Atmospheric and Oceanic Sciences

To address the tradeoff between environmental robustness and fine-grained accuracy in single-sensor human behavior recognition, this paper proposes a non-contact system fusing 77 GHz SIFT (mmWave) radar and a 40 kHz ultrasonic array. The system leverages radar’s long-range penetration and low-visibility adaptability, paired with ultrasound’s centimeter-level short-range precision and electromagnetic clutter immunity. A synchronized data acquisition platform ensures multi-modal signal consistency, while wavelet transform (for radar) and STFT (for ultrasound) extract complementary time–frequency features. The proposed Attention-CNN-BiLSTM architecture integrates local spatial feature extraction, bidirectional temporal dependency modeling, and salient cue enhancement. Experimental results on 1600 synchronized sequences (four behaviors: standing, sitting, walking, falling) show a 98.6% mean class accuracy with subject-wise generalization, outperforming single-sensor baselines and traditional deep learning models. As a privacy-preserving, lighting-agnostic solution, it offers promising applications in smart homes, healthcare monitoring, and intelligent surveillance, providing a robust technical foundation for contactless behavior recognition.

The extensive availability of electricity and hydrogen in Texas provides enormous potential for adopting alternative energy for transportation. The operational phase of alternative energy infrastructure is an essential element in its environmental impact assessment and has not been evaluated up to this point, especially in terms of its utilization and vehicle mix. This study aims to evaluate the environmental impacts of alternative energy options, including fast/slow electric vehicle charging and hydrogen refueling at charging/refueling stations. The AFLEET Charging and Fueling Infrastructure (CFI) Emissions Tool was used to analyze the burden reductions in life cycle GHGs and air pollutants. The station operation considered infrastructure type, utilization, and vehicle mix (LDVs and HDVs). High utilization of the station yielded more burden reductions. Fast-charging supply equipment resulted in higher GHG burden reductions compared to the slow counterpart (367% in moderate utilization). Elevated burden reductions were observed in GHGs, VOC, and CO pollutants with more LDVs. There was an increase in NOx burden reductions of approximately 5200 lb. (moderate utilization), while transiting from 100% LDV to 75% LDV. Increased burden reductions were noted in particulate matter for 50% LDV. Also, enhanced burden reductions were observed in SOx with more HDVs for EVs and equal vehicle mix in hydrogen. Increased GHG burden reductions were identified in SMR than electrolysis. These results recommend policy makers focus on maximized utilization of the new or existing infrastructure to reduce environmental loads.

Sandy soils in arid and semi-arid regions of Egypt are characterized by poor structure, low fertility, and a limited capacity to retain irrigation water, which collectively constrain nutrient availability and crop productivity under arid conditions. Despite these limitations, improving the performance and sustainability of sandy soils has become essential to meet increasing agricultural demands. Therefore, this study aimed to evaluate the individual and combined effects of biochar, mineral NPK fertilization, and seaweed extract on the growth performance, biomass production, nutrient status, and overall productivity of spearmint (Mentha spicata L.) cultivated in sandy soil. Field experiments were conducted over two successive growing seasons (2024 and 2025) at the Agricultural Research Station, Al-Marashda, Qena Governorate, Egypt, using a split-plot design with biochar application (0 and 12.5 ton ha−1) as the main factor and foliar growth stimulants (control, NPK, NPK + 2 mL L−1 seaweed extract, and NPK + 4 mL L−1 seaweed extract) as sub-factors. Results revealed that biochar application significantly improved all vegetative growth parameters, herbage fresh and dry yields, essential oil percentage, oil yield per plant, photosynthetic pigment concentrations, and leaf N, P, and K contents compared with untreated soil. Foliar application of NPK fertilizer, particularly when combined with seaweed extract, further enhanced plant performance. The greatest improvements across all measured traits were consistently obtained from the integrated application of biochar at 12.5 ton ha−1 combined with foliar spraying of NPK (5 g L−1) and seaweed extract 4 mL L−1. This treatment produced the highest biomass accumulation, essential oil yield, chlorophyll content, and nutrient uptake during both seasons. The findings conclude that integrating biochar with balanced mineral fertilization and natural biostimulants represents an effective and sustainable strategy for improving productivity and essential oil yield of spearmint grown in nutrient-poor sandy soils.

The transition to clean photovoltaic sustainable generation sources has motivated several developments in required power electronics interface systems. The conventional solution is based on two cascaded conversion stages, leading to reduced efficiency, inevitable leakage currents, and/or a high number of required components. Another solution is the use of integrated two-stage solutions suffering from asymmetrical switch structures, discontinuous input side currents, and/or complex modulation and control requirements. This paper presents a modified configuration with symmetrical six switches based on the common ground boost inverter solution. Furthermore, the proposed solution presents a continuous input side current and a simple modulation strategy. Moreover, the proposed CG topology offers a reduction of the current stress on the power switch by diverting the load current away from the power switch during the inductor charging. The operation, modulation, and control of the developed solution are presented in the paper, including comprehensive performance comparisons with boost inverter solutions in the literature. Simulation and experimental prototype-based results confirm the advantages and superiority of the proposed topology over existing topologies.

China’s urban development is shifting from extensive expansion to stock-oriented renewal, making the precise identification of inefficient land stock essential for sustainable spatial governance. To overcome the limitations of single-source data and coarse delineation, we propose a multi-source geospatial framework integrating land-use surveys, socioeconomic statistics, spatiotemporal mobility trajectories, and ecological indicators. Using Shenzhen as a case study, we construct an eight-indicator system across social, economic, and ecological dimensions and apply entropy-based objective weighting to support GIS-based weighted overlay evaluation. The results identify 65.37 km2 of inefficient land in 2019, accounting for approximately 7% of Shenzhen’s construction land, with a distinctive “edge aggregation and corridor extension” pattern concentrated along urban–rural fringes and administrative boundaries. Inefficient land is highly uneven across districts, with Longgang (21.11 km2) and Bao’an (12.57 km2) contributing 51.5% of the total and exhibiting statistically spatial clustering (p < 0.01). The observed configuration reflects path-dependent historical development and policy–ecology constraints, including the interaction between ecological control boundaries and peripheral expansion. Overall, by integrating multi-source spatiotemporal big data within a multi-dimensional evaluation framework, the framework offers an objective and transferable approach for diagnosing inefficient land stock and informing targeted urban renewal strategies in high-density cities worldwide.

The global push for energy decarbonization has increased interest in hydrogen as a clean energy carrier. Biohydrogen from agricultural residues is a promising pathway for countries with strong agro-industrial sectors. This study evaluates the technical, economic, and environmental feasibility of hydrogen production from palm oil rachis in two post-conflict regions of Colombia: a large-scale facility in Bolívar and a small-scale plant in Santander. The assessment integrates Aspen Plus® (version 14) simulations using the NRTL thermodynamic model, an attributional gate-to-gate Life Cycle Assessment (LCA) with ReCiPe Midpoint (H), and a techno-economic analysis. The simulated process includes biomass drying, decomposition, steam gasification, syngas cleaning, and methane reforming. A key technical finding was the non-linear relationship between feedstock composition and process yield. Although Santander’s biomass had a higher hydrogen content (9.42% vs. 6.58%), Bolívar achieved a much higher conversion efficiency (0.198 kg H2/kg biomass) and produced over seven times more hydrogen while processing only 5.8 times more biomass. Environmental results showed clear advantages for Bolívar, which presented lower impacts across most categories compared to Santander and the fossil-based hydrogen benchmark. Bolívar achieved a Global Warming Potential of 2.47 kg CO2 eq/kg H2, far below the 15.03 kg CO2 eq/kg H2 of Santander, and showed favorable performance in particulate matter formation, acidification, and fossil resource scarcity. Economically, Bolívar was viable, with a Net Present Value of USD 25.01 million, a Benefit–Cost Ratio of 3.29, and a discounted payback period of 4.54 years. Santander was economically unfeasible under all conditions. Hydrogen production from palm rachis is technically feasible, environmentally beneficial, and economically viable when biomass availability and process integration are adequate, as illustrated by the Bolívar case.

Climate change is a pressing issue that has far-reaching effects on the global ecosystem, societies, and economies. Climate-induced disasters exacerbate multidimensional poverty through economic, social, and environmental pathways. This study examines the relationship between climate-induced disasters and multidimensional poverty, applying a mixed-method design comprising a PRISMA-guided systematic review and thematic analysis. Articles published between 1999 and 2025 were retrieved from Scopus and Web of Science, yielding 3587 articles. After reference checking and screening for relevance and availability, we finally reviewed 17 articles. The results highlight that climate-induced disasters disrupt economic and livelihood activities, negatively impact GDP, slow financial development, reduce per capita expenditure ability, and harm agricultural production. Disasters also have negative impacts on health and well-being, education, gender, the natural environment, and culture; these disasters promote intergenerational poverty. Among all stressors, floods and droughts are the most pervasive, and they have different magnitudes and durations of impacts. The assessment identifies governance quality, gender inequality, education, social positions, and environmental degradation as the significant mediating systems influencing vulnerability and recovery. To cope with vulnerabilities, individuals employ a variety of strategies based on their socioeconomic status. Building on these insights, the study develops the Multidimensional Climate–Poverty Dynamics (MCPD) Framework to conceptually capture climate–poverty as a socially constructed and institutionally mediated process. The study contributes theoretically to environmental sociology and empirically to climate policy by framing adaptation as a social process of transformation rather than as solely a survival mechanism.

A well-developed national park scheme is of great significance in ensuring the sustainability of the ecological environment and addressing the risks of community green development. As a key mechanism for the transformation of ecological product value, the implementation and optimization of the concession system contribute to the coordinated development of national park protection and community interests. How to promote the transition of national park concession from “system establishment” to “system efficiency improvement” is an important issue in system optimization, and the key to solving this issue is to assessing the credibility of national park concession systems in the community. Community residents are important stakeholders in the national park concession system, and their perception of this system’s credibility reflects its levels of rationality, efficiency and sustainability. This study takes residents of four villages in Wuyishan National Park as an example, obtains data through a questionnaire survey, and uses linear regression analysis to study the influencing factors of residents’ credibility of national park concession systems from five aspects, including household location, demographic characteristics, functional perceptions of the concession system, normative perceptions of the concession system, and expectation perceptions of the concession system. The results show that: (1) Residents show different perceptions of the concession system in national parks; they generally believe that it conforms to the collective interests and should be permanent. (2) The normative perceptions and expectation perceptions of the concession system in national parks have a significant positive impact on its credibility. Furthermore, age and educational attainment significantly influence residents’ perceptions of the system’s credibility. The impact of household location on the credibility of national park concession systems is not significant.

Driven by advances in cloud computing, cryptocurrency, and artificial intelligence, the rapid proliferation of data centers may pose severe human and planetary health risks. This paper, to the best of our knowledge, is the first-of-its-kind assessment of the potential health implications associated with data centers. Here, we examine the case of Virginia’s Data Center Alley, the world’s largest concentrated data center hub, and argue that data centers can cause public health harms, harms that can be at least partially mitigated through improved planning and design. We assess the health risks associated with data centers, including air pollution, extensive water use, noise pollution, and detrimental land use’s risk of disrupting natural ecosystems and community well-being. We also address how rising energy costs can worsen social determinants of health. To mitigate these risks, we recommend transitioning data centers to renewable energy, implementing strict regulations to minimize water consumption, and optimizing site planning with acoustic treatments and green zoning to reduce noise pollution. Additionally, we advocate enforcing responsible site selection and zoning regulations to curb adverse land use changes, equitable energy pricing to alleviate economic burdens, and strengthening health communication for informed public and governmental decision-making, action-oriented advocacy, and policy changes. Finally, we emphasize the need for transdisciplinary research integrating physical sciences, engineering, and public health to quantify specific health outcomes linked to data centers.

