Search Results (28)

Crop rotation is a key practice for improving soil health, reducing chemical inputs, and ensuring sustainable agricultural productivity. This review synthesizes research from major agricultural regions worldwide, including North America, Europe, South America, South Asia, and Africa, with additional case studies from China to illustrate regional applications. This study presents a streamlined framework that integrates climate adaptability, crop combinations, yield-enhancing mechanisms, technological support, and regional optimization. By analyzing plant–soil–microbe interactions—including improvements in soil physical structure, nutrient cycling, microbial processes, and suppression of pests and diseases—we elucidate how rotation systems enhance the yield of subsequent crops. Precision agriculture technologies, such as variable-rate fertilization and remote sensing, have been shown to improve resource use efficiency and reduce labor input in certain cropping systems. Diversified crop rotations can substantially offset direct greenhouse gas emissions through enhanced soil carbon sequestration. The design of region-specific recommended rotation patterns should follow the principles of resource matching, stable yield and efficiency enhancement, and sustainability. This framework provides a practical reference for designing region-specific rotation systems and advancing sustainable agricultural development. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

Landfills are complex repositories where macroplastics degrade into MPs. This review examines mechanical, chemical, and biological pathways of plastic fragmentation, as well as the occurrence, characteristics, and removal efficiency of MPs in landfill leachate. We also explore the landfill plastisphere from the perspective of this complex matrix, considering how plastic surfaces and microbial life may potentially converge to form a key biogeochemical interface that could influence carbon and nitrogen transformations. The plastisphere’s complex surface structure drives microbial differentiation. Given its established links to GHG production in soil and water, we propose it likely represents a key contributor to GHG emissions in the more complex landfill environment. To bridge this conceptual gap, we review a mathematical scaffolding encompassing biofilm growth, polymer degradation kinetics, and gas flux, which can as a theoretical baseline requiring future in situ parameterization to evaluate plastisphere-driven biogeochemical interactions. Building on recent advances in monitoring and remote sensing technologies, including IOT networks, UAV imagery, and AI analysis, we outline a low-carbon landfill framework designed to optimize operational controls. This framework is described to simultaneously mitigate MP release and reduce GHG emissions, lowering carbon footprints. Amid surging plastic pollutants, this review underscores the necessity of holistic, integrated mitigation strategies. Full article

Emerging contaminants (ECs) are trace-level chemical and biological compounds detected in the environment, particularly in water, including personal care products, microplastics, nanoplastics, antibiotic resistance genes, etc., which have the potential to endanger ecosystems and human health. Forensic techniques, which are used to detect and trace the sources of ECs with specificity and accuracy, like gas and liquid chromatography coupled with mass spectrometry, Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, isotope ratio mass spectrometry, remote sensing, geographical information systems, and statistical and machine learning approaches are discussed in this study. The present analysis also outlines the types of emerging contaminants, along with their possible sources, including domestic and municipal wastewater, wastewater from healthcare institutions, urban runoff and stormwater, industrial and commercial discharges, effluents from agricultural and animal husbandry, and pollution from recreational activities in water. The review concludes by emphasizing the future need for real-time detection systems, integrated data analysis, and stronger regulatory frameworks to manage ECs more effectively. Full article

The transition toward Industry 4.0 requires advanced sensing platforms capable of delivering real-time, high-fidelity data under extreme industrial conditions. Thin-film sensors, leveraging both photonic and functional approaches, are emerging as key enablers of this transformation. By exploiting optical phenomena such as Fabry–Pérot interference, guided-mode resonance, plasmonics, and photonic crystal effects, thin-film photonic devices provide highly sensitive, electromagnetic interference-immune, and remotely interrogated solutions for monitoring temperature, strain, and chemical environments. Complementarily, functional thin films including oxide-based chemiresistors, nanoparticle coatings, and flexible electronic skins extend sensing capabilities to diverse industrial contexts, from hazardous gas detection to structural health monitoring. This review surveys the fundamental optical principles, material platforms, and deposition strategies that underpin thin-film sensors, emphasizing advances in nanostructured oxides, 2D materials, hybrid perovskites, and additive manufacturing methods. Application-focused sections highlight their deployment in temperature and stress monitoring, chemical leakage detection, and industrial safety. Integration into Internet of Things (IoT) networks, cyber-physical systems, and photonic integrated circuits is examined, alongside challenges related to durability, reproducibility, and packaging. Future directions point to AI-driven signal processing, flexible and printable architectures, and autonomous self-calibration. Together, these developments position thin-film sensors as foundational technologies for intelligent, resilient, and adaptive manufacturing in Industry 4.0. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

