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Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3–7%) and by 13% (95% CI 10–16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

High levels of air pollution in China may contribute to the urban population’s reported low level of happiness 1 – 3 . To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo 4 – 6 , and studied its dynamics relative to daily local air quality index and PM 2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM 2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China’s government about rising quality of life concerns. A daily, city-level happiness metric constructed from the sentiment expressed in 210 million tweets on Sina Weibo from 144 cities shows that high levels of air pollution significantly reduce Chinese urbanites’ expressed happiness on social media.

HighlightsNanocapsules made from metal–organic frameworks were designed for sustained release of additive (LiNO3) to passivate Li anode in commercial carbonate-based electrolyte.The nanocapsules with continuous supply of LiNO3 formed a nitride-rich solid electrolyte interphase layer on Li anode and persistently remedied the interphase during prolonged cycling.The practical Li-metal full cell delivered a prolonged lifespan with 90% capacity retention after 240 cycles which has been hardly achieved in commercial electrolyte.A robust solid-electrolyte interphase (SEI) enabled by electrolyte additive is a promising approach to stabilize Li anode and improve Li cycling efficiency. However, the self-sacrificial nature of SEI forming additives limits their capability to stabilize Li anode for long-term cycling. Herein, we demonstrate nanocapsules made from metal–organic frameworks for sustained release of LiNO3 as surface passivation additive in commercial carbonate-based electrolyte. The nanocapsules can offer over 10 times more LiNO3 than the solubility of LiNO3. Continuous supply of LiNO3 by nanocapsules forms a nitride-rich SEI layer on Li anode and persistently remedies SEI during prolonged cycling. As a result, lifespan of thin Li anode in 50 μm, which experiences drastic volume change and repeated SEI formation during cycling, has been notably improved. By pairing with an industry-level thick LiCoO2 cathode, practical Li-metal full cell demonstrates a remarkable capacity retention of 90% after 240 cycles, in contrast to fast capacity drop after 60 cycles in LiNO3 saturated electrolyte.

Temperature inversion (TI) constitutes a crucial component in the physicochemical processes of the lower troposphere, but disentangling human contributions to its generation from complex environmental factors poses significant challenges. We leveraged the unique natural experiment prompted by the coronavirus disease 2019 (COVID‐19) pandemic to estimate changes in TI incidence and temperature difference (∆T) caused by the economic shutdown in the first half of 2020 across 500 major cities worldwide. We found that ∆T declined by 2.5% and TI incidence declined by 18.2% compared to 2016–2019, exhibiting spatial‐temporal heterogeneity and pronounced declines in cities with higher levels of economic development and emission reduction. Moreover, we demonstrated that fine particulate matter (PM2.5) may serve as a mediating pathway through which human activities influence air thermal properties, and climate categories modulate this mediating effect. Our analysis provides empirical evidence of human influence on the vertical thermal structure of the atmosphere. Plain Language Summary Near‐surface temperature inversion (TI) is a typical indicator reflecting the vertical structure of the atmosphere and is closely related to human socioeconomic activities. However, due to the complicated interplay between anthropogenic and natural factors, quantifying human contributions to TI generation poses significant challenges. In this study, we utilized the economic shutdown during the first wave of COVID‐19 (the first half of 2020) across 500 cities worldwide to estimate changes in TI incidence and temperature difference (∆T) between atmospheric pressure layers compared to parallel levels in 2016–2019. We found that ∆T declined by 2.5% and TI incidence declined by 18.2% during the shutdown period as a consequence of disrupted socioeconomic activities. The magnitude of this effect varies with urban climate and economic level and is highly correlated with anthropogenic emissions. Furthermore, we demonstrated that fine particulate matter (PM2.5) may serve as a mediating pathway through which human activities impact atmospheric thermal properties. Notably, the effect of PM2.5 is climate‐dependent, being most pronounced in boreal cities but least significant in tropical regions. This study enriches our understanding of human influence on the vertical thermal structure of the atmosphere. Key Points Reduced human activities during the COVID‐19 led to a 18.2% decrease in the incidence of temperature inversion (TI) across global cities TI responses monotonously to reduced economic activities, prominent for urban areas with greater reduction in anthropogenic emissions PM2.5 serves as a mediator through which humans impact the vertical thermal structure, with the effect size being climate‐dependent

The rising demand for calories and protein together with urbanization, pose significant challenges to China’s food security. The determination of policy actions requires accurate estimates of climatic impacts on both crop yields (intensive margin) and cropland area (extensive margin). However, the analysis of the latter has been limited, especially in developing countries. Here, we assess the impact of temperature on land use in China by matching high-resolution satellite data on land use with daily weather data from 1980 to 2010. We find that extremely hot weather (daily average temperature above 30 °C) has a long-lasting effect on reducing cropland in China. Combining climate projections from 39 downscaled climate models, we predict that climate change is likely to reduce China’s cropland area by 2.09%–25.51% under IPCC’s slowest and fastest-warming scenarios by the end of this century. In addition, we find that non-irrigated land is more susceptible to rising temperatures in the short term; however, irrigated land is subject to a similar impact in the long term. This result suggests that the adaptive effect of irrigation could be limited under persistent rising in temperature.

Anthropogenic climate change is driving summer heat toward more humid conditions, accompanied by more frequent day-night compound heat extremes (high temperatures during both day and night). As the fast-warming and aging continent, Europe faces escalating heat-related health risks. Here, we projected future heat-related mortality in Europe using a distributed lag nonlinear model that incorporates humid heat and compound heat extremes, strengthened by a health risk-based definition of extreme heat and a scenario matrix integrating time-varying adaptation trajectories. Under 2010–2019 adaptation baselines, future heat-related mortality is projected to increase annually by 103.7-135.1 deaths per million people by 2100 across various population-climate scenarios for every degree of global warming, with Western and Eastern Europe suffering the most. If global warming exceeds 2 °C, climate change will dominate (84.0–96.8%) projected increase in heat-related mortality. Across all socioeconomic pathways, even a 50% reduction in heat-related relative risk through physiological adaptation will be insufficient to offset the climate change-driven escalation of future heat-related mortality. This study projects heat-related mortality in Europe across various adaptation scenarios by modelling humid and compound day-night heat, using a health-based heat definition. Without heat adaptation, mortality could rise by 103.7–135.1 deaths per million people per 1 °C of global warming.

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants. Non-pharmaceutical interventions (NPIs) and COVID-19 vaccination have been implemented concurrently, making their relative effects difficult to measure. Here, the authors show that effects of NPIs reduced as vaccine coverage increased, but that NPIs could still be important in the context of more transmissible variants.

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of ‘X-ray’ scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted ‘idea tree’ that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.

The COVID-19 pandemic has created unprecedented burdens on people’s physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.Using tweets in over 100 countries, Wang et al. examine evidence of global sentiment during the COVID-19 pandemic. They find that COVID-19 outbreaks caused a decline in sentiment worldwide, and the effects of lockdowns differed across countries.

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people’s daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders (“nightmare” and “insomnia”) take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.