scholarly journals Monitoring of wastewater quality in Lodz sewage system (Poland)—do the current solutions enable the protection of WWTP and receiving water?

2021 ◽  
Author(s):  
Grazyna Sakson ◽  
Agnieszka Brzezinska ◽  
Dawid Bandzierz ◽  
Dorota Olejnik ◽  
Małgorzata Jedrzejczak ◽  
...  
Keyword(s):  
Treatment Plant ◽  
Principal Component ◽  
Quality Parameters ◽  
Wastewater Management ◽  
Combined Sewer Overflows ◽  
Multivariate Statistical ◽  
Wastewater Quality ◽  
Urban Catchments ◽  
And Cluster Analysis ◽  
Measurement Campaigns

AbstractSolving urban wastewater management problems requires knowledge of wastewater composition and variability. In the case of combined sewerage, this applies to both dry and wet weather. Wastewater composition is changing as a result of the appearance of new substances on the market, the changes in inhabitant lifestyle and the catchment characteristic; therefore, it must be constantly monitored. At the same time, due to the time-consuming and high costs of measurement campaigns, solutions that could limit their scope and facilitate the interpretation of the results are sought. This paper presents the results of the measurement campaign conducted in 2018–2021. The aim of the monitoring was, inter alia, assessment of wastewater composition in terms of threats to wastewater treatment plant and urban rivers, which are receivers of discharge from combined sewer overflows. The obtained results were analyzed using the multivariate statistical methods: Principal Component Analysis and Cluster Analysis. However, the applied methods did not allow for the full identification of the relationship between the wastewater quality parameters as well as the differences and similarities in the wastewater composition from individual parts of the city, which could simplify and reduce the measurement campaigns in the future. Therefore, in the case of large urban catchments, it is necessary to introduce other solutions to control the wastewater composition.

scholarly journals Suitability of SBR for Wastewater Treatment and Reuse: Pilot-Scale Reactor Operated in Different Anoxic Conditions

2020 ◽  
Vol 17 (5) ◽  
pp. 1617 ◽  
Author(s):  
Omar Alagha ◽  
Ahmed Allazem ◽  
Alaadin A. Bukhari ◽  
Ismail Anil ◽  
Nuhu Dalhat Mu'azu
Keyword(s):  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Batch Reactor ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Pilot Scale ◽  
Quality Parameters ◽  
Wastewater Management ◽  
Wastewater Quality

The present study investigates the performance of a pilot-scale Sequencing Batch Reactor (SBR) process for the treatment of wastewater quality parameters, including turbidity, total suspended solids (TSS), total solids (TS), nitrogen (ammonia (NH3–N), nitrite (NO2−), and nitrate (NO3−), phosphate (PO43−), the chemical oxygen demand (COD), and the 5-day biological oxygen demand (BOD5), from municipal wastewater. Two scenarios, namely, pre-anoxic denitrification and post-anoxic denitrification, were investigated to examine the performance of a pilot-scale SBR on the wastewater quality parameters, particularly the nitrogen removal. The correlation statistic was applied to explain the effects of operational parameters on the performance of the SBR system. The results revealed that the post-anoxic denitrification scenario was more efficient for higher qualify effluent than the first scenario. The effluent concentrations of the targeted wastewater quality parameters obtained for the proposed SBR system were below those of the local standards, while its performance was better than that of the North Sewage Treatment Plant, Dharan, Eastern province, Kingdom of Saudi Arabia (KSA), in terms of the BOD5, COD, TN, and PO43- treatment efficiencies. These results indicated the suitability of SBR technology for wastewater treatment in remote areas in the KSA, with a high potential of reusability for sustainable wastewater management.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

scholarly journals Danube water quality and assessment on ecotourism in the biosphere reserve ‘Bačko Podunavlje’ in Serbia

2020 ◽  
Vol 20 (4) ◽  
pp. 1215-1228
Author(s):  
Sanja Obradović ◽  
Milana Pantelić ◽  
Vladimir Stojanović ◽  
Aleksandra Tešin ◽  
Dragan Dolinaj
Keyword(s):  
Water Quality ◽  
Ecological Status ◽  
Biosphere Reserve ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Parameters ◽  
Danube River ◽  
Multivariate Statistical ◽  
Limit Values ◽  
Starting Point

