Biosorption is a technique widely used in the remediation of contaminated effluents, and its main advantages are its easy applicability, high efficiency rate, versatility, and its economic viability. Associated with nanotechnology, this work proposes the use of nanocomposites of sugarcane bagasse (SB) and ferromagnetic nanoparticles (Fe₃O₄) in the removal of metallic ions present in contaminated water. SB is a promising adsorbent material since it is an abundant agricultural residue, easily accessed. By using the coprecipitation method, two nanocomposites were obtained from in natura (SB-NP) or acid-treated (MSB-NP) sugarcane bagasse. These materials were synthetized by impregnation of Fe₃O₄ to gain paramagnetic properties and to facilitate the removal of the contaminant-containing adsorbent. The characterization of the nanocomposites was performed using pHPCZ, FTIR, XRD, and SEM/EDS techniques, to evaluate the synthesis efficiency and investigate the morphology of the materials. The efficiency of magnetite impregnation on the SB was assessed by SEM/EDS and XRD, while the main functional groups (carbonyl, carboxyl, hydroxyl, amine, amide, and nitrate) responsible for adsorption were found by FTIR. In the surface charge characterization by pHPCZ sorption of dyes, it was found that negative charges are predominant. The pHPCZ for SB-NP and MSB-NP was 5.95 and 5.59, respectively, and the chosen Cu(II) adsorption pH was 6.2 ± 0.1. The adsorption equilibrium was reached between 10 and 60 min of contact time. The maximum experimental sorption capacity (SCₑₓₚ) was 2.53 ± 0.09 (SB-NP) and 2.61 ± 0.01 mg/g (MSB-NP). The isotherm models applied to the experimental data were Langmuir, Freundlich, Sips, Temkin, and Dubinin-Radushkevich, and Temkin best described the adsorption phenomena for Cu(II) by SB-NP (r² = 0.9976 and χ² = 3.965) and MSB-NP (r² = 0.9990 and χ² = 1.816). Reuse cycles of the nanocomposites were also performed employing ten cycles of sorption using 50 mg/L Cu(II) solutions, after which the materials showed SCₑₓₚ = 7.47 ± 0.04 mg/g (SB-NP) and 7.82 ± 0.04 mg/g (MSB-NP). Therefore, the investigated materials exhibited promising results to be used as biosorbents in the remediation of effluents contaminated with toxic metal ions, such as copper.

Flue gases contain SO₂ and NOₓ that can be treated together for elemental sulphur recovery in bioscrubbers, a technology that couples physical-chemical and biological processes for gaseous emissions treatment in a more economic manner than classical absorption. Sequential wet absorption of SO₂ and NOₓ from flue gas is thoroughly studied in this work in a two-stage bioscrubber towards elemental sulphur valorisation pursuing reuse of biological process effluents as absorbents. The optimal operating conditions required for SO₂ and NOₓ absorption in two consecutive spray absorbers were defined using NaOH-based absorbents. Overall, removal efficiencies of 98.9% and 55.9% for SO₂ and NOₓ abatement were obtained in two in-series scrubbers operated under a gas contact time of 1 and 100 s, and a liquid-to-gas ratio of 7.5 and 15 L m⁻³, respectively. Higher NOₓ removal efficiency to clean gas emission was obtained by oxidants dosing in the absorber for NOₓ absorption. High NaHCO₃ concentration in a two-stage bioscrubber effluent was exploited as alkaline absorbent for flue gas treatment. The performance of scrubbers using an absorbent mimicking a reused effluent exhibited the same removal efficiencies than those observed using NaOH solutions. In addition, the reuse of bioprocess effluent reduced reagents’ consumption by a 63.7%. Thus, the two-stage bioscrubber proposed herein offers an environmentally friendly and economic alternative for flue gas treatment.

This paper presents a quantitative pollutant discharge model for a typical molybdenum roasting plant, which combines the best available technology and object-oriented Petri net concepts. The proposed model was used to verify whether the best available technology in a molybdenum roasting process meeting the current pollutant emission limits by comparing the results of multiple simulations with online monitoring data records. Theoretical SO₂ emission values were obtained via multiple simulations and compared with the online monitoring data of a typical molybdenum roasting plant to verify the authenticity of the online monitoring data. The relationship between the different operating parameters and desulfurization efficiency is established through analyzing the historical operation parameters of the enterprise and response surface method. It was found that the optimal operating parameters for the flue gas desulfurization system of this plant could be characterized by a flue gas temperature of 90–93 °C, a pH range of 6.20–6.30, and a liquid-gas ratio of 23–25 L/m³.