Coastal salt marshes (CSMs) are vital blue carbon (BC) reservoirs, yet accurately quantifying their gross primary productivity (GPP) remains challenging due to limitations in terrestrial biosphere models (TBMs), which often overlook coastal-specific processes. Here, we present SAL-GPP, a process-based model that incorporates coastal-specific modules to capture the effects of salinity and temperature stress on photosynthesis, as well as light-use efficiency across salinity gradients in diverse CSM plant species. Model validation showed strong agreement with observations, with <i>R</i>² of 0.82 and model efficiencies of 0.82 and 0.74 for daily and seasonal GPP, respectively. Driven with global inputs, SAL-GPP produced high-resolution global simulations, yielding a mean annual GPP of 66.89 ± 11.68 TgC yr^-1 (2011-2020), with 64% concentrated in key hotspots across the southeastern United States, western Europe, southeastern China, and Australia. From 2011 to 2016, global CSM GPP increased by 1.56 TgC yr^-1, then declined, rebounded after 2018, and peaked at 71.45 ± 12.02 TgC yr^-1 in 2020. Model evaluation showed that SAL-GPP outperformed existing remote sensing-based GPP products and TBMs at both site and grid levels. By explicitly incorporating coastal ecosystem dynamics, SAL-GPP supports global BC accounting and climate mitigation strategies aligned with nature-based solutions for carbon neutrality.

Abstract. MAMAP2D-Light is an airborne passive remote sensing imaging push-broom spectrometer developed at the Institute for Environmental Physics at the University of Bremen to determine atmospheric methane (CH4) and carbon dioxide (CO2) column anomalies in the 1.6 µm-band to quantify point-source emissions. In its initial version, as flown in 2022 in Canada, a significant stray light level of 5.6 % of the measured signal has been observed post-campaign, causing apparent error patterns in the retrieved CO2 and CH4 column anomalies. Measurement data collected during an airborne campaign in 2022 in Canada offer the unique opportunity to investigate the end-to-end impact of stray light and its correction on the retrieved CO2 and CH4 column anomalies, as well as the derived emission rates. We successfully developed and applied a stray light correction to the instrument and investigated its impact on the CH4/CO2 proxy method, the CH4 column, and derived point-source emissions. In nearly all cases, applying the CH4/CO2 proxy method reduced the stray-light-related column errors below the CH4 column noise. The derived emission rates for the proxy-retrieval with and without stray light corrected spectra are comparable, proving the ability of the CH4/CO2 proxy method to correct stray-light-related artifacts. In this paper, we additionally investigate the impact on the CH4 total column retrieval for a high contrast scene condition under which the correction by applying the proxy method is no longer sufficient. Following the initial campaign in 2022, the post-campaign stray light characterization and analysis revealed that a significant fraction of stray light was attributed to reflective surfaces in the object plane of the spectrometer. Based on these findings, the total stray light was reduced by ∼ 63 % by implementing a hardware modification from 2023 onward.

ABSTRACT A comprehensive understanding of long‐term trends in near‐surface wind speed (SWS) and their underlying physical mechanisms is imperative for progress in atmospheric science, climatology, and energy‐related fields. Utilising observational data and simulations from 10 models, this study investigates the role of anthropogenic activities in the observed decline of SWS over the Tibetan Plateau (TP) from 1961 to 2014. The results show a widespread and statistically significant decline in the annual mean SWS across the TP. The models qualitatively captured this decreasing trend under all‐forcing scenarios. Detection and attribution analysis attributes the observed wind speed decline primarily to anthropogenic forcings, which account for most of the reduction, while natural forcings show no detectable influence. Among these anthropogenic factors, greenhouse gas (GHG) emissions were responsible for the greatest decrease in SWS over the TP. In comparison, the contributions from aerosol forcing and land use change were marginal; their negative regression coefficients indicate that they partially offset the overall weakening trend. The underlying mechanism involves GHG‐induced asymmetric warming. This warming weakened the pressure gradient over the TP by causing a greater increase in geopotential height over the mid‐high latitudes north of the plateau than over the regions to its south. These findings highlight the dominant influence of human activities on wind speed changes over the TP, with important implications for wind resource planning and ecological management.

