Search Results (790)

Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or released as a self-contained deliverable. This paper presents an open dataset that combines four sensor-derived sources covering the whole of Spain over the period from 2022 to 2024: hourly air quality observations from the 588 stations of the national network operated by the Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO), daily meteorological records from the Agencia Estatal de Meteorología (AEMET), daily mobility indicators derived from anonymised mobile telephony events published by the Ministerio de Transportes y Movilidad Sostenible (MITMA) at the municipality level, and a calendar of national and Autonomous Community public holidays. The processing pipeline harmonises sources that differ in temporal resolution, spatial codification and quality regime into a tidy hourly table indexed by station and timestamp, with a fixed feature schema of 56 variables per record. Air quality stations are paired with their nearest AEMET station through a three-tier distance rule, and the daily exogenous features are aligned to the air quality time axis through a two-variant temporal-alignment scheme (lag-and-expand to the hourly grid for the hourly release, same-calendar-day join for the daily release). A complementary daily resolution variant of the dataset is also released, with 72 columns and the same feature schema except for the air quality block, which is aggregated to daily mean, minimum and maximum. The integrated dataset contains approximately 15 million hourly records across the 588 stations and is released on Zenodo (DOI 10.5281/zenodo.20196221) under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. It is intended as a substrate for research on air quality forecasting, environmental epidemiology and multi-source data fusion at the nationwide scale. Full article

The Kubuqi Desert serves as a critical zone for both renewable energy development and ecological management in China. Large-scale photovoltaic (PV) deployment has fundamentally altered the regional underlying surface, impacting near-surface wind–sand dynamics. To elucidate these disturbance mechanisms, we selected three representative surfaces—a PV area, a resource base, and Qixing Lake—and conducted field observations from September to December 2023 using meteorological towers and wind erosion sensors. Results indicate that all surfaces significantly attenuated near-surface wind speeds by over 30% through modified flow field structures. A strong linear positive correlation existed between wind speed and friction velocity (R² ≈ 0.99). Notably, for the same friction velocity, the actual wind speed required to initiate sand movement was lowest in the PV zone (high k) and highest at Qixing Lake (low k), signifying enhanced surface stability due to PV infrastructure and moisture. Threshold analysis revealed distinct initiation speeds: >6.0 m·s⁻¹ in peripheral quicksand, >4.3 m·s⁻¹ in inter-panel zones, and >4.6 m·s⁻¹ beneath panels. The tilted PV panels accelerate airflow downward, generating cyclonic vortices that intensify sand particle impacts under and between panels. This study reveals the tri-dimensional mechanism of wind regulation–sand suppression–stability enhancement, providing theoretical support for mitigating wind–sand disasters while advancing green energy in desert regions. Full article

Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment. Full article

Climate change threatens ecosystem stability in arid Central Asia, yet regional vegetation responses remain poorly resolved at the operational scale of land-use policy. We integrated long-term meteorological records (2000–2024) from Kazhydromet with Landsat surface-reflectance imagery for four epochs (2010, 2015, 2020, 2025) across the five administrative regions of Southern Kazakhstan (≈710,000 km²). After cross-sensor harmonization of Landsat 5 TM and Landsat 8 OLI, dense vegetation cover (NDVI > 0.4) increased modestly across all regions, with the cumulative area growing from 9.09 to 9.60 million hectares (+5.6%) and a transient 2020 minimum linked to the 2018–2020 drought. Per-region OLS trend slopes were not statistically significant at p < 0.05, given the four-epoch sampling (n = 4). A composite Biodiversity–Climate Sensitivity Index (BCSI), constructed from four normalized components (temperature trend, precipitation deficit, NDVI trend, and the coefficient of variation of dense-vegetation cover as a biodiversity–vulnerability proxy), identifies the lower Syr Darya floodplain and former Aral Sea margins as the most sensitive territories and the Northern Tien Shan as the most resilient. The framework provides an operational evidence base for climate-adaptive conservation aligned with SDG 13 and SDG 15. Full article

