Recent applications of AI to environmental disciplines: A review

A reliable model to predict mercury solubility in natural gas components: A robust machine learning framework and data assessment

Mercury contamination in natural gas poses serious risks to production, processing, and transportation, leading to equipment corrosion, worker safety hazards, environmental pollution, and economic losses. Accurately predicting mercury solubility in methane, ethane, and multicomponent systems is essential for effective mitigation and regulatory compliance. This study employs advanced machine learning (ML) approaches, namely multilayer perceptron (MLP), generalized regression neural network (GRNN), and extra trees (ET), to estimate mercury solubility under varying pressure and temperature conditions. A high-quality dataset was used to train and validate these models, ensuring accuracy and reliability. The MLP model demonstrated the highest predictive performance with a determination coefficient of 0.9998, and a root mean square error of 1.7430 ppb. Besides, the MLP model effectively captured solubility trends, while feature importance analysis identified temperature as the dominant factor. The Leverage approach confirmed dataset reliability, with 96.5 % of data points within the trust region. This pioneering ML-based framework, the first of its kind for mercury solubility estimation, holds great industrial potential. It enables real-time monitoring, minimizes risks of equipment failure and human exposure, and supports environmental protection by reducing mercury emissions. By integrating this intelligent approach, operators can enhance safety, efficiency, and sustainability in natural gas operations.

Artificial intelligence: A key fulcrum for addressing complex environmental health issues

Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, health risks, and strategies for mitigating harmful environmental factors. Traditional EH research investigates correlations between risk factors and health outcomes through control variables, but this route is difficult to address complex EH issue. Artificial intelligence (AI) technology not only has accelerated the innovation of the scientific research paradigm but also has become an important tool for solving complex EH problems. However, the in-depth and comprehensive implementation of AI in the field of EH still faces many barriers, such as model generalizability, data privacy protection, algorithm transparency, and regulatory and ethical issues. This review focuses on the compound exposures of EH and explores the potential, challenges, and development directions of AI in four key phases of EH research: (1) data collection, fusion, and management, (2) hazard identification and screening, (3) risk modeling and assessment and (4) EH management. It is not difficult to see that in the future, artificial intelligence technology will inevitably carry out multidimensional simulation of complex exposure factors through multi-mode data fusion, so as to achieve accurate identification of environmental health risks, and eventually become an efficient tool for global environmental health management. This review will help researchers re-examine this strategy and provide a reference for AI to solve complex exposure problems.

Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation

This paper presents an automated procedure for optimizing datasets used in land/water segmentation tasks with deep learning models. The proposed method employs the Normalized Difference Water Index (NDWI) with a variable threshold to automatically assess the quality of annotations associated with multispectral satellite images. By systematically identifying and excluding low-quality samples, the method enhances dataset quality and improves model performance. Experimental results on two different publicly available datasets-the SWED and SNOWED-demonstrate that deep learning models trained on optimized datasets outperform those trained on baseline datasets, achieving significant improvements in segmentation accuracy, with up to a 10% increase in mean intersection over union, despite a reduced dataset size. Therefore, the presented methodology is a promising scalable solution for improving the quality of datasets for environmental monitoring and other remote sensing applications.

Multiview angle UAV infrared image simulation with segmented model and object detection for traffic surveillance

With the rapid development of infrared (IR) imaging UAV technology, infrared aerial image processing technology has been applied in different fields. But it is not very convenient to obtain real aerial images in some cases because of flight limitations, acquisition costs and other factors. So, it is necessary to simulate UAV infrared images by computer. This paper proposed an improved infrared aerial image simulation method based on open source AirSim. By improving the original AirSim infrared image simulation method, the simulation quality of the infrared image is improved via 3-dimensional segmented model processing. The infrared aerial images of the traffic scene with different viewing angles are simulated via the proposed method in this paper and we constructed infrared traffic scene simulation dataset (IR-TSS) containing seven types of objects. We propose the efficient EfficientNCSP-Net net for the IR-TSS dataset and use popular methods for comparative experiments. The experimental results show that the proposed EfficientNCSP-Net has an mAP50 greater than 96% for object detection on IR-TSS dataset, which is better than those of the existing methods. This paper not only contributes to research on infrared image simulations of traffic scenes, but also has referential significance in other aerial image simulation fields.

