The research was possible due to funding from the Conservation Effects Assessment Project (CEAP). We are especially thankful for site-access permission from the producers, research assistance from members of the USDA-ARS-Delta Water Management Research Unit, and sample analysis by staff at the Ecotoxicology Research Facility, Arkansas State University. Part of this research was supported by an appointment to the ARS Participation Program, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and the USDA. ORISE is managed by ORAU under DOE contract number DE-AC05-06OR23100. All opinions expressed in this paper are the author&#39;s and do not necessarily reflect the policies and views of USDA, ARS, DOE, or ORAU/ORISE.

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The authors have nothing to disclose.

Overall, the continuous monitoring of nutrients and sediment has several advantages over monitoring using the grab sampling method. Hydrological and water quality processes are affected by rainfall over a very short span of time. Users can obtain high temporal-resolution data on nutrients and sediment to study complex problems. Other water quality parameters, such as conductivity, pH, temperature, and DO, can be obtained simultaneously and at the same cost as for monitoring nitrate, ammonium, and turbidity. Moreover, there are other sensors from manufacturers that allow for the measurement of even more water quality parameters, such as chlorophyll, salinity, and oxidation-reduction potential, along with nutrients and sediment.
This protocol can be used to identify the temporal variation of the pollutants over a chosen period of study; the spatial variation of pollutants in a watershed, if monitoring is carried out at multiple stations; and the cross-sectional variation of pollutants, if monitoring is carried out at several points in a cross-section. As shown in this protocol, the diurnal variation in pH, conductivity, DO, nitrate, ammonium, turbidity, and temperature can demonstrate cause-effect relationships and contribute to a better understanding of the drivers of pollutant loads.
Despite the successful continuous measurement of nutrients and sediment, the greatest limitation of the method is the loss of data or the collection of a low-quality data due to sensor failure, loss of power, and sediment/debris buildup. While site selection is important, it is equally important to frequently check the calibration or to calibrate when necessary, replace internal and external batteries (if not solar powered), and download and check data. Data quality can be compromised at several stages, from data acquisition to data processing. At the acquisition stage, the focus of this paper, remedies for possible problems are discussed below.
Data Loss:
Inappropriate programming of sensors, loss of power to the sensor, etc., can cause gaps in the data. If possible, a solar charger can be installed at the stations to recharge the battery. Otherwise, the frequent replacement of internal (for sondes) and/or external batteries is required. Downloading the data frequently will help to identify the problem quickly and to address it, reducing the loss of data due to memory limitations. Rodents can damage cables and incur losses of data. These losses can be avoided by using wire guards to cover the cables.
Low-quality Data Due to Fouling:
The fouling of sensor surfaces and the resulting drift or inaccuracy in the data can be minimized by covering the sensor guard with copper tape, by using copper guard, and by using copper mesh around the sensor guard. We found that covering the sonde surfaces (not sensors) with all-weather adhesive tape greatly facilitated the cleaning of the sensors. Self-cleaning sondes with wipers and brushes, like in the one used in this study, helped to clean the surfaces of the sensors (Figure 7). The use of copper materials, such as tape, guard, or mesh, reduced the growth of microorganisms and the resulting fouling.
Low-quality Data Due to Debris Buildup:
Positioning of the sensor and the sonde and burying the cables under sediment can limit debris buildup. For example, placing the sonde a certain depth above the stream bed but below the water surface helps to limit sediment buildup. Similarly, placing the sonde on the downstream side of the Telspar post reduces the debris, as the Telspar post catches the large woods, grasses, etc. Cleaning the sonde during every field visit can help to produce better-quality data. Wrapping the sensor guard with copper mesh reduces the sediment and debris buildup, interference from aquatic plants and macroinvertebrates, and fouling.
While the sonde can be placed upstream or downstream of the Telspar post, suspending the sonde on the downstream side is recommended. The requirement for the sensors in the sonde to measure without bias is having the movement of water across the sensor surfaces or having no standing water. The thin width of the post (4.0 cm) and the holes in the post ensure that the water flows through the sensor surfaces. Additionally, when the sonde is on the upstream side of the post, aquatic weeds and plant material/debris may enclose the sonde guard, as observed in this study. Another drawback of placing the sonde on the upstream side is that, while the guard protects the sensors, the sonde body is still in danger of being damaged by debris/wood on the upstream side of the post. The effect of the post on the velocity measurement can be tested by visually observing and comparing the velocity readings with and without the post. In this protocol, the area-velocity sensor was approximately 50 cm upstream of the Telspar post, and the presence of the Telspar post did not affect the velocity.
Identifying the frequency of calibration under site-specific conditions is important. It is a balance of not compromising the data quality by under-calibrating and not wasting resources by over-calibrating. In the agricultural streams in this study (i.e., hot, humid tropical climate), laboratory calibration every 2 weeks in the summer (Figure 6) and every 3 weeks in the winter was sufficient. However, sensors were cleaned on the site every week during the summer.
The preparation of a QAPP for all activities, including quality control checks in advance of the project, helps to identify potential problems, keeps the study consistent and uniform, and produces better-quality data. Following the guidelines provided in the QAPP procedure is required.
Documentation of the events or unusual observations in notebooks or photographs is very important. Many times, the results of monitoring are linked to events that are atypical. For example, the dredging (i.e., cleaning) of a stream (ditch), which is infrequent, will increase the turbidity of the water sample, even without increased discharge.
The safety of the personnel involved in the field work, as well as instrument safety, are very important. A safety, health, and welfare plan should be devised before the start of a project. Some of the safety concerns include snakes, temperature hazards, flood, high wind, driving conditions, lightning, etc. Logistics and recommended items to take during field visits are provided in Table 1.
One of the limitations of the current technology for measuring the nitrate and ammonium (i.e., ion-selective electrode) is that it does not measure them precisely up to very low nutrient values. While the resolution of the sensors is 0.01 mg/L for both nitrate and ammonium sensors, the accuracy is 5% of the reading, or up to &#177; 2 mg/L. The accuracy of the DO, turbidity, pH, and conductivity sensors are &#177; 0.1 - 0.2 mg/L, or 0.1%; &#177; 1 - 3% up to 400 NTU; &#177; 0.2; and &#177; 5 &#181;S, respectively. Moreover, the protocol is difficult to follow during flooding due to inaccessibility.
While this protocol was tested in agricultural watersheds, it can also be applied to other watersheds in other regions, such as watersheds impacted by other land use activities, including mining. This method is also useful in assessing interactions between multiple contaminants. Future applications of the method described here include sensor advancement to cope with the fouling of sensors and the accumulation of debris/sediment on the sonde guard; further improvements in the accuracy and precision of the sensors; the development of wireless networks and the remote transfer of data to the servers; and the buildup of larger networks for standard data acquisition systems, data management, and applications.

