Atmospheric and Oceanic Sciences

Reliable exposure assessment is vital for epidemiological research, but weaknesses in land-use regression (LUR) models undermine its validity. Using mobile ultrafine particle (UFP) data in Toronto, we compared LUR models trained under random, spatial, temporal, and spatiotemporal cross-validation (CV), with and without forward feature selection (FFS). Model hyperparameters and feature subsets were optimized within each CV scheme. Spatial CV folds were designed at fine scales to reflect UFP autocorrelation. Each approach was evaluated on a hold-out test set, across CV schemes, and against independent stationary backyard measurements. Models based on spatiotemporal CV coupled with FFS were able to reduce overfitting, improve generalization, and produce stable exposure surfaces. These surfaces avoided the spatial artifacts and exaggerated variable effects typically seen in models trained with random CV. Models tuned with random CV overfit, performed poorly on independent samples, and were sensitive to outliers. The average percentage error (APE) decreased from ∼217% for a model with random-CV to ∼79% with spatiotemporal CV and FFS. Our findings demonstrate that proper alignment of model design with the data's spatiotemporal structure and modeling objective ensures reliability, minimizes data reproduction, and enables true prediction.

The acceleration of industrialization has driven the increased emission of volatile organic compounds (VOCs), posing significant threats to both the ecological environment and public health. The deficiency of reactive oxygen species fundamentally restricts the low-temperature catalytic toluene combustion in transition-metal oxide catalysts. Herein, we report a strategy for intelligently designing active Cu⁺-O_v-Ti ensembles by coupling isolated Cu with adjacent oxygen vacancy, which can synergistically activate chemisorbed O₂ into reactive superoxide species (O₂^-). The defective Cu/TiO_2-<i>x</i> catalyst exhibited remarkable catalytic performance for toluene oxidation, achieving a <i>T</i>₉₀ of 225 °C, significantly 100 °C lower than that of the pristine Cu/TiO₂ catalyst. The low coordination geometry and electron transfer within Cu⁺-O_v-Ti ensembles synergistically activated O₂ to form the Cu-(O-O)_ad-Ti bridged superoxide O₂^- intermediate with an elongated O═O bond. In addition, the distinctive Cu-(O-O)_ad-Ti bridging structure with localized electrons facilitated the chemisorbed O₂ dissociation into electrophilic monatomic O^- species, which subsequently nucleophilically attack the methyl C-H of toluene. These benzyl alcohol-derived Ph-CH₂-O^- intermediates can be readily and flexibly converted into reactive benzaldehyde and benzoic acid species, which were available for subsequent aromatic ring-opening reactions. This study not only advances mechanistic insights into the Cu⁺-O_v-Ti ensembles and electrophilic O^- species in toluene catalytic oxidation but also establishes a design Cu⁺-O_v-Ti principle for engineering efficient VOC elimination catalysts.

Achieving sustainability under accelerating climate and socioeconomic pressures requires moving beyond siloed sectoral management toward a system-thinking approach. The water-energy-food-ecosystem (WEFE) Nexus offers a holistic lens, yet most applications remain conceptual, short-term, or treat ecosystems as external constraints. This study operationalizes the WEFE Nexus by embedding ecosystems as a coequal, quantified pillar through a hydrologic-regime-based method, since streamflow is a master variable shaping riverine ecosystem health. Long-term foresight is incorporated via dynamically downscaled climate projections and Shared Socioeconomic Pathways within a coupled water and energy systems (WEAP-LEAP) model. Applied to the semiarid Sakarya Basin in Türkiye, the framework evaluates three future periods (2020-2030, 2055-2065, and 2090-2100) across seven subbasins. Results show systemic trade-offs: municipal water security remains high (>90%), but ecosystem integrity and renewable energy goals are consistently compromised. Overall, WEFE Nexus Index values (0.53-0.86) show significant spatial disparities, with arid upstream regions consistently underperforming. Strikingly, SSP2 (business-as-usual) and SSP5 (fossil-fueled growth) yield nearly identical outcomes, underscoring the systemic unsustainability of current trajectories. This framework advances nexus assessment from theory to practice by integrating reproducible metrics, scenario planning, and spatial modeling, creating a practical tool for developing adaptive and resilient sustainability strategies.

Forestation and renewable energy sources are both critical tools for climate mitigation and human sustainable development. However, climate feedback from forestation, such as changes in wind speed and downwelling shortwave radiation following forestation driven by modified land-atmosphere heat, momentum, and moisture exchanges, could potentially compromise the availability of weather-dependent renewable energy. Here, based on coupled land-atmosphere simulations with contrasting forest distributions, our results showed that, in idealized forestation scenarios, pixels of forestation remaining suitable for renewable energy deployment are projected to experience a 27.8 ± 1.1% decline in wind energy potential and a 1.9 ± 0.1% reduction in solar energy potential. For the future emission scenario of SSP1-2.6, which is compatible with the goal of the Paris Agreement that limits global warming below 2 °C, forestation-driven reduction (-9.5 ± 1.9%) in wind energy potential for current onshore wind installations can even surpass that caused by climate change (-2.1 ± 1.5%). In contrast, solar energy change for current onshore solar installations in the SSP1-2.6 scenario is projected to be dominated by climate-driven increases (+3.9 ± 0.7%) rather than forestation-induced change (+0.3 ± 0.2%). Our study highlights the need to account for the trade-off between forestation-based carbon removal and renewable-based emission reduction when formulating climate mitigation pathways.

Environmental incidents can release multiple chemicals simultaneously, as seen in the recent JP-5 leak in Hawaii, raising concerns about human exposure through contaminated drinking water. Assessing the toxicity of such mixtures is essential, yet experimental data are often unavailable. This study evaluates the feasibility of applying publicly available in silico tools, including TTC, QSAR models, and read-across, to estimate oral points of departure (PODs) for acute, subchronic, and chronic exposures to JP-5. Applicability domain assessments indicated broad QSAR model coverage, with OPERA and ToxTree fully inclusive and VEGA models showing mostly moderate-to-high confidence, aside from a few additives. Dosimetric adjustments and uncertainty factors were applied to extrapolate rat PODs to in silico-derived health guidance values (HGV). Mixture toxicity was estimated by using the concentration addition model and compared with existing HGV-based values to derive screening levels. In silico-derived HGVs generally aligned with available guidance values, and the TTC estimates were consistently conservative. Potential health effects were also curated based on available animal and mechanistic studies. Overall, this case study demonstrates that in silico models can help fill toxicological data gaps and provide screening-level insights into evaluating chemical mixture exposure when empirical data are limited.

Improving carbon productivity is essential for simultaneously achieving sustainable development goals and tackling climate change. While the Global Value Chains (GVCs) division enhances productive efficiency, its impact on carbon productivity remains elusive. Here we integrated the GVCs theory with the Environmental Expanded Input-output model to investigate the highly globalized automotive manufacturing industry. We found that CO₂ emissions intensity, the inverse of carbon productivity, fluctuated between 0.37 and 0.47 kg/USD in automotive manufacturing GVCs during 2001-2021. Notably, developing economies nearly doubled their CO₂ emissions intensity during this period, whereas developed economies almost halved theirs. The global distribution of CO₂ emissions and value added is becoming increasingly unequal in industrial production and service segments. Lower production levels and energy efficiency in developing economies, coupled with their upstream roles in GVCs (raw materials and industrial parts suppliers), exacerbate these disparities. Our findings indicate that merely global labor division is insufficient to create low-carbon automotive manufacturing GVCs. Formulating emission reduction targets that consider the diverse roles of economies within GVCs, and supporting developing economies in boosting energy productivity, labor value added efficiency, and skill can help narrow the distribution gaps and enhance the carbon productivity of the entire automotive manufacturing GVCs.

Accelerating the low-carbon transition of an urban centralized heating system is a key concern for policymakers. We propose a novel equilibrium analysis framework to assess the feasibility of implementing ETS in China's central heating sector. The results demonstrate that implementing an emission trading scheme (ETS) within the central heating sector can accelerate decarbonization process by shifting the focus of the current <i>Clean Heating Campaign</i> from gas boilers to industrial surplus heat and geothermal energy, aligning with China's goal of reaching 25% nonfossil energy by 2030. It can provide a more cost-effective and sustainable decarbonization pathway in which a 1% annual reduction in carbon quotas leads to emissions peak at 2033, as well as it is expected to generate significant co-benefits by reducing heating-related air pollution. A well-designed subsidy reallocation and phase-out strategy can enable the ETS to drive decarbonization and alleviate fiscal pressures. This combination of ETS and subsidies ensures a smooth transition to low-carbon heating in the short term and is sustainable in the long run through endogenous technological advancement and market self-regulation.

People of color in the United States are disproportionately and unfairly exposed to air pollution. Equity-oriented scientific evaluations quantifying these disparities often use population-average exposure metrics to capture the overall inequality within a system. Utilizing these metrics involves choices about the exposure input for assessing disparity, the study geography, and the reference population, which are critical to understanding disparities and effectively designing interventions. Here, we use a case study of exposure to fine particulate matter (PM_2.5) from California's agricultural sector to dissect the implications of these decisions. Using a reduced-complexity model and emissions of PM_2.5 and precursors, we compare estimates of racial and ethnic disparities in exposure resulting from different combinations of these methodological choices. The full population distributions highlight differences between disparities at the extremes (e.g., 90th percentile) and at the mean. Additionally, the selection of study geography and reference population can influence the magnitude and relative ordering of exposure disparities. Thus, methodological choices can lead to different conclusions for the same concentration and population surfaces; this can impact not only the findings of an individual study but also have implications for mitigation strategies. We conclude with recommendations for best practices for making, justifying, and communicating these methodological decisions.

The desire to protect aquatic ecosystems and drinking water supplies from the adverse effects of nutrients, trace organic contaminants, and other constituents in municipal wastewater has led to a need for additional treatment, particularly in watersheds with insufficient dilution. Constructed wetlands have emerged as viable alternatives to advanced wastewater treatment processes for effluent polishing due to their low cost and ancillary benefits. Starting in the late 1980s, experiences gained from several decades of operating wetlands in small communities increased confidence that these nature-based treatment systems could be effective and reliable. As a result, investments were made in larger constructed wetlands, with surface areas greater than a million square meters (1 Mm²) and flows often exceeding 1 m³ s^-1, on effluent-dominated rivers and as part of potable water reuse projects. More recently, new wetland designs have improved the constructed wetland system performance by taking advantage of sunlight-mediated processes in the water column and microbial processes on subsurface porous media. Research that provides additional insight into contaminant removal mechanisms, demonstrates long-term viability, and further improves treatment performance could expand the application of constructed wetlands to other difficult-to-solve water quality challenges, including the treatment of municipal water reuse concentrate and the mitigation of nonpoint source pollution.

Most microplastics (MPs) in wastewater are retained within the sewage sludge. These MPs enter the soil environment through land application, posing a threat to ecosystems. This study proposes an effective control strategy using alkali pretreatment (pH 10, 5 days) followed by hydrothermal treatment at 180 °C (AHT), achieving a degradation rate of 81.83% for polyethylene terephthalate MPs (PET-MPs) in sludge. AHT promotes the formation and solubilization of key active components in sludge, such as alkalinity, which drives nucleophilic attack, metal ions, which catalyze reactions, and organic matter, which acts as radical donors. These components synergistically disintegrate PET-MPs through hydrolysis and radical oxidation pathways during hydrothermal treatment. Meanwhile, hydrothermal treatment induces polymer chain motion and physical structural disruption, accelerating the penetration and reaction of active components, thereby achieving efficient degradation of PET-MPs. Spectral and high-resolution mass spectrometry analyses reveal that sludge AHT facilitates the transformation of MP-derived dissolved organic matter (MP-DOM) into molecules characterized by low-aromaticity, low-molecular-weight, high-saturation, and high-bioavailability. Concurrently, MP-DOM exhibits low acute toxicity toward aquatic organisms and the immortalized human liver cell line (THLE-2 cells). Therefore, sludge AHT effectively degrades and converts polyester MPs into MP-DOM with low-toxicity, thereby mitigating the risks of sludge-based MPs to ecosystems.

The presence and spread of antibiotic resistance genes (ARGs) across various habitats have increased the risks of antibiotic resistance, highlighting the urgent need for effective monitoring methods. One key challenge in method development lies in balancing sensitivity, speed, and portability. To address it, a one-step assay targeting the carbapenem resistance gene <i>bla</i>_NDM was developed based on recombinase polymerase amplification (RPA) combined with CRISPR/Cas12a. A sensitivity optimization paradigm─MOSAIC (multistrategy optimized sensitive assay via integrated CRISPR/Cas12a)─was proposed, incorporating component optimization, suboptimal-PAM-mediated CRISPR inhibition, and glycerol-assisted phase separation. The glycerol-assisted strategy exhibited the largest enhancement, followed by the suboptimal-PAM strategy and component optimization. When combined, these strategies demonstrated a synergistic effect, yielding greater improvement (10 000-fold) than a single strategy alone. MOSAIC reached a limit of detection (LOD) of 260 copies/μL, comparable to that of qPCR, and enabled faster quantification of <i>bla</i>_NDM at 37 °C within 1 h on a standard plate reader. It achieved 100% diagnostic sensitivity and 95.45% specificity in clinical isolates, and 77.41-99.73% accuracy in environmental matrix-spiked samples, comparable to that of qPCR. It provides a technological foundation for on-site detection of <i>bla</i>_NDM and offers an optimization paradigm and new insights for the development of one-step RPA-CRISPR/Cas12a assays targeting various genes.

Abstract. The Arctic is experiencing unprecedented environmental changes with rapidly rising temperatures. Emissions of methane (CH4) – a potent greenhouse gas – may be increasing from the region, making accurate monitoring essential. The TROPOspheric Monitoring Instrument (TROPOMI) instrument offers high spatial and temporal coverage of CH4 column mole fractions. However, its data in the Arctic has historically exhibited seasonal and latitudinal biases and low-quality retrievals. A major challenge is the lack of ground-based validation data in high-latitude regions, which are used to improve satellite retrievals. This study evaluates inverse modelling to estimate CH4 emissions using TROPOMI measurements over the North Slope of Alaska. Using two retrieval products – the operational SRON product and the scientific WFMD product from the University of Bremen – we assess the alignment of derived emissions with surface measurement-derived inversions over 2018–2020 and test their robustness through sensitivity analyses. Our results show that tundra emissions from SRON inversions align more closely with surface measurement-derived emissions than WFMD inversions. Both TROPOMI-product derived emissions have anomalously low emissions in August 2018 compared to surface measurement-derived emissions, likely due to low data density resulting from high cloud cover. TROPOMI inversions provided stronger constraints on fugitive anthropogenic emissions compared to surface inversions. However, each retrieval produced different emission estimates, highlighting retrieval-dependent differences. Sensitivity tests revealed a strong prior dependence in both retrievals, raising concerns about robustness in northern high latitudes. This study highlights the importance of using multiple retrievals and rigorous sensitivity testing in high-latitude satellite inversions.

Abstract. This study investigates the impact of non-ideal instrumental effects on the performance of high-resolution Fourier Transform Infrared (FTIR) spectrometers, with a focus on the Bruker FTS 120M. Key non-idealities, including retroreflector misalignments, baseline drift, and spectral channeling, were systematically analyzed using advanced diagnostic tools such as ALIGN60 and LINEFIT. The nominal configuration exhibited significant anomalies, notably modulation efficiency (ME) deviations of up to +10.9 %, phase error (PE) variability of 2.11 × 10−2 radians, and spectral channeling frequencies such as a persistent 2.9044 cm−1, along with emerging frequencies around 0.24 cm−1 attributed to retroreflector wear and CaF2 beamsplitter degradation. A pronounced anomaly at 40.672 cm−1, likely induced by environmental factors such as external vibrations or mechanical instability, was also identified. Implementation of a modified configuration effectively addressed these issues, reducing PE variability to 0.042 × 10−2 radians, aligning ME within the NDACC-acceptable threshold of 1.1, and achieving substantial improvements in the instrument line shape (ILS), including sharper peaks, narrower full-width at half maximum (FWHM), and reduced sidelobe asymmetry. Analysis of HBr transmission spectra revealed improved fitting of the P(2) line, characterized by lower residuals and enhanced spectral quality. Simulated Haidinger fringes near zero path difference (ZPD) highlighted alignment degradation patterns, underscoring the necessity for precise optical adjustments. Temporal trends showed an increase in ILS peak height of &gt;14 % associated with the instrument upgrade, together with significant mean absolute error (MAE) reductions achieved by the modified configuration. In addition, a targeted retrieval case study demonstrates that explicit propagation of the empirically characterized instrumental response into the forward model reduces spectral residuals and retrieval uncertainties while increasing the retrieved total column by approximately 6 %–7 % relative to the nominal configuration. Overall, this study provides a robust framework for diagnosing and correcting instrumental artifacts, ensuring the accuracy, reproducibility, and long-term stability of FTIR measurements essential for atmospheric trace gas retrievals.

Abstract European forests have been intensively managed for the provision of renewable materials and are a valuable asset for climate change mitigation, adaptation, and biodiversity conservation. Forest harvest fundamentally alters the physical structure and composition of forests, with consequences for climate regulation services. It can impact land surface temperature (LST) diurnally and seasonally, but the net LST impact of historical forest harvest activities in Europe is yet unknown. This study integrates satellite-derived data of LST and forest harvest for the period 2004-2023 to unravel spatial and temporal patterns of the surface temperature response to harvest disturbances in Europe. We find a consistent diurnal asymmetry: harvesting induces daytime warming alongside nighttime cooling across all seasons and forest types. However, the magnitude, net effect, and temporal evolution have strong heterogeneity. Regionally, daytime warming dominates in southern Europe (+0.075±0.007°C, mean ± standard error; +0.719°C at the 90th percentile), peaking in summer (+0.103±0.010°C; +0.823°C), while eastern Europe shows the strongest annual nighttime cooling (-0.060±0.005°C; -0.937°C). The response is weaker in Western Europe. Seasonally, net daily cooling prevails in spring, whereas net warming dominates in summer. Forest composition modulates the response: needleleaf forests show the strongest diurnal contrast, broadleaf forests exhibit the weakest daytime signal, and mixed forests display spring daytime cooling. After harvest, most regions transition from initial post-harvest warming towards longer-term cooling or neutrality after one or two decades, with the post-harvest pattern varying regionally and by forest types. Overall, this study elucidates strong spatiotemporal variation in net microclimate responses to forest harvesting across Europe, driven by geography, seasonality, and forest composition. By explicitly characterizing how forest harvesting alters surface temperature across space and time, these findings contribute to improved region-specific forest management strategies that can better balance timber production with climate regulation and biodiversity conservation under changing environmental conditions.

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