Artificial intelligence in the era of planetary health: insights on its application for the climate change-mental health nexus in the Philippines

This review explores the transformative potential of Artificial Intelligence (AI) in the light of evolving threats to planetary health, particularly the dangers posed by the climate crisis and its emerging mental health impacts, in the context of a climate-vulnerable country such as the Philippines. This paper describes the country's mental health system, outlines the chronic systemic challenges that it faces, and discusses the intensifying and widening impacts of climate change on mental health. Integrated mental healthcare must be part of the climate adaptation response, particularly for vulnerable populations. AI holds promise for mental healthcare in the Philippines, and be a tool that can potentially aid in addressing the shortage of mental health professionals, improve service accessibility, and provide direct services in climate-affected communities. However, the incorporation of AI into mental healthcare also presents significant challenges, such as potentially worsening the existing mental health inequities due to unequal access to resources and technologies, data privacy concerns, and potential AI algorithm biases. It is crucial to approach AI integration with ethical consideration and responsible implementation to harness its benefits, mitigate potential risks, and ensure inclusivity in mental healthcare delivery, especially in the era of a warming planet.

AI-driven participatory environmental management: Innovations, applications, and future prospects

The rapid advancement of Artificial Intelligence (AI) presents unprecedented opportunities for participatory environmental management. This paper explores the integration of AI technologies into participatory approaches, which engage diverse stakeholders in environmental decision-making processes. Using artificial intelligence, a corpus of 80 papers was compiled and subsequently analyzed with text mining tools. By identifying and systematizing academics' contributions to knowledge about AI-driven tools, this study also discusses the challenges and ethical considerations inherent in AI deployment, emphasizing the need for transparent, equitable, and accountable AI systems. Looking ahead, we outline future prospects for AI in participatory environmental management, focusing on the potential for AI to foster adaptive management strategies, enhance stakeholder collaboration, and support sustainable development goals.

Artificial intelligence and Eddy covariance: A review

The Eddy Covariance (EC) method allows for monitoring carbon, water, and energy fluxes between Earth's surface and atmosphere. Due to its varying interdependent data streams and abundance of data as a whole, EC is naturally suited to Artificial Intelligence (AI) approaches. The integration of AI and EC will likely play a crucial role in the climate change mitigation and adaptation goals defined in the Sustainable Development Goals (SDGs) of the Agenda 2030. To aid this, we present a scoping review in which the novelty of various AI techniques in monitoring fluxes through the EC method from the past two decades has been collected. Overall, we find a clear positive trend in the quantity of research in this area, particularly in the last five years. We also find a lack of uniformity in available techniques, due to the diverse technologies and variables employed across environmental conditions and ecosystems. We highlight the most applied Machine Learning (ML) models, over the 71 algorithms identified in the scoping review, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Support Vector Regression (SVR), and K-Nearest Neigbor (KNN). We suggest that future progress in this field requires an international, collaborative effort involving computer scientists and ecologists. Modern Deep Learning (DL) techniques such as Transformers and generative AI must be investigated to find how they may benefit our field. A forward-looking strategy must be formed for the optimal utilization of AI combined with EC to define future actions in flux monitoring in the face of climate change.

Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy

In the ongoing Anthropocene era, air quality monitoring constitutes a primary axis of European and international policies for all sectors, including Waste Water Treatment Plants (WWTPs). Unmanned Aerial Systems (UASs) with proper sensing equipment provide an edge technology for air quality and odor monitoring. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in civil engineering, environmental (water) quality assessment and lately for industrial facilities monitoring. This study constitutes a systematic review of the late advances and limitations of germane equipment and implementations. Despite their unassailable flexibility and efficiency, the employment of the aforementioned technologies in WWTP remote monitoring is yet sparse, partial, and concerns only particular aspects. The main finding of the review was the lack of a tailored UAS for WWTP monitoring in the literature. Therefore, to fill in this gap, we propose a fit-for-purpose remote monitoring system consisting of a UAS with a platform that would integrate all the required sensors for air quality (i.e., emissions of H2S, NH3, NOx, SO2, CH4, CO, CO2, VOCs, and PM) and odor monitoring, multispectral and thermal cameras for photogrammetric structural health monitoring (SHM) and wastewater/effluent properties (e.g., color, temperature, etc.) of a WWTP. It constitutes a novel, supreme and integrated approach to improve the sustainable management of WWTPs. Specifically, the developments that a fit-for-purpose WWTP UAS would launch, are fostering the decision-making of managers, administrations, and policymakers, both in operational conditions and in case of failures, accidents or natural disasters. Furthermore, it would significantly reduce the operational expenditure of a WWTP, ensuring personnel and population health standards, and local area sustainability.

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants

In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (Pele) and calorimetric power (Pcal), the collected data were divided into two groups: data with Pele and data with Pcal. Eight input variables, including frequency, power density, pH, temperature, initial concentration, solubility, vapor pressure, and octanol-water partition coefficient (Kow), and one target variable of the degradation rate constant, were selected for ML. Statistical analysis was conducted, and outliers were determined separately for the two groups. ML models, including random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGB), were used to predict the pseudo-1st-order reaction rate constants for the removal of aqueous pollutants. The prediction performance of the ML models was evaluated using different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), and R squared (R2). A significantly higher prediction performance was obtained using data without outliers and augmented data. Consequently, all the applied ML models could be used to predict the sonochemical degradation of aqueous pollutants, and the XGB model showed the highest accuracy in predicting the rate constants. In addition, the power density and frequency were the most influential factors among the eight input variables in prediction with the Shapley additive explanation (SHAP) values method. The degradation rate constants of the two pollutants over a wide frequency range (20-1,000 kHz) were predicted using the trained ML model (XGB) and the prediction results were analyzed.

Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis.

Effective degradation of bentazone by two-dimensional and three-phase, three-dimensional electro-oxidation system: kinetic studies and optimization using ANN

Bentazone is a broad-leaved weed-specific herbicide in the pesticide industry. This study focused on removing bentazone from water using three different methods: a two and three-dimensional electro-oxidation process (2D/EOP and 3D/EOP) with a fluid-type reactor arrangement using tetraethylenepentamine-loaded particle electrodes and an adsorption method. Additionally, we analysed the effects of two types of supporting electrolytes  (Na2SO4 and NaCl) on the degradation process. The energy consumption amounts were calculated to evaluate the obtained results. The degradation reaction occurs 3.5 times faster in 3D/EOP than in 2D/EOP at 6 V in Na2SO4. Similarly, the degradation reaction of bentazone in NaCl occurs 2.5 times faster in 3D/EOP than in 2D/EOP at a value of 7.2 mA/cm2. Removal of bentazone is significantly better in 3D/EOPs than in 2D/EOPs. The use of particle electrodes can significantly enhance the degradation efficiency. The study further assessed the prediction abilities of the machine learning model (ANN). The ANN presented reasonable accuracy in bentazone degradation with high R2 values of 0.97953, 0.98561, 0.98563, and 0.99649 for 2D with Na2SO4, 2D with NaCl, 3D with Na2SO4, and 3D with NaCl, respectively.