Water quality monitoring provides information on the concentrations of pollutants at different spatial scales, depending upon the size of the contributing area, which can range from a plot or a field to a watershed. This monitoring takes place over a period of time, such as a single event, a day, a season, or a year. The information garnered from monitoring water quality, mainly relating to nutrients (e.g., nitrogen and phosphorus) and sediment, can be used to: 1) understand hydrological processes and the transport and transformation of pollutants in streams, such as agricultural drainage ditches; 2) evaluate the efficiency of management practices applied to the watershed to reduce the nutrient and sediment load and to increase the water quality; 3) assess the delivery of the sediment and nutrients to the water downstream; and 4) improve the modeling of nutrients and sediment to understand the hydrological and water quality processes that determine pollutant transport and dynamics over the range of temporal and spatial scales.
This information is crucial to aquatic ecosystem restoration, sustainable planning, and the management of water resources1.
The most commonly used method for nutrient and sediment monitoring in a watershed is grab sampling. Grab sampling accurately represents a snapshot concentration at the time of sampling2. It can also depict a variation of pollutant concentrations with time if frequent sampling is done. However, frequent sampling is time intensive and expensive, often making it impractical2. Additionally, grab sampling may under- or overestimate the actual pollutant concentrations outside of the sampling time2,3,4. Consequently, loads calculated using such concentrations may not be accurate.
Alternatively, continuous monitoring provides accurate and timely information on water quality in a predetermined time interval, such as a minute, an hour, or a day. Users can select the appropriate time intervals based on their needs. Continuous monitoring enables the researchers, planners, and managers to optimize sample collection; develop and monitor time-integrated metrics, such as total maximum daily loads (TMDLs); evaluate the recreational use of the water body; assess baseline stream conditions; and spatially and temporally evaluate the variation of pollutants to determine cause-effect relationships and develop a management plan5,6. Continuous monitoring of nutrients and sediment has recently received increased attention due to advances in computing and sensor technology, the improved capacity of storage devices, and the increasing data requirements needed to study more complex processes1,5,7. In a global survey of over 700 water professionals, the use of multi-parameter sondes increased from 26% to 61% from 2002 to 2012 and is expected to reach 66% by 20225. In the same survey, 72% of respondents indicated the need for the expansion of their monitoring network to meet their data needs5. The number of stations in a monitoring network and the number of variables monitored per station in 2012 are expected to increase by 53% and 64%, respectively, by 20225.
However, continuous water quality and quantity monitoring in agricultural watersheds is challenging. Large rainfall events wash away sediment and macrophytes, contributing to high sediment load and debris buildup in the sensors and sondes. The runoff of excess nitrogen and phosphorus applied to agricultural fields creates ideal conditions for the growth of microscopic and macroscopic organisms and for the fouling of instream sensors and sondes, especially during the summer. Fouling and sediment buildup can cause sensors to fail, drift, and produce unreliable data. Despite these challenges, finer temporal resolution (as low as per minute) data are required to study the runoff processes and non-point source contamination, as they are affected by watershed characteristics (e.g., size, soil, slope, etc.) and the timing and intensity of rainfall7. Careful field observation, frequent calibration, and proper cleaning and maintenance can ensure good-quality data from the sensors and sondes, even at the finer time resolution.
Here, we discuss a method for the in situ continuous monitoring of two agricultural watersheds using multi-parameter water quality sondes, area-velocity and pressure transducer sensors, and autosamplers; their calibration and field maintenance; and data processing. The protocol demonstrates a way in which continuous water quality monitoring can be performed. The protocol is generally applicable to continuous water quality and quantity monitoring at any type or size of watershed.
The protocol was carried out in Northeast Arkansas in Little River Ditches Basin (HUC 080202040803, 53.4 km2 area) and Lower St. Francis Basin (HUC 080202030801, 23.4 km2 area). These two watersheds drain into tributaries of the Mississippi River. A need for monitoring tributaries of the Mississippi River was identified by the Lower Mississippi River Conservation Committee and the Gulf of Mexico Hypoxia Task Force to develop a watershed management plan and to record the progress of management activities8,9. Moreover, these watersheds are characterized as focus watersheds by the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS), based on the potential for reducing nutrient and sediment pollution and for improving water quality10. Edge-of-field monitoring is being carried out in these watersheds as part of the statewide Mississippi River Basin Healthy Watershed Initiative (MRBI) network11. More details of the watersheds (i.e., site locations, watershed characteristics, etc.) are provided in Aryal and Reba (2017)6. In short, the Little River Ditches Basin has predominantly silt loam soils, and cotton and soybean are the major crops, whereas the Lower St. Francis Basin has predominantly Sharkey clay soil, and rice and soybean are the major crops. At each watershed, in situ continuous water quantity and quality monitoring (i.e., discharge temperature, pH, DO, turbidity, conductivity, nitrate, and ammonium) was carried out at three stations in the mainstream using this protocol to understand the spatial and temporal variability in the pollutant loads and the hydrological processes. Additionally, weekly water samples were collected and analyzed for suspended sediment concentration.

