scholarly journals Hubungan Kemelimpahan Chlorella sp Dengan Kualitas Lingkungan Perairan Pada Skala Semi Masal di BBBPBAP Jepara

2016 ◽  
Vol 14 (2) ◽  
pp. 77 ◽  
Author(s):  
Siska Aprilliyanti ◽  
Tri Retnaningsih Soeprobowati ◽  
Bambang Yulianto
Keyword(s):  
Water Quality ◽  
Environmental Quality ◽  
Water Environment ◽  
Quality Parameters ◽  
Mass Scale ◽  
Regression Equation ◽  
Water Quality Parameters ◽  
Coefficient Of Determination ◽  
Culture Vessel ◽  
Chlorella Sp

ABSTRAKChlorella   sp merupakan salah satu mikroalga yang sering dibudidayakan untuk berbagai keperluan seperti obat-obatan, kosmetik, atau untuk alternatif  biodiesel Chlorella    sp  merupakan suatu agen bioremediasi yang baik, selain dapat hidup pada lingkungan yang tercemar juga dapat memakai logam berat sebagai logam esensial untuk metabolisme. Banyaknya manfaat yang akan dapat diambil apabila dapat mengembangkan Chlorella    sp pada skala masal. Dengan kemanfaatannya dari Chlorella    sp maka penulis melakukan penelitian dengan menggunakan Chlorella    sp sebagai objeknya. Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara kemelimpahan Chlorella    sp dengan kualitas lingkungan perairan di Kabupaten Jepara. Chlorella      sp ini dikultivasi di luar ruangan dengan sumber cahaya berasal dari sinar matahari secara langsung, pengudaraan untuk pencampuran media menggunakan blower yang dialirkan melalui selang dan kran aerasi untuk mencampur media. Aerasi dalam penelitian ini digunakan dengan tujuan agar sel Chlorella   sp dapat memperoleh nutrisi dalam media kultivasi secara merata karena adanya sirkulasi air dalam wadah kultur (Amini, 2006). Dari hasil analisis data diperoleh nilai koefisien determinasi (R2) = 0,995. Hal ini memberikan gambaran bahwa terdapat hubungan yang sangat kuat antara variabel bebas yakni kelima parameter kualitas air (nitrat, fosfat, temperature, pH dan salinitas) dengan variabel terikat yakni kemelimpahan Chlorella   sp . Selanjutnya diperoleh persamaan regresi linier berganda sebagai berikut:Y =  -5323.54 -16.80 nitrat -60.78 fosfat   + 111.09 temperatur  + ; 997.26 pH -191.92 salinitas. Dari persamaan regresi tersebut memperlihatkan bahwa parameter kualitas air yang memiliki hubungan searah (berbanding lurus) adalah temperature  dan pH. Sedangkan parameter kualitas air yang memiliki hubungan berbanding terbalik yaitu; nitrat,fosfat dan salinitas. Hubungan kemelimpahan Chlorella   sp dengan kualitas lingkungan perairan skala semi masal kuat, hasil analisis regresi didapat nilai Adjusted R2 0,995, artinya persentase sumbangan pengaruh variabel nitrat ,fosfat,temperature, pH dan salinitas terhadap kemelimpahan Chlorella   adalah sebesar 99,5% dan sisanya dipengaruhi oleh faktor lain. Nilai koefisien / pengaruh tertinggi terdapat pada parameter pH yaitu (997,49).Kata kunci: Chlorella  sp, kualitas lingkungan, semi masal, Jepara ABSTRACTChlorella sp is one of the microalgae are often cultivated for various purposes such as pharmaceuticals, cosmetics, or for alternative biodiesel Chlorella sp an agent of bioremediation good, but can live in a polluted environment can also wear a heavy metal as the metal essential for metabolism. The many benefits that will be taken if it can develop Chlorella sp on a mass scale. With the emergence of Chlorella sp author conducted research using Chlorella sp as its object. The purpose of this study was to determine the relationship between the abundance of Chlorella sp with the quality of the water environment in the district of Jepara.Chlorella sp is cultivated outdoors with a light source coming from direct sunlight, aeration for mixing media using a blower that flowed through the hose and faucet aeration to mix media. Aeration used in this study with the aim of Chlorella sp cells can obtain nutrients evenly in cultivation media for their water circulation in the culture vessel (Amini, 2006). From the analysis of data obtained by the coefficient of determination (R2) = 0.995. This illustrates that there is a very strong relationship between the independent variables namely the five parameters of water quality (nitrates, phosphates, temperature, pH and salinity) with the dependent variable abundance of Chlorella sp. Furthermore, multiple linear regression equation as follows: Y = -5323.54 -16.80 -60.78 nitrate phosphate + 111.09 + temperature; 997.26 -191.92 pH salinity. From the regression equation shows that the water quality parameters that have a unidirectional relationship (proportional) is temperature and pH. While water quality parameters which have an inverse relationship, namely; nitrate, phosphate and salinity. Chlorella sp abundance relationships with water environmental quality semi massive scale strong, the results of the regression analysis obtained Adjusted R2 value of 0.995, meaning that the percentage contribution of variables influence nitrates, phosphates, temperature, pH and salinity of the abundance of Chlorella is 99.5% and the rest is influenced by factors other. The coefficient of impact / highest in pH parameters ie (997.49).Keywords:  Chlorella sp, environmental quality, semi-massive, JeparaCara sitasi: Apriliyanti, S., Soeprobowati, T. R., Yulianto, B. (2016). Hubungan Kemelimpahan Chlorella sp dengan Kualitas Lingkungan Perairan pada Skala Semi Masal di BBBPBAP Jepara. Jurnal Ilmu Lingkungan,14(2),77-81, doi:10.14710/jil.14.2.77-81

