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Prospecting toxicity of the avobenzone sunscreen in plants

environmental Science and Pollution Research 2024年第31期31卷 44308-44317页

作者： Beijora, Sara Splendor Vaz, Thalia Aparecida Correia Santo, Diego Espirito de Almeida, Edson Araújo Junior, Osvaldo Valarini Parolin, Mauro da Silva Gonzalez, Regiane de Souza, Débora Cristina Peron, Ana Paula Chemical Engineering Course Federal Technological University of Paraná Campo Mourão Paraná Brazil Graduate Program in Environmental Engineering Federal Technological University of Paraná Paraná Francisco Beltrão Brazil Graduate Program in Chemistry State University of Maringá Paraná Maringá Brazil Academic Department of Food and Chemical Engineering Federal Technological University of Paraná Paraná Campo Mourão Brazil Graduate Program in Geography State University of Maringá Paraná Maringá Brazil Graduate Program in Food Technology Federal Technological University of Paraná Paraná Campo Mourão Brazil Graduate Program in Water Resources and Environmental Engineering Federal University of Paraná Paraná Brazil Graduate Program in Technological Innovations Federal Technological University of Paraná Via Rosalina Maria Dos Santos 1233 Paraná Campo Mourão87.301-899 Brazil

Avobenzone (AVO) is a sunscreen with high global production and is constantly released into the environment. Incorporating sewage biosolids for fertilization purposes, the leaching from cultivated soils, and the use of wastewater for irrigation explain its presence in the soil. There is a lack of information about the impact of this sunscreen on plants. In the present study, the ecotoxicity of AVO was tested at concentrations 1, 10, 100, and 1,000 ng/L. All concentrations caused a reduction in root growth of Allium cepa, Cucumis sativus, and Lycopersicum esculentum seeds, as well as a mitodepressive effect, changes in the mitotic spindle and a reduction in root growth of A. cepa bulbs. The cell cycle was disturbed because AVO disarmed the enzymatic defense system of root meristems, leading to an accumulation of hydroxyl radicals and superoxides, besides lipid peroxidation in cells. Therefore, AVO shows a high potential to cause damage to plants and can negatively affect agricultural production and the growth of non-cultivated plants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Cell proliferation

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Unveiling the mechanisms of Fe(Ⅲ)-loaded chitosan composite(CTS-Fe)in enhancing anaerobic digestion of waste activated sludge

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Journal of environmental Sciences 2024年第4期138卷 200-211页

作者： Boaiqi Zhang Ziwen Zhao RuiMa Nan Chen Zhe Kong Zhongfang Lei Zhenya Zhang Graduate School of Life and Environmental Sciences University of Tsukuba1-1-1 TennodaiTsukubaIbaraki 305-8572Japan The Key Laboratory of Water and Sediment Sciences Ministry of Education/State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems/College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China South China Institute of Environmental Sciences Ministry of Ecology and EnvironmentGuangzhou 510345China School of Water Resources and Environment China University of Geosciences(Beijing)Beijing 100083China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou 215009China

Anaerobic digestion(AD)of waste activated sludge(WAS)is usually limited by the low generation efficiency of ***(Ⅲ)-loaded chitosan composite(CTS-Fe)have been reported to effectively enhanced the digestion of WAS,but its role in promoting anaerobic sludge digestion remains *** present study,the effects of CTS-Fe on the hydrolysis and methanogenesis stages of WAS anaerobic digestion were *** addition of CTSFe increased methane production potential by 8%-23%under the tested conditions with the addition of 5-20 g/L ***,the results demonstrate that the addition of CTS-Fe could effectively promote the hydrolysis of WAS,evidenced by lower protein or polysaccharides concentration,higher soluble organic carbon in rector adding CTS-Fe,as well as the increased activity of extracellular hydrolase with higher CTS-Fe ***,the enrichment of Clostridia abundance(iron-reducing bacteria(IRBs))was observed in CTS-Fe adding reactor(8.9%-13.8%),which was higher than that in the control reactor(7.9%).The observation further suggesting the acceleration of hydrolysis through dissimilatory iron reduction(DIR)process,thus providing abundant substrates for ***,the presence of CTS-Fe was inhibited the acetoclastic and hydrogenotrophic methanogenesis process,which could be ascribed to the Fe(Ⅲ)act as electron acceptor coupled to methane for anaerobic ***,coenzyme F420 activity in the CTS-Fe added reactor was 34.9% lower than in the blank,also abundance of microorganisms involved in hydrogenotrophic methanogenesis was *** from this study could provide theoretical support for the practical applications of CTS-Fe.

