Qin Qian

Water quality monitoring using STORM 3 Data Loggers and a wireless sensor network

Sustainable water management decisions are often made with the support of water quantity and water quality models with a focus on prediction uncertainty. Unfortunately, limited observational data severely constrains the design of accurate water models for these decisions. This paper presents our initial efforts to deploy STORM 3 data loggers and a wireless sensor network WSN to collect real-time and in-situ data at fine temporal granularities to monitor the pond at Lamar University in Beaumont, TX. Specifically, we present the details about how to set up STORM 3, integrate H-377 water temperature sensor probe from WaterLOG, and validate collected water temperature data. We further explain our prototype WSN and a variety of third-party probes to collect water Dissolved Oxygen DO and Water pH values. Our deployed STORM 3 and NI-based WSN have been able to collect water temperature, DO, and pH values consistently and periodically in a real-time manner.

Development and Implementation of Wireless Sensor Networks (WSNs) for Measuring Water Quality

Distributed wireless sensor networks (WSNs) have a great potential to revolutionize water sustainability research through providing measurements in situ at fine temporal and spatial granularities that were previously impossible. Unfortunately, this potential is still hampered by the poor integration of WSNs into water sustainability research. To address this challenge, after extensive testing of various research products, we report our preliminary and incremental research to deploy a wireless sensor network system from National Instruments. We have built and deployed a prototype WSN and adopted a variety of third-party probes to collect water dissolved oxygen (DO) and water pH values from the pond at Lamar University in Beaumont, Texas. Since September 2013, our deployed WSN has been able to collect DO and pH values consistently and periodically in a real-time manner. Ongoing research is underway to verify the measurements and apply the data to the water quality model.

Deployment of STORM 3 Data Loggers to Monitor Water Quality

Sustainable water management decisions are often made on the support of water quantity and quality models with a focus on prediction uncertainty. Unfortunately, limited observational data severely constrains the design of accurate water models for these decisions. To address this challenge, this paper presents our initial efforts to deploy STORM 3 data loggers from WaterLOG to collect real-time and in-situ data at fine temporal granularities to monitor the pond at Lamar University in Beaumont, TX. We present the details about how to set up STORM 3, integrate H-377 water temperature sensor probe from WaterLOG, and validate collected water temperature data. Since July 2014, our deployed STORM 3 is collecting water temperature data once every 15 minutes. On-going research is underway to revise the deployment of our STORM 3, and to provide water temperature data for decision making on water sustainability management.

Modeling of Solute Transport in Pore Scale Sediment Beds: A Summary of Hydrodynamic Interactions Induced by Surface Wave, Bed Form and Near Bed Turbulence

Solute transport in a pore scale sediment bed of river or lake has a significant effect on chemical mass balances and microbial activities in the water and sediment. The solute transfer between water and a pore scale sediment bed is often described by a 1-D vertical dispersion model and estimated using molecular diffusion and porosity. However, surface waves, bed forms, and near bed turbulence create periodic pressure waves along the sediment/water interface, which in turn induces flows in the pores of the sediment bed. A coupled 2-D hydrodynamic and solute transport model has been developed to study the solute transport in the pore scale sediment bed, and the solute transport has been incorporated in a 1-D depth dependent enhanced dispersion coefficient (D E ). Typically, D E diminishes exponentially with depth in the sediment bed. It is a function of the near-bed coherent motion due to the turbulent current, relative dispersivity (longitudinal dispersivity/wave length), wave steepness, sediment hydraulic conductivity, and sediment porosity. Maximum values of D E near the sediment surface can be much larger than molecular diffusion coefficients, e.g., D E ~ 10cm 2 /s in a gravel bed with pressure standing waves, D E ~1 cm 2 /s in a gravel bed under progressive surface waves, and D E ~0.1 cm 2 /s in a gravel bed under near bed turbulent current. The penetration depth due to turbulent is only about 1/5 ~ 1/10 of that caused by the pressured surface wave. Therefore, the pressured surface wave is a dominant process, and the other processes can be ignored. However, the near-bed turbulent can enhance the transport at least 1 order magnitude than the underflow process along the sediment/water interface.

Experimental Study of the Solute Transport in the Interfacial Exchange Zone (IEZ) of a Gravel Stream Bed

Solute exchange between overlying water and the sediment bed of a stream have important effects on the chemical mass balance and biological activities in both the water column and the sediment. Previous studies did not consider the coupled and cumulative contributions of turbulence in the overlying water, water surface waves, and underflow to the solute transport in the Interfacial Exchange Zone (IEZ), although these processes have been studied individually. To investigate the interaction, eight carefully designed experiments of solute exchange in the pore system of a stream gravel bed were conducted. The data analysis showed that the turbulent flow over the bed, the underflow and the wave induced advection enhanced the solute exchange and penetration of a conservative solute into a stream gravel bed. The maximum penetration depth under the turbulent current was 15cm in 3000s; however, the penetration depth reached 20cm in less than 1000s when surface waves were present. A coupled hydrodynamic and solute transport model was developed to estimate vertical dispersion coefficients for solutes in a porous and permeable streambed. The enhanced dispersion coefficient that combines all three processes, is a function of the near-bed coherent motion due to the turbulent current, relative dispersivity (longitudinal dispersivity/wave length), wave steepness, sediment hydraulic conductivity and sediment porosity, and decreases exponentially with depth; however, the turbulence-enhanced dispersion without the wave effects is smaller and diminishes faster with depth compared to the wave-enhanced dispersion.