Abstract Various forms of machine learning (ML) methods have historically played a valuable role in environmental remote sensing research. With an increasing amount of “big data” from earth observation and rapid advances in ML, increasing opportunities for novel methods have emerged to aid in earth environmental monitoring. Over the last decade, a typical and state-of-the-art ML framework named deep learning (DL), which is developed from the traditional neural network (NN), has outperformed traditional models with considerable improvement in performance. Substantial progress in developing a DL methodology for a variety of earth science applications has been observed. Therefore, this review will concentrate on the use of the traditional NN and DL methods to advance the environmental remote sensing process. First, the potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed. A typical network structure will then be introduced. Afterward, the applications of DL environmental monitoring in the atmosphere, vegetation, hydrology, air and land surface temperature, evapotranspiration, solar radiation, and ocean color are specifically reviewed. Finally, challenges and future perspectives will be comprehensively analyzed and discussed.
AbstractIn materials science, “green” synthesis has gained extensive attention as a reliable, sustainable, and eco-friendly protocol for synthesizing a wide range of materials/nanomaterials including metal/metal oxides nanomaterials, hybrid materials, and bioinspired materials. As such, green synthesis is regarded as an important tool to reduce the destructive effects associated with the traditional methods of synthesis for nanoparticles commonly utilized in laboratory and industry. In this review, we summarized the fundamental processes and mechanisms of “green” synthesis approaches, especially for metal and metal oxide [e.g., gold (Au), silver (Ag), copper oxide (CuO), and zinc oxide (ZnO)] nanoparticles using natural extracts. Importantly, we explored the role of biological components, essential phytochemicals (e.g., flavonoids, alkaloids, terpenoids, amides, and aldehydes) as reducing agents and solvent systems. The stability/toxicity of nanoparticles and the associated surface engineering techniques for achieving biocompatibility are also discussed. Finally, we covered applications of such synthesized products to environmental remediation in terms of antimicrobial activity, catalytic activity, removal of pollutants dyes, and heavy metal ion sensing.
The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.
Emerging contaminants include many synthetic or natural substances, such as pharmaceuticals and personal care products, hormones, and flame retardants that are not often controlled or monitored in the environment. The consumption or use of these substances is on an ever-rising trend, which dangerously increases their prevalence in practically all environmental matrices. These contaminants are present in low environmental concentrations and cause severe effects on human health and the biota. The present review analyzed 2012-2022 years papers via PubChem, science direct, National Center for Biotechnology Information, web of science on the legislations and policies of emerging contaminants globally. A state-of-the-art review of several studies in the literature focus on examining and evaluating the emerging contaminants and the frameworks adopted by developed and developing countries to combat the release of emerging contaminants and form footprints towards water sustainability which includes water availability, usage patterns, generation and pollution management, the health of aquatic systems, and societal vulnerability. The paper aims to provide a comprehensive view of current global policies and framework regarding evaluating and assessing the chemicals, in light of being a threat to the environment and biota. The review also highlights the future global prospects, including current governmental activities and emerging contaminant policy measures. The review concludes with suggestions and way forward to control the inventory and disposal of emerging contaminants in the environment.
Environmental Microbiology: Advanced Research and Multidisciplinary Applications focus on the current research on microorganisms in the environment. Contributions in the volume cover several aspects of applied microbial research, basic research on microbial ecology and molecular genetics. The reader will find a collection of topics with theoretical and practical value, allowing them to connect environmental microbiology to a variety of subjects in life sciences, ecology, and environmental science topics. Advanced topics including biogeochemical cycling, microbial biosensors, bioremediation, application of microbial biofilms in bioremediation, application of microbial surfactants, microbes for mining and metallurgical operations, valorization of waste, and biodegradation of aromatic waste, microbial communication, nutrient cycling and biotransformation are also covered. The content is designed for advanced undergraduate students, graduate students, and environmental professionals, with a comprehensive and up-to-date discussion of environmental microbiology as a discipline that has greatly expanded in scope and interest over the past several decades.
