Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data

The association between childhood adiposity in northeast China and anthropogenic heat flux: A new insight into the comprehensive impact of human activities

Anthropogenic heat has been reported to have significant health impacts, but research on its association with childhood adiposity is still lacking. In this study, we matched the 2008-2012 average anthropogenic heat flux, as simulated by a grid estimation model using inventory methods, with questionnaire and measurement data of 49,938 children randomly recruited from seven cities in Northeast China in 2012. After adjusting for social demographic and behavioral factors, we used generalized linear mixed-effect models to assess the association between anthropogenic heat flux and adiposity among children. We also examined the effect modification of various social demographic and behavioral confounders. We found that each 10 W/m2 increase in total anthropogenic heat flux and that from the industry source was associated with an increase of 5.82% (95% CI = 0.84%-11.05%) and 6.62% (95% CI = 0.87%-12.70%) in the odds of childhood adiposity. Similarly, the excess rate of adiposity among children were 5.26% (95% CI = -1.33%-12.29%) and 8.51% (95% CI = 2.24%-15.17%) per 1 W/m2 increase in the anthropogenic heat flux from transportation and buildings, and was 7.94% (95% CI = 2.28%-13.91%) per 0.001 W/m2 increase in the anthropogenic heat flux from human metabolism. We also found generally greater effect estimates among female children and children who were exposed to passive smoking during pregnancy, born by caesarean section, non-breastfed/mixed-fed, or lived within 20 m adjacent to the main road. The potential deleterious effect of anthropogenic heat exposure on adiposity among children may make it a new but major threat to be targeted by future mitigation strategies.

Interactions among spatial configuration aspects of urban tree canopy significantly affect its cooling effects

Increasing urban tree canopy (UTC) has been widely recognized as an effective means for urban heat mitigation and adaptation. While numerous studies have shown that both percent cover of UTC and its spatial configuration can significantly affect urban temperature, the pathways governing these relationships are largely unexplored. Here we present a cross-city comparison aiming to fill this gap by explicitly quantifying the pathways on which percent cover of UTC and its spatial configuration affect land surface temperature (LST) using structural equation modeling (SEM), based on UTC mapped from high resolution imagery and LST derived from Landsat thermal bands. We found: 1) Although both the direct and indirect pathways significantly affected LST regardless of scales and cities, the direct pathway played a more important role in affecting LST in Baltimore, Beijing, and Shenzhen. In contrast, an opposite result was found in Sacramento, likely due to the effects of buildings and their interactions with UTC. 2) Similarly, the direct pathway of mean patch size (MPS) and mean shape index (MSI) played a more important role in affecting LST than their indirect effects via altering edge density (ED). Our results highlighted the necessity for discomposing the effects of different spatial configuration variables on LST. Understanding the pathways through which UTC affects LST can provide insights into urban heat mitigation and adaptation.

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.

An Estimation of the Anthropogenic Heat Emissions in Darwin City Using Urban Microclimate Simulations

The energy consumption due to urbanization and man-made activities has resulted in production of waste, heat, and pollution in the urban environment. These have further resulted in undesirable environmental issues such as the production of excessive Anthropogenic Heat Emissions (AHE), thus leading to an increased Urban Heat Island (UHI) effect. The aim of this study was to estimate the total AHE based on the contribution of three major sources of waste heat generation in an urban environment, i.e., buildings, vehicular traffic, and human metabolism. Furthermore, a comparison of dominating anthropogenic heat factor of Darwin with that of other major international cities was carried out. Field measurements of microclimate (temperatures, humidity, solar radiation, and other factors of climate measures) were conducted along Smith Street, Darwin City. Then, surveys were conducted to collect information regarding the buildings, vehicle traffic and Human population (metabolism) in the study area. Each individual component of AHE was calculated based on a conceptual framework of the anthropogenic heat model developed within this study. The results showed that AHE from buildings is the most dominant factor influencing the total AHE in Darwin, contributing to about 87% to 95% of total AHE. This is followed by vehicular traffic (4–13%) and lastly, human metabolism (0.1–0.8%). The study also shows that Darwin gains an average of 990 Wm−2 solar power on a peak day. This study proves that building anthropogenic heat is the major dominating factor influencing the UHI in tropical urban climates.

