A new Pseudomonas putida strain (AQ8) was isolated from a decommissioned oil refinery’s soil in Italy and characterized for its ability to degrade BTEX. The draft genome of the new strain was sequenced and annotated for genes that encode enzymes putatively involved in BTEX degradation and quorum sensing. The strain was transformed with a plasmid expressing lactonase, which cleaves the autoinducer quorum sensing signal molecule, the acyl-homoserine lactone, to obtain a quorum sensing minus strain. P. putida AQ8 depleted the 40% on average of all the components of the initial BTEX concentration in 36 h. The quorum sensing minus strain, in the same time interval, depleted only the 10% of the initial BTEX concentration. The role of quorum sensing in regulating the expression of the annotated benzene/toluene dioxygenase gene (benzA) and biphenyl/toluene/benzene dioxygenase (bphA) genes, which are involved in BTEX degradation, was studied by quantitative RT-real-time quantitative (q)PCR analysis. The qPCR data showed decreased levels of expression of the benzA and bphA genes in the quorum sensing minus strain. Our results showed, for the first time, quorum sensing modulation of the level of transcription of dioxygenase genes in the upper BTEX oxidation pathway.

Energy security and environmental sustainability have become an integral policy agenda worldwide whereby the global economic growth policies are being restructured to ensure the reliability of energy supply and safeguard environmental well-being as well. However, technological inefficiency is one of the major hindrances in attaining these over-arching goals. Hence, this paper probed into the non-linear impacts of ICT trade on the prospects of undergoing renewable energy transition, improving energy use efficiencies, enhancing access to cleaner cooking fuels, and mitigating carbon dioxide emissions across selected South Asian economies: Bangladesh, India, Pakistan, Sri Lanka, Nepal, and Maldives. The results from the econometric analyses reveal that ICT trade directly increases renewable energy consumption, enhances renewable energy shares, reduces intensity of energy use, facilitates adoption of cleaner cooking fuels, and reduces carbon-dioxide emissions. Moreover, ICT trade also indirectly mitigates carbon-dioxide emissions through boosting renewable energy consumption levels, improving energy efficiencies, and enhancing cleaner cooking fuel access. Hence, these results, in a nutshell, portray the significance of reducing the barriers to ICT trade with respect to ensuring energy security and environmental sustainability across South Asia. Therefore, it is ideal for the government to gradually lessen the trade barriers to boost the volumes of cross-border flows of green ICT commodities. Besides, it is also recommended to attract foreign direct investments for the potential development of the respective ICT sectors of the South Asian economies.

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM₁₀ and PM₂.₅. Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 μ g/m³), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the corrective alert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM₁₀ and PM₂.₅ concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM₁₀ and PM₂.₅ as a tool for an early alert system (EAS) at least 24 h in advance.

In tropical estuaries, fish diversity varies spatially and temporally due to behavioral processes such as reproductive migrations, predator avoidance, and foraging, which are affected by water quality. Eutrophication is one of the main factors affecting water quality in estuaries. The objective of this study was to determine variation in fish assemblage explained by fluctuating water quality in the Buenaventura Bay. Fish were captured using artisanal trawl nets during the wet, dry, and transitional seasons at four sampling sites. Additionally, alkalinity; phosphate, nitrite, and nitrate concentrations; dissolved oxygen; pH; temperature; and suspended solids were measured. Multivariate analysis was used to assess the effect of water quality on fish assemblage. In Buenaventura Bay, the assemblage composition of Pseudupeneus grandisquamis, Daector dowi, and Citharichthys gilberti was affected by nitrate concentration. Moreover, large fish biomasses were associated with high nitrite concentration, intermediate salinity, and low dissolved oxygen, suggesting that these estuaries are dominated by species tolerant to poor water quality. Species richness was associated with low nitrate and phosphate concentrations, more suitable water quality indicators, and intermediate temperatures. These results suggest that the deteriorating water quality of estuaries as a result of the anthropogenic impact could increase dominance and decrease richness, resulting in structural changes of fish assemblages.

To study the influence factors of calcined layer-like Mg-Al hydrotalcites nanosheets adsorbing perfluorooctanoic acid (PFOA) in aqueous solution, Mg-Al hydrotalcite (HMA) nanosheets were prepared by one-step hydrothermal synthesis. The effect of calcination temperature on adsorption properties and structure of HMA (CHMA-x, x means different calcination temperature) was investigated. The prepared samples were systematically characterized by the Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG), X-ray diffraction (XRD), scanning electronic microscopy (SEM), and nitrogen adsorption-desorption isotherms. The adsorption isotherms and kinetics showed the adsorption equilibrium reached within 2 h, and the factors, such as adsorption dosage, pH, and cycles were investigated. It was found that CHMA with 600 °C displayed a uniformly morphology, higher surface area about 106.3 m²/g, and excellent adsorption properties (1969 mg/g). The equilibrium adsorption data perfectly fitted to the pseudo-second-order kinetic model (R² = 0.999) and the Freundlich model (R² = 0.994). The main mechanism of CHMA adsorbing PFOA might be the “memory effect.” This study provided a new insight to prepare highly effective adsorbents in water treatment.