关键词： Chitosan-Fe(Ⅲ)composite Methanogenesis Dissimilatory iron reduction Enzyme activity Microbial community structure

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Enhanced nitrogen removal from low strength anaerobic membrane bioreactor(AnMBR)permeate using complete nitrification and partial denitrification-anammox processes

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Frontiers of environmental Science & engineering 2024年第12期18卷 153-166页

作者： Jingwei Fu Zhaoyang Hou Hexiang Zhao Qian Li Rong Chen Yu-You Li Key Laboratory of Environmental Engineering Shaanxi ProvinceXi’an University of Architecture and TechnologyXi’an 710055China Hualu Engineering&Technology Co. Ltd.Xi’an 710000China International Science&Technology Cooperation Center for Urban Alternative Water Resources Development Key Laboratory of Northwest Water ResourceEnvironment and EcologyMOEXi’an University of Architecture and TechnologyXi’an 710055China Department of Civil and Environmental Engineering Graduate School of EngineeringTohoku UniversitySendai Miyagi 980-8579Japan

In this study,an anaerobic membrane bioreactor coupled with a complete nitrification and partial denitrification-anammox process(AnMBR-NF-PDA)was developed to efficiently remove both chemical oxygen demand(COD)and *** control of raw water ratios was utilized to adjust the ratio of COD/NO_(3)^(-)-N,resulting in maximum nitrogen removal efficiency of 90.14% at a ratio of ***,specific anammox activity(SAA)increased with the proportion of raw water,peaking at 17.83 mg-N/(g-VSS∙d)in stage Ⅱ before *** variation was attributed to the significant presence of filamentous bacteria,especially“Acinetobacter”(13.58%-31.59%),which facilitated nitrite generation,supporting the nitrous oxide hypothesis in partial denitrification processes and enabling cross-feeding with ***,the average particle size of granular sludge increased from 300 to 528μm under the influence of filamentous *** analysis revealed an upsurge in genes associated with partial denitrification(NarG and NapA)as the COD/NO_(3)-N ratio *** abundance of genes closely correlated with anammox(Hzs and Hdh)peaked during stage Ⅱ,indicating the beneficial role of filamentous bacteria in the stable conversion of nitrite in PDA *** study offers valuable insights into optimizing the synergistic metabolism and granulation processes involving filamentous bacteria and AnAOB,thereby laying the groundwork for the practical application of AnMBR coupled with anammox processes in wastewater treatment.

关键词： Anammox Anaerobic membrane bioreactor COD/NO_(3)^(-)-N Filamentous bacteria

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Deep Neural Networks for ENSO Prediction in the Niño 3.4 and Niño 1+2 Regions

Deep Neural Networks for ENSO Prediction in the Niño 3.4 an...

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World environmental and water resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Gomez-Tunque, Kennedy Richard Ingol-Blanco, Eusebio Mejia-Marcacuzco, Abel Chávarri-Velarde, Eduardo Pino-Vargas, Edwin Graduate Program in Water Resources Engineering School of Civil Engineering Universidad Nacional de Huancavelica Lima Peru Graduate Program in Water Resources Engineering Universidad Nacional Agraria la Molina Lima Peru Dept. of Civil Engineering Universidad Nacional Jorge Basadre Grohmann Tacna Peru

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

Forecasting the El Niño-Southern Oscillation (ENSO) is one of the most challenging tasks because ENSO events show large differences in their amplitude, temporal evolution, and spatial pattern. This research discusses a deep neural network model that integrates convolutional short-term and long-term memory (ConvLSTM) to forecast the patterns of sea surface temperature that represent the evolution of ENSO over different time horizons with simultaneous space-time sequences in the Niño 3.4 and Niño 1+2 regions. Monthly gridded sea surface temperature and anomaly index data were used in El Niño regions for the period 1854-2021. In general, the methodology includes the sea surface temperature (SST) normalization of the tropical Pacific, the creation and selection of the architecture of the deep neural network model, and the training and validation. Furthermore, the forecast and evaluation of the SST and SST anomalies in space-time with the El Niño of 1982/1983 and 1997/1998 and with different time horizons for the test stage were developed. The results show a good model performance with ENSO data for El Niño 1982/1983 (training) and El Niño 1997/1998 (validation) in space and time, and with climatic indices for regions 3.4 and 1+2. The model performance was evaluated using statistical metrics of observed and forecasted data over six months in the equatorial Pacific Ocean (latitude 10°S-10°N and longitude 140°E-80°W). In general, all the metrics used to evaluate the accuracy and forecast of the model show a range from good to excellent for both El Niño regions in the six months of forecast. © World environmental and water resources Congress *** rights reserved