Low-carbon chemical fires pose significant hazards, and remote sensing of high-temperature gas emissions from these fires is a critical method for identifying and assessing their environmental impact. Analyzing the spectral characteristics of gases produced by low-carbon chemical pool fires and developing spectral radiation models can establish a foundation for remote pollution monitoring. However, such studies remain scarce. Using a custom-built high-temperature gas spectroscopy platform, this study extracts spectral features of gases emitted by low-carbon chemical pool fires. We investigate spectral interference mechanisms among combustion products and develop a high-precision spectral radiation model to support remote fire pollution monitoring. Experimental results reveal distinct spectral bands for key gases: CO₂ peaks near 2.7 μm and 4.35 μm, SO₂ at 4.05 μm, 7.5 μm, and 9.0 μm, NO at 5.5 μm, and NO₂ at 3.6 μm and 6.3 μm. The proposed spectral radiation model accurately simulates the position and shape of spectral peaks. For carbon disulfide and acetonitrile combustion products, the model achieves prediction accuracies of 83.4–96.9% and 79.2–95.3%, respectively. Full article

Conservation agriculture (CA) is widely recognized as the cornerstone of sustainable agriculture. It prioritizes minimizing soil disturbance, maintaining permanent soil cover, and diversifying crop species to restore soil health and ecosystem resilience. This review synthesizes the effects of CA on the soil’s physical–chemical and biological properties. It demonstrates its effectiveness in improving soil structure, enhancing organic carbon sequestration, promoting microbial activity, increasing water-use efficiency, and reducing erosion and nutrient losses. The paper then highlights the broad environmental, economic, and social benefits of CA. These include biodiversity conservation, reduced greenhouse gas emissions, improved yields, and increased food system resilience. The review explores the synergistic role of technological innovations such as precision agriculture, remote sensing, and digital tools in scaling CA for higher productivity and sustainability. The review then examines how socioeconomic conditions, institutional frameworks, and policy interventions shape CA adoption and impact. Despite its growing adoption, CA’s successful implementation will require strategies adapted for local needs, capacity-building, and supportive, inclusive policies. Finally, the review identifies key CA research gaps and future directions. This provides a comprehensive foundation to advance CA as a climate-smart, resilient, and sustainable pathway to ensure global food security and environmental stewardship. Full article

Remote sensing imaging technology is one of the safest and most effective tools for gas leakage monitoring in chemical parks, as it enables fast and accurate access to detailed information about the gas cloud (e.g., volume, distribution, diffusion, and location) in the case of gas leakage. While multi-spectral imaging systems are commonly used for hazardous gas leakage detection, efforts to realize the three-dimensional reconstruction of gas clouds through data obtained from multi-spectral imaging systems remain scarce. In this study, we propose a method for realizing the three-dimensional reconstruction of gas clouds with only two multi-spectral imaging systems; in particular, the two multi-spectral imaging systems are used to simultaneously observe the three-dimensional space with gas leakage and reconstruct gas cloud images in real time. A geometric method is used for the localization in the monitoring space and the construction of a three-dimensional spatial grid. The non-axisymmetric inverse Abel transform (IAT) is then applied to the extracted gas absorbance images in order to realize the reconstruction of each layer, and these are then stacked to form a 3D gas cloud. Through the above measurement, identification, and reconstruction processes, a 3D gas cloud with geometric information and concentration distribution characteristics is generated. The results of simulation experiments and external field tests prove that gas clouds can be localized under the premise that they are completely covered by the field of view of both scanning systems, and the 3D distribution of the leakage gas cloud can be reconstructed quickly and accurately with the proposed system. Full article

