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Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model

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environmental Science and Pollution Research 2024年第22期31卷 32382-32406页

作者： Hai, Tao Ahmadianfar, Iman Halder, Bijay Heddam, Salim Al-Areeq, Ahmed M. Demir, Vahdettin Kilinc, Huseyin Cagan Abba, Sani I. Tan, Mou Leong Homod, Raad Z. Yaseen, Zaher Mundher School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China Ajman University Ajman United Arab Emirates Department of Civil Engineering Behbahan Khatam Alanbia University of Technology Behbahan Iran Department of Earth Sciences and Environment Faculty of Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Faculty of Science Agronomy Department University 20 Août 1955 Skikda Route El Hadaik BP Skikda26 Algeria Dhahran Saudi Arabia Department of Civil Engineering KTO Karatay University Konya42020 Turkey Department of Civil Engineering Istanbul Aydın University Istanbul Turkey GeoInformatic Unit Geography Section School of Humanities Universiti Sains Malaysia 11800 Minden Penang Malaysia Department of Oil and Gas Engineering Basrah University for Oil and Gas Basra Iraq Civil and Environmental Engineering Department King Fahd University of Petroleum & ampMinerals Dhahran31261 Saudi Arabia

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash–Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in fo

关键词： Variational mode decomposition

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MXene nanosheet-derived N, S-codoped graphene quantum dots for ultrasensitive and selective detection of 3-nitro-L-tyrosine in human serum

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Analytica Chimica Acta 2024年 1292卷 342237页

作者： Anh, Nguyen Thi Ngoc Huynh, Trung Viet Nguyen, Van Thanh Nguyen, Thi Kim Anh Doong, Ruey-an Institute of Analytical and Environmental Sciences National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu300044 Taiwan Vinh Long University of Technology Education 73 Nguyen Hue Street Vinh Long City Viet Nam Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu300044 Taiwan

3-Nitro-L-tyrosine (3NT) is an oxidative stress metabolite associated with neurodegenerative diseases such as Parkinson's disease and rheumatoid arthritis. In this study, the N, S-co-doped graphene quantum dots (NSGQDs) derived from nitrogen-doped Ti3C2Tx MXene nanosheet via the hydrothermal method in the presence of mercaptosuccinic acid was synthesized as an optical sensing probe to detect 3NT in human serum. Tetramethyl ammonium hydroxide, the nitrogen source and delamination agent, was used to prepare nitrogen-doped MXene nanosheets via one step at room temperature. The as-prepared NSGQDs are uniform with an average size of 1.2 ± 0.6 nm, and can be stable in aqueous solution for at least 90 d to serve as the fluorescence probe. The N atoms in N-MXene reduce the restacking and aggregation of MXene nanosheets, while the sulfur dopant in NSGQDs increases the quantum yield from 6.2 to 12.1 % as well as enhances the selectivity of 3NT over the other 12 interferences via coordination interaction with nitro group in 3NT. A linear range of 0.02–150 μM in PBS and 0.05–200 μM in human serum with a recovery of 97–108 % for 3NT detection is observed. Moreover, the limit of detection can be lowered to 4.2 and 7 nM in PBS and 1 × diluted human serum, respectively. Results obtained clearly indicate the potential application of the N–Ti3C2Tx derived NSGQD for effective detection of 3NT, which can open a window for the synthesis of doped GQDs via 2D MXene materials for ultrasensitive and selective detection of other biometabolites and biomarkers of neurodegenerative diseases in biological fluids. © 2024 Elsevier B.V.

关键词： Fluorescence

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A Three-Tier Incremental Approach for Development of Smart Corridor Digital Twins

A Three-Tier Incremental Approach for Development of Smart C...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Abhilasha J. Saroj Michael Hunter Somdut Roy Angshuman Guin Oak Ridge National Laboratory Vehicle and Mobility Systems Research Section Oak Ridge USA School of Civil and Environmental Engineering Georgia Institute of Technology Atlanta USA

Development of an arterial smart corridor digital twin requires the integration of real time data streams, data storage, and simulation model execution. This process may be complex, time consuming, and susceptible to errors. To aid in smart corridor digital twin development this paper seeks to provide a framework, best practices, and development guidance. As such, a three-tier incremental approach to smart corridor digital twin development is presented. The paper highlights practical issues in digital twin construction along with key data challenges based on experiences from the development of digital twins for two large Smart Corridor deployments, one in Chattanooga, Tennessee, and the other in Atlanta, Georgia. The presented three-tier incremental approach includes: 1) development of a prepopulated offline simulation, 2) development of a pseudo digital twin that is driven by archived data, and 3) integration of real time data streams to create the online digital twin model. The three-tiered approach facilitates conducting multiple trials and scenarios with increasing complexity, allowing for incremental error processing and updating of the digital twin.

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Inter- and sub-harmonics estimation: Treatise on raptor-inspired Harris Hawks collective wisdom

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International Journal of Modern Physics C 2024年第12期35卷

作者： Malik, Naveed Ahmed Chang, Ching-Lung Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Shu, Chi-Min Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology University Road Douliu 123Section 3 Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA's performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10^-3 to 10^-10, an evidence of valuable precision, stability and robustness of the proposed algorithm. © 2024 World Scientific Publishing Company.