关键词： Surface temperature

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Methanation and chemolitrophic nitrogen removal by an anaerobic membrane bioreactor coupled partial nitrification and Anammox

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Frontiers of environmental Science & engineering 2023年第6期17卷 37-47页

作者： Qian Li Zhaoyang Hou Xingyuan Huang Shuming Yang Jinfan Zhang Jingwei Fu Yu-You Li Rong Chen Key Laboratory of Environmental Engineering of Shaanxi Province Xi'an University of Architecture and TechnologyXi'an 710055China Department of Civil and Environmental Engineering Graduate School of EngineeringTohoku UniversitySendai Miyagi 980-8579Japan International S&T Cooperation Center for Urban Alternative Water Resources Development Key Laboratory of Northwest Water ResourceEnvironment and Ecology(Ministry of Education)Xi'an University of Architecture and TechnologyXi'an 710055China

An AnMBR-PN/A system was developed for mainstream sewage *** verify the efficient methanation and subsequent chemolitrophic nitrogen removal,a long-term experiment and analysis of microbial activity were carried *** performance was less affected by the change of hydraulic retention time(HRT),which could provide a stable influent for subsequent PN/A *** COD removal efficiency of AnMBR was>93%during the experiment,85.5%of COD could be recovered in form of *** the HRT of PN/A being shortened from 10 to 6 h,nitrogen removal efficiency(NRE)of PN/A increased from 60.5%to 80.4%,but decreased to 68.8%when the HRTPN/A further decreased to 4 *** analysis revealed that the highest specific ammonia oxidation activity(SAOA)and the ratio of SAOA to specific nitrate oxidation activity(SNOA)provide stable NO_(2)^(−)-N/NH_(4)^(+)-N for anammox,and anammox bacteria(mainly identified as Candidatus Brocadia)enriched at the bottom of Anammox-UASB might play an important role in nitrogen *** addition,the decrease of COD in Anammox-UASB indicated partial denitrification occurred,which jointly promoted nitrogen removal with anammox.

关键词： Anaerobic membrane bioreactor Partial nitrification/Anammox Carbon separation Chemolitrophic nitrogen removal

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ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE UNIVERSITY RESTAURANT-UNB 32nd

ANALYSIS OF NITROGEN FLOW IN FOOD WASTE: A CASE STUDY OF THE...

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32nd European Biomass Conference and Exhibition, EUBCE 2024

作者： Ramos, Arthur Augusto Frasson De Brites, Eneida Campos Felipe Soares, Raquel Moraes Silveira, Edgar A. Contreras, Francisco University of Brasilia Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil University of Brasilia Graduate Program on Environmental Technology and Water Resources Department of Civil and Environmental Engineering DF Brasilia70910-900 Brazil Federal Institute of Education Science and Technology of Brasília Estrutural Campus DF Brasília71250-000 Brazil University of Brasília Mechanical Sciences Graduate Program Laboratory of Energy and Environment DF Brasilia70910-900 Brazil

