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Water quality monitoring in recirculating aquaculture systems

Water quality monitoring in recirculating aquaculture systems INTRODUCTIONRecirculating aquaculture systems (RAS) are land‐based intensive aquaculture systems where water is re‐used typically via mechanical and biological treatment to reduce the consumption of water and yet maintain adequate water quality (Martins et al., 2010). RAS utilises modern knowledge of biology, environmental sciences, mechanical engineering and information technology (Xiao et al., 2019). Circulating water is reused multiple times, and only a small proportion is replaced with clean water (Chun et al., 2018). Currently, variety of freshwater and marine species from hatchery to grow‐out is reared in RAS. RAS have been used for several decades for fish production, although only a few very large (production over 10 million kg per year) systems are in operation (Molleda, 2007).The idea of RAS originates from Japanese biological purification in the 1960s (Wu et al., 2008; Lin, 2011; Xiao et al., 2019). Since the 1990s, biological engineering, automation control, bottom discharge and controls of dissolved oxygen (DO) and temperature have been employed. Currently, the development of RAS focuses on improving of water purification technology and equipment, sustainability and the level of wastewater discharge (Becke et al., 2019). However, since RAS are often energy and cost intensive, and suggestions have been made to improve their economy.Water quality can be divided into categories which include physical parameters, organic contaminants, biological contaminants and pathogens (Su et al., 2020). Physical parameters depend on climate and the surrounding conditions. Water quality control is a key aspect in any aquaculture management, but especially in RAS, where the water is circulated in the system. The main parameters for quality monitoring include pH, temperature, oxidation–reduction potential (ORP/Redox), turbidity, salinity and DO. In the ideal range, water quality can enhance the fish growth rate and reduce the occurrence of fish diseases (Stigebrandt et al., 2004; Tolentino et al., 2021). Technologies suitable for rapid, real‐time and automated monitoring and data storing are therefore greatly needed.Water quality in RAS is affected by feed loading and composition (Blancheton et al., 2013; von Ahnen et al., 2015). Dissolved compounds and particulate matter can be difficult to remove from the system and they can act as substrate material for bacterial growth. Some of these bacterial species can induce off‐flavours in the system (Lindholm‐Lehto et al., 2019; Suurnäkki et al., 2020). A high abundance of bacteria can also affect the fish, acting as pathogens or competing for oxygen (Michaud et al., 2014).In RAS, water quality parameters are kept stable to ensure suitable conditions for the fish (Rojas‐Tirado et al., 2018). It is therefore important to understand and control factors that change the water quality. Water quality needs to be monitored closely to maintain optimal growth conditions in RAS (Naughton et al., 2020). In RAS, fish are often reared in high densities, causing a build‐up of organic material and nutrients (Bentzon‐Tilia et al., 2016). These factors need to be controlled and monitored, but the available tools are often complex or include a considerable lag between sampling and the results (Rojas‐Tirado, 2018). There are no regulations concerning which parameters to measure and each farmer has their own view of and possibilities for water quality measurements. Additionally, there are no guidelines for acceptable ranges and fluctuations.Previous articles have comprehensively studied and discussed fish health, stress levels, diseases (Venugopal, 2002; Endo & Wu, 2019), pathogens (Adams & Thompson, 2011) and exposure to environmental contaminants such as polycyclic aromatic hydrocarbons (Srogi, 2007) and cyanotoxins (Vogiazi et al., 2019). Additionally, analytical technologies and biosensors used in aquaculture have recently also been reviewed (Hu et al., 2020; Su et al., 2020). However, a comprehensive study of current sensors, monitoring equipment and the typically monitored water quality variables has been missing.In this study, it was hypothesised that only the very basic water quality parameters are typically monitored in commercial RAS, possibly with very traditional monitoring methods. However, in recent years, new possibilities are available due to many technical innovations. This review aims at (1) combining new technologies available for monitoring of water quality by using the most modern information technologies, (2) listing the technology‐based possibilities in prediction of detrimental occurrences in water quality and (3) presenting which parameters can be measured by the modern online‐measurement tools and which still need more advanced laboratory analyses. All this might encourage adopting new measurement equipment for monitoring of water quality at commercial aquaculture facilities.WATER QUALITY PARAMETERSIn aquaculture, several parameters need to be monitored and measured to understand the aquatic conditions (Tziortzioti et al., 2019). These include pH, temperature, DO, salinity and turbidity (Mwegoha et al., 2010). DO is the most crucial parameter for aquatic species. Oxygen is not readily soluble in water, and its ability to dissolve decreases rapidly as the temperature and salinity increase (Timmons et al., 2018; Chumkiew et al., 2019).The fluctuation of physical parameters directly affects the health, growth and feed utilisation of the raised species. For example, DO affects feed consumption, feeding efficiency, metabolism and growth in fish (Buentello et al., 2000; Zhang et al., 2011). Fish use oxygen to convert feed to biomass and depending on the species, varies from 5 mg L−1 (warm water species) to 7 mg L−1 (cold water species) (Pillay & Kutty, 2005). However, some species (silver catfish, Rhamdia quelen) can tolerate DO levels below 3 mg L−1 but DO levels below 5 mg L−1 affect their behaviour, survival and growth (Braun et al., 2006). For salmonids, oxygen saturation should be at least at 70–80% (above 6 mg L−1). Fluctuations or suboptimal temperatures affect feed use (lower FCR at temperatures above optimal, Timmons et al., 2018) and induce stress which can lead to disease outbreaks. Additionally, organic material consumes oxygen (biological oxygen demand, BOD) via bacterial degradation.Water conductivity is related to the ionic content of water and salinity, but it also depends on temperature and pressure (for a 1°C increase, conductivity increases by 2–4%) due to increased ion mobility and solubility (Tziortzioti et al., 2019). Hydrogen ion concentration and relative acidity are measured as pH. Aquatic species generally require a neutral pH, but for example, the optimum pH for chichlids (Cichlidae; Takahashi‐Kariyazono et al., 2017) and for tilapia (Oreochromis sp.; Milud et al., 2013) can be in mildly basic conditions. A biofilter achieves its best performance in mildly basic conditions. The pH can be affected by, for example, bacteria, CO2 content and chemicals in the system. Water with a low pH can be harmful to aquatic species but can also corrode pipes or release lead, cadmium, copper and zinc into water.Turbidity means the degree of cloudiness in water due to suspended organic or inorganic particles (silt, clay, organic matter, plankton, etc.) and is a valuable parameter for evaluating water quality (Tziortzioti et al., 2019). The degree of turbidity is based on scattering of light from the suspended particles. High turbidity reduces the depth of light can penetrate which can prevent or decrease the efficiency of UV light treatment.Nitrogen speciesIn RAS, nitrogen compounds can accumulate in water (ammonium‐N, nitrite), which are toxic to fish even at low concentrations (Timmons et al., 2018). Intensified production in RAS leads to an increase in dissolved waste, including ammonia (NH3) (Ip et al., 2001). Ammonia (NH3) is highly toxic to fish and can deteriorate gills and result in mortality (Wicks et al., 2002). Typically, the upper safe limit for NH3‐N for salmonids is 0.0125 mg L−1 (Timmons et al., 2018).Ammonia can exist in two forms: NH3 and in the ionised form NH4+, which are in equilibrium in water based on temperature and pH (Timmons et al., 2018). Together, they are referred to as total ammonia nitrogen (TAN). The proportion of free ammonia (NH3) increases with increasing temperature and pH. Fish excrete nitrogen mainly as ammonia‐N (53–68%), but also as urea‐N (6–10%), amino acids (4–10%) and as protein (3–11%) (Kajimura et al., 2004). Fish have varying tolerance of ammonia‐nitrogen, depending on species, age and physiological status. For example, adult fish are more tolerant to ammonia than juveniles and fingerlings (Dauda et al., 2019). Generally, levels of ammonia below 1 mg L−1 and of TAN below 5 mg L−1 are recommended (Table 1) (Boyd, 2003; Ajani et al., 2011). However, in some studies, increased concentrations of ammonium have been beneficial in terms of growth rate and sublethal NH3‐N (Kolarevic et al., 2013).1TABLELimit concentrations of alkalinity (mg L−1), CO2 (mg L−1), DO (mg L−1), H2S (μg L−1), nitrogen species (mg L−1), pH, selected metals (μg L−1) and temperature (°C)ParameterLimit concentrationSpecies/additional infoReferencesAlkalinity (as CaCO3), mg L−150–30070–200Atlantic salmonTimmons et al. (2018)Summerfelt et al. (2015)CO2, mg L−115–207–10<20<80Salmonids in freshwater;salmonids in seawater‐sensitive species, salmonids‐tolerant species, tilapiaMolleda (2007)Timmons et al. (2018)Dissolved oxygen (DO), mg L−16–9 (70–80%)<5 (70%)Molleda (2007)Timmons et al. (2018)H2S, μg L−125In freshwaterin marineHjeltnes et al. (2019)Sommerset et al. (2020)NO3‐N, mg L−11002000–400Chen et al. (2002)Molleda (2007)Timmons et al. (2018)NO2‐N, mg L−1<0.10.1In soft waterTimmons et al. (2018)NH3‐N, mg L−10.030.322.2<0.0125–0.03Arctic charr;rainbow trout;common carpsalmonidsMolleda (2007)Timmons et al. (2018)Summerfelt et al. (2015)TAN (NH3‐N +NH4‐N), mg L−1<1.0<3.0Long‐term, cool‐water fishwarm‐water fish, Arctic charrMolleda (2007)Timmons et al. (2018)Temperature, °C<1515–20>2014–1628–32Cold‐water speciescool‐water specieswarm‐water speciesrainbow trouttilapiaTimmons et al. (2018)pH4.26.5–8.5Rainbow trout, roach LC 8‐day mediangeneral suitable pH rangeAlabaster and Lloyd (1980)Timmons et al. (2018)Cadmium (Cd), μg L−111Salmonid juveniles and adults, (hardness 4 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Copper (Cu), μg L−15.0<30Rainbow trout (hardness 10 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Davidson et al. (2009)Zinc (Zn), μg L−130<269Salmonids (hardness 10 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Davidson et al. (2009)In RAS, ammonia is converted by certain bacteria to less toxic nitrite (ammonia‐oxidising bacteria: AOB, e.g., Nitrosomonas) and further to nitrate (nitrite‐oxidising bacteria: NOB, e.g., Nitrospira, Nitrobacter) via nitrification process which allows lower water use rates (Suurnäkki et al., 2020). Typically, this is performed in nitrifying bioreactors, such as moving bed bioreactors (Kamstra et al., 2017) or fixed bed bioreactors (Pedersen et al., 2015) where biofilm containing the nitrifying bacteria, is formed on plastic carrier material or on natural material, such as sand particles.Nitrite is toxic to fish, and concentrations below 0.5 mg L−1 are recommended (Ajani et al., 2011). Nitrate is a less toxic oxidation product, and most fish species can tolerate levels up to 200 mg L−1 (Dauda & Akinwole, 2015), although NO3‐N concentrations of 100–1000 mg L−1 are not uncommon (Preena et al., 2021). However, high concentrations of nitrate can inhibit fish growth and increase mortality (Table 1) (Davidson et al., 2014). Typically, nitrate concentrations in RAS are controlled by adjusting the water exchange rate and biofilters, adding denitrification, or even phytoremediation into the system (Martins et al., 2010; Schipper et al., 2010; Pulkkinen et al., 2021).Fish feeds and feed load are important factors when dimensioning ammonia removal capacity in RAS. TAN production in fish depends on the feeding interval (Pedersen et al., 2012) and digestibility (Médale et al., 1995) and it may increase if the amino acid profile in the fish feed is not balanced (Bureau & Hua, 2010). It is beneficial to disperse the feed throughout the day in RAS to ensure a steady ammonia load into the bioreactors.The feed loading (kg feed per m3 make‐up water) reflects the intensity of recirculation which is the main critical parameter that influences water quality deterioration (Pedersen et al., 2012). Upper limits of feed loading are determined based on the raised fish species, the prevailing environmental conditions, the type of feed and the used water treatment equipment. Higher feed loads are possible with increased biofilter nitrification capacity, when fish performance is not restricted by nitrate accumulation or by deteriorated water quality in general (Davidson et al., 2011a). For example, declined health of rainbow trout (Oncorhynchus mykiss) and increased mortality were observed in a commercial RAS with 1.3–2.0 kg m−3 feed loads (Davidson et al., 2009). At very high feed loads, even abnormal swimming behaviour, side swimming and deformities in rainbow trout have been observed (Davidson et al., 2011b).When aiming for very low water exchange rates in RAS or even zero‐discharge systems, nitrate need to be removed from the system. Nitrogen removal is most commonly achieved via heterotrophic denitrification which converts nitrate to nitrogen gas and uses carbon as the electron donor and for microbial growth (Schipper et al., 2010). There are other options for denitrification, such as anaerobic ammonium oxidation (Anammox) and chemo‐autotrophic denitrification (Burgin & Hamilton, 2007). Additionally, biofloc system or biofloc technology absorbs inorganic nitrogen from the circulating water and improves water quality. In Biofloc system, microbial protein is produced for the raised species which improves feed conversion ratio, FCR (Khanjani & Sharifinia, 2020). Biofloc has been widely studied in recent decades for example in shrimp and finfish farming (Abbaszadeh et al., 2019; Ebrahimi et al., 2020).Aquaponic systems are also able to remove nitrogen species via denitrification and utilise nitrogen formed in aquaculture for the needs of plant growth (Wongkiew et al., 2017). Denitrification is carried out in anoxic conditions by various archaea and heterotrophic bacteria, such as Aerobacter, Acinetobacter and Pseudomonas (Wonkiew et al., 2017). Organic material in circulating water acts as electron donor and the source of carbon. However, sufficient carbon source need to be maintained to avoid accumulation of intermediate products NO and N2O.The most detrimental problems experienced in RAS have been associated with both low DO and high concentrations of metabolic waste, such as TAN, ammonia (NH3‐N), nitrite (NO2‐N) and CO2 (Molleda, 2007). Typically, fish produce 1.0–1.4 mg L−1 TAN, 13–14 mg L−1 CO2 and 10–20 mg L−1 total suspended solids (TSS) per every 10 mg L−1 of consumed DO (Hagopian & Riley, 1998; Molleda, 2007).Carbon dioxideCarbon dioxide (CO2) is toxic to fish and is a limiting factor in intensive systems. In RAS, fish density is much higher than with more traditional aquaculture methods. High fish density leads to a significant increase in CO2 content in water. Additionally, nitrifying bacteria produce CO2 in nitrification (Xiao et al., 2019). A high CO2 level in water reduces the diffusion gradient between fish blood and water, resulting in blood acidification and reduction in oxygen uptake (Sanni & Forsberg, 1996; Molleda, 2007). Fish ventilate CO2 via the gills as molecular CO2 gas, which forms carbonic acid H2CO3, bicarbonate (HCO3−) and carbonate (CO32−) in water which are in equilibrium, based on pH and partial pressure of CO2 (Fig. 1) (Boyd, 2000; Molleda, 2007). For rainbow trout (Oncorhynchus  mykiss), excretion of CO2 has been estimated to 1–2 mg CO2 kg−1 min−1. Typically, a limit value for CO2 is 20 mg L−1 in water, but tilapia (Oreochromis niloticus), for example, can tolerate as much as 60 mg CO2 L−1 (Timmons et al., 2018).1FIGUREMole fractions as a function of pH on proportions of CO2, HCO3− and CO32− (Boyd, 2000; Molleda, 2007).Excessively high concentrations can damage the respiratory system of fish or