The contribution of wildfire to PM2.5 trends in the USA.

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy1,2, have led to public health benefits1-4. However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 µm (PM2.5), a pollutant regulated under the Clean Air Act1, have stagnated or begun to reverse throughout much of the USA5. Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM2.5 concentrations are unregulated under current air pollution law6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm.

Drought impacts on the electricity system, emissions, and air quality in the western United States.

The western United States has experienced severe drought in recent decades, and climate models project increased drought risk in the future. This increased drying could have important implications for the region's interconnected, hydropower-dependent electricity systems. Using power-plant level generation and emissions data from 2001 to 2021, we quantify the impacts of drought on the operation of fossil fuel plants and the associated impacts on greenhouse gas (GHG) emissions, air quality, and human health. We find that under extreme drought, electricity generation from individual fossil fuel plants can increase up to 65% relative to average conditions, mainly due to the need to substitute for reduced hydropower. Over 54% of this drought-induced generation is transboundary, with drought in one electricity region leading to net imports of electricity and thus increased pollutant emissions from power plants in other regions. These drought-induced emission increases have detectable impacts on local air quality, as measured by proximate pollution monitors. We estimate that the monetized costs of excess mortality and GHG emissions from drought-induced fossil generation are 1.2 to 2.5x the reported direct economic costs from lost hydro production and increased demand. Combining climate model estimates of future drying with stylized energy-transition scenarios suggests that these drought-induced impacts are likely to remain large even under aggressive renewables expansion, suggesting that more ambitious and targeted measures are needed to mitigate the emissions and health burden from the electricity sector during drought.

Unraveling complex causal processes that affect sustainability requires more integration between empirical and modeling approaches.

Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature-society systems is hampered by conceptual and methodological challenges that arise from nature-society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature-society systems.

High-Volumetric Density Atomic Cobalt on Multishell ZnxCd1-xS Boosts Photocatalytic CO2 Reduction.

The volumetric density of the metal atomic site is decisive to the operating efficiency of the photosynthetic nanoreactor, yet its rational design and synthesis remain a grand challenge. Herein, we report a shell-regulating approach to enhance the volumetric density of Co atomic sites onto/into multishell ZnxCd1-xS for greatly improving CO2 photoreduction activity. We first establish a quantitative relation between the number of shell layers, specific surface areas, and volumetric density of atomic sites on multishell ZnxCd1-xS and conclude a positive relation between photosynthetic performance and the number of shell layers. The triple-shell ZnxCd1-xS-Co1 achieves the highest CO yield rate of 7629.7 µmol g-1 h-1, superior to those of the double-shell ZnxCd1-xS-Co1 (5882.2 µmol g-1 h-1) and single-shell ZnxCd1-xS-Co1 (4724.2 µmol g-1 h-1). Density functional theory calculations suggest that high-density Co atomic sites can promote the mobility of photogenerated electrons and enhance the adsorption of Co(bpy)32+ to increase CO2 activation (CO2 → CO2* → COOH* → CO* → CO) via the S-Co-bpy interaction, thereby enhancing the efficiency of photocatalytic CO2 reduction.

FeNC nanozyme-based electrochemical immunoassay for sensitive detection of human epidermal growth factor receptor 2.

The proof-of-concept of sensitive electrochemical immunoassay for the quantitative monitoring of human epidermal growth factor receptor 2 (HER2) is reported. The assay is carried out on iron nitrogen-doped carbon (FeNC) nanozyme-modified screen-printed carbon electrode using chronoamperometry. Introduction of target HER2 can induce the sandwiched immunoreaction between anti-HER2 monoclonal antibody-coated microplate and biotinylated anti-HER2 polyclonal antibody. Thereafter, streptavidin-glucose oxidase (GOx) conjugate is bonded to the detection antibody. Upon addition of glucose, 3,3',5,5'-tetramethylbenzidine (TMB) is oxidized through the produced H2O2 with the assistance of GOx and FeNC nanozyme. The oxidized TMB is determined via chronoamperometry. Experimental results revealed that electrochemical immunosensing system exhibited good amperometric response, and allowed the detection of target HER2 as low as 4.5 pg/mL. High specificity and long-term stability are acquired with FeNC nanozyme-based sensing strategy. Importantly, our system provides a new opportunity for protein diagnostics.

Smartphone-Based Electrochemical Immunoassay for Point-of-Care Detection of SARS-CoV-2 Nucleocapsid Protein.

Large-scale, rapid, and inexpensive serological diagnoses of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) are of great interest in reducing virus transmission at the population level; however, their development is greatly plagued by the lack of available point-of-care methods, leading to low detection efficiency. Herein, an ultrasensitive smartphone-based electrochemical immunoassay is reported for rapid (less than 5 min), low-cost, easy-to-implement detection of the SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 N protein). Specifically, the electrochemical immunoassay was fabricated on a screen-printed carbon electrode coated with electrodeposited gold nanoparticles, followed by incubation of anti-N antibody (Ab) and bovine serum albumin as the working electrode. Accompanied by the antigen-antibody reaction between the SARS-CoV-2 N protein and the Ab, the electron transfer between the electroactive species [Fe(CN)6]3-/4- and the electrode surface is disturbed, resulting in reduced square-wave voltammetry currents at 0.075 V versus the Ag/AgCl reference electrode. The proposed immunoassay provided a good linear range with SARS-CoV-2 N protein concentrations within the scope of 0.01-1000 ng/mL (R2 = 0.9992) and the limit of detection down to 2.6 pg/mL. Moreover, the detection data are wirelessly transmitted to the interface of the smartphone, and the corresponding SARS-CoV-2 N protein concentration value is calculated and displayed. Therefore, the proposed portable detection mode offers great potential for self-differential diagnosis of residents, which will greatly facilitate the effective control and large-scale screening of virus transmission in resource-limited areas.

Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US.

Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine learning model of daily wildfire-driven PM2.5 concentrations using a combination of ground, satellite, and reanalysis data sources that are easy to update. We apply our model across the contiguous US from 2006 to 2020, generating daily estimates of smoke PM2.5 over a 10 km-by-10 km grid and use these data to characterize levels and trends in smoke PM2.5. Smoke contributions to daily PM2.5 concentrations have increased by up to 5 µg/m3 in the Western US over the last decade, reversing decades of policy-driven improvements in overall air quality, with concentrations growing fastest for higher income populations and predominantly Hispanic populations. The number of people in locations with at least 1 day of smoke PM2.5 above 100 µg/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. Our data set can bolster efforts to comprehensively understand the drivers and societal impacts of trends and extremes in wildfire smoke.

Ultrasensitive fluorometric biosensor based on Ti3C2 MXenes with Hg2+-triggered exonuclease III-assisted recycling amplification.

Herein a rapid and sensitive fluorometric bioanalysis platform for mercury(ii) (Hg2+) detection was innovatively developed using ultrathin two-dimensional MXenes (Ti3C2) as fluorescence quencher and Hg2+-induced exonuclease III (Exo III)-assisted target recycling strategy for efficient signal amplification. Initially, fluorophore-labeled single-stranded DNA (FAM-labeled probe) can be easily adsorbed onto the surface of ultrathin Ti3C2 nanosheets by hydrogen bonding and metal chelating interaction, and the fluorescence signal emitted by the FAM-labeled probe is quenched strongly owing to the fluorescence resonance energy transfer between the FAM and ultrathin Ti3C2 nanosheets. Upon sensing the target Hg2+, the protruding DNA fragment at the 3' end of hairpin will hybridize with primer (hairpin-Hg2+-primer), and then further digested by Exo III to produce a probe (nicker). The released target Hg2+ and primer continue to participate in the next recycling, resulting in more hairpin probes becoming nickers. The combination of a large number of nickers and FAM-probe resulted in a significant increase in the fluorescence signal of the system, which was attributed to the fact that the double helix DNA was more rigid and separated from the surface of the ultrathin Ti3C2 nanosheets. The obvious fluorescence signal change of the Ti3C2-based Exo III-assisted target recycling can be accurately monitored by fluorescence spectrometry, which is also proportional to the concentration of Hg2+. Under optimum operating conditions, the peak intensity (520 nm wavelength) of fluorescence increased with increasing Hg2+ within a wide dynamic working range from 0.05 nM to 50 nM (R2 = 0.9913) with a limit of detection down to 42.5 pM. The proposed strategy uses ultrathin MXenes as a platform for binding nucleic acids, which contributes to its potential in nucleic acid hybridization-based biosensing and/or nucleic acid signal amplification bio-applications.

Improving Evaluation of Energy Policies with Multiple Goals: Comparing Ex Ante and Ex Post Approaches.

