The co-occurrence of heavy metals, antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs) from hospital effluents spreading into the river receiving systems and evaluating associated risks are topics of scientific interest and still under-studied in developing countries under tropical conditions. To understand the selectors of the ARGs, we examined the occurrence of heavy metals (Cr, Co, Ni, Cu, Cd, Pb and Zn), associated ARB (β-lactam-resistant Escherichia coli, β-lactam-resistant Enterobacteriaceae, and carbapenem-resistant Enterobacteriaceae) and ARGs (blaOXA, blaCTX₋M, blaIMP, blaTEM) in water and sediments from two sub-urban rivers receiving urban and hospital effluent waters in the Democratic Republic of the Congo (DRC). High abundances of ARB and ARGs were observed in all sediment samples. All the metal contents correlated negatively with grain size (− 0.94 ≤ r ≤ − 0.54, p < 0.05) except for Ni and positively with organic matter content and total copies of 16 s rRNA (0.42 ≤ r ≤ 0.79, p < 0.05), except for Ni and Zn. The metals had a significant positive correlation with the faecal indicator Enterococcus except for Ni and Cd (0.43 ≤ r ≤ 0.67, p < 0.05). Carbapenem-resistant Enterobacteriaceae correlated negatively with Zn (r = − 0.44, p < 0.05) and positively with all the rest of toxic metals (0.58 ≤ r ≤ 1.0, p < 0.05). These results suggested that some metals had a great influence on the persistence of ARB and ARGs in sediments. Overall, this study strongly recommends the managing urban wastewater to preserve water resources used for human and agricultural purposes. Additionally, we recommend the utilizing biological indicators (faecal indicator bacteria, ARB, ARGs) when investigating urban wastewater pollutions.

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.

Uranium tailing ponds are a potential major source of radioactive pollution. Solidification treatment can control the diffusion and migration of radioactive elements in uranium tailings to safeguard the surrounding ecological environment. A literature review and field investigation were conducted in this study prior to fabricating 11 solidified uranium tailing samples with different proportions of PVA fiber, basalt fiber, metakaolin, and fly ash, and the weight percentage of uranium tailings in the solidified body is 61.11%. The pore structure, volume resistivity, compressive strength, radon exhalation rate variations, and U(VI) leaching performance of the samples were analyzed. The pore size of the solidified samples is mainly between 1 and 50 nm, the pore volume is between 2.461 and 5.852 × 10⁻² cm³/g, the volume resistivity is between 1020.00 and 1937.33 Ω·m, and the compressive strength is between 20.61 and 36.91 MPa. The radon exhalation rate is between 0.0397 and 0.0853 Bq·m⁻²·s⁻¹. The cumulative leaching fraction of U(VI) is between 2.095 and 2.869 × 10⁻² cm, and the uranium immobilization rate is between 83.46 and 85.97%. Based on a comprehensive analysis of the physical and mechanical properties, radon exhalation rates, and U(VI) leaching performance of the solidified samples, the basalt fiber is found to outperform PVA fiber overall. The solidification effect is optimal when 0.6% basalt fiber is added.

Soil contamination represents a serious and significant issue, especially when it comes to soil used in agricultural practices. This research was carried out in order to investigate the accumulation level of potentially toxic trace elements (Cr, Cd, Cu, Mn, Ni, Pb and Zn) in soil and vegetables (Solanum lycopersicum and Daucus carota). The transfer of the trace elements from soil to vegetables and the potential risk assessment were studied as well. Results indicated relatively high levels of heavy metals. Cd, Cu and Pb exceeded the alert limits established by the Romanian legislation. Zn was high as well. Positive correlations between the Cr, Cu and Pb indicated similar source of pollution, possibly related to the activities occurred in the non-metallic facility, nearby the study area. The heavy metals determined in the Solanum lycopersicum fruits and Daucus carota roots were below the maximum allowable concentrations, according to the WHO/FAO guideline. Slightly higher amounts of Cr and Cu were measured in tomatoes, compared to the carrots. