Search Results (39,622)

As vital carbon pools within terrestrial ecosystems, wetlands store sediment organic carbon (SOC), a process influenced by plant communities, seasonal variations, and wetland types. Microbial communities, fundamental to wetland ecosystems, are hypothesized to regulate carbon storage. We investigated sediment microbial communities and carbon storage in different seasonal and plant conditions in two types of wetlands. Sediment organic carbon, the associated environmental factors, and microbial community characteristics were detected to explore the impacts of seasons and plants on SOC. Plants and seasons significantly influenced the content of SOC in constructed wetland, while only altered the content of dissolved organic carbon (DOC) in river wetland. In river wetland, plants increased the microbial function of Amino Acid Metabolism through the input of exogenous dissolved organic carbon (DOC) and the effect on moisture content. The functional traits of Carbohydrate Metabolism in sediment were higher in river wetland than that in constructed wetland. Our results indicated that plants and seasons influenced SOC in wetlands through their effects on sediment microbial community and function. Compared with the river wetland, the constructed wetland had more stable microbial communities and might be easier to fix organic carbon from plants. This study highlights the importance of the carbon sequestration potential of constructed wetlands due to the stable microbial communities. Full article

Wastewater containing high pollutant loads is discharged into the municipal sewerage system by industrial facilities operating within the industrial zones of Konya, Türkiye. Although regulations mandate that wastewater be treated to comply with specified discharge standards, some facilities lack pretreatment systems due to high capital and operational costs, while existing systems experience operational deficiencies. As a consequence, operational disruptions and increased environmental risks occur within the municipal sewerage system. Periodic sampling and inspection activities conducted by municipal authorities are becoming increasingly challenging for effective monitoring and evaluation as the number of facilities increases. In this study, a Geographic Information System (GIS)-based approach was developed to enhance monitoring effectiveness, and industrial wastewater quality data were analyzed using ArcGIS Pro 2.9 software (Esri, Redlands, CA, USA) to generate spatial pollution distribution maps. Samples were collected from five industrial facilities and four sewer junction points located in the Hacıyusufmescit, Emirgazi, and Fetih neighborhoods, where odor problems are frequently reported, during the 2022–2023 period. It was determined that COD (24,960 mg/L), BOD (2970 mg/L), and oil and grease (254 mg/L) concentrations significantly exceeded the regulatory discharge limits, particularly during the summer season. The results demonstrate that GIS-based monitoring systems constitute an effective tool for the early detection of pollution and odor-related problems at the urban scale, for the systematic management of control processes, and for the facilitation of evidence-based decision-making. Full article

This study investigated the effects of different straw return practices—no-tillage with straw mulching (SM), shallow tillage with straw incorporation (SS), and deep tillage with straw incorporation (DS)—on the content and structural characteristics of soil water-soluble organic carbon (WSOC) under a maize–soybean rotation in the black soil region in the Northeast of China. Compared with SM, SS and DS increased WSOC content by 39.0% and 28.8% in the 0~20 cm layer (p < 0.05), and by 28.4% and 8.5% in the 20–40 cm layer, respectively. Deep tillage combined with straw return reduced the WSOC/SOC ratio. The DS treatment exhibited the highest levels under maize straw incorporation, while SM treatment showed the highest levels under soybean straw incorporation. Spectral indices in both maize and soybean seasons—including the fluorescence index (FI, ranging from 1.53 to 1.57 in the maize season and from 1.53 to 1.67 in the soybean season), biological index (BIX, ranging from 0.84 to 1.79 in the maize season and from 0.61 to 0.74 in the soybean season), and humification index (HIX, ranging from 0.51 to 0.79 in the maize season and from 0.84 to 0.97 in the soybean season)—collectively indicated that WSOC predominantly consisted of microbially processed organic matter with a low degree of humification. PARAFAC modeling resolved two fluorescent components in maize season: C1 (humic acid-like substances, accounting for 34.8–54.9%) and C2 (Tryptophan-like substance, accounting for 45.1–65.2%), and two components in the soybean season: C1 (humic-like substances, 51.0–53.7%), and C2 (Fulvic acid-like substance 46.3–49.0%). Overall, deep straw return promotes soil humification but increases the structural complexity of WSOC. This systematic investigation provides mechanistic insights into how straw return practices regulate the quantity and quality of labile carbon pools in agricultural ecosystems over time. Full article