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.

The association between anthropogenic heat and adult hypertension in Northeast China

OBJECTIVES

Although the potential serious threat of anthropogenic heat on human health was receiving considerable attention worldwide, its long-term health effect on blood pressure (BP) remained unknown. We aimed to evaluate the associations of long-term anthropogenic heat exposure with different components of BP and hypertension.

METHODS

In this cross-sectional study (Liaoning province, China) conducted in 2009, we included a total of 24,845 Chinese adults (18-74 years). We estimated the anthropogenic heat exposure in 2008 using multisource remote sensing images and ancillary data. We measured systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP), and defined hypertension. We used generalized linear mixed model to examine the associations.

RESULTS

In the adjusted model, the estimates indicated that the difference in SBP, MAP and PP for those in highest quartiles of total anthropogenic heat exposure was greater compared with the lowest quartile (highest quartile: β = 1.11 [95% CI: 0.28-1.94], 0.60 [95% CI: 0.04-1.17], 0.76 [95% CI: 0.17-1.35]). Compared with the lowest quartile, the odds of hypertension were higher among those in higher quartiles (second quartile: OR = 1.17 [95% CI: 1.05-1.30]; third quartile:1.10 [95% CI: 1.1.01-1.21]; highest quartile: 1.17 [95% CI: 1.06-1.28]). These associations were stronger in female participants.

CONCLUSION

Our study showed that long-term exposure to anthropogenic heat was associated with elevated BP and higher odds of hypertension. These findings suggest that mitigation strategies to reduce anthropogenic heat should be considered.

Associations between metabolic syndrome and anthropogenic heat emissions in northeastern China

BACKGROUND

Recent research attention has been paid to anthropogenic heat emissions (AE), temperature increase generated by human activity such as lighting, transportation, manufacturing, construction, and building climate controls. However, there is no epidemiological data available to investigate the association between anthropogenic heat emissions and metabolic syndrome (MetS), a cluster of conditions that increase risk of stroke, heart disease and diabetes.

OBJECTIVE

To explore the relationships between AE and MetS in China.

METHODS

We recruited 15,477 adults from the 33 Communities Chinese Health Study, a cross-sectional study in northeastern China. We retrieved anthropogenic heat flux by collecting socio-economic and energy consumption data as well as satellite-based nighttime light and Normalized Difference Vegetation Index datasets, including emissions from buildings, transportation, human metabolism, and industries. We also measured MetS components consisting of triglycerides, high density lipoprotein cholesterol, fasting glucose, systolic blood pressure, and diastolic blood pressure, and waist circumference. Restricted cubic spline models were applied to assess the associations between AE and MetS.

RESULTS

The median flux of total AE was 30.98 W/m² and industrial AE was the dominant contributor (87.64%). The adjusted odds ratio and 95% confidence interval (CI) of MetS for the 75th and 95th percentiles of the total AE against the threshold were 1.29 (95% CI: 1.21, 1.38) and 1.65 (95% CI: 1.47, 1.85). Greater AE was associated with higher odds of MetS in a dose-response pattern, and the lowest point of U-shape curve indicated the threshold effect. Participants who are young and middle-aged exhibited stronger associations between AE and MetS.

CONCLUSIONS

Our novel findings reveal that AE are positively associated with MetS and that associations are modified by age. Further investigations into the mechanisms of the effects are needed.

