Hasil untuk "Environmental sciences"

Qiangqiang Yuan, Huanfeng Shen, Tongwen Li et al.

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.

H. Ali, Ezzat Khan, I. Ilahi

Heavy metals are well-known environmental pollutants due to their toxicity, persistence in the environment, and bioaccumulative nature. Their natural sources include weathering of metal-bearing rocks and volcanic eruptions, while anthropogenic sources include mining and various industrial and agricultural activities. Mining and industrial processing for extraction of mineral resources and their subsequent applications for industrial, agricultural, and economic development has led to an increase in the mobilization of these elements in the environment and disturbance of their biogeochemical cycles. Contamination of aquatic and terrestrial ecosystems with toxic heavy metals is an environmental problem of public health concern. Being persistent pollutants, heavy metals accumulate in the environment and consequently contaminate the food chains. Accumulation of potentially toxic heavy metals in biota causes a potential health threat to their consumers including humans. This article comprehensively reviews the different aspects of heavy metals as hazardous materials with special focus on their environmental persistence, toxicity for living organisms, and bioaccumulative potential. The bioaccumulation of these elements and its implications for human health are discussed with a special coverage on fish, rice, and tobacco. The article will serve as a valuable educational resource for both undergraduate and graduate students and for researchers in environmental sciences. Environmentally relevant most hazardous heavy metals and metalloids include Cr, Ni, Cu, Zn, Cd, Pb, Hg, and As. The trophic transfer of these elements in aquatic and terrestrial food chains/webs has important implications for wildlife and human health. It is very important to assess and monitor the concentrations of potentially toxic heavy metals and metalloids in different environmental segments and in the resident biota. A comprehensive study of the environmental chemistry and ecotoxicology of hazardous heavy metals and metalloids shows that steps should be taken to minimize the impact of these elements on human health and the environment.

Jagpreet Singh, T. Dutta, Ki‐Hyun Kim et al.

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.

Shifa Zhong, Kai Zhang, M. Bagheri et al.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

K. Deiner, Holly M. Bik, E. Mächler et al.

The genomic revolution has fundamentally changed how we survey biodiversity on earth. High‐throughput sequencing (“HTS”) platforms now enable the rapid sequencing of DNA from diverse kinds of environmental samples (termed “environmental DNA” or “eDNA”). Coupling HTS with our ability to associate sequences from eDNA with a taxonomic name is called “eDNA metabarcoding” and offers a powerful molecular tool capable of noninvasively surveying species richness from many ecosystems. Here, we review the use of eDNA metabarcoding for surveying animal and plant richness, and the challenges in using eDNA approaches to estimate relative abundance. We highlight eDNA applications in freshwater, marine and terrestrial environments, and in this broad context, we distill what is known about the ability of different eDNA sample types to approximate richness in space and across time. We provide guiding questions for study design and discuss the eDNA metabarcoding workflow with a focus on primers and library preparation methods. We additionally discuss important criteria for consideration of bioinformatic filtering of data sets, with recommendations for increasing transparency. Finally, looking to the future, we discuss emerging applications of eDNA metabarcoding in ecology, conservation, invasion biology, biomonitoring, and how eDNA metabarcoding can empower citizen science and biodiversity education.

Wondimagegn Mengist, T. Soromessa, G. Legese

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D. McKinley, A. Miller‐Rushing, H. Ballard et al.

