Chemical pollution poses significant environmental and human health problems. Some researchers have put forward the planetary boundary framework as a way to contextualize knowledge about Earth system challenges, including chemicals. In this Perspective, we argue that further application of the planetary boundary framework has limited conceptual and practical value for advancing research and policy-making to address environmental and human health risk from chemicals, and may end up having negative unintended consequences for inclusive and effective governance. We present four sets of issues which challenge the application of the planetary boundary framework to chemicals, connected to conceptual inconsistencies, conflicts with established norms and practices on risk and precaution, a limited ability to help protect the most vulnerable, and a lack of legitimacy in political processes. As an alternative, we discuss three ways in which researchers can help advance knowledge and action on chemicals, working with stakeholders through existing policy efforts and treaties.

Climate models exhibit an approximately invariant surface warming pattern in typical end-of-century projections. This observation has been used extensively in climate impact assessments for fast calculations of local temperature anomalies, with a linear procedure known as pattern scaling. At the same time, emerging research has also shown that time-varying warming patterns are necessary to explain the time evolution of effective climate sensitivity in coupled models, a mechanism that is known as the pattern effect and that seemingly challenges the pattern scaling understanding. Here we present a simple theory based on local energy balance arguments to reconcile this apparent contradiction. Specifically, we show that the pattern invariance is an inherent feature of exponential forcing, linear feedbacks, a constant forcing pattern and diffusive dynamics. These conditions are approximately met in most CMIP6 Shared Socioeconomic Pathways (SSP), except in the Arctic where nonlinear feedbacks are important and in regions where aerosols considerably alter the forcing pattern. In idealized experiments where concentrations of CO2 are abruptly increased, such as those used to study the pattern effect, the warming pattern can change considerably over time because of spatially inhomogeneous ocean heat uptake, even in the absence of nonlinear feedbacks. Our results illustrate why typical future projections are amenable to pattern scaling, and provide a plausible explanation of why more complicated approaches, such as nonlinear emulators, have only shown marginal improvements in accuracy over simple linear calculations.

Effective assessment of potential climate impacts requires the ability to rapidly predict the time-varying response of climate variables. This prediction must be able to consider different combinations of forcing agents at high resolution. Full-scale ESMs are too computationally intensive to run large scenario ensembles due to their long lead times and high costs. Faster approaches such as intermediate complexity modeling and pattern scaling are limited by low resolution and invariant response patterns, respectively. We propose a generalizable framework for emulating climate variables to overcome these issues, representing the climate system through spatially resolved impulse response functions. We derive impulse response functions by directly deconvolving effective radiative forcing and near-surface air temperature time series. This enables rapid emulation of new scenarios through convolution and derivation of other impulse response functions from any forcing to its response. We present results from an application to near-surface air temperature based on CMIP6 data. We evaluate emulator performance across 5 CMIP6 experiments including the SSPs, demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to CMIP6 ensemble outputs. Global mean relative error in emulated temperature averages 1.49% in mid-century and 1.25% by end-of-century. These errors are likely driven by state-dependent climate feedbacks, such as the non-linear effects of Arctic sea ice melt. We additionally show an illustrative example of our emulator for policy evaluation and impact analysis, emulating spatially resolved temperature change for a 1,000 member scenario ensemble in less than a second.

We utilize ideas from signal processing to demonstrate a novel methodology for climate emulation based on the response of the climate system to effective radiative forcing (ERF). While previous work has demonstrated the efficacy of impulse response functions as a tool for climate emulation, these methods are largely non-generalizable to new scenarios and are inaccessible to more general audiences. To remedy this, we propose a generalizable framework for emulation of climate variables such as near-surface air temperature, representing the climate system through the surrogate of spatially resolved impulse response functions. These response functions are derived through the deconvolution of ERF and near-surface air temperature profiles, treating ERF and near-surface air temperature as input and output signals, respectively. Using this framework, new scenarios can be quickly and easily emulated through convolution and other sets of impulse response functions can be derived from any pair of climate variables. We present results from an application to near-surface air temperature based on ERF and temperature data taken from experiments in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We evaluate the emulator using additional experiments taken from the CMIP6 archive, including the Shared Socioeconomic Pathways (SSPs), demonstrating accurate emulation of global mean and spatially resolved temperature change with respect to the outputs of the CMIP6 ensemble. Global absolute error in emulated temperature averages 0.25 degrees Celsius with a bias ranging from -0.14 to -0.04 degrees Celsius. We additionally show how our emulator can be implemented as a tool for climate education through integration with the En-ROADS platform, providing fast visualizations of spatially resolved temperature change for a number of policy-relevant scenarios. While it is unable to capture state-dependent climate feedbacks, such as the non-linear effects of Arctic sea ice melt in high-warming scenarios, our results show that the emulator is generalizable to any scenario independent of the specific forcings present.