关键词： harmonics analysis Harris Hawks optimization parameter estimation Power quality swarm intelligence

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Remediation of carcinogenic PAHs from landfill leachate by Electro-Fenton process – Optimization and modeling

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Chemosphere 2024年 359卷 142248页

作者： Singa, Pradeep Kumar Rajamohan, Natarajan Isa, Mohamed Hasnain Azner Abidin, Che Zulzikrami Ibrahim, Abdul Haqi Department of Civil Engineering Guru-Nanak Dev Engineering College Karnataka Bidar585403 India Civil and Environmental Engineering Department Universiti Teknologi PETRONAS 32610 Malaysia Chemical Engineering Section Faculty of Engineering Sohar University PC-311 Sohar Oman Department of Civil Engineering Universiti Teknologi Brunei Jalan Tungku Link GadongBE1410 Brunei Faculty of Civil Engineering and Technology Universiti Malaysia Perlis Perlis 01000 Malaysia Water Research Group School of Environmental Engineering Universiti Malaysia Perlis Perlis 01000 Malaysia

PAHs is the group of emerging micro-pollutants present in most environmental matrices that has the tendency to bioaccumulate and cause carcinogenic effects to human health. The present research involved the quantification and treatment of leachate produced from secured landfill, to eliminate the PAHS. Electro-Fenton process, a class of advanced oxidation process, is adopted to degrade the PAHs using titanium electrodes as both anode and cathode. Artificial intelligence based statistical tool "Central Composite Design" a module of JMP -19 software was used to design the experiments and optimize the critical parameters involved in the research. It was observed that the value of P is significant (P 2 obtained was 0.96 and 0.97 for COD and PAHs respectively. The maximum removal efficiency of COD and PAH was found to be 84.24% and 90.78% respectively. The optimized conditions obtained from the central composite design were: pH = 5;Fe2+ = 0.1 g/L;H2O2 = 2 g/L;reaction time = 60 min;and electric intensity = 0.2 A. Additionally, optimized experimental conditions were used to study the removal efficiencies of individual 16 PAHs and are also reported. From the close proximity of experimental and predicted results of the software it can be proved that central composite design is efficient enough to be used as a statistical tool in design and analysis for treatment of landfill leachate. © 2024 Elsevier Ltd

关键词： Efficiency

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Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States

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Agricultural Water Management 2023年 283卷

作者： Valipour, Mohammad Khoshkam, Helaleh Bateni, Sayed M. Jun, Changhyun Band, Shahab S. Department of Engineering and Engineering Technology Metropolitan State University of Denver DenverCO80217 United States Department of Civil and Environmental Engineering and Water Resources Research Center University of Hawaii at Manoa HonoluluHI96822 United States Department of Civil and Environmental Engineering College of Engineering Chung-Ang University Seoul06974 Korea Republic of Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The daily reference evapotranspiration (ETo) must be accurately forecasted to improve real-time irrigation scheduling and decision-making for water resources allocation. In this study, multi-step (i.e., 1, 3, 7, and 10)-ahead daily ETo at 30 sites is forecasted using three hybrid machine learning approaches: wavelet long short-term memory (WLSTM), wavelet group method of data handling (WGMDH), and wavelet genetic algorithm-adaptive neuro-fuzzy inference system (WGA-ANFIS). The 30 sites are chosen to sample nine climate regions across the contiguous United States. Three input scenarios are considered. This study emphasizes on forecasting ETo using limited meteorological variables. In the first scenario, we consider only solar radiation (Rs) as the input variable owing to the largest correlation coefficient (R) between ETo and Rs compared with the other meteorological variables in most of the study sites. In the second scenario, in addition to Rs, the daily maximum (Tx), minimum (Tn), and mean (Tm) air temperatures are used. The input variables for the third scenario are Rs, Tx, Tn, Tm, and the relative humidity (RH). Data pertaining to 2005–2014 and 2015–2019 are used for the training and forecasting phases, respectively. The model forecasts are compared against ETo estimates from the Penman–Monteith (PM) equation. The third input scenario yields the most accurate results based on the average over all the study sites. In this input scenario, the WLSTM outperforms the other models for 1-day-ahead ETo forecasting in terms of the 30-site average root mean square error (RMSE) = 0.541 mm/d, Nash–Sutcliffe coefficient (NS) = 0.946, and R = 0.973. In contrast, WGMDH outperforms WLSTM and WGAANFIS for 3-, 7-, and 10-day-ahead ETo forecasting with RMSEs of 0.636, 0.649, and 0.651 mm/d;NS of 0.925,0.922, and 0.921;and R of 0.962, 0.961, and 0961, respectively. The highest performances of all models are observed in the Northwest and West climate regions, which exhibit the stron

关键词： Fuzzy inference

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The application of strategy based on LSTM for the short-term prediction of PM2.5 in city

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Science of the Total Environment 2024年 906卷