Abstract. We propose a novel method for computing the effects of TROPOMI observational uncertainties on emissions calculation arising from the nonlinearity of the gradient terms and non-biased filtering in space and time. Application using TROPOMI XCH4 data in clean areas of Western China with long-term WMO background observations quantifies a minimum detectable emission threshold of 0.3 µgm-2s-1, lower than existing community thresholds using TROPOMI. By combining threshold-based and stochastic approaches that incorporates pixel-by-pixel and day-by-day XCH4 uncertainties, we identify and filter physically unrealistic emission values in both space and time. The resulting emissions reveal both missing sources and emission bias caused by the nonlinearity of the gradient term. Validation was performed by applying the method to the Permian Basin, where comparisons with airborne observations demonstrate the method's ability to align with independent datasets. The importance and implications of our results are related to this being a new methodology for methane emissions estimate from TROPOMI which enables precise identification of emission sources and improved handling of observational noise, offering a more accurate framework for methane emission monitoring across diverse regions using existing satellite platforms. Our results yield a non-negative emissions dataset using an objective selection and filtration method, which includes a lower minimum emissions threshold on all grids and reduction of false positives. Finally, the new approach can be adopted to other satellite platforms to provide a more robust and reliable quantification of emissions under data uncertainty that moves beyond traditional plume identification and background subtraction.

Abstract. Atmospheric aerosols significantly contribute to air pollution and influence atmospheric chemistry, impacting air quality and public health. Decrease in aerosols can hinder the radical uptake sink of HO2, and thus increase NOx and OH, and subsequently increase ozone levels. This study investigates the seasonal variations of PM10 and aerosol surface area and their effect on surface ozone levels in India, using the GEOS-Chem Chemical Transport Model for the years 2018 and 2022, two years with high and low simulated PM10 concentrations, respectively. The results reveal substantial seasonal variations in PM10 and aerosol surface area. In winter (DJF), higher PM10 and aerosol surface area in the Indo-Gangetic Plain (IGP) and western Central India (CI) result from biomass burning and industrial activity, while coastal regions show lower aerosol surface area. A decrease in aerosol surface area is seen during the pre-monsoon (March–April–May; MAM) and monsoon (June–July–August–Septmember; JJAS), followed by an increase in the post-monsoon (ON) season. As a result, aerosol-induced HO2 uptake during winter and post-monsoon suppress surface ozone by approximately 5–10 µg m−3 in 2022 when compared to that of 2018. In contrast, during monsoon in 2022, the decrease in aerosol surface area caused an ozone increase of 5–7.5 µg m−3 when compared to that of 2018. On average, this increase in surface ozone due to the decrease in aerosols can be mitigated by reducing anthropogenic NOx emissions by about 25 %–50 %. Thus, we recommend integrated strategies addressing aerosols, precursor emissions and regional meteorology to combat ozone pollution.

Abstract. Ground-level ozone (O3) pollution has recently become of increasing concern in China. Studies have shown that conventional models often fail to predict accurately the net O3 production rate (P(O3)net) due to the absence of certain mechanisms, particularly the kinetics from missing reactive volatile organic compounds (VOCs) species, and hence affects the reliability of evaluation for O3 formation sensitivity (OFS). Therefore, we conducted a field observation of P(O3)net and OFS using a P(O3)net (NPOPR) detection system based on a dual-channel reaction chamber technique at the Guangdong Atmospheric Supersite of China in Heshan, Pearl River Delta (PRD) in autumn of 2023. The in-situ monitoring data were then compared with results from a zero-dimensional model incorporating the Master Chemical Mechanism (MCM v3.3.1). We tested the model performance by incorporating parameterization for 4 processes including HO2 uptake by ambient aerosols, dry deposition, N2O5 uptake, and ClNO2 photolysis, and found that the discrepancies between the modelled P(O3)net (P(O3)net_Mod) and measured data (P(O3)net_Mea) did not change evidently, the maximum daily P(O3)net differed by ∼ 44.8 %. Meanwhile, we found that the agreement of OFS assessment results between the direct measurements and the modelling study was lower in the P(O3)net rising phase (08:00–09:00 LT, 63.6 %) than in the P(O3)net stable phase (10:00–12:00 LT, 72.7 %) and P(O3)net declining phase (13:00–17:00 LT, 72.7 %). The results in this study reflected that unmeasured oxygenated VOCs (OVOCs) were the most effective compensating factor for the discrepancies between observed and computed P(O3)net and OFS, hinting clearly at the importance of quantitative understanding the total reactivity of VOCs in O3 chemistry.