even result in mortality (Fivelstad et al., 2003) and decrease biofilter efficiency (Ren, 2014; Xiao et al., 2019). Removal of CO2 also affects the water pH. For example, an 80% decrease of CO2 can lead to a significant increase of pH (Chen et al., 2009). The rate of CO2 removal is affected by the initial CO2 content, water flow, gas‐water volume ratio, type and structure of equipment and the inlet air flow rate (Gong et al., 2017).Over the years, a variety of equipment has been used for CO2 removal. CO2 degassing towers have been used since the 1980s and their effect has been evaluated by (Summerfelt et al., 2003). Additionally, the injection of micron‐sized air bubbles into a foam fractionator increases aeration and CO2 removal (Barrut et al., 2013). For example, an airlift pump with CO2‐stripping can remove 13‐20 g CO2 kWh−1 (Loyless & Malone, 1998) and paddlewheels 1.2 kg CO2 kWh−1 (Eshchar et al., 2003).AlkalinityTotal alkalinity is described as the concentration of bases in water which can react with a hydrogen ion (H+). Several compounds can react in water with hydrogen ion and the total alkalinity describes the contribution of different ions to alkalinity (Boyd et al., 2016). However, in natural waters alkalinity is mostly derived from HCO3−, CO32− and OH− (Eq. 1, Boyd et al., 2016). Traditionally, alkalinity has been expressed as mg CaCO3 L−1 because alkalinity mainly originates from limestone (mixture of CaCO3 and MgCO3) or as milliequivalents per litre of calcium oxide (CaO). Limestone composition varies from CaCO3 (calcite), MgCO3⋅CaCO3 (dolomite) to mixture of CaCO3 and MgCO3 (Boyd et al., 2016). Besides alkalinity, limestone is a major source of hardness, the term used to describe the ability of water to precipitate soap and defined as the concentrations of primarily calcium (Ca2+) and magnesium (Mg2+; Timmons et al., 2018).1Alkalinity=HCO3−+2CO32−+OH−−H+$$\begin{equation}{\rm{Alkalinity}} = \left[ {{\rm{HCO}}_3^ - } \right] + 2\left[ {{\rm{CO}}_3^{2 - }} \right] + \left[ {{\rm{OH}}^ - } \right] - \left[ {{{\rm{H}}}^ + } \right]\end{equation}$$Alkalinity is also connected to buffering capacity. A buffer includes a mixture of a weak acid and its conjugate base (salt) or a weak base and its conjugate acid, such as acetic acid and its conjugate base sodium acetate or ammonium hydroxide and its conjugate acid ammonium chloride (Boyd et al., 2016). Dissolved CO2, HCO3− and CO32− buffer water against sudden changes in pH and water with low alkalinity will lead to greater fluctuation in pH (Boyd et al., 2016).The pH of most freshwaters is based on HCO3− ions and dissolved acid CO2 from air, excluding effects of biological activities and pollution. Typical alkalinity in freshwater ranges from below 5 mg L−1 to over 500 mg CaCO3 L−1, based the geology of the aquifer, while the alkalinity of seawater is about 120 mg CaCO3 L−1 (Timmons et al., 2018).In aquaculture, acids and bases may be added in circulating water which leads to change in alkalinity and pH. For example, removal of CO2 from water increases pH, and at pH 8.3 the concentration of CO2 is reduced to negligible (Boyd et al., 2016). Besides total alkalinity and pH, concentration of dissolved CO2 is affected by temperate; its concentration decreases with increasing pH and temperature. Variety of bases, for example, CaCO3, Na2CO3 and NaOH, can be added as alkalinity supplements (Timmons et al., 2018).Maintaining adequate alkalinity concentrations has been reported to be critical for nitrification. For example, alkalinities of 40–80 mg CaCO3 L−1 have been reported for wastewater. For aquaculture, Chen et al. (2006) recommend maintaining an alkalinity level at 200 mg CaCO3 L−1 to support nitrification. Summerfelt et al. (2015) suggested alkalinity of 70 mg CaCO3 L−1, while a wide range of 50–300 mg CaCO3 L−1 has also been reported (Timmons et al., 2018).TemperatureEach aquaculture farm has a unique optimum temperature range. Fish can be classified into three classifications depending on their temperature preference: cold‐water, cool‐water and warm‐water species. For cold‐water species, optimum water temperatures are below 15°C, for cool‐water species between 15 and 20°C, and for warm‐water species above 20°C (Timmons et al., 2018, Table 1). Temperature affects the physiological processes of the reared species, such as respiration rate, efficiency of feeding, growth, behaviour, reproduction and depuration (Drake et al., 2010). Additionally, several factors affect the tolerance of fish to different temperatures, including species, age, size and past thermal history (Timmons et al., 2018).In RAS, temperature should be measured from several locations such as in rearing tank water and slaughtered fish (Lekang, 2013). Especially, measurement of inlet water temperature is important for designing and selecting a suitable heating equipment and for operating a RAS. The control of rearing temperature allows maintaining good rearing conditions for optimum growth and feed utilisation, although the inlet water temperature may vary considerably between seasons (Dalsgaard et al., 2013). A monitoring equipment based on a programmable logic controller (PLC) for real‐time monitoring of recirculating water and water level was reported by Wu et al. (2011). On the other hand, water source heat pumps generate low‐grade heat from natural sources and can be used for heating and cooling.For heating, four different heating methods are typically used; boilers, geothermal energy, factory waste heat and heat pumps or other equipment (Xiao et al., 2019). Industrial heat is limited by geographical conditions. Heating by boilers is common, but increasing attention to environmental pollution is likely to reduce the attractiveness of boiler heating. So far, only a limited number of studies have been related to temperature control in RAS (Good et al., 2011; Davidson et al., 2014; Xin et al., 2017).Water source heat pumps have been used to heat aquaculture water and to maintain stable temperature around the year (Xin et al., 2017), while Good et al. (2011) used a heat exchanger in RAS rearing rainbow trout (O. mykiss) and Davidson et al. (2014) a geothermal heat exchanger. Geothermal energy has been used for heating in RAS farms producing salmon, trout, tropical fish, lobsters, shrimp and prawns (Ragnarsson, 2014). Waste heat from industry has been used for commercial oyster, penaeid shrimp and salmon farming (Rickard, 1998), while heat from power plants has been used for eel and salmonid fingerlings production (Lemercier & Serene, 1980; Ingebrigtsen & Torrissen, 1980). Additionally, ground heat has been used as a heat source for temperature control in RAS (Jiang et al., 2017). In recent years, there has been interest in solar technologies to maintain selected water temperature in RAS, but only a simulation model regarding solar heating systems in RAS has so far been published (Fuller, 2007).SalinitySalinity represents a content of dissolved salt in water, including calcium, sodium, potassium, bicarbonate, chloride and sulphate ions (Timmons et al., 2018). Water is commonly described as fresh, brackish or saltwater based on its salinity.Cl− and Na+ ions are predominant in sea water, while carbonates and HCO3− in fresh water. Typically, sea water contains 35 g L−1 salt (mainly 77.8% NaCl and 10.9% MgCl2; Boeuf & Payan, 2001). It is more difficult to determine general values for fresh water, but 0.32 g L−1 (35.1% CO32−, 20.4% HCO23−, 12.1% SO42−, 11.7% SiO2 and 5.8% Na+) have been reported (Boeuf & Payan, 2001).Fish maintain concentrations of dissolved salts in their body fluids via osmoregulation by regulating the uptake of ions from the environment and through the restriction of ion loss (Timmons et al., 2018). For example, the best salinity range on growth for Atlantic salmon Salmo salar is 22–28 ppt or g L−1 (0–35 tolerated), 8 for tilapia O. niloticus (0–16 tolerated) and 0 g L−1 for rainbow trout O. mykiss (0–34 tolerated) (Boeuf & Payan, 2001). Most freshwater fish reproduce and grow well at salinities up to at least 4–5 g L−1. Freshwater aquaculture systems are generally maintained at 2–3 g L−1 salinity (Timmons et al., 2018).Seawater RAS may have higher operating costs compared with freshwater or brackish water RAS because of the lower efficiency of CO2 (Moran, 2010) and ammonia removal in seawater and requirement to increased scale of equipment (Bakke et al., 2017). On the other hand, problems related to accumulation of off‐flavours have been observed less often in saline water (15–33 g L−1; Tucker, 2000).Salinity affects reaction rates for ammonia oxidising (AOB) and NOB in brackish or saltwater conditions (NaCl above 5 g L−1; Kinyage et al., 2019). At very low salinity, NaCl below 3.7 g L−1, only minor effects have reported whereas complete inhibition was observed at a salinity of NaCl above 24 g L−1 (Cortés‐Lorenzo et al., 2015).Feed intake, feed conversion and growth rate are improved in brackish conditions (8–20 g L−1) due to reduced energy expenditure on osmoregulation and lower standard metabolic rate (Árnason et al., 2013). Furthermore, brackish water (12 g L−1) combined with moderate exercise (water velocity of minimum 1 body lengths s−1) has shown a positive effect on salmon growth, survival and welfare in RAS (Ytrestøyl et al., 2020).Hydrogen sulphideThe smell of hydrogen sulphide (H2S) resembles rotten eggs. It is highly soluble in water (4000 mg L−1 at 20°C (Barton et al., 2014). H2S gas is also colourless, somewhat heavier than air (1.19 at 15°C) and flammable (Rubright et al., 2017). In water, H2S equilibrates with bisulphide (HS−) and sulphide (S2−) (Eq. 2). H2S and S2− are toxic, while HS− is not (Tanudjaja, 2021). The dissociation of H2S greatly depends on pH, and HS− formation begins at pH 5 (Fig. 2). In marine water systems, pH below 6.8 is a matter of concern due to sulphides, which can trigger the production of H2S (Rojas‐Tirado, 2018).2H2Sg⇔H2Saq⇔HS−+H+⇔S2−+2H+$$\begin{equation}{{\rm{H}}}_2{\rm{S}}\left( {\rm{g}} \right)\mathop \Leftrightarrow \limits {{\rm{H}}}_2{\rm{S}}\left( {{\rm{aq}}} \right)\mathop \Leftrightarrow \limits {\rm{HS}}^ - + {{\rm{H}}}^ + \mathop \Leftrightarrow \limits {{\rm{S}}}^{2 - } + 2{{\rm{H}}}^ + \end{equation}$$2FIGUREMole fractions of hydrogen sulphide (H2S), bisulphide (HS−) and sulphide (S2−) as a function of pH (Applied analytics, 2022).H2S is toxic to all aquatic species at very low concentrations (Lien et al., 2022). Long‐term exposure can cause growth reduction and great stress on fish due to disruption of the respiration process and cellular anoxia (Kiemer et al., 1995). In marine water, sulphate concentration is about 1000 times higher than in freshwater (Letelier‐Gordo et al., 2020), which can lead to H2S formation. Marine RAS are therefore vulnerable to H2S outbreaks. In recent years, H2S poisoning has caused severe fish mortalities and resulted in high economic losses (Hjeltnes et al., 2019; Sommerset et al., 2020).Although H2S is known to be toxic, toxicity levels have not been set by the authorities. However, for freshwater species, over 2 μg L−1 and marine species, over 5 μg L−1 are not recommended (Table 1) (Boyd, 2014; Hjeltnes et al., 2019; Sommerset et al., 2020). The 96‐h lethal concentration for H2S in freshwater ranges from 20 to 50 μg L−1, and in a marine environment from 50 to 500 μg L−1 (Boyd, 2014).H2S poisoning occurs and harms the fish quickly (Lien et al., 2022). Frequent measurements are therefore required to obtain specific information regarding H2S formation. Unfortunately, the monitoring of H2S levels may not be enough, and the prediction of potential occurrences is required to prevent the problems in advance.Possible factors leading to H2S formation are sulphate content in water (freshwater 5–50 mg L−1, sea water 2700 mg L−1), the presence of carbon‐based compounds in water, biodegradation reactions in anoxic conditions with accumulated organic material and bacteria, for example, sulphate‐reducing bacteria or methane‐reducing bacteria (Li & Lancaster, 2013; Letelier‐Gordo et al., 2020). Certain physical properties such as high turbidity, sedimentation of organic matter and low water flow rate can also affect H2S formation (Yu & Bishop, 2001). Additionally, carbon‐based material from uneaten feed or faeces can react with sulphate, forming HS− and later toxic H2S (Eq. 3). Organic matter can also participate in H2S production via biodegradation which occurs via oxidation and reduction reactions; electrons are transferred from organic carbon donor to electron acceptor (Lien et al., 2022).3CH4+SO42−→HCO3−+HS−+H2O$$\begin{equation}{\rm{CH}}_4 + {\rm{SO}}_4^{2 - }\mathop \to \limits {\rm{HCO}}_3^ - + {\rm{HS}}^ - + {{\rm{H}}}_2{\rm{O}}\end{equation}$$OzoneOzone is an environmentally safe disinfectant (Hansen et al., 2016) which improves water quality in RAS due to the oxidisation reactions of nitrite, organic material, chemical oxygen demand, colour and suspended solids (Summerfelt et al., 2009; Davidson et al., 2011a). Ozone can also attack off‐flavour‐causing compounds such as geosmin, bacteria and fish pathogens (Summerfelt et al., 2009). Ozone reacts rapidly with any organic matter, but residual ozone fed directly to a rearing tank severely threatens farmed species (Powell & Scolding, 2018).Several commercial ozone sensors are available. However, they can be very expensive or unreliable or do not show the changes in ozone concentration. ORP is therefore widely used to monitor the ozone addition (Davidson et al., 2011a; Powell & Scolding, 2018). ORP records the readiness of substances in solution to give or receive electrons. In RAS, an ORP sensor should be placed at a point where ozone is completely consumed and downstream of the ozone addition point to prevent ozone damaging the sensors (Bullock et al., 1997). However, ORP gives only an indirect result by the difference in readings before and after the ozone treatment, and other methods such as mediated electrochemical oxidation and changes in UV absorbance at 254 nm or at 272 nm have been suggested (Hansen et al., 2010; Wenk et al., 2013).The ozone production result can be determined by titration with an iodometric standard method (IOA Standardization Committee Europe, 001/87 (F), 1987), while dissolved ozone in water with the standard colorimetric indigo ‐method (IOA Standardization Committee – Europe, 006/ 89 (F), 1989), as recently reported in Pettersson et al. (2022).MEASUREMENTSMeasurements of water qualitySuccessful intensive aquaculture requires the monitoring of several water quality parameters, such as DO, salinity, temperature, pH and electrical conductivity (EC) (Jamroen et al., 2023). Traditionally, water quality parameters have been measured at certain intervals with handheld sensors. However, this requires staff, can be time consuming and is possible only during working hours. In recent years, instrumentation, automation and monitoring systems have developed and become more common and many of the basic measurements can be carried out by online instrumentation (Lekang, 2013). The development of information technology and low‐cost sensors has increased the feasibility of monitoring via wireless sensor networks (WSN). The special conditions in aquaculture must be considered when designing and installing instruments (Lekang, 2013). Besides the price of instrumentation, maintenance, running costs and calibration are required, and sensors may require changing at fixed intervals.Real‐time and efficient monitoring of water quality is essential to ensure cost‐effective RAS management. For example, Zhu et al. (2010) reported an online water monitoring system which was able to monitor DO, pH, temperature and EC. Zhang et al. (2011) used an Orion5‐Star Portable pH/ORP/DO/EC Multimeter to measure these factors in a RAS. Later, Odey and Li (2013) used an AquaMesh wireless mesh sensor network to measure pH, DO, temperature and EC. Several studies have reported methods to measure these variables online in aquaculture (Odey & Li, 2013; Schmidt et al., 2018 Danh et al., 2020) but they all measured the basic variables of water quality.The most common basic water quality parameters, such as temperature, pH and DO, can be measured with portable instruments such as Multi 3410 (WTW GmbH) (Chun et al., 2018). Although portable alternatives are available, nitrite, nitrate and phosphate are often measured more accurately in a laboratory, using an ion‐exchange chromatograph and suppressed conductivity detector (e.g., Dionex DX‐500, Dionex