In this study, different types of magnetic biochar nanocomposites were synthesized using the co-precipitation method. Two biochar materials, namely, sewage sludge biochar and woodchips biochar, were prepared at two different temperatures, viz., 450 and 700 °C. These biochars were further modified with magnetic nanoparticles (Fe₃O₄). The modified biochar nanocomposites were characterized using field emission–scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET), SQUID analysis, X-ray photoelectron spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR). The potential of prepared adsorbents was examined for the removal of hexavalent chromium (Cr(VI)) and Acid orange 7 (AO7) dye from water as a function of various parameters, namely, contact time, pH of solution, amount of adsorbents, and initial concentrations of adsorbates. Various kinetic and isotherm models were tested to discuss and interpret the adsorption mechanisms. The maximum adsorption capacities of modified biochars were found as 80.96 and 110.27 mg g⁻¹ for Cr(VI) and AO7, respectively. Magnetic biochars showed high pollutant removal efficiency after 5 cycles of adsorption/desorption. The results of this study revealed that the prepared adsorbents can be successfully used for multiple cycles to remove Cr(VI) and AO7 from water. Graphical Abstract

The present study aims to explore the co-movement between urbanization and environmental sustainability for the period 1950 to 2014 using the wavelet coherence technique within the global framework. The wavelet coherence technique allows us to investigate both the long-run and short-term causal relationships between urbanization and environmental sustainability within the global framework. The findings reveal that (i) wavelet correlation from the global perspectives indicates a significant wavelet relationship between urbanization and environmental sustainability for medium- and long-term horizons; however, the correlations are not significant for the short-term horizons; (ii) significant vulnerabilities in urbanization and environmental sustainability are observed at different periods and different frequency levels; (iii) urbanization has reliable power for explaining environmental sustainability at different periods between 1970 and 2000. Based on our study, we suggest that global urban planners and policymakers should support modern environment-friendly technologies and renewable energies to control global CO₂ emissions and pollutions created by industrialization; policy action to fight climate change is as well recommended as evidence supports to compact city theory.

Our world is subject to various kinds of pollution and contamination due to rapid growth and development of industrialization. Though, industries are helping to improve the human life style in many ways in day to day life such as power generation to treatment of diseases. At the same time, industries emit the waste which causes major environmental pollution and leads to harmful for all living organism. As the renewable energy sources are depleting, energy/power generation become a major research around the world. Nuclear energy is one of the promising energy to sort out the energy demand, but the problem associated with the nuclear energy is the management and treatment of radioactive waste/emission/effluent since which is more dangerous to all living organism. There is a large scale contamination of radioactive waste associated for the past 60 years of global nuclear activity. It is necessary to pay special attention to the management of radioactive wastes in order to approach pollution-free environment and avoid diseases to living organism through various clean-up strategies. In this review, we discussed the wide ranges of strategies available for radioactive waste management such as physical, chemical, and biological methods. Bioremediation may be the powerful tool for treatment of radioactive wastes. Additionally, discussed on recent advancement have been made in treatment of radioactive waste through microbial transformation as well as phytoremediation which play a major role in disposal of radioactive waste.

Accurate estimation of reference evapotranspiration (ETₒ) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETₒ-based estimation is a major concern in the hydrological cycle. The estimation of ETₒ can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETₒ estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETₒ on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETₒ at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tₘₐₓ and Tₘᵢₙ), solar radiation (Rₛ), and wind speed (Uₛ) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETₒ at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.

Acid rain is a global environmental issue due to inhibiting severely plant growth and productivity. To discover the tolerant mechanism in plants under acid rain stress, we studied the difference in response of two crops (rice and soybean) to simulated acid rain (pH 5.0 ~ 2.5) at growth and physiological, biochemical and molecular levels during exposure and recovery periods by hydroponics. By analyzing the change in relative growth rate, chlorophyll content and plasma permeability in rice and soybean, we found that rice could tolerate acid rain above pH 3.0 whereas soybean could tolerate acid rain above pH 4.5. By RT-PCR analyses, immunoprecipitation and enzyme kinetics study, we observed that pH 4.5 acid rain promoted the transcriptional expression of H⁺-ATPase genes and the phosphorylation of H⁺-ATPase and increased H⁺-ATPase activity in the two crops for resisting acid stress. The increased degree in soybean was larger than that in rice. Acid rain at pH 3.0 still promoted the transcription regulation to maintain H⁺-ATPase activity higher in rice for resisting stress but caused irreversible inhibition on express of H⁺-ATPase and decreased H⁺-ATPase activity in soybean. All results suggest that the different tolerance in rice and soybean to acid rain stress could be associated with difference in plasma membrane H⁺-ATPase at transcriptional regulation, post-translational modification and the substrate affinity.