<table border="0" cellpadding="0" cellspacing="0" width="812">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Multiparameter sonde</td>
			<td style="width:109px;">Hach Hydrolab</td>
			<td style="width:135px;">DS5X</td>
			<td style="width:419px;">measures temperature, pH, conductivity, dissolved oxygen, nitrate, ammonium, turbidity</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:149px;">Area velocity flow module and sensor</td>
			<td style="width:109px;">Teledyne Isco</td>
			<td style="width:135px;">2150</td>
			<td style="width:419px;">measures average stream velocity and flow depth, and calculates flow rate and total flow based on provided cross-section area of the ditch. Stored data can be downloaded directly to computer.</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Automatic portable water sampler</td>
			<td style="width:109px;">Teledyne Isco</td>
			<td style="width:135px;">ISCO 6712</td>
			<td style="width:419px;">automatically samples water in the set interval or in conjunction with flow module and sensor</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Pressure Transducer</td>
			<td style="width:109px;">In-situ</td>
			<td style="width:135px;">Rugged Troll 100</td>
			<td style="width:419px;">measures presure, level and temperature in the water. Stored data can be directly downloaded to the computer</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Portable flow meter</td>
			<td style="width:109px;">Flo-mate (Hach)</td>
			<td style="width:135px;">Marsh-McBirney 2000</td>
			<td style="width:419px;">For manual discharge measurement</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Battery, 12 v, rechargeable</td>
			<td style="width:109px;">UPG</td>
			<td style="width:135px;">UB 1270</td>
			<td style="width:419px;">To power sonde</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Battery, 12 v, rechargeable</td>
			<td style="width:109px;">Interstate Batteries</td>
			<td style="width:135px;">SRM 27</td>
			<td style="width:419px;">Lead acid battery to power autosampler</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Solar panel</td>
			<td style="width:109px;">Alt E</td>
			<td style="width:135px;">ALT20-12P</td>
			<td style="width:419px;">To recharge battery at the site</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">C-8 batteries</td>
			<td style="width:109px;"></td>
			<td style="width:135px;"></td>
			<td style="width:419px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Calibration standards</td>
			<td style="width:109px;">Hach or Fisher Scientific</td>
			<td style="width:135px;">mulitple</td>
			<td style="width:419px;">Standards of pH (4,7,10), conductivity (1412 uS/cm), nitrate (5 and 50 mg/L), ammonium (5 and 50 mg/L), and turbidity (50,100,200 NTU)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">High nitrate standard</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">013810HY</td>
			<td style="width:419px;">50 mg/L</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Low nitrate standard</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">013800HY</td>
			<td style="width:419px;">5 mg/L</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">High ammonium standard</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">002588HY</td>
			<td style="width:419px;">50 mg/L</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Low ammonium standard</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">002587HY</td>
			<td style="width:419px;">5 mg/L</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Turbidity standard</td>
			<td style="width:109px;">Fisher scientific</td>
			<td>R8819050-500G</td>
			<td style="width:419px;">50 NTU</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Turbidity standard</td>
			<td style="width:109px;">Fisher scientific</td>
			<td>88-061-6</td>
			<td style="width:419px;">100 NTU</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Turbidity standard</td>
			<td style="width:109px;">Fisher scientific</td>
			<td>R8819200500 C</td>
			<td style="width:419px;">200 NTU</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Potassium chloride salt pellets</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">005376HY</td>
			<td style="width:419px;">to maintain electrolyte for pH electrode</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Potassium chloride standard</td>
			<td style="width:109px;">Fisher scientific</td>
			<td style="width:135px;">5890-16</td>
			<td style="width:419px;">1412 us/cm</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Buffer solution, pH 4</td>
			<td style="width:109px;">Fisher scientific</td>
			<td style="width:135px;">SB99-1</td>
			<td style="width:419px;">for pH sensor calibration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Buffer solution, pH 7</td>
			<td style="width:109px;">Fisher scientific</td>
			<td style="width:135px;">SB108-1</td>
			<td style="width:419px;">for pH sensor calibration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Buffer solution, pH 10</td>
			<td style="width:109px;">Fisher scientific</td>
			<td style="width:135px;">SB116-1</td>
			<td style="width:419px;">for pH sensor calibration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Silicon sealant</td>
			<td style="width:109px;">Hach</td>
			<td style="width:135px;">00298HY</td>
			<td style="width:419px;">For sealing sensor battery cover water tight</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">All purpose cleaner</td>
			<td style="width:109px;">Sunshine Makers Inc</td>
			<td style="width:135px;">Simple green</td>
			<td style="width:419px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Wipes</td>
			<td style="width:109px;">Kimberly-Clark</td>
			<td style="width:135px;"></td>
			<td style="width:419px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">L-bracket</td>
			<td style="width:109px;"></td>
			<td style="width:135px;"></td>
			<td style="width:419px;"></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:149px;">Telsbar post</td>
			<td style="width:109px;">Unistrut Service Company</td>
			<td style="width:135px;"></td>
			<td style="width:419px;">Secure sensors and sondes in the stream</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Steel wire</td>
			<td style="width:109px;"></td>
			<td style="width:135px;"></td>
			<td style="width:419px;">supend sonde and PT sensor</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Carabiner</td>
			<td style="width:109px;"></td>
			<td style="width:135px;"></td>
			<td style="width:419px;">supend sonde and PT sensor</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Allen wrench</td>
			<td style="width:109px;"></td>
			<td style="width:135px;"></td>
			<td style="width:419px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:149px;">Copper wire mesh</td>
			<td style="width:109px;">Bird B Gone</td>
			<td style="width:135px;"></td>
			<td style="width:419px;">Rodent and bird control copper mesh roll</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:149px;">Adhesive Tape</td>
			<td style="width:109px;">Agri Drain Corporation</td>
			<td style="width:135px;"></td>
			<td style="width:419px;">Tile tape, works in wet and cold weather</td>
		</tr>
	</tbody>
</table>