Effective conservation requires knowledge exchange among scientists and decision-makers to enable learning and support evidence-based decision-making. Efforts to improve knowledge exchange have been hindered by a paucity of empirically-grounded guidance to help scientists and practitioners design and implement research programs that actively facilitate knowledge exchange. To address this, we evaluated the Ningaloo Research Program (NRP), which was designed to generate new scientific knowledge to support evidence-based decisions about the management of the Ningaloo Marine Park in north-western Australia. Specifically, we evaluated (1) outcomes of the NRP, including the extent to which new knowledge informed management decisions; (2) the barriers that prevented knowledge exchange among scientists and managers; (3) the key requirements for improving knowledge exchange processes in the future; and (4) the core capacities that are required to support knowledge exchange processes. While the NRP generated expansive and multidisciplinary science outputs directly relevant to the management of the Ningaloo Marine Park, decision-makers are largely unaware of this knowledge and little has been integrated into decision-making processes. A range of barriers prevented efficient and effective knowledge exchange among scientists and decision-makers including cultural differences among the groups, institutional barriers within decision-making agencies, scientific outputs that were not translated for decision-makers and poor alignment between research design and actual knowledge needs. We identify a set of principles to be implemented routinely as part of any applied research program, including; (i) stakeholder mapping prior to the commencement of research programs to identify all stakeholders, (ii) research questions to be co-developed with stakeholders, (iii) implementation of participatory research approaches, (iv) use of a knowledge broker, and (v) tailored knowledge management systems. Finally, we articulate the individual, institutional and financial capacities that must be developed to underpin successful knowledge exchange strategies.
Cíntia Cármen de Faria Melo, Danilo Silva Amaral, Renato de Mello Prado
et al.
Abstract Inadequate nitrogen (N) fertilization management in pastures is common and can lead to N deficiency or excess, resulting in physiological imbalances in forage grasses across different regions of the world. Silicon (Si) fertigation is a promising strategy to mitigate these issues due to its anti-stress properties. However, its effects on the morphogenic growth processes of grasses and their influence on forage nutritional value remain unclear. This study investigated the detrimental effects of low, adequate, and excessive N-urea supply on the morphogenesis, production, and chemical-bromatological composition of Zuri grass in two tropical soils (Ferralsol and Arenosol), with a focus on the mitigating role of nanosilica in these parameters. Low N levels inhibited leaf growth and tillering, whereas excessive N led to excessive increases in morphogenic activity, compromising leaf lifespan and dry matter (DM) production. Si fertigation balanced morphogenesis under both low and excessive N conditions, reducing dead material and lignin content in forage grown in Arenosol. Well-nourished plants exhibited higher DM production in both soils when supplemented with Si. Fertigation with silicon is beneficial for the morphogenesis of grass under low or high N, mitigating DM production losses under N excess, but not under N deficiency. Silicon can optimize forage production in adequately fertilized systems without compromising forage digestibility.
Geospatial technologies are rapidly emerging as pivotal tools for advancing sustainable urban and rural development through citizen empowerment in India and worldwide. This study systematically reviews peer-reviewed and grey literature to examine their integration with global frameworks, such as the SDGs, Paris Agreement, and Sendai Framework, while aligning with Indian initiatives like NAPCC, Smart Cities, Digital India, SVAMITVA, AMRUT, and the National Geospatial Policy 2022, with emphasis on the citizen as a crucial feedback factor. Employing thematic mapping and comparative analysis between the Global North and South, we evaluate applications in urban planning, mobility, energy, resilience, and health, highlighting platforms like PPGIS, VGI, Bhuvan, and 'Know Your DIGIPIN' for participatory data collection and decision-making.</p>
<p>Our analysis reveals regional disparities in India, with the southern zone leading in innovation (35% adoption) and the eastern region focussing on disaster management (15%), along with global successes in disaster relief, welfare targeting, and immunisation tracking. Quantitative impacts include India's geospatial market growth to ₹63,000 crores by 2025 and AMRUT 2.0's rapid water and sewerage coverage expansion in many major cities. However, persistent challenges include technical knowledge gaps in academia, insufficient institutional support for geospatial startups, and barriers like low digital literacy and language limitations that restrict broader participation.</p>
<p>We recommend enhanced geospatial education, open data policies, vernacular interfaces, and inclusive citizen science frameworks to bridge these gaps, foster equitable participation, and realise geospatial intelligence's full potential for resilient, data-driven sustainability.
Cheng-Hong Yang, Chih-Hsien Wu, Kuei-Hau Luo
et al.
Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM2.5, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.
Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits often used to reflect plant resource utilization strategies and predict plant responses to environmental changes. In general, grassland plants at different elevations exhibit varying survival strategies. However, it remains unclear how grassland plants adapt to changes in elevation and their driving factors. To address this issue, we utilized SLA and LDMC data of grassland plants from 223 study sites at different elevations in China, along with climate and soil data, to investigate variations in resource utilization strategies of grassland plants along different elevational gradients and their dominant influencing factors employing linear mixed-effects models, variance partitioning method, piecewise Structural Equation Modeling, etc. The results show that with increasing elevation, SLA significantly decreases, and LDMC significantly increases (P < 0.001). This indicates different resource utilization strategies of grassland plants across elevation gradients, transitioning from a “faster investment-return” at lower elevations to a “slower investment-return” at higher elevations. Across different elevation gradients, climatic factors are the main factors affecting grassland plant resource utilization strategies, with soil nutrient factors also playing a non-negligible coordinating role. Among these, mean annual precipitation and hottest month mean temperature are key climatic factors influencing SLA of grassland plants, explaining 28.94% and 23.88% of SLA variation, respectively. The key factors affecting LDMC of grassland plants are mainly hottest month mean temperature and soil phosphorus content, with relative importance of 24.24% and 20.27%, respectively. Additionally, the direct effect of elevation on grassland plant resource utilization strategies is greater than its indirect effect (through influencing climatic and soil nutrient factors). These findings emphasize the substantive impact of elevation on grassland plant resource utilization strategies and have important ecological value for grassland management and protection under global change.
Dalia D. Hadi, Mohammed Dheyaa Marsool Marsool, Ali Dheyaa Marsool Marsool
et al.
Abstract Background Idiopathic pulmonary fibrosis (IPF) is a progressive and debilitating lung disease characterized by irreversible scarring of the lungs. The cause of IPF is unknown, but it is thought to involve a combination of genetic and environmental factors. There is no cure for IPF, and treatment is focused on slowing disease progression and relieving symptoms. Aims We aimed in this review to investigate and provide the latest insights into IPF management modalities, including the potential of Saracatinibas a substitute for current IPF drugs. We also investigated the therapeutic potential of Sotatercept in addressing pulmonary hypertension associated with IPF. Materials and Methods We conducted a comprehensive literature review of relevant studies on IPF management. We searched electronic databases, including PubMed, Scopus, Embase, and Web of science. Results The two Food and Drug Administration‐approved drugs for IPF, Pirfenidone, and Nintedanib, have been pivotal in slowing disease progression, yet experimental evidence suggests that Saracatinib surpasses their efficacy. Preclinical trials investigating the potential of Saracatinib, a tyrosine kinase inhibitor, have shown to be more effective than current IPF drugs in slowing disease progression in preclinical studies. Also, Sotatercept,a fusion protein, has been shown to reduce pulmonary vascular resistance and improve exercise tolerance in patients with PH associated with IPF in clinical trials. Conclusions The advancements discussed in this review hold the promise of improving the quality of life for IPF patients and enhancing our understanding of this condition. There remains a need for further research to confirm the efficacy and safety of new IPF treatments and to develop more effective strategies for managing exacerbations.
Vadthya Lokya, Sejal Parmar, Arun K. Pandey
et al.
Abstract In addition to the challenge of meeting global demand for food production, there are increasing concerns about food safety and the need to protect consumer health from the negative effects of foodborne allergies. Certain bio‐molecules (usually proteins) present in food can act as allergens that trigger unusual immunological reactions, with potentially life‐threatening consequences. The relentless working lifestyles of the modern era often incorporate poor eating habits that include readymade prepackaged and processed foods, which contain additives such as peanuts, tree nuts, wheat, and soy‐based products, rather than traditional home cooking. Of the predominant allergenic foods (soybean, wheat, fish, peanut, shellfish, tree nuts, eggs, and milk), peanuts (Arachis hypogaea) are the best characterized source of allergens, followed by tree nuts (Juglans regia, Prunus amygdalus, Corylus avellana, Carya illinoinensis, Anacardium occidentale, Pistacia vera, Bertholletia excels), wheat (Triticum aestivum), soybeans (Glycine max), and kidney beans (Phaseolus vulgaris). The prevalence of food allergies has risen significantly in recent years including chance of accidental exposure to such foods. In contrast, the standards of detection, diagnosis, and cure have not kept pace and unfortunately are often suboptimal. In this review, we mainly focus on the prevalence of allergies associated with peanut, tree nuts, wheat, soybean, and kidney bean, highlighting their physiological properties and functions as well as considering research directions for tailoring allergen gene expression. In particular, we discuss how recent advances in molecular breeding, genetic engineering, and genome editing can be used to develop potential low allergen food crops that protect consumer health.