Jiayan Li, Jinman Wang

Abstract The amount of coal gangue, a by-product of coal mining and washing, is rapidly increasing with the growing trend of energy consumption. The accumulated coal gangue without appropriate utilization has resulted in a squander of resources, waste disposal and environmental pollution issues. Over the past few decades, there has been wide attention in developing strategies for the utilization of coal gangue due to a cultural shift towards sustainable development coupled with increasing demand for disposing the challenge of coal gangue accumulation. However, to our knowledge, there is no thorough and in-depth review on the series address of coal gangue reuse. In spite of some advantages of using coal gangue, it is notable that negative environmental problems cannot be ignored, so the scientific utilization is necessary to control the environmental impacts. Therefore, the main objective of this paper is to provide a comprehensive literature review of coal gangue utilization in building material production, energy generation, soil improvement and other high-added applications, analyze the worldwide dynamics of the studies on coal gangue utilization and identify the potential environmental risks in various pathways. The key focus of the review is on detecting the potential problems and thus giving recommendations for the solution. In addition, based on the progress of previous research, this paper also points the directions for further research within the field. A bibliometric analysis was developed in China National Knowledge Internet and Web of Science and a systematic review was conducted for related 237 articles to understand the physicochemical properties and utilization characteristics of coal gangue as well as corresponding environmental risks. The results indicated that the number of published articles has increased in recent years, and the researches were mainly from China, with the contribution of 78.94% of the total selected publications. Besides, these researches mainly focused on the utilization of coal gangue, while there was a lack of attention to environmental risks. The findings of the present study open up a new gate for the further application in coal gangue, hopefully motivate future relevant studies and guide the policy-making.

N. Bennett

The conservation community is increasingly focusing on the monitoring and evaluation of management, governance, ecological, and social considerations as part of a broader move toward adaptive management and evidence‐based conservation. Evidence is any information that can be used to come to a conclusion and support a judgment or, in this case, to make decisions that will improve conservation policies, actions, and outcomes. Perceptions are one type of information that is often dismissed as anecdotal by those arguing for evidence‐based conservation. In this paper, I clarify the contributions of research on perceptions of conservation to improving adaptive and evidence‐based conservation. Studies of the perceptions of local people can provide important insights into observations, understandings and interpretations of the social impacts, and ecological outcomes of conservation; the legitimacy of conservation governance; and the social acceptability of environmental management. Perceptions of these factors contribute to positive or negative local evaluations of conservation initiatives. It is positive perceptions, not just objective scientific evidence of effectiveness, that ultimately ensure the support of local constituents thus enabling the long‐term success of conservation. Research on perceptions can inform courses of action to improve conservation and governance at scales ranging from individual initiatives to national and international policies. Better incorporation of evidence from across the social and natural sciences and integration of a plurality of methods into monitoring and evaluation will provide a more complete picture on which to base conservation decisions and environmental management.

F. Lange, S. Dewitte

Abstract Any scientific attempt to understand, predict, or promote pro-environmental behavior requires an adequate measurement tool for the assessment of pro-environmental behavior. The multidisciplinary interest in pro-environmental behavior has generated a large variety of such tools, ranging from domain-general and domain-specific self-report measures, field observations conducted with the help of informants, trained observers, or technical devices, to behavioral tasks for use in the laboratory. The present review discusses this broad spectrum of existing approaches to the measurement of pro-environmental behavior, their strengths and weaknesses, as well as possibilities to improve upon them. From this review, we deduce several recommendations for the development, selection, and application of measures in pro-environmental behavior research. We conclude by stressing the importance of established and validated measures for a cumulative science of pro-environmental behavior.

J. Willard, X. Jia, Shaoming Xu et al.

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.

Huabin Zhang, J. Nai, Le Yu et al.

A. Singer, H. Shaw, Vicki Rhodes et al.

The environment is increasingly being recognized for the role it might play in the global spread of clinically relevant antibiotic resistance. Environmental regulators monitor and control many of the pathways responsible for the release of resistance-driving chemicals into the environment (e.g., antimicrobials, metals, and biocides). Hence, environmental regulators should be contributing significantly to the development of global and national antimicrobial resistance (AMR) action plans. It is argued that the lack of environment-facing mitigation actions included in existing AMR action plans is likely a function of our poor fundamental understanding of many of the key issues. Here, we aim to present the problem with AMR in the environment through the lens of an environmental regulator, using the Environment Agency (England’s regulator) as an example from which parallels can be drawn globally. The issues that are pertinent to environmental regulators are drawn out to answer: What are the drivers and pathways of AMR? How do these relate to the normal work, powers and duties of environmental regulators? What are the knowledge gaps that hinder the delivery of environmental protection from AMR? We offer several thought experiments for how different mitigation strategies might proceed. We conclude that: (1) AMR Action Plans do not tackle all the potentially relevant pathways and drivers of AMR in the environment; and (2) AMR Action Plans are deficient partly because the science to inform policy is lacking and this needs to be addressed.