Localized operating conditions inside boilers, heat recovery steam generators, or other large thermal systems have a huge impact on the efficiency, environmental performance, and lifetime of components. It is extremely difficult to measure species accurately within these systems due to the high temperatures and harsh environments, locally oxidizing or reducing atmospheres, ash, other particulates, and other damaging chemical species. Physical probes quickly suffer damage and are rendered nonfunctional. This work has attempted to adapt the measurement approach based on Scheimpflug light detection and ranging (S-LIDAR) for the remote sensing of gas species inside the high-temperature boiler environment. For a proof-of-concept, the detection of Raman signals of N₂, O₂, and CO₂ and their behavior with increasing temperature have been presented. Full article

The high surface area and transfer-less growth of graphene on dielectric materials is still a challenge in the production of novel sensing devices. We demonstrate a novel approach to graphene synthesis on a C-plane sapphire substrate, involving the microwave plasma-enhanced chemical vapor deposition (MW-PECVD) technique. The decomposition of methane, which is used as a precursor gas, is achieved without the need for remote plasma. Raman spectroscopy, atomic force microscopy and resistance characteristic measurements were performed to investigate the potential of graphene for use in sensing applications. We show that the thickness and quality of graphene film greatly depend on the CH₄/H₂ flow ratio, as well as on chamber pressure during the synthesis. By varying these parameters, the intensity ratio of Raman D and G bands of graphene varied between ~1 and ~4, while the 2D to G band intensity ratio was found to be 0.05–0.5. Boundary defects are the most prominent defect type in PECVD graphene, giving it a grainy texture. Despite this, the samples exhibited sheet resistance values as low as 1.87 kΩ/□. This reveals great potential for PECVD methods and could contribute toward efficient and straightforward graphene growth on various substrates. Full article

The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects. Full article

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO₄), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O₂), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle. Full article

The Michigan–Ontario Ozone Source Experiment (MOOSE) is an international air quality field study that took place at the US–Canada Border region in the ozone seasons of 2021 and 2022. MOOSE addressed binational air quality issues stemming from lake breeze phenomena and transboundary transport, as well as local emissions in southeast Michigan and southern Ontario. State-of-the-art scientific techniques applied during MOOSE included the use of multiple advanced mobile laboratories equipped with real-time instrumentation; high-resolution meteorological and air quality models at regional, urban, and neighborhood scales; daily real-time meteorological and air quality forecasts; ground-based and airborne remote sensing; instrumented Unmanned Aerial Vehicles (UAVs); isotopic measurements of reactive nitrogen species; chemical fingerprinting; and fine-scale inverse modeling of emission sources. Major results include characterization of southeast Michigan as VOC-limited for local ozone formation; discovery of significant and unaccounted formaldehyde emissions from industrial sources; quantification of methane emissions from landfills and leaking natural gas pipelines; evaluation of solvent emission impacts on local and regional ozone; characterization of the sources of reactive nitrogen and PM_2.5; and improvements to modeling practices for meteorological, receptor, and chemical transport models. Full article

The Patras Gulf Pockmark field is located in shallow waters offshore Patras City (Greece) and is considered one of the most spectacular and best-documented fluid seepage activities in the Ionian Sea. The field has been under investigation since 1996, though surveying was partially sparse and fragmentary. This paper provides a complete mapping of the field and generates new knowledge regarding the fluid escape structures, the fluid pathways, their origin and the link with seismic activity. For this, data sets were acquired utilising high-resolution marine remote sensing techniques, including multibeam echosounders, side-scan sonars, sub-bottom profilers and remotely operated vehicles, and laboratory techniques focusing on the chemical composition of the escaping fluids. The examined morphometric parameters and spatial distribution patterns of the pockmarks are directly linked to tectonic structures. Acoustic anomalies related to the presence of gas in sediments and in the water column document the activity of the field at present and in the past. Methane is the main component of the fluids and is of microbial origin. Regional and local tectonism, together with the Holocene sedimentary deposits, appear to be the main contributors to the growth of the field. The field preserves evidence that earthquake activity prompts the activation of the field. Full article