The intercropping of maize (<i>Zea mays</i> L.) and peanuts (<i>Arachis hypogaea</i> L.) (M||P) significantly enhances crop yield. In a long-term M||P field experiment with two P fertilizer levels, we examined how long-term M||P affects topsoil aggregate fractions and stability, organic carbon (SOC), available phosphorus (AP), and total phosphorus (TP) in each aggregate fraction, along with crop yields. Compared to their respective monocultures, long-term M||P substantially increased the proportion of topsoil mechanical macroaggregates (7.6–16.3%) and water-stable macroaggregates (>1 mm) (13.8–36.1%), while reducing the unstable aggregate index (E<sub>LT</sub>) and the percentage of aggregation destruction (PAD). M||P significantly boosted the concentration (12.9–39.9%) and contribution rate (4.1–47.9%) of SOC in macroaggregates compared to single crops. Moreover, the concentration of TP in macroaggregates (>1 mm) and AP in each aggregate fraction of M||P exceeded that of the respective single crops (<i>p</i> < 0.05). Furthermore, M||P significantly increased the Ca<sub>2</sub>-P, Ca<sub>8</sub>-P, Al-P, and Fe-P concentrations of intercropped maize (IM) and the Ca<sub>8</sub>-P, O-P, and Ca<sub>10</sub>-P concentrations of intercropped peanuts (IP). The land equivalent ratio (LER) of M||P was higher than one, and M||P stubble improved the yield of subsequent winter wheat (<i>Triticum aestivum</i> L.) compared with sole-crop maize stubble. P application augmented the concentration of SOC, TP, and AP in macroaggregates, resulting in improved crop yields. In conclusion, our findings suggest that long-term M||P combined with P application sustains farmland productivity in the North China Plain by increasing SOC and macroaggregate fractions, improving aggregate stability, and enhancing soil P availability.
By improving its total factor productivity, China may attain higher quality and more sustainable economic growth. As a key market-based incentive for environmental regulation, does environmental protection tax increase total factor productivity and provide a win-win situation for both economic and environmental performance? It is a debate-worthy topic. Based on data of Chinese listed companies, this paper uses the triple difference method to analyze China’s environmental protection tax reform as a natural experiment. The results show that the environmental protection tax can significantly boost the firm’s total factor productivity by encouraging technological innovation and enhancing resource allocation. Based on analysis of heterogeneity, it appears that state-owned enterprises, larger corporations, and regions with more strict environmental enforcement are more responsive to environmental protection tax policies. This report provides critical empirical evidence for upgrading China’s tax framework to protect the environment.
Students who completed the online version of an introductory course on climate change performed 2% worse than those who completed the in-person version, according to a study of 1790 undergraduate students in California, USA.
Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.
The upstream of bioenergy industry has suffered from unreliable operations of granular biomass feedstocks in handling equipment. Computational modeling, including continuum-mechanics models and discrete-particle models, offers insightful understandings and predictive capabilities on the flow of milled biomass and can assist equipment design and optimization. This paper presents a benchmark study on the fidelity of the continuum and discrete modeling approaches for predicting granular biomass flow. We first introduce the constitutive law of the continuum-mechanics model and the contact law of the coarse-grained discrete-particle model, with model parameters calibrated against laboratory characterization tests of the milled loblolly pine. Three classical granular material flow systems (i.e., a lab-scale rotating drum, a pilot-scale hopper, and a full-scale inclined plane) are then simulated using the two models with the same initial and boundary conditions as the physical experiments. The close agreement of the numerical predictions with the experimental measurements on the hopper mass flow rate, the hopper critical outlet width, the material stopping thickness on the inclined plane, and the dynamic angle of repose, clearly indicates that the two methods can capture the critical flow behavior of granular biomass. The qualitative comparison shows that the continuum-mechanics model outperforms in parameterization of materials and wall friction, and large-scale systems, while the discrete-particle model is more preferred for discontinuous flow systems at smaller scales. Industry stakeholders can use these findings as guidance for choosing appropriate numerical tools to model biomass material flow in part of the optimization of material handling equipment in biorefineries.