Search Results (188)

La Paz Lagoon is a semi-enclosed coastal ecosystem in the southwestern Gulf of California that was historically affected by wastewater discharges and other anthropogenic disturbances. This study evaluated the taxonomic diversity and community structure of soft-bottom fish assemblages across two climatic periods (Neutral, 2016–2017, and La Niña, 2022–2023) using alpha (α), beta (β), and gamma (γ) diversity metrics together with taxonomic distinctness indices (Δ, Δ*, Δ+, and Λ+). Six bimonthly surveys were conducted during each climatic period at seven sampling stations. A total of 93 fish species, representing 65 genera, 33 families, and 17 orders, were recorded across the two climatic periods. Species richness exhibited marked temporal variation, reaching its highest value in April and its lowest value in March. Periods of reduced richness were associated with increased species turnover among stations. Taxonomic distinctness indices remained within the expected confidence limits, indicating a stable taxonomic structure despite historical anthropogenic impacts. Dominant species included Paralabrax maculatofasciatus, Diapterus brevirostris, Eucinostomus gracilis, Urobatis halleri, and Synodus scituliceps. Overall, the results indicate that the soft-bottom fish assemblage of La Paz Lagoon exhibits characteristics consistent with a stable and well-structured community. Multivariate analyses further identified water temperature as the principal environmental variable associated with community variation. Collectively, these findings support the hypothesis that La Paz Lagoon is undergoing ecological recovery and provide a robust ecological baseline for long-term monitoring, conservation, and ecosystem management. Full article

Amidst global carbon neutrality goals, China’s building energy consumption gains prominence, with heating and cooling exceeding 50% of the total. Underground structures leverage inherent geological thermal inertia to significantly reduce ventilation energy demands. This study employs combined field measurements and numerical simulations to investigate heat exchange mechanisms and performance in underground hydropower station air intake tunnels. Four representative tunnels (Sichuan, Fujian, Hebei, Yunnan) served as case studies, monitoring air temperature, humidity, velocity, and wall temperature. Field-monitored parameters informed a computational fluid dynamics (CFD) model, enabling quantitative analysis of rock thermal conductivity, inlet air velocity, and wall temperature effect on heat exchange efficiency. Research shows that: (1) significant seasonal adaptive characteristics exist, achieving peak cooling efficiency (69.04%, summer) and heating efficiency (78.86%, winter); (2) rock thermal conductivity is the primary efficiency determinant—quartzite tunnels exhibited 11.8% higher average efficiency than tuff tunnels; and (3) inlet air velocity negatively correlates with efficiency, exceeding 90% at 0.1 m/s but declining to 69% at 1.5 m/s. This work provides a theoretical basis for optimizing energy-efficient ventilation in underground engineering and validates the pivotal role of rock thermal inertia in reducing operational building energy consumption. Full article

While global climate change is fundamentally reshaping thermal regimes, capturing these shifts in topographically diverse regions remains a significant hurdle for standard gridded datasets. This study provides a comprehensive spatiotemporal analysis of 16 extreme temperature indices across Northern Carpathians from 1980 to 2024 using the E-OBS dataset. The QDM framework proved highly effective in neutralizing elevation-induced temperature biases, which reached up to 5.1 °C in raw E-OBS data. Beyond simple bias removal, the correction significantly improved the daily accuracy of the dataset, with RMSE values at high-altitude stations, such as the Chopok summit (1995 m), decreasing from 5.1 °C to 2.3 °C. Both Warm Days (TX90p) and Summer Days (SU) show near-perfect Field Coherence ($C_f$ = 100% and 98%, respectively). A prominent feature of this temporal national average trend is its inherent asymmetry; the Annual Minimum (TNn) is climbing nearly twice as fast (+1.1 °C/decade) as the Annual Maximum (TXx) (+0.6 °C/decade), though the warming of these coldest nights is more localized (74.5% coherence). We also identified a clear signal of Elevation-Dependent Warming (EDW), with absolute maximums surging most aggressively in the Northern Carpathians at +1.6 °C/decade. Conversely, cold-tail indices like Ice Days are in a concurrent nationwide retreat ($C_f$ = 97%), a shift that significantly reduces the physical window for winter tourism and alters the climatic envelope for fragile mountain ecosystems. Ultimately, these results position Slovakia as a high-sensitivity climate region where observed trends often outpace broader Central European averages, highlighting the urgent need for localized, nature-based adaptation strategies. Full article