continuous instream monitoring of nutrients and sediment in agricultural watersheds

Pollutant concentrations and loads in watersheds vary considerably with time and space. Accurate and timely information on the magnitude of pollutants in water resources is a prerequisite for understanding the drivers of the pollutant loads and for making informed water resource management decisions. The commonly used &#34;grab sampling&#34; method provides the concentrations of pollutants at the time of sampling (i.e., a snapshot concentration) and may under- or overpredict the pollutant concentrations and loads. Continuous monitoring of nutrients and sediment has recently received more attention due to advances in computing, sensing technology, and storage devices. This protocol demonstrates the use of sensors, sondes, and instrumentation to continuously monitor in situ nitrate, ammonium, turbidity, pH, conductivity, temperature, and dissolved oxygen (DO) and to calculate the loads from two streams (ditches) in two agricultural watersheds. With the proper calibration, maintenance, and operation of sensors and sondes, good water quality data can be obtained by overcoming challenging conditions such as fouling and debris buildup. The method can also be used in watersheds of various sizes and characterized by agricultural, forested, and/or urban land.

In the Aryal and Reba (2017) publication, this protocol was used to study the transport and transformation of nutrients and sediment in two small agricultural watersheds6. Additional outcomes from this protocol are described below.
Rainfall-runoff Water Quality Relationships:
The strength of continuous monitoring is that users can choose a fine time resolution to study cause-effect relationships, such as the relationship between rainfall, runoff, and turbidity, using 15-min data (Figure 2A). Rainfall data were downloaded from weather stations (www.weather.astate.edu), one inside the Little River Ditches Basin and the other 6.3 miles away from the Lower St. Francis Basin. From 00:00 to 09:00 on 7/22, a total of 25.4 mm of rainfall occurred. The rainfall increased the discharge from 0.71 m3/s at 00:00 to 4.89 m3/s at 17:45 on 7/22. There were multiple local discharge peaks during the event, likely tied to the spatial variability of rainfall and the drainage patterns of the rice and soybean fields that contributed to the majority of the flow. The Lower St. Francis Basin had approximately 94% of the area in row crops, primarily soybean and rice. As the discharge gradually subsided, another 14-mm rain event occurred on 7/23 at 07:00 and lasted for 5 h. Consequently, another increase in discharge was measured.
As expected, turbidity increased with discharge following the rain event and subsided gradually (Figure 2A). Turbidity increased from 13 NTU at 23:34 on 7/21 to 409 NTU at 02:04 on 7/23. The highest turbidity was obtained during the increasing discharge portion of the hydrograph. It was likely due to the first flush that washed soil particles from the agricultural fields. As with discharge, the turbidity also showed two clear peaks.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56036/56036fig2.jpg" /> 
Figure 2. Variation of Rainfall, Discharge, and Water Quality on an Event Basis in the Lower St. Francis Basin, an Agricultural Watershed. 
(A) Rainfall, discharge and turbidity. (B) Nitrate, ammonium, and conductivity from 7/21 to 7/26. The majority of the watershed crops were soybean and rice. The rainfall, discharge, and turbidity plots are based on 60-, 15-, and 15-min data, respectively. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Similarly, nitrate, ammonium, and conductivity showed variations with runoff and time (Figure 2B). During a runoff event, nitrate concentration can either decrease due to a dilution effect or increase due to a mixing of concentrated runoff from fields. In the considered time frame, nitrate increased up to 4.52 mg/L at 02:04 on 7/22 and gradually decreased. The highest concentration of nitrate coincided with the first flush runoff, as recently applied but unused soluble nitrogen was washed away. The second peak of the nitrate concentration corresponded with the second peak in the discharge, but it had a lower concentration than the first peak. This is likely due to the washout of easily soluble nitrogen by the first flush. The shape of the nitrate peaks was similar during both events, despite differences in magnitude.
The mean ammonium concentration was 0.80 mg/L, likely due to the contribution from rice fields. The ammonium concentration varied slightly with two discharge peaks (i.e., increased with an increase in discharge). However, the increase in the ammonium concentration with the second discharge peak was less than that with first discharge peak, for the same reasons as nitrate (Figure 2B). As with nitrate, the ammonium concentration peaked before the discharge peaked.
The conductivity ranged from 93 - 495 &#181;S/cm during the period. The conductivity showed an inverse relationship to discharge (Figure 2A and 2B) (i.e., conductivity was high during base flow and decreased with an increase in flow during both peak discharges). Nitrate and ammonium were likely minor contributors to the water conductivity, since the conductivity of water decreased during peak discharge, even though the nitrate and ammonium were higher than during base conditions. The dilution of rain water, which has lower conductivity, may have contributed to the lower conductivity of water in the stream.
Diurnal variations of pH, temperature, and DO are clearly illustrated by the sonde results (Figure 3). The temperature varied from 36.1 to 24.6 &#176;C from 7/9 - 7/10. The water temperature in the stream was the lowest at 06:00-07:00 and the highest at 17:00-18:00.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56036/56036fig3.jpg" /> 
Figure 3. Diurnal Variation of pH, Temperature, and DO at a Stream Section in the Lower St. Francis Basin, an Agricultural Watershed. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The dissolved oxygen was lowest from midnight to 06:00. As the photosynthesis activity of plants starts after sunrise, the DO increased steadily until it peaked at 16:19 on 7/9 (9.98 mg/L, 144.9% saturation) and at 15:34 on 7/10 (11.21 mg/L, 159.9% saturation). The DO steadily decreased until midnight and remained constant. Bacterial and algal respiration, photosynthesis, carbonaceous and nitrogenous oxidation, and temperature likely affected the diurnal variation of DO18.
The pH varied between 7.4 and 7.8 from 7/9-7/10. The pH was highest at 17:34 on 7/9 (7.78) and at 17:04 on 7/10 (7.77). Diurnal variation in pH was also affected by the rate of respiration, photosynthesis, and buffering capacity, since carbon dioxide, which decreases pH, is removed during photosynthesis and is added during respiration in the aquatic systems.
The concentrations shown in Figure 2 and Figure 3, if measured over a longer period (i.e., a month, season, year) can provide information on how the water quality changes with time under natural or managed conditions.
Temporal (Monthly) Variation of Pollutant Loads:
