Assessment of Environmental Impact of Un-Engineered Aquaculture Ponds In the Delta Region of Andhra Pradesh

Abstract

Aquaculture is persistent and well-established in the delta regions of Andhra Pradesh. Since 2014, the expansion of aquacultures confers positive economic growth in the newly formed state. However, the enormous development of aqua ponds increases the effluents from aquacultures and could impact the ecosystem negatively. This research work presents the effects of un-engineered aquaculture on the environment in the western delta region of Andhra Pradesh. A quantitative and topography survey, experimental investigation on aquaculture water and pond bottom soil, contaminant exposed soils behaviour, and assessment of ammonia levels using soft computing techniques were carried out. Based on the questionnaire survey data, the aquaculture practice in the delta region of Andhra Pradesh was classified as intensive, semi-intensive and traditional zones. Land use and land cover changes shows that aquaculture ponds extended towards the northeast from the southwest. Between 2017 and 2021, aquaculture significantly increased by 54.35 km2 and agriculture land decreased by 87.06 km2. The physicochemical characteristics of the aquaculture water found higher levels of alkalinity, salinity, calcium, magnesium, and bicarbonates. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceeded the permissible limits; and are a significant concern in aquaculture waters due to toxicity. 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 the second crop. The physicochemical characteristics of the pond subsoil shows higher concentration of potassium, Sulphur, and sodium in the intensive zone than the traditional farming zone. The results of the swell-shrink behaviour of expansive clays blended with aquaculture sludge show a significant decrease in swell potential, swelling pressure, and linear shrinkage. Furthermore, the microstructural analysis revealed the formation of a crystalline structure and the development of flocs and aggregation of clay particles with aquaculture sludge. ii Pelican optimization algorithm (POA) and novel hybrid approach discrete wavelet transforms coupled with POA (DWT-POA) were used to predict ammonia levels in aquaculture ponds. DWT-POA model shows a higher performance compared with 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.