Effect of Health-Related Uncertainty and Natural Variability on Health Impacts and Cobenefits of Climate Policy

Equity and modeling in sustainability science: Examples and opportunities throughout the process

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

Health and equity implications of individual adaptation to air pollution in a changing climate

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone's need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

Is the economic uncertainty- human health relationship nonlinear? An empirical analysis for the China

The health costs of economic uncertainty always remain a major concern among policymakers of China. The theoretical and empirical literature on the economic uncertainty-human health nexus is still in its infancy stage. This study is firmly rooted in the economic uncertainty theory advanced by Baker, Bloom, & Davis. In this study, the primary objective of the analysis is to estimate the asymmetric impact of economic uncertainty on human health in China's economy. In order to evaluate the short and long-run estimates of economic uncertainty on human health across various quantiles, we have employed the linear and nonlinear QARDL models. The linear QARDL model shows that the long-run relationship between economic uncertainty and the infant mortality rate is positive and significant at all quantiles, while the long-run relationship between economic uncertainty and the death rate is positive and significant at higher quantiles. The nonlinear QARDL model reveals that, in the long run, the relationship between the positive shock of economic uncertainty and the infant mortality rate is positive and significant at quantiles 0.30 to 0.95, while the long-run relationship between the positive shock of economic uncertainty and the death rate is positive and significant at higher quantiles. The relationship between the negative shock of economic uncertainty and the infant mortality rate is negative and significant at the highest quantiles, while the relationship between the negative shock of economic uncertainty and death rate is negative and significant at higher quantiles in the long run. The findings indicate a positive relationship between economic uncertainty in China and higher rates of infant mortality and death. Thus, adopting suitable policies for controlling economic uncertainty can help in improving human health in China.

Influence of Air Pollution Exposures on Cardiometabolic Risk Factors: a Review

PURPOSE OF REVIEW

The increasing prevalence of cardiometabolic risk factors (CRFs) contributes to the rise in cardiovascular disease. Previous research has established a connection between air pollution and both the development and severity of CRFs. Given the ongoing impact of air pollution on human health, this review aims to summarize the latest research findings and provide an overview of the relationship between different types of air pollutants and CRFs.

RECENT FINDINGS

CRFs include health conditions like diabetes, obesity, hypertension etc. Air pollution poses significant health risks and encompasses a wide range of pollutant types, air pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O2). More and more population epidemiological studies have shown a positive correlation between air pollution and CRFs. Although various pollutants have diverse effects on specific cellular molecular pathways, their main influence is on oxidative stress, inflammation response, and impairment of endothelial function. More and more studies have proved that air pollution can promote the occurrence and development of cardiovascular and metabolic risk factors, and the research on the relationship between air pollution and CRFs has grown intensively. An increasing number of studies are using new biological monitoring indicators to assess the occurrence and development of CRFs resulting from exposure to air pollution. Abnormalities in some important biomarkers in the population (such as homocysteine, uric acid, and C-reactive protein) caused by air pollution deserve more attention. Further research is warranted to more fully understand the link between air pollution and novel CRF biomarkers and to investigate potential prevention and interventions that leverage the mechanistic link between air pollution and CRFs.

Progress in modeling dynamic systems for sustainable development

This Perspective evaluates recent progress in modeling nature-society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice-defining purpose, selecting components, analyzing interactions, and assessing interventions-we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature-society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Reducing Planetary Health Risks Through Short-Lived Climate Forcer Mitigation

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short-lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5-related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (-0.085 K), agriculture (-0.034 K), and waste/landfill (-0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5-related health risk at around 5%-7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration-response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co-benefits through SLCF mitigation.

Regional policy synergy and haze governance-empirical evidence from 281 prefecture-level cities in China

The spatial spillover effect of haze pollution makes the local independent governance model inefficient, and it requires regional synergy to achieve the relatively high efficiency of haze governance. This article counts the frequency of environmental policy vocabulary in the government work reports of 30 provinces in China (mainly include the words ecology, pollution, energy, atmosphere, low carbon, etc.) and multiplies it with the proportion of the added value of the secondary industry in the GDP of the 281 prefecture-level cities under the jurisdiction of each province to obtain quantitative policy information and then use gray relational analysis to measure the degree of policy synergy of 281 prefecture-level cities in China. Finally, based on the data of PM2.5 and the degree of policy synergy of 281 prefecture-level cities in China from 2007 to 2016, combined with the data of a series of urban characteristic variables, the dynamic panel model is used to empirically examine the impact of regional policy synergy on haze governance. The results of the study show that between 2007 and 2016, the level of haze pollution is relatively high, ranging from 30 to 65 μg/m³, with the highest level in the central and eastern regions, ranging from 50 to 65 μg/m³, and relatively low in the northeast and west, ranging from 30 to 50 μg/m³. The degree of policy synergy among China's regions has been volatile during the evolution and is between primary synergy and mild imbalance. The degree of policy synergy has a significant negative impact on haze pollution, and the impact coefficient is about - 0.3; that is, policy synergy has a significant positive effect on haze governance. In addition, the industrial structure with a high proportion of secondary industry, the agglomeration of the population, and the production activities it brings will increase the haze pollution in China, and the increase in government expenditure on science and technology will help alleviate the haze pollution. The government can strengthen regional policy synergy, improve the mechanism of synergistic governance in various regions, and formulate strict and unified standards of regional environmental management based on the conditions of each region.