Against the global imperative to address climate change and accelerate energy transitions, the rapid growth of the electric vehicle (EV) industry has turned public charging infrastructure into a key foundation of urban operations, driven by carbon peaking and carbon neutrality goals. However, effective supply depends not only on scale but on deep association with urban functional spaces. This study compares Gusu District (a historic preservation district) and Industrial Park District (a new development district) in Suzhou. The goal is to reveal how public electric vehicle charging stations (EVCSes) associate with functional spaces under different urban development models. The study employs Standard Deviational Ellipse (SDE), Kernel Density Estimation (KDE), Bivariate Spatial Autocorrelation, and Coupling Coordination Degree (CCD) models to compare layout patterns, clustering features and functional association. The research findings are as follows: (1) The EVCS layout in Gusu District shows strong dependence on roads and administrative boundaries, while EVCSes in Industrial Park District show clear planning intervention, less constrained by its administrative boundary. (2) KDE analysis confirms that Gusu District has continuous clustering centered on the ancient city, but Industrial Park District shows a multi-center layout. (3) Bivariate Spatial Autocorrelation reveals different priorities in facility allocation. In Gusu District, spatial association is mainly driven by high-mobility nodes, while in Industrial Park District, EVCSes are more deeply embedded in social services and daily life scenarios. (4) CCD analysis reveals that the coordination in Gusu District forms a monocentric, spatially continuous gradient centered on the ancient city, whereas in Industrial Park District it displays a polycentric but fragmented pattern, with high coordination areas confined to planned cores. This comparative study reveals the EVCS spatial layout, which is shaped by both administrative boundaries and policy constraints, and the heterogeneity in spatial association between two districts. It provides scientific evidence and decision support for different spatial governance and facility optimization in various types of urban areas. Full article

This work proposes a multiscale spatial and temporal approach to assess the impacts of the ionosphere and neutrosphere (neutral atmosphere including both tropospheric and stratospheric) through an independent analysis of each component on Precise Point Positioning (PPP) accuracy and stability during selected representative geomagnetic events of Solar Cycle 25. Geomagnetically quiet and disturbed days were selected using the Kp index, with 21 multi-GNSS stations distributed across latitude bands. Kinematic PPP processing was performed using APPPOLO software (v1.0) with ionosphere-free dual-frequency combinations, precise products, and robust filtering, totaling 924 solutions. Results show improvements in geometry and satellite availability with multi-GNSS, achieving discrepancies within 0–10 cm in more than 89% of the solutions. The VMF3 model confirmed the deterministic behavior of ZHD and the latitudinal variability of ZWD, with increased stability in multi-GNSS solutions. Greater degradation was observed at high latitudes under disturbed geomagnetic conditions, particularly for GPS-only processing. Residual analysis indicated elevation-dependent effects and constellation-related differences. The analysis of ionospheric irregularities using ROTI revealed that PPP degradation is strongly associated with spatial distribution and satellite geometry, with enhanced effects at high latitudes and low elevation angles. Full article

