Although liquefaction technology has been extensively applied, plenty of biomass remains tainted with heavy metals (HMs). A meta-analysis of literature published from 2010 to 2023 was conducted to investigate the effects of liquefaction conditions and biomass characteristics on the remaining ratio and chemical speciation of HMs in biochar, aiming to achieve harmless treatment of biomass contaminated with HMs. The results showed that a liquefaction time of 1–3 h led to the largest HMs remaining ratio in biochar, with the mean ranging from 84.09% to 92.76%, compared with liquefaction times of less than 1 h and more than 3 h. Organic and acidic solvents liquefied biochar exhibited the greatest and lowest HMs remaining ratio. The effect of liquefaction temperature on HMs remaining ratio was not significant. The C, H, O, volatile matter, and fixed carbon contents of biomass were negatively correlated with the HMs remaining ratio, and N, S, and ash were positively correlated. In addition, liquefaction significantly transformed the HMs in biochar from bioavailable fractions (F1 and F2) to stable fractions (F3) ( P < 0.05) when the temperature was increased to 280–330 ℃, with a liquefaction time of 1–3 h, and organic solvent as the liquefaction solvent. N and ash in biomass were positively correlated with the residue state (F4) of HMs in biochar and negatively correlated with F1 or F2, while H, O, fixed carbon, and volatile matter were negatively correlated with F4 but positively correlated with F3. Machine learning results showed that the contribution of biomass characteristics to HMs remaining ratio was higher than that of liquefaction factor. The most prominent contribution to the chemical speciation changes of HMs was the characteristics of HMs themselves, followed by ash content in biomass, liquefaction time, and C content. The findings of this meta-analysis contribute to factor selection, modification, and application of liquefied biomass to reducing risks.

Excess fluoride in drinking water causes both dental and skeletal fluorosis among other problems. As such there is need to develop affordable and easily accessible techniques for fluoride removal from drinking water. This work assessed surface modified diatomite mixed with brick for fluoride removal. Diatomite samples were modified using aluminium hydroxide and the mixture was optimized for fluoride removal through response surface methodology (RSM) using the Box-Wilson central composite design. Batch experiments showed that, individually, a 28 g/L dose of the surface modified diatomite sufficiently removed fluoride to the acceptable level of 1.5 mg/L from an initial concentration of 10 mg/L fluoride while a 300 g/L dose of brick powder was required to remove an equal amount of fluoride in the same water samples. RSM optimization showed that a mixture of surface modified diatomite and brick in the mass ratio 1.8:17.8 grams per milligram of fluoride in water can be used to remove fluoride in water to an acceptable level. Adsorption of fluoride by surface modified diatomite fit better into the Freundlich adsorption isotherm (R²=0.9753) compared to the Langmuir (R²=0.8954), while adsorption by brick better fit the Langmuir adsorption mechanism (R²=0.9804) in comparison to the Freundlich adsorption (R² =0.9372). Kinetic studies revealed that chemisorption was the main mechanism for both surface modified diatomite and brick adsorbents. Conclusively, an optimal mixture of surface modified diatomite and brick can be successfully used for fluoride removal in areas for which water has high fluoride contamination.

Ecuador is a developing country that relies on mining as a significant source of economic income every year; however, there needs to be more studies on the soil pollution caused by mining over time. Biological remediation as an alternative to the use of physical and chemical methods offers a more cost-effective and environmentally friendly means to counteract the negative impacts that the presence of heavy metals in mining tailings soils can cause. This study focused on soil sampling from the mining tailings of the San Carlos de las Minas sector, in the Zamora Chinchipe province in Ecuador, to find potential bacterial strains that can degrade two specific contaminants, mercury (Hg) and cyanide (CN^-). For this purpose, 68 soil subsamples were collected. pH, electrical conductivity, moisture, and the concentration of the contaminants were analyzed and measured. The initial concentration of CN^- was 0.14 mg/kg, and of Hg was 88.76 mg/kg. From the soil samples, eight bacterial strains were isolated, characterized at macroscopic and microscopic levels, and identified at the molecular level. The bacteria were then subjected to degradability tests for CN^- and Hg, obtaining interesting results. The degradation capacity of CN^- stood out for the strains Micrococcus aloeverae and Pseudomonas alcaliphila, and for the degradation of Hg, the strains Hydrogenophaga laconesensis and Micrococcus aloeverae were highlighted, achieving degradation percentages of up to 98.80%. These results emphasize the discovery of these bacterial species with potential use in cyanide and mercury remediation processes.

The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O₃ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O₃ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi- step-ahead manner.