Climate variability and change introduce a range of air quality outcomes for a given emissions policy. Quantifying this range helps us evaluate the likelihood of meeting air quality targets in an uncertain future climate. Using a global atmospheric chemistry model ensemble, we project how the ozone response to a 10% reduction in anthropogenic nitrogen oxides (NOx) emissions varies across three northern midlatitude source regions under two future climate scenarios. In Eastern North America and West-Central Europe, where ambient ozone is NOx-sensitive in the historical scenario, the magnitude of ozone reductions from reduced NOx is smaller under high warming relative to that under the historical scenario because of the dominant effect of increased soil NOx emissions. In North East Asia, where ambient ozone is NOx-insensitive in some areas in the historical scenario, the magnitude of ozone reductions from reduced NOx increases under high warming due to the dominant effect of enhanced HOx production. In North East Asia, these changes lead to a significant decrease in the frequency of days with ozone disbenefits from NOx reductions under the high-warming scenario relative to the historical scenario, while no statistically significant increase in ozone disbenefits is found for Eastern North America or West-Central Europe.

The Multi-Compartment Hg (mercury) Modeling and Analysis Project (MCHgMAP) is an international multimodel research initiative intended to simulate and analyze the geospatial distributions and temporal trends of environmental Hg to inform effectiveness evaluations of two multilateral environmental agreements (MEAs): the Minamata Convention on Mercury (MC) and the Convention on Long-Range Transboundary Air Pollution (LRTAP). This MCHgMAP overview paper presents its science objectives, background, and rationale; experimental design (multimodel ensemble (MME) architecture, inputs and evaluation data, simulations, and reporting framework); and methodologies for the evaluation and analysis of simulated environmental Hg levels. The primary goals of the project are to facilitate detection and attribution of recent (observed) and future (projected) spatial patterns and temporal trends of global environmental Hg levels and identification of key knowledge gaps in Hg science and modeling to improve future effectiveness evaluation cycles of the MEAs. The current advances and challenges of Hg models, emission inventories, and observational data are examined, and an optimized multimodel experimental design is introduced to address the key policy questions of the MEAs. A common set of emissions, environmental conditions, and observation datasets is proposed (where possible) to enhance the MME comparability. A novel harmonized simulation approach between atmospheric, land, oceanic, and multimedia models is proposed to account for the short- and long-term changes in secondary Hg exchanges and to achieve mechanistic consistency of Hg levels across environmental matrices. A comprehensive set of model experiments is proposed and prioritized to ensure systematic analysis and participation of a variety of models from the scientific community.

Large volumes of liquid water transiently existed on the surface of Mars more than 3 billion years ago. Much of this water is hypothesized to have been sequestered in the subsurface or lost to space. We use rock physics models and Bayesian inversion to ...

Carbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time‐dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time‐dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

Anthropogenic activities emit ~2000 Mg yr-1 of the toxic pollutant mercury (Hg) into the atmosphere, leading to long-range transport and deposition to remote ecosystems. Global anthropogenic emissions inventories report increases in Northern Hemispheric (NH) Hg emissions during the last three decades, in contradiction with the observed decline in atmospheric Hg concentrations at NH measurement stations. Many factors can obscure the link between anthropogenic emissions and atmospheric Hg concentrations, including trends in the re-emissions of previously released anthropogenic (“legacy”) Hg, atmospheric sink variability, and spatial heterogeneity of monitoring data. Here we assess the observed trends in gaseous elemental mercury (Hg0) in the NH and apply biogeochemical box modeling and chemical transport modeling to understand the trend drivers. Using linear mixed effects modeling of observational data from 51 stations, we find negative Hg0 trends in most NH regions, with an overall trend for 2005–2020 of ‑0.011 ± 0.006 ng m-3 yr-1 (±2 SD). In contrast to existing emission inventories, our modelling analysis suggests that NH anthropogenic emissions must have declined by at least 140 Mg yr-1 between the years 2005 and 2020 to be consistent with observed trends. Faster declines in 95th percentile Hg0 values than median values in Europe, North America, and East Asian measurement stations corroborate that the likely cause is a decline in nearby anthropogenic emissions rather than background legacy re-emissions. Our results are relevant for evaluating the effectiveness of the Minamata Convention on Mercury, demonstrating that existing emissions inventories are incompatible with the observed Hg0 declines.

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, emissions of NOx, NH3 and N2O resulting from ammonia combustion could cause impacts on air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NOx, NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 vs pure NH3 combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies). We then use the GEOS–Chem High Performance global chemical transport model to simulate the air quality impacts of switching to ammonia-powered ships. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 33,100 (18900 to 47300, 95% confidence interval) mortalities annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr-1) from pure NH3 engines could lead to 595,100 additional mortalities annually under current legislation. Requiring NH3 scrubbing within current Emission Control Areas leads to smaller improvements in public health outcomes (38,000 avoided mortalities for NH3–H2 and 554,200 additional mortalities for pure NH3 annually, respectively), while extending both Tier III NOx standard and NH3 scrubbing requirements globally leads to larger improvement in public health outcomes associated with a switch to ammonia-powered ships (79,100 and 21,100 avoided mortalities for NH3–H2 and pure NH3 annually, respectively). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