Accurate characterization of wind-speed distributions is essential for preliminary wind-resource assessment, vertical wind-profile evaluation, and energy-yield estimation. This study presents a CFD-assisted reconstruction and validation framework that integrates two-parameter Weibull statistics with class-conditioned OpenFOAM v13 simulations to reconstruct wind-speed distributions at different measurement heights. Hourly wind-speed records measured at 10 m and 30 m at the Sakarya–Esentepe station during the period of 2009–2010 were used. The 2009 dataset was employed to estimate the Weibull shape and scale parameters by maximum likelihood estimation, while the 2010 dataset was reserved for independent validation. To ensure methodological consistency between statistical wind characterization and steady CFD modeling, the fitted Weibull distribution was discretized into representative wind-speed classes. For each class, a steady Reynolds-averaged Navier–Stokes simulation was performed in OpenFOAM under neutral atmospheric boundary-layer assumptions using the standard k–ε turbulence model, a logarithmic inlet velocity profile, and rough-wall boundary treatment. The class-wise CFD velocity responses extracted at 10 m and 30 m were then weighted by the corresponding Weibull class probabilities to reconstruct height-specific wind-speed probability distributions. The reconstructed distributions showed good agreement with the measured and fitted Weibull references. The RMSE values obtained by CFD for measurements at heights of 10 m and 30 m on the measurement mast were 0.45 m s⁻¹ and 0.52 m s⁻¹, respectively, and the Pearson correlation coefficients were 0.97 and 0.96, respectively; these values indicate that the CFD analyses are reliable. For the Lilliefors-adjusted Kolmogorov–Smirnov statistics, there is no value higher than 0.06. The differences between the reference and CFD-reconstructed AEP estimates were +0.40% at 10 m and −1.97% at 30 m. These findings indicate that the proposed Weibull–OpenFOAM framework provides a reproducible engineering approach for CFD-assisted wind-speed distribution reconstruction and height-specific consistency assessment. However, the method should be interpreted as a class-conditioned reconstruction framework rather than a stand-alone transient atmospheric wind prediction model. Full article

This study investigates the low-latitude ionospheric response over the Eastern Hemisphere during two successive geomagnetic storms on 12–13 November 2025. BDS-GEO observations from 20 GNSS stations, CODE GIM data, Swarm satellite observations, and simulations from the TIEGCM and HWM14 models were integrated to investigate regional ionospheric disturbances, single-station responses, and Equatorial Ionization Anomaly (EIA) evolution. During the first storm, with SYM-H reaching −254 nT, EIA intensification and poleward expansion beyond ±20° magnetic latitude were observed, with VTEC approaching 100 TECU at stations over Australia and rTEC exceeding 80% over Australia and the adjacent Pacific Ocean. Swarm observations showed TEC decreases within the EIA crest region and TEC increases in the surrounding areas. In contrast, the second storm, with SYM-H reaching −154 nT, produced disturbances with lower amplitudes, mainly characterized by localized positive TEC anomalies near the magnetic equator within 100°E–180°E, together with negative TEC anomalies in the surrounding low-latitude regions. The first storm was associated with southward IMF Bz reaching −54 nT and electrodynamic uplift related to PPEF, which contributed to the superfountain effect, whereas the second storm was influenced by residual disturbed neutral winds, reduced O/N₂ ratios at low latitudes, and the preconditioned ionospheric state inherited from the first storm. These results demonstrate that successive geomagnetic storms can produce different ionospheric responses in terms of intensity, spatial morphology, and driving mechanisms, highlighting the event dependence and regional variability of low-latitude ionospheric storm responses. BDS-GEO observations offer distinct advantages for monitoring localized ionospheric disturbances over the Eastern Hemisphere. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

With ongoing urban warming and the increasing frequency of extreme heat events, thermal comfort at highly exposed public spaces like bus stops has attracted significant attention. However, existing studies largely rely on linear assumptions and limited environmental variables, leaving the complex, multidimensional mechanisms driving thermal perception unclear. This study investigates the nonlinear impacts of microclimate, urban morphology, and station design on passengers’ thermal perception during summer in Chongqing, China. Drawing on field measurements, questionnaire surveys, and spatial data, linear regression was first applied to estimate neutral temperatures and acceptable thermal ranges. Subsequently, an interpretable machine learning framework integrating XGBoost and SHAP analysis was developed to explore the nonlinear effects and interactions mechanisms among these variables. The results reveal a dual regulatory pattern. Mechanism variables exhibit distinct nonlinear thresholds, with wind speeds above 0.98 m/s showing cooling associations and PET values exceeding 34.70 °C corresponding to more rapid increases in thermal discomfort. Concurrently, urban morphology and station design factors contextually modify these direct effects by altering their magnitude and direction. Furthermore, significant spatial heterogeneity in thermal adaptation was observed, with neutral temperatures ranging from 23.19 to 31.01 °C. These findings provide a basis for developing adaptive and context-specific thermal environment management strategies for urban bus stops. Full article

