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环境科学与生态技术（英文）
环境科学与生态技术（英文）
环境科学与生态技术（英文）/Journal Environmental Science & EcotechnologySCI
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    Persistent organic pollutants and chemicals of emerging Arctic concern in the Arctic environment

    Da LüYihe LüGuangyao GaoSiqi Sun...
    1-3页
      原文链接:
    • NETL
    • NSTL
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    In memory of Robie W.Macdonald(1948-2022):A scientist and a friend

    Baowei ZhangXiang TangQiuxiang XuChangzheng Fan...
    4-6页
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    Piezoelectricity activates persulfate for water treatment:A perspective

    Xiaofei WangShenyu LanKorneel RabaeyAntonin Prévoteau...
    7-12页
    查看更多>>摘要：Advanced oxidation processes(AOPs)utilizing persulfate(PS)offer great potential for wastewater treatment.Yet,the dependency on energy and chemical-intensive activation techniques,such as ultra-violet radiation and transition metal ions,constrains their widespread adoption.Recognizing this limi-tation,researchers are turning towards the piezoelectric effect-a novel,energy-efficient method for PS activation that capitalizes on the innate piezoelectric characteristics of materials.Intriguingly,this method taps into weak renewable mechanical forces omnipresent in nature,ranging from wind,tides,water flow,sound,and atmospheric forces.In this perspective,we delve into the burgeoning realm of piezoelectric/PS-AOPs,elucidating its fundamental principles,the refinement of piezoelectric materials,potential mechanical force sources,and pertinent application contexts.This emerging technology har-bors significant potential as a pivotal element in wastewater pretreatment and may spearhead in-novations in future water pollution control engineering.
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    Persistent organic pollutants in global surface soils:Distributions and fractionations

    Dou WangGuiling YangXiao YunTing Luo...
    13-27页
    查看更多>>摘要：The distribution and fractionation of persistent organic pollutants(POPs)in different matrices refer to how these pollutants are dispersed and separated within various environmental compartments.This is a significant study area as it helps us understand the transport efficiencies and long-range transport po-tentials of POPs to enter remote areas,particularly polar regions.This study provides a comprehensive review of the progress in understanding the distribution and fractionation of POPs.We focus on the contributions of four intermedia processes(dry and wet depositions for gaseous and particulate POPs)and determine their transfer between air and soil.These processes are controlled by their partitioning between gaseous and particulate phases in the atmosphere.The distribution patterns and fractionations can be categorized into primary and secondary types.Equations are developed to quantificationally study the primary and secondary distributions and fractionations of POPs.The analysis results suggest that the transfer of low molecular weight(LMW)POPs from air to soil is mainly through gas diffusion and particle deposition,whereas high molecular weight(HMW)POPs are mainly via particle deposition.HMW-POPs tend to be trapped near the source,whereas LMW-POPs are more prone to undergo long-range atmo-spheric transport.This crucial distinction elucidates the primary reason behind their temperature-independent primary fractionation.However,the secondary distribution and fractionation can only be observed along a temperature gradient,such as latitudinal or altitudinal transects.An animation is produced by a one-dimensional transport model to simulate conceptively the transport of CB-28 and CB-180,revealing the similarities and differences between the primary and secondary distributions and fractionations.We suggest that the decreasing temperature trend along latitudes is not the major reason for POPs to be fractionated into the polar ecosystems,but drives the longer-term accumulation of POPs in cold climates or polar cold trapping.
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    Sedimentary records of contaminant inputs in Frobisher Bay,Nunavut