ICS1600, Dionex Integrion HPIC; Chun et al., 2018; Lindholm‐Lehto et al., 2020, 2021).Instruments applying laboratory methods have been modified for aquaculture facilities. A typical detector is a spectrophotometer, which measures the absorption of light in a sample. Integration of monitoring equipment and intelligent data management have been commercially available, for example, from Greenspan and Fleck in Australia, Gimat in Germany, and Isoc, Hydrolab and Emnet in the USA (Zhang, 2007; Lv, 2015). Additionally, Siemens has developed a YSI5200 monitoring systems (Shi et al., 2011). YSI has designed monitoring and control systems specifically for aquaculture with sophisticated alarm modes (Timmons et al., 2018). Model 5200A can continuously monitor and log DO, pH, conductivity, ORP, salinity and temperature, and Model 5400 for four DO probes and four additional inputs such as temperature, pH and ORP.As a result of the development of online instruments, CO2, ammonia and nitrate sensors are widely available (Pulkkinen et al., 2018). The sensors can be for individual parameters or several sensors included in one instrument (Lekang, 2013). For example, automatic monitoring of DO, pH and temperature in a RAS was performed with WATT TriO Matic 700IQ (SW), WATT Sensolyt 700 IQ (SW) and WATT TrioxyTherm sensors (Zhang, 2007). WSN equipment greatly reduces the requirement for maintenance and costs, but especially in marine systems, coating of sensors is required to prevent corrosion (Hu et al., 2020).Water quality can also be monitored by electrochemical analysis. This includes salinity, pH, DO, total organic carbon (TOC), temperature, BOD, dissolved carbon and reduction potential (Bergheim & Fivelstad, 2014; Kolarevic et al., 2014). For example, companies supplying electrochemical sensors commercially include Oxygard Pacific (OxyGuard International A/S, Denmark) and Pentair Point Four (Pentair, UK). These sensors´ measurements are not specific but give a rough indication of water quality, and more accurate analyses for specific quantification can be performed in the laboratory (da Silva et al., 2018).Tziortzioti et al. (2019) reported an inexpensive, portable device for water quality measurements without the need for a communications infrastructure and network connection. The Arduino device was equipped with SD card reader and an Ethernet module, and the measurement data were stored on the SD card (Tziorzioti et al., 2019). The card can be removed and connected to a laptop or a smart phone for data transfer if necessary. The device included a digital temperature sensor, a total dissolved solids meter, an analogue turbidity sensor for measuring suspended particles, an analogue DO meter and an analogue pH Meter.Santos et al. (2022) reported a pilot design of electrochemical oxidation of ammonium nitrogen in marine RAS water rearing gilthead sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax). They measured TAN, fish growth (specific growth rate SGR, FCR, specific feed rate SFR), microbial count (FCU mL−1) and water and energy consumption (Santos et al., 2022). The system included incorporated automatic online analysers for monitoring pH, DO, temperature, ORP, TAN and chlorine content. The TAN concentration was monitored in real time, while the other parameters were periodically measured.Lien et al. (2022) developed a sensor to detect H2S at sub μg L−1 levels. The AquaSense system was a mobile wireless and a real‐time monitoring system which consisted of units in different locations in a RAS. The sensors measured H2S (μg L−1), DO (% saturation), CO2 (mg L−1), pH, salinity (‰), alkalinity (mg L−1), turbidity (NTU) and temperature (°C) (Lien et al., 2022). The sensors are not exposed to water. The system was automatically calibrated by feeding calibration gas to sensors at selected intervals. The sensors in a RAS communicated via an open network and collected the data for storage locally or stored in the cloud service. The H2S sensor covered the 0–80 μg L−1 range (Lien et al., 2022).A future trend in aquaculture is an automatic remote monitoring and computer control of systems. For example, Zhu et al. (2010) used a Code Division Multiple Access (CDMA) service and an IPsec‐based virtual private network to monitor water quality. CDMA is a competing cell phone service technology to GSM. The system consisted of water quality control components, data acquisition and data transformation and transmission. It was able to monitor water and room temperature, DO saturation, DO concentration, pH, EC and salinity, and notify any abnormalities (Zhu et al., 2010).Measurement frequencyMonitoring of water quality should include many parameters (Table 2). Some of them require constant monitoring (e.g., DO, CO2, pH) to avoid detrimental disturbances in RAS, while others can be monitored less frequently (e.g., off‐flavours, trace elements and metals). However, effective online monitoring systems are unavailable for all parameters, especially not at reasonable cost.2TABLESelected water quality parameters (alkalinity, DO, CO2, EC, nitrite‐N, nitrate‐N, pH, ORP, salinity TAN, temperature and TOC) typically monitored in RAS, examples of their analysis methods and instrumentationParameterMethod of analysisInstrumentationReferencesAlkalinityStandard titrationmethod (ISO 9963−1:1994)Hach Method 8203–Sulfuric Acid Digital TitrationTitraLab AT1000, HachLindholm‐Lehto et al. (2021)Davidson et al. (2017)Pulkkinen et al. (2018)Dissolved oxygen (DO)Portable instrumentsonline‐monitoring,electrochemical analysisOrion5‐Star Portable pH/ORP/DO/EC MultimeterOxygard PacificZhang et al. (2011)Schmidt et al. (2018)Danh et al. (2020)Carbon dioxide (CO2)Online‐monitoringquick spectrophotometric testsFranatechHach Method 8223Pulkkinen et al. (2018)Davidson et al. (2019)Electrical conductivity (EC)Portable instrumentsonline‐monitoringOrion5‐Star Portable pH/ORP/DO/EC MultimeterAquaMesh networkOxygard PacificZhang et al. (2011)Odey and Li (2013)da Silva et al. (2018)Hydrogen sulphide (H2S)Wireless online monitoring systemAquaSense systemLien et al. (2022)Nitrite‐NNitrate‐NIon chromatography, ICportable instrumentsonline‐ monitoringquick spectrophotometric testsDionex DX‐500 (Dionex)Multi 3410 (WTW GmbH)LCK340, LCK341,Chun et al. (2018)Lindholm‐Lehto et al. (2020, 2021)Pulkkinen et al. (2018)Oxidative reduction potential (ORP/Redox)Portable instrumentsonline‐monitoringelectrochemical analysisLAQUAact, Horiba scientific D‐74LoRaWAN (Long Range Wide Area Network),Tolentino et al. (2021)Pettersson et al. (2022)Schmidt et al. (2018)Danh et al. (2020)pHPortable instrumentsonline‐monitoringelectrochemical analysisAquaMesh networkOxygard PacificProMinentZhu et al. (2010)Odey and Li (2013)Pulkkinen et al. (2018)SalinityIon chromatography, ICportable instrumentsDionex DX‐500Oxygard PacificYSI 30 Salinity MeterDanh et al. (2020)Lindholm‐Lehto et al. (2020, 2021)Total nitrogen (TAN)Quick spectrophotometric testsHach Method 8038 USEPA NesslerDavidson et al. (2019)TemperatureElectrochemical analysisportable instrumentsonline‐monitoringOxygard PacificTemperature MeterDanh et al. (2020)Bergheim and Fivelstad (2014)Kolarevic et al. (2014)Total organic carbon (TOC)Electrochemical analysisOxygard PacificBergheim and Fivelstad (2014)Kolarevic et al. (2014)To maintain water quality at optimal range, quality parameter data need to be gathered regularly. Traditionally, water quality has been monitored by manually taking samples and sent for analysis in a laboratory (Pasika & Gandla, 2020). This takes time, requires analytical equipment and can be labour intensive (Pasika & Gandla, 2020), but does not provide real‐time data. There are various opinions for, how often water quality parameters should be measured (Jamroen et al., 2023). For example, Ferreira et al. (2011) suggested daily monitoring of DO and temperature, and salinity, but weekly monitoring of pH and turbidity. However, dramatic changes in water quality can be missed which can lead to major fish mortalities and financial losses. New sensor technology and wireless communication for remote monitoring is possible in real‐time (Jamroen et al., 2023). Real‐time monitoring of selected water quality parameters is required for farmers to recognise low water quality and make management decisions (Oberle et al., 2019).In aquaculture, economic constrains, high costs of human resources and real‐time monitoring of all the important water quality parameters have long been real challenges for farmers (Rashid et al., 2021). It is impossible to monitor water quality based only on experience, but an intelligent system can monitor water quality parameters in real time and help maintain good water quality (Saha et al., 2019; Rashid et al., 2021). A water quality prediction model can be useful for predicting dynamic changes in water. Furthermore, a smart aquaculture system can reduce labour costs and be beneficial for fish health (Ghose, 2014).A monitoring system with sensors and a microcontroller can monitor pH, turbidity, water level, temperature and humidity (Pasika & Gandla, 2020). The data were collected continuously and in real time sent wirelessly to the monitoring station. The system included a microcontroller unit with a sensor network for sampling every 10 s from the water tank (Pasika & Gandla, 2020). Additionally, an algorithm was used to monitor the changes in water quality parameters.Real‐time measurementsVarious methods for water quality monitoring have been reported (Zhu et al., 2010; Huan et al., 2020; Lin et al., 2021). Typically, they can measure the basic water quality factors (e.g., DO, pH, temperature) which greatly affect the survival of the reared species.In recent years, a variety of monitoring systems has been reported, such as a remote monitoring system for aquaculture (Wang et al., 2012), WSN (Nam et al., 2015) and a WSN automatic monitoring system for fisheries that combines WiFi and ZigBee technologies (Chen et al., 2016). Zigbee is built for control and sensor networks for wireless personal area networks (WPANs) and operates on 2.4 GHz, 900 MHz and 868 MHz frequencies. WSN consists of self‐organised sensors that measure, collect, transmit and process data in real time. The information is displayed on a computer or conveyed for remote monitoring (Chen et al., 2016). Over the last decade, WSN has been widely used in aquaculture (Zhang et al., 2011; Huang et al., 2013; Cario et al., 2017).Odey and Li (2013) used an AquaMesh wireless mesh sensor network to measure pH, DO and EC. Later, WSN and a general packet radio service (GPRS) were applied to remotely monitor water quality parameters and feed consumption (Lorena et al., 2018). Lin et al. (2021) used online monitoring of temperature, pH, DO and EC in an intensive culture system. They used a portable pH/ORP/DO/EC Multimeter to measure these variables in a RAS connected with a constructed wetland. The applications have been in used in variety of industries, also in fish farms (Zhang et al., 2011; Huang et al., 2013; Chandanapalli, 2014; Luo et al., 2015; Cario et al., 2017). Physical water quality parameters were measured in real time and stored in a database. However, these systems included only the most basic variables (temperature, DO, pH, water level). A responsible person was informed of a problem by SMS or email (Zhang et al., 2011). Graphical display of real‐time data was available (Huang et al., 2013), while solar and lithium cells have also been used as power supply (Luo et al., 2015).Long transmission distance, fast communication and high transmission distance quality have been achieved with the 4G technology. In future, 5G technology will be applied to further enhance the data transfer. Addition of data processing to support subsequent analysis can improve data quality. It consists of data cleaning, integration, transformation and data reduction (Fang, 2009). This usually means data repair and noise reduction. Several different data repairing mechanisms have been suggested, such as request retransmission mechanism (Li et al., 2014a), distributed storage mechanism (Omiwade & Zheng, 2012), multi‐path disjoint data transmission (Li et al., 2014b) and data redundancy backup (Srouji et al., 2011). Additionally, a compression sensing algorithm can be incorporated (Zhao, 2015).Many noise reduction methods have been proposed, such as least square method, quadratic bilinear time frequency analysis, Fourier transform and wavelet transform, to eliminate noise interference in measurements (Wickersham et al., 2014; Hu et al., 2020). The wavelet analysis can realise the separation and extraction of frequencies for water quality signals (Wickersham et al., 2014). It is a convenient tool which has been used, for example, in hydrological and water quality prediction (Ebrahimi et al., 2013; Yu et al., 2014).Tolentino et al. (2021) reported a system which monitored and automatically corrected water quality parameters in an intensive system rearing Nile tilapia (O. niloticus). They developed a monitoring system for pH, ORP, turbidity, temperature, salinity and DO in water. Furthermore, the system included an internet‐based access and online‐applications to monitor the status of the system and parameters (Tolentino et al., 2021). The system consisted of sensors, microcontrollers, a Long Range Wide Area Network (LoRaWAN), and correcting devices: a microcontroller, water pump, heater, water bottle and drum. Water quality was improved by adding new replacement water, while pH was corrected by adding sodium bicarbonate solution.Internet of Things in aquacultureIn recent years, Internet of Things (IoT) has become increasingly common. In aquaculture, the IoT has been applied in real‐time monitoring and adjusting of water levels and pumping (Kassem et al., 2021). Anand and Regi (2018) used a Narrow Band (NB)‐IoT system for remote monitoring of water levels in rearing tanks. This technology had low costs, low power consumption and wide coverage which is suitable for aquaculture. For example, an IoT‐based water quality monitoring system for temperature, pH, DO and salinity (Dahn et al., 2020). Lin et al. (2021) showed that a wireless multi‐sensor IoT integrated with sensors measuring pH, temperature, DO and EC with WiFi communication for aquaculture, showing good accuracy and confidence.Huan et al. (2020) reported a monitoring system for aquaculture water quality based on NB‐IoT with chip and sensor technology to collect aquatic environment information. It was applied for the non‐gateway data report through the NB module and a cloud platform to save the received data. Temperature, pH and DO sensor devices collected the water quality data and encoded them for the data frame format. Additionally, the system enabled the remote control of equipment.In the study of Kassem et al. (2021), the RAS monitoring system consisted of three components: monitoring of the most vital water quality parameters, monitoring the water sampling inlets connected to basin water, and the components responsible for cleaning pipes. No cleaning fluid was fed into the rearing tanks. The sensor case contained a separate cleaning mechanism to ensure the cleaning of sensors and their calibration (Kassem et al., 2021). The system run‐time depended on the distance from the sampling point to the sensor and controller (about two hours, including the cleaning sequence). The systems showed high accuracy due to automated sampling and cleaning. It was run non‐stop to ensure multiple measurements per day (Kassem et al., 2021). A decreased labour requirement led to decreased costs, but it could still measure a wide range of parameters: ammonia, nitrite, pH, total vibrio (cfu mL−1), alkalinity, temperature, salinity and DO.Rashid et al. (2021) reported a system applying an IoT‐based monitoring system for real‐time water quality, which focused on water level parameters. They collected data via sensors, analysed the data with machine learning tools to study and monitor the water quality. The water quality parameters included pH, temperature and TSS, and the correlation between the parameters was also studied (Rashid et al., 2021). The prediction model was trained based on the collected data. They showed that the system increased efficiency, reduced labour requirements and ensured more sustainable and economical production. They also reported good testing accuracy and performance but still wished to improve it in the future.IoT systems in aquaculture typically use WSN with wide coverage area and good