Occurrence of microplastics (MPs) in the gastrointestinal tract of fishes: A global systematic review and meta-analysis and meta-regression.

The presence of Microplastics (MPs) in food has become a global health concern in the last two decades. In this study, an attempt was made to obtain articles about the occurrence of MPs in the gastrointestinal tract (gt) of fishes using searching the Scopus and PubMed databases from 1 January 1990 to 10 August 2021. The occurrence of MPs was meta-analyzed using the random effect model (REM). The results indicate that pooled occurrence of MPs in gastrointestinal of fishes was 2.76 P/gt: 95%CI:2.65-2.86 P/gt. Occurrence MPs in gastrointestinal of fishes in closed water sources (5.86 P/gt) was higher than free water sources (2.46 P/gt). In addition, the rank order of water sources based on occurrence MPs in gastrointestinal of fish was Lake (5.50 P/gt) > Estuary (5.46 P/gt) > River (2.91 P/gt) > Bay (2.85 P/gt) > Sea (2.58 P/gt) > Ocean (1.29 P/gt). The lowest and highest occurrence MPs in gastrointestinal of fishes were observed in high-income economies (1.45 P/gt) and low-income economies (8.08 P/gt), respectively. The higher frequency of color in MPS was related to blue with polyethylene-type polymers. Therefore, control plans to reduce the occurrence of MPs in fishes is recommended.

Application of soft computing to predict water quality in wetland.

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.