With the projected global population reaching 9.7 billion by 2050, the production and consumption of food continue to escalate. Nitrogen is pivotal in this landscape due to its significance in living organisms and the environmental repercussions associated with nutrient-rich waste. Inappropriate disposal of such residues contributes to nitrogen release into the environment, impacting aquatic ecosystems and precipitating the generation of greenhouse gases. This study tackles the global challenge of effectively managing nitrogen in food waste by utilizing Material Flow Analysis (MFA) as a tool to comprehend this cycle. The analysis uncovered that vegetables, legumes, and fruits constitute the primary sources of waste generation, while meats, despite their lower mass, account for a substantial proportion of total nitrogen depletion. In the surveyed month of October 2023, 174,834 meals were served, resulting in an average food consumption N-footprint of 0.003 kg of nitrogen discarded per individual meal within the restaurant’s organic waste. These findings indicate that 65% of nitrogen is consumed in meal form, while 35% is discarded as organic solid waste. However, 53% of the nitrogen in the residues originates from food preparation processes, with food preparation responsible for over half of this figure. A deeper process analysis reveals that vegetables have low nitrogen concentrations, although they significantly contribute to waste at all stages. In contrast, meats and eggs, with higher nitrogen concentrations, emerge as noteworthy contributors to the overall nitrogen content in waste. Vegetables and meats contribute about 50% and 45% of the total nitrogen, respectively. These outcomes substantially enhance our comprehension of waste generation dynamics and nutrient utilization at the university restaurant, assisting with waste management, design of sustainable menus, reduction of food waste, and optimized resource utilization, contributing to sustainability and re

关键词： Effluent treatment

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Assessment of the Impact of COVID-19 Lockdown on the Spatio-Temporal Distribution of PM2.5 in Thailand 1

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8th Asia Conference on Environment and Sustainable Development, ACESD 2023

作者： Simatupang, Cathleen Ariella Piyaviriyakul, Pitchaya Pongkiatkul, Prapat Strezov, Vladimir Boontanon, Narin Jindal, Ranjna Boontanon, Suwanna Kitpati Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom73170 Thailand School of Natural Sciences Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia Department of Environmental Engineering King Mongkut’s University of Technology Thonburi 10800 Bangkok Thailand Faculty of Environment and Resource Studies Mahidol University Nakhon Pathom73170 Thailand Environmental Engineering and Management Program Department of Energy Environment and Climate Change School of Environment and Resources Development Asian Institute of Technology 12120 Pathumthani Thailand Graduate School of Global Environmental Studies Kyoto University 606-8501 Yoshida Sakyo-Ku Kyoto Japan

ISBN: (数字)9789819733200

ISBN: (纸本)9789819733194

During COVID-19 pandemic several countries have implemented quarantines in response to the virus’ spread, which was considered an emergency. Thailand was under a state of emergency from January to December 2020, which has had an impact on the state of environment. Based on real-time data from the Pollution Control Department, the impact of COVID-19 lockdown on PM2.5 pollution in Thailand was examined. Inverse Distance Weighted interpolation was used to generate a more accurate visual representation of the PM2.5 pollution through a spatial distribution of atmospheric pollutants across the provinces of Thailand. PM2.5 levels in all considered areas reduced considerably between 2017 and 2020, with the most significant decrease occurring during the lockdown period of April-July in 2020. As the pre-COVID-19 period occurred during late winter in Thailand, the monthly average PM2.5 concentrations in all selected provinces were gradually increased from December 2019 to March 2020. During the lockdown period, the concentration of PM2.5 was reduced in Pathum Thani (48.9%), Samut Sakhon (35.6%) Samut Prakarn (33.6%), and Bangkok (31.7%) The spatial–temporal distribution of the PM2.5 levels in Samut Sakhon and Samut Prakarn provinces were analyzed for October 2020, during the new normal period. The results showed a significant decrease in concentration of up to 31% compared to previous years where industrial operations were situated. This study showed that the government enforced lockdown during COVID-19 pandemic was beneficial in understanding the impact of reduced human activities on anthropogenic air pollutants’ concentrations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Identification of Extreme Precipitation Changes Due to Climate Change in Indonesia 3rd

Identification of Extreme Precipitation Changes Due to Clima...

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3rd International Conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Aminah, Siti Riawan, Edi Syahputra, Muhammad Ridho Kuntoro, Arno Adi Master Program in Water Resource Management Faculty of Civil and Environmental Engineering Bandung Institute of Technology Bandung Indonesia Atmospheric Sciences Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung Indonesia Water Resources Engineering Research Group Faculty of Civil and Environmental Engineering Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9789819707393