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

scholarly journals PENGARUH KOMBINASI FITOHORMON DAN ZAT PENGATUR TUMBUH (ZPT) UNTUK MENINGKATKAN PERTUMBUHAN Chlorella sp.

2018 ◽  
Vol 46 (2) ◽  
pp. 80
Author(s):  
Erlangga Erlangga ◽  
Zulfikar Zulfikar ◽  
Muhammad Anggi
Keyword(s):  
Water Quality ◽  
Cell Proliferation ◽  
Experimental Method ◽  
Growth Regulator ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Chlorella Cell ◽  
Factor B ◽  
Chlorella Sp ◽  
Population Peak

This study aims to determine the dosage of phytohormone and the appropriate growth regulator in Chlorella sp culture to increase the growth of Chlorella sp.The method used in this study was the experimental method with fitohormon as a factor A which were cytokines (0,02 gr/l), auksin (0,065 gr/l) and the growth regulator (PGR) as a factor B which were TSP (0,03 gr/l), NPK (0,04 gr/l). the treatment would be undertaken for the treatment of hormone auxin A (0,065 gr/l) anf TSP (0,03 gr/l) B.The result is the hormone cytokinins (0,02 gr/l) and NPK (0,04 gr/l) the highest growth of Chlorella sp cells was in the treatment D cytokinins (0,02 gr/l) and NPK (0,04 gr/l), then it was followed by treatment B hormone auxin (0,065 gr/l) and NPK (0,04 gr/l), C hormone cytokinins (0,02 gr/l) and TSP (0,03 gr/l) and A auxin (0,065 gr/l) anf TSP (0,03 gr/l). population peak of chlorella cell in each treatment were : D D (2041.67 x 103 sel/ml), B (1610.67 x 103 sel/ml), C (1592.67 x 103 sel/ml), treatment A showed the lowest value which was (1589.33 x 103 sel/ml). water quality parameters strongly supported theprocess of cell proliferation or growth of Chlorella sp with pH 7.0-7.3, salinity 27-30 ppt, and temperature of 22.4-25 0C.

scholarly journals Variation in palladium and water quality parameters and their relationship in the urban water environment

2020 ◽  
Author(s):  
Yuyan Liu ◽  
Fangfang Ding ◽  
Caiye Ji ◽  
Dan Wu ◽  
Lin Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
Correlation Coefficient ◽  
Water Body ◽  
Wet Deposition ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Urban Water ◽  
Rainfall Runoff ◽  
Receiving Water

