In recent decades, soil contamination with heavy metals has become an environmental crisis due to their long-term stability and adverse biological effects. Therefore, bioremediation is an eco-friendly technology to remediate contaminated soil, which the efficiency requires further research. This study was designed to comparatively investigate two strategies: bioaugmentation by using a cyanobacterial species (Oscillatoria sp.) and bioaugmentation-assisted phytoremediation by using Oscillatoria sp. and purslane (Portulaca oleracea L.) for the bioremediation of soil contaminated by heavy metals (Cr (III), Cr (VI), Fe, Al, and Zn). Various quantities of biochar (0.5, 2, and 5% (w/w)) were used as an amendment in the experiments to facilitate the remediation process. The results of the bioaugmentation test showed that applying biochar and cyanobacteria into contaminated soil significantly increased the chlorophyll a, nitrogen, and organic carbon contents. In contrast, the extractable fractions of Cr (III), Cr (VI), Zn, Al, and Fe declined compared with those of the control treatment. The highest reduction content (up to 87 %) in the extractable portion was obtained for Cr (VI). The development of longer root and hypocotyl lengths and vigour index from lettuces and radish seeds grown in the remediated soil confirmed the success of remediation treatments. Moreover, the findings of the bioaugmentation-assisted phytoremediation test displayed a reduction in the bioavailable fraction of Cr (III), Cr (VI), Zn, Al, and Fe. Cr (III) presented the highest reduction (up to 90 %) in metal bioavailability. With cyanobacteria inoculation and biochar addition, the shoot and root lengths of purslane grew 4.6 and 3-fold while the heavy metal accumulation decreased significantly. Besides, these treatments enhanced the tolerance index (TI) quantities of purslane whereas diminished its bioaccumulation coefficient (BAC) and bioconcentration factor (BCF) values. For all heavy metals (except Zn), translocation factor (TF) and BAC values were found to be less than 1.0 at all treatments, indicating the successful phytoextraction by the purslane. These results suggest that the purslane can be considered an excellent phytoextracting agent for soils contaminated with heavy metals.

In the process of exploiting mineral resources, dust enters the environment through air suspended particles and surface runoff, which has a serious impact on the atmospheric environment and human health. From all-year and seasonal scenarios, the migration trajectories and cumulative concentration based on the secondary development of Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) in four mining areas (SF, BC, SJZ, and MJT) in Northwest China are studied. The convergent cross mapping (CCM) method is used to study the causal relationship between concentration and meteorological factors. In this process, the problem of missing non-station meteorological data is solved with the help of the inverse distance weighted interpolation method, and the problem in which the convergence requirements of the CCM algorithm cannot meet the requirements is solved with the bootstrap method. The results indicated that the short path has the characteristics of slow movement, short migration path, low altitude(< 1 km), and high contribution rate, while the long path has the opposite characteristics. Furthermore, the results demonstrated that the concentration is centered on the pollution source and diffuses around, with a diffusion radius of 220–270 km, showing a serious pollution center and slight gradient settlement on the edge, but the overall distribution of accumulated concentration is uneven. The results also show that temperature (TEMP and S_TEMP), evaporation, and air pressure are the main meteorological factors affecting the all-year concentration. The concentration and meteorological factors in the four mining areas also show significant seasonal characteristics, and the correlation in spring, summer, and autumn is stronger than that in winter. This study not only provides a reference for the green and sustainable exploitation of mineral resources but also provides theoretical support for the joint prevention and control of transboundary pollution.

The degree of developmental stability of individuals and populations is most often estimated by their level of fluctuating asymmetry (FA) — the random deviations from perfect symmetry. In our previous work, we recorded high levels of FA (FAMI index: frequency of asymmetric manifestation of an individual) in Pelophylax ridibundus populations that inhabit biotopes at Sazliyka River, south Bulgaria with high levels of anthropogenic pollution (domestic sewage pollution). At the same time, in the biotopes located in the upper reaches of the river (less disrupted habitats), the populations showed low levels of FA. Currently, we present the results of the study of the values of several morphological parameters: snout-vent length (SVL), body weight (BW), and body condition factor (CF) in the same populations of P. ridibundus. In addition, we evaluate the correlation between the values of these morphological parameters and the values of fluctuating asymmetry (the FAMI index), using the Kendall rank correlation analysis. The analysis of the relationships between the parameters characterizing the physical fitness of frogs and the indicator of developmental stability — the FAMI index — did not establish statistically significant correlations in the analyses in the whole groups of P. ridibundus from each site and in the correlations between sexes. We believe that the approaches to the study of developmental stability (analysis of fluctuating asymmetry levels) and those related to the assessment of physical fitness (health status) of frogs should be applied independently of each other.

