Physico-chemical Attributes Study from Different Sources of Freshwater Bodies in and around Barishal University Campus, Barishal, Bangladesh

2021 ◽  
Vol 1 (01) ◽  
pp. 21-26
Author(s):  
AFROJA NASRIN
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Chemical Properties ◽  
University Campus ◽  
Surface Water Temperature ◽  
Physico Chemical ◽  
Freshwater Bodies ◽  
Water Ph ◽  
Chemical Attributes ◽  
Different Sources

This study measured surface water temperature, water pH, TDS, EC, DO, nitrate, ammonium, sulphate and DIP as water physico-chemical properties of four freshwater reservoirs from Barishal Sadar upozilla. The surface water temperature was recorded highest (22.4 °C) in station 4 and loest was in station 1. Water pH, nitrate, ammonium and sulphate showed almost same results among the four reservoirs. TDS and EC values range from 188 to 215 mg/l and 195 to 225 µS/cm, respectively. DO level was almost similar in all the stations except station 1 which showed comparatively lowest amount (4.55 mg/l). DIP ranges from 3.20 to 4.15 mg/l. Among the four reservoirs, comparatively newly established BU pond (station 4) showed poor results than the others.

scholarly journals Evaluation of Surface Waters Quality Obtained from Different Sources of Mymensingh District of Bangladesh

2016 ◽  
Vol 13 (1) ◽  
pp. 189-195
Author(s):  
Zannatul Ferdous ◽  
M F Haque ◽  
M A Hosain ◽  
M M Rahman ◽  
Abu Sayed
Keyword(s):  
Heavy Metals ◽  
Surface Water ◽  
Surface Waters ◽  
Chemical Properties ◽  
Quality Parameters ◽  
University Campus ◽  
Standard Value ◽  
Metal Properties ◽  
Total Dissolved Solid ◽  
Different Sources

The present study was conducted to assess some  chemical properties and heavy metals of  surface waters collected from different sources at Bangladesh Agricultural University Campus and Mymensingh Sadar Upazila  for common use.  Twelve water samples were collected from 4 different locations during February to April 2011 for analyzing their chemical quality parameters. The recorded values of pH, EC (Electrical Conductivity), TDS (Total Dissolved Solid), DO (Dissolved Oxygen), As, Pb and Fe were found to range from 6.153 to 7.043, 0.194 to 0.679 mS/cm,106.333 to 389.333 ppm, 4.433 to 4.9 ppm, 0.064 to 1.756 ppb, 0.00053 to 0.00083 ppm and 0.567 to 1.795 ppm, respectively.  pH and TDS of the surface water were within the standard value but other parameters such as As, Pb, and Fe were significantly higher than the standard for drinking water. The correlation analysis among chemical and heavy metal properties indicated that all the parameters had significant contribution to pollute the water except DO. The correlation between TDS and EC was strongly significant (r = 0.99**). pH showed positive significant correlation with EC and TDS. Fe showed negative (r = -0.636) significant correlation with As. The above findings show that the surface water in Mymensingh area is being gradually polluted by unplanned discharge of chemicals and heavy metals contained effluents. It might be concluded that the water is not so harmful yet for irrigation, aquaculture and livestock usage.The Agriculturists 2015; 13(1) 189-195

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

Impact of climate warming on the surface water temperature of plateau lake

2021 ◽  
Author(s):  
Zongqi Peng ◽  
Jiaying Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Chunxue Shang
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Climate Warming ◽  
Surface Water Temperature ◽  
Plateau Lake

scholarly journals Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

2021 ◽  
Vol 13 (17) ◽  
pp. 3461
Author(s):  
Pavel Kishcha ◽  
Boris Starobinets ◽  
Yury Lechinsky ◽  
Pinhas Alpert
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Dead Sea ◽  
Lake Kinneret ◽  
Surface Water Temperature ◽  
Water Lake ◽  
Increasing Trend ◽  
Atmospheric Warming ◽  
Fresh Water Lake

This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period (2003–2019). In spite of the presence of increasing atmospheric warming, in summer when evaporation is maximal, in fresh-water Lake Kinneret, satellite data revealed the absence of surface water temperature (SWT) trends. The absence of SWT trends in the presence of increasing atmospheric warming is an indication of the influence of increasing evaporation on SWT trends. The increasing water cooling, due to the above-mentioned increasing evaporation, compensated for increasing heating of surface water by regional atmospheric warming, resulting in the absence of SWT trends. In contrast to fresh-water Lake Kinneret, in the hypersaline Dead Sea, located ~100 km apart, MODIS records showed an increasing trend of 0.8 °C decade−1 in summer SWT during the same study period. The presence of increasing SWT trends in the presence of increasing atmospheric warming is an indication of the absence of steadily increasing evaporation in the Dead Sea. This is supported by a constant drop in Dead Sea water level at the rate of ~1 m/year from year to year during the last 25-year period (1995–2020). In summer, in contrast to satellite measurements, in-situ measurements of near-surface water temperature in Lake Kinneret showed an increasing trend of 0.7 °C  decade−1.

scholarly journals Diel dynamics of heleoplankton in relation to some physico-chemical attributes of water in an aquaculture tank in Guwahati, Assam

2012 ◽  
Vol 4 (1) ◽  
pp. 16-22
Author(s):  
Janardan Pathak ◽  
M. M. Goswami
Keyword(s):  
Water Temperature ◽  
T Test ◽  
Total Alkalinity ◽  
Diurnal Fluctuation ◽  
Total Hardness ◽  
Numerical Density ◽  
Physico Chemical ◽  
Free Carbon Dioxide ◽  
Chemical Attributes ◽  
Positive Correlations

Diurnal fluctuation of the phytoplankton and zooplankton population (numerical densities) and the physicochemical attributes of water are studied in an aquaculture pond at Guwahati, Assam. In the 24 hour diel cycle, the phytoplankton and zooplankton show significant (t-test, p < 0.01) variation in their numerical density at an interval of 4 hours. The studied physico-chemical parameters of water included Temperature, Total Alkalinity (TA), Total hardness (TH), Dissolved Oxygen (DO), Free Carbon dioxide (FCO2) and pH are analyzed concurrently, which reveals a significant fluctuation (t-test, p< .01) in the day and night hours. Multivariate correlation analysis portrays significant and positive correlations among the total population density of phytoplankton with respect to water temperature (r = 0.845, p<0.05) and pH (r = 0.881, p< 0.01) while it maintains significant negative correlations with TA (r = -0.837, p<0.05), TH (r = -0.768, p< 0.05) and FCO2 (r = -0.830, p<0.05). However, total numerical density of zooplankton reveals positive and significant correlations with TA (r = 0.842, p< 0.05) and FCO2 (r= 0.758, p< 0.05) while it shows significant negative correlations with water temperature (r = -0.906, p< 0.01) and pH (r =- 0.912, p< 0.01).

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