Insights into the Reactive and Deactivation Mechanisms of Manganese Oxides for Ozone Elimination: The Roles of Surface Oxygen Species

Manganese oxides with varied Mn valance states but identical morphologies were synthesized via a facile thermal treatment of γ-MnOOH. Also, their catalytic performance on ozone decomposition was investigated following the order of Mn₃O₄ < Mn₂O₃ < MnO₂ < MnO₂-H-200. In combination with X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), transmission electron microscopy (TEM), H₂-temperature-programmed reduction (TPR), O₂-temperature-programmed desorption (TPD), and X-ray photoelectron spectroscopy (XPS) characterization, it was deduced that the superior O₃ decomposition capacity for MnO₂-H-200 was strongly associated with abundant oxygen vacancies on its surface. Among Mn₃O₄, Mn₂O₃, and MnO₂, the difference in O₃ decomposition efficiency was dependent on the divergent nature of oxygen vacancy. Density functional theory (DFT) calculation revealed that Mn₃O₄ and MnO₂ possessed lower formation energy of oxygen vacancy, while MnO₂ had the minimum desorption energy of peroxide species (O₂*). It was deduced that the promotion of the O₃ decomposition capability was attributed to the easier O₂* desorption. Insights into the deactivation mechanism for MnO₂-H-200 further validated the assumptions. As the reaction proceeded, adsorbed oxygen species accumulated on the catalyst surface, and a portion of them were transformed to lattice oxygen. The consumption of oxygen vacancy led to the deactivation of the catalyst.

How will air quality effects on human health, crops and ecosystems change in the future?

Future air quality will be driven by changes in air pollutant emissions, but also changes in climate. Here, we review the recent literature on future air quality scenarios and projected changes in effects on human health, crops and ecosystems. While there is overlap in the scenarios and models used for future projections of air quality and climate effects on human health and crops, similar efforts have not been widely conducted for ecosystems. Few studies have conducted joint assessments across more than one sector. Improvements in future air quality effects on human health are seen in emission reduction scenarios that are more ambitious than current legislation. Larger impacts result from changing particulate matter (PM) abundances than ozone burdens. Future global health burdens are dominated by changes in the Asian region. Expected future reductions in ozone outside of Asia will allow for increased crop production. Reductions in PM, although associated with much higher uncertainty, could offset some of this benefit. The responses of ecosystems to air pollution and climate change are long-term, complex, and interactive, and vary widely across biomes and over space and time. Air quality and climate policy should be linked or at least considered holistically, and managed as a multi-media problem. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Projections of Ambient Temperature- and Air Pollution-Related Mortality Burden Under Combined Climate Change and Population Aging Scenarios: a Review

PURPOSE OF REVIEW

Climate change will affect mortality associated with both ambient temperature and air pollution. Because older adults have elevated vulnerability to both non-optimal ambient temperature (heat and cold) and air pollution, population aging can amplify future population vulnerability to these stressors through increasing the number of vulnerable older adults. We aimed to review recent evidence on projections of temperature- or air pollution-related mortality burden (i.e., number of deaths) under combined climate change and population aging scenarios, with a focus on evaluating the role of population aging in assessing these health impacts of climate change. We included studies published between 2014 and 2019 with age-specific population projections.

RECENT FINDINGS

We reviewed 16 temperature projection studies and 15 air pollution projection studies. Nine of the temperature studies and four of the air pollution studies took population aging into account by performing age-stratified analyses that utilized age-specific relationships between temperature or air pollution exposures and mortality (i.e., age-specific exposure-response functions (ERFs)). Population aging amplifies the projected mortality burden of temperature and air pollution under a warming climate. Compared with a constant population scenario, population aging scenarios lead to less reduction or even increases in cold-related mortality burden, resulting in substantial net increases in future overall (heat and cold) temperature-related mortality burden. There is strong evidence suggesting that to accurately assess the future temperature- and air pollution-related mortality burden of climate change, investigators need to account for the amplifying effect of population aging. Thus, all future studies should incorporate age-specific population size projections and age-specific ERFs into their analyses. These studies would benefit from refinement of age-specific ERF estimates.

Air quality co-benefits from climate mitigation for human health in South Korea

Climate change mitigation efforts to reduce greenhouse gas (GHG) emissions have associated costs, but there are also potential benefits from improved air quality, such as public health improvements and the associated cost savings. A multidisciplinary modeling approach can better assess the co-benefits from climate mitigation for human health and provide a justifiable basis for establishment of adequate climate change mitigation policies and public health actions. An integrated research framework was adopted by combining a computable general equilibrium model, an air quality model, and a health impact assessment model, to explore the long-term economic impacts of climate change mitigation in South Korea through 2050. Mitigation costs were further compared with health-related economic benefits under different socioeconomic and climate change mitigation scenarios. Achieving ambitious targets (i.e., stabilization of the radiative forcing level at 3.4 W/m²) would cost 1.3-8.5 billion USD in 2050, depending on varying carbon prices from different integrated assessment models. By contrast, achieving these same targets would reduce costs by 23 billion USD from the valuation of avoided premature mortality, 0.14 billion USD from health expenditures, and 0.38 billion USD from reduced lost work hours, demonstrating that health benefits alone noticeably offset the costs of cutting GHG emissions in South Korea.