scalability (Pule et al., 2017). These technologies include Zigbee, Bluetooth (Hu et al., 2020) and General Packet Radio Services (GPRS)/CDMA/long‐term evolution (Zhu et al., 2010). Zigbee is widely used in WSN for water quality monitoring. However, sensitive systems can easily malfunction and have high energy consumption in long distance transmission.A fusion of digital technologies and use of IoT can transform a traditional farming model into smart farming (Narwane et al., 2022). WSNs have made significant contribution to various farming applications (Jamroen et al., 2020, 2023). Wang et al. (2012) designed a remote monitoring system to increase the level of automation and used a 3G cellular technology with an Android‐based mobile operating architecture. Nam et al. (2015) developed a WSN‐based remote water quality monitoring system by combining a ZigBee network protocol and CDMA technology. Postolache et al. (2014) proposed an IoT‐based system for water quality monitoring (EC, temperature and turbidity) in aquaculture, using WSN architecture. Recently, a photovoltaic (PV)/battery energy storage (BES)‐powered water quality monitoring system was introduced, based on the NB‐IoT (Jamroen et al., 2023). The system could measure DO, pH, temperature, turbidity and salinity, as well as electrical parameters’ PV power, system consumption, BES power and state of charge for system functionality.Besides the real‐time information they provide on water quality parameters, early warning signs are also valuable for farmers in detecting abnormalities (Hu et al., 2020). In smart aquaculture, the integration of artificial intelligence and IoT systems can control the risks and improve the quality of aquatic production and ensure the survival of the reared species. This requires real‐time collection and processing of reliable water quality data.More advanced analysesOrganic compounds such as veterinary drugs, antibiotics, off‐flavour compounds and ozonation by‐products in marine systems can enter the system, for example, via inlet water, or are formed in the system. Medications can also be needed in aquaculture, including anaesthetics, vaccines and antibiotics (Costello et al., 2001; Dauda et al., 2019). However, these chemicals are important for aquaculture purposes but are used in low concentrations. Additionally, the detection of cortisol levels and other hormones in aquaculture water and fish have become more important for fish welfare studies (Rollo et al., 2006; Mota et al., 2017; Hanke et al., 2019; Höglund et al., 2022;). Some compounds may be undesirable in fish products (e.g., off‐flavours geosmin and 2‐methylisoborneol), or they can pose a risk to the raised species (e.g., ozonation by‐products). They need to be reliably detected, identified and quantified. This usually requires more advanced chemical knowledge and equipment, and these analyses are therefore performed in a laboratory instead of on a fish farm (Table 3).3TABLESelected water quality parameters measured in RAS which require more advanced analyses in the laboratory and examples of instrumentation required for their analysisParameterInstrumentationReferencesAntibioticsLC‐MS/MSaGorito et al. (2022)Shi et al. (2022)CortisolLC‐MS/MSaradioimmunoassay (RIA)Cortisol EIA kitHanke et al. (2019)Mota et al. (2017)Höglund et al. (2022)Rollo et al. (2006)DOC, TOCbAurora 1030 WTOC analyzerShimdadzu TOC‐L analyzerLi et al. (2016)Pettersson et al. (2022)MetalsICP‐OESc: Perkin‐Elmer Optima 8300ICP‐MSd: Perkin Elmer NexION® 350DLindholm‐Lehto et al. (2020, 2021)Molecular mass of NOMeHPSEC‐EEMf/UVJokubauskaite et al. (2015)Ignatev and Tuhkanen (2019)Molecular mass of NOMeHPSEC‐DAD/FLDgEEM‐PARAFAChLi et al. (2014c, 2016)Off‐flavoursiDynamic HSj‐GC‐MSSPME‐GC‐MSSPME‐GC‐MS/MSPodduturi et al. (2017)Lindholm‐Lehto et al. (2019)Lindholm‐Lehto (2022)TrihalomethanesBrominated oxidation by‐productsGC‐MS USEPA Method 524.2DR/2800 SpectrophotometerSchroeder et al. (2011)Li et al. (2016)aLiquid chromatography (LC)–tandem mass spectrometry (MS/MS).bDissolved organic matter (DOC), total organic carbon (TOC).cInductively coupled plasma optical emission spectrometer (ICP‐OES).dInductively coupled plasma mass spectrometry (ICP‐MS).eOrganic material, including humic and fulvic acids, protein‐like compounds (NOM).fExcitation emission matrix (EEM).gHigh performance size exclusion chromatography (HPSEC)‐diode array detector (DAD)/fluorescence detector (FLD).hFluorescence excitation‐emission‐matrix combined with parallel factor analysis (EEM‐PARAFAC).iOff‐flavours include a variety of compounds, such as geosmin, 2‐methylisoborneol and other terpenes, aldehydes, fatty acids and so on.jDynamic HS Dynamic Headspace sampling of volatiles in Tenax TA traps.Trace metals such as zinc and copper can be led in the system via feed or by erosion of materials, or via inlet water (Kristensen et al., 2009; Bergheim & Fivelstad, 2014). Heavy metals are toxic to fish, as they can interact with osmoregulation and the accumulation of other toxic compounds such as ammonia, or remain in fish flesh, posing a risk to fish consumers (da Silva et al., 2018). This highlights the importance of monitoring trace metals and elements in RAS.Compound can be analysed with techniques based on liquid (LC) or gas chromatography (GC) based on its properties. Additionally, for the detection of low concentrations of analytes in complex RAS water matrix, a variety of pretreatment methods is required, including solid phase extraction (Gorito et al., 2022; Höglund et al., 2022), solid phase micro extraction (Lindholm‐Lehto, 2022) and dynamic headspace (HS) (Podduturi et al., 2017), to name a few (Table 3). In the case of volatile and semi‐volatile compounds, GC‐based methods are preferred (Lindholm‐Lehto et al., 2019; Lindholm‐Lehto, 2022), while LC with tandem mass spectrometry (MS/MS) detection is more suitable for antibiotics (Freitas et al., 2014).EFFECTS OF WATER QUALITYWater quality and fish behaviourThe stress response of fish is related to water quality (Iryna et al., 2013; Hu et al., 2020). Stress in fish can lead to inhibited growth, a weaker immune system and changes in fish behaviour (Hjeltnes et al., 2022). These changes can be crucial, especially as optimal growth is highly desirable (da Silva et al., 2018). For example, stressed zebrafish (Danio rerio) showed increased body temperature (2–4°C) which can be considered as a fever response (Rey et al., 2015). Rey et al. (2015) suggested that the observed emotional fever is associated with consciousness in fish. The zebrafish clearly showed the capacity for emotional fever but the link between consciousness is still debated. On the other hand, water properties (temperature) can affect fish growth and genetic sex determination based on temperature (Santi et al., 2017). High temperatures (36°C) led to masculinisation of African catfish (Clarias gariepinus) when exposed 6–8 days after hatching. The exposure during the short thermosensitive period led to 90–100% males (Santi et al., 2017).Certain pollutants can change the movement behaviour of fish, and by measuring the intensity of this behaviour, pollutant concentration can be calculated (Zhang et al., 2013). For a pollutant, models can show if the water quality is polluted or unpolluted but not determine a specific concentration (Peng, 2017). So far, more advanced interactions remain subjects for further studies.Water quality has been evaluated by studying the average movement speed, height, distant and position distribution of fish (Bernatowicz et al., 2009). Fen (2014) on the other hand, used group behaviour parameters: swimming speed and distance to nearest neighbor, to quantify normal and abnormal water quality. However, all these methods are applicable only in certain conditions, and only a few studies report interactions between multiple water quality parameter and behavioural characteristics of fish.Based on fish behaviour, it is possible to obtain and predict changes in water quality. The detection of fish behaviour can be based on video imaging, and includes characteristics’ shape, texture, background, colour, swimming speed and direction, fin beating frequency, feeding condition and metabolic rate (Yang et al., 2013; Cook et al., 2014). However, video imaging may be unsuitable for aquaculture environment, for example, in the case of high turbidity. Fish can be separated from their backgrounds, based on background modelling, temporal difference method and optical flow method (Hu et al., 2020). For example, Zhang et al. (2015) used a computer vision algorithm to measure pectoral fin and background subtraction to obtain fish location more accurately.Changes in swimming activity, swimming depth and acceleration can reflect the response of fish to its surroundings (Martins et al., 2012; Kolarevic et al., 2021). Although water quality parameters are frequently monitored in RAS, fish behaviour is not typically as an integral part of the systems (Kolarevic et al., 2016, 2021), and the conditions in RAS on welfare and performance of the raised species need to be improved.A variety of water quality parameters (DO, CO2, nitrate, pH) and feeding regimes can affect the swimming behaviour of fish (Davidson et al., 2011a; Martins et al., 2012). Techniques applying biotelemetry have been used to monitor the behaviour of individual farmed and wild fish (Kolarevic et al., 2016, 2021; Sinisalo, 2022). With tags, algorithms of acceleration and gravity forces can be measured accurately and transmitted to acoustic receivers (AccelTag; Thelma Biotel, Norway) (Martins et al., 2012). This technology has been used to monitor swimming activity in sea cages (Føre et al., 2011).An acoustic acceleration tag, AccelTag, allows real‐time monitoring of fish behaviour (Kolarevic et al., 2016). However, they are limited by the available tag sizes (7 and 11 g). The tag size should be about 2%/body mass (Jepsen et al., 2004). By tagging Atlantic salmon post smolts in RAS, swimming behaviour of fish can be monitored and observe changes in water quality and stress events (Kolarevic et al., 2016). Acoustic acceleration tags can therefore be a suitable tool for welfare and management practice studies and for responses to changes in water quality and chronic thresholds of water quality parameters.Fish movement in good water quality differs from those in abnormal water quality. Cheng et al. (2019) monitored fish movements by two cameras combined a three‐dimensional motion trajectory system with a Kuhn‐Munkres algorithm. They showed that the learning model successfully reflected the water quality. However, they could not assess water quality in time and were not able to show which water quality parameter caused the abnormal behaviour.WSNs have also been applied for monitoring of fish behaviour and water quality. For example, Parra et al. (2018) used a WSN to monitor feeding of fish and water quality in rearing tanks. Sensors were used to monitor temperature, turbidity, water level and conductivity, while monitoring of fish behaviour included swimming depth and velocity. Based on the monitored variables and fish behaviour, system was able to control the changes in water quality.FeedingFish appetite can be affected by water quality, especially temperature and DO (Sun et al., 2016). Several issues affect feed intake, which can be due to physiological, nutritional or environmental issues (Zhou et al., 2018). Frequency and time of feeding affects feeding intensity, and fish behaviour can vary based on how feed is sprinkled in the tank (Martins et al., 2012).Excessive feeding increases costs and faeces and uneaten feed lead to increased solid waste in the system and affect the water quality and microbial abundance in the system (Rojas‐Tirado et al., 2018; Zhou et al., 2018). Currently, automated feeding systems are widely used in aquaculture. However, predicting when feeding should be stopped is often based on subjective experience (Zhou et al., 2018).Feeding can be automatically adjusted based on fish appetite (Zhou et al., 2017a). There are feeding systems that can calculate feed demand real‐time based on the quantity of fish. Fish behaviour is monitored by machine vision, underwater acoustic technology and water quality sensors. For example, acoustic sensors can detect feed (Juell et al., 1993) and count the pellets by computer vision (Li et al., 2017) to assess the fish appetite. Computer vision has been widely used because it is relatively inexpensive and does not harm the fish (Sadoul et al., 2014; Lin et al., 2018).An intelligent feedback control can observe fish behaviour and based on specific control algorithm, stop feeding in the case of aggregation behaviour (Chang et al., 2005). However, computer vision often requires good illumination conditions which can be challenging in turbid water. A near infrared machine vision is more suitable, and with image enhancement algorithms good results can be achieved, also in poorly illuminated conditions (Zhou et al., 2017b). Zhou et al. (2018) reported an automatic feeding control method based on near infrared computer vision and a neuro‐fuzzy model for on‐demand feeding. They found significant increase in fish growth and the FCR was reduced by 10%, and water quality was improved (Zhou et al., 2018).CONCLUSIONSIn aquaculture, several water quality parameters need to be monitored to understand the aquatic conditions. There are currently no regulations for which parameters to measure, and each farmer has their own view of and possibilities for water quality measurements. So far, there are no guidelines for acceptable ranges and fluctuations. These have been listed in this study. Although suitable ranges vary between different species, certain guidelines would be a valuable tool for designing and running a RAS facility.Traditionally, water quality parameters have been measured at certain intervals with handheld sensors and quick laboratory tests, but this can time‐consuming and possible only during working hours. In recent years, the development of information technology and low‐cost sensors has enabled monitoring via WSNs, online monitoring systems and applications of IoT which allow real‐time monitoring, automatic data collection and storing.Intelligent systems may include the prediction of problems beforehand based on changes in water quality. Early warning signs are valuable for farmers to detect abnormalities and act before major issues occur. However, many of the modern sensors and monitoring systems need competent users and regular maintenance and calibration. The effects of water quality can also be observed by monitoring fish behaviour such as swimming activity, depth, acceleration and feeding. Fish behaviour can be monitored by, for example, acoustic acceleration tags and computer vision. This can be a valuable addition to the monitoring regime with the more traditional parameters.All the new IoT‐based monitoring systems can monitor the basic water quality parameters (DO, pH, temperature, turbidity and salinity), which can be sufficient for regular monitoring purposes. However, the more advanced analyses (e.g., off‐flavours, cortisol levels) still need to be performed in a laboratory and cannot be performed in real‐time. These may also require more advanced analysis equipment and can only be performed by trained personnel. Development of new techniques which allows real‐time monitoring of the more advanced water quality parameters remains as a subject of future studies.AUTHOR CONTRIBUTIONPetra Lindholm‐Lehto: Formal analysis, investigation, writing – original draft, resourcesACKNOWLEDGEMENTSFinancial support from the NordForsk: Sustainable Aquaculture – Research and Innovation Projects is gratefully acknowledged.CONFLICT OF INTEREST STATEMENTThe author of this research project would like to disclose the absence any personal or financial relationship with people or organisations that may inappropriately influence this work. This naturally includes all grant and sources of funding described in the paper.DATA AVAILABILITY STATEMENTNot applicable.ETHICS STATEMENTThis material is the authors’ own original work, which has not been previously published elsewhere.REFERENCESAbbaszadeh, A., Keyvanshokooh, S., Yavari, V. & Naderi, M. 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(2010) A remote wireless system for water quality online monitoring in intensive fish culture. Computers and Electronics in Agriculture, 71S, S3S9. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aquaculture Fish and Fisheries Wiley

