Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

Comparison of mediating effects of air pollutants on urban morphology and urban heat Island intensity at block scale

The urban heat island effect seriously challenges the sustainability and livability of urban development. Air pollutants (AP) may play a mediating role in the impact of urban morphology (UM) on the canopy layer urban heat island intensity (CLUHII) and the surface urban heat island intensity (SUHII). To verify this hypothesis, taking Urumqi as an example, we use the ridge regression model to reveal the differences in the impacts of UM and AP on the two types of urban heat island intensity (UHII). A structural equation model was established to verify the mediating effect of AP. The results show that: (1) There are differences in the optimal research units for UM and CLUHII and SUHII, which are 500 m and 300 m respectively. (2) Whether it is CLUHII or SUHII, the impact of two - dimensional urban morphology indicators are greater than that of three - dimensional urban morphology indicators. (3) There are similarities and differences in the impact of urban morphology indicators on the two types of UHII. The effects of standard deviation of building height, floor area ratio, and sky view factor on the two are opposite. (4) Air pollutants (PM10, PM2.5, NO2) have significant mediating effects between building density, impervious surface percent, green coverage ratio, mean building height, standard deviation of building height, floor area ratio, sky view factor, and the two types of UHII. This study provides a reliable reference for urban planning aimed at mitigating the urban heat island effect and air pollution.

Urban green spaces and flood disaster management: toward sustainable urban design

This paper explores how rapid urbanization and frequent extreme rainfall affect urban flood risk, highlighting that stormwater management challenges worsen due to increased impervious surfaces, altered urban hydrological cycles, and damaged natural retention systems. This paper, grounded in sustainable urban design principles, underscores the significance of green infrastructure for bolstering urban flood resilience. Urban green spaces not only reduce surface runoff through natural storage and infiltration but also improve water quality, regulate urban microclimate, and enhance biodiversity. Furthermore, this paper examines the application of green infrastructure in stormwater management, drawing inspiration from international success stories such as the Amsterdam Canal Network, Singapore's ABC Waters Program, and Kazan's "Resilient Belt" project. The study suggests that an integrated "blue-green-gray" strategy, which combines natural ecosystems with engineered facilities, should be adopted to optimize stormwater management efficiency. Despite the significant advantages of green infrastructure, there are still challenges in scaling up the application, connecting decentralized green spaces, and integrating real-time monitoring systems for dynamic regulation. Future research should focus on the synergistic effects of green infrastructure at different urban scales and explore a balanced path between urban expansion and ecological sustainability.

Uncertainty evaluation for aneroid barometer measurement. Part I. "Guide to the expression of uncertainty in measurement method"

Addressing the oversight in the evaluation of measurement uncertainty for traditional aneroid barometers, where the performance of calibration equipment (in particular, the temperature coefficient calibration box, henceforth termed the coefficient box, and the indication calibration box, referred to as the indication box) and the uncertainty associated with the temperature coefficient are frequently neglected, this paper introduces an enhanced evaluation framework. The objective of this framework is to achieve a more precise evaluation of measurement uncertainty and to enhance the accuracy of calibration outcomes. Initially, a comprehensive calibration methodology is devised, accounting for the impact of the coefficient box's performance on the temperature coefficient and the influence of the indication box's performance on the indication error. By executing this methodology, detailed empirical test data are procured. Subsequently, incorporating various factors that affect the measurement outcomes, the measurement uncertainty is evaluated utilizing the collected data. The evaluation results reveal that the uncertainty of the temperature coefficient is U = 0.004 hPa/°C (k = 2), and the uncertainty of the indication error is U = 0.12 hPa (k = 2). For comparison, the traditional calibration approach yields an uncertainty of the temperature coefficient of U = 0.004 hPa/°C (k = 2) and an uncertainty of the indication error of U = 0.09 hPa (k = 2). Through a comparative analysis, it is evident that the coefficient box's performance has little effect on the temperature coefficient's uncertainty; however, neglecting the performance of the indication box and the temperature coefficient's uncertainty can result in the evaluation of the indication error's uncertainty deviating from actuality. Consequently, it is recommended that the effects of the indication box's performance and the temperature coefficient's uncertainty be thoroughly considered when evaluating the measurement uncertainty of an aneroid barometer's indication error. By doing so, more authentic and reliable evaluation results can be obtained, ultimately enhancing the accuracy of the aneroid barometer's calibration outcomes.

