Emission Scenarios Research Articles

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

The heat-related health impacts of extremely high temperatures have become increasingly severe worldwide. Quantitative information on these impacts is necessary to plan and implement adaptation measures. The present study aimed to predict the number of heatstroke ambulance calls (HAC) and ambulance occupancy rate of patients with heatstroke (AORPH) under extremely high temperatures in Tokyo, Japan. Extremely high temperatures were defined as the annual highest value of daily maximum temperature that occurs once every 30 y (30 y temp) or 50 y (50 y temp). Combinations of three greenhouse gas (GHG) emission scenarios and five climate models were adopted to predict the impacts during the base years (1985–2014), mid-21st century (2021–2050), and late 21st century (2071–2100). Furthermore, two types of populations were adopted: fixed and changing. In the case of 50 y temp, the HAC during the peak hour in the base years ranged from 52 to 130 persons across the climate models. In the case of a fixed population, in the late 21st century it ranged from 123 to 2618 persons across the climate models and GHG scenarios. In the case of a changing population, the corresponding values in late 21st century ranged from 128 to 3078 persons. The AORPH during the peak hour ranged from 32% to 80% during the base years for 50 y temp, assuming that all ambulances were dispatched for heatstroke only. In the late 21st century, it ranged from 75% to 1605% in the fixed population case. With population change, the corresponding values in late 21st century ranged from 83% to 1781%. In many scenarios, not all patients with heatstroke could be transported (AORPH > 100%) even if all ambulances have been dispatched. Therefore, adaptation measures that mitigate such an ambulance transport system collapse must be planned and implemented.

Precipitation Seasonality Amplifies as Earth Warms

AbstractPrecipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world.

Magnesium Isotopes of Carbonate Reveal Seasonal Climate Variation in the Central East Asia During the Middle Eocene

AbstractIt is debated whether there was strong climate seasonality during the Eocene, which provides a close geological analogy for near‐future scenarios of greenhouse gas emissions. Lithological data suggest the existence of a broad arid zone centered around 30°N paleo‐latitude, while a humid climate was supported by palaeobotanic assemblages in East Asia. Here, we report the occurrence of massive primary lacustrine dolomite and magnesite in the central East Asia during the middle Eocene. We provide a novel perspective from magnesium isotopes to link the formation of Mg‐carbonates to seasonal dry‐wet cycles. Rapid magnesium input during the rainy season and intense evaporation in the dry season likely caused the formation of magnesium carbonates in an enclosed lake. These findings provide insights into hydroclimatic seasonality during the Eocene, contributing to our understanding of the hydrological cycle response to a greenhouse climate.

The role of financing constraints and environmental policy on green investment

Despite ambitious carbon reduction targets set by policymakers worldwide, current investments fall well short of the net-zero emissions scenario. This paper investigates the impact of financing constraints (FCs) on green investment among large, publicly listed firms across diverse sectors and countries. We find that FCs significantly reduce the propensity to undertake green investments by 2.5 percentage points. This result is even more pronounced in a difference-in-differences framework when focusing on the exogenous increase in financing costs resulting from the Great Financial Crisis of 2007/08. The study reveals further that the negative impact of FCs can be reduced by market-based environmental policies.

Historical and Future global warming under different RCP emission scenarios

Understanding the relationship between carbon emissions and climate change has become increasingly urgent amidst the universal endorsement of sustainable development and the establishment of net-zero carbon emission target (The Paris Agreement). In this study, model projections based on Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to examine future climate change under different representative concentration pathway (RCP) emission scenarios till 2300. Besides analysis of future global average temperature change under the emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP 8.5, historical data is first collected during the past three decades (1990-2021). The results show that the simulated global warming ranges from 0.9 to 1.3 between 1990 and 2021, consistent with the observations (0.93). Carbon emissions in China arose mostly from manufacturing and construction, electricity and heat and transport. Emissions from electricity, heat and transport increase by a high ratio of 7.7 and 3.5 times due to industry development. Under different RCP scenarios, the global surface temperature anomaly will increase by 0.2-1.0, 1.2-2.0, 3.5-4.3 and 3.0-8.5 , respectively. The surface temperature anomaly values under RCP8.5 are much higher than those under other emission scenarios. Only in the RCP2.6 scenario (sustainability), the surface warming in all simulations stays below a 2 threshold at the end of the 21st century. To attain net zero emissions, it is highly demanded to develop strict emission policies, chart a course toward decarbonization, and institute effective measures aimed at realizing net zero emissions within the short to medium term. Concurrently striving for economic objectives, managing the equilibrium between economic growth and environmental sustainability emerges as a critical imperative.

Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate

The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

An assessment of the distributional impacts of autonomous adaptation to climate change from European agriculture

Farmers facing a durable change in climate conditions may autonomously adapt through the intensive margin, the extensive margin, or through the adoption of new practices. Based on a coupling between a microeconomic model of European agriculture (AROPAj) and a crop model (STICS), this article investigates the potential distributional impacts of farm-level autonomous adaptation to climate change within the European Union (EU-27). Considering the representative concentration pathway (RCP) 4.5 of the second report on emission scenario of the fifth assessment report (SRES AR5), we implement two levels of autonomous adaptation for farmers, and three time horizons. The results indicate that ceteris paribus, climate change may lead in terms of social welfare to a slightly worse situation in the middle term and a slightly better situation in the long term with respect to the present. However, the ranking of agents in the distribution is importantly impacted. Our Shapley inequality decomposition shows that income inequality is largely explained by the region and type of farming. Climate change barely affects the marginal contribution of these two characteristics to overall income inequality.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

Anthropogenic warming is a key climate indicator of rising urban fire activity in China

Abstract China, one of the most populous countries in the world, has suffered the highest number of natural disaster-related deaths from fire. On local scales, the main causes of urban fires are anthropogenic in nature. Yet, on regional to national scales, little is known about the indicators of large-scale co-varying urban fire activity in China. Here, we present the China Fire History Atlas (CFHA), which is based on 19 947 documentary records and represents fires in urban areas of China over the twentieth century (1901–1994). We found that temperature variability is a key indicator of urban fire activity in China, with warmer temperatures are correlated with more urban fires, and that this fire–temperature relationship is seasonally and regionally explicit. In the early twentieth century, however, the fire–temperature relationship was overruled by war-related fires in large urban areas. We further used the fire–temperature relationship and multiple emissions scenarios to project fire activity across China into the twenty-first century. Our projections show a distinct increase in future urban fire activity and fire-related economic loss. Our findings provide insights into fire–climate relationships in China for densely-populated areas and on policy-relevant time scales and they contribute spatial coverage to efforts to improve global fire models.

Future perfect climates: A phenomenological rejoinder to the performativity of climate change mitigation pathways

From charting out climate change mitigation pathways to estimating price risks associated with the social cost of carbon, as environmentally concerned citizens of the twenty-first century, we live in a culture of foresight. Because of a growing integration of an ever-wider sample space of possible climate futures into the present, historical experience has become seemingly irrelevant for effectively predicting where our climate transitions are headed, in effect restricting our sense of futurity to its performativity in the present. What has been surprisingly absent as a theoretical and methodological approach among sociologists, however, are treatments of the performativity of the future as the expression of a historical praxis for prognosis, with its own mode of disclosure. By interrogating the temporal structure of anticipation that characterizes computer-based simulations of emissions scenarios, the paper illustrates how this praxis discloses the future in accordance with the grammatical tense of the future perfect. It then argues that this relationship between past and future is the cultural product of a historically particular set of prognostic techniques and technologies, namely, model-based scenario analysis. Against this background, the paper seeks to contribute to the rehabilitation of the relevance of historical experience by historicizing the social ontological status of the future that theories of performativity take as their starting point.

Experimental and numerical approach for the evaluation of PV-system performance on energy and environmental behavior of nearly zero energy buildings: Case study in Mediterranean climate

Nowadays, the performance of photovoltaic (PV) systems has strategic role for satisfying the standard of net or nearly zero energy building with in-situ production. In this frame, the novelty of the paper is the proposal of a combined experimental and numerical approach for evaluating the mismatch between PV production and consumptions during design, operational and future performance under expected emissions scenarios. First of all, it proposes a comparison of production estimation by means of several tools for the evaluation of the energy balance in the design phase; then the expected and real behavior is compared with also the evaluation of the performance ratio (PR) evolution as function of cell temperature. Finally, a sensitivity analysis is proposed in the short and medium scenarios by varying the size of installed system and considering the degradation rate under future weather conditions. The approach is applied to a case study in a typical Mediterranean climate, starting from the monitoring data available for the building energy request and the renewable production (2016–2022). The results of the case study indicate that the PR greatly influences the performance, mainly under expected climate changes, since the operational emissions could increase of more than 15 % compared to actual performance also if the zero energy balance is satisfied. Furthermore, through a sensitivity analysis on the PV-system size, the study demonstrates that with a maximum linear degradation rate of 1.51 %/year, the building could be configured as nearly zero energy in the medium term (20 years) also with lower peak power. The analysis also suggests that the maximization of installed power is a prerequisite to follow the implementation of positive energy goal because, under all considered climatic evolutions it can balance the penalties both due to technical aspects that to modification of consumption and production profiles.

