Prediction of ammonia contaminants in the aquaculture ponds using SCC with wavelet analysis

What is it about?

Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh. The average water quality index (WQI) was 126, with WQI values ranging from 21 to 456. Approximately 78% of the water samples were very poor and unsafe for drinking and domestic usage. The mean ammonia content in aquaculture water was 0.15 mg/L, and 78% of the samples were above the acceptable limit set by the World Health Organization (WHO) of 0.5 mg/L. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceed the permissible limits and are a significant concern in aquaculture waters due to toxicity. This paper also presents an intelligent soft computing approach to predicting ammonia levels in aquaculture ponds, using two novel approaches, such as the pelican optimization algorithm (POA) and POA coupled with discrete wavelet analysis (DWT-POA). The modified and enhanced POA with DWT can converge to higher performance when compared to standard POA, with an average percentage error of 1.964 and a coefficient of determination (R2) value of 0.822. Moreover, it was found that prediction models were reliable with good accuracy and simple to execute. Furthermore, these prediction models could help stakeholders and policymakers to make a real-time prediction of ammonia levels in intensive farming inland aquaculture ponds.

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Why is it important?

Inland aquaculture is one of the significant activities in the coastal region of Andhra Pradesh (Belton et al., 2017; Jayanthi et al., 2019). In recent times, due to the demand for aquaculture-based food, economic growth of the state, and employment for local people, aquaculture practices were rapidly expanded, and farming intensity also increased (Kolli et al., 2022). Current aquaculture practices in the western delta region are more concerned due to the negative impact on the environment (Latha et al., 2022; Nagaraju et al., 2022). Further, without the stricter regime of monitoring leads to a hazardous environment. For instance, many researchers have shown significant studies in aquaculture ponds such as variations in the dissolved oxygen levels, salinity, temperature, pH, total dissolved solids, nitrates, ammonia, calcium, potassium, etc., which have effect on shrimp or fish (Ariadi et al., 2019; Aksoy et al., 2022). The most noxious and concern of the above parameters is total ammonia, because abnormal levels of ammonia become toxic, thereby causing stress to aquatic species (Liu et al., 2020; Duan et al., 2021). Aquaculture effluents total ammonia bloom at the bottom of the ponds for many months, leading to highly toxic substances (Ahmad et al., 2022). It is essential for quality monitoring band assessment of healthy aquaculture ponds (Zhou and Boyd, 2015). Higher ammonia levels in the aquaculture pond lead to phenoloxidase hemolymph antimicrobial activity, reduced dissolved oxygen, and growth disease in shrimps (Liu et al., 2020; Zhao et al., 2020). Furthermore, it causes a decrease in the shrimp survival rate, economic losses to aquaculture framers, and water pollution (Dauda et al., 2019; Chatla et al., 2020). In general, ammonia varies with the pH level; when the pH value exceeds 9.5, ammonia form changes from NH4+ to NH3. Ammonia level depends on many factors such as pH, temperature, dissolved oxygen, total dissolved solids, and algal growth (Kim et al., 2006; Collos and Harrison, 2014). In aquaculture ponds, ammonia is usually generated due to many factors such as organic matter, uneaten feed, algae bloom, shrimp feces, decay of aquatic animals, and exogenous substances with nitrogen (Hu et al., 2012; John et al., 2020; Yu et al., 2021). In municipal solid waste landfills, ammonia toxicity is the most concern (Akindele and Sartaj, 2018). Ammonia is an inorganic pollutant that accumulates at the bottom of the aquaculture pond and landfills (Mook et al., 2012; Akindele and Sartaj, 2018). Furthermore, ammonia leachate may affect the groundwater bodies (Kjeldsen et al., 2002). To monitor or evaluate the aquaculture pond ammonia, it is a predominance water parameter, not only for assessing the survival rate of shrimps, but also to know the level of water pollution (Karri et al., 2018). So, prediction models are much needed to assess the water quality to make sustainable water management (Gyawali et al., 2013). Nowadays, artificial intelligence is gaining potential in solving complex problems. For example, in measuring ammonia, many procedures such as spectrophotometer, electrochemical sensors method, sodium hypobromite method, and Nessler's reagent method are in vogue (Zhou and Boyd, 2016; Wang et al., 2018). However, due to the long detection time, toxic chemicals usage in the test procedure and weak scattering of the traces with interference signals, detecting ammonia content is complex (Yu et al., 2021).

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