The global concentration of fine particulate matter (PM_2.5) is experiencing an upward trend. This study investigates the utilization of space-time cubes to visualize and interpret PM_2.5 data in South Africa over multiple temporal intervals spanning from 1998 to 2022. The findings indicated that the mean PM_2.5 concentrations in Gauteng Province were the highest, with a value of 53 μg/m³ in 2010, whereas the lowest mean PM_2.5 concentrations were seen in the Western Cape Province, with a value of 6.59 μg/m³ in 1999. In 2010, there was a rise in the average concentration of PM_2.5 across all provinces. The increase might be attributed to South Africa being the host nation for the 2010 FIFA World Cup. In most provinces, there has been a general trend of decreasing PM_2.5 concentrations over the previous decade. Nevertheless, the issue of PM_2.5 remains a large reason for apprehension. The study also forecasts South Africa’s PM_2.5 levels until 2029 using simple curve fitting, exponential smoothing and forest-based models. Spatial analysis revealed that different areas require distinct models for accurate forecasts. The complexity of PM_2.5 trends underscores the necessity for varied models and evaluation tools.

Biogas is a cost-effective, efficient, environmentally friendly, and renewable alternative energy source. While biogas contains CH₄, it also contains impurities in the form of 27–45% CO₂ gas. Therefore, it is necessary to purify biogas by removing CO₂ gas as it can reduce the calorific value of CH₄, the main component of biogas. The process of methane purification can be achieved through adsorption. Bottom ash, derived from palm oil mill waste, shows great potential for methane purification by effectively adsorbing CO₂. This research simulated the methane purification process using the computational fluid dynamics (CFD) method with the student version of the ANSYS R20 software. The study utilized an adsorbent made from bottom ash obtained from palm oil mill waste. The main objective was to investigate the performance of bottom ash as an adsorbent for removing CO₂ gas in a continuous gas flow within an adsorption column. The study involved varying the column bed height (4 cm, 8 cm, 12 cm) and gas flow rate (10 L/min, 15 L/min, 20 L/min). The results showed that the highest efficiency in removing CO₂ gas was 84.53% with a bed height of 12 cm and a flow rate of 10 L/min, while the lowest efficiency was 47.87% with a bed height of 4 cm and a flow rate of 20 L/min. Furthermore, the highest adsorption capacity for CO₂ gas was 1.64 mg/g with a bed height of 12 cm and a flow rate of 10 L/min, while the lowest capacity was 0.93 mg/g with a bed height of 4 cm and a flow rate of 20 L/min. The linearization of adsorption isotherm data indicated that the CO₂ gas adsorption process using bottom ash adsorbent followed the Langmuir model.

Chromium (Cr(VI)) is a type of hazardous waste generated by the batik industry. In this study, the gamma irradiation technique was applied to precipitate chromium in simulated batik mordanting waste. Gamma irradiation induced the generation of active species, including hydrated electrons (e_aq^-) and hydrogen atoms (•H), which effectively reduced Cr(VI) ions to Cr(III) and caused precipitation as chromium hydroxide (Cr(OH)₃) upon interaction with hydroxide ions (OH^-). Optimal precipitation conditions were observed at 40 kGy irradiation dose, 4M of 2-propanol work as scavenger, and pH 9. The applied reaction conditions led to a decrease in the concentration of Cr(VI) in simulated waste from 11,673 ppm to 177 ppm. The Cr(OH)₃ was calcined to form chromium oxide (Cr₂O₃) and through a chemical process was synthesized back into K₂Cr₂O₇ with the total Cr recovery of 63.39%. SEM and FTIR analysis indicated that the recovery of Cr(VI) into Cr(OH)₃, Cr₂O₃, and K₂Cr₂O₇ can be considered successful.

The social cost of carbon (SCC) has emerged as one of the relevant measures in integrated assessment models in climate economics, to quantify costs related to global warming and climate change. While the SCC is used in different models, including DICE (Dynamic Integrated model of Climate and Economy), PAGE (Policy Analysis of the Greenhouse Effect), and FUND (Climate Framework for Uncertainty, Negotiation, and Distribution), its exact definition and computation depend on the reference and, frequently lacking consistency within research streams whether focusing on a single model or on different models. In this study, we investigated three different methods for the computation of the SCC using the integrated assessment model DICE. While the first two methods are commonly known and used, the novel formula derived for the third method allows a direct analysis of the impact of the discount factor in the calculation of the SCC. We provide a detailed proof for the correctness of the third method and validate the consistency of all three methods by numerical experiments.