    Qingsong JiangJincheng LiYanxin SunJilin Huang...
    28-42页
    查看更多>>摘要：Contaminants,such as polychlorinated biphenyls(PCBs),polycyclic aromatic hydrocarbons(PAHs),heavy metals,and per and polyfluoroalkyl substances(PFASs),primarily reach the Arctic through long-range atmospheric and oceanic transport.However,local sources within the Arctic also contribute to the levels observed in the environment,including legacy sources and new sources that arise from activities associated with increasing commercial and industrial development.The City of Iqaluit in Frobisher Bay,Nunavut(Canada),has seen rapid population growth and associated development during recent decades yet remains a site of interest for ocean protection,where Inuit continue to harvest country food.In the present study,seven dated marine sediment cores collected in Koojesse Inlet near Iqaluit,and from sites in inner and outer Frobisher Bay,respectively,were analyzed for total mercury(THg),major and trace elements,PAHs,PCBs,and PFASs.The sedimentary record in Koojesse Inlet shows a period of Aroclor 1260-like PCB input concurrent with military site presence in the 1950-60s,followed by decades of input of pyrogenic PAHs,averaging about ten times background levels.Near-surface sediments in Koo-jesse Inlet also show evidence of transient local-source inputs of THg and PFASs,and recycling or continued slow release of PCBs from legacy land-based sources.Differences in PFAS congener compo-sition clearly distinguish the local sources from long-range transport.Outside Koojesse Inlet but still in inner Frobisher Bay,9.2 km from Iqaluit,sediments showed evidence of both local source(PCB)and long-range transport.In outer Frobisher Bay,an up-core increase in THg and PFASs in sediments may be explained by ongoing inputs of these contaminants from long-range transport.The context for ocean protection and country food harvesting in this region of the Arctic clearly involves both local sources and long-range transport,with past human activities leaving a long legacy insofar as levels of persistent organic pollutants are concerned.
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    Sampling efficiency of a polyurethane foam air sampler:Effect of temperature

    Zhe DengJulian Muñoz SierraAna Lucia Morgado FerreiraDaniel Cerqueda-Garcia...
    43-52页
    查看更多>>摘要：Effective monitoring of atmospheric concentrations is vital for assessing the Stockholm Convention's effectiveness on persistent organic pollutants(POPs).This task,particularly challenging in polar regions due to low air concentrations and temperature fluctuations,requires robust sampling techniques.Furthermore,the influence of temperature on the sampling efficiency of polyurethane foam discs re-mains unclear.Here we employ a flow-through sampling(FTS)column coupled with an active pump to collect air samples at varying temperatures.We delved into breakthrough profiles of key pollutants,such as polycyclic aromatic hydrocarbons(PAHs),polychlorobiphenyls(PCBs),and organochlorine pesticides(OCPs),and examined the temperature-dependent behaviors of the theoretical plate number(N)and breakthrough volume(VB)using frontal chromatography theory.Our findings reveal a significant rela-tionship between temperature dependence coefficients(KTN,KTV)and compound volatility,with decreasing values as volatility increases.While distinct trends are noted for PAHs,PCBs,and OCPs in KTN,KTV values exhibit similar patterns across all chemicals.Moreover,we establish a binary linear correlation between log(VB/m3),1/(T/K),and N,simplifying breakthrough level estimation by enabling easy con-version between N and VB.Finally,an empirical linear solvation energy relationship incorporating a temperature term is developed,yielding satisfactory results for N at various temperatures.This approach holds the potential to rectify temperature-related effects and loss rates in historical data from long-term monitoring networks,benefiting polar and remote regions.
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    Poisson rectangular pulse(PRP)model establishment based on uncertainty analysis of urban residential water consumption patterns

