Impact of the As Samra wastewater treatment plant upgrade on the water quality (COD, electrical conductivity, TP, TN) of the Zarqa River

2013 ◽  
Vol 67 (7) ◽  
pp. 1455-1464 ◽  
Author(s):  
A. Al-Omari ◽  
Z. Al-houri ◽  
R. Al-Weshah
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Time Series ◽  
Wastewater Treatment ◽  
Time Series Data ◽  
Wastewater Reuse ◽  
Treatment Plant ◽  
Series Data ◽  
Before And After ◽  
Zarqa River

The impact of the As Samra wastewater treatment plant upgrade on the quality of the Zarqa River (ZR) water was investigated. Time series data that extend from October 2005 until December 2009 obtained by a state-of-the-art telemetric monitoring system were analyzed at two monitoring stations located 4 to 5 km downstream of the As Samra effluent confluence with the Zarqa River and about 25 km further downstream. Time series data that represent the ZR water quality before and after the As Samra upgrade were analyzed for chemical oxygen demand (COD), electrical conductivity (EC), total phosphorus (TP) and total nitrogen (TN). The means of the monitored parameters, before and after the As Samra upgrade, showed that the reductions in the COD, TP and TN were statistically significant, while no reduction in the EC was observed. Comparing the selected parameters with the Jordanian standards for reclaimed wastewater reuse in irrigation and with the Ayers & Westcot guidelines for interpretation of water quality for irrigation showed that the ZR water has improved towards meeting the required standards and guidelines for treated wastewater reuse in irrigation.

Applications of intervention analysis to model the impact of drought and bushfires of Water Quality

1989 ◽  
Vol 40 (3) ◽  
pp. 241 ◽  
Author(s):  
DR Welsh ◽  
DB Stewart
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Time Series ◽  
Time Series Data ◽  
Statistical Modelling ◽  
Quality Data ◽  
Series Data ◽  
Intervention Analysis ◽  
Water Quality Data ◽  
The Impact

Intervention analysis is a rigorous statistical modelling technique used to measure the effect of a shift in the mean level of a time series, caused by an intervention. A general formulation of an intervention model is applied to water-quality data for two streams in north-eastern Victoria, measuring the effect of drought on the electrical conductivity of one stream, and the effect of bushfires on the flow and turbidity of the other. The nature of the intervention is revealed using exploratory data-analysis techniques, such as smoothing and boxplots, on the time-series data. Intervention analysis is then used to confirm the identified changes and estimate their magnitude. The increased level of electrical conductivity due to drought is determined by three techniques of estimation and the results compared. The best of these techniques is then used to model changes in stream flow and turbidity following bushfires in the catchment.

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

Water reuse in Israel - the Dan Region Project: evaluation of water quality and reliability of plant's operation

2003 ◽  
Vol 3 (4) ◽  
pp. 231-237 ◽  
Author(s):  
N. Icekson-Tal ◽  
O. Avraham ◽  
J. Sack ◽  
H. Cikurel
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Water Reuse ◽  
Wastewater Reuse ◽  
Treatment Plant ◽  
Water Quality Monitoring ◽  
Water Shortage ◽  
Soil Aquifer Treatment ◽  
Biofilm Growth ◽  
Reclamation Project

Israel is a semi-arid country with insufficient natural water resources. Wastewater effluent reuse and desalination have become the main source of water to compensate for the future water shortage. Today, between 65 and 70% of wastewater of urban and industrial origin is reused in agriculture after treatment in biological treatment plants around the country. The Dan Region Reclamation Project (Shafdan) is the largest wastewater treatment and reclamation project in Israel. 130 Mm3/yr of reclaimed water is used for unrestricted irrigation after soil aquifer treatment (SAT). Extensive water quality monitoring is performed to keep an efficient and safe wastewater reuse system. After 25 years of operation, the Shafdan deals with the following operational issues on an ongoing basis: Biofouling of the effluent pipelines from the wastewater treatment plant to the SAT, and a lack of capacity in the SAT system. Biofilm growth in the pipelines is controlled by intermittently applying chlorine based compounds at a 10 mg/L dosage for a few hours.

scholarly journals Use of e-cigarettes by young people in Great Britain before and after Tobacco Products Directive

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
G Moore ◽  
R Brown ◽  
N Page ◽  
B Hallingberg ◽  
L Gray ◽  
...  
Keyword(s):  
Time Series ◽  
Young People ◽  
School Health ◽  
Time Series Data ◽  
Research Network ◽  
Series Data ◽  
Initial Increase ◽  
Cigarette Use ◽  
Tobacco Products ◽  
Before And After