Water quality monitoring in recirculating aquaculture systems

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Abstract

INTRODUCTIONRecirculating aquaculture systems (RAS) are land‐based intensive aquaculture systems where water is re‐used typically via mechanical and biological treatment to reduce the consumption of water and yet maintain adequate water quality (Martins et al., 2010). RAS utilises modern knowledge of biology, environmental sciences, mechanical engineering and information technology (Xiao et al., 2019). Circulating water is reused multiple times, and only a small proportion is replaced with clean water (Chun et al., 2018). Currently, variety of freshwater and marine species from hatchery to grow‐out is reared in RAS. RAS have been used for several decades for fish production, although only a few very large (production over 10 million kg per year) systems are in operation (Molleda, 2007).The idea of RAS originates from Japanese biological purification in the 1960s (Wu et al., 2008; Lin, 2011; Xiao et al., 2019). Since the 1990s, biological engineering, automation control, bottom discharge and controls of dissolved oxygen (DO) and temperature have been employed. Currently, the development of RAS focuses on improving of water purification technology and equipment, sustainability and the level of wastewater discharge (Becke et al., 2019). However, since RAS are often energy and cost intensive, and suggestions have been made to improve their economy.Water quality can be divided into categories which include physical parameters, organic contaminants, biological contaminants and pathogens (Su et al., 2020). Physical parameters depend on climate and the surrounding conditions. Water quality control is a key aspect in any aquaculture management, but especially in RAS, where the water is circulated in the system. The main parameters for quality monitoring include pH, temperature, oxidation–reduction potential (ORP/Redox), turbidity, salinity and DO. In the ideal range, water quality can enhance the fish growth rate and reduce the occurrence of fish diseases (Stigebrandt et al., 2004; Tolentino et al., 2021). Technologies suitable for rapid, real‐time and automated monitoring and data storing are therefore greatly needed.Water quality in RAS is affected by feed loading and composition (Blancheton et al., 2013; von Ahnen et al., 2015). Dissolved compounds and particulate matter can be difficult to remove from the system and they can act as substrate material for bacterial growth. Some of these bacterial species can induce off‐flavours in the system (Lindholm‐Lehto et al., 2019; Suurnäkki et al., 2020). A high abundance of bacteria can also affect the fish, acting as pathogens or competing for oxygen (Michaud et al., 2014).In RAS, water quality parameters are kept stable to ensure suitable conditions for the fish (Rojas‐Tirado et al., 2018). It is therefore important to understand and control factors that change the water quality. Water quality needs to be monitored closely to maintain optimal growth conditions in RAS (Naughton et al., 2020). In RAS, fish are often reared in high densities, causing a build‐up of organic material and nutrients (Bentzon‐Tilia et al., 2016). These factors need to be controlled and monitored, but the available tools are often complex or include a considerable lag between sampling and the results (Rojas‐Tirado, 2018). There are no regulations concerning which parameters to measure and each farmer has their own view of and possibilities for water quality measurements. Additionally, there are no guidelines for acceptable ranges and fluctuations.Previous articles have comprehensively studied and discussed fish health, stress levels, diseases (Venugopal, 2002; Endo & Wu, 2019), pathogens (Adams & Thompson, 2011) and exposure to environmental contaminants such as polycyclic aromatic hydrocarbons (Srogi, 2007) and cyanotoxins (Vogiazi et al., 2019). Additionally, analytical technologies and biosensors used in aquaculture have recently also been reviewed (Hu et al., 2020; Su et al., 2020). However, a comprehensive study of current sensors, monitoring equipment and the typically monitored water quality variables has been missing.In this study, it was hypothesised that only the very basic water quality parameters are typically monitored in commercial RAS, possibly with very traditional monitoring methods. However, in recent years, new possibilities are available due to many technical innovations. This review aims at (1) combining new technologies available for monitoring of water quality by using the most modern information technologies, (2) listing the technology‐based possibilities in prediction of detrimental occurrences in water quality and (3) presenting which parameters can be measured by the modern online‐measurement tools and which still need more advanced laboratory analyses. All this might encourage adopting new measurement equipment for monitoring of water quality at commercial aquaculture facilities.WATER QUALITY PARAMETERSIn aquaculture, several parameters need to be monitored and measured to understand the aquatic conditions (Tziortzioti et al., 2019). These include pH, temperature, DO, salinity and turbidity (Mwegoha et al., 2010). DO is the most crucial parameter for aquatic species. Oxygen is not readily soluble in water, and its ability to dissolve decreases rapidly as the temperature and salinity increase (Timmons et al., 2018; Chumkiew et al., 2019).The fluctuation of physical parameters directly affects the health, growth and feed utilisation of the raised species. For example, DO affects feed consumption, feeding efficiency, metabolism and growth in fish (Buentello et al., 2000; Zhang et al., 2011). Fish use oxygen to convert feed to biomass and depending on the species, varies from 5 mg L−1 (warm water species) to 7 mg L−1 (cold water species) (Pillay & Kutty, 2005). However, some species (silver catfish, Rhamdia quelen) can tolerate DO levels below 3 mg L−1 but DO levels below 5 mg L−1 affect their behaviour, survival and growth (Braun et al., 2006). For salmonids, oxygen saturation should be at least at 70–80% (above 6 mg L−1). Fluctuations or suboptimal temperatures affect feed use (lower FCR at temperatures above optimal, Timmons et al., 2018) and induce stress which can lead to disease outbreaks. Additionally, organic material consumes oxygen (biological oxygen demand, BOD) via bacterial degradation.Water conductivity is related to the ionic content of water and salinity, but it also depends on temperature and pressure (for a 1°C increase, conductivity increases by 2–4%) due to increased ion mobility and solubility (Tziortzioti et al., 2019). Hydrogen ion concentration and relative acidity are measured as pH. Aquatic species generally require a neutral pH, but for example, the optimum pH for chichlids (Cichlidae; Takahashi‐Kariyazono et al., 2017) and for tilapia (Oreochromis sp.; Milud et al., 2013) can be in mildly basic conditions. A biofilter achieves its best performance in mildly basic conditions. The pH can be affected by, for example, bacteria, CO2 content and chemicals in the system. Water with a low pH can be harmful to aquatic species but can also corrode pipes or release lead, cadmium, copper and zinc into water.Turbidity means the degree of cloudiness in water due to suspended organic or inorganic particles (silt, clay, organic matter, plankton, etc.) and is a valuable parameter for evaluating water quality (Tziortzioti et al., 2019). The degree of turbidity is based on scattering of light from the suspended particles. High turbidity reduces the depth of light can penetrate which can prevent or decrease the efficiency of UV light treatment.Nitrogen speciesIn RAS, nitrogen compounds can accumulate in water (ammonium‐N, nitrite), which are toxic to fish even at low concentrations (Timmons et al., 2018). Intensified production in RAS leads to an increase in dissolved waste, including ammonia (NH3) (Ip et al., 2001). Ammonia (NH3) is highly toxic to fish and can deteriorate gills and result in mortality (Wicks et al., 2002). Typically, the upper safe limit for NH3‐N for salmonids is 0.0125 mg L−1 (Timmons et al., 2018).Ammonia can exist in two forms: NH3 and in the ionised form NH4+, which are in equilibrium in water based on temperature and pH (Timmons et al., 2018). Together, they are referred to as total ammonia nitrogen (TAN). The proportion of free ammonia (NH3) increases with increasing temperature and pH. Fish excrete nitrogen mainly as ammonia‐N (53–68%), but also as urea‐N (6–10%), amino acids (4–10%) and as protein (3–11%) (Kajimura et al., 2004). Fish have varying tolerance of ammonia‐nitrogen, depending on species, age and physiological status. For example, adult fish are more tolerant to ammonia than juveniles and fingerlings (Dauda et al., 2019). Generally, levels of ammonia below 1 mg L−1 and of TAN below 5 mg L−1 are recommended (Table 1) (Boyd, 2003; Ajani et al., 2011). However, in some studies, increased concentrations of ammonium have been beneficial in terms of growth rate and sublethal NH3‐N (Kolarevic et al., 2013).1TABLELimit concentrations of alkalinity (mg L−1), CO2 (mg L−1), DO (mg L−1), H2S (μg L−1), nitrogen species (mg L−1), pH, selected metals (μg L−1) and temperature (°C)ParameterLimit concentrationSpecies/additional infoReferencesAlkalinity (as CaCO3), mg L−150–30070–200Atlantic salmonTimmons et al. (2018)Summerfelt et al. (2015)CO2, mg L−115–207–10<20<80Salmonids in freshwater;salmonids in seawater‐sensitive species, salmonids‐tolerant species, tilapiaMolleda (2007)Timmons et al. (2018)Dissolved oxygen (DO), mg L−16–9 (70–80%)<5 (70%)Molleda (2007)Timmons et al. (2018)H2S, μg L−125In freshwaterin marineHjeltnes et al. (2019)Sommerset et al. (2020)NO3‐N, mg L−11002000–400Chen et al. (2002)Molleda (2007)Timmons et al. (2018)NO2‐N, mg L−1<0.10.1In soft waterTimmons et al. (2018)NH3‐N, mg L−10.030.322.2<0.0125–0.03Arctic charr;rainbow trout;common carpsalmonidsMolleda (2007)Timmons et al. (2018)Summerfelt et al. (2015)TAN (NH3‐N +NH4‐N), mg L−1<1.0<3.0Long‐term, cool‐water fishwarm‐water fish, Arctic charrMolleda (2007)Timmons et al. (2018)Temperature, °C<1515–20>2014–1628–32Cold‐water speciescool‐water specieswarm‐water speciesrainbow trouttilapiaTimmons et al. (2018)pH4.26.5–8.5Rainbow trout, roach LC 8‐day mediangeneral suitable pH rangeAlabaster and Lloyd (1980)Timmons et al. (2018)Cadmium (Cd), μg L−111Salmonid juveniles and adults, (hardness 4 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Copper (Cu), μg L−15.0<30Rainbow trout (hardness 10 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Davidson et al. (2009)Zinc (Zn), μg L−130<269Salmonids (hardness 10 mg L−1 as CaCO3)Alabaster and Lloyd (1980)Davidson et al. (2009)In RAS, ammonia is converted by certain bacteria to less toxic nitrite (ammonia‐oxidising bacteria: AOB, e.g., Nitrosomonas) and further to nitrate (nitrite‐oxidising bacteria: NOB, e.g., Nitrospira, Nitrobacter) via nitrification process which allows lower water use rates (Suurnäkki et al., 2020). Typically, this is performed in nitrifying bioreactors, such as moving bed bioreactors (Kamstra et al., 2017) or fixed bed bioreactors (Pedersen et al., 2015) where biofilm containing the nitrifying bacteria, is formed on plastic carrier material or on natural material, such as sand particles.Nitrite is toxic to fish, and concentrations below 0.5 mg L−1 are recommended (Ajani et al., 2011). Nitrate is a less toxic oxidation product, and most fish species can tolerate levels up to 200 mg L−1 (Dauda & Akinwole, 2015), although NO3‐N concentrations of 100–1000 mg L−1 are not uncommon (Preena et al., 2021). However, high concentrations of nitrate can inhibit fish growth and increase mortality (Table 1) (Davidson et al., 2014). Typically, nitrate concentrations in RAS are controlled by adjusting the water exchange rate and biofilters, adding denitrification, or even phytoremediation into the system (Martins et al., 2010; Schipper et al., 2010; Pulkkinen et al., 2021).Fish feeds and feed load are important factors when dimensioning ammonia removal capacity in RAS. TAN production in fish depends on the feeding interval (Pedersen et al., 2012) and digestibility (Médale et al., 1995) and it may increase if the amino acid profile in the fish feed is not balanced (Bureau & Hua, 2010). It is beneficial to disperse the feed throughout the day in RAS to ensure a steady ammonia load into the bioreactors.The feed loading (kg feed per m3 make‐up water) reflects the intensity of recirculation which is the main critical parameter that influences water quality deterioration (Pedersen et al., 2012). Upper limits of feed loading are determined based on the raised fish species, the prevailing environmental conditions, the type of feed and the used water treatment equipment. Higher feed loads are possible with increased biofilter nitrification capacity, when fish performance is not restricted by nitrate accumulation or by deteriorated water quality in general (Davidson et al., 2011a). For example, declined health of rainbow trout (Oncorhynchus mykiss) and increased mortality were observed in a commercial RAS with 1.3–2.0 kg m−3 feed loads (Davidson et al., 2009). At very high feed loads, even abnormal swimming behaviour, side swimming and deformities in rainbow trout have been observed (Davidson et al., 2011b).When aiming for very low water exchange rates in RAS or even zero‐discharge systems, nitrate need to be removed from the system. Nitrogen removal is most commonly achieved via heterotrophic denitrification which converts nitrate to nitrogen gas and uses carbon as the electron donor and for microbial growth (Schipper et al., 2010). There are other options for denitrification, such as anaerobic ammonium oxidation (Anammox) and chemo‐autotrophic denitrification (Burgin & Hamilton, 2007). Additionally, biofloc system or biofloc technology absorbs inorganic nitrogen from the circulating water and improves water quality. In Biofloc system, microbial protein is produced for the raised species which improves feed conversion ratio, FCR (Khanjani & Sharifinia, 2020). Biofloc has been widely studied in recent decades for example in shrimp and finfish farming (Abbaszadeh et al., 2019; Ebrahimi et al., 2020).Aquaponic systems are also able to remove nitrogen species via denitrification and utilise nitrogen formed in aquaculture for the needs of plant growth (Wongkiew et al., 2017). Denitrification is carried out in anoxic conditions by various archaea and heterotrophic bacteria, such as Aerobacter, Acinetobacter and Pseudomonas (Wonkiew et al., 2017). Organic material in circulating water acts as electron donor and the source of carbon. However, sufficient carbon source need to be maintained to avoid accumulation of intermediate products NO and N2O.The most detrimental problems experienced in RAS have been associated with both low DO and high concentrations of metabolic waste, such as TAN, ammonia (NH3‐N), nitrite (NO2‐N) and CO2 (Molleda, 2007). Typically, fish produce 1.0–1.4 mg L−1 TAN, 13–14 mg L−1 CO2 and 10–20 mg L−1 total suspended solids (TSS) per every 10 mg L−1 of consumed DO (Hagopian & Riley, 1998; Molleda, 2007).Carbon dioxideCarbon dioxide (CO2) is toxic to fish and is a limiting factor in intensive systems. In RAS, fish density is much higher than with more traditional aquaculture methods. High fish density leads to a significant increase in CO2 content in water. Additionally, nitrifying bacteria produce CO2 in nitrification (Xiao et al., 2019). A high CO2 level in water reduces the diffusion gradient between fish blood and water, resulting in blood acidification and reduction in oxygen uptake (Sanni & Forsberg, 1996; Molleda, 2007). Fish ventilate CO2 via the gills as molecular CO2 gas, which forms carbonic acid H2CO3, bicarbonate (HCO3−) and carbonate (CO32−) in water which are in equilibrium, based on pH and partial pressure of CO2 (Fig. 1) (Boyd, 2000; Molleda, 2007). For rainbow trout (Oncorhynchus  mykiss), excretion of CO2 has been estimated to 1–2 mg CO2 kg−1 min−1. Typically, a limit value for CO2 is 20 mg L−1 in water, but tilapia (Oreochromis niloticus), for example, can tolerate as much as 60 mg CO2 L−1 (Timmons et al., 2018).1FIGUREMole fractions as a function of pH on proportions of CO2, HCO3− and CO32− (Boyd, 2000; Molleda, 2007).Excessively high concentrations can damage the respiratory system of fish or even result in mortality (Fivelstad et al., 