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It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.A systematic review shows that >58% of infectious diseases confronted by humanity, via 1,006 unique pathways, have at some point been affected by climatic hazards sensitive to GHGs. These results highlight the mounting challenge for adaption and the urgent need to reduce GHG emissions.

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives. The risk of heat-mortality is increasing sharply. The authors report that heat-mortality levels of a 1-in-100-year summer in the climate of 2000 can be expected once every ten to twenty years in the current climate and at least once in five years with 2 °C of global warming.

Extreme heat undermines the working capacity of individuals, resulting in lower productivity, and thus economic output. Here we analyse the present and future economic damages due to reduced labour productivity caused by extreme heat in Europe. For the analysis of current impacts, we focused on heatwaves occurring in four recent anomalously hot years (2003, 2010, 2015, and 2018) and compared our findings to the historical period 1981–2010. In the selected years, the total estimated damages attributed to heatwaves amounted to 0.3–0.5% of European gross domestic product (GDP). However, the identified losses were largely heterogeneous across space, consistently showing GDP impacts beyond 1% in more vulnerable regions. Future projections indicate that by 2060 impacts might increase in Europe by a factor of almost five compared to the historical period 1981–2010 if no further mitigation or adaptation actions are taken, suggesting the presence of more pronounced effects in the regions where these damages are already acute. Heatwaves are becoming increasingly frequent and more intense, causing severe economic impacts through reduced labour productivity. Here, the authors show that economic damages in Europe exceed 1% of the GDP in vulnerable areas, which might increase by a factor of almost five in the medium term without climate action.

Minimum mortality temperature (MMT) is an important indicator to assess the temperature–mortality relationship. It reflects human adaptability to local climate. The existing MMT estimates were usually based on case studies in data rich regions, and limited evidence about MMT was available at a global scale. It is still unclear what the most significant driver of MMT is and how MMT will change under global climate change. Here, by analysing MMTs in 420 locations covering six continents (Antarctica was excluded) in the world, we found that although the MMT changes geographically, it is very close to the local most frequent temperature (MFT) in the same period. The association between MFT and MMT is not changed when we adjust for latitude and study year. Based on the MFT~MMT association, we estimate and map the global distribution of MMTs in the present (2010s) and the future (2050s) for the first time. Minimum mortality temperature (MMT) changes geographically and over time. Here, by analysing MMTs in 420 global locations during 1984-2018, the authors found that MMT is very close to the local most frequent temperature (MFT) in the same period, and the association between MFT and MMT is not changed when adjusted for lattitude and study year.

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems 1 – 5 . However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy 4 , 5 . Here we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr −1 , lower than previous estimates that did not fully consider fertilizer management practices 6 – 9 . Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH 3 emissions by about 38% (1.6 ± 0.4 Tg N yr −1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH 3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH 3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases. A machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally shows that global NH 3 emissions in 2018 were lower than previous estimates that did not fully consider fertilizer management practices.

Cost-effective on-demand computing resources can help to process the increasing number of large, diverse datasets generated from smart internet-enabled technology, such as sensors, CCTV cameras, and mobile devices, with high temporal resolution. Category 1 emergency services (Ambulance, Fire and Rescue, and Police) can benefit from access to (near) real-time traffic- and weather data to coordinate multiple services, such as reassessing a route on the transport network affected by flooding or road incidents. However, there is a tendency not to utilise available smart city data sources, due to the heterogeneous data landscape, lack of real-time information, and communication inefficiencies. Using a systems engineering approach, we identify the current challenges faced by stakeholders involved in incident response and formulate future requirements for an improved system. Based on these initial findings, we develop a use case using Microsoft Azure cloud computing technology for analytical functionalities that can better support stakeholders in their response to an incident. Our prototype allows stakeholders to view available resources, send automatic updates and integrate location-based real-time weather and traffic data. We anticipate our study will provide a foundation for the future design of a data ontology for multi-agency incident response in smart cities of the future.

Premature mortality from current ambient fine particulate (PM 2.5 ) exposure in India is large, but the trend under climate change is unclear. Here we estimate ambient PM 2.5 exposure up to 2100 by applying the relative changes in PM 2.5 from baseline period (2001–2005) derived from Coupled Model Inter-comparison Project 5 (CMIP5) models to the satellite-derived baseline PM 2.5 . We then project the mortality burden using socioeconomic and demographic projections in the Shared Socioeconomic Pathway (SSP) scenarios. Ambient PM 2.5 exposure is expected to peak in 2030 under the RCP4.5 and in 2040 under the RCP8.5 scenario. Premature mortality burden is expected to be 2.4–4 and 28.5–38.8% higher under RCP8.5 scenario relative to the RCP4.5 scenario in 2031–2040 and 2091–2100, respectively. Improved health conditions due to economic growth are expected to compensate for the impact of changes in population and age distribution, leading to a reduction in per capita health burden from PM 2.5 for all scenarios except the combination of RCP8.5 exposure and SSP3. Modulation of ambient PM 2.5 exposure and premature mortality burden in India under climate change scenarios is unclear. Here the authors show that the premature mortality burden is projected to decrease in 2100 relative to present day under all possible combined climate change and socioeconomic pathways scenarios.

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue’s distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue’s expansion throughout Vietnam. The geographic distribution of dengue has been expanding in recent decades, and Vietnam is one of the most severely affected countries. In this study, the authors use Bayesian hierarchical modelling to investigate the socio-environmental and climatic drivers of dengue incidence in Vietnam and how they vary across the country.

The global population is aging at the same time as heat exposures are increasing due to climate change. Age structure, and its biological and socio-economic drivers, determine populations’ vulnerability to high temperatures. Here we combine age-stratified demographic projections with downscaled temperature projections to mid-century and find that chronic exposure to heat doubles across all warming scenarios. Moreover, >23% of the global population aged 69+ will inhabit climates whose 95th percentile of daily maximum temperature exceeds the critical threshold of 37.5 °C, compared with 14% today, exposing an additional 177–246 million older adults to dangerous acute heat. Effects are most severe in Asia and Africa, which also have the lowest adaptive capacity. Our results facilitate regional heat risk assessments and inform public health decision-making. By 2050 > 23% of the global population aged 69 + will live in climates with acute heat exposure– the 95th percentile of the distribution of maximum daily temperatures–greater than the critical threshold of 37.5C, compared with 14% in 2020, an increase of 177–246 million older adults exposed to dangerous acute heat.

Organic pollution of rivers by wastewater discharge from human activities negatively impacts people and ecosystems. Without treatment, pollution control relies on a combination of natural degradation and dilution by natural runoff to reduce downstream effects. We quantify here for the first time the global sanitation crisis through its impact on organic river pollution from the threats of (1) increasing wastewater discharge due to urbanization and intensification of livestock farming, and (2) reductions in river dilution capacity due to climate change and water extractions. Using in-stream Biochemical Oxygen Demand (BOD) as an overall indicator of organic river pollution, we calculate historical (2000) and future (2050) BOD concentrations in global river networks. Despite significant self-cleaning capacities of rivers, the number of people affected by organic pollution (BOD >5 mg/l) is projected to increase from 1.1 billion in 2000 to 2.5 billion in 2050. With developing countries disproportionately affected, our results point to a growing need for affordable wastewater solutions.