检索结果-南通市图书馆

Big Data Analytics with Artificial Intelligence Enabled Environmental Air pollution monitoring Framework

Computers, Materials & Continua 2022年第11期73卷 3235-3250页

作者： Manar Ahmed Hamza Hadil Shaiba Radwa Marzouk Ahmad Alhindi Mashael M.Asiri Ishfaq Yaseen Abdelwahed Motwakel Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia&Research and InnovationSalla Holding LimitedMakkahSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent *** this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is *** addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution *** improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

关键词： Sustainability environmental air quality predictive model pollution monitoring statistical models artificial intelligence

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

pollution　monitoring　of　marine　organisms　grown　in　the　Zhoushan　Sea　of　China

引用

Journal of Environmental Sciences 1997年第3期9卷 35-39页

作者： Zhou Qixing, Zhu Yinmei Department of Environmental Sciences, Zhejiang Agricultural University, Hangzhou 310029, China

The sustainable regeneration of the biggest fishery resource in the Zhoushan Sea area of China has been adversely affected in recent years. Wastewater discharged into the marine ecosystem is the main source of pollution. Affected organisms such as hairtail prawn, jellyfish, crab, laver and kelp were monitored, and the contributions and fluxes of three sort of pollutants(oils, Cr and phenol) from the expansion of rural enterprises in the Yangtze River valley, the Qiantang River valley, the Ningbo coastal area and the Zhoushan islands were calculated. More than 16 chemical pollutants were jointly responsible for the decrease in the yield and quality of marine organisms. Furthermore, combined contamination effects and their joint toxicity differed between summer and winter, because they were varied with different temperature, salinity, pH and E h.

关键词： pollution monitoring marine environment affected organism.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Study on the monitoring Malfunction of Water pollution during Drought or Flood Period and Low-carbon and High-value Methodology--A Case Study of the Correlation Test of Water,Soil and Gas pollution in Xiangxiang County

引用

Meteorological and Environmental Research 2011年第8期2卷 67-73页

作者： LI Jin-song LI Lin-jie College of Chemistry and Chemical Engineering Central South UniversityChangsha 410083China Postdoctoral Research Station of Chemical Engineering and Technology Central South UniversityChangsha 410083China

Based on the low-carbon and high-value methodology of chemical ecology and chemical informatics,combining theory and methods,taking saving,environmental protection,low carbon,high production,high value and circulation as values and aims,the relationship between human and land as a basis,ecosystem as a center,overall control as a goal and agricultural ecological engineering as a mean,environmental pollution detection,as one of bottlenecks for agricultural products and food security,should be solved firstly;through the field survey in dry years from 2009 to 2010 when drought and flood were frequent and the frequency of drought was higher than that of flood,plus the determination of surface water flow and water quantity in a small typical river basin,the correlation of local water,soil and gas in the county could be found,and the transfer of monitoring focus from water environment to atmospheric environment was possible and *** study would promote the quantitative research on the correlation among water,soil and gas,and the results were in accordance with the conclusions of related studies.

关键词： pollution monitoring Representative Accuracy Correlation among water,soil and gas data Low-carbon and high-value methodology China

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Study of cadmium(Cd)-induced oxidative stress in Eisenia fetida based on mathematical modelling

引用

Pedosphere 2021年第3期31卷 460-470页

作者： Yucui NING Haoran ZHOU Enze WANG Congmin JIN Ying YU Xu CAO Dongxing ZHOU College of Resources and Environmental Science Northeast Agricultural UniversityHarbin 150030China College of Horticulture Science and Engineering Shandong Agricultural UniversityTaian 271018China Department of Grain Engineering Heilongjiang Communications PolytechnicQiqihar 161002China Institute of Microbiology Heilongjiang Academy of SciencesHarbin 150030China

As a sensitive biological indicator,earthworms are widely used to monitor various pollutants of soil and provide an early warning for soil ***,because many indices are involved in the exposure-induced oxidative stress response,practical applications of these indices are quite ***,it is appropriate to investigate the key monitoring index for use in early warning and pollution *** Eisenia fetida as an experimental model in an indoor simulation experiment,the mathematical modelling of the effect on oxidative stress in earthworms under cadmium(Cd)stress was *** test lasted 40 d,with the removal of one earthworm every 10 *** Cd2+concentration gradient was set as 0,1,10,20,100,200,400,and 800 mg kg^(-1) dry *** earthworms were divided into two sections from the clitellum for the determination of total protein(TP)and peroxidase(POD),superoxide dismutase(SOD),glutathione-S-transferase(GST),glutathione peroxidase(GPX),catalase(CAT),malondialdehyde(MDA),and acetylcholinesterase(AChE)*** showed that POD was the key index of oxidative stress in head tissues after 10 d of exposure,TP was the key index at 20 d,and POD became the key index again at 30 and 40 *** contrast,in tail tissues,MDA and SOD were the key indices at an exposure time of 10 d,GPX at 20 d,CAT and TP at 30 d,and POD and MDA at 40 *** results contribute to establishing a scientific method for ecotoxicological diagnosis and revealing the mechanism of soil Cd toxicity.

关键词： biological indicator biomarker earthworm factor analysis heavy metal pollution monitoring natural soil soil pollutant TOPSIS

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Atmospheric remote sensing with MiniMPL

Atmospheric remote sensing with MiniMPL

引用

第九届海峡两岸气溶胶技术研讨会暨第二届海峡两岸环境保护...

作者： Yunhui Zheng Savyasachee Mathur Brian Kamamia Daniel Caplan Dan Reed Ed Leventhal Taylor Jones Phil DeCola Sigma Space Corp

There is a trend in Lidar industry to develop compact,lightweight and low power consumption Aerosol Lidar for fast and ubiquitous monitoring of the atmospheric *** paper introduces a new product Mini Micro Pulse Lidar（MPL）from Sigma Space *** advantages for aerosol measurements with the new MiniMPL design are *** affecting mobile measurement quality such as thermal stability are also *** MiniMPL data is compared to the data from Legacy Micro Pulse Lidar（MPL）.The results show good *** some data from NASA Discover-AQ campaign are presented,in which three MiniMPLs are deployed to monitor to air quality across the Washington DC area in summer 2011.

关键词： Aerosol Lidar thermal stability pollution monitoring fast deployment

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Investigating of Correlation between MODIS Satellite Data and Ground Data Related to Aerosols and Modeling Aerosols Using Neural Networks over Shiraz

Investigating of Correlation between MODIS Satellite Data an...

引用

2012 Asian Pacific Conference on Energy,Environment and Sustainable Development(APEESD 2012)

作者： Alireza Dehghani Ali Akbar Safavi Mohammad Jafar Nazemossadat Seyed Mohammad Hessam Mohammadi School of E.Learning instrumentation and automation Shiraz University School of Electrical and Computer Engineering Shiraz University School of Agriculture Shiraz University School of E. Learning instrumentation and automation Shiraz University

This paper presents an investigation of satellite data and ground data about aerosols and then modelsthe mentioned data over Shiraz using artificial neural networks. MODIS satellite data are available on 36 various frequency bands. In this study, a good correlation between ground data and the 10 first satellite image bands is being shown. Specially, the best correlation was found in band number 8. Therefore, using neural networks and ground data along with satellite information, a model of aerosols is constructed. In the mentioned model, satellite data of band 8 and ground data are used as network input and output, respectively. The results show the effectiveness of the proposed model.

关键词： Aerosols pollution monitoring Modeling Neural Network Shiraz MODIS

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：