查看更多>>摘要：China is now confronting the intertwined challenges of air pollution and climate change.Given the high synergies between air pollution abatement and climate change mitigation,the Chinese government is actively promoting synergetic control of these two issues.The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators.The Synergetic Roadmap 2022 report is the first annual update,featuring 20 indicators across five aspects:synergetic governance system and practices,progress in structural transition,air pollution and associated weather-climate interactions,sources,sinks,and mitigation pathway of atmo-spheric composition,and health impacts and benefits of coordinated control.Compared to the comprehensive review presented in the 2021 report,the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones.These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time,a decline in the production of crude steel and cement after years of growth,and the surging penetration of electric vehicles.Additionally,in 2022,China issued the first national policy that synergizes abatements of pollution and carbon emissions,marking a new era for China's pollution-carbon co-control.These changes highlight China's efforts to reshape its energy,eco-nomic,and transportation structures to meet the demand for synergetic control and sustainable development.Consequently,the country has witnessed a slowdown in carbon emission growth,improved air quality,and increased health benefits in recent years.
查看更多>>摘要：Rapid advancement in aerospace technology has successfully enabled long-term life and economic ac-tivities in space,particularly in Low Earth Orbit(LEO),extending up to 2000 km from the mean sea level.However,the sustainance of the LEO Economy and its Environmental Control and Life Support System(ECLSS)still relies on a regular cargo supply of essential commodities(e.g.,water,food)from Earth,for which there still is a lack of adequate and sustainable technologies.One key challenge in this context is developing water treatment technologies and standards that can perform effectively under microgravity conditions.Solving this technical challenge will be a milestone in providing a scientific basis and the necessary support mechanisms for establishing permanent bases in outer space and beyond.To identify clues towards solving this challenge,we looked back at relevant scientific research exploring novel technologies and standards for deep space exploration,also considering feedback for enhancing these technologies on land.Synthesizing our findings,we share our outlook for the future of drinking water treatment in microgravity.We also bring up a new concept for space aquatic chemistry,considering the closed environment of engineered systems operating in microgravity.
Simon Elias BibriJohn KrogstieAmin KaboliAlexandre Alahi...
29-59页
查看更多>>摘要：The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities.These strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental challenges.This aligns with the visionary concept of smarter eco-cities,an emerging paradigm of ur-banism characterized by the seamless integration of advanced technologies and environmental strate-gies.However,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions.To bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability.To ensure thoroughness,the study em-ploys a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis approaches.At the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices,as well as the formidable nature of the challenges they pose.Beyond theoretical enrichment,these findings offer invaluable insights and new perspectives poised to empower policymakers,practitioners,and researchers to advance the inte-gration of eco-urbanism and AI-and AIoT-driven urbanism.Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions,stakeholders gain the necessary groundwork for making well-informed decisions,implementing effec-tive strategies,and designing policies that prioritize environmental well-being.
查看更多>>摘要：Silver nanoparticles(AgNPs),revered for their antimicrobial prowess,have become ubiquitous in a range of products,from biomedical equipment to food packaging.However,amidst their rising popularity,concerns loom over their possible detrimental effects on fetal development and subsequent adult life.This review delves into the developmental toxicity of AgNPs across diverse models,from aquatic species like zebrafish and catfish to mammalian rodents and in vitro embryonic stem cells.Our focus encom-passes the fate of AgNPs in different contexts,elucidating associated hazardous results such as embry-otoxicity and adverse pregnancy outcomes.Furthermore,we scrutinize the enduring adverse impacts on offspring,spanning impaired neurobehavior function,reproductive disorders,cardiopulmonary lesions,and hepatotoxicity.Key hallmarks of developmental harm are identified,encompassing redox imbal-ances,inflammatory cascades,DNA damage,and mitochondrial stress.Notably,we explore potential explanations,linking immunoregulatory dysfunction and disrupted epigenetic modifications to AgNPs-induced developmental failures.Despite substantial progress,our understanding of the developmental risks posed by AgNPs remains incomplete,underscoring the urgency of further research in this critical area.
查看更多>>摘要：Waterborne viral epidemics are a major threat to public health.Increasing interest in wastewater reclamation highlights the importance of understanding the health risks associated with potential mi-crobial hazards,particularly for reused water in direct contact with humans.This study focused on identifying viral epidemic patterns in municipal wastewater reused for recreational applications based on long-term,spatially explicit global literature data during 2000-2021,and modelled human health risks from multiple exposure pathways using a well-established quantitative microbial risk assessment methodology.Global median viral loads in municipal wastewater ranged from 7.92 × 104 to 1.4 × 106 GC L-1 in the following ascending order:human adenovirus(HAdV),norovirus(NoV)GII,enterovirus(EV),NoV GI,rotavirus(RV),and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Following secondary or tertiary wastewater treatment,NoV GI,NoV GII,EV,and RV showed a relatively higher and more stable log reduction value with medians all above 0.8(84％),whereas SARS-CoV-2 and HAdV showed a relatively lower reduction,with medians ranging from 0.33(53％)to 0.55(72％).A subsequent disinfection process effectively enhanced viral removal to over 0.89-log(87％).The predicted event probability of virus-related gastrointestinal illness and acute febrile respiratory illnesses in reclaimed recreational water exceeded the World Health Organization recommended recreational risk benchmark(5％and 1.9％,respectively).Overall,our results provided insights on health risks associated with reusing wastewater for recreational purposes and highlighted the need for establishing a regulatory framework ensuring the safety management of reclaimed waters.
查看更多>>摘要：Cornstalks show promise as a raw material for polysaccharide production through xylanase.Rapid and accurate prediction of polysaccharide yield can facilitate process optimization,eliminating the need for extensive experimentation in actual production to refine reaction conditions,thereby saving time and costs.However,the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately.Here,we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production.We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations.Notably,Random Forest(RF)and eXtreme Gradient Boost(XGB)demonstrate robust performance,achieving prediction accuracies of 93.0％and 95.6％,respectively,while an independently developed deep neural network(DNN)model achieves 91.1％ac-curacy.A feature importance analysis of XGB reveals the enzyme solution volume's dominant role(43.7％),followed by time(20.7％),substrate concentration(15％),temperature(15％),and pH(5.6％).Further interpretability analysis unveils complex parameter interactions and potential optimization strategies.This data-driven approach,incorporating machine learning,deep learning,and interpretable analysis,offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.
查看更多>>摘要：Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle con-version process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO3)).The mechanism between ε(NO3-)and its drivers is highly complex and nonlinear,and can be charac-terized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-).Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory ap-proaches.Our approach robustly identifies NH4+,SO42-,and temperature as pivotal drivers for ε(NO3-).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4+during both daytime(30％)and nighttime(40％)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
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