ETH/Eawag Aquatic Chemistry field campaign in the Zambezi River Basin (March 2018 – March 2019)
Date of last edit: 2021.02.22
Reference people: Dr. Elisa Calamita, Dr. R. Scott Winton, Dr. Cristian R. Teodoru, Prof. Bernhard Wehrli      
Analytical Institutions: Aquatic Chemistry Group, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland
Zambian host institutions: Prof. Imasiku Nyambe, Dr. Kawawa Banda, Institute for Water Resource Management, School of Mines, University of Zambia, Lusaka
Funding: Decision Analytic Framework to explore the water-energy-food Nexus in complex transboundary water resource systems of fast developing countries (DAFNE) project, from the European Union’s Horizon 2020 research and innovation programme under grant agreement           no. 690268; ETH Zurich, Bernhard Wehrli
Technical support for laboratory analyses at Eawag: Patrick Kathriner, Serge Robert, Daniel Steiner

Campaigns
Campaign 1 
Dates: March 9 to March 31 2018
Aquatic Chemistry team: Cristian R. Teodoru, R. Scott Winton, Elisa Calamita, Davide Vanzo
Other participants: Fritz Kleinschroth, Simon Spratley, Ethel Namafe, Namafe Namafe, Rowen Jani, Aaron Kachinga,  Goodfellow Mphane, Miyande Mombe, Simian Luhanga, Stefaan Dondeyne, Ine Rosier

Campaign 2 
Dates: June to July 2018
Aquatic Chemistry team: Cristian R. Teodoru, Elisa Calamita, Cristian Dinkel

Campaign 3
Dates: October to November 2018
Aquatic Chemistry team: Cristian R. Teodoru, Elisa Calamita, Davide Vanzo

Campaign 4
Dates: February 9 to March 10, 2019
Aquatic Chemistry team: R. Scott Winton, Cristian R. Teodoru