Mauritius, as a Small Island Developing State (SIDS), depends heavily on tourism and is therefore exposed to external shocks; this study examines how the sector’s performance and value capture evolved from 2010 to 2025, with particular attention to the COVID-19 disruption and subsequent recovery. The analysis uses only secondary data, combining arrivals and source-market breakdowns published by the Ministry of Tourism with the monthly series of gross tourism earnings released by the Bank of Mauritius. Trends and seasonality are described for both arrivals and earnings, and three indicators are derived to support interpretation: revenue per arrival (as a proxy for value capture), the intensity of seasonality, and the concentration of source markets. The results document the magnitude of the pandemic-related break, trace the timing of the rebound, and show how value capture and market concentration shifted between the pre- and post-COVID periods. The paper concludes by discussing the implications for resilience in island destinations, highlighting the need for diversification and higher-value positioning, and proposing a replicable monitoring approach that can be updated as new official data become available. Full article

Lake Villarrica, located in southern Chile, is a vital freshwater resource whose ecological status requires continuous evaluation. Chlorophyll-a (Chl-a) is a key indicator of phytoplankton biomass and estimating it using satellite sensors enables efficient and large-scale monitoring. This study compared the performance of different empirical models based on reflectance data obtained from atmospherically corrected satellite images using ACOLITE software (Generic Version 20231023.0), calibrated with in situ measurements of Chl-a collected during the spring and summer seasons between 2014 and 2024. For each sensor, the best combination of spectral bands was selected, and retrieval models were generated using a bootstrapping procedure with 1000 iterations to obtain robust regression coefficients; the final models were defined using the median of these coefficients. The top-performing model for Landsat-8 and 9 was based on a blue-red band combination (R² = 0.79, RMSE = 2.1 µg·L⁻¹, MAE = 1.2 µg·L⁻¹, n = 74). In contrast, the optimal model for Sentinel-2A utilized green and blue bands, yielding higher precision (R² = 0.75, RMSE = 0.8 µg·L⁻¹, MAE = 0.72 µg·L⁻¹, n = 112). In general, the results obtained through remote sensing reveal a gradual increase in Chl-a levels over the last decade, reflected in recurrent summer biomass increases primarily along the shoreline near the urban area of Pucón and in the vicinity of the Pucón River inflow into Lake Villarrica. These results support the development of an operational satellite-based monitoring framework for inland lake water quality assessment. Full article

This study evaluates the performance of the Ain El Houtz wastewater treatment plant (WWTP) in Tlemcen, Algeria, by applying a fuzzy logic-based framework to multi-scale temporal data. A total of 2192 effluent samples collected between 2020 and 2022 were analyzed for Biochemical Oxygen Demand over five days (BOD₅), Chemical Oxygen Demand (COD), dissolved oxygen (O₂), pH, nitrate (NO₃⁻), phosphate (PO₄³⁻), and temperature. Expert-derived parameter weights were integrated into a Mamdani fuzzy inference system to compute a Fuzzy Water Quality Index (FWQI). Sensitivity analysis was conducted to assess the robustness of the model to variations in weights and membership functions. Results revealed satisfactory performance in 2020 and 2022 (FWQI > 85%), while 2021 showed critical degradation (FWQI ≈ 50%), unrelated to seasonal climate variability. Comparison with raw parameters and regulatory thresholds validated the FWQI’s ability to capture operational fluctuations. This work represents the first multi-scale fuzzy logic application to wastewater treatment monitoring in Algeria, highlighting both the potential and limitations of fuzzy indices in semi-arid contexts. The approach provides a transferable decision-support tool for improving effluent quality management and guiding corrective actions in WWTPs. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