The influence of weather and urban environment characteristics on upper respiratory tract infections: a systematic review

Background

Weather can independently affect the occurrence of respiratory tract infections (RTIs) in urban areas. Built environments of cities could further modify exposure to weather and consequently the risk of RTIs, but their combined effects on infections are not known.

Objectives

Our aim was to synthesize evidence of the influence of weather on RTIs in urban areas and to examine whether urban built environments are associated with both weather and RTIs.

Methods

A systematic search of Scopus, PubMed, and Web of Science databases was conducted on 9th of August 2022 following PRISMA guidelines. Studies were included in the review based on predefined criteria by screening 5,789 articles and reviewing reference lists of relevant studies. The quality of the studies was assessed using the AXIS appraisal tool, and the results analyzed by narrative synthesis.

Results

Twenty-one eligible studies focusing on COVID-19 and influenza transmissions, were included in the review. All studies were register based ecological studies by design. Low temperature (11/19 studies) was most often associated with increased risk of RTI. Humidity showed either negative (5/14 studies), positive (3/14 studies) or no (6/14 studies) relation with RTIs. The association between wind and solar radiation on infections was inconclusive. Population density was positively associated with RTIs (14/15 studies).

Conclusions

Our review shows that exposure to low temperature increases the occurrence of RTIs in urban areas, and where also high population density increases the infection risk. The study highlights the need to further assess the relationship between built environment characteristics, weather, and RTIs.

Predicting the impact of dynamic global urban expansion on urban soil organic carbon

Urban expansion has triggered significant changes in soil organic carbon (SOC), profoundly affecting the global carbon cycle. The accurate prediction of the global distribution of urban SOC and assessment of the impact of future urban expansion on SOC are essential for urban soil carbon management. By using data from 377 urban locations, this study estimated the global distribution of urban SOC and projected future SOC changes under two socioeconomic scenarios: SSP126 and SSP585. The results showed that compared with the urban SOC density in 2000 (64.75 Mg C ha- 1), the global urban SOC density by 2100 decreased by 1.19% and 4.72% under the SSP126 and SSP585 scenarios, respectively. However, urban SOC stocks increased by 66.13% and 178.75% by 2100 under SSP126 and SSP585, respectively, reflecting a "carbon transfer" from natural ecosystems to urban land use. This study provides critical insights into the dynamics of urban SOC and underscores the necessity to balance urban expansion with ecosystem protection to reduce carbon loss and stabilise the global carbon balance.

Long-term variability of extreme precipitation with WRF model at a complex terrain River Basin

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions.

Impacts of urban expansion on air temperature and humidity during 2022 mega-heatwave over the Yangtze River Delta, China

The Yangtze River Delta (YRD) experienced record-breaking heat in the summer of 2022. However, the urban heat pattern and the role of urban expansion over the last two decades in this hot summer have not been explored. Using the advanced mesoscale Weather Research and Forecasting (WRF) model, we reproduced the fine spatial features and investigated the urban heat island (UHI) and dry island (UDI) effects during the two identified heatwaves in 2022. We further replace the current (2020) land use with the historical (2001) land use in WRF to evaluate the impacts of urban expansion from 2001 to 2020 on air temperature and moisture. Our finding revealed that the conversion of land use resulted in near-surface warming and drying, with pronounced diurnal variations, especially during the July heatwave. The analysis of surface energy balance demonstrated that the substantial decrease in evapotranspiration (ET) was the primary driver of daytime warming, elevating temperatures by 7 °C (July heatwave) and 2 °C (August heatwave). This ET reduction also led to the strong daytime coupling of warming and drying effects over new urban areas. At night, the release of stored heat resulted in the temperature increase of 2 °C (1 °C) during July (August) heatwave, highlighting the nighttime as a critical period for heightened thermal risk. Additionally, urban expansion at the periphery contributed modestly to the warming of urban cores, exacerbating conditions in an already hot environment. This study enhances understanding of the impacts of urban expansion on air temperature and humidity during extreme heatwaves, thereby supporting targeted adaptation and mitigation for extreme events within large cities.