Methods
Study area
The Zambezi is Africa’s fourth largest river and the most important draining into the Indian Ocean. We focused our field study on the south-western half of Zambia, with sampling points bracketing major hydropower reservoirs on the Zambezi and Kafue River main stems. Zambezi sites were: above Victoria Falls (VIC), below Kariba Dam near Siavonga (SIA), above the Kafue River confluence at Breezer’s Lodge (BRE) and below the Kafue confluence above the upstream boundary of Lower Zambezi National Park (LOW). Kafue River sites were near Hook Bridge above the Itezhi-Tezhi Reservoir at Mukambia Lodge (MUK), below the Itezhi-Tezhi Dam (DIT), at the lower end of the Kafue Flats near Mazambuka (MAZ), near Kafue Town at the Kafue River Bridge (KBR), and upstream of the Zambezi confluence at Gwabi Lodge (GWA). We also sampled surface and deep waters (50 m depth) of the Kariba and Itezhi-Tezhi reservoirs near the dam wall (KAS, KAD ITS, ITD). To test for effects of anthropogenic landcover change on stream water quality, we sampled three smaller streams (MAR, CHO, LCH) draining sub-catchments characterized by dense urban and/or agricultural areas. The study region’s climate is characterized by intense hydrologic seasonality, with a 5 to 6-month rainy season accounting for 95% of annual precipitation that gives way to a half-year of nearly no rainfall. As a result, river flows and the connectivity between surface waters and catchments follow a corresponding high-low, on-off modality. Discharge of the Zambezi River at Victoria Falls spans nearly an order of magnitude from minimum flows of a few hundred m3 s-1 in October/November to a few thousand m3 s-1 in April/May (Beilfuss, 2012). 
Field sampling
In order to capture the region’s seasonally variable hydrologic conditions, we collected surface water samples from all sampled points (with a few exceptions) four times between March 2018 and March 2019 at approximately even intervals spanning both wet and dry seasons. In most cases we were able to sample from a boat or floating pontoon to avoid bias from hyporheic or littoral influence at the river margins. We measured pH, conductivity and water temperature in situ using an YSI Professional Series multi-parameter sonde (Yellow Springs, Ohio). We measured dissolved oxygen using a second YSI optical dissolved oxygen probe. We calibrated pH and oxygen sensors daily before taking measurements. We filtered water samples through pre-combusted and pre-weighed glass fiber F filters, collecting subsamples of the filtrate for analysis of dissolved ions, nutrients, and dissolved organic carbon (in borosilicate glass vials). We sun-dried filters for subsequent analysis of particulate matter. For analysis of chlorophyll we collected a second filter, which we stored in 95% ethanol and in the dark for subsequent field processing and analysis. We also collected unfiltered surface water samples for analysis of digestible nutrients and alkalinity. We separately collected water samples for dissolved inorganic carbon, which we filtered through 0.2 µM syringe filters in 12 ml exetainers without headspace. We collected all water samples in triplicate in order to estimate error associated with our entire sampling and analytical workflow and if necessary, discard outliers. We kept samples cool during handling and storage.
During the first campaign we brought an ultraportable Infrared greenhouse gas analyzer (Los Gatos Research) for direct measurements in the field. Because of complications with transporting this instrument by plane, we instead collected water samples in 113 ml serum bottles for headspace equilibrium and analysis of headspace on a gas chromatograph in the laboratory at Eawag. We only measured flux using a static chamber at a few sites during the first campaign.
Sample analysis
Nutrients
We pursued two parallel strategies for nutrient analysis, one based on manual analysis in a field laboratory and the other relying on transportation of samples to Eawag laboratories in Kastanienbaum, Switzerland. For field laboratory we analyzed NO3, NH4, and PO4 colorimetrically on a Merck (Darmstadt, Germany) Spectroquant NOVA60 portable spectrophotometer. We designed our field laboratories to analyze all filtered water samples for these mineral nutrients within 48 hours of collection to avoid potential loss of analytes during storage. We tested for loss by re-analyzing extra replicates of the first collected at increasing time intervals and found that NH4 loss was relatively rapid (presumably via volatilization), but that NO3 and PO4 were stable for the duration of our handling times. This result gave us the confidence in our transported samples and to batch our samples for less frequent field analysis. Unfortunately, much of the field nutrients data ended up being contaminated during analysis by tainted deionized water, which seemed to have high background concentrations of NO3 and PO4, presumably from HNO3, which is commonly used to clean lab materials and/or P-containing detergents. For the laboratory analysis we used a Skalar (Breda, Netherlands) SAN++ automated flow injection analyzer following standard procedure ISO 13395:1996. We used the same instrument to analyze potassium-persulfate digests of unfiltered samples. We note that this method typically underestimates true “total” P and N because of the potential for fine particles to settle during sample storage (Zhang, 2012) and are better labelled “digestible” N and P. 
Ions, Alkalinity, DIC and DOC
We analyzed filtered samples for anions (Cl-, SO42-, NO3-, NO2-) on a Metrohm 882 Compact IC Pro and cations (NH4+, Na+, Mg2+, K+, Ca2+) on a Metrohm 881 Compact IC Pro. We analyzed unfiltered samples for total alkalinity on a Metrohm 862 Compact Titrosampler. We analyzed filtered samples for dissolved organic carbon (non-purgeable organic carbon), dissolved nitrogen and dissolved inorganic carbon using a Shimadzu TOC-L. 
Chlorophyll
We sonicated plastic centrifuge tubes containing filters and ethanol for 30 minutes to release photosynthetic pigments into solution. We filled the water bath with ice cubes to avoid excessive heating of the samples during sonication and kept the device covered by a black cloth to avoid photo-degradation. We used a 0.45 µM syringe filter to remove glass fiber particles while transporting the solution to a photometric cell. We recorded absorbance at 665nm using a portable spectrophotometer and then transferred the sample to an amber vial, which we stored at -20 ?C (or as cold as possible) for transport to the Eawag laboratories for analysis via a Jasco Instruments (Easton, Maryland) High-Performance Liquid Chromatograph (HPLC) system. Total suspended solids
We oven dried and then weighed filters to calculate total suspended solids (TSS). In some cases, we yielded negative values, which we found to be associated with light actually TSS burdens and anomalously heavy pre-weights coupled, possibly caused by humidity and/or weighing error. We sought to avoid issue by passing as much water as possible before filter clogging, but this was not always enacted in practice. In one of the campaigns the entire supply of pre-weighed filters was lots. For these samples with no pre-weighed or suspiciously heavy pre-weights, we instead used a synthetic mean filter weight based on a sample of 16 clean and dry filters with weights fitting a normal distribution. Relative error associated with some of the low TSS values may be very high, but the heavier TSS values are probably reasonably accurate. TSS is also strongly biased for samples taken shortly after or during rainfall. We observed relatively turbid river water in several instances, usually regaining clarity within 48 hours of dry conditions. 
After collecting particulate matter in the field, we sun-dried filters and then packed them in plastic cases for transport to Eawag laboratories for weighing and subsequent analysis. 
To remove inorganic carbon, we fumigated the filters for 48 h under an HCl atmosphere. We measured the C and N elemental and isotopic compositions with an EA-IRMS (vario PYRO cube, Elementar coupled with an IsoPrime IRMS, GV Instruments). Acetanilide #1 (Indiana University, CAS # 103-84-4) was used as an internal standard. The isotopic ratios of the samples are reported in the delta notation VPDB for carbon and air for nitrogen. 
Greenhouse Gases
CO2, CH4, N2O water concentrations by GC
Samples for CO2, CH4 and N2O analysis were collected for laboratory-based analysis by gas chromatography. Water, collected with a Niskin bottle, was filled, bubble-free, into 120 mL septa vials by allowing overflow of approximately 3 times the sample volume before preserving the sample by adding CuCl2. Depending on the expected concentrations, a headspace of 15 - 25 mL was created in the lab using pure N2. Samples were equilibrated overnight at 23°C on a shaker and the headspace was analyzed using gas chromatography (GC 9350 with flame ionization detector and ion capture detector, Agilent Technologies, US).

