Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China

Quantifying the contribution of industrial zones to urban heat islands: Relevance and direct impact

Industrial production activities are an important source of urban heat emissions. Quantifying the contribution of industrial zones to urban heat islands (UHIs) is crucial for urban planning and management. However, few studies have explored the quantitative relationship between land surface temperature (LST) and urban industrial zones (UIZs) at the urban scale, especially the direct impact of industrial expansion or contraction on LST. Linyi City, the largest city in Shandong Province, was selected as the study area. This study aims to analyze the spatial-temporal variation in the UIZs in Linyi City from 2013 to 2022, focusing on the quantitative relationship between LST and UIZs. Using remote sensing images, a novel spectral index (called the BCCSI) was constructed to identify factory buildings. The performance of the BCCSI was validated using five existing indices and Google Earth images. Over the past 10 years, the UIZ area of Linyi has increased by 137.16 km2. The UIZs in Linyi are mainly distributed in counties near the urban center, and counties with large UIZ areas are also hotspots for UIZ changes. Moreover, we found that the contraction or expansion of UIZs has obvious effects on LST. After the contraction (or expansion) of UIZs, the LST decreased (or increased) by 0.48 °C (0.39 °C). In addition, we found that there is an exponential relationship between LST and the industrial unit area (P value less than 0.01). This research is valuable for environmental assessment and fine management of industrial cities.

Anthropogenic heat variation during the COVID-19 pandemic control measures in four Chinese megacities

Anthropogenic heat (AH) is an important input for the urban thermal environment. While reduction in AH during the Coronavirus disease 2019 (COVID-19) pandemic may have weakened urban heat islands (UHI), quantitative assessments on this are lacking. Here, a new AH estimation method based on a remote sensing surface energy balance (RS-SEB) without hysteresis from heat storage was proposed to clarify the effects of COVID-19 control measures on AH. To weaken the impact of shadows, a simple and novel calibration method was developed to estimate the SEB in multiple regions and periods. To overcome the hysteresis of AH caused by heat storage, RS-SEB was combined with an inventory-based model and thermal stability analysis framework. The resulting AH was consistent with the latest global AH dataset and had a much higher spatial resolution, providing objective and refined features of human activities during the pandemic. Our study of four Chinese megacities (Wuhan, Shanghai, Beijing, and Guangzhou) indicated that COVID-19 control measures severely restricted human activities and notably reduced AH. The reduction was up to 50% in Wuhan during the lockdown in February 2020 and gradually decreased after the lockdown was eased in April 2020, similar to that in Shanghai during the Level 1 pandemic response. In contrast, AH was less reduced in Guangzhou during the same period and increased in Beijing owing to extended central heating use in winter. AH decreased more in urban centers and the change in AH varied in terms of urban land use between cities and periods. Although UHI changes during the COVID-19 pandemic cannot be entirely attributed to AH changes, the considerable reduction in AH is an important feature accompanying the weakening of the UHI.

Landscape and vegetation traits of urban green space can predict local surface temperature

Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient Boost Regression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Improved anthropogenic heat flux model for fine spatiotemporal information in Southeast China

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m^-2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m^-2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.