    Yao YangMengzhen XuXingyu ChenJiahao Zhang...
    53-59页
    查看更多>>摘要：The commonly used Poisson rectangular pulse(PRP)model,employed for simulating high-resolution residential water consumption patterns(RWCPs),relies on calibration via medium-resolution RWCPs obtained from practical measurements.This introduces inevitable uncertainty stemming from the measured RWCPs,which consequently impacts the precision of model simulations.Here we enhance the accuracy of the PRP model by addressing the uncertainty of RWCPs.We established a critical sampling size of 2000 household water consumption patterns(HWCPs)with a data logging interval(DLI)of 15 min to attain dependable RWCPs.Through Genetic Algorithm calibration,the optimal values of the PRP model's parameters were determined:pulse frequency A=91 d-1,mean of pulse intensity E(I)=0.346 m3 h-1,standard deviation of pulse intensity STD(I)=0.292 m3 h-1,mean of pulse duration E(D)=40 s,and standard deviation of pulse duration STD(D)=55 s.Furthermore,validation was conducted at both HWCP and RWCP levels.We recommend a sampling size of ≥2000 HWCPs and a DLI of＜30 min for PRP model calibration to balance simulation precision and practical implementation.This study significantly advances the theoretical foundation and real-world application of the PRP model,enhancing its role in urban water supply system management.
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    • NETL
    • NSTL
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    Surveillance-image-based outdoor air quality monitoring

    Yurong ZhangXudong BuYajun WangZhenyu Hang...
    60-69页
    查看更多>>摘要：Air pollution threatens human health,necessitating effective and convenient air quality monitoring.Recently,there has been a growing interest in using camera images for air quality estimation.However,a major challenge has been nighttime detection due to the limited visibility of nighttime images.Here we present a hybrid deep learning model,capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images.Our model,which integrates a convolutional neural network(CNN)and long short-term memory(LSTM),adeptly captures spatial-temporal image features,enabling air quality estimation at any time of day,including PM2.5 and PM10 concentrations,as well as the air quality index(AQI).Compared to independent CNN networks that solely extract spatial features,our model demonstrates superior accuracy on self-constructed datasets with R2=0.94 and RMSE=5.11 μg m-3 for PM2.5,R2=0.92 and RMSE=7.30 μg m-3 for PM10,and R2=0.94 and RMSE=5.38 for AQI.Furthermore,our model excels in daytime air quality estimation and enhances nighttime predictions,elevating overall accuracy.Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model,reaffirming its appli-cability and superiority for air quality monitoring.
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    Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system

    Mohamed MannaaAbdelaziz MansourInmyoung ParkDae-Weon Lee...
    70-82页
    查看更多>>摘要：The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system man-agement.The proliferation of model complexity results in more significant uncertainty and computa-tional requirements.However,conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples.Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search.MLPS,termed the machine learning parallel system for fast parameter search of in-tegrated process-based models,aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness,accuracy,and stability.MLPS was constructed upon the concept of model surrogation+algorithm optimization using Ant Colony Optimization(ACO)coupled with Long Short-Term Memory(LSTM).The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0,and the average absolute percent bias decreases from 124.3％to 8.8％.The model outputs closely align with the monitoring data,and parameter calibration time is reduced by 89.94％.MLPS enables the efficient optimization of integrated process-based models,facilitating the application of highly precise complex models in environmental management.The design of MLPS also presents valuable insights for optimizing complex models in other fields.
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    Incorporating adaptive genomic variation into predictive models for invasion risk assessment

    Shixiang DaiFalk HarnischMicjel Chávez MorejónNina Sophie Keller...
    83-90页
    查看更多>>摘要：Global climate change is expected to accelerate biological invasions,necessitating accurate risk fore-casting and management strategies.However,current invasion risk assessments often overlook adaptive genomic variation,which plays a significant role in the persistence and expansion of invasive pop-ulations.Here we used Molgula manhattensis,a highly invasive ascidian,as a model to assess its invasion risks along Chinese coasts under climate change.Through population genomics analyses,we identified two genetic clusters,the north and south clusters,based on geographic distributions.To predict invasion risks,we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability,respectively.These approaches yielded distinct predictions:the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster(i.e.,lower invasion risks),while the species distribution model indicated higher future habitat suitability for the same cluster(i.e,higher invasion risks).By integrating these models,we found that the south cluster exhibited minor genome-niche disruptions in the future,indicating higher invasion risks.Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change.Moreover,incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.
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    • NETL
    • NSTL
    • 万方数据