Understanding the Relationship between China’s Eco-Environmental Quality and Urbanization Using Multisource Remote Sensing Data

The rapid development of urbanization and population growth in China has posed a major threat to the green sustainable development of the ecological environment. However, the impact of urbanization on the eco-environmental quality (EEQ) in China remains to be developed. Understanding their interactive coupling mechanism is of great significance to achieve the urban sustainable development goals. By using multi-source remote sensing data and the coupling coordination degree model (CCDM), we intended to answer the question “What are the temporal and spatial characteristics of urbanization and EEQ in China on the pixel scale during 2000–2013, and what is the coupling mechanism between the urbanization and the EEQ?”. To answer these questions, we explored the coupling mechanism between urbanization and the EEQ in China with a combined mathematical and graphics model. The results show that the urbanization and the coupling coordination degree (CCD) of the whole region continually increased from 2000 to 2013, especially in the three major urban agglomerations, with a spatial distribution pattern that was “high in the east and low in the west”. Most importantly, from 2000 to 2013, the CCD type of cities in China gradually evolved from uncoordinated cities to coordinated cities. Additionally, the decisive factor affecting the CCD from 2000 to 2013 was the development of urbanization, and the degree at which urbanization had an impact on CCD was about 8.4 times larger than that of the EEQ. At the same time, the rapid urbanization that has occurred in some areas has led to a significant decline in the EEQ, thus indicating that China needs to increase its protection of the ecological environment while pursuing social and economic development in the future. This study makes up for the deficiencies in the existing literature and investigates the long-term coupling of the EEQ and urbanization in China, thereby providing a new research perspective for the sustainable development of China and even the world in the future.

Appraising regional anthropogenic heat flux using high spatial resolution NTL and POI data: A case study in the Beijing-Tianjin-Hebei region, China

Rapid urbanization and the aggregation of human activities in cities have resulted in large amounts of anthropogenic heat (AH) emission, which affects urban climate. Quantifying and assessing the AH emission values accurately and analyzing their spatial distribution characteristics is important to understand the energy exchange processes of urban areas. In this study, the high spatial resolution anthropogenic heat flux (AHF) quantification and spatial distribution analysis were conducted using multi-source data in the Beijing-Tianjin-Hebei region (BTH region) of China. First, the AH emission in district and city level were estimated using inventory method based on energy consumption and socio-economic statistical data; Then, AHF spatial quantification models were constructed based on high spatial resolution nighttime light (NTL) data and Point of interests (POI) data, and 130 m × 130 m gridded AHF quantification result in BTH region was realized; Finally, the potential numerical and spatial distribution patterns of AHF were analyzed using various indicators including contribution rate and aggregation index. The results show that: (1) The parameterized index constructed based on NTL and POI data shows a strong correlation with AHF, with R² ranging from 0.79 to 0.94 and a mean absolute error (MAE) value of 0.72 w·m^-2, which can be applied to the quantification of gridded AHF values with high resolution. The highest total AHF in the study area is 214 w·m^-2, and the average value is 2.24 w·m^-2. (2) Considering the sources of AHF, industrial emission sources in BTH region contribute the most to the total AHF, but commercial building emission sources in Beijing have a higher contribution, which can reach 33.8%. (3) Different types of AHF have different spatial aggregation levels. Commercial building emission and human metabolic emission have the highest aggregation level, and transportation emission has the lowest aggregation level.

Impacts of the evolving urban development on intra-urban surface thermal environment: Evidence from 323 Chinese cities

Urban development has significantly modified the surface thermal environment in urban areas. This study provides the first attempt to characterize the urban development imprint on surface thermal environment for 323 cities across the entire country of China, using an intra-urban perspective. Specifically, it investigates the variation of surface thermal environment in terms of land surface temperature (LST) difference triggered by significant urban evolution of intra-urban division containing two primary classes: old urban areas developed by 1992 and new ones expanded in the 1992-2015 period. Under this "old-new" dichotomy, the relationship between urban development and the LST difference is explored through Multi-scale Geographically Weighted Regression (MGWR). Results reveal that urban development is closely related to the difference in LST between old and new urban areas in 2015, which varies from -2.66 °C to 2.46 °C, up to -6.27 °C in western China. 264 cities manifest relatively "cooler" urban environments in the generally larger-sized new urban areas. The seven selected urban development indicators can explain 75% of the variance in the LST difference through MGWR. Among them, the old-new elevation difference, the normalized difference vegetation index (NDVI) difference, and Gini coefficient are found to influence the LST difference in various spatially varying manners. The elevation difference, a generally underestimated nature-driven indicator, is found dominant in explaining the LST difference for 252 cities, among which 216 cities demonstrate higher LSTs in the urban areas with lower elevations. Overall, this study provides valuable information of human-environment interaction across many cities in a generalized way, which complements similar studies at local level, and helps to depict a complete picture of environmental impacts of urban development. The integrated workflow can also be promoted to other periods or other countries to examine the corresponding urbanization imprint on intra-urban surface warming.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Temporal and Spatial Variation of Anthropogenic Heat in the Central Urban Area: A Case Study of Guangzhou, China

