The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

Water quality analysis is essential to understand the ecological status of aquatic life. Conventional water quality index (WQI) assessment methods are limited to features such as water acidic or basicity (pH), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH 3 -N), and suspended solids (SS). These features are often insufficient to represent the water quality of a heavy metal-polluted river. Therefore, this paper aims to explore and analyze novel input features in order to formulate an improved WQI. In this work, prospective insights on the feasibility of alternative water quality input variables as new discriminant features are discussed. The new discriminant features are a step toward formulating adaptive water quality parameters according to the land use activities surrounding the river. The results and analysis obtained from this study have proven the possibility of predicting WQI using new input features. This work analyzes 17 new input features, namely conductivity (COND), salinity (SAL), turbidity (TUR), dissolved solids (DS), nitrate (NO 3 ), chloride (Cl), phosphate (PO 4 ), arsenic (As), chromium (Cr), zinc (Zn), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), E. coli, and total coliform, in predicting WQI using machine learning techniques. Five regression algorithms-random forest (RF), AdaBoost, support vector regression (SVR), decision tree regression (DTR), and multilayer perception (MLP)-are applied for preliminary model selection. The results show that the RF algorithm exhibits better prediction performance, with R 2 of 0.974. Then, this work proposes a modified RF by incorporating the synthetic minority oversampling technique (SMOTE) into the conventional RF method. The proposed modified RF method is shown to achieve 77.68%, 74%, 69%, and 71% accuracy, precision, recall, and F1-score, respectively. In addition, the sensitivity analysis is included to highlight the importance of the turbidity variable in WQI prediction. The results of sensitivity analysis highlight the importance of certain water quality variables that are not present in the conventional WQI formulation.

Although the use of phthalates has been restricted worldwide, they remain an issue due to health concerns. Diet is one of the most important exposure pathways for humans and due to their solubility in oil, phthalates are commonly found in edible oil and food high in fat. Gas chromatography–mass spectrometry (GC-MS) using electron ionization (EI) has been commonly used for the analysis of the phthalates in foodstuffs, including edible oil. However, this method suffers from issues with sensitivity and selectivity, as most phthalates are fragmented to generate a common phthalic anhydride fragment ion at m/z 149. The molecular ion cannot be observed due to strong fragmentation in EI. In contrast, atmospheric pressure gas chromatography (APGC) is a soft ionization technique with less fragmentation, whereby the molecular ion can be used as the precursor ion for multiple reaction monitoring (MRM). In this study, a simple and quick method for the determination of phthalates in vegetable oil using APGC-MS/MS was developed, and performance was assessed. The method was based on dilution of the oil in solvent and direct injection without the need for further cleanup. The established method was evaluated for linearity, recovery, precision, method detection limit (MDL), and method quantitation limit (MQL). The obtained MQL in vegetable oil was in the range of 0.015–0.058 mg/kg, despite limiting the injection volume to 1 µL, which is suitable for investigating dietary exposure and future proof against decreases to the regulatory limit. Finally, the developed method was successfully applied to analyze nine phthalates in eight commercially available vegetable oil.

No abstract

No abstract