The COVID-19 pandemic not only has caused a global health crisis but also has significant environmental consequences. Although many studies are confirming the short-term improvements in air quality in several countries across the world, the long-term negative consequences outweigh all the claimed positive impacts. As a result, this review highlights the positive and the long-term negative environmental effects of the COVID-19 pandemic by evaluating the scientific literature. Remarkable reduction in the levels of CO (3 − 65%), NO₂ (17 − 83%), NOₓ (24 − 47%), PM₂.₅ (22 − 78%), PM₁₀ (23 − 80%), and VOCs (25 − 57%) was observed during the lockdown across the world. However, according to this review, the pandemic put enormous strain on the present waste collection and treatment system, resulting in ineffective waste management practices, damaging the environment. The extensive usage of face masks increased the release of microplastics/nanoplastics (183 to 1247 particles piece⁻¹) and organic pollutants in land and water bodies. Furthermore, the significant usages of anti-bacterial hand sanitizers, disinfectants, and pharmaceuticals have increased the accumulation of various toxic emerging contaminants (e.g., triclocarban, triclosan, bisphenol-A, hydroxychloroquine) in the treated sludge/biosolids and discharged wastewater effluent, posing great threats to the ecosystems. This review also suggests strategies to create long-term environmental advantages. Thermochemical conversions of solid wastes including medical wastes and for treated wastewater sludge/biosolids offer several advantages through recovering the resources and energy and stabilizing/destructing the toxins/contaminants and microplastics in the precursors.

COVID-19, which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly spread over the world, posing a global health concern. The ongoing epidemic has necessitated the development of novel drugs and potential therapies for patients infected with SARS-CoV-2. Advances in vaccination and medication development, no preventative vaccinations, or viable therapeutics against SARS-CoV-2 infection have been developed to date. As a result, additional research is needed in order to find a long-term solution to this devastating condition. Clinical studies are being conducted to determine the efficacy of bioactive compounds retrieved or synthesized from marine species starting material. The present study focuses on the anti-SARS-CoV-2 potential of marine-derived phytochemicals, which has been investigated utilizing in in silico, in vitro, and in vivo models to determine their effectiveness. Marine-derived biologically active substances, such as flavonoids, tannins, alkaloids, terpenoids, peptides, lectins, polysaccharides, and lipids, can affect SARS-CoV-2 during the viral particle’s penetration and entry into the cell, replication of the viral nucleic acid, and virion release from the cell; they can also act on the host’s cellular targets. COVID-19 has been proven to be resistant to several contaminants produced from marine resources. This paper gives an overview and summary of the various marine resources as marine drugs and their potential for treating SARS-CoV-2. We discussed at numerous natural compounds as marine drugs generated from natural sources for treating COVID-19 and controlling the current pandemic scenario.

The high-quality development of agriculture is closely related to technological innovation, but the evolutionary characteristics of the relationship between agricultural transformation and technological innovation have received little study. This study takes 13 main grain-producing areas of China as the research object. Data collection was from 2004 to 2019. Based on the coupling coordination and responsiveness models, we analyze the spatio-temporal agriculture comprehensive level and the associated response degree of agricultural transformation to technological innovation. The results showed that (1) the comprehensive development of technological innovation showed a growth trend, while the agricultural transformation showed a U-shaped growth trend; (2) the coordinated development of these two systems has been significantly improved, but there are differences in the development speed of each province; (3) the coordinated gravity center moved southward in the spatial pattern, eventually presenting the characteristics of “higher level in the east and lower level in the west, while the higher level in the south and lower level in the north”; (4) the influence of technological innovation on agricultural transformation gradually changed from inhibition to positive promotion. In the end, this paper puts forward suggestions on the high-quality development of agriculture from the relationship of technological innovation and agricultural transformation.