Research integrity in the era of artificial intelligence: Challenges and responses

The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Advances in amelioration of air pollution using plants and associated microbes: An outlook on phytoremediation and other plant-based technologies

Globally, air pollution is an unfortunate aftermath of rapid industrialization and urbanization. Although the best strategy is to prevent air pollution, it is not always feasible. This makes it imperative to devise and implement techniques that can clean the air continuously. Plants and microbes have a natural potential to transform or degrade pollutants. Hence, strategies that use this potential of living biomass to remediate air pollution seem to be promising. The simplest future trend can be planting suitable plant-microbe species capable of removing air pollutants like SO2, CO2, CO, NOX and particulate matter (PM) along roadsides and inside the buildings. Established wastewater treatment strategies such as microbial fuel cells (MFC) and constructed wetlands (CW) can be suitably modified to ameliorate air pollution. Green architecture involving green walls and green roofs is facile and aesthetic, providing urban ecosystem services. Certain microbe-based bioreactors such as bioscrubbers and biofilters may be useful in small confined spaces. Several generative models have been developed to assist with planning and managing green spaces in urban locales. The physiological limitations of using living organisms can be circumvent by applying biotechnology and transgenics to improve their potential. This review provides a comprehensive update on not just the plants and associated microbes for the mitigation of air pollution, but also lists the technologies that are available and/or can be modified and used for air pollution control. The article also gives a detailed analysis of this topic in the form of strengths-weaknesses-opportunities-challenges (SWOC). The strategies mentioned in this review would help to attain corporate Environmental Social and Governance (ESG) and Sustainable Development Goals (SDGs), while reducing carbon footprint in the urban scenario. The review aims to emphasise that urbanization is possible while tackling air pollution using facile, green techniques involving plants and associated microbes.

Cultivating a sustainable future in the artificial intelligence era: A comprehensive assessment of greenhouse gas emissions and removals in agriculture

Agriculture is a leading sector in international initiatives to mitigate climate change and promote sustainability. This article exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry. It also investigates an extensive range of GHG sources, including rice cultivation, enteric fermentation in livestock, and synthetic fertilisers and manure management. This research reveals the complex array of obstacles that are faced in the pursuit of reducing emissions and also investigates novel approaches to tackling them. This encompasses the implementation of monitoring systems powered by artificial intelligence, which have the capacity to fundamentally transform initiatives aimed at reducing emissions. Carbon capture technologies, another area investigated in this study, exhibit potential in further reducing GHGs. Sophisticated technologies, such as precision agriculture and the integration of renewable energy sources, can concurrently mitigate emissions and augment agricultural output. Conservation agriculture and agroforestry, among other sustainable agricultural practices, have the potential to facilitate emission reduction and enhance environmental stewardship. The paper emphasises the significance of financial incentives and policy frameworks that are conducive to the adoption of sustainable technologies and practices. This exhaustive evaluation provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable. Agriculture can significantly contribute to climate change mitigation and the promotion of a sustainable future by adopting a comprehensive approach that incorporates policy changes, technological advancements, and technological innovations.

Unearthing Earth's secrets: Exploring the environmental legacy of contaminants in soil, water, and sediments

The Earth's history is documented in human civilizations, soil layers, river movement, and quiet sediments throughout millennia. This investigation explores the significant legacy of environmental toxins in these key planet components. Understanding how ancient activity shaped the terrain is crucial as mankind faces environmental issues. This interdisciplinary study uses environmental science, archaeology, and geology to uncover Earth's mysteries. It illuminates the dynamic processes that have built our globe by studying pollutants and soil, water, and sediments. This research follows human actions, both intentional and unintentional, from ancient civilizations through contemporary industrialization and their far-reaching effects. Environmental destiny examines how contaminants affect ecosystems and human health. This study of past contamination helps solve modern problems including pollution cleanup, sustainable land management, and water conservation. This review studies reminds us that our previous activities still affect the ecosystem in a society facing rapid urbanisation and industrialization. It emphasises the importance of environmental stewardship and provides a framework for making educated choices to reduce toxins in soil, water, and sediments. Discovery of Earth's secrets is not only a historical curiosity; it's a necessary step towards a sustainable and peaceful cohabitation with our home planet.

Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? - A proof-of-concept study

Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.