Seasonal ammonia losses from spray-irrigation with secondary-treated recycled water

2012 ◽  
Vol 65 (4) ◽  
pp. 676-682 ◽  
Author(s):  
Jose A. Saez ◽  
Thomas C. Harmon ◽  
Sarika Doshi ◽  
Francisco Guerrero
Keyword(s):  
Nutrient Management ◽  
Time Series Data ◽  
Irrigation System ◽  
Treatment Plant ◽  
Series Data ◽  
Irrigation Season ◽  
Recycled Water ◽  
Application Rates ◽  
Treated Effluent ◽  
Agricultural Site

This work examines ammonia volatilization associated with agricultural irrigation employing recycled water. Effluent from a secondary wastewater treatment plant was applied using a center pivot irrigation system on a 12 ha agricultural site in Palmdale, California. Irrigation water was captured in shallow pans and ammonia concentrations were quantified in four seasonal events. The average ammonia loss ranged from 15 to 35% (averaging 22%) over 2-h periods. Temporal mass losses were well-fit using a first-order model. The resulting rate constants correlated primarily with temperature and secondarily with wind speed. The observed application rates and timing were projected over an entire irrigation season using meteorological time series data from the site, which yielded volatilization estimates of 0.03 to 0.09 metric tons NH3-N/ha per year. These rates are consistent with average rates (0.04 to 0.08 MT NH3-N/ha per year) based on 10 to 20 mg NH3-N/L effluent concentrations and a 22% average removal. As less than 10% of the treated effluent in California is currently reused, there is potential for this source to increase, but the increase may be offset by a corresponding reduction in synthetic fertilizers usage. This point is a factor for consideration with respect to nutrient management using recycled water.

scholarly journals Roles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics

2020 ◽  
Vol 6 (1) ◽  
pp. 123-135
Author(s):  
Susana Martínez Arbas ◽  
Shaman Narayanasamy ◽  
Malte Herold ◽  
Laura A. Lebrun ◽  
Michael R. Hoopmann ◽  
...  
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Microbial Consortium ◽  
Control Strategies ◽  
Treatment Plant ◽  
Series Data ◽  
Dynamic Interactions ◽  
Microbial Community Dynamics ◽  
Host Interaction

AbstractViruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE–host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR–Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid–host and phage–host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant ‘Candidatus Microthrix parvicella’ population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes.

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.

scholarly journals Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy

2020 ◽  
Vol 12 (8) ◽  
pp. 1275 ◽  
Author(s):  
Salvatore Falanga Bolognesi ◽  
Edoardo Pasolli ◽  
Oscar Belfiore ◽  
Carlo De Michele ◽  
Guido D’Urso
Keyword(s):  
Vegetation Index ◽  
Southern Italy ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Campania Region ◽  
Machine Learning Classification ◽  
Sentinel 2

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.

Mapping paddy rice using Landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988−2017

2020 ◽  
Vol 42 (4) ◽  
pp. 1556-1576
Author(s):  
Jingjing Cao ◽  
Xueliang Cai ◽  
Junwei Tan ◽  
Yuanlai Cui ◽  
Hengwang Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Southern China ◽  
Irrigation System ◽  
Paddy Rice ◽  
Series Data

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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