Pesticide treatment dramatically reduces crop loss and enhances agricultural productivity, promoting global food security and economic growth. However, owing to high accrual and persistent tendency, pesticides could create significant ecological consequences when used often. Lately, the perspective has transitioned to implementing biological material, environmentally sustainable, and economical strategies via bioremediation approaches to eradicate pesticides contaminations. Microalgae were regarded as a prominent option for the detoxification of such hazardous contaminants. Sustainable application and remediation strategies of pesticides pollutants in the agriculture system by microalgae from the past studies, and recent advancements were integrated into this review. Bibliometric strategies to enhance the research advancements in pesticide bioremediation by microalgae between 2010 and 2020 were implemented through critical comparative analysis of documents from Scopus and PubMed databases. As a result, this study identified a growing annual research trend from 1994 to 2020 (nScopus > nPubMed). Global production of pesticide remediation by microalgae demonstrated significant contributions from India (23.8%) and China (16.7%). The author’s keyword clustering was visualized using bibliometric software (VOSviewer), which revealed the strongest network formed by “microalgae”, “bioremediation”, “biodegradation”, “cyanobacteria”, “wastewater”, and “pesticide” as significant to the research topic. Hence, this bibliometric review will facilitate the future roadmap for many scholars and authors who were drawing attention to the burgeoning research on bioremediation of pesticides to counteract environmental impacts while maintaining food sustainability.

Rice straw, an agricultural waste product generated in huge quantities worldwide, is utilized to remediate diesel pollution as it possesses excellent characteristics as a natural sorbent. This study aimed to optimize factors that significantly influence the sorption capacity and the efficiency of oil absorption from diesel-polluted seawater by rice straw (RS). Spectroscopic analysis by attenuated total reflectance infrared (ATR-IR) spectroscopy and surface morphology characterization by variable pressure scanning electron microscopy (VPSEM) and energy-dispersive X-ray microanalysis (EDX) were carried out in order to understand the sorbent capability. Optimization of the factors of temperature pre-treatment of RS (90, 100, 110, 120, 130 or 140 °C), time of heating (10, 20, 30, 40, 50, 60 or 70 min), packing density (0.08, 0.10, 0.12, 0.14 or 0.16 g cm⁻³) and oil concentration (5, 10, 15, 20 or 25% (v/v)) was carried out using the conventional one-factor-at-a-time (OFAT) approach. To eliminate any non-significant factors, a Plackett–Burman design (PBD) in the response surface methodology (RSM) was used. A central composite design (CCD) was used to identify the presence of significant interactions between factors. The quadratic model produced provided a very good fit to the data (R² = 0.9652). The optimized conditions generated from the CCD were 120 °C, 10 min, 0.148 g cm⁻³ and 25% (v/v), and these conditions enhanced oil sorption capacity from 19.6 (OFAT) to 26 mL of diesel oil, a finding verified experimentally. This study provides an improved understanding of the use of a natural sorbent as an approach to remediate diesel pollution.

Oil pollution such as diesel poses a significant threat to the environment. Due to this, there is increasing interest in using natural materials mainly from agricultural waste as organic oil spill sorbents. Oil palm’s empty fruit bunch (EFB), a cost-effective material, non-toxic, renewable resource, and abundantly available in Malaysia, contains cellulosic materials that have been proven to show a good result in pollution treatment. This study evaluated the optimum screening part of EFB that efficiently absorbs oil and the physicochemical characterisation of untreated and treated EFB fibre using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). The treatment conditions were optimised using one-factor-at-a-time (OFAT), which identified optimal treatment conditions of 170 °C, 20 min, 0.1 g/cm³, and 10% diesel, resulting in 23 mL of oil absorbed. The predicted model was highly significant in statistical Response Surface Methodology (RSM) and confirmed that all the parameters (temperature, time, packing density, and diesel concentration) significantly influenced the oil absorbed. The predicted values in RSM were 175 °C, 22.5 min, 0.095 g/cm³, and 10%, which resulted in 24 mL of oil absorbed. Using the experimental values generated by RSM, 175 °C, 22.5 min, 0.095 g/cm³, and 10%, the highest oil absorption achieved was 24.33 mL. This study provides further evidence, as the data suggested that RSM provided a better approach to obtain a high efficiency of oil absorbed.

Owing to its excellent qualities as a natural sorbent, rice husk (RH), a significant agricultural waste product obtained from the milling process, is employed as a biosorbent for engine oil. Engine oil spillages in rivers will flow to the ocean, exposing marine life to deadly contaminants. To date, there are very few natural sorbent studies specifically targeting engine oil removal. The purpose of this study was to optimise the significant factors in the efficiency of engine oil sorption by RH. Spectroscopic analyses using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed to characterise the chemical composition and surface morphology of RH sorbent after pre- and post-treatment. A conventional optimisation approach, one-factor-at-a-time (OFAT), was used to assess the range of factors affecting the efficiency of engine oil sorption through heat treatment, heating time, packing density, and concentration of engine oil. The efficiency of engine oil removal obtained from this method was 74.5%. All the factors were assessed using a Plackett–Burman design (PBD) to eliminate non-significant factors. Furthermore, a central composite design (CCD) was employed to explore significant interactions among the factors. The quadratic model generated (R² = 0.9723) fitted the data well. The optimised conditions from the CCD were 160 °C, 20 min, 0.16 g/cm³, and 12.5% (v/v), with improved oil sorption from 74.5% (OFAT) to 78.89% (RSM).