Abstract Palladium (Pd) is widely used in vehicle exhaust catalysts (VECs) to reduce toxic emissions from motor vehicles. The study aimed to quantitatively determine Pd content and water quality parameters, to analyze the variation differences and to explore the effect of water quality parameters on Pd content in the urban water environment system (wet deposition–rainfall runoff–receiving water body–estuary) of the city of Haikou, Hainan Island, China. The method used in this study included microwave digestion under high pressure and temperature, analysis by inductively coupled plasma mass spectrometry, quality control of the experimental procedure and guaranteed recovery (85% −125%). The results showed that the dissolved Pd average content in the urban water environment system was the highest in rainfall runoff (4.93 ng/L), followed by that in the receiving water body (4.56 ng/L), and it was the lowest in wet deposition (0.1 ng/L). The suspended Pd average content was the highest in the estuary (2.83 ng/L), followed by that in rainfall runoff (1.26 ng/L), and it was the lowest in wet deposition (6 × 10−4 ng/L). The particle–water partition ratio of the estuary Pd was the highest (1.26), followed by that of Pd in rainfall runoff (0.26). The particle–water partition ratio of the wet deposition Pd was the lowest (6 × 10−3). The dissolved Pd was correlated with the pH, Cl−, and total suspended solids (TSS) (correlation coefficient = 0.52, −0.68, 0.39, p < 0.05; regression coefficient = 1.27, −1.39, 0.01). The suspended Pd was only correlated with Cl− and TSS (correlation coefficient = −0.36, 0.76, p < 0.05; regression coefficient = −1.45, 0.01). Cl− and TSS were the most closely related to Pd in the water environment system. Although individual factors such as pH, Cl−, and TSS had certain migration and transformation effects on Pd in the wet deposition–rainfall runoff–receiving water body–estuary system, the probability of strong correlations was not high. In particular, Eh was not related to the dissolved nor suspended Pd content (correlation coefficient = 0.14, 0.13), which may be due to the synergistic effect of the multiple physical factors on Pd. This study was helpful to better understand the environmental behavior of Pd and provided important theoretical support for the prevention and protection against urban water environmental pollution.

scholarly journals Estimation of Water Quality Parameters with High-Frequency Sensors Data in a Large and Deep Reservoir

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2632
Author(s):  
Cunli Li ◽  
Cuiling Jiang ◽  
Guangwei Zhu ◽  
Wei Zou ◽  
Mengyuan Zhu ◽  
...  
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Environmental Influence ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
High Temporal Resolution ◽  
Regression Equations ◽  
Chemical Parameters ◽  
Frequency Sensor

High-frequency sensors can monitor water quality with high temporal resolution and without environmental influence. However, sensors for detecting key water quality parameters, such as total nitrogen(TN), total phosphorus(TP), and other water environmental parameters, are either not yet available or have attracted limited usage. By using a large number of high-frequency sensor and manual monitoring data, this study establishes regression equations that measure high-frequency sensor and key water quality parameters through multiple regression analysis. Results show that a high-frequency sensor can quickly and accurately estimate dynamic key water quality parameters by evaluating seven water quality parameters. An evaluation of the flux of four chemical parameters further proves that the multi-parameter sensor can efficiently estimate the key water quality parameters. However, due to the different optical properties and ecological bases of these parameters, the high-frequency sensor shows a better prediction performance for chemical parameters than for physical and biological parameters. Nevertheless, these results indicate that combining high-frequency sensor monitoring with regression equations can provide real-time and accurate water quality information that can meet the needs in water environment management and realize early warning functions.

scholarly journals Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 147
Author(s):  
Mohammad Hajigholizadeh ◽  
Angelica Moncada ◽  
Samuel Kent ◽  
Assefa M. Melesse
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Coefficient Of Determination ◽  
Lake Okeechobee ◽  
Wet Season ◽  
Chl A

The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R2) were found (R2 = 0.84 for chl-a and R2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R2 values were moderate (R2 = 0.48 for chl-a and R2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R2 = 0.92 for TP and R2 = 0.94 for TKN) and in wet season (R2 = 0.89 for TP and R2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

scholarly journals The performance of Chlorella vulgaris growth on mass-scale cultivation

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Dini Alvateha ◽  
Siska Falentina ◽  
Rarasrum Dyah Kasitowati ◽  
Sutianto Pratama Suherman ◽  
Luthfiana Aprilianita Sari ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorella Vulgaris ◽  
Culture Media ◽  
Initial Density ◽  
Quality Parameters ◽  
Mass Scale ◽  
Water Quality Parameters ◽  
Natural Food ◽  
Culture Activity ◽  
Brackish Water Aquaculture