2003) and decrease biofilter efficiency (Ren, 2014; Xiao et al., 2019). Removal of CO2 also affects the water pH. For example, an 80% decrease of CO2 can lead to a significant increase of pH (Chen et al., 2009). The rate of CO2 removal is affected by the initial CO2 content, water flow, gas‐water volume ratio, type and structure of equipment and the inlet air flow rate (Gong et al., 2017).Over the years, a variety of equipment has been used for CO2 removal. CO2 degassing towers have been used since the 1980s and their effect has been evaluated by (Summerfelt et al., 2003). Additionally, the injection of micron‐sized air bubbles into a foam fractionator increases aeration and CO2 removal (Barrut et al., 2013). For example, an airlift pump with CO2‐stripping can remove 13‐20 g CO2 kWh−1 (Loyless & Malone, 1998) and paddlewheels 1.2 kg CO2 kWh−1 (Eshchar et al., 2003).AlkalinityTotal alkalinity is described as the concentration of bases in water which can react with a hydrogen ion (H+). Several compounds can react in water with hydrogen ion and the total alkalinity describes the contribution of different ions to alkalinity (Boyd et al., 2016). However, in natural waters alkalinity is mostly derived from HCO3−, CO32− and OH− (Eq. 1, Boyd et al., 2016). Traditionally, alkalinity has been expressed as mg CaCO3 L−1 because alkalinity mainly originates from limestone (mixture of CaCO3 and MgCO3) or as milliequivalents per litre of calcium oxide (CaO). Limestone composition varies from CaCO3 (calcite), MgCO3⋅CaCO3 (dolomite) to mixture of CaCO3 and MgCO3 (Boyd et al., 2016). Besides alkalinity, limestone is a major source of hardness, the term used to describe the ability of water to precipitate soap and defined as the concentrations of primarily calcium (Ca2+) and magnesium (Mg2+; Timmons et al., 2018).1Alkalinity=HCO3−+2CO32−+OH−−H+$$\begin{equation}{\rm{Alkalinity}} = \left[ {{\rm{HCO}}_3^ - } \right] + 2\left[ {{\rm{CO}}_3^{2 - }} \right] + \left[ {{\rm{OH}}^ - } \right] - \left[ {{{\rm{H}}}^ + } \right]\end{equation}$$Alkalinity is also connected to buffering capacity. A buffer includes a mixture of a weak acid and its conjugate base (salt) or a weak base and its conjugate acid, such as acetic acid and its conjugate base sodium acetate or ammonium hydroxide and its conjugate acid ammonium chloride (Boyd et al., 2016). Dissolved CO2, HCO3− and CO32− buffer water against sudden changes in pH and water with low alkalinity will lead to greater fluctuation in pH (Boyd et al., 2016).The pH of most freshwaters is based on HCO3− ions and dissolved acid CO2 from air, excluding effects of biological activities and pollution. Typical alkalinity in freshwater ranges from below 5 mg L−1 to over 500 mg CaCO3 L−1, based the geology of the aquifer, while the alkalinity of seawater is about 120 mg CaCO3 L−1 (Timmons et al., 2018).In aquaculture, acids and bases may be added in circulating water which leads to change in alkalinity and pH. For example, removal of CO2 from water increases pH, and at pH 8.3 the concentration of CO2 is reduced to negligible (Boyd et al., 2016). Besides total alkalinity and pH, concentration of dissolved CO2 is affected by temperate; its concentration decreases with increasing pH and temperature. Variety of bases, for example, CaCO3, Na2CO3 and NaOH, can be added as alkalinity supplements (Timmons et al., 2018).Maintaining adequate alkalinity concentrations has been reported to be critical for nitrification. For example, alkalinities of 40–80 mg CaCO3 L−1 have been reported for wastewater. For aquaculture, Chen et al. (2006) recommend maintaining an alkalinity level at 200 mg CaCO3 L−1 to support nitrification. Summerfelt et al. (2015) suggested alkalinity of 70 mg CaCO3 L−1, while a wide range of 50–300 mg CaCO3 L−1 has also been reported (Timmons et al., 2018).TemperatureEach aquaculture farm has a unique optimum temperature range. Fish can be classified into three classifications depending on their temperature preference: cold‐water, cool‐water and warm‐water species. For cold‐water species, optimum water temperatures are below 15°C, for cool‐water species between 15 and 20°C, and for warm‐water species above 20°C (Timmons et al., 2018, Table 1). Temperature affects the physiological processes of the reared species, such as respiration rate, efficiency of feeding, growth, behaviour, reproduction and depuration (Drake et al., 2010). Additionally, several factors affect the tolerance of fish to different temperatures, including species, age, size and past thermal history (Timmons et al., 2018).In RAS, temperature should be measured from several locations such as in rearing tank water and slaughtered fish (Lekang, 2013). Especially, measurement of inlet water temperature is important for designing and selecting a suitable heating equipment and for operating a RAS. The control of rearing temperature allows maintaining good rearing conditions for optimum growth and feed utilisation, although the inlet water temperature may vary considerably between seasons (Dalsgaard et al., 2013). A monitoring equipment based on a programmable logic controller (PLC) for real‐time monitoring of recirculating water and water level was reported by Wu et al. (2011). On the other hand, water source heat pumps generate low‐grade heat from natural sources and can be used for heating and cooling.For heating, four different heating methods are typically used; boilers, geothermal energy, factory waste heat and heat pumps or other equipment (Xiao et al., 2019). Industrial heat is limited by geographical conditions. Heating by boilers is common, but increasing attention to environmental pollution is likely to reduce the attractiveness of boiler heating. So far, only a limited number of studies have been related to temperature control in RAS (Good et al., 2011; Davidson et al., 2014; Xin et al., 2017).Water source heat pumps have been used to heat aquaculture water and to maintain stable temperature around the year (Xin et al., 2017), while Good et al. (2011) used a heat exchanger in RAS rearing rainbow trout (O. mykiss) and Davidson et al. (2014) a geothermal heat exchanger. Geothermal energy has been used for heating in RAS farms producing salmon, trout, tropical fish, lobsters, shrimp and prawns (Ragnarsson, 2014). Waste heat from industry has been used for commercial oyster, penaeid shrimp and salmon farming (Rickard, 1998), while heat from power plants has been used for eel and salmonid fingerlings production (Lemercier & Serene, 1980; Ingebrigtsen & Torrissen, 1980). Additionally, ground heat has been used as a heat source for temperature control in RAS (Jiang et al., 2017). In recent years, there has been interest in solar technologies to maintain selected water temperature in RAS, but only a simulation model regarding solar heating systems in RAS has so far been published (Fuller, 2007).SalinitySalinity represents a content of dissolved salt in water, including calcium, sodium, potassium, bicarbonate, chloride and sulphate ions (Timmons et al., 2018). Water is commonly described as fresh, brackish or saltwater based on its salinity.Cl− and Na+ ions are predominant in sea water, while carbonates and HCO3− in fresh water. Typically, sea water contains 35 g L−1 salt (mainly 77.8% NaCl and 10.9% MgCl2; Boeuf & Payan, 2001). It is more difficult to determine general values for fresh water, but 0.32 g L−1 (35.1% CO32−, 20.4% HCO23−, 12.1% SO42−, 11.7% SiO2 and 5.8% Na+) have been reported (Boeuf & Payan, 2001).Fish maintain concentrations of dissolved salts in their body fluids via osmoregulation by regulating the uptake of ions from the environment and through the restriction of ion loss (Timmons et al., 2018). For example, the best salinity range on growth for Atlantic salmon Salmo salar is 22–28 ppt or g L−1 (0–35 tolerated), 8 for tilapia O. niloticus (0–16 tolerated) and 0 g L−1 for rainbow trout O. mykiss (0–34 tolerated) (Boeuf & Payan, 2001). Most freshwater fish reproduce and grow well at salinities up to at least 4–5 g L−1. Freshwater aquaculture systems are generally maintained at 2–3 g L−1 salinity (Timmons et al., 2018).Seawater RAS may have higher operating costs compared with freshwater or brackish water RAS because of the lower efficiency of CO2 (Moran, 2010) and ammonia removal in seawater and requirement to increased scale of equipment (Bakke et al., 2017). On the other hand, problems related to accumulation of off‐flavours have been observed less often in saline water (15–33 g L−1; Tucker, 2000).Salinity affects reaction rates for ammonia oxidising (AOB) and NOB in brackish or saltwater conditions (NaCl above 5 g L−1; Kinyage et al., 2019). At very low salinity, NaCl below 3.7 g L−1, only minor effects have reported whereas complete inhibition was observed at a salinity of NaCl above 24 g L−1 (Cortés‐Lorenzo et al., 2015).Feed intake, feed conversion and growth rate are improved in brackish conditions (8–20 g L−1) due to reduced energy expenditure on osmoregulation and lower standard metabolic rate (Árnason et al., 2013). Furthermore, brackish water (12 g L−1) combined with moderate exercise (water velocity of minimum 1 body lengths s−1) has shown a positive effect on salmon growth, survival and welfare in RAS (Ytrestøyl et al., 2020).Hydrogen sulphideThe smell of hydrogen sulphide (H2S) resembles rotten eggs. It is highly soluble in water (4000 mg L−1 at 20°C (Barton et al., 2014). H2S gas is also colourless, somewhat heavier than air (1.19 at 15°C) and flammable (Rubright et al., 2017). In water, H2S equilibrates with bisulphide (HS−) and sulphide (S2−) (Eq. 2). H2S and S2− are toxic, while HS− is not (Tanudjaja, 2021). The dissociation of H2S greatly depends on pH, and HS− formation begins at pH 5 (Fig. 2). In marine water systems, pH below 6.8 is a matter of concern due to sulphides, which can trigger the production of H2S (Rojas‐Tirado, 2018).2H2Sg⇔H2Saq⇔HS−+H+⇔S2−+2H+$$\begin{equation}{{\rm{H}}}_2{\rm{S}}\left( {\rm{g}} \right)\mathop \Leftrightarrow \limits {{\rm{H}}}_2{\rm{S}}\left( {{\rm{aq}}} \right)\mathop \Leftrightarrow \limits {\rm{HS}}^ - + {{\rm{H}}}^ + \mathop \Leftrightarrow \limits {{\rm{S}}}^{2 - } + 2{{\rm{H}}}^ + \end{equation}$$2FIGUREMole fractions of hydrogen sulphide (H2S), bisulphide (HS−) and sulphide (S2−) as a function of pH (Applied analytics, 2022).H2S is toxic to all aquatic species at very low concentrations (Lien et al., 2022). Long‐term exposure can cause growth reduction and great stress on fish due to disruption of the respiration process and cellular anoxia (Kiemer et al., 1995). In marine water, sulphate concentration is about 1000 times higher than in freshwater (Letelier‐Gordo et al., 2020), which can lead to H2S formation. Marine RAS are therefore vulnerable to H2S outbreaks. In recent years, H2S poisoning has caused severe fish mortalities and resulted in high economic losses (Hjeltnes et al., 2019; Sommerset et al., 2020).Although H2S is known to be toxic, toxicity levels have not been set by the authorities. However, for freshwater species, over 2 μg L−1 and marine species, over 5 μg L−1 are not recommended (Table 1) (Boyd, 2014; Hjeltnes et al., 2019; Sommerset et al., 2020). The 96‐h lethal concentration for H2S in freshwater ranges from 20 to 50 μg L−1, and in a marine environment from 50 to 500 μg L−1 (Boyd, 2014).H2S poisoning occurs and harms the fish quickly (Lien et al., 2022). Frequent measurements are therefore required to obtain specific information regarding H2S formation. Unfortunately, the monitoring of H2S levels may not be enough, and the prediction of potential occurrences is required to prevent the problems in advance.Possible factors leading to H2S formation are sulphate content in water (freshwater 5–50 mg L−1, sea water 2700 mg L−1), the presence of carbon‐based compounds in water, biodegradation reactions in anoxic conditions with accumulated organic material and bacteria, for example, sulphate‐reducing bacteria or methane‐reducing bacteria (Li & Lancaster, 2013; Letelier‐Gordo et al., 2020). Certain physical properties such as high turbidity, sedimentation of organic matter and low water flow rate can also affect H2S formation (Yu & Bishop, 2001). Additionally, carbon‐based material from uneaten feed or faeces can react with sulphate, forming HS− and later toxic H2S (Eq. 3). Organic matter can also participate in H2S production via biodegradation which occurs via oxidation and reduction reactions; electrons are transferred from organic carbon donor to electron acceptor (Lien et al., 2022).3CH4+SO42−→HCO3−+HS−+H2O$$\begin{equation}{\rm{CH}}_4 + {\rm{SO}}_4^{2 - }\mathop \to \limits {\rm{HCO}}_3^ - + {\rm{HS}}^ - + {{\rm{H}}}_2{\rm{O}}\end{equation}$$OzoneOzone is an environmentally safe disinfectant (Hansen et al., 2016) which improves water quality in RAS due to the oxidisation reactions of nitrite, organic material, chemical oxygen demand, colour and suspended solids (Summerfelt et al., 2009; Davidson et al., 2011a). Ozone can also attack off‐flavour‐causing compounds such as geosmin, bacteria and fish pathogens (Summerfelt et al., 2009). Ozone reacts rapidly with any organic matter, but residual ozone fed directly to a rearing tank severely threatens farmed species (Powell & Scolding, 2018).Several commercial ozone sensors are available. However, they can be very expensive or unreliable or do not show the changes in ozone concentration. ORP is therefore widely used to monitor the ozone addition (Davidson et al., 2011a; Powell & Scolding, 2018). ORP records the readiness of substances in solution to give or receive electrons. In RAS, an ORP sensor should be placed at a point where ozone is completely consumed and downstream of the ozone addition point to prevent ozone damaging the sensors (Bullock et al., 1997). However, ORP gives only an indirect result by the difference in readings before and after the ozone treatment, and other methods such as mediated electrochemical oxidation and changes in UV absorbance at 254 nm or at 272 nm have been suggested (Hansen et al., 2010; Wenk et al., 2013).The ozone production result can be determined by titration with an iodometric standard method (IOA Standardization Committee Europe, 001/87 (F), 1987), while dissolved ozone in water with the standard colorimetric indigo ‐method (IOA Standardization Committee – Europe, 006/ 89 (F), 1989), as recently reported in Pettersson et al. (2022).MEASUREMENTSMeasurements of water qualitySuccessful intensive aquaculture requires the monitoring of several water quality parameters, such as DO, salinity, temperature, pH and electrical conductivity (EC) (Jamroen et al., 2023). Traditionally, water quality parameters have been measured at certain intervals with handheld sensors. However, this requires staff, can be time consuming and is possible only during working hours. In recent years, instrumentation, automation and monitoring systems have developed and become more common and many of the basic measurements can be carried out by online instrumentation (Lekang, 2013). The development of information technology and low‐cost sensors has increased the feasibility of monitoring via wireless sensor networks (WSN). The special conditions in aquaculture must be considered when designing and installing instruments (Lekang, 2013). Besides the price of instrumentation, maintenance, running costs and calibration are required, and sensors may require changing at fixed intervals.Real‐time and efficient monitoring of water quality is essential to ensure cost‐effective RAS management. For example, Zhu et al. (2010) reported an online water monitoring system which was able to monitor DO, pH, temperature and EC. Zhang et al. (2011) used an Orion5‐Star Portable pH/ORP/DO/EC Multimeter to measure these factors in a RAS. Later, Odey and Li (2013) used an AquaMesh wireless mesh sensor network to measure pH, DO, temperature and EC. Several studies have reported methods to measure these variables online in aquaculture (Odey & Li, 2013; Schmidt et al., 2018 Danh et al., 2020) but they all measured the basic variables of water quality.The most common basic water quality parameters, such as temperature, pH and DO, can be measured with portable instruments such as Multi 3410 (WTW GmbH) (Chun et al., 2018). Although portable alternatives are available, nitrite, nitrate and phosphate are often measured more accurately in a laboratory, using an ion‐exchange chromatograph and suppressed conductivity detector (e.g., Dionex DX‐500, Dionex ICS1600, Dionex Integrion HPIC; Chun et al., 