This study uses Global Precipitation Measurement (GPM) and Coupled Model Intercomparison Project Phase 6 (CMIP6) precipitation data to identify extreme precipitation intensity and frequency changes in Indonesia. Extreme precipitation calculations were performed using the Gumbel distribution and return period fitted to annual maxima data. Extreme precipitation and frequency of extreme events were compared between the historical period and the near-future period (2026–2055) and the far-future period (2070–2099) to identify changes. The results showed that there were changes in extreme precipitation in Indonesia both in intensity and frequency of occurrence. Extreme precipitation with a 5-year return period in Indonesia generally has a change value in the range of − 5 to + 25% with most areas likely to increase. The increased value reaches + 20% in the near-future period and + 40% in the far-future period. For the frequency of extreme precipitation above the 5-year return period in Indonesia, the change value is generally in the range of − 40 to + 120% with most areas likely to increase. The increased value reaches + 160% in the near-future period and + 200% in the far-future period. In the higher emission scenario (SSP5-8.5), the areas with higher values of increase will expand both in the change in intensity and frequency of occurrence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Climate change

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Development of Rainfall-Runoff Model Using Mock Formula with the Calibration of Stream Discharge in Cisadane watershed - Indonesia 1st

Development of Rainfall-Runoff Model Using Mock Formula with...

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1st International Conference on Emerging Smart Cities, ICESC 2022

作者： Hidayat, Dina P. A. Legowo, W. D. Sri Farid, Mohammad Doctoral Program of Civil Engineering Institut Teknologi Bandung Bandung Indonesia Water Resources Engineering Research Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Bandung Indonesia Civil Engineering Department Universitas Trisakti Jakarta Indonesia

ISBN: (纸本)9789819911103

Rainfall-runoff model is one way to model the amount of rainfall that will become runoff and baseflow. Many models have been developed such as the Mock model which is quite simple but has a fairly good level of accuracy. This study aims to model rainfall runoff with the Mock formula and calibrate the model with measured discharge at one discharge station. This study uses the Cisadane watershed in West Java Indonesia for the location study and Serpong station as the discharge station. The modeling will be carried out with 2 scenarios, a monthly rainfall-runoff model in 1 year and the average monthly rainfall-runoff model for several years. The calibration parameter used is Nash Sutcliffe Efficiency (NSE) and coefficient of determination (R2). From the model results, it is obtained that the model calibration results with a fairly good measured discharge with a satisfactory NSE value and R2 is close to 1. © 2024, Institute of Technology PETRONAS Sdn Bhd.

关键词： Rain

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Assessing the Performance of Machine Learning Algorithms for water Level Prediction in the Chao Phraya River and its Tributaries: A Focus on Low and High water Levels

SSRN

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SSRN 2024年

作者： Priyasiri, Wilmat D.S.M. Rittima, Areeya Kraisangka, Jidapa Sawangphol, Wudhichart Phankamolsil, Yutthana Talaluxmana, Yutthana Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Faculty of Information and Communication Technology Mahidol University Thailand Environmental Engineering and Disaster Management Program Mahidol University Kanchanaburi Campus Thailand Department of Water Resources Engineering Faculty of Engineering Kasetsart University Thailand

High precision of hydrological prediction is crucial for real–time operation of flood and drought risk mitigation and strategic planning. This study assessed the predictive performances of three Machine Learning (ML) algorithms;XGBoost, Random Forest (RF), and Multi–Layer Neural Networks (MLNNs) for water level prediction with a focus on low and high water levels. The one–day and one–week water level prediction models for six key gauged stations along the Chao Phraya River and its major tributaries were accordingly developed. Selecting the combination of input features was carried out and structured in the prediction model at all gauged stations based on the physical river–reservoir system using past water level, rainfall, controlled reservoir outflow, and upstream discharges with different travel times. The statistical evaluation indicated that both XGBoost and RF, the tree–based ML models, with rainfall input robustly outperformed than MLNNs, as it strongly achieved higher R2 from 0.957 to 0.999 for model training and from 0.743 to 0.995 for model testing and lower MAE, MSE, and RMSE values for all prediction scenarios. Among these three ML algorithms applied for daily prediction, RF demonstrated the superior performance for low water level prediction exhibiting the smallest percentage error of overestimating lying between +0.0088% and +0.9380%. XGBoost, RF, and MLNNs, demonstrated acceptable performance level for high water level prediction as it shows small percentage error of both overestimating and underestimating lying between –2.2696% and +1.1587%. Additionally, it can capture the entire testing dataset with high precision than weekly model. © 2024, The Authors. All rights reserved.

关键词： Rain

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