Applications > Contaminant Observation and Management > Embedded Networked Sensing for Observation and Analysis of Groundwater Arsenic Mobilization in a Bangladeshi Aquifer
Jenny Jay (UCLA), Thomas Harmon (UC Merced), Eddie Kohler (UCLA), Deborah Estrin (UCLA), Charles Harvey (MIT)
Arsenic (As) in well water has led to the largest environmental poisoning in history; tens of millions of people in the Ganges Delta continue to drink groundwater that is dangerously contaminated with arsenic. Despite the tragic public health implications of this problem, we do not yet have a complete answer to the question of why dissolved arsenic concentrations are so high in the groundwater of the Ganges Delta. Answering this question has proven to be a complex scientific challenge requiring a combined understanding of biogeochemical, hydrological, and geological processes. A current working hypothesis is that As is mobilized in the near surface environment where sediments are weathered by seasonal changes in the redox state that drive a cycle of pyrite oxidation and iron oxide reduction, and the dissolved As is transported into the aquifers by recharge.
In January 2006, we deployed an embedded sensing system in a rice paddy in Bangladesh to evaluate the relationship between irrigation and arsenic contamination in the groundwater. We deployed 42 ion-selective electrodes (ISEs) to monitor ammonium, calcium, carbonate, chloride, pH, oxidation-reduction potential, and nitrate, and 8 soil temperature, moisture and pressure sensors at 3 different depths in 3 locations. The network collected 26,000 measurements over a period of 12 days. We observed several unexpected phenomena (already discussed in last year's report) which have spawned further study, and subsequent deployments; several UCLA students will be leaving in March, 2007 to deploy more sensors and collect physical samples for lab analysis.
Data from 2006 sensor deployments at our field site show large daily oscillations in soil water pressures and aqueous geochemistry that occur within the slower seasonal changes driven by the monsoonal climate. We propose to investigate the mechanisms leading to these daily oscillations, and the effects of this cycling on As mobility and transport. The importance of these newly recognized diurnal processes will be investigated throughout the annual cycle of flooding, drying and rice cultivation.
The ultimate science aims for this project are:
Specific Aim 1: We hypothesize that hydrological processes (due to barometric changes and plant evapotranspiration) and plant-induced chemical changes (due to root oxygen release and surface water changes) drive the daily oscillations observed at our site.
San Joaquin River Deployment. We deployed 21 embedded networked sensors at the confluence of the Merced and San Joaquin Rivers. This deployment is discussed earlier in the report. We deployed Confidence with this wireless network of 14 ion-selective electrodes (ISEs) and 7 temperature sensors. Our short-term goal was to validate the embedded networked system, and the functioning of Confidence. Our longer-term goal is to design a system that addresses the problems we experienced in Bangladesh, and deploy this system in Bangladesh in December, 2007.
We took two steps to validate this system. First, we deployed our sensors alongside the sensors connected to Hobo data-loggers described earlier in the report. This was useful in order to validate the data collection hardware we used in our system, as the Hobo data-loggers are rugged, and have been field-tested at many sites, providing a good baseline validation point. Second, we periodically extracted physical water samples from each of the sites in order to further validate the data we collected. This process is also described earlier in the report, and we do not expand further. Using the output of the Hach Kit as ground truth, we validate our system by comparing sensor output to this reading. We find that of the 14 ISEs, and 7 temperature sensors, 5 ISEs and 1 temperature sensor are definitely faulty at some point during the deployment and require attention. Confidence detects 5 of these 6 faults.
For each of the 7 functioning sensors, Confidence never reports a fault. Data from two example sensors are shown in Figure 1. For all deployment graphs, circles corresponds to ammonium, and squares correspond to nitrate; the small points correspond to sensor readings, and the big points correspond to physical sample readings. Confidence never reports a fault for any of these functioning sensors.
Figure 1
3 of the 4 faults Confidence detected were broken or dying sensors. Within several days of deployment, Confidence notified us to check the ammonium sensor connected to mote 22, and the nitrate sensor connected to mote 10. We disconnected the sensors from the board and connected them to a portable meter, and validated the readings obtained fromthe board. We then compared the sensor output with nitrate and ammonium levels measured from physical samples extracted near each sensor tip. Data from these sensors and the physical samples are shown in Figure 2. From the graph it is easy to see that readings from the sensors are very far from the ground truth, indicative of a dead sensor. We later removed the sensors and re-calibrated them, validating that the sensors were completely broken. Confidence also notified us to check the nitrate sensor connected to mote 16. Physical samples validated that the sensor was not operating correctly. And re-calibration of the sensor revealed that it was not completely dead, but only responded to changes in very high concentrations.
Figure 2
The final fault was simply a disconnected temperature sensor wire. Once notified, we re-connected the wire and fixed the problem. The results from Confidence monitoring of network quality were not as good unfortunately. When there is only one fault, Confidence always notified us. However, we did not have time to add the ability for Confidence to detect multiple correlated faults before the deployment. So, when there were multiple correlated faults, Confidence did not detect them. We have since added and evaluated this functionality.
Bangladesh Field Trip 2007. The upcoming field deployment at the experimental rice paddy in Munshiganj, Bangladesh will consist of two components. In the first, oxygen sensors will be deployed to determine the chemical composition of an unsaturated zone that was observed under the plough pan. The origin and composition (i.e. redox status) of this layer is unknown, but has great bearing on the geochemistry of the surrounding groundwater. Introduction of oxygen to the subsurface by plant- or infiltration-driven processes can drive redox cycling that can lead to arsenic mobilization (oxidation of arsenic-bearing pyrite minerals, then subsequent reductive mobilization).
Secondly, we will deploy javelins with nitrate, ammonium, carbonate, and chloride sensors in transects from the bund toward the center of the field. We aim to determine if the diurnal cycling in redox active species that was observed in the 2006 deployment is repeatable. We will identify location and times in the rice paddy where this cycling is most prevalent, and which will inform decisions regarding locations for further studies (arsenic measurements, enclosure studies).
In the next year, we will test hypotheses outlined in Specific Aim 2. Namely, we hypothesize that the observed daily oscillations in redox state may drive As mobilization at our site. Through 1) As measurements alongside our embedded sensor deployments, 2) enclosure studies in the field, 3) and microcosm studies, we will investigate effects of diurnal oscillations in hydrological regime and redox condition on the biogeochemistry of As at our field site.
Using this experimental microcosm, we will investigate the effects of the following on As speciation in sediments and mobilization to the dissolved phase: (a) presence versus absence of plants; (b) variant of rice plant (previous work has shown Boro (dry season) rice accumulated less arsenite and arsenate compared with Aman (wet season) rice); (c) growth phase of rice (While the rice growth cycle has been shown to result in short-term fluctuations in microbial guilds, no research has yet tied this to As cycling); and (d) soil type. We will use soil from our site, as well as other typical soils in Bangladesh; and (d) water management. This will be accomplished by varying the hydraulic regime in the microcosms. Effects of daily oxidizing conditions in the upper sediments can be investigated.
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