Climate change and rising global temperature values lead to a cascade of effects on human health and well-being. Methodologies for assessing thermal conditions and identifying areas with increased thermal stress are important for enhancing the quality of life in urban environments. This study is aimed at developing a methodology that combines high-resolution simulation data with surface meteorological observations for application in urban thermal stress assessment. Eleven urban public sites within the metropolitan area of Athens, Greece (i.e., squares and parks) were simulated using the three-dimensional microclimate model ENVI-met. The model was validated using micrometeorological data from field campaigns conducted in summer, autumn and winter. The validation results confirmed that ENVI-met showed satisfactory performance for further research analysis. Subsequently, Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) were calculated using data from weather stations operated by the National Observatory of Athens and the Hellenic National Meteorological Service. PET and UTCI were then spatially interpolated using a mixed modeling and kriging method, with parameters optimized based on statistical validation metrics derived from the ENVI-met simulations. Finally, seasonal bioclimatic maps were produced to identify areas experiencing unfavorable thermal conditions. The spatial analysis revealed distinct seasonal patterns in the distribution of unfavorable thermal conditions across the Athens metropolitan area. Full article

As climate change worsens, irrigation modernization has become critical for better water distribution and maintaining rice production in the face of increasing water constraints. However, there remains a gap in quantification regarding the environmental trade-offs between pump-managed and gravity-based irrigation systems, especially in integrated assessments that relate economic performance, carbon emissions, and water use. This study used an integrated framework of water productivity (WP), consumptive water footprint (WF), carbon footprint, and eco-efficiency to compare gravity-based and pump-managed systems in the Don Chedi Operation and Maintenance Project, Thailand, from 2021 to 2023. The results showed no significant differences in WP and WF between systems. WP averaged 0.39 kg m⁻³ during the wet seasons and 0.54 kg m⁻³ during the dry seasons, while the WF averaged 2517 m³ t⁻¹ and 1854 m³ t⁻¹, respectively. These findings indicate that pump-managed irrigation enhanced operational flexibility and yield stability but did not substantially improve water use efficiency. However, compared with the gravity-based system, the pump-managed system produced much greater carbon emissions, with total carbon footprints ranging from 1.252 to 1.333 tCO₂eq t⁻¹, or five times higher in the irrigation process. Eco-efficiency metrics rose by up to 8.11% despite this environmental burden, indicating enhanced economic resilience amid fluctuating water conditions. These results show a recurring trade-off between low-carbon agricultural development and irrigation modernization. The study therefore emphasizes the importance of integrating renewable energy and low-carbon technologies into pump-based irrigation systems to support climate-resilient and sustainable agricultural transitions. Full article

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

Urban stormwater is often viewed as a drainage problem rather than a local water resource, even in areas where runoff capture could simultaneously reduce flooding and promote the reuse of non-potable water. This study develops, installs, and field-tests a decentralized, school-scale stormwater harvesting system that relocates permeable concrete, transforming it from a passive infiltration surface into a purpose-built capture and pretreatment interface. The system integrates a 3 m × 3 m permeable concrete slab with load-bearing sections, an impermeable underlayer to ensure controlled flow, a double-compartment sump for staged sedimentation and hydraulic damping, sequential filtration with sand/gravel and activated carbon, and a 5000 L storage tank. The prototype was implemented at CETis 105 in Querétaro, Mexico, and evaluated during its commissioning and operation in the 2023 rainy season. Field operations demonstrated reduced ponding in the catchment area and a reliable flow of runoff to the pretreatment units. In the sump compartments, apparent color decreased from 221 to 59 Pt-Co, turbidity from 46.8 to 12.9 NTU, and COD from approximately 30–35 to 15–18 mg·L⁻¹, corresponding to approximate pretreatment reductions of 73.3%, 72.4%, and 40–57%, respectively, before post-filtration. Conversely, the elevated pH, electrical conductivity, and total dissolved solids indicated interaction with fresh cementitious materials and dissolved ionic residues during initial operation, highlighting the need for curing, initial washing, and post-filtration verification before declaring compliance with reuse requirements. Therefore, the results support the feasibility of the proposed configuration as a decentralized, low-infrastructure architecture for localized runoff control and pretreatment, while confirming that full reuse validation still requires microbiological and post-filtration evaluation. The study provides a field-proven system design adaptable to school campuses and similar institutional environments for distributed stormwater management and non-potable water storage. Full article