CH4, N2O water concentrations by Los Gatos (Ultraportable CH4/N2O analyzer LGR)
For in-situ determination of CH4 and N2O concentration, 3×1 L Schott-Bottles were filled completely, bubble-free, with water from a Niskin bottle taken from 0.5 - 1 m below the water surface. After the air-tight sealing, a headspace was created inside the Schott -bottle by abstracting water from the bottom of the bottle by the use of a 100 ml syringe while allowing same amount of atmospheric air (with known CH4 and N2O concentration) to enter the bottle at the top. The volume of the created headspace depends on the expected concentrations. After mixing for over 10 minutes by gently shaken, bottles were allowed to equilibrate for a minimum of 1 hour in a cool-box filled with river water to mimic the temperature of the water samples to be determined. The gas from the headspace was then transferred into gasbags which were connected to the inflow of the Ultraportable CH4/N2O analyzer (Los Gatos Research, LGR) for instantaneous determination of CH4 and N2O concentration of the gas mixture (air and water). Water pCH4 and pN2O was calculated from the ratio between the air and water volumes (headspace ratio) using the gas solubility at sampling temperature.

CO2 water concentration by EGM-4 non-dispersive, infrared gas analyzer (PP System)
The partial pressure of CO2 (pCO2) in the water was measured in-situ with a PP Systems EGM-4 non-dispersive, infrared gas analyzer using the headspace technique. For this, 30 mL of water, collected (from 0.5 m below the water surface) into five 60 mL polypropylene syringes, was mixed with 30 mL of air of known CO2 concentration and gently shaken for 5 min to allow for equilibration of the two phases. The headspace volume (30 mL) was then transferred into a new syringe and directly injected into the EGM-4 analyzer for instantaneous determination of CO2 concentration of the two phases. Water pCO2 was then calculated from the ratio between the air and water volumes using the gas solubility at sampling temperature.



CO2, CH4 and N2O flux measurements by EGM-4 and Los Gatos
CO2, CH4 and N2O fluxes from water to the atmosphere were measured in the field using a custom-designed floating chamber (polyvinyl chloride cylinder) with an internal area of 829.6 cm2 and an internal volume of 10080 cm3, leading to a Volume/Area ratio of 12.15 cm. After deploying the chamber on the water surface and allowing for the pressure to equilibrate, changes in gas concentrations inside the chamber were monitored over 30 minutes using a non-dispersive infrared gas analyzer (EGM-4, PP SYSTEM) for CO2 and an Ultraportable CH4/N2O analyzer (Los Gatos Research) for CH4 and N2O, both connected at the top of the chamber with rubber-polymer tubes (interior diameter 0.45 cm) which circulate the air inside the chamber in a close loop. The temperature inside the chamber was monitored continuously with a VWR 4039 waterproof thermometer and further used in the flux calculation.
Fluxes to the atmosphere were estimated from the change in concentrations over time using the following equation: 
       F = [(s × V)/(mV × S)] × f
where F is the flux in ?mol m-2 d-1, s is the slope in ?atm min-1, V is the volume of the chamber in liters (L), mV (molar Volume) is the volume of 1 mol of gas in L atm mol-1, S is the surface area of the floating chamber over the water in m2, and f is the conversion factor from minutes to days (1 d = 1440 min). The slope was calculated using both linear and exponential fit yielding two values of the flux (linear and exponential).


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