The presence of arsenic in the groundwater 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. A current working hypothesis is that arsenic 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. In order to test the supporting hypothesis that subsurface geochemical changes may be induced by agricultural activity, we deployed 42 wirelessly networked ion-selective electrodes, including calcium, ammonium, nitrate, ORP, chloride, carbonate, and pH in a rice paddy in the Munshiganj district of Bangladesh in January of 2006. Each sensor was connected to an MDA300 sensor board and Mica2 wireless transceiver and computational device. Over a period of 11 days, we observed clear diel, and diurnal trends in 4 of the electrodes (calcium, ammonium, chloride and carbonate). The trends may be due to hydrological changes, or geochemical changes induced either by photosynthesis in the overlying water (which then infiltrated to the depth of the sensors) or in the root zone of rice plants. While the spatiotemporally dense measurements from wireless sensor networks enable scientists to ask new questions and elucidate complex relationships in heterogeneous physical environments such as soil, there are many practical issues to address in order to collect data usable for scientific purposes. For example, in response to a stream of faults in one of our sensor network deployments, we designed Sympathy to enable users to find and fix problems impacting the quantity of data collected in the field. Sympathy detects packet loss experienced at the base station and systematically assigns blame to faulty components in the network for remediation, replacing the prior policy of ad-hoc node rebooting and battery replacements. Sympathy has been deployed in many habitat monitoring sensor networks. While using Sympathy at our Bangladesh field site we received 80% of the sensor data expected at the base station, upon returning, post-deployment analysis revealed that 42% of these sensor data were potentially faulty. Due to the remote location of the deployment, we were unable to go back and validate the questionable segments of the data set, forcing us to discard potentially interesting information. In addition to being undesirable, this response is often avoidable as well. Even simple actions such as checking sensor connections and quickly validating sensors in the field could have increased our confidence in the quality of the data, minimizing doubts that data observations were simply caused by badly behaving hardware. To improve data quality, we have designed a system called Confidence, which continuously monitors data collected at a base-station to identify faulty data and notify the user in the field of actions they can take to validate the data or remediate the sensor fault. Augmenting a sensor network deployment with Confidence and Sympathy enables users in the identification and remediation of faults impacting the quality and quantity of data respectively.

The presence of arsenic in the groundwater 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 (As). Rice fields receive large loads of arsenic with irrigation water and provide recharge to the underlying aquifer. It is currently not known whether rice fields are a sink or source of arsenic in the hydrologic system. In the dry season, as As(III)-containing minerals are oxidized, As(V) is released and will adhere to Fe hydr(oxide) minerals. When sediments are inundated with water, reducing conditions will then drive reduction of Fe hydr(oxides) and release of As. We have been intensively studying a field site in Munshiganj, Bangladesh with extremely high levels of arsenic in groundwater (up to 1.2 mg/L). To better understand geochemical and microbial processes leading to As mobilization in surface sediment, we deployed sensors to take temporally dense measurements across our experimental rice paddy. Data collected in both 2006 and 2007 showed trends in geochemical parameters indicating that diurnal, possibly plant-induced, processes may be important. Over a two month period, nitrate concentrations decrease consistently each day as ammonium levels increase, presumably through temperature driven reductive processes. Nitrate concentrations in the subsurface then increase while ammonium levels decrease, possibly due to root oxygen leakage or rapid infiltration of oxygen rich surface water. Using sediment from the rice paddy and artificial irrigation water, laboratory microcosms were constructed to simulate the diurnal cycles observed at the field site. In carbon-ammended treatments, Fe and As cycling can occur on the order of days. Oscillations in redox conditions on diurnal as well as seasonal time scales may be important in the mobilization of arsenic into aquifers. By elucidating As mobilization mechanisms at an experimental rice paddy, this work could ultimately lead to solutions that minimize As exposure in critical populations.