Oil sludge washing (OSW) with surfactants and co-solvents is used to recover the oil, and this process leaves some residuals (sediments and surfactant solution). Currently, there are no data on the ecotoxicological effects of these OSW residuals from different sludges. This study evaluated the toxicity of OSW residuals from washing four types of oil sludges with five surfactants (Triton X-100 and X-114, Tween 80, sodium dodecyl sulphate (SDS) and rhamnolipid) and a co-solvent (cyclohexane). The toxicity of the residuals was evaluated with the impact on the soil microbial dehydrogenase activity (DHA) and ryegrass (Lolium perenne) seed germination. There was a high DHA detected directly in the sludges and all OSW residual combinations, but this activity could not be attributed to the DHA itself but to some chemical interferences. The DHA was then tested in the soils amended with the OSW residuals to simulate a bioremediation scenario. There were no chemical interferences in this case. In general, the INTF concentrations were significantly higher at low concentrations, 1 and 5% (p < 0.01). There were no significant differences in the DHA at high concentrations of OSW residuals (10, 25 and 50%) which implied that the concentration of the contaminants is not directly proportional to the levels of ecotoxicity. Unexpectedly, the INTF values of the 10, 25 and 50% rhamnolipid-OSW residuals were significantly lower than the Triton X-100 residuals. The ryegrass germination rates were higher than 70% with no apparent phytotoxicity symptoms in the seedlings. Particularly, there was a highly significant negative effect of the residuals on the germination rates at high concentrations (p < 0.01). Given that the extractable petroleum hydrocarbon (EPH) concentrations in the OSW residual-amended soils in both DHA and germination tests were very low (13–21 ppm), other co-contaminants could be contributing to the toxicity. These findings implied that biotreatment techniques can be applied to treat the OSW residuals if necessary.

We employ the new Method of Moments Quantile Regression approach to expose the role of natural resources, renewable energy, and globalization in testing Environment Kuznets Curve (EKC) in MINT panel covering the years 1995–2018. The outcome validates the EKC curve between economic progress and carbon emissions from the third quantile to the extreme highest quantile. The result also shows that natural resources increase CO₂ emissions at the lowest quantile and then turn insignificant from the middle to the highest quantiles due to the potential utilization of resources in a sustainable manner. The renewable energy mitigates CO₂ emissions at the lower half quantiles. Still, for upper quantiles, the results are unexpected and imply that the countries’ total energy mix depends heavily on fossil fuels. As far as globalization is concerned, the significant results from medium to upper quantiles reveal that as globalization heightens due to foreign direct investment or trade, energy consumption also expands, leading to the worst environment quality. Thus, the present study’s consequences deliver guidelines for policymakers to utilize natural resources sustainably and opt technologies based on clean energy, which may offset environmental degeneration.

Nanopriming is an emerging field of science which uses nanoparticles in solution to improve parameters of seed vigor. This leads to an initial advantage to the crop plant at the germination phase of its life cycle, which is also the most vulnerable phase and may lead to an improved yield. In this study, we have synthesized copper oxide (CuO) and (3-Aminopropyl) triethoxysilane (APTES)-coated CuO (CuO@APTES) nanoparticles via environmentally friendly green synthesis using the extract of Coriandrum sativum (coriander) herb. The synthesized nanoparticles were used as nanoprimers on Vigna radiata (moong bean), a model legume, to promote seed vigor via increase in germination. This was followed by characterization and comparison of both types of nanoparticles using various physicochemical techniques; UV-Visible (UV-Vis), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), zeta potential, and transmission electron microscopy (TEM). All characterization techniques pointed out to the successful synthesis and coating of CuO nanoparticles. Seed imbibition and germination assays were performed, which indicated increased imbibition potential and germination promotion at low nanoparticle concentration. Such studies can be used in the development of simple prepackaged nanoprimer products, which can be used by farmers before sowing to provide a boost to their crops and productivity.

Ground and satellite measurements of particulate pollution play an important role in determining the particulate pollutant-Aerosol Optical Depth (AOD) relationship. The daily observed PM₁₀ and PM₂.₅ concentration varied from 11–757 μg/m³ and 8–630 μg/m³ with the mean concentrations of 137 ± 119 μg/m³ and 86 ± 90 μg/m³, respectively. The long-term mean annual PM₁₀ and PM₂.₅ levels are several times higher than the WHO permissible limits. The 1377 satellite-derived AOD observations from the Moderate Resolution Imaging Spectrometer, ground-based particulate matter (PM) and meteorological observations from 2013–2017 were analysed to develop two-variate linear model (TVM) (AOD versus PM₁₀ or PM₂.₅) and multi-variate regression models (MVMs) (AOD + meteorological parameters versus PM₁₀ or PM₂.₅) for estimation of the ground level PM₁₀ and PM₂.₅ in the Kashmir Himalaya, India. The model evaluation showed that the PM predication estimates are significant at 99% confidence level for all the models. The TVM predicts daily PM₁₀ concentration better than PM₂.₅ explaining 82% and 74% variance in the observed data, respectively. By adding meteorological data to the regression analysis, there is an improvement of 5% and 11% in R² for PM₁₀ and PM₂.₅ estimates which inter alia reduced the RMSE by 11.8% and 20.47%, respectively. Estimation of the particulate pollution, utilising satellite-based AOD, observed PM and meteorology, would encourage satellite-based air quality monitoring in the data-scarce Himalaya. However, it is suggested that more studies are required to improve the operational prediction of PM pollution by incorporating satellite observations of other pollutants, and processes in the model using advanced approaches.