Temporal variation at a section of the stream can be studied over different time scales. Monthly variation at the Little River Ditches Basin, a small agricultural watershed in northeast Arkansas, revealed a pattern of nitrogen and sediment loss from the watershed throughout the year (Figure 4). Pollutant loads were high in the early summer and late fall. The months of September and October were characterized by low pollutant loading, mainly due to low flow. The SSC was highest in November and December due to high rainfall on recently harvested and disturbed fields. The data also showed that variations were very high, since daily loads were driven by rainfall events that varied significantly. The high loads during late fall (November and December) demonstrated that nutrient reduction programs may be more effective if they focus on reducing November/December loads. Consequently, techniques that reduce the loss of pollutants in the winter, such as the use of cover crops19, must be considered in watershed management programs.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56036/56036fig4.jpg" /> 
Figure 4. Monthly Variation of Nitrate, Ammonium, and SSC Load (kg/d) at the Outlet of the Little River Ditches Basin. 
The values are median &#177; interquartile range. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Spatial Variation of Pollutant Loads:
The protocol can also provide the data for spatial variations in addition to temporal variations if multiple stations within a watershed are chosen. Pollutant loads in an agricultural watershed (Figure 5) show distinctly increasing nitrate and ammonium loads as the water travels downstream. The loss in 9.6 kg/ha nitrate per year was within the 8 - 14 kg/ha per year range reported in Missouri in small agricultural watersheds with similar soil typs20. This type of information can be used to evaluate the effectiveness of instream water management practices and pollutant transport, among others.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56036/56036fig5.jpg" /> 
Figure 5. Nitrate and Ammonium Transport in the Little River Ditches Basin. 
Upstream, midstream, and downstream sites were located approximately 2 km apart. The values are the mean &#177; standard error of mean on a daily basis for August 2015. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Sensor Fouling and Sediment Buildup:
In agricultural watersheds, the presence of nutrients, such as nitrogen and phosphorus, in the runoff water at high concentrations can accelerate the rate at which bio-fouling occurs at a given temperature. Additionally, runoff water can carry high sediment loads that originate from the tilled fields and eroded waterways. The high sediment load can lead to the deposition of the sediment particles at the sensor and sonde surfaces and to the buildup of sediment. Such fouling and sediment buildup can result in drift and in inaccurate results.
The diurnal variation of DO decreased until 7/15, increased on 7/16 after the sensor was cleaned at the site, and abruptly decreased after 13 or 14 days (Figure 6) due to fouling. The growth and resulting accumulation of microorganisms on the surfaces of the sonde are visible in Figure 7. The fouling is severe on the surfaces where wipes or brushes do not clean. The effect of sediment buildup on the turbidity reading was observed on 12/26 (Figure 8). The rainfall on 12/23 and 12/25 increased the turbidity up to 1595 NTU and 1073 NTU. The turbidity decreased once the discharge decreased in the stream. However, the big rain event on 12/26 caused the turbidity to reach the upper limit of 3000 NTU. The turbidity reading remained stable at 3000 NTU due to the accumulation of debris on the sonde guard and the presence of weeds and plants on the Telspar post. Once the debris accumulated, the turbidity readings were erratic (i.e., changed abruptly from 3000 NTU to less than 50 NTU in 15 min) and incorrect. Hence, the turbidity data from 12/26 to 12/29 are not of good quality.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56036/56036fig6.jpg" /> 
Figure 6. Drift of the DO Sensor Reading after the Sonde Remained in the Stream for Two Weeks. 
After calibration, the sonde was installed on 7/8, and the drift started on 7/22. The drift in the sensor reading after 7/21 resulted in a lower DO than normal. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56036/56036fig7.jpg" /> 
Figure 7. Images Showing the Fouling on the Sensor Surfaces (left) and Clean Sensing Surfaces of the Sensors (right) after Wiping with a Brush and Wiper. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56036/56036fig8.jpg" /> 
Figure 8. Turbidity (NTU) in the Stream Before and After Sediment Buildup in the Sonde Guard. 
Rainfall (mm) is shown on the secondary y-axis. The turbidity showed an excellent response to rainfall on 12/16, 12/23, and 12/25. However, the large rainfall event of 12/26 created sediment buildup in the sonde guard, and the turbidity readings after 12/26 were faulty (mostly 3000 NTU) and erratic. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56036/56036fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Item listing</td>
			<td colspan="4">Item listing</td>
			<td>Check</td>
		</tr>
		<tr>
			<td colspan="2" rowspan="5">Documents</td>
			<td colspan="4">QAPP (Quality Assurance Project Plan)</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Chain of Custody Sheets</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Field notebook</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Navigation maps/GPS</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Pen, Marker, label tape</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2" rowspan="8">Safety</td>
			<td colspan="4">Sunscreen/sunglasses</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Wasp spray</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">First-aid kit</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Drinking water</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Communication (cell phone)</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Personal Protective Equipment-Wader, rubber boot, gloves, hat</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Rope and anchor</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Antiseptic hand wash</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2" rowspan="3">Sample collection, storage, transport</td>
			<td colspan="4">Cooler and ice</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Sample bottle and lid</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Labeling tape</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2" rowspan="3">Sensor/Instrumentation</td>
			<td colspan="4">Communication cables</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Charged external batteries</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Field laptop</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2" rowspan="4">Sonde</td>
			<td colspan="4">Communication cable</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">&#8216;C&#8217; batteries</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Brush and soap</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">Field laptop</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">Other</td>
			<td colspan="4">Tool box (screw drivers, volt meter, zip ties, wrench, &#8230;)</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1. Checklist of Items Recommended for a Field Visit to Sample Water and Repair and Maintain Sensors.