Organic coatings are the most widely utilized corrosion protection strategy for metallic materials. Nevertheless, they can degrade over time through the effects of UV, moisture, and corrosive media, compromising their protective performance. In order to monitor the coating performance for predictive maintenance, an electrochemical sensor was fabricated using 6005A aluminum alloy and coated with four coating systems: (1) epoxy primer, (2) epoxy primer/polyurethane topcoat, (3) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin, and (4) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin/acrylic coat. The sensors and corresponding coupon samples were exposed for 24 months at two sites in Thailand: Pathum Thani (PTI, suburban) and Chon Buri (CBI, mild marine). Electrochemical impedance spectroscopy (EIS) measurements were conducted at a fixed frequency of 117 Hz, synchronized with on-site meteorological monitoring. Impedance data were converted into a coating aging index (AI) to quantitatively assess the coating degradation. Coating deterioration was observed in PTI as early as at 6 months of exposure. Machine learning modeling revealed that cumulative rainfall was the dominant environmental factor influencing coating degradation. The single epoxy primer layer exhibited the poorest durability, while the incorporation of polyurethane, aluminum-pigmented polyester, and acrylic layers significantly prolonged the protective service life of the coating system. Full article

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM_2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

Reliable short-term agroclimatic forecasting is essential for precision agriculture, irrigation planning, disease-risk assessment, and microclimatic decision support. However, field-deployed sensor systems often operate under degraded data conditions, including missing measurements, noise, temporal interruptions, and limited local computational resources. These constraints make it necessary to evaluate not only forecasting accuracy under clean data, but also model robustness under realistic sensor-data degradation. The objective of this study is to compare machine-learning models for one-step-ahead agroclimatic time-series forecasting under degraded sensor-data conditions. Using a real meteorological dataset collected by a field weather station in the Dnipro region of Ukraine, twelve regression models were evaluated: Ridge Regression, Random Forest, Extra Trees, Gradient Boosting, HistGradientBoosting, Support Vector Regression, Linear SVR, KNN, PLSRegression, ElasticNet, Lasso, and MultiTaskElasticNet. The models were tested under five controlled scenarios: baseline data, missing values, additive noise, reduced training history, and combined noise–missingness degradation. Quantitatively, Ridge Regression achieved the strongest baseline temperature-forecasting performance, with MAE = 0.318 and R² ≈ 0.98 under clean data. It also maintained R² > 0.90 when trained on only 50% of the available history. Under Gaussian noise with σ = 0.05–0.10, Ridge Regression and HistGradientBoosting maintained R² values in the range of 0.95–0.97, whereas under combined degradation with σ = 0.10 and 20% missing data, HistGradientBoosting retained R² > 0.85. These findings indicate that machine-learning models differ substantially in their sensitivity to sensor-data degradation and that robustness-oriented benchmarking is necessary before selecting models for agroclimatic forecasting systems. Full article

As the initial link in the dust cycle, sand saltation directly determines the release of dust aerosols into the atmosphere. Suspended dust can modify meteorological conditions, potentially altering sand saltation characteristics, though this relationship requires further investigation. The recent deployment of automatic visibility sensors has provided new observational support for this study. Using hourly observations from five meteorological stations in the Taklamakan Desert during March–August 2016–2024, together with reanalysis data, this study estimated threshold wind speeds under different dust-intensity conditions, as indicated by horizontal visibility. We analyzed sand saltation frequency and horizontal dust flux across the region under varying visibility conditions, and reassessed recent trends and drivers of dust emission. Results indicate that threshold wind speeds range between 4.67 and 4.71 m·s⁻¹, with a notable increase in wind speed when horizontal visibility falls below 5 km. Based on these thresholds, our analysis reveals significant regional differences in both dust emission frequency and flux under varying visibility conditions, with clear skies also identified as an important contributor to dust emission. Specifically, horizontal dust flux in the Taklamakan Desert showed a decreasing trend during 2016–2021 and a slight increase during 2021–2024, and this recent change in dust-emission trends may be linked to changes in atmospheric circulation and meteorological conditions. These findings provide a scientific basis for dust forecasting, early warning, disaster prevention, and mitigation in the Taklamakan Desert and its surrounding areas. Full article

Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R²) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. Full article

