Ferrihydrite often precipitates with humic acid in natural ways, affecting the fate of lead ions, the stabilization of humic acid, and the aging process of ferrihydrite. A series of 2-line ferrihydrite-humic (Fh-HA) acid with varying C loadings has been prepared, the morphology and surface properties of Fh-HA organo-minerals have been characterized, and the adsorption property of Pb ions onto Fh-HA has been studied. The results indicated that a strong interference of HA to ferrihydrite 2-line Fh dominated mineral phase in all samples, but with increasing C/Fe molar ratios, the crystallinity gradually weakened, particles became smaller, and SSA decreased significantly. The data of Mössbauer spectra confirmed C loadings changed the unit structure of ferrihydrite. Fh-HA performed good adsorption properties to Pb (II): high efficiency and big capacity, especially Fh-HA_2.0. pH had great effect on Pb (II) sorption, the pH change affects not only the amounts of competitive ions in solutions, but also the dissociation and protonation of functional groups on the surface of Fh-HA. Sorption kinetics can be well modeled by a pseudo-second-order model, and the process was controlled by film and intraparticle adsorption simultaneously. The adsorption isotherms can be well described by Freundlich isotherm model. The detailed determination results of Fe 2p, O 1 s, and Pb 4f spectra before and after lead adsorption showed mononuclear bidentate or binuclear bidentate ligands occurring on Fh-HA surface, forming stable inner-sphere complex. By comparison of Mössbauer spectra and TEM images, with aging time, a slower evolution of iron oxide/oxyhydroxide phases in Fh-HA-Pb system happened compared to pure ferrihydrite. Ferrihydrite has transformed to a combination of ferrihydrite, goethite, and hematite phases. In this study, the determination of C-Fe interaction, Pb fate influenced by Fh-HA, and transformation of ferrihydrite would have a great implication to application of Fh-HA precipitates in remediation for surface water or groundwater polluted by heavy metals.

Backfilling mining is a green mining method. The rational design of the compressed ratio of the backfilling body can reduce backfilling mining costs while avoiding mining-induced environmental damage. At present, the enumeration method is a common design method for the compressed ratio of the backfilling body of backfilling mining under buildings (structures). However, the design method has some problems, such as cumbersome processes and time consumption. Therefore, this paper proposes an intelligent optimization design method of the compressed ratio of backfilling body and constructs the design flow of the method. The method takes economic benefits as the optimization goal and takes the mining-induced subsidence of protected objects to meet their fortification requirements as the constraint condition. Within the feasible region of the compressed ratio, the intelligent optimization algorithm is used to search for the minimum compressed ratio that satisfies the constraints. This paper used the method to design the compressed ratio of the backfilling panel in the Dongtan Coal Mine. The research results show that the method can quickly design the optimal compressed ratio compared with the enumeration method and cut the production cost on the premise of the safety of ground buildings (structures).

The world is confronted with a slew of environmental issues, one of which is attenuating the detrimental impacts of carbon dioxide (CO₂) emission-induced climate change. The ever-increasing use of energy is eroding natural resources to the point that our economic future may be jeopardized. The Tunisian economic growth indicates the excellent performance in the industrial sector as the minimum required input for these developments which necessitate additional energy consumption, resulting in increased CO₂ emissions and environmental degradation. This study explores the role of energy efficiency, urbanization, economic growth, and natural gas energy usage in the industrial sector on the CO₂ emissions of Tunisia. The research mainly employs the vector autoregressive model (VAR) to examine the factors driving the evolution of CO₂ emissions through the industrial sector from 2000 to 2018. The findings assess that natural gas as an energy source and efficiency is crucial for reducing CO₂ emissions. The study has shown the existence of the environmental Kuznets curve (EKC), which demonstrates that economic development in Tunisia has an inverted U-shape connection with CO₂ emissions. The findings show that energy consumption and GDP have a considerable impact on CO2 emissions due to large-scale population changes and industrial structure alteration. In contrast, energy efficiency is a key factor in lowering CO2 emissions. Based on the study’s results, the article will enable economic decision-makers and relevant authorities to develop an appropriate energy strategy for the industrial sector to safeguard environmental deterioration in the long term by lowering carbon emissions.

Beaches with monazitic sands show high natural radiation, and the knowledge of this radiation is fundamental to simulate the effects of natural terrestrial radiation on biological systems. Monazite-rich sand from a beach in the southeastern Brazil were collected and analyzed by X-ray fluorescence, X-ray diffraction, and magnetic susceptibility. The natural terrestrial radiation of the beach sand showed a positive correlation with the Th and Y elements, which are closely associated with Ce, Nd, Ca, and P, suggesting that this grouping is mainly associated with local natural radiation. Based on the sand characterization, a physical simulator of natural gamma radiation was built with parameters similar to those of the monazite beach sand, considering areas with high natural radiation levels. The simulation revealed that the natural radiation of the monazite sands has a significant effect on reducing the growth of the bacteria strains of E. coli and S. aureus present in the beach sand, with a reduction of 23.8% and 18.4%, respectively.

This study aims to analyze the impact of ICT, renewable energy consumption, and financial development on CO₂ emissions in selected developing countries of East and South Asia. Using panel data spanning 1985–2020, Pooled Mean Group (PMG) estimator is used to analyze the short-run and long-run effects. Results suggest that ICT and financial development positively contribute to the degradation of the environment in the long run, while their impact on CO₂ emissions is insignificant in the short run. On the other hand, renewable energy consumption affects environmental quality positively in both the long run and short run. It is also examined that economic growth affects CO₂ emissions positively but the squared economic growth reduces CO₂ emissions which validates inverted U-shaped EKC hypothesis. The empirical findings of the Granger Causality test suggest unidirectional causality from ICT and financial development to CO₂ emissions, while a bi-directional relationship is found among renewable energy and CO₂ emissions. Results imply that governments in these countries need to invest in renewable energy to control environmental degradation.