M. Basner, S. Mcguire

To evaluate the quality of available evidence on the effects of environmental noise exposure on sleep a systematic review was conducted. The databases PSYCINFO, PubMed, Science Direct, Scopus, Web of Science and the TNO Repository were searched for non-laboratory studies on the effects of environmental noise on sleep with measured or predicted noise levels and published in or after the year 2000. The quality of the evidence was assessed using GRADE criteria. Seventy four studies predominately conducted between 2000 and 2015 were included in the review. A meta-analysis of surveys linking road, rail, and aircraft noise exposure to self-reports of sleep disturbance was conducted. The odds ratio for the percent highly sleep disturbed for a 10 dB increase in Lnight was significant for aircraft (1.94; 95% CI 1.61–2.3), road (2.13; 95% CI 1.82–2.48), and rail (3.06; 95% CI 2.38–3.93) noise when the question referred to noise, but non-significant for aircraft (1.17; 95% CI 0.54–2.53), road (1.09; 95% CI 0.94–1.27), and rail (1.27; 95% CI 0.89–1.81) noise when the question did not refer to noise. A pooled analysis of polysomnographic studies on the acute effects of transportation noise on sleep was also conducted and the unadjusted odds ratio for the probability of awakening for a 10 dBA increase in the indoor Lmax was significant for aircraft (1.35; 95% CI 1.22–1.50), road (1.36; 95% CI 1.19–1.55), and rail (1.35; 95% CI 1.21–1.52) noise. Due to a limited number of studies and the use of different outcome measures, a narrative review only was conducted for motility, cardiac and blood pressure outcomes, and for children’s sleep. The effect of wind turbine and hospital noise on sleep was also assessed. Based on the available evidence, transportation noise affects objectively measured sleep physiology and subjectively assessed sleep disturbance in adults. For other outcome measures and noise sources the examined evidence was conflicting or only emerging. According to GRADE criteria, the quality of the evidence was moderate for cortical awakenings and self-reported sleep disturbance (for questions that referred to noise) induced by traffic noise, low for motility measures of traffic noise induced sleep disturbance, and very low for all other noise sources and investigated sleep outcomes.

Xian Liu, Dawei Lu, A. Zhang et al.

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.

S. Banzhaf, Lala Ma, C. Timmins

The grassroots movement that placed environmental justice issues on the national stage around 1980 was soon followed up by research documenting the correlation between pollution and race and poverty. This work has established inequitable exposure to nuisances as a stylized fact of social science. In this paper, we review the environmental justice literature, especially where it intersects with work by economists. First we consider the literature documenting evidence of disproportionate exposure. We particularly consider the implications of modeling choices about spatial relationships between polluters and residents, and about conditioning variables. Next, we evaluate the theory and evidence for four possible mechanisms that may lie behind the patterns seen: disproportionate siting on the firm side, “coming to the nuisance” on the household side, market-like coordination of the two, and discriminatory politics and/or enforcement. We argue that further research is needed to understand how much weight to give each mechanism. Finally, we discuss some policy options.

N. Latulippe, Nicole L. Klenk

The global environmental change research community that engages with Indigenous knowledge holders commonly practice engagement in an extractive way: knowledge is treated as data that can be aggregated and understood in abstract and universal form. This assumes that knowledge and governance are separate and gives knowledge co-production the appearance of playing an informative and facilitative role in global environmental change governance. But seeking Indigenous knowledge to inform environmental decision-making implies that Indigenous peoples are stakeholders as opposed to self-determining nations with rights and responsibilities regarding their knowledge systems and lands. Indigenous sovereignty is not respected when knowledge is treated as mere data for collective decision-making. This paper brings literatures on knowledge co-production together with Indigenous knowledge, research, and environmental governance to explain why co-production scholars must move away from seeking to better ‘integrate’ Indigenous knowledges into western science and make way for Indigenous research leadership.

Mehak Puri, K. Gandhi, M. Kumar

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.

Hao Tan, Jialing Li, M. He et al.

S. M. Popescu, Sheikh Mansoor, O. A. Wani et al.

Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.