) It can effectively address issues found in the MLR model, such as multicollinearity among independent variables and the number of sample points being less than the number of variables in the modelling analysis [57,59,61]. We used regional average SSR all-sky /SSR no-TCC as dependent variables, and the TCC and aerosol/precursors (BC, OC, NH 3 , NO X , SO 2 and NMVOC) in the corresponding as independent variables for the PLSR modeling.…”