The expansion of climate monitoring networks has generated an increasing accumulation of end-of-life meteorological sensors, creating a specialized stream of waste electrical and electronic equipment (WEEE) that remains largely unaddressed in developing countries. This study presents a material characterization and sustainable management framework for obsolete meteorological sensors installed in automatic weather stations in Ecuador. A hybrid methodological approach was applied, combining field inventory of 16 stations, gravimetric measurements, and analysis of manufacturer technical specifications to estimate material composition and recovery potential. Results show that 65–90% of the total sensor mass consists of recyclable materials, including aluminum, stainless steel, copper, glass, and engineering polymers. A smaller fraction contains components requiring controlled management due to the potential presence of hazardous additives, such as PVC (polyvinyl chloride) elements and electronic microdevices. Based on these findings, a multi-phase management protocol is proposed, incorporating selective disassembly, material segregation, traceability mechanisms, and processing under extended producer responsibility principles. The framework supports circular economy strategies and offers a replicable model for improving sustainability in climate monitoring infrastructure and specialized WEEE management in low- and middle-income countries. Full article

Air pollution associated with public transport systems constitutes a critical yet highly heterogeneous component of urban exposure and represents an important challenge for sustainable urban mobility and environmental health governance. Commuters and transport workers are frequently subjected to pollutant concentrations that exceed those reported by ambient background monitoring networks. This review provides a comprehensive synthesis of the global scientific literature on air quality in public transport microenvironments—including buses, bus stops, terminals, and underground stations—through a multidimensional analytical framework that considers climatic classification, socio-economic context, meteorological drivers, transport microenvironment typology, sampling strategies, analytical techniques, and exposure metrics. A large body of peer-reviewed studies published worldwide was examined to identify dominant patterns, methodological trends, and persistent knowledge gaps. Across regions, the evidence consistently reports elevated concentrations of particulate matter (PM_2.5, PM₁₀, and ultrafine particles) and traffic-related gaseous pollutants, particularly within confined or poorly ventilated environments and during peak traffic periods. Marked geographical, climatic, and socio-economic imbalances are evident, with most studies conducted in temperate and tropical climates and in countries with very high or high Human Development Index, whereas arid, continental, and low-HDI regions remain substantially underrepresented. From a methodological perspective, the literature is dominated by short- to intermediate-term monitoring campaigns relying on active sampling, mobile measurements, and increasingly calibrated low-cost sensors, while long-term stationary observations and standardized integrative monitoring frameworks remain scarce. Although advanced analytical approaches—such as chemical characterization, environmental magnetism, receptor modeling, computational fluid dynamics, and inhaled dose assessment—are increasingly applied, their systematic integration remains limited. Overall, this review reveals persistent methodological, geographical, and conceptual gaps and highlights the urgent need for standardized, interdisciplinary, and long-term monitoring strategies to improve exposure assessment and support evidence-based mitigation policies and sustainable urban transport planning aimed at reducing health risks associated with public transport-related air pollution. Full article

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Against the background of accelerated green energy development and the deep integration of intelligent sensing technologies, wind power forecasting is evolving toward a multimodal sensor collaborative perception paradigm within nonlinear multi-source integrated energy systems. To address the limitations of conventional methods, including the lack of dynamic importance modeling and constrained stability under complex wind conditions, a forecasting framework based on multimodal sensor importance perception is proposed. This study emphasizes the framework’s role in decoding the complex nonlinear dependencies between atmospheric drivers and turbine responses. Through a multimodal feature encoding architecture, unified temporal representations of meteorological environments and turbine operational states are established. A sensor-importance-aware attention mechanism and a cross-modal relational modeling strategy are introduced to adaptively allocate contributions under varying contexts. Furthermore, prediction compensation and uncertainty characterization modules are integrated to enhance robustness. Systematic experiments on real-world multi-source data validate the method. Overall performance comparisons demonstrate that MAE, RMSE, and MAPE reach 30.48, 42.37, and 9.16 percent, respectively, with the coefficient of determination $R^2$ achieving 0.957, significantly outperforming the Transformer baseline. In multi-horizon tasks, the model exhibits superior error accumulation suppression, with twelve-step forecasting errors remaining at 41.27 and 56.48. These findings reveal that the framework captures the context-dependent nonlinear mapping of energy systems, providing effective technical support for green energy dispatch and intelligent sensing applications. Full article