Phytoplankton have many benefits, including as a primary producer, natural food, bioindicator, and water pollution treatment. For this reason, their availability needs to be managed, one of which is through cultivation. The purpose of this study was to analyze the mass scale cultivation of Chlorella vulgaris. The research was conducted at the Technical Implementation Unit of Freshwater and Brackish Water Aquaculture, Situbondo, using a descriptive method. The data were analyzed statistically using MS. Excel 2016 software, and a multiple linear regression test was carried out to determine the effect of water quality parameters on the growth of C. vulgaris using the SPSS 16.0 application. The cultivation process started from strain preparation, water preparation, tank and culture media preparation, inoculation, fertilization, and then harvesting. The initial density of C. vulgaris used was 145x104 Cell. mL-1 in tank 1 and 188x104 Cell. mL-1 in tank 2. The results showed that the cell density value of C. vulgaris increased every day until it entered the exponential phase, namely on the 4th day of the culture activity, which was 507 x 104 in tank 1 and 536 x 104 Cell. mL-1 in tank 2. Furthermore, the value of water quality parameters that affected the growth of C. vulgaris in tank 1 and tank 2 was dissolved oxygen of 4.82-6.97 mg. L-1, pH 8.2-9.1, transparency of 20-45 cm, temperature was 26.8-28.2 oC, nitrate of 0.10-0.50 mg. L-1, phosphate of 0.75-2 mg. L-1, and salinity of 30-39 ppt.

scholarly journals Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1273
Author(s):  
Jianzhuo Yan ◽  
Jiaxue Liu ◽  
Yongchuan Yu ◽  
Hongxia Xu
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Prediction Algorithm ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Quality Prediction ◽  
Water Quality Data ◽  
Water Quality Prediction

The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

Correlation Analysis between Primary Productivity Factors and Water Environment Factors

2013 ◽  
Vol 448-453 ◽  
pp. 902-907
Author(s):  
Shih Chieh Chen ◽  
Chao Cheng Chung ◽  
Wen Liang Lai ◽  
Chung Yi Chung ◽  
Hwa Sheng Gau ◽  
...  
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Explanatory Power ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Canonical Variables ◽  
The Relationship

In this study, we use canonical correlation analysis to interpret the relationship between water quality parameters (T, Alk, Cl, EC, TN, TP, UV-254, pH, HPC, DO) and primary productivity parameters (algae and chlorophyll-a). In these two sets of constructed canonical variables, the water quality parameters can account for 39.25% of the total variance of primary productivity. The majority of the explanatory power is from the first set of canonical variables, which has a correlation coefficient of 0.84. The main factors that control chlorophyll-a are HPC, Alk, T, TN, and pH.

scholarly journals Growth performance of tilapia fingerling in cage in ponds managed by Adivasi households: An assessment through length-weight relationship

2012 ◽  
Vol 10 (1) ◽  
pp. 149-155 ◽  
Author(s):  
N Gupta ◽  
M M Haque ◽  
M Khan
Keyword(s):  
Water Quality ◽  
Condition Factor ◽  
Pond Water ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Coefficient Of Determination ◽  
Weight Relationship ◽  
Growth Performances ◽  
Relationship Of

This study was conducted to assess the growth performances of cage reared GIFT strain of tilapia (Oreochromis niloticus) fingerling using length-weight (LW) relationship technique. Along with this, condition factor (K) of fish and   pond water quality parameters were also brought under this study to have broader understanding. For LW  relationship and K, a sample size of 120 fingerlings was made from randomly selected three different cages in a pond  at Tarala village in Kaharole Upazila of Dinajpur District, Bangladesh. The length-weight relationship of tilapia  fingerlings reared in cages managed by Adivasi people was significant. The value of correlation coefficient (r) and the coefficient of determination (r2) were 0.97 and 0.94 respectively. This suggests that growth of tilapia from fry to  fingerling was normal in cages. The condition factor of different size group of fish was almost closed to 2, indicating  fish health as satisfactory. All the water quality parameters including temperature, transparency, dissolved oxygen,ammonia-nitrogen, phosphate-phosphorus, nitrate-nitrogen and pH were within suitable range both in cages and  outside the cage in pond. About 5 phyla and 25 genera of phytoplankton from Bacillariophyceae, Cyanophyceae, Chlorophyceae, Euglenophyceae and Rhodophyceae groups and one phylum and 5 genera of zooplankton from Rotifera group were found in cage installed in ponds. These all indicate the growth of tilapia fingerling in cages was satisfactory which was technically sound for landless adivasi households.   DOI: http://dx.doi.org/10.3329/jbau.v10i1.12107   J. Bangladesh Agril. Univ. 10(1): 149–155, 2012  

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