Geopolymers were obtained from ashes through an alternative geopolymerization process and applied to remove Ni²⁺, Cu²⁺, Co²⁺, and Ag⁺ from synthetic aqueous media and real effluents. The study in synthetic solutions revealed that pseudo-second-order and general order models were the best to fit the kinetic curves. To represent the equilibrium curves, Langmuir and Freundlich were the most adequate. The geopolymer derived from bottom ash (GHA) was superior to adsorb Cu⁺², Co⁺², and Ag⁺¹ than the geopolymer derived from fly ash (GFA). GHA reached adsorption capacities of 279.5, 288.2, and 462.8 mg g⁻¹ for Co⁺², Cu⁺², and Ag⁺¹, respectively. Otherwise, GFA was the best for Ni⁺² removal, with an efficiency of 95% in low concentrations. In treating real effluents of the E-coat printing process, both GHA and GFA were efficient, with the removal of higher than 85% for all the metals. In brief, it can be stated that GFA and GHA prepared are promising materials to remove metals from aqueous media (synthetic and real), presenting fast adsorption kinetics, high adsorption capacity, and high metal removal percentage.

As the eutrophication of natural water bodies becomes more and more serious, the frequency of outbreaks of harmful algal blooms (HABs) mainly formed by harmful algae also increases. HABs have become a global ecological problem that poses a serious threat to human health and food safety. Therefore, it is extremely important to establish methods that can rapidly detect harmful algal species for early warning of HABs. The traditional morphology-based identification method is inefficient and inaccurate. In recent years, the rapid development of molecular biology techniques has provided new ideas for the detection of harmful algae and has become a research hotspot. The current molecular detection methods for harmful algal species mainly include fluorescence in situ hybridization, sandwich hybridization, and quantitative PCR (qPCR), but all of these methods can only detect single harmful algal species at a time. The establishment of methods for the simultaneous detection of multiple harmful algal species has become a new trend in the development of molecular detection technology because various harmful algal species may coexist in the natural water environment. The established molecular techniques for multiple detections of harmful algae mainly include gene chip, multiplex PCR, multiplex qPCR, massively parallel sequencing, antibody chip, and multiple isothermal amplification. This review mainly focuses on the principles, advantages and disadvantages, application progress, and application prospects of these multiple detection technologies, aiming at providing effective references not only for the fisheries but also for economic activities, environment, and human health.

The adsorption method is the most widely used technique for the reduction of different types of pollutants from an aqueous solution. Nowadays, in comparison to the commercial adsorbent, researchers are currently discovering economically cheap, require less processing, and are abundantly available in nature. The present study aimed to determine the reduction percentage of COD and ammoniacal nitrogen (NH₃-N) by using coconut shell activated carbon (CSAC) and peat soil (PS) as an economically composite adsorbent as well as partly reducing the amount of CSAC. The method of determining the optimum condition of the composite was conducted by using static batch techniques. The optimum reduction conditions were achieved at 200-rpm rotational speed, 120-min rotational time, and pH 7. The optimum dosage ratio condition of CSAC:PS was achieved at 2.0:2.0 for the reduction percentage of COD and NH₃-N. The best reduction percentage of COD and NH₃-N were 79% and 70% respectively. The coefficient of determination values indicates that the Langmuir model for COD and NH₃-N was in accordance with the experimental data for the elimination of contaminants investigated with 0.9982 and 0.9885, respectively. The coefficient of determination values indicates that pseudo-second order better fitted the equilibrium data over the entire concentration range studied compared to the pseudo-first-order kinetic model. The coefficient of determination for pseudo-second order was generally in the range of 0.9965 for COD and 0.9260 for NH₃-N, respectively. This shows that the utilization of PS as a low-cost adsorbent as well as reduces the use of commercial adsorbent.