Watch this Scientific Journal Video about Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds at JoVE.com

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds | Environment | Video

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

With the advancement of technology and the rise in end-user expectations, the need and use of higher temporal resolution data for pollutant load estimation has increased. This protocol describes a method for continuous in situ water quality monitoring to obtain higher temporal resolution data for informed water resource management decisions.

Pollutant concentrations and loads in watersheds vary considerably with time and space. Accurate and timely information on the magnitude of pollutants in ...

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SCIENTISTS IN ACTION

11.1K Views.  Oak Ridge Institute of Science and Education (ORISE). With the advancement of technology and the rise in end-user expectations, the need and use of higher temporal resolution data for pollutant load estimation has increased. This protocol describes a method for continuous in situ water quality monitoring to obtain higher temporal resolution data for informed water resource management decisions.

Video: Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. Radioisotope tracing is a major technique used to measure such fluxes, both within plants, and between plants and their environments. Flux data obtained with radiotracer protocols can help elucidate the capacity, mechanism, regulation, and energetics of transport systems for specific mineral nutrients or toxicants, and can provide insight into compartmentation and turnover rates of subcellular mineral and metabolite pools. Here, we describe two major radioisotope protocols used in plant biology: direct influx (DI) and compartmental analysis by tracer efflux (CATE). We focus on flux measurement of potassium (K+) as a nutrient, and ammonia/ammonium (NH3/NH4+) as a toxicant, in intact seedlings of the model species barley (Hordeum vulgare L.). These protocols can be readily adapted to other experimental systems (e.g., different species, excised plant material, and other nutrients/toxicants). Advantages and limitations of these protocols are discussed.

In planta measurement of nutrient and toxicant fluxes is essential to the study of plant nutrition and toxicity. Here, we cover radiotracer protocols for influx and efflux determination in intact plant roots, using potassium (K+) and ammonia/ammonium (NH3/NH4+) fluxes as examples. Advantages and limitations of such techniques are discussed.

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. ...