This study aimed to develop a precision model between inputs and yield, and also between inputs (indirect emissions) and environmental final index (EFI) in onion farms through regression models (classic computing) and artificial intelligence models (soft computing). Required data were collected through direct measurement and questionnaire. To this end, 85 and 70 questionnaires were distributed among onion farmers in Fereydan and Falavarjan regions (Isfahan province, center of Iran), respectively. In the Fereydan region, the total energy input, onion yield, and water use efficiency (WUE) were obtained as 239496 MJ.ha⁻¹, 97658 kg.ha⁻¹, and 9.08 kg.m⁻³, respectively, while for Falavarjan region, these were obtained as 232221 MJ.ha⁻¹, 94485 kg.ha⁻¹, and 10.8 kg m⁻³, respectively. Electricity and diesel fuel were the most widely used inputs in the study areas. Based on the results related to the environmental indices, EFI was obtained as 547.38 and 363.54 pPt.t⁻¹ for Fereydan and Falavarjan regions, respectively. The contribution of direct (such as CO₂ and NH₃) and indirect emissions (especially electricity) to the total EFI was 74 and 26% in Fereydan and 63 and 37% in Falavarjan region, respectively. Results related to the Cobb–Douglas regression model (CDR) showed that the effects of seed, manure, and labor on the onion yield were significant at 1% level of confidence. However, despite meeting the regression assumptions, the CDR model has predicted the yield and EFI with lower accuracy and higher error compared to artificial neural network models (ANNs), multi-layer perceptron (MLP), and adaptive neuro-fuzzy inference system (ANFIS). Soft computing (artificial intelligence) modeling showed that the ANFIS model (Grid Partitioning (GP)) has higher computational speed an lower error compared to multi-layer perceptron (MLP) models. Therefore, the comparison of the best GP and MLP models showed that the root-mean-square-error (RMSE) was obtained as 10.649 and 52.321 kg.ha⁻¹ for yield and 25.08 and 40.94 pPt.ha⁻¹ for EFI, respectively.

The Ore Treatment Unit was a uranium mining company that is currently being decommissioned. The local rainfall index makes it necessary to release effluents into the environment. After releasing, the wastewater is available for unrestricted use. Current study aims to use national and international recommendations to assess the radiological potability of released effluents at one of the three points of company’s interface with the environment. Twenty-four samples of water were collected and activity concentrations (AC) were obtained by gross alpha count, gross beta count, and for arsenazo spectrophotometry. Statistical analysis techniques were applied to the data with the purpose of understanding the results for the soluble, particulate, and total fractions. The mean AC for effluents were 3.580, 0.082, 0.103, 0.063, and 0.090 Bq L⁻¹ for Uₙₐₜ, ²²⁶Ra, ²¹⁰Pb, ²³²Th, and ²²⁸Ra, respectively, for the total fraction. The analysis of variance pointed to Uₙₐₜ as a critical radionuclide, since it presented more than 90% of the total AC released into the environment. Pearson’s R² pointed to soluble fraction as a major contributor to the total AC released. The guidance level proposed by WHO was used to assess the radiological potability of the effluents. The results obtained indicated the need for trigger other analyses. Committed effective dose was estimated due to the unrestricted use of effluents and the value obtained, 0.23 mSv year⁻¹, was below the maximum allowed limit. Finally, the radiotoxicity of the released effluent was evaluated and the value obtained was ~ 50% of the maximum allowed limit. In conclusion, the present study showed that the level of radioactivity released into the environment by the Ore Treatment Unit does not present a radiological risk to the surrounding population.

This paper investigates the solar evacuated tube heat pipe system (SETHP) coupled with a thermoelectric generator (TEG) using the internet of things (IoT). The TEGs convert heat energy into electricity through the Seebeck effect that finds application in the waste heat recovery process for the generation of power. The present work deals with the theoretical study on solar evacuated tube heat pipe integrated TEG and it is validated experimentally using with and without parabolic trough concentrating collector. However, it is found that the maximum power output due to the influence of the parabolic trough concentrator results in increased efficiency when compared with the non-concentrating SETHP-TEG system. Thus, the thermoelectric generator’s electrical energy efficiency for the concentrating system was 0.151% greater than the latter one. A power electronic boost converter may enhance the acquired TEG output power to a maximum of 5.98 V. This would be directly used for both mobile charging and lighting applications in distant places and military camps where the community lacks sufficient electrical access. And the carbon credit of the TEG system is determined to find its potential in the environmental aspects of carbon emission per watt, carbon mitigation, and carbon credit and its results are 2.34 × 10⁻³ g/W, 0.027 tonnes, and 0.681 dollars respectively for a TEG module. Besides, the recorded real sensor data with Arduino is implemented in the experimental process for automatic remote monitoring of the temperature.

Haze pollution is one of the most concerning environmental issues, and controlling haze pollution without affecting economic development is of immense significance. Using the panel data composed of PM₂.₅ concentration and other data from 278 cities in China between 2003 and 2016, this paper empirically investigates the impact of urban innovation on haze pollution and its transmission mechanism. Based on the fixed effect model, the research finds that increasing urban innovation significantly reduces haze pollution. Even after dealing with possible endogenous problems, the result still holds. Energy consumption and industrial agglomeration are two important transmission channels through which urban innovation affects haze pollution. Furthermore, time heterogeneity analysis shows that the negative effect of urban innovation on haze pollution increases with time. Spatial heterogeneity analysis shows that urban innovation has a more significant mitigation effect on haze pollution in eastern cities than in central and western cities in China. This paper indicates that technological innovation, as the main driving force for development, can provide vital support to China to improve the ecological environment.