Abstract ‘Bačko Podunavlje’ represents one of the largest and the best-preserved wetland areas of the upper Danube. Water quality is crucial for nature in protected areas and ecotourism. The paper is based on data for the period 1992–2016. Using multivariate statistical analysis, water quality was defined. One-factor analysis of variations is the starting point for the analysis of time variables (annual and monthly analysis). The principal component analysis (PCA) of the ten quality parameters is in the three factors that determine the greatest impact on the change in water quality. Results revealed the satisfactory ecological status of the Danube River in these sections (Bezdan and Bogojevo) and there is no threat that the biodiversity of this area is endangered by poor water quality, which fully justifies the possibilities for intensive development of ecotourism in the biosphere reserve. Suspended solids are the only parameter that exceeds the allowed limit values in a larger number of measurements, especially in the summer period of the year. Other analyzed water quality parameters range within the allowed limit values for the second class of surface water quality based on the Law on Waters (Republic of Serbia) and in accordance with the Water Quality Classification Criteria of ICPDR.

scholarly journals DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2136 ◽  
Author(s):  
Patrycja Garbacz ◽  
Marek Wesolowski
Keyword(s):  
Principal Component ◽  
Water Soluble ◽  
Crystal Formation ◽  
Multivariate Statistical ◽  
Molar Ratios ◽  
Pharmaceutical Ingredients ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Dsc Curves ◽  
Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 31 ◽  
Author(s):  
Oghenero Ohwoghere-Asuma ◽  
Kizito Aweto ◽  
Chukwuma Ugbe
Keyword(s):  
Groundwater Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Clayey Sand ◽  
Multivariate Statistical ◽  
Subsurface Geology ◽  
Dissolved Ions ◽  
Considerable Impact ◽  
And Cluster Analysis

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

Characterization of Balsamic Vinegar by Multivariate Statistical Analysis of Trace Element Content

1998 ◽  
Vol 81 (5) ◽  
pp. 1087-1095 ◽  
Author(s):  
Antonella Del Signore ◽  
Barbara Campisi ◽  
Franco Di Giacomo
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Trace Element Content ◽  
The Other ◽  
Statistical Techniques ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Balsamic Vinegar ◽  
And Cluster Analysis

Abstract To characterize vinegars according to the types prescribed by Italian regulations, 8 trace elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, and Pb) were determined. The data collected were successively elaborated by 3 statistical techniques: linear principal component analysis (LPCA), linear discriminant analysis (LDA), and cluster analysis (CA). LDA and LPCA best classified and discriminated the 3 types of vinegar under study, separating traditional balsamic vinegars from the other 2 types, nontraditionally aged balsamic vinegars and common vinegars. The latter 2 types were appreciably distinguished only by LDA through bidimensional analysis of discriminant scores

Assessment of Water Quality Parameters on Uzuncayır Dam Lake Using Principal Component and Cluster Analysis

2016 ◽  
Vol 16 (3) ◽  
Author(s):  
Banu KUTLU ◽  
Azime KÜÇÜKGÜL ◽  
Osman SERDAR ◽  
Rahmi AYDIN ◽  
Durali DANABAŞ
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
And Cluster Analysis

scholarly journals Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Sukhnag Stream: An Inflow Stream of Lake Wular (Ramsar Site), Kashmir Himalaya

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Gowhar Meraj ◽  
Sayar Yaseen ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Principal Component ◽  
Pollution Source ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Accumulation Factor ◽  
Kashmir Himalaya ◽  
And Cluster Analysis ◽  
Maximum Similarity

The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis (PCA), regression analysis, and cluster analysis, were applied to 26 water quality parameters. PCA identified a reduced number of mean 2 varifactors, indicating that 96% of temporal and spatial changes affect the water quality in this stream. First factor from factor analysis explained 66% of the total variance between velocity, total-P, NO3–N, Ca2+, Na+, TS, TSS, and TDS. Bray-Curtis cluster analysis showed a similarity of 96% between sites IV and V and 94% between sites II and III. The dendrogram of seasonal similarity showed a maximum similarity of 97% between spring and autumn and 82% between winter and summer clusters. For nitrate, nitrite, and chloride, the trend in accumulation factor (AF) showed that the downstream concentrations were about 2.0, 2.0, and 2.9, times respectively, greater than upstream concentrations.

The climatic regions of New Brunswick: a multivariate analysis of meteorological data

1984 ◽  
Vol 14 (3) ◽  
pp. 389-394 ◽  
Author(s):  
H. van Groenewoud
Keyword(s):  
New Brunswick ◽  
Classification Scheme ◽  
Meteorological Data ◽  
Principal Component ◽  
Site Classification ◽  
Forest Site ◽  
Multivariate Statistical ◽  
Climatic Regions ◽  
Geological Maps ◽  
And Cluster Analysis

New Brunswick was divided into 11 climatic regions by means of three multivariate statistical analyses (principal component analysis, R and Q type, and cluster analysis) of data on precipitation, various temperature parameters, elevation, latitude, and longitude for 76 climatological stations. These regions form the first-level division for a forest site classification scheme being implemented in New Brunswick. Comparison of the climatic and geological maps of New Brunswick with the plant community distribution shows that either climatic or geologic parameters may control the distribution of the vegetation.

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