Sediment Core Sectioning and Extraction of Pore Waters under Anoxic Conditions

We demonstrate a method for sectioning sediment cores and extracting pore waters while maintaining oxygen-free conditions. A simple, inexpensive system is built and can be transported to a temporary work space close to field sampling site(s) to facilitate rapid analysis. Cores are extruded into a portable glove bag, where they are sectioned and each 1-3 cm thick section (depending on core diameter) is sealed into 50 ml centrifuge tubes. Pore waters are separated with centrifugation outside of the glove bag and then returned to the glove bag for separation from the sediment. These extracted pore water samples can be analyzed immediately. Immediate analyses of redox sensitive species, such as sulfide, iron speciation, and arsenic speciation indicate that oxidation of pore waters is minimal; some samples show approximately 100% of the reduced species, e.g. 100% Fe(II) with no detectable Fe(III). Both sediment and pore water samples can be preserved to maintain chemical species for further analysis upon return to the laboratory.

A protocol for sectioning sediment cores and extracting pore waters under anoxic conditions in order to permit analysis of redox sensitive species in both solids and fluids is presented.

We demonstrate a method for sectioning sediment cores and extracting pore waters while maintaining oxygen-free conditions. A simple, inexpensive system is ...

Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study provides a protocol for the passive sampler preparation, calibration, extraction method and instrumental analysis. Sampling rates (RS) and passive sampler-water partition coefficients (KPW) were calculated for silicone rubber, polar organic chemical integrative sampler POCIS-A, POCIS-B, SDB-RPS and C18 disk. The uptake of the selected compounds depended on their physicochemical properties, i.e., silicone rubber showed a better uptake for more hydrophobic compounds (log octanol-water partition coefficient (KOW) &#62; 5.3), whereas POCIS-A, POCIS-B and SDB-RPS disk were more suitable for hydrophilic compounds (log KOW &#60; 0.70).

A protocol about the characterization and application of five different passive sampling devices is presented.

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study ...

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in nutrient and gas cycles. After cores with intact sediment-water interfaces are collected, they are submerged in incubation tanks and kept under aerobic conditions at in situ temperatures. To initiate a time course of overlying water chemistry, cores are sealed without bubbles using a top cap with a suspended stirrer. Time courses of 4-7 sample points are used to determine the rate of sediment water exchange. Artificial illumination simulates day-time conditions for shallow photosynthetic sediments, and in conjunction with dark incubations can provide net exchanges on a daily basis. The net measurement of N2 is made possible by sampling a time course of dissolved gas concentrations, with high precision mass spectrometric analysis of N2:Ar ratios providing a means to measure N2 concentrations. We have successfully applied this approach to lakes, reservoirs, estuaries, wetlands and storm water ponds, and with care, this approach provides valuable information on biogeochemical balances in aquatic ecosystems.

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

Here, we present small core incubations for the measurement of sediment-water gas and solute exchange. These will provide reliable measurements of sediment-water exchange that assess the role of sediment in influencing biological and biogeochemical processes in aquatic ecosystems.

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in ...

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonate (CaCO3) profiles. The objective is to describe a simple conceptual model and detailed protocol for repeatable field and laboratory measurements of these quantities. Here, accurate elevation is measured using a ground-based differential global positioning system (GPS); other data acquisition methods could be applied to the same basic method. Soil samples are collected from prescribed depth intervals and analyzed in the lab using an efficient and precise modified pressure-calcimeter method for quantitative analysis of inorganic carbon concentration. Standard statistical methods are applied to point data, and representative results show significant correlations between changes in soil surface layer CaCO3 and changes in elevation consistent with the conceptual model; CaCO3 generally decreased in depositional areas and increased in erosional areas. Maps are derived from point measurements of elevation and soil CaCO3 to aid analyses. A map of erosional and depositional patterns at the study site, a rain-fed winter wheat field cropped in alternating wheat-fallow strips, shows the interacting effects of water and wind erosion affected by management and topography. Alternative sampling methods and depth intervals are discussed and recommended for future work relating soil erosion and deposition to soil CaCO3.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes in elevation are related to changes in near-surface soil carbonates. Repeatable methods for field and laboratory measurements of these quantities and data analysis methods are described here.

Spatial patterns of soil erosion and deposition can be inferred from differences in ground elevation mapped at appropriate time increments. Such changes ...

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Vernal ponds, also referred to as vernal pools, provide critical ecosystem services and habitat for a variety of threatened and endangered species. However, they are vulnerable parts of the landscapes that are often poorly understood and understudied. Land use and management practices, as well as climate change are thought to be a contribution to the global amphibian decline. However, more research is needed to understand the extent of these impacts. Here, we present methodology for characterizing a vernal pond's morphology and detail a monitoring station that can be used to collect water quantity and quality data over the duration of a vernal pond's hydroperiod. We provide methodology for how to conduct field surveys to characterize the morphology and develop stage-storage curves for a vernal pond. Additionally, we provide methodology for monitoring the water level, temperature, pH, oxidation-reduction potential, dissolved oxygen, and electrical conductivity of water in a vernal pond, as well as monitoring rainfall data. This information can be used to better quantify the ecosystem services that vernal ponds provide and the impacts of anthropogenic activities on their ability to provide these services.

Understanding the ecosystem services and processes provided by vernal ponds and the impacts of anthropogenic activities on their ability to provide these services requires intensive hydrologic monitoring. This sampling protocol using in-situ monitoring equipment was developed to understand the impact of anthropogenic activities on water levels and quality.