Based on sodium lidar observations obtained at the Haikou station (20° N, 110.2° E) of the Chinese Meridian Project during 2012–2024, this study systematically investigates the climatology of sporadic sodium layers (SSLs) at low latitudes and their relationship with ionospheric sporadic E (Es) layers, and it further analyzes their morphological features and evolution through a case study. The results show that the occurrence rate of SSLs over Hainan exhibits significant interannual variability. In terms of the monthly occurrence rate for individual years, while there is no fixed intra-annual pattern, February and October repeatedly appear as months with high occurrence rates, with February appearing in 4 of the 12 analyzed years and October in 5 of the 12 analyzed years, indicating that SSLs have a certain seasonal preference for late winter and autumn. This feature seems to be related to meteoric injection. The monthly occurrence rate of SSLs averaged over 2012–2024 further shows pronounced maxima in February, June, and October. Comparison with the climatology of Es layers over the same period reveals that the Es occurrence rate reaches its annual maximum in June, while the SSL occurrence rate also shows a local peak in June, indicating that Es layers may play an important role in SSL formation. Nevertheless, the high SSL occurrence rates in February and October indicate that other physical and chemical processes also play important modulating roles. Statistical analysis of SSL over local time indicates that SSLs mostly occur between 21:00 and 01:00 LT, with both onset and peak times concentrated in this interval, durations mostly of the order of tens of minutes, and peak heights at 94–96 km. Overall, SSLs over Hainan exhibit significant interannual variability and a weak seasonal preference, and their formation is jointly influenced by direct meteoric injection, Es-related ionospheric processes, and neutral Na chemistry. Full article

Comprehensive utilization of crop straw (CUCS) is a critical pathway toward sustainable agricultural development, synergizing food security and carbon neutrality goals. However, there remains a lack of systematic empirical evidence regarding its macro-level productivity associations and the conditions under which they materialize. Based on China’s provincial panel data from 2011 to 2023, this paper takes the CUCS pilot policy launched in 2016 as a quasi-natural experiment and employs the difference-in-differences (DID) model to examine the association between CUCS and grain yield, along with its moderating factors and environmental co-benefits. This study yields four main findings. First, CUCS is associated with higher grain yield in pilot regions, and this finding remains robust after a series of endogeneity and robustness checks. Second, the positive association between CUCS and grain output appears to be moderated by fiscal support and innovation–entrepreneurship. The relationship is more pronounced in regions with higher fiscal expenditures on agriculture and environmental protection, as well as more agricultural patents and agricultural enterprises. Third, heterogeneity analysis suggests that the CUCS–grain output association tends to be stronger in regions with richer groundwater resources and more agricultural meteorological observation stations. Fourth, extended analysis indicates that CUCS is also associated with lower particulate matter and agricultural carbon emissions, a pattern consistent with synergistic environmental benefits. By integrating economic and environmental dimensions into a unified analytical framework, this study provides empirical evidence on the contribution of comprehensive straw utilization to grain output and highlights the enabling role of fiscal and innovation environments. These findings offer integrated evidence from China for the policy evaluation of climate-smart agriculture and contribute to the broader sustainable development agenda. Full article