2018; Lindholm‐Lehto et al., 2020, 2021).Instruments applying laboratory methods have been modified for aquaculture facilities. A typical detector is a spectrophotometer, which measures the absorption of light in a sample. Integration of monitoring equipment and intelligent data management have been commercially available, for example, from Greenspan and Fleck in Australia, Gimat in Germany, and Isoc, Hydrolab and Emnet in the USA (Zhang, 2007; Lv, 2015). Additionally, Siemens has developed a YSI5200 monitoring systems (Shi et al., 2011). YSI has designed monitoring and control systems specifically for aquaculture with sophisticated alarm modes (Timmons et al., 2018). Model 5200A can continuously monitor and log DO, pH, conductivity, ORP, salinity and temperature, and Model 5400 for four DO probes and four additional inputs such as temperature, pH and ORP.As a result of the development of online instruments, CO2, ammonia and nitrate sensors are widely available (Pulkkinen et al., 2018). The sensors can be for individual parameters or several sensors included in one instrument (Lekang, 2013). For example, automatic monitoring of DO, pH and temperature in a RAS was performed with WATT TriO Matic 700IQ (SW), WATT Sensolyt 700 IQ (SW) and WATT TrioxyTherm sensors (Zhang, 2007). WSN equipment greatly reduces the requirement for maintenance and costs, but especially in marine systems, coating of sensors is required to prevent corrosion (Hu et al., 2020).Water quality can also be monitored by electrochemical analysis. This includes salinity, pH, DO, total organic carbon (TOC), temperature, BOD, dissolved carbon and reduction potential (Bergheim & Fivelstad, 2014; Kolarevic et al., 2014). For example, companies supplying electrochemical sensors commercially include Oxygard Pacific (OxyGuard International A/S, Denmark) and Pentair Point Four (Pentair, UK). These sensors´ measurements are not specific but give a rough indication of water quality, and more accurate analyses for specific quantification can be performed in the laboratory (da Silva et al., 2018).Tziortzioti et al. (2019) reported an inexpensive, portable device for water quality measurements without the need for a communications infrastructure and network connection. The Arduino device was equipped with SD card reader and an Ethernet module, and the measurement data were stored on the SD card (Tziorzioti et al., 2019). The card can be removed and connected to a laptop or a smart phone for data transfer if necessary. The device included a digital temperature sensor, a total dissolved solids meter, an analogue turbidity sensor for measuring suspended particles, an analogue DO meter and an analogue pH Meter.Santos et al. (2022) reported a pilot design of electrochemical oxidation of ammonium nitrogen in marine RAS water rearing gilthead sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax). They measured TAN, fish growth (specific growth rate SGR, FCR, specific feed rate SFR), microbial count (FCU mL−1) and water and energy consumption (Santos et al., 2022). The system included incorporated automatic online analysers for monitoring pH, DO, temperature, ORP, TAN and chlorine content. The TAN concentration was monitored in real time, while the other parameters were periodically measured.Lien et al. (2022) developed a sensor to detect H2S at sub μg L−1 levels. The AquaSense system was a mobile wireless and a real‐time monitoring system which consisted of units in different locations in a RAS. The sensors measured H2S (μg L−1), DO (% saturation), CO2 (mg L−1), pH, salinity (‰), alkalinity (mg L−1), turbidity (NTU) and temperature (°C) (Lien et al., 2022). The sensors are not exposed to water. The system was automatically calibrated by feeding calibration gas to sensors at selected intervals. The sensors in a RAS communicated via an open network and collected the data for storage locally or stored in the cloud service. The H2S sensor covered the 0–80 μg L−1 range (Lien et al., 2022).A future trend in aquaculture is an automatic remote monitoring and computer control of systems. For example, Zhu et al. (2010) used a Code Division Multiple Access (CDMA) service and an IPsec‐based virtual private network to monitor water quality. CDMA is a competing cell phone service technology to GSM. The system consisted of water quality control components, data acquisition and data transformation and transmission. It was able to monitor water and room temperature, DO saturation, DO concentration, pH, EC and salinity, and notify any abnormalities (Zhu et al., 2010).Measurement frequencyMonitoring of water quality should include many parameters (Table 2). Some of them require constant monitoring (e.g., DO, CO2, pH) to avoid detrimental disturbances in RAS, while others can be monitored less frequently (e.g., off‐flavours, trace elements and metals). However, effective online monitoring systems are unavailable for all parameters, especially not at reasonable cost.2TABLESelected water quality parameters (alkalinity, DO, CO2, EC, nitrite‐N, nitrate‐N, pH, ORP, salinity TAN, temperature and TOC) typically monitored in RAS, examples of their analysis methods and instrumentationParameterMethod of analysisInstrumentationReferencesAlkalinityStandard titrationmethod (ISO 9963−1:1994)Hach Method 8203–Sulfuric Acid Digital TitrationTitraLab AT1000, HachLindholm‐Lehto et al. (2021)Davidson et al. (2017)Pulkkinen et al. (2018)Dissolved oxygen (DO)Portable instrumentsonline‐monitoring,electrochemical analysisOrion5‐Star Portable pH/ORP/DO/EC MultimeterOxygard PacificZhang et al. (2011)Schmidt et al. (2018)Danh et al. (2020)Carbon dioxide (CO2)Online‐monitoringquick spectrophotometric testsFranatechHach Method 8223Pulkkinen et al. (2018)Davidson et al. (2019)Electrical conductivity (EC)Portable instrumentsonline‐monitoringOrion5‐Star Portable pH/ORP/DO/EC MultimeterAquaMesh networkOxygard PacificZhang et al. (2011)Odey and Li (2013)da Silva et al. (2018)Hydrogen sulphide (H2S)Wireless online monitoring systemAquaSense systemLien et al. (2022)Nitrite‐NNitrate‐NIon chromatography, ICportable instrumentsonline‐ monitoringquick spectrophotometric testsDionex DX‐500 (Dionex)Multi 3410 (WTW GmbH)LCK340, LCK341,Chun et al. (2018)Lindholm‐Lehto et al. (2020, 2021)Pulkkinen et al. (2018)Oxidative reduction potential (ORP/Redox)Portable instrumentsonline‐monitoringelectrochemical analysisLAQUAact, Horiba scientific D‐74LoRaWAN (Long Range Wide Area Network),Tolentino et al. (2021)Pettersson et al. (2022)Schmidt et al. (2018)Danh et al. (2020)pHPortable instrumentsonline‐monitoringelectrochemical analysisAquaMesh networkOxygard PacificProMinentZhu et al. (2010)Odey and Li (2013)Pulkkinen et al. (2018)SalinityIon chromatography, ICportable instrumentsDionex DX‐500Oxygard PacificYSI 30 Salinity MeterDanh et al. (2020)Lindholm‐Lehto et al. (2020, 2021)Total nitrogen (TAN)Quick spectrophotometric testsHach Method 8038 USEPA NesslerDavidson et al. (2019)TemperatureElectrochemical analysisportable instrumentsonline‐monitoringOxygard PacificTemperature MeterDanh et al. (2020)Bergheim and Fivelstad (2014)Kolarevic et al. (2014)Total organic carbon (TOC)Electrochemical analysisOxygard PacificBergheim and Fivelstad (2014)Kolarevic et al. (2014)To maintain water quality at optimal range, quality parameter data need to be gathered regularly. Traditionally, water quality has been monitored by manually taking samples and sent for analysis in a laboratory (Pasika & Gandla, 2020). This takes time, requires analytical equipment and can be labour intensive (Pasika & Gandla, 2020), but does not provide real‐time data. There are various opinions for, how often water quality parameters should be measured (Jamroen et al., 2023). For example, Ferreira et al. (2011) suggested daily monitoring of DO and temperature, and salinity, but weekly monitoring of pH and turbidity. However, dramatic changes in water quality can be missed which can lead to major fish mortalities and financial losses. New sensor technology and wireless communication for remote monitoring is possible in real‐time (Jamroen et al., 2023). Real‐time monitoring of selected water quality parameters is required for farmers to recognise low water quality and make management decisions (Oberle et al., 2019).In aquaculture, economic constrains, high costs of human resources and real‐time monitoring of all the important water quality parameters have long been real challenges for farmers (Rashid et al., 2021). It is impossible to monitor water quality based only on experience, but an intelligent system can monitor water quality parameters in real time and help maintain good water quality (Saha et al., 2019; Rashid et al., 2021). A water quality prediction model can be useful for predicting dynamic changes in water. Furthermore, a smart aquaculture system can reduce labour costs and be beneficial for fish health (Ghose, 2014).A monitoring system with sensors and a microcontroller can monitor pH, turbidity, water level, temperature and humidity (Pasika & Gandla, 2020). The data were collected continuously and in real time sent wirelessly to the monitoring station. The system included a microcontroller unit with a sensor network for sampling every 10 s from the water tank (Pasika & Gandla, 2020). Additionally, an algorithm was used to monitor the changes in water quality parameters.Real‐time measurementsVarious methods for water quality monitoring have been reported (Zhu et al., 2010; Huan et al., 2020; Lin et al., 2021). Typically, they can measure the basic water quality factors (e.g., DO, pH, temperature) which greatly affect the survival of the reared species.In recent years, a variety of monitoring systems has been reported, such as a remote monitoring system for aquaculture (Wang et al., 2012), WSN (Nam et al., 2015) and a WSN automatic monitoring system for fisheries that combines WiFi and ZigBee technologies (Chen et al., 2016). Zigbee is built for control and sensor networks for wireless personal area networks (WPANs) and operates on 2.4 GHz, 900 MHz and 868 MHz frequencies. WSN consists of self‐organised sensors that measure, collect, transmit and process data in real time. The information is displayed on a computer or conveyed for remote monitoring (Chen et al., 2016). Over the last decade, WSN has been widely used in aquaculture (Zhang et al., 2011; Huang et al., 2013; Cario et al., 2017).Odey and Li (2013) used an AquaMesh wireless mesh sensor network to measure pH, DO and EC. Later, WSN and a general packet radio service (GPRS) were applied to remotely monitor water quality parameters and feed consumption (Lorena et al., 2018). Lin et al. (2021) used online monitoring of temperature, pH, DO and EC in an intensive culture system. They used a portable pH/ORP/DO/EC Multimeter to measure these variables in a RAS connected with a constructed wetland. The applications have been in used in variety of industries, also in fish farms (Zhang et al., 2011; Huang et al., 2013; Chandanapalli, 2014; Luo et al., 2015; Cario et al., 2017). Physical water quality parameters were measured in real time and stored in a database. However, these systems included only the most basic variables (temperature, DO, pH, water level). A responsible person was informed of a problem by SMS or email (Zhang et al., 2011). Graphical display of real‐time data was available (Huang et al., 2013), while solar and lithium cells have also been used as power supply (Luo et al., 2015).Long transmission distance, fast communication and high transmission distance quality have been achieved with the 4G technology. In future, 5G technology will be applied to further enhance the data transfer. Addition of data processing to support subsequent analysis can improve data quality. It consists of data cleaning, integration, transformation and data reduction (Fang, 2009). This usually means data repair and noise reduction. Several different data repairing mechanisms have been suggested, such as request retransmission mechanism (Li et al., 2014a), distributed storage mechanism (Omiwade & Zheng, 2012), multi‐path disjoint data transmission (Li et al., 2014b) and data redundancy backup (Srouji et al., 2011). Additionally, a compression sensing algorithm can be incorporated (Zhao, 2015).Many noise reduction methods have been proposed, such as least square method, quadratic bilinear time frequency analysis, Fourier transform and wavelet transform, to eliminate noise interference in measurements (Wickersham et al., 2014; Hu et al., 2020). The wavelet analysis can realise the separation and extraction of frequencies for water quality signals (Wickersham et al., 2014). It is a convenient tool which has been used, for example, in hydrological and water quality prediction (Ebrahimi et al., 2013; Yu et al., 2014).Tolentino et al. (2021) reported a system which monitored and automatically corrected water quality parameters in an intensive system rearing Nile tilapia (O. niloticus). They developed a monitoring system for pH, ORP, turbidity, temperature, salinity and DO in water. Furthermore, the system included an internet‐based access and online‐applications to monitor the status of the system and parameters (Tolentino et al., 2021). The system consisted of sensors, microcontrollers, a Long Range Wide Area Network (LoRaWAN), and correcting devices: a microcontroller, water pump, heater, water bottle and drum. Water quality was improved by adding new replacement water, while pH was corrected by adding sodium bicarbonate solution.Internet of Things in aquacultureIn recent years, Internet of Things (IoT) has become increasingly common. In aquaculture, the IoT has been applied in real‐time monitoring and adjusting of water levels and pumping (Kassem et al., 2021). Anand and Regi (2018) used a Narrow Band (NB)‐IoT system for remote monitoring of water levels in rearing tanks. This technology had low costs, low power consumption and wide coverage which is suitable for aquaculture. For example, an IoT‐based water quality monitoring system for temperature, pH, DO and salinity (Dahn et al., 2020). Lin et al. (2021) showed that a wireless multi‐sensor IoT integrated with sensors measuring pH, temperature, DO and EC with WiFi communication for aquaculture, showing good accuracy and confidence.Huan et al. (2020) reported a monitoring system for aquaculture water quality based on NB‐IoT with chip and sensor technology to collect aquatic environment information. It was applied for the non‐gateway data report through the NB module and a cloud platform to save the received data. Temperature, pH and DO sensor devices collected the water quality data and encoded them for the data frame format. Additionally, the system enabled the remote control of equipment.In the study of Kassem et al. (2021), the RAS monitoring system consisted of three components: monitoring of the most vital water quality parameters, monitoring the water sampling inlets connected to basin water, and the components responsible for cleaning pipes. No cleaning fluid was fed into the rearing tanks. The sensor case contained a separate cleaning mechanism to ensure the cleaning of sensors and their calibration (Kassem et al., 2021). The system run‐time depended on the distance from the sampling point to the sensor and controller (about two hours, including the cleaning sequence). The systems showed high accuracy due to automated sampling and cleaning. It was run non‐stop to ensure multiple measurements per day (Kassem et al., 2021). A decreased labour requirement led to decreased costs, but it could still measure a wide range of parameters: ammonia, nitrite, pH, total vibrio (cfu mL−1), alkalinity, temperature, salinity and DO.Rashid et al. (2021) reported a system applying an IoT‐based monitoring system for real‐time water quality, which focused on water level parameters. They collected data via sensors, analysed the data with machine learning tools to study and monitor the water quality. The water quality parameters included pH, temperature and TSS, and the correlation between the parameters was also studied (Rashid et al., 2021). The prediction model was trained based on the collected data. They showed that the system increased efficiency, reduced labour requirements and ensured more sustainable and economical production. They also reported good testing accuracy and performance but still wished to improve it in the future.IoT systems in aquaculture typically use WSN with wide coverage area and good scalability (Pule et al., 2017). These technologies