作者： Lin, Min-Der Liu, Ping-Yu Huang, Chi-Wei Lin, Yu-Hao Department of Environmental Engineering National Chung Hsing University 145 Xingda Rd. Taichung402 Taiwan General Education Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Many cities have long suffered from the events of fine particulate matter (PM2.5) pollutions. The Taiwanese Government has long strived to accurately predict the short-term hourly concentration of PM2.5 for the warnings on air pollution. Long Short-Term Memory neural network (LSTM) based on deep learning improves the prediction accuracy of daily PM2.5 concentration but PM2.5 prediction for next hours still needs to be improved. Therefore, this study proposes innovative Application-Strategy-based LSTM (ASLSTM) to accurately predict the short-term hourly PM2.5 concentrations, especially for the high PM2.5 predictions. First, this study identified better spatiotemporal input feature of a LSTM for obtaining this Better LSTM (BLSTM). In doing so, BLSTM trained by appropriate datasets could accurately predict the next hourly pollution concentration. Next, the application strategy was applied on BLSTM to construct ASLSTM. Specifically, from a timeline perspective, ASLSTM concatenates several BLSTMs to predict the concentration of PM2.5 at the following next several hours during which the predicted outputs of BLSTM at this time t was selected and included as the inputs of the next BLSTM at the next time t + 1, and the oldest input used as BLSTM at the time t was removed. The result demonstrated that BLSTM were trained by the dataset collected from 2008 to 2010 at Dali measurement station because there is a relatively large amount of data on high PM2.5 concentration in this dataset. Besides, a comparison of the performance of the ASLSTM with that of the LSTM was made to validate this proposed ASLSTM, especially for the range of higher PM2.5 concentration that people concerned. More importantly, the feasibility of this proposed application strategy and the necessity of optimizing the input parameters of LSTM were validated. In summary, this ASLSTM could accurately predict the short-term PM2.5 in Taichung city. © 2023 Elsevier B.V.

关键词： Long short-term memory

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Simultaneous Removal of Cyanobacteria and Algal Organic Matter (Aom) with CacO3 Precipitation Strategy

SSRN

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SSRN 2024年

作者： Nie, Xiaobao Li, Yinan Ouyang, Shuai Wang, Zhengbo Wang, Guoqi School of Hydraulic & Environmental Engineering Changsha University of Science and Technology No. 960 Section 2 Wanjiali South Road Tianxin District Changsha410114 China Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province Changsha410114 China

Simultaneous removal of cyanobacteria and algal organic matter (AOM) is still a difficult task for drinking water treatment plants (DWTPs) during cyanobacteria blooms. CaCO3 precipitation, an environment-friendly drinking water treatment technique, has been recommended to removal cyanobacteria or AOM from drinking water. However, literature concerning simultaneous removal performance is rare. Here, the simultaneous removal performance to cells and AOM of CaCO3 precipitation, combined with polyaluminum chloride (PACl) coagulation, was investigated, as well as the underlying mechanisms. The results showed that CaCO3 precipitation strategy significantly improved the simultaneous removal performance of PACl, with removal efficiency up to 98% for cells and 48% for AOM. CaCO3 precipitation exhibited reliable simultaneous removal effects at various initial cells densities and initial AOM concentrations. Scanning electron microscope (SEM), transmission electron microscope (TEM) and zeta potential results suggested that mutual flocculation between positively charged CaCO3 crystalline particles and negatively charged cells is the main responsible mechanism for cells removal. Despite that the adsorption of AOM on CaCO3 nuclei surface delays its precipitation, co-precipitation removal mechanism of AOM is realized during CaCO3 nucleation. Moreover, further AOM removal can be obtained by CaCO3 crystalline particles via chemisorption between organic molecules and the particles surface. © 2024, The Authors. All rights reserved.

关键词： Flocculation

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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Effect of bio-oil on low-intermediate temperature properties of organosolv lignin-bitumen 6

Effect of bio-oil on low-intermediate temperature properties...

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6th Chinese-European Workshop on Functional Pavement Design, CEW 2020

作者： Zhang, Y. Liu, X. Ren, S. Jing, R. Gard, W. Apostolidis, P. Erkens, S. Skarpas, A. Section of Pavement Engineering Delft University of Technology Delft Netherlands School of Highway Chang’an University Xi’an China Department of Civil Infrastructure and Environmental Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9780367726102

Lignin, one of the most abundant natural polymers, has been extensively studied as liquid or solid additive in bituminous binders. Despite the fact the organosolv lignin in bitumen improves the overall resistance against oxidative aging, lignin could lead to binders of high thermal cracking sensitivity. In this study, a bio-based oil is implemented in a lignin modified bitumen to ameliorate characteristics, such as fatigue and thermal cracking resistance. Pressure aging vessel conditioning was applied to new binders formulated by different proportions of bio-oil to simulate the long-term aging. A series of rheological tests were performed. Based on the linear amplitude sweep test results, fatigue damage of lignin-bitumen could be reduced by increasing the oil content. According to relaxation test results, the addition of oil significantly decreased the ratio of residual stress and relaxation time. This study has shown preliminary conclusions on the use of bio-oil to improve the low-intermediate temperature performance of lignin-bitumen binders. © 2021 Taylor & Francis Group, London,

关键词： Lignin

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