Study on the Water Quality and Quantity Scheduling Scheme in Xinxue River Constructed Wetland of China under the Constraint Condition of Class III of Surface Water Quality

2011 ◽  
Vol 374-377 ◽  
pp. 923-927
Author(s):  
Chao Liu ◽  
Xiao Jie Cao ◽  
Chao Wang ◽  
Jing Jing Sun ◽  
Yu Ting Gu

Using Xinxue River Constructed Wetland as the study object, the wetland prediction models based on BP neural network were established through the seasonal division of the wetland, and the maximum influent water load was determined on the constraint condition that effluent water quality achieved class Ⅲ of surface water quality. Then nonlinear functions of water quality and quantity scheduling were constructed by Origin software. The optimal influent load was determined adopting prediction results of the models as constraint conditions of the functions. Thus the water quality and quantity scheduling scheme of the wetland was established. The results show that optimal influent load for Feb. ~ May: the influent water quantity is no more than 8560m3/d, CODCr is 25.47mg/l~26.37mg/l, ammonia nitrogen 0.11mg/l~1.0mg/l, TN 10.28mg/l~10.51mg/l, TP 0.16mg/l; for Jun. ~ Sept.: the water quantity is no more than 31750m3/d, CODCr is 26mg/l~32.36mg/l, or 37.15mg/l~45.37mg/l, ammonia nitrogen 0.48 mg/l~1.78mg/l, TN 5.15mg/l~6.18mg/l, TP 0.07mg/l~0.09mg/l; for Oct. ~ Dec.: the water quantity is no more than 11070m3/d, CODCr is 24.55mg/l~26.91mg/l, ammonia nitrogen no more than 0.75, TN no more than 8.61 mg/l, TP 0.10mg/l~0.12mg/l, or 0.16mg/l~0.17mg/l.

Author(s):  
Nguyen Minh Ky ◽  
Nguyen Cong Manh ◽  
Phan Van Minh ◽  
Nguyen Tri Quang Hung ◽  
Phan Thai Son ◽  
...  

The paper presented results of the comparative assessment of nutrient absorption capacity by plants, including reed grass (Phragmites australis L.) and vetiver (Vetiveria zizanioides L.). The constructed wetland models were designed with experiments (i) - Loading 1 (T1): reed grass (S1), vetiver (V1) + control (C1); (ii) - Loading 2 (T2): reed grass (S2), vetiver (V2) + control (C2); (iii) - Load 3 (T3): reed grass (S3), vetiver (V3) + control (C3). The study investigated the surface water quality parameters including nutrients such as TKN (Total Kieldalh Nitrogen), ammonium (NH4-N), nitrite (NO2-N), nitrate (NO3-N), total phosphorus (TP) and phosphate (PO43-). Results showed that there was significantly decreasing change related to pollutant concentration in the tanks. The studied results showed that the water treatment efficiency of Loading 1 (T1) possessed highly nutrient absorption capacities such as nitrogen and phosphorus. Comparing the nitrogen and phosphorus removal efficiency, there was no statistically significant difference between reed grass and vetiver in the same loading (P>0.05). In general, in the same loading levels, the plants’ nutrient removal efficiencies were often higher than the control experiments (P<0.05). The effluent findings illustrated some parameters of water quality that met to National Technical Regulation of surface water quality for agricultural irrigation purposes (QCVN 08-MT:2015/BTNMT). Therefore, the constructed wetland technology obtained highly effective characteristics and supplying the environmental friendly advantages.


Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 84
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Vimbayi G. P. Chimonyo ◽  
Alistair D. Clulow ◽  
Cletah Shoko ◽  
...  

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.


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