The coral health of Pulau Anak Datai (PAD), located off the northwest of Langkawi, Malaysia, was assessed using the Coral Health Index (CHI) method. Three ecological parameters, namely, benthic cover, fish biomass, and microbes (Vibrio) were determined at four sites around the island in 2019. In addition, community parameters such as coral mortality index, coral richness, relative abundance, diversity index, Evenness tests, and reef morphology were measured for each site. The results revealed that the benthic cover consists of less than 40% of scleractinian corals at all sites. A total of 25 genera of hard corals comprising of 11 families and 1 scleractinian Incertae sedis were observed, with the most dominant corals belong to the genera Porites, Favites, and Diploastrea. The average fish biomass of PAD was low (16.76 g/m²), with only 19 non-cryptic fish species observed. The abundance of Vibrio around the island was within the average range of 29.58 cfu/ml. Based on the benthos, fish, and Vibrio values, the Coral Health Index (CHI) of PAD was classified on the low side of the fair status. All sites tended toward high values of the mortality index (MI > 0.33). Reef morphology was strongly influenced by stress-tolerant corals, dominated by massive and sub-massive corals. The data presented here suggested that the reefs of PAD could be rated as stressed and becoming unhealthy and disturbed. However, in view of the rarity of coral reef ecosystems in the Straits of Malacca, this island deserves increased attention for conservation planning and coral reef protection.

The treatment of coal gangue solid waste and microbially induced calcium carbonate precipitation (MICP) consolidate technology is a focus of research at home and abroad. MICP technology was used to solidify and cement coal gangue particles and endows them with a certain strength. The process does not use the traditional cementitious material (Portland cement) and is eco-friendly and pollution-free. The mechanical properties including unconfined compressive strength, CaCO₃ content, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and infrared spectroscopy (FTIR) were used for macroscopic and microscopic analyses. The results show that the average strength of CG-based bio-mineralized backfill materials reaches 1.55 MPa and the maximum strength is 2.17 MPa, suggesting the potential for its use as an underground fill. Compared with unmineralized gangue, the CaCO₃ crystal content in CG-based mineralized material is increased by 8.75% on average, and the maximum content is 13.34%. In the process of bacterial liquid perfusion, there is uneven distribution of bacteria in the material, which results in fewer CaCO₃ crystals being locally generated in the mineralized material and affects the overall compressive strength of gangue columns. Moreover, the greater the amount of calcium carbonate, the larger the strength of the mineralized material. SEM analysis results indicate that the gaps between gangue particles are filled with CaCO₃ crystals, and the calcium carbonate crystals are mostly polyhedral, showing stacked growth and contain a small number of spherical crystals that exist alone. The results of FTIR and EDS analysis show that the CaCO₃ crystals in the mineralized material are mainly in the form of calcite, followed by a small amount of vaterite. Comprehensive analysis demonstrates that the preparation of CG-based bio-mineralized backfilling materials is successful, and this experiment provides new ideas and methods for the treatment of solid waste such as coal gangue and building material waste.

Identifying and improving the existing ecological security patterns (ESPs) are of great importance to promoting ecological security and achieving sustainable development goals. The Three Gorges Reservoir Region (TGRR) is an area with a sensitive, fragile, and complex ecological environment in the Upper Reaches of the Yangtze River. The construction of ESPs for the TGRR is significant for maintaining regional ecosystem stability and promoting peaceful coexistence between humans and nature. The main objective of the study is to identify the ecological nodes, ecological corridors, and ecological sources that play essential roles in the ecosystem. Based on land use data and human interference factors, we have evaluated the current habitat quality using the InVEST model and identified vital ecological sources for the TGRR. The negative exponential transformation function was used to convert habitat suitability into a landscape resistance layer. Circuit theory modeling was utilized to identify ecological corridors, and the final ESPs of the TGRR were then constructed. Results showed that (a) the spatial distribution of habitat varied significantly in the TGRR. The optimal habitats were concentrated in the northeast, east, and southwest, accounting for 45.98% of the total suitable habitats; (b) habitat quality varied through space, with habitat quality being higher in the northeast and lower in the western regions. (c) Ecological sources were distributed primarily in the forests with high vegetation coverage in the east. The total area of ecological sources was about 15,412 km², approximately accounting for 34% of the study area; (d) the ESPs were dominated by ecological sources composed of forests, which were radially connected by ecological corridors. In total, these included 14 significant ecological sources, 25 clusters of ecological corridors, and 23 ecological nodes. The results are of great significance to promote the ecological security of the TGRR and could provide theoretical support for biodiversity conservation and territorial space planning for the Three Gorges Region.