More extremely hot days, more heat exposure and fewer cooling options for people of color in Connecticut, U.S

It is well-documented that people of color in the U.S. are disproportionately exposed to extreme urban heat. However, most studies have focused on large cities for one point in time, and less is known about how heat exposure changes over time in smaller cities. Here, we present a study of the changing nature of urban heat exposure and cooling strategies for ten cities in Connecticut in the U.S. Our results show that people of color experience more heat exposure and fewer adaptation strategies. They experienced higher overall temperatures, more extremely hot days, and larger increases in heat exposure. Also, they have lower air conditioning ownership rates and lower tree cover. Taken together, the results indicate that people of color are not only exposed to higher temperatures but also disproportionately exposed to increasing temperatures over time. With lower heat adaptation capacity, people of color are more vulnerable to increasing urban heat.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Climate data for building simulations with urban heat island effects and nature-based solutions

As cities face a changing climate, buildings will be subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under climate change is essential for maintaining sustainable and resilient communities. To better prepare building simulation climate data with urban effects, a computationally efficient approach is used to generate "urbanized" data, where the city's unique signature is obtained through the dynamic Weather Research and Forecasting model for the Ottawa, Canada region. We demonstrate this process using existing climate data and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. The data consists of several 31-year time series of climate variables such as temperature, humidity, wind speed and direction, pressure, cloud cover, and precipitation over different global warming thresholds. Such a dataset allows building practitioners to evaluate building performance under both historical and future climate conditions, as well as to evaluate the impacts of nature-based solutions to mitigate future climate change risks.

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 μg/m3 to 73 μg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Association of temperature and precipitation with malaria incidence in 57 countries and territories from 2000 to 2019: A worldwide observational study

BACKGROUND

The transmission of malaria is known to be affected by climatic factors. However, existing studies on the impact of temperature and precipitation on malaria incidence offer no clear-cut conclusions, and there is a lack of research on a global scale. We aimed to estimate the association of temperature and precipitation with malaria incidence globally from 2000 to 2019.

METHODS

We used meteorological data from the National Centers for Environmental Information and malaria incidence data from the Global Burden of Disease Study 2019 to calculate effect sizes through quasi-Poisson generalised linear models while controlling for confounders.

RESULTS

231.4 million malaria cases occurred worldwide in 2019. National annual average temperature and precipitation were associated with malaria incidence, with an increase in the age-standardised incidence rate (ASIR) of 2.01% (95% confidence interval (CI) = 2.00, 2.02) and 6.04% (95% CI = 6.00, 6.09) following one unit increase of national annual average temperature and precipitation. In subgroup analysis, we found that malaria incidence in Asian countries was most affected by temperature, while the incidence in African countries was most affected by precipitation (P < 0.05). Stratified by age, children under five were most affected by both temperature and precipitation (P < 0.05). We additionally found that the impact of the national annual average temperature on malaria incidence increased over time (P < 0.05).

CONCLUSIONS

We advocate for a comprehensive approach to malaria prevention, focussed on addressing the impact of climate factors through international collaboration, adaptive measures, and targeted interventions for vulnerable populations.

Mapping urban form into local climate zones for the continental US from 1986-2020

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

Urban land patterns can moderate population exposures to climate extremes over the 21st century

Climate change and global urbanization have often been anticipated to increase future population exposure (frequency and intensity) to extreme weather over the coming decades. Here we examine how changes in urban land extent, population, and climate will respectively and collectively affect spatial patterns of future population exposures to climate extremes (including hot days, cold days, heavy rainfalls, and severe thunderstorm environments) across the continental U.S. at the end of the 21st century. Different from common impressions, we find that urban land patterns can sometimes reduce rather than increase population exposures to climate extremes, even heat extremes, and that spatial patterns instead of total quantities of urban land are more influential to population exposures. Our findings lead to preliminary suggestions for embedding long-term climate resilience in urban and regional land-use system designs, and strongly motivate searches for optimal spatial urban land patterns that can robustly moderate population exposures to climate extremes throughout the 21st century.

Application of advanced trend analysis techniques with clustering approach for analysing rainfall trend and identification of homogenous rainfall regions in Delhi metropolitan city

In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann-Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991-2018. But the rate of increase was low as the trend slope of ITA and Sen's slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.

Continental United States climate projections based on thermodynamic modification of historical weather

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

Sulfur dioxide improves the thermotolerance of maize seedlings by regulating salicylic acid biosynthesis