Wind energy is one of the important renewable energy alternatives due to its wide potential and meeting increasing energy demand. However, location selection in wind farms is a complex spatial decision process for decision-makers. This study aimed to determine suitable wind farm locations by combining Fuzzy-Analytical Hierarchical Process (F-AHP) and Maximum Entropy (MaxEnt) methods for Hatay Province, Turkey. Firstly, nine decision criteria for selecting suitable wind farm locations were determined by climate, environmental, social and economic factors. Secondly, the F-AHP and MaxEnt models were implemented and suitable areas were mapped according to five suitability classes. Finally, F-AHP and MaxEnt model results were combined to define and classify priority locations for the wind farm. Study results show that wind speed, air densities and elevation are important criteria for F-AHP, while wind speed, wind power density and distance from power criteria are the most important factors for MaxEnt. Very high and high suitable wind farm locations of Hatay Province cover 21.6% in F-AHP and 29.8% in the MaxEnt model, while very low and low suitable areas cover 48.1% of the study area in both model results. To determine the priority wind farm location, F-AHP and MaxEnt model results were overlapped and reclassified according to the combination of suitability classes. The priority classes show that 62.9% of the study area is unsuitable for the wind farm. However, the limited area was determined as the 1st-priority area (3.2%), 2nd-priority area (4.9%) and 3rd-priority area (6.2%) to locate the wind farm. Consequently, the study methodology enables a more precise evaluation by combining different model results for decision-makers to select the optimum wind farm location selection.

Evaluations of field sites containing multiple contaminants are commonly impacted by ammonia from anthropogenic and natural sources. Scientifically defensible lines of evidence regarding which contaminants cause toxicity inform management decisions. Methods are needed to isolate ammonia toxicity from other contaminant effects without the treatment confounding results. Treatment columns packed with zeolite remove ammonia, but loose powder cannot be recovered and can physically harm organisms. Solutions such as in situ bioassay cages containing resins or immobilizing zeolite in a non-toxic matrix are needed. This study employed 3D printable polylactic acid (PLA) to immobilize zeolite and enable on-demand customization of high surface area, deployable and retrievable structures. The impact of incorporating zeolite loadings (8–32% w/w) was investigated for relative efficacy. PLA–zeolite structures in ammonia-contaminated water indicated 24 h treatment reduced ammonia below toxic levels. To demonstrate efficacy for complex environmental samples, contaminated sediments were collected and used to prepare sediment–water slurries (elutriates) and analyzed for ammonia, metals and polyaromatic hydrocarbons (PAH) before and after treatment. The 32% zeolite composite reduced 44 mg/L ammonia-N by 74% (24 h) and 83% (48 h). Results indicated successful treatment of sediment elutriates by 3D printed PLA–zeolite in bioassay chambers, based on reduced toxicity to Ceriodaphnia dubia. The study provides evidence that in situ PLA–zeolite treatment did not alter metals or PAH concentrations, which is desirable for ammonia-specific toxicity reduction evaluations of complex samples. This technology is applicable to adsorption of other chemicals by integration of different adsorbent powders into printable polymer.

This work investigates the relationship of financial development with energy efficiency and economic growth. Due to the coexistence of economic expansion, trade openness, financial development, and urbanization in Indonesia and Turkey, these two countries are considered. Johansen cointegration, error correction, and Granger causality tests are applied to validate the predicted effects of economic activity on the environment. Results show a long-term relationship of Indonesia’s CO₂ emissions with five out of six macroeconomic factors, except for urbanization, which has a detrimental effect on carbon emissions. On the other hand, no cointegration across variables is found in the case of Turkey. However, unidirectional causality is observed from energy consumption and economic growth to economic growth. Furthermore, economic growth, energy consumption, and trade openness have a two-way causal effect on financial development. This work encourages Turkish and Indonesian policymakers and regulators to strengthen environmental laws. It also encourages other economies and governments to conduct similar analyses and determine the best course of action.

The objectives of this study were investigating the photodegradation of the polycyclic aromatic hydrocarbons (PAHs) in modified petroleum impregnated bentonite mulch through solar radiation, determining PAHs’ translocation in the soils that underlay the mulch and finding a solution to prevent the uncontrolled release of petroleum into the environment. For this research, various formulated mulches were prepared: mulch no. 1 was a mixture of 5:1 sandy soil: natural bentonite + petroleum; mulch no. 2 composed a mixture of 5:1 sandy soil: modified bentonite + natural bentonite + petroleum; and mulch no. 3 composed a mixture of 5:1:0.5 ratio of sandy soil: natural bentonite: modified bentonite mixed with petroleum at a ratio of 1:1. PAHs in surface mulches and subsurface sandy soil were monitored over 5, 20, 40 and 80 days. The results demonstrated that PAHs undergo numerous changes over time because of sunlight. Photodegradation is the most dominant process for low molecular weight (LMW) PAHs (≤ 3 fused aromatic rings) and high molecular weight (HMW) PAHs (≥ 4 fused aromatic rings). HMW PAHs could be sequestrated strongly within the soil particles because of their higher aromaticity and lower polarity; they were more resilient in the soil matrices than LMW PAHs. Mulch no. 2 retained more PAHs compounds (p > 95%) than mulch nos. 1 and 3, which could be attributed to the retention of numerous PAHs in its interlayers, preventing its movement into the underlying soil, environment and atmosphere.

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.