include Zigbee, Bluetooth (Hu et al., 2020) and General Packet Radio Services (GPRS)/CDMA/long‐term evolution (Zhu et al., 2010). Zigbee is widely used in WSN for water quality monitoring. However, sensitive systems can easily malfunction and have high energy consumption in long distance transmission.A fusion of digital technologies and use of IoT can transform a traditional farming model into smart farming (Narwane et al., 2022). WSNs have made significant contribution to various farming applications (Jamroen et al., 2020, 2023). Wang et al. (2012) designed a remote monitoring system to increase the level of automation and used a 3G cellular technology with an Android‐based mobile operating architecture. Nam et al. (2015) developed a WSN‐based remote water quality monitoring system by combining a ZigBee network protocol and CDMA technology. Postolache et al. (2014) proposed an IoT‐based system for water quality monitoring (EC, temperature and turbidity) in aquaculture, using WSN architecture. Recently, a photovoltaic (PV)/battery energy storage (BES)‐powered water quality monitoring system was introduced, based on the NB‐IoT (Jamroen et al., 2023). The system could measure DO, pH, temperature, turbidity and salinity, as well as electrical parameters’ PV power, system consumption, BES power and state of charge for system functionality.Besides the real‐time information they provide on water quality parameters, early warning signs are also valuable for farmers in detecting abnormalities (Hu et al., 2020). In smart aquaculture, the integration of artificial intelligence and IoT systems can control the risks and improve the quality of aquatic production and ensure the survival of the reared species. This requires real‐time collection and processing of reliable water quality data.More advanced analysesOrganic compounds such as veterinary drugs, antibiotics, off‐flavour compounds and ozonation by‐products in marine systems can enter the system, for example, via inlet water, or are formed in the system. Medications can also be needed in aquaculture, including anaesthetics, vaccines and antibiotics (Costello et al., 2001; Dauda et al., 2019). However, these chemicals are important for aquaculture purposes but are used in low concentrations. Additionally, the detection of cortisol levels and other hormones in aquaculture water and fish have become more important for fish welfare studies (Rollo et al., 2006; Mota et al., 2017; Hanke et al., 2019; Höglund et al., 2022;). Some compounds may be undesirable in fish products (e.g., off‐flavours geosmin and 2‐methylisoborneol), or they can pose a risk to the raised species (e.g., ozonation by‐products). They need to be reliably detected, identified and quantified. This usually requires more advanced chemical knowledge and equipment, and these analyses are therefore performed in a laboratory instead of on a fish farm (Table 3).3TABLESelected water quality parameters measured in RAS which require more advanced analyses in the laboratory and examples of instrumentation required for their analysisParameterInstrumentationReferencesAntibioticsLC‐MS/MSaGorito et al. (2022)Shi et al. (2022)CortisolLC‐MS/MSaradioimmunoassay (RIA)Cortisol EIA kitHanke et al. (2019)Mota et al. (2017)Höglund et al. (2022)Rollo et al. (2006)DOC, TOCbAurora 1030 WTOC analyzerShimdadzu TOC‐L analyzerLi et al. (2016)Pettersson et al. (2022)MetalsICP‐OESc: Perkin‐Elmer Optima 8300ICP‐MSd: Perkin Elmer NexION® 350DLindholm‐Lehto et al. (2020, 2021)Molecular mass of NOMeHPSEC‐EEMf/UVJokubauskaite et al. (2015)Ignatev and Tuhkanen (2019)Molecular mass of NOMeHPSEC‐DAD/FLDgEEM‐PARAFAChLi et al. (2014c, 2016)Off‐flavoursiDynamic HSj‐GC‐MSSPME‐GC‐MSSPME‐GC‐MS/MSPodduturi et al. (2017)Lindholm‐Lehto et al. (2019)Lindholm‐Lehto (2022)TrihalomethanesBrominated oxidation by‐productsGC‐MS USEPA Method 524.2DR/2800 SpectrophotometerSchroeder et al. (2011)Li et al. (2016)aLiquid chromatography (LC)–tandem mass spectrometry (MS/MS).bDissolved organic matter (DOC), total organic carbon (TOC).cInductively coupled plasma optical emission spectrometer (ICP‐OES).dInductively coupled plasma mass spectrometry (ICP‐MS).eOrganic material, including humic and fulvic acids, protein‐like compounds (NOM).fExcitation emission matrix (EEM).gHigh performance size exclusion chromatography (HPSEC)‐diode array detector (DAD)/fluorescence detector (FLD).hFluorescence excitation‐emission‐matrix combined with parallel factor analysis (EEM‐PARAFAC).iOff‐flavours include a variety of compounds, such as geosmin, 2‐methylisoborneol and other terpenes, aldehydes, fatty acids and so on.jDynamic HS Dynamic Headspace sampling of volatiles in Tenax TA traps.Trace metals such as zinc and copper can be led in the system via feed or by erosion of materials, or via inlet water (Kristensen et al., 2009; Bergheim & Fivelstad, 2014). Heavy metals are toxic to fish, as they can interact with osmoregulation and the accumulation of other toxic compounds such as ammonia, or remain in fish flesh, posing a risk to fish consumers (da Silva et al., 2018). This highlights the importance of monitoring trace metals and elements in RAS.Compound can be analysed with techniques based on liquid (LC) or gas chromatography (GC) based on its properties. Additionally, for the detection of low concentrations of analytes in complex RAS water matrix, a variety of pretreatment methods is required, including solid phase extraction (Gorito et al., 2022; Höglund et al., 2022), solid phase micro extraction (Lindholm‐Lehto, 2022) and dynamic headspace (HS) (Podduturi et al., 2017), to name a few (Table 3). In the case of volatile and semi‐volatile compounds, GC‐based methods are preferred (Lindholm‐Lehto et al., 2019; Lindholm‐Lehto, 2022), while LC with tandem mass spectrometry (MS/MS) detection is more suitable for antibiotics (Freitas et al., 2014).EFFECTS OF WATER QUALITYWater quality and fish behaviourThe stress response of fish is related to water quality (Iryna et al., 2013; Hu et al., 2020). Stress in fish can lead to inhibited growth, a weaker immune system and changes in fish behaviour (Hjeltnes et al., 2022). These changes can be crucial, especially as optimal growth is highly desirable (da Silva et al., 2018). For example, stressed zebrafish (Danio rerio) showed increased body temperature (2–4°C) which can be considered as a fever response (Rey et al., 2015). Rey et al. (2015) suggested that the observed emotional fever is associated with consciousness in fish. The zebrafish clearly showed the capacity for emotional fever but the link between consciousness is still debated. On the other hand, water properties (temperature) can affect fish growth and genetic sex determination based on temperature (Santi et al., 2017). High temperatures (36°C) led to masculinisation of African catfish (Clarias gariepinus) when exposed 6–8 days after hatching. The exposure during the short thermosensitive period led to 90–100% males (Santi et al., 2017).Certain pollutants can change the movement behaviour of fish, and by measuring the intensity of this behaviour, pollutant concentration can be calculated (Zhang et al., 2013). For a pollutant, models can show if the water quality is polluted or unpolluted but not determine a specific concentration (Peng, 2017). So far, more advanced interactions remain subjects for further studies.Water quality has been evaluated by studying the average movement speed, height, distant and position distribution of fish (Bernatowicz et al., 2009). Fen (2014) on the other hand, used group behaviour parameters: swimming speed and distance to nearest neighbor, to quantify normal and abnormal water quality. However, all these methods are applicable only in certain conditions, and only a few studies report interactions between multiple water quality parameter and behavioural characteristics of fish.Based on fish behaviour, it is possible to obtain and predict changes in water quality. The detection of fish behaviour can be based on video imaging, and includes characteristics’ shape, texture, background, colour, swimming speed and direction, fin beating frequency, feeding condition and metabolic rate (Yang et al., 2013; Cook et al., 2014). However, video imaging may be unsuitable for aquaculture environment, for example, in the case of high turbidity. Fish can be separated from their backgrounds, based on background modelling, temporal difference method and optical flow method (Hu et al., 2020). For example, Zhang et al. (2015) used a computer vision algorithm to measure pectoral fin and background subtraction to obtain fish location more accurately.Changes in swimming activity, swimming depth and acceleration can reflect the response of fish to its surroundings (Martins et al., 2012; Kolarevic et al., 2021). Although water quality parameters are frequently monitored in RAS, fish behaviour is not typically as an integral part of the systems (Kolarevic et al., 2016, 2021), and the conditions in RAS on welfare and performance of the raised species need to be improved.A variety of water quality parameters (DO, CO2, nitrate, pH) and feeding regimes can affect the swimming behaviour of fish (Davidson et al., 2011a; Martins et al., 2012). Techniques applying biotelemetry have been used to monitor the behaviour of individual farmed and wild fish (Kolarevic et al., 2016, 2021; Sinisalo, 2022). With tags, algorithms of acceleration and gravity forces can be measured accurately and transmitted to acoustic receivers (AccelTag; Thelma Biotel, Norway) (Martins et al., 2012). This technology has been used to monitor swimming activity in sea cages (Føre et al., 2011).An acoustic acceleration tag, AccelTag, allows real‐time monitoring of fish behaviour (Kolarevic et al., 2016). However, they are limited by the available tag sizes (7 and 11 g). The tag size should be about 2%/body mass (Jepsen et al., 2004). By tagging Atlantic salmon post smolts in RAS, swimming behaviour of fish can be monitored and observe changes in water quality and stress events (Kolarevic et al., 2016). Acoustic acceleration tags can therefore be a suitable tool for welfare and management practice studies and for responses to changes in water quality and chronic thresholds of water quality parameters.Fish movement in good water quality differs from those in abnormal water quality. Cheng et al. (2019) monitored fish movements by two cameras combined a three‐dimensional motion trajectory system with a Kuhn‐Munkres algorithm. They showed that the learning model successfully reflected the water quality. However, they could not assess water quality in time and were not able to show which water quality parameter caused the abnormal behaviour.WSNs have also been applied for monitoring of fish behaviour and water quality. For example, Parra et al. (2018) used a WSN to monitor feeding of fish and water quality in rearing tanks. Sensors were used to monitor temperature, turbidity, water level and conductivity, while monitoring of fish behaviour included swimming depth and velocity. Based on the monitored variables and fish behaviour, system was able to control the changes in water quality.FeedingFish appetite can be affected by water quality, especially temperature and DO (Sun et al., 2016). Several issues affect feed intake, which can be due to physiological, nutritional or environmental issues (Zhou et al., 2018). Frequency and time of feeding affects feeding intensity, and fish behaviour can vary based on how feed is sprinkled in the tank (Martins et al., 2012).Excessive feeding increases costs and faeces and uneaten feed lead to increased solid waste in the system and affect the water quality and microbial abundance in the system (Rojas‐Tirado et al., 2018; Zhou et al., 2018). Currently, automated feeding systems are widely used in aquaculture. However, predicting when feeding should be stopped is often based on subjective experience (Zhou et al., 2018).Feeding can be automatically adjusted based on fish appetite (Zhou et al., 2017a). There are feeding systems that can calculate feed demand real‐time based on the quantity of fish. Fish behaviour is monitored by machine vision, underwater acoustic technology and water quality sensors. For example, acoustic sensors can detect feed (Juell et al., 1993) and count the pellets by computer vision (Li et al., 2017) to assess the fish appetite. Computer vision has been widely used because it is relatively inexpensive and does not harm the fish (Sadoul et al., 2014; Lin et al., 2018).An intelligent feedback control can observe fish behaviour and based on specific control algorithm, stop feeding in the case of aggregation behaviour (Chang et al., 2005). However, computer vision often requires good illumination conditions which can be challenging in turbid water. A near infrared machine vision is more suitable, and with image enhancement algorithms good results can be achieved, also in poorly illuminated conditions (Zhou et al., 2017b). Zhou et al. (2018) reported an automatic feeding control method based on near infrared computer vision and a neuro‐fuzzy model for on‐demand feeding. They found significant increase in fish growth and the FCR was reduced by 10%, and water quality was improved (Zhou et al., 2018).CONCLUSIONSIn aquaculture, several water quality parameters need to be monitored to understand the aquatic conditions. There are currently no regulations for which parameters to measure, and each farmer has their own view of and possibilities for water quality measurements. So far, there are no guidelines for acceptable ranges and fluctuations. These have been listed in this study. Although suitable ranges vary between different species, certain guidelines would be a valuable tool for designing and running a RAS facility.Traditionally, water quality parameters have been measured at certain intervals with handheld sensors and quick laboratory tests, but this can time‐consuming and possible only during working hours. In recent years, the development of information technology and low‐cost sensors has enabled monitoring via WSNs, online monitoring systems and applications of IoT which allow real‐time monitoring, automatic data collection and storing.Intelligent systems may include the prediction of problems beforehand based on changes in water quality. Early warning signs are valuable for farmers to detect abnormalities and act before major issues occur. However, many of the modern sensors and monitoring systems need competent users and regular maintenance and calibration. The effects of water quality can also be observed by monitoring fish behaviour such as swimming activity, depth, acceleration and feeding. Fish behaviour can be monitored by, for example, acoustic acceleration tags and computer vision. This can be a valuable addition to the monitoring regime with the more traditional parameters.All the new IoT‐based monitoring systems can monitor the basic water quality parameters (DO, pH, temperature, turbidity and salinity), which can be sufficient for regular monitoring purposes. However, the more advanced analyses (e.g., off‐flavours, cortisol levels) still need to be performed in a laboratory and cannot be performed in real‐time. These may also require more advanced analysis equipment and can only be performed by trained personnel. Development of new techniques which allows real‐time monitoring of the more advanced water quality parameters remains as a subject of future studies.AUTHOR CONTRIBUTIONPetra Lindholm‐Lehto: Formal analysis, investigation, writing – original draft, resourcesACKNOWLEDGEMENTSFinancial support from the NordForsk: Sustainable Aquaculture – Research and Innovation Projects is gratefully acknowledged.CONFLICT OF INTEREST STATEMENTThe author of this research project would like to disclose the absence any personal or financial relationship with people or organisations that may inappropriately influence this work. This naturally includes all grant and sources of funding described in the paper.DATA AVAILABILITY STATEMENTNot applicable.ETHICS STATEMENTThis material is the authors’ own original work, which has not been previously published elsewhere.REFERENCESAbbaszadeh, A., Keyvanshokooh, S., Yavari, V. & Naderi, M. 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Journal

Aquaculture Fish and Fisheries – Wiley

Published: Apr 1, 2023

Keywords: aquaculture; fish behaviour; Internet of Things (IoT); monitoring equipment; recirculating aquaculture systems (RAS); water quality

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