Heat stress (HS) has become a serious threat to crop growth and yield. Sulfur dioxide (SO₂) is being verified as a signal molecule in regulating the plant stress response. However, it is unknown whether SO₂ plays a significant role in the plant heat stress response (HSR). Herein, maize seedlings were pretreated with various concentrations of SO₂ and then kept at 45 °C for heat stress treatment, aiming to study the effect of SO₂ pretreatment on HSR in maize by phenotypic, physiological, and biochemical analyses. It was found that SO₂ pretreatment greatly improved the thermotolerance of maize seedlings. The SO₂-pretreated seedlings showed 30-40% lower ROS accumulation and membrane peroxidation, but 55-110% higher activities of antioxidant enzymes than the distilled water-pretreated seedlings under heat stress. Interestingly, endogenous salicylic acid (SA) levels were increased by ∼85% in SO₂-pretreated seedlings, as revealed by phytohormone analyses. Furthermore, the SA biosynthesis inhibitor paclobutrazol markedly reduced SA levels and attenuated SO₂-triggered thermotolerance of maize seedlings. Meanwhile, transcripts of several SA biosynthesis and signaling, and heat stress-responsive genes in SO₂-pretreated seedlings were significantly elevated under HS. These data have demonstrated that SO₂ pretreatment increased endogenous SA levels, which activated the antioxidant machinery and strengthened the stress defense system, thereby improving the thermotolerance of maize seedlings under HS. Our current study provides a new strategy for mitigating heat stress damage for safe crop production.

Impact of different roofing mitigation strategies on near-surface temperature and energy consumption over the Chicago metropolitan area during a heatwave event

This study examined the impact of cool roofs, green roofs, and solar panel roofs on near-surface temperature and cooling energy demand through regional modeling in the Chicago metropolitan area (CMA). The new parameterization of green roofs and solar panel roofs based on model physics has recently been developed, updated, and coupled to a multilayer building energy model that is fully integrated with the Weather Research and Forecasting model. We evaluate the model performance against with observation measurements to show that our model is capable of being a suited tool to simulate the heatwave event. Next, we examine the impact by characterizing the near-surface air temperature and its diurnal cycle from experiments with and without the different rooftops. We also estimate the impact of the rooftop on the urban island intensity (UHII), surface heat flux, and the boundary layer. Finally, we measure the impact of the different rooftops on citywide air-conditioning consumption. Results show that the deployment of the cool roof can reduce the near-surface temperature most over urban areas, followed by green roof and solar panel roof. The cool roof experiment was the only one where the near-surface temperature trended down as the urban fraction increased, indicating the cool roof is the most effective mitigation strategy among these three rooftop options. For cooling energy consumption, it can be reduced by 16.6 %, 14.0 %, and 7.6 %, when cool roofs, green roofs, and solar panel roofs are deployed, respectively. Although solar panel roofs show the smallest reduction in energy consumption, if we assume that all electricity production can be applied to cooling demand, we can expect almost a savings of almost half (46.7 %) on cooling energy demand.

Compound events and associated impacts in China

Owing to amplified impacts on human society and ecosystems, compound events (or extremes) have attracted ample attention in recent decades. China is particularly vulnerable to compound events due to the fast warming rate, dense populations, and fragile ecological environment. Recent studies have demonstrated tangible effects of climate change on compound events with mounting impacts on the economy, agriculture, public health, and infrastructure in China, posing unprecedented threats that are increasingly difficult to manage. Here, I synthesize recent progress in studies of compound events and associated impacts in China. Several lines of evidence indicate an increase in the frequency and intensity of multiple types of compound events across China. Future directions in studying compound events in China are suggested, including investigating extremes from a compound perspective, modeling compound events in the Anthropocene, quantitative evaluations of risks, and holistic adaptation measures of compound events.

Watershed Ecohydrological Processes in a Changing Environment: Opportunities and Challenges

Basin ecohydrological processes are essential for informing policymaking and social development in response to growing environmental problems. In this paper, we review watershed ecohydrology, focusing on the interaction between watershed ecological and hydrological processes. Climate change and human activities are the most important factors influencing water quantity and quality, and there is a need to integrate watershed socioeconomic activities into the paradigm of watershed ecohydrological process studies. Then, we propose a new framework for integrated watershed management. It includes (1) data collection: building an integrated observation network; (2) theoretical basis: attribution analysis; (3) integrated modeling: medium- and long-term prediction of ecohydrological processes by human–nature interactions; and (4) policy orientation. The paper was a potential solution to overcome challenges in the context of frequent climate extremes and rapid land-use change.

The Curious Relationship Between COVID-19 Lockdowns and Urban Heat Islands

The COVID-19 pandemic has been a life-altering shock to society. However, there have been serendipitous outcomes from the associated lockdowns ranging from improved air quality to reductions in carbon emissions. Liu et al. (2022, https://doi.org/10.1029/2021GL096842) revealed that even the magnitude of the heat islands in Chinese cities were reduced due to a decline in human activities and their associated anthropogenic contributions. These surprising findings have significant implications for understanding intersections among climate, health, energy, urban planning, transportation, and infrastructure.