Vernal ponds, also referred to as vernal pools, provide critical ecosystem services and habitat for a variety of threatened and endangered species. ...

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide

Coffee berry borer (CBB) is the most devastating insect pest for coffee crops worldwide. We developed a scientific monitoring protocol that is aimed at capturing and quantifying the dynamics and impact of this invasive insect pest as well as the development of its host plant across a heterogeneous landscape. The cornerstone of this comprehensive monitoring system is timely georeferenced data collection on CBB movement, coffee berry infestation, mortality by the fungus Beauveria bassiana, and coffee plant phenology via a mobile electronic data recording application. This electronic data collection system allows field records to be georeferenced through built-in global positioning systems, and is backed by a network of weather stations and records of farm management practices. Comprehensive monitoring of CBB and host plant dynamics is an essential part of an area-wide project in Hawaii to aggregate landscape-level data for research to improve management practices. Coffee agroecosystems in other parts of the world that experience highly variable environmental and socioeconomic factors will also benefit from implementing this protocol, in that it will drive the development of customized integrated pest management (IPM) to manage CBB populations.

Comprehensive monitoring of coffee berry borer and host plant dynamics is essential for aggregating landscape-level data to improve management of this invasive pest. Here, we present a protocol for scientific monitoring of coffee berry borer movement, infestation, mortality, coffee plant phenology, weather, and farm management via a mobile electronic data recording application.

Coffee berry borer (CBB) is the most devastating insect pest for coffee crops worldwide. We developed a scientific monitoring protocol that is aimed at ...

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current measures to combat such pathogens have proved too conservative and, thus, not sufficiently effective, and they can potentially pose environmental risks. Therefore, it is extremely necessary to identify host-resistance factors to assist in controlling plant diseases naturally through the identification of resistant germplasm, the isolation and characterization of resistance genes, and the molecular breeding of resistant cultivars. In this regard, there is need to establish an accurate, rapid, and large-scale inoculation method to breed and develop plant resistance genes. The rice blast fungal pathogen Magnaporthe grisea causes severe disease symptoms and yield losses. Recently, M. grisea has emerged as a model organism for studying the mechanisms of plant-fungal pathogen interactions. Hence, we report the development of a plant virulence test method that is specific for M. grisea. This method provides for both spray inoculation with a conidial suspension and wounding inoculation with mycelium cubes or droplets of conidial suspension. The key step of the wounding inoculation method for detached rice leaves is to make wounds on plant leaves, which avoids any interference caused by host penetration resistance. This spray/wounding protocol contributes to the rapid, accurate, and large-scale screening of the pathotypes of M. grisea isolates. This integrated and systematic plant infection method will serve as an excellent starting point for gaining a broad perspective of issues in plant pathology.

The Plant Infection Test: Spray and Wound-Mediated Inoculation with the Plant Pathogen Magnaporthe Grisea

Here, we present a protocol to test plant virulence with the plant pathogen Magnaporthe grisea. This report will contribute to the large-scale screening of the pathotypes of fungi isolates and serve as an excellent starting point for understanding the resistant mechanisms of plants during molecular breeding.

Plants possess a powerful system to defend themselves against potential threats by pathogenic fungi. For agriculturally important plants, however, current ...

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Denitrification is the primary biogeochemical process removing reactive nitrogen from the biosphere. The quantitative evaluation of this process has become particularly relevant for assessing the anthropogenic-altered global nitrogen cycle and the emission of greenhouse gases (i.e., N2O). Several methods are available for measuring denitrification, but none of them are completely satisfactory. Problems with existing methods include their insufficient sensitivity, and the need to modify the substrate levels or alter the physical configuration of the process using disturbed samples.This work describes a method for estimating sediment denitrification rates that combines coring, acetylene inhibition, and microsensor measurements of the accumulated N2O. The main advantages of this method are a low disturbance of the sediment structure and the collection of a continuous record of N2O accumulation; these enable estimates of reliable denitrification rates with minimum values up to 0.4-1 &#181;mol N2O m-2 h-1. The ability to manipulate key factors is an additional advantage for obtaining experimental insights. The protocol describes procedures for collecting the cores, calibrating the sensors, performing the acetylene inhibition, measuring the N2O accumulation, and calculating the denitrification rate. The method is appropriate for estimating denitrification rates in any aquatic system with retrievable sediment cores. If the N2O concentration is above the detection limit of the sensor, the acetylene inhibition step can be omitted to estimate the N2O emission instead of denitrification. We show how to estimate both actual and potential denitrification rates by increasing nitrate availability as well as the temperature dependence of the process. We illustrate the procedure using mountain lake sediments and discuss the advantages and weaknesses of the technique compared to other methods. This method can be modified for particular purposes; for instance, it can be combined with 15N tracers to assess nitrification and denitrification or field in situ measurements of denitrification rates.

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

This method estimates sediment denitrification rates in sediment cores using the acetylene inhibition technique and microsensor measurements of the accumulated N2O. The protocol describes procedures for collecting the cores, calibrating the sensors, performing the acetylene inhibition, measuring the N2O accumulation, and calculating the denitrification rate.

Denitrification is the primary biogeochemical process removing reactive nitrogen from the biosphere. The quantitative evaluation of this process has ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.