The concentration of PM₂.₅ is one of the main factors in evaluating the air quality in environmental science. The severe level of PM₂.₅ directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of the air quality, it is difficult to predict the volatile changes of PM₂.₅ over time. In this paper, a hybrid deep learning model VMD-BiLSTM is constructed, which combines variational mode decomposition (VMD) and bidirectional long short-term memory network (BiLSTM), to predict PM₂.₅ changes in cities in China. VMD decomposes the original PM₂.₅ complex time series data into multiple sub-signal components according to the frequency domain. Then, BiLSTM is employed to predict each sub-signal component separately, which significantly improved forecasting accuracy. Through a comprehensive study with existing models, such as the EMD-based models and other VMD-based models, we justify the outperformance of the proposed VMD-BiLSTM model over all compared models. The results show that the prediction results are significantly improved with the proposed forecasting framework. And the prediction models integrating VMD are better than those integrating EMD. Among all the models integrating VMD, the proposed VMD-BiLSTM model is the most stable forecasting method.

We evaluated, under controlled laboratory conditions, the insecticidal activity of polydimethylsiloxane (PDMS) for the control of the house mosquito, Culex pipiens. In a first series of bioassays, we tested the residual effect of different PDMS doses to control 3rd instar larvae. The label dose caused high mortality rates (>80%) even after 50 days from the initial application. Mortality levels at half the label dose were significantly higher compared to control, during the entire experimental period following a gradual decrease over time. Similar trends were observed when the 1/4 and the 1/8 of the label dose were applied with a much steeper decrease at long time intervals since the initial application especially for the lowest dose. However, after the 10th day of the bioassays and until the end of the experiments, mortality rates in the case of the label dose were higher compared to all other doses after the initial 10 days post application. Conversely, the insecticidal efficacy of PDMS against pupae was generally lower in comparison with larvae. Indeed, 10 days after the application, mortality was notably reduced, regardless of the dose rate tested. In a second series of bioassays, we assessed the efficacy of short exposures of different instars of larvae and nymphs to registered formulation and label dose. Mortality was well over 95% for 2nd instar larvae at exposure intervals ranging from 1 to 4 days. Significant mortality was recorded to 3rd instar larvae even at 15 min of exposure. Exposures longer than 30 min caused high mortality rates to 3rd instar larvae. Similar results were also recorded in pupae. Finally, in a third series of bioassays, the efficacy of PDMS in egg hatch was tested. Egg hatch was completely suppressed in the treated substrate (all egg rafts were “sinking” at the bottom of the test cups). Moreover, there was no oviposition in the case of the application of the label dose, while some egg rafts were recorded at one-eighth of the label dose. In both cases, it was recorded that PDMS acts as an oviposition deterrent. Τhe results of this study show that PDMS is effective in a wide range of conditions and application scenarios.

In Wuhan city, China, a pneumonia-like disease of unknown origin triggered a catastrophe. This disease has spread to 215 nations, affecting a diverse variety of persons. It was formally called extreme acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as coronavirus disease, by the World Health Organization as a pandemic. This pandemic forced countries to enforce a socio-economic lockdown to avoid its widespread presence. This study focuses on how the pollution of particulate matter during the coronavirus pandemic in the period from 23 March 2020 to 31 December 2020 was reduced compared to the pre-pandemic situation in the country. The improvement in air quality and atmosphere due to the coronavirus pandemic in Pakistan was identified by both ground-based and satellite observations with a primary focus on the four provincial capitals and country capitals, namely, Peshawar, Karachi, Quetta, Lahore, and Islamabad, and statistically verified through paired Student’s t test. Both datasets have shown a significant decrease in the levels of PM₂.₅ pollutions across Pakistan (ranging from 15 to 35% for satellite observations, while 27 to 61% for ground-based observations). The result shows that poor air quality is one of the key factors for a higher COVID-19 spread rate in major Pakistani cities. By extending the same investigation across the nation, there is a greater need to investigate the connections between COVID-19 spread and air pollution. However, both higher population density rates and frequent population exposure can be partially attributed to increased levels of PM₂.₅ concentrations before the pandemic of the coronavirus.