Urban canals in rapidly growing cities often experience water quality deterioration from wastewater inputs and stormwater runoff, with impacts that vary across space and time. This study aimed to quantify five-year spatiotemporal patterns of key water quality indicators in the Mae Kha Canal, Chiang Mai, Thailand, and to identify persistent degradation hotspots to support management. Monthly longitudinal data (2020–2024) for dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, and water temperature (WT) were collected at 18 monitoring stations and analyzed using locally estimated scatterplot smoothing (LOESS) for trend exploration, repeated-measures correlation for association between parameters, and Geographic Information Systems-based spatiotemporal mapping using inverse-distance-weighted interpolation. Results showed that DO remains very low across much of the canal, while BOD was persistently high; pH was relatively stable near neutral and WT exhibited clear seasonal variability. Spatial mapping indicated that upstream sections generally had better quality, whereas the urban middle reaches repeatedly exhibited hotspots of low DO and high BOD. BOD and DO levels positively correlate with pH level (p < 0.001). In conclusion, the Mae Kha Canal has sustained impairment over 2020–2024, highlighting the need for strengthened wastewater control, stormwater management, and targeted remediation guided by hotspot-based monitoring. Full article

Against the backdrop of advancing the “dual carbon” goals (carbon peaking and carbon neutrality), the “fishery–photovoltaic complementary” model—integrating solar power generation with salt pan production—has been widely adopted in Tianjin. However, large-scale photovoltaic (PV) facility construction exerts complex impacts onsalt panns, a wetland ecosystem of unique ecological value, by blocking sunlight, altering local microclimates, and regulating water evaporation. Currently, systematic field studies on the comprehensive effects of PV facilities onsalt pans ecosystems remain scarce, particularly those focusing on impacts on primary producers and key environmental factors. Pond sediments harbor the densest and most diverse aquatic microbial communities. In this study, sediment samples were collected from four typical ponds in Tianjin’salt panan region in April, July, and September 2024. Post sample processing, multiple statistical analyses were conducted, including alpha diversity indexing, species abundance clustering, and beta diversity analysis (non-metric multidimensional scaling, NMDS). The results showed the following: (1) Microbial communities existed in both PV-equipped and non-PV areas, indicating no significant correlation between PV presence and alpha diversity indices. (2) Species and genus compositions aggregated in PV-equipped areas with generally consistent community structures, whereas they displayed high dispersion in non-PV areas. This regulatory effect of PV facilities was relatively stable, with deviations only at a few sampling sites, confirming that PV presence significantly affects community composition patterns at both species and genus levels. (3) Cluster heatmap analysis revealed distinct seasonal variations in clustering relationships between sampling stations and microbial genera. Among dominant genera, only Desulfotignum was unaffected by PV facilities or seasonal changes, while the distribution of other dominant genera was significantly influenced by PV construction. Full article

The role of power converters in modern electrical infrastructure (such as electric vehicle charging stations, battery energy storage systems and photovoltaic energy systems) has become critical. Given the high reliability required by these converters, continuous condition monitoring for predictive maintenance is mandatory. Traditional SCADA and HMI systems often face scalability bottlenecks and lack the flexibility in data aggregation and storage scalability required for long-term predictive maintenance. This paper proposes a scalable, containerized microservices-based architecture for degradation tracking and State-of-Health (SoH) monitoring in power conversion systems. The architecture features a decoupled four-layer structure, utilizing dedicated UDP servers for low-latency data ingestion, RabbitMQ (AMQP) for robust message routing, and a NoSQL (MongoDB) storage layer with a FastAPI interface. The proposed system was validated using a Hardware-in-the-Loop (HiL) setup with a Typhoon HIL606 simulator monitoring an Active Neutral Point Clamped (ANPC) power converter. Experimental stress tests demonstrated a Packet Delivery Ratio (PDR) of 1.0 at ingestion rates up to 100 messages per second (msgs/s) per node. The system exhibits transmission and processing overheads consistently below 5 ms, ensuring timely data availability for tracking thermal dynamics and parametric aging trends. This operational performance significantly exceeds the nominal requirement of 2 msgs/s for condition monitoring, ensuring robust data integrity. Finally, this modular approach provides the horizontal scalability necessary for Industry 4.0 integration, offering a high-performance framework for long-term health monitoring in modern power electronics. Full article

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below $-20$ °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at $-5$ °C (over 25.9% even at $-35$ °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. Full article