Sustainability policies are often motivated by the potential to achieve multiple goals, such as simultaneously mitigating the climate change and air quality impacts of energy use. Ex ante analysis is used prospectively to inform policy decisions by estimating a policy's impact on multiple objectives. In contrast, ex post analysis of impacts that may have multiple causes can retrospectively evaluate the effectiveness of policies. Ex ante analyses are rarely compared with ex post evaluations of the same policy. These comparisons can assess the realism of assumptions in ex ante methods and reveal opportunities for improving prospective analyses. We illustrate the benefits of such a comparison by examining a case of two energy policies in China. Using ex post analysis, we estimate the impacts of two policies, one that targets energy intensity and another that imposes quantitative targets on SO2 emissions, on energy use and pollution outcomes in two major energy-intensive industrial sectors (cement, iron and steel) in China. We find that the ex post effects of the energy intensity policy on both energy and pollution outcomes are very limited on average, while the effects of the SO2 emissions policy are large. Compared with ex ante analysis, ex post estimates of benefits of the energy intensity policy are on average smaller, and differ by location in both sign and magnitude. Accounting for firm-level heterogeneity in production processes and policy responses, as well as the use of empirically grounded counterfactual baselines, can improve the realism of ex ante analysis and thus provide a more reliable basis for policy design.

Air quality related equity implications of U.S. decarbonization policy.

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 (CO2) emissions, when compared with 2005 emissions. The 50% CO2 reduction policy case reduces average fine particulate matter (PM2.5) exposure across racial/ethnic groups, with greatest benefit for non-Hispanic Black (-0.44 µg/m3) and white populations (-0.37 µg/m3). 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 CO2 sources would substantially mitigate exposure disparities. Results suggest that CO2-based strategies for this range of reductions are insufficient for fully mitigating PM2.5 exposure disparities between white and racial/ethnic minority populations; addressing disparities may require larger-scale structural changes.

Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality.

Ever-increasing ambient ozone (O3) pollution in China has been exacerbating cardiopulmonary premature deaths. However, the urban-rural exposure inequity has seldom been explored. Here, we assess population-scale O3 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence. We find Chinese population have been suffering from climbing O3 exposure by 4.3 ± 2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities. Rural residents are broadly exposed to 9.8 ± 4.1 ppb higher ambient O3 than the adjacent urban citizens, and thus urbanization-oriented migration compromises the exposure-associated mortality on total population. Cardiopulmonary excess premature deaths attributable to long-term O3 exposure, 373,500 (95% uncertainty interval [UI]: 240,600-510,900) in 2019, is underestimated in previous studies due to ignorance of cardiovascular causes. Future O3 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States.

Understanding impacts of renewable energy on air quality and associated human exposures is essential for informing future policy. We estimate the impacts of U.S. wind power on air quality and pollution exposure disparities using hourly data from 2011 to 2017 and detailed atmospheric chemistry modeling. Wind power associated with renewable portfolio standards in 2014 resulted in $2.0 billion in health benefits from improved air quality. A total of 29% and 32% of these health benefits accrued to racial/ethnic minority and low-income populations respectively, below a 2021 target by the Biden administration that 40% of the overall benefits of future federal investments flow to disadvantaged communities. Wind power worsened exposure disparities among racial and income groups in some states but improved them in others. Health benefits could be up to $8.4 billion if displacement of fossil fuel generators prioritized those with higher health damages. However, strategies that maximize total health benefits would not mitigate pollution disparities, suggesting that more targeted measures are needed.

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 PM2.5 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.

The contribution of the Beijing, Tianjin and Hebei region's iron and steel industry to local air pollution in winter.

The Beijing, Tianjin and Hebei region (BTH) in China is a highly populated area that has recently experienced frequent haze episodes in winter. With high production capacities, the iron and steel industry (ISI) has long been a key source of air pollutants in BTH and is thus considered responsible for the degradation of local air quality. Here, we conducted a cross-disciplinary research combining the Weather Research and Forecasting with Chemistry (WRF/Chem) model, the multiregional input-output model (MRIO) and the health assessment model to explore the impacts of the ISI on air pollution in the BTH region in January 2012. Our results show large increases in air pollution due to direct ISI emissions, with up to a 90 µg/m3 monthly average of fine particulate matter (PM2.5) and sulfur dioxide (SO2) in eastern Tangshan and western Handan. In addition to direct emissions, the ISI has induced large quantities of indirect emissions from upstream sectors (e.g., the electricity and transportation sectors), leading to PM2.5, SO2 and NOx increases of 2-10 µg/m3 in BTH. Considering the direct and indirect emissions, we estimated that 275 (233-313) PM2.5-related mortalities occurred in January, and approximately 42% of these premature deaths occurred in Tangshan. A high rate of premature deaths also occurred in urban Beijing due to its high population density. Revealing the great health burden caused by the ISI, our results underscore the necessity for the Chinese government to reduce air pollutant emissions from the ISI and its upstream industries in BTH.