Deforestation reduces the capacity of the terrestrial biosphere to take up toxic pollutant mercury (Hg) and enhances the release of secondary Hg from soils. The consequences of deforestation for Hg cycling are not currently considered by anthropogenic emission inventories or specifically addressed under the global Minamata Convention on Mercury. Using global Hg modeling constrained by field observations, we estimate that net Hg fluxes to the atmosphere due to deforestation are 217 Mg year-1 (95% confidence interval (CI): 134-1650 Mg year-1) for 2015, approximately 10% of global primary anthropogenic emissions. If deforestation of the Amazon rainforest continues at business-as-usual rates, net Hg emissions from the region will increase by 153 Mg year-1 by 2050 (CI: 97-418 Mg year-1), enhancing the transport and subsequent deposition of Hg to aquatic ecosystems. Substantial Hg emissions reductions are found for two potential cases of land use policies: conservation of the Amazon rainforest (92 Mg year-1, 95% CI: 59-234 Mg year-1) and global reforestation (98 Mg year-1, 95% CI: 64-449 Mg year-1). We conclude that deforestation-related emissions should be incorporated as an anthropogenic source in Hg inventories and that land use policy could be leveraged to address global Hg pollution.

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We explore how nuclear shut-downs in the United States could affect air pollution, climate and health with existing and alternative grid infrastructure. We develop a dispatch model to estimate emissions of CO 2 , NO x and SO 2 from each electricity-generating unit, feeding these emissions into a chemical transport model to calculate effects on ground-level ozone and fine particulate matter (PM 2.5 ). Our scenario of removing nuclear power results in compensation by coal, gas and oil, resulting in increases in PM 2.5 and ozone that lead to an extra 5,200 annual mortalities. Changes in CO 2 emissions lead to an order of magnitude higher mortalities throughout the twenty-first century, incurring US$11–180 billion of damages from 1 year of emissions. A scenario exploring simultaneous closures of nuclear and coal plants redistributes health impacts and a scenario with increased penetration of renewables reduces health impacts. Inequities in exposure to pollution are persistent across all scenarios—Black or African American people are exposed to the highest relative levels of pollution.

Climate policies that target greenhouse gas emissions can improve air quality by reducing co-emitted air pollutant emissions. However, the extent to which climate policy could contribute to the targets of reducing existing pollution disparities across different populations remains largely unknown. We quantify potential air pollution exposure reductions under U.S. federal carbon policy, considering implications of resulting health benefits for exposure disparities across U.S. racial/ethnic groups. We focus on policy cases that achieve reductions of 40-60% in 2030 economy-wide carbon dioxide (CO 2 ) emissions, when compared with 2005 emissions. The 50% CO 2 reduction policy case reduces average fine particulate matter (PM 2.5 ) exposure across racial/ethnic groups, with greatest benefit for non-Hispanic Black (−0.44 μg/m 3 ) and white populations (−0.37 μg/m 3 ). The average exposure disparity for racial/ethnic minorities rises from 12.4% to 13.1%. Applying an optimization approach to multiple emissions reduction scenarios, we find that no alternate combination of reductions from different CO 2 sources would substantially mitigate exposure disparities. Results suggest that CO 2 -based strategies for this range of reductions are insufficient for fully mitigating PM 2.5 exposure disparities between white and racial/ethnic minority populations; addressing disparities may require larger-scale structural changes.

Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, direct attribution of fires to human activities may provide opportunities for near term smoke mitigation by focusing policy, management, and funding efforts on particular ignition sources. We analyze how fires associated with human ignitions (agricultural fires and human-initiated wildfires) impact fire particulate matter under 2.5 µ m (PM _2.5 ) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these agricultural and human-initiated wildfires dominate fire PM _2.5 in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Smoke from these human levers also makes meaningful contributions to total PM _2.5 (∼5%–10% in 2003 and 2018). Across CONUS, these two human ignition processes account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM _2.5 . These findings indicate that a large portion of the smoke exposure and impacts in CONUS are from fires ignited by human activities with large mitigation potential that could be the focus of future management choices and policymaking.

Reducing greenhouse gases can benefit air quality and health overall, but the magnitude and distribution of these benefits remain uncertain. Now a study shows that while air quality gains from carbon policies are widespread, some regions could see pollution increases.

The ability to rapidly simulate the climate implications of a large number of CO2 emissions trajectories is helpful for implementing mitigation and adaptation policies. A key variable of interest is near-surface air temperature, which is approximately proportional to cumulative CO2 emissions. We take advantage of this relationship, diagnosing Green’s Functions for the spatial temperature response to CO2 emissions based on CMIP6 experiment data, creating an emulator that can be used across emissions scenarios to estimate local temperature responses. As compared to CMIP6 experiments, this approach captures the spatial temperature response with some limited accuracy in polar regions. It incorporates emissions path dependency and is useful for evaluating large ensembles of policy scenarios that are otherwise prohibitively expensive to simulate using earth system models. We apply this emulator to show differing local temperature responses when a global mean of 2ºC is reached and to varying trajectories with the same cumulative emissions.

Errors in clinical decision-making are disturbingly common. Here, we show that structured information–sharing networks among clinicians significantly reduce diagnostic errors, and improve treatment recommendations, as compared to groups of ...Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information–sharing ...