Studies have shown intriguing associations between gestational PM₂.₅ exposure and preeclampsia (PE), as well as fetal growth restriction (FGR). This study investigated the impact of PM₂.₅ exposure on gestational hypertension and fetal outcome in a preeclampsia-like rat model. Pregnant Sprague Dawley rats were exposed to either filtered (FA) or PM₂.₅-contaminated air during the whole pregnancy period. A PE-like rat model was established by intraperitoneal injection of L-NAME (300 mg/kg) from gestational day (GD) 12 to until GD20. Systolic blood pressure (SBP), weight gain, pup weight and placental weight were measured. The percentages of rat Treg/Th17 cells and Th17-related cytokines were examined by flow cytometry. Gene expression profiles were analyzed by microarray, and the expression of differentially expressed genes was validated by qRT-PCR. The results showed that maternal PM₂.₅ exposure had no effect on SBP but was associated with low birth weight (LBW) and a higher labyrinth/basal zone ratio. The percentages of splenic Th17 cells from the PM₂.₅ group of PE-like rats were higher than those from the FA or PM₂.₅ groups of healthy controls. A significantly decreased Treg/Th17 cell ratio was found in the PM₂.₅ group of PE-like rats. The mRNA expression of Foxp3 was downregulated, while the mRNA expression of RORα and RORγτ was upregulated after PM₂.₅ exposure. Furthermore, we observed that both the mRNA and protein levels of TNF-a, CCL2, CCL3 and CCR1 increased in the PM₂.₅ groups. Our study suggested that systemic inflammation may contribute to the development of FGR associated with PM₂.₅ exposure throughout pregnancy.

Tropical peatlands have high potential function as a major source of atmospheric methane (CH₄) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification–decomposition (DNDC) model was used to simulate CH₄ fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH₄ emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R²) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R² at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at − 0.11, 3.38, 3.05 and 11.45 for 2015 and − 1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH₄ fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E = 6.79% and difference = 3.3%). Principal component analysis (PCA) showed that CH₄, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH₄ fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC model for predicting CH₄ fluxes from the pristine tropical peatland in Sarawak on a monthly basis.

Petroleum sludge is a solid emulsified waste and contaminant commonly produced in the petroleum industry. In the recent past, there has been increased business growth in the oil sector, resulting in increased volumes of oily sludge characterized by high viscosity and toxicity. Therefore, sludge treatment before discarding is extremely necessary. This review seeks to highlight various conventional and evolving approaches in the treatment, recovery, and disposal of petroleum sludge and assess their suitability under various conditions.

Biochar derived from Lentinus edodes (LBC) and CuFe₂O₄ (CuFe₂O₄@LBC) composites were prepared by the hydrothermal method, and were applied to activate persulfate (PDS) for degrading tetracycline (TC) in a wide pH range. The CuFe₂O₄@LBC composites were characterized by XRD, FTIR, SEM, and XPS. LBC-derived biochars greatly reduced the aggregation of CuFe₂O₄ particles and enhanced the catalytic performance of CuFe₂O₄. CuFe₂O₄@LBC catalyst could remove 85% of tetracycline within 100 min under visible light. In addition, the removal rate of TC reached 76% after five cycles, indicating that the composite had good stability and reusability. Simple classical quenching experiments suggested that the degradation of TC could be mainly attributed to •OH and •S [Formula: see text].