Abstract Background Young people’s experimentation with e-cigarettes has increased in recent years, although regular use remains rare. In May 2016, the EU Tobacco Products Directive (TPD) introduced regulations aimed in part at preventing use by young people. It imposed warnings on e-cigarette packets, banned many forms of advertising, and restricted nicotine strength. This paper examines change in young people’s e-cigarette use after TPD, as well as complementary and alternative causal explanations for change, from young people’s perspectives. Methods Quantitative data sources were 2013, 2015 and 2017 School Health Research Network/Health Behaviour in School-aged Children surveys in Wales and 2014 and 2016 Smoking Drinking and Drug Use surveys in England. Data were analysed using segmented binary logistic regression in Wales, with simpler before and after analyses in England. Results were considered alongside qualitative interview data from young people aged 14-15 years in England, Wales and Scotland, collected in 2017 and 2018. Results Ever-use of e-cigarettes almost doubled from 2013-15, though subsequent increases were smaller. In Wales, where pre-legislation time series data were available, under a range of assumptions, prior growth in e-cigarette ever-use did not continue post-TPD. Change in trend post-TPD did not reach significance (OR = 0.96; 95%CI=0.91 to 1.01), but became significant after adjusting for change in smoking rates across the time-series (OR = 0.93; 95%CI=0.88 to 0.98). Regular use did not increase significantly from 2015 to 2017 in Wales, although ever and regular use in England both increased from 2014 to 2016. Young people described limited interactions with core components of TPD, while commonly describing e-cigarette use as a fad which was beginning to run its course. Conclusions Growth in youth experimentation with e-cigarettes may be slowing. Qualitative data from young people provide a range of explanations which appear largely unrelated to TPD itself. Key messages Survey data provide preliminary evidence that use of e-cigarettes may be plateauing among young people in the UK after a rapid initial increase in experimentation. Explanations position e-cigarettes as a passing fad which is beginning to lose its appeal in UK youth. Longer term monitoring of trends and perceptions remain vital.

scholarly journals Explainable AI Framework for Multivariate Hydrochemical Time Series

2021 ◽  
Vol 3 (1) ◽  
pp. 170-204
Author(s):  
Michael C. Thrun ◽  
Alfred Ultsch ◽  
Lutz Breuer
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Multivariate Time Series ◽  
Environmental Parameters ◽  
Series Data ◽  
Topographic Map ◽  
Domain Experts ◽  
Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series. The XAI provides explanations that are interpretable by domain experts. In three steps, it combines a data-driven choice of a distance measure with supervised decision trees guided by projection-based clustering. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The framework, called DDS-XAI, does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two state of the art XAIs called eUD3.5 and iterative mistake minimization (IMM) were unable to provide meaningful and relevant explanations from the three multivariate time series data. The DDS-XAI framework can be swiftly applied to new data. Open-source code in R for all steps of the XAI framework is provided and the steps are structured application-oriented.

scholarly journals Explainable AI Framework for Multivariate Hydrochemical Time Series

2020 ◽  
Author(s):  
Michael Thrun ◽  
Alfred Ultsch ◽  
Lutz Breuer
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Time Series Data ◽  
Distance Measure ◽  
Multivariate Time Series ◽  
Environmental Parameters ◽  
Series Data ◽  
Domain Expert ◽  
Topographic Map ◽  
Explainable Ai

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

scholarly journals Perbandingan Kontribusi Bruto Asuransi Syariah Sebelum dan Sesudah Pandemi Covid-19

2021 ◽  
Vol 2 (2) ◽  
pp. 142
Author(s):  
Arin Ramadhiani Soleha ◽  
Iza Hanifuddin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Insurance Industry ◽  
Series Data ◽  
Islamic Finance ◽  
Mutual Help ◽  
Paired Samples ◽  
Significant Difference ◽  
Before And After ◽  
Islamic Insurance

AbstractIslamic insurance has a big role in the Islamic finance sector with the principle of mutual help. Gross contribution is one of the funds that can be utilized for insurance participants and companies. Covid19, which has an impact on the economic sector, makes understanding the growth of sharia insurance before and after the pandemic in terms of gross contribution very important. This study aims to further review the gross contribution from March-December to find out whether there is a significant difference from the gross contribution of sharia insurance before and after the Covid-19 pandemic. The study was conducted using a comparative quantitative approach with two paired samples. The research sample uses time series data, namely the gross contribution of sharia insurance in 2019 and 2020 for the period from March to December. The results of this study found that the comparison of gross contribution to the Islamic insurance industry seen before the 2019 pandemic and after the 2020 pandemic which was taken from March to December was normally distributed. This means that the development of sharia insurance when viewed before the 2019 pandemic and after the 2020 pandemic according to the gross contributions from sharia insurance participants did not experience a significant difference and will certainly increase.AbstrakAsuransi syariah memiliki peran besar pada sektor keuangan syariah dengan prinsip saling tolongmenolong. Kontribusi bruto merupakan salah satu dana yang dapat dimanfaatkan bagi peserta asuransi maupun perusahaan. Covid-19 yang berdampak pada sektor perekonomian, menjadikan pemahaman mengenai pertumbuhan asuransi syariah sebelum dan sesudah pandemi ditinjau dari kontribusi bruto sangat penting. Penelitian ini bertujuan untuk meninjau lebih lanjut kontribusi bruto dari Maret-Desember untuk mengetahui apakah ada perbedaan yang signifikan dari kontribusi bruto asuransi syariah sebelum dan sesudah pandemi Covid-19. Penelitian dilakukan dengan pendekatan kuantitatif komparatif dengan dua sampel berpasangan. Sampel penelitian menggunakan data time series yaitu kontribusi bruto asuransi syariah tahun 2019 dan 2020 periode bulan Maret hingga Desember. Hasil penelitian ini menemukan bahwa perbandingan kontribusi bruto pada industri asuransi syariah dilihat saat sebelum pandemi tahun 2019 dan sesudah pandemi 2020 yang diambil pada periode Maret hingga Desember berdistribusi normal. Hal tersebut berarti bahwa perkembangan asuransi syariah jika ditinjau pada saat sebelum pandemi tahun 2019 dan sesudah pandemi tahun 2020 menurut kontribusi bruto yang berasal dari para peserta asuransi syariah tidak mengalami perbedaan yang signifikan dan dapat dipastikan akan mengalami peningkatan.

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