This paper investigates solar energy production forecasting at a monthly temporal resolution using a pooled neural network framework applied to the Chikalov photovoltaic systems in southwestern Bulgaria. The study considers several related PV installations with unequal time-series lengths and formulates the forecasting task as one-step-ahead prediction of the next monthly total energy yield, measured in kWh, in a global pooled setting. Two complementary neural architectures are compared: a multilayer perceptron (MLP), which serves as a nonlinear feed-forward benchmark based on lagged observations and seasonal descriptors, and a gated recurrent unit (GRU), which explicitly models sequential temporal dependence. In both cases, seasonality is represented through cyclical calendar encodings, while model selection is performed by chronological hyperparameter search using a separate validation block. Forecast accuracy is assessed by RMSE, MAE, coefficient of determination (R²), MAPE, and sMAPE, and uncertainty is quantified through validation residual prediction intervals. The results show that the MLP achieves stronger validation performance, whereas the GRU provides better final out-of-sample generalization after refitting on the combined training and validation data. For both architectures, the best configurations are obtained with a 12-month input horizon, indicating that one full annual cycle contains the most informative memory for forecasting monthly aggregated photovoltaic energy yield in the considered dataset. After refitting on the combined training and validation data, the GRU achieved the best final out-of-sample performance, with RMSE = 296.38 kWh, MAE = 213.16 kWh, R² = 0.9231, MAPE = 7.52%, and sMAPE = 7.49%. Overall, the findings demonstrate that pooled neural modeling is an effective framework for monthly PV production forecasting and can provide practically useful support for sustainable energy planning, monitoring, and optimization. Full article

The integration of quantitative mathematical methods and artificial intelligence into agricultural monitoring systems represents a critical pathway toward data-driven decision-making in the contemporary precision agriculture economy. This study applies mathematical modeling and quantitative analysis to temporal NDVI (Normalized Difference Vegetation Index) raster datasets from six agricultural parcels in the Dobrogea region of Romania (2017 growing season), with the objective of supporting agronomic performance evaluation and operational decision-making. Higher-order statistical descriptors—variance, kurtosis, and skewness—were extracted from XML raster files and subjected to comprehensive visual analytics using kernel density estimation, three-dimensional surface modeling, and polynomial regression in Python. A feedforward Artificial Neural Network (ANN) with a 4-15-9-3-1 architecture was trained under four activation function and solver combinations (tanh/ReLU × Adam/SGD) to classify satellite sensing-date authenticity (is_sensing_date), a key data-quality indicator for operational crop monitoring workflows. Permutation-based feature importance analysis confirmed that variance is the dominant mathematical predictor (~35.8%), followed by kurtosis (~31.5%) and skewness (~26.6%), while the temporal month variable contributed least (~6.1%). The tanh–SGD configuration yielded the best training–test error balance for most individual datasets, while tanh–Adam performed optimally on the combined dataset. The inverse mathematical relationship between variance and kurtosis, and the direct co-variation between kurtosis and skewness, were consistent across all parcels, demonstrating the universality of these quantitative patterns in agricultural remote sensing data. These findings establish a replicable mathematical modeling framework applicable to predictive analytics, risk assessment of data quality, and performance evaluation in agricultural decision-making systems, with direct relevance to digital transformation strategies in the agri-economy sector. Full article