The urban heat island effect caused by the rapid increase in urban anthropogenic heat has gradually become an important factor affecting the living environment of urban residents. Studying the temporal and spatial variation characteristics of urban anthropogenic heat is of great significance for urban planning and urban ecological service systems. In this study, the urban anthropogenic heat flux (AHF) in 2004, 2009, 2014, and 2020 in the central urban area of Guangzhou was retrieved based on Landsat data and the surface energy balance equation, and the temporal and spatial characteristics of different types of anthropogenic heat were explored by combining the transfer matrix and the migration of the gravity center. The results showed that: (1) The overall change trend of anthropogenic heat in the central urban area of Guangzhou was enhanced, and the degree of enhancement was related to the type of urban functional land. (2) Different types of anthropogenic heat had different characteristics in terms of area expansion and spatial changes. Low-value anthropogenic heat (zero-AHF zone, low-AHF zone, medium-AHF zone) changed drastically in terms of area expansion. High-value anthropogenic heat (medium-AHF zone, high-AHF zone) changed more drastically in space. The increase in urban population, rapid economic development, and increased industrial production activities have stimulated the emission of anthropogenic heat, which has a positive impact on the intensity of anthropogenic heat.

Changing Effect of Urban Form on the Seasonal and Diurnal Variations of Surface Urban Heat Island Intensities (SUHIIs) in More Than 3000 Cities in China

Based on the indicators of more than 3000 cities in China, this study shows that the relationship between the urban form and surface urban heat island intensity (SUHII) demonstrates seasonal and diurnal variations, and also changes along urban development and elevation gradients. SUHIIs show seasonal and diurnal change patterns along urban development and elevation gradients, but there is no obvious change trend along temperature and humidity gradients. Among them, the seasonal variation of the SUHII went up about 0.4 ℃ from the first level of urban development to the highest level, while the diurnal variation of the SUHII decreased by 0.4 °C. With urban development, the correlations between the anthropogenic heat flux (AHF), population density (POPDEN) and morphological continuity (CONTIG) with the SUHII of summer days, summer nights and winter nights continued to be enhanced, with the correlation coefficients (β) increased by about 0.3. The effect of area size (AREA) became more influential on the SUHII of summer days and nights, but its effect on the SUHII of winter nights increased first and then decreased along the urban development gradient. With the increase of elevation, the correlations of the AHF, POPDEN, AREA, CONTIG and summer day and night SUHII were gradually reduced (β decreased by about 0.4), but their impact on the SUHII of winter nights was gradually enhanced (β increased by about 0.2 to 0.3). Along temperature and humidity gradients, the positive effect of POPDEN on the summer SUHII decreased gradually (β decreased by about 0.3). However, the enhancement effects of the AHF, AREA, CONTIG and POPDEN on the SUHII of winter nights increased generally (β increased by about 0.2). According to the Random Forest model, for the SUHIIs at night, the relative importance (RI) of urban form factors was greater, while for the SUHIIs of daytime, the RIs of natural factors were greater. The contribution of the urban form to the seasonal variation of the SUHII is similar to that of natural factors, but their contribution to diurnal variation is lower. Our results suggest that it is more necessary to control the urban scale, avoid excessive urban agglomeration and reasonably control the anthropogenic heat emission in more developed and low altitude cities to reduce their summer heat exposure.