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

IntroductionCrops are vulnerable to precipitation and heat extremes during late spring through summer.MethodsWe analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041-2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap.ResultsIdentified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type.DiscussionThe information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40 %. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36 %. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change.

Impacts of Climate Change on the Ascension Island Marine Protected Area and Its Ecosystem Services

AbstractThis is the first projection of marine circulation and biogeochemistry for the Ascension Island Marine Protected Area (AIMPA). Marine Protected Areas are a key management tool used to safeguard biodiversity, but their efficacy is increasingly threatened by climate change. To assess an MPA's vulnerability to climate change and predict biological responses, we must first project how the local marine environment will change. We present the projections of an ensemble from the Sixth Coupled Model Intercomparision Project. Relative to the recent past (2000–2010), the multi‐model means of the mid‐century (2040–2050) project that the AIMPA will become warmer (+0.9 to +1.2°C), more saline (+0.01 to +0.10), with a shallower mixed layer depth (−1.3 to −0.8 m), a weaker Atlantic Equatorial Undercurrent (AEU) (−1.5 to −0.4 Sv), more acidic (−0.10 to −0.07), with lower surface nutrient concentrations (−0.023 to −0.0141 mmol N m−3 and −0.013 to −0.009 mmol P m−3), less chlorophyll (−6 to −3 µg m−3) and less primary production (−0.31 to −0.20 mol m−2 yr−1). These changes are often more extreme in the scenarios with higher greenhouse gases emissions and more significant climate change. Using the multi‐model mean for two scenarios in the years 2090–2100, we assessed how five key ecosystem services in both the shallow subtidal and the pelagic zone were likely to be impacted by climate change. Both low and high emission scenarios project significant changes to the AIMPA, and it is likely that the provision of several ecosystem services will be negatively impacted.

Earth system responses to carbon dioxide removal as exemplified by ocean alkalinity enhancement: tradeoffs and lags

Carbon dioxide removal (CDR) is discussed for offsetting residual greenhouse gas emissions or even reversing climate change. All emissions scenarios of the Intergovernmental Panel on Climate Change that meet the ‘well below 2 °C’ warming target of the Paris Agreement include CDR. Ocean alkalinity enhancement (OAE) may be one possible CDR where the carbon uptake of the ocean is increased by artificial alkalinity addition. Here, we investigate the effect of OAE on modelled carbon reservoirs and fluxes in two observationally-constrained large perturbed parameter ensembles. OAE is assumed to be technically successful and deployed as an additional CDR in the SSP5-3.4 temperature overshoot scenario. Tradeoffs involving feedbacks with atmospheric CO2 result in a low efficiency of an alkalinity-driven atmospheric CO2 reduction of −0.35 [−0.37 to −0.33] mol C per mol alkalinity addition (skill-weighted mean and 68% c.i.). The realized atmospheric CO2 reduction, and correspondingly the efficiency, is more than two times smaller than the direct alkalinity-driven enhancement of ocean uptake. The alkalinity-driven ocean carbon uptake is partly offset by the release of carbon from the land biosphere and a reduced ocean carbon sink in response to lowered atmospheric CO2 under OAE. In a second step we use the Bern3D-LPX model in CO2 peak-decline simulations to address hysteresis and temporal lags of surface air temperature change (ΔSAT) in an idealized scenario where ΔSAT increases to ~2 °C and then declines to ~1.5 °C as result of CDR. ΔSAT lags the decline in CO2-forcing by 18 [14–22] years, depending close to linearly on the equilibrium climate sensitivity of the respective ensemble member. These tradeoffs and lags are an inherent feature of the Earth system response to changes in atmospheric CO2 and will therefore be equally important for other CDR methods.

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

AbstractWhen different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Modeling of air pollution due to marine traffic in Marseille

Marine traffic is constantly increasing and is unlikely to decrease in the years to come. However, merchant and tourist ships still use fuels with a high pollutant content, particularly in the Mediterranean sea where regulations are more flexible than those applied in northern European waters. The pollutants of interest in this work, which include particulate matter (PM10, PM2.5), NO2, SO2 and ozone, are responsible for respiratory diseases such as airway irritation, cardiopulmonary and lung cancers. The aim of this study is to estimate the impact of maritime traffic on air quality in Marseille in case of high port occupancy. The same ship traffic emission scenario will be studied under a series of typical meteorological conditions selected on the basis of a statistical seasonal analysis. This approach therefore differs from studies based on annualized ship traffic scenarios, which do not capture worst-case pollution. In order to do so, we have parameterized the numerical weather forecasting model WRF (Weather Research and Forecasting) that allows the inclusion of chemistry and atmospheric transport equations. The relative difference in concentration between a situation with and without ship traffic allows us to evaluate the influence of ships on air quality. The results show an average ship contribution of between 1.5% and 5.7% for PM2.5, with some local maximum of about 25% near the port. Contribution levels for gaseous species are much higher, with values reaching 40% for SO2 and 80% for NO2. The analysis of various meteorological conditions has highlighted a significant influence of wind speed and sea–land breeze dynamic on ship contribution.

Modelling Habitat Suitability and Distribution of the Endemic Mindanao Horned Frog (Pelobatrachus stejnegeri) and its Response to Changing Climate

Climate change is already affecting biodiversity, with special concern to endemics whose range is restricted and limited. This study focuses on the Mindanao horned frog (Pelobatrachus stejnegeri), an endemic species to the Philippines, susceptible to climate-induced habitat changes. Using MaxEnt species distribution model (SDM), the current and future (year 2050 projections) habitat suitability and distribution of P. stejnegeri were modelled. Results showed that annual mean temperature, elevation, and annual precipitation were the environmental variables having the highest influence on P. stejnegeri's distribution. The model predicts a significant range contraction under representative concentration pathways (RCP) future scenarios (RCP 2.6 and RCP 8.5), with a more pronounced decrease in distribution (31.72%) under the high emission scenario (RCP 8.5). These findings emphasize the vulnerability of P. stejnegeri to climate change and highlight the importance of integrating SDM into conservation and management strategies to protect endemic species under changing climatic conditions.

Factors affecting pedestrian-level ship pollution in port areas: CFD in the service of policy-making

Examining the impact of various factors associated with plume dispersion is essential for quantifying ship pollution in port areas, particularly adjacent to urban environments. This paper concerns the application of Computational Fluid Dynamics (CFD) modelling to study the effect of such factors on air pollution at the passenger port of Marseille, France. Pedestrian-level pollution maps are created for several emission scenarios involving variables such as wind conditions, docking location, emission reduction technology, different emission sources, as well as parameters related to the vessel. Depending on wind direction, docking location and vessel's engine power, different parts of the studied area are exposed to ship pollution. Within a typical examined range, funnel exhaust temperature, funnel height and wind speed deviate up to 78 %, 45 % and 59 % from the baseline scenario, respectively, indicating significant sensitivity of ship pollution to changes in these factors. The lower buoyancy effects of using a scrubber lead to a 17 % larger area exposed to increased ship pollution instead of using Ultra Low Sulphur Fuel Oil (ULSFO). Additionally, it is estimated that an individual vessel may contribute up to six times more to the NOx pollution of a roundabout located 800 m downwind, compared to the local road traffic. The current work can provide valuable information to various stakeholders such as port authorities, decision-makers, or even air quality modelers regarding the impact of shipping emissions on air pollution in port areas, constituting a useful assessment and advisory tool.

Wasted efforts of elite Marathon runners under a warming climate primarily due to atmospheric oxygen reduction

Meteorological conditions exert influences on sport performances via complex processes. Previous studies conventionally investigate the effects of weather conditions on marathon performance by following events held at the same places, which brings large uncertainties due to the changed participants. Via following each athlete to eliminate these uncertainties, we show that elite athletes’ marathon-running performance tends to monotonically worsen as ambient temperature rises except when it is extremely cold based on the best 16 athletes from each of the sex continents. It worsens by 0.56 (0.39 for men and 0.71 for women) min/°C when it rises beyond 15 °C. Theoretical analysis based on global marathon performance and weather observation datasets shows that more than half of this effect is mediated by reduced oxygen partial density as warmer temperature expands air and increases vapor pressure for both the men and women athletes, which is confirmed by the methods of Baron–Kenny. This atmospheric thermodynamic mechanism has not been emphasized previously. We also show that world-top athletes’ marathon performance approximately linearly worsens as ambient pressure decreases and slightly worsens as relative humidity rises if not extremely wet. These results substantially differ from the previous ones following the events instead of each athlete. Multi-variable changes in climate system are projected to slow Olympic marathon athletes by 2.51 and 1.06 min by the end of the 21st century in relative to 2020 under the high and intermediate emission scenario, respectively. Therefore, future progression of marathon performance is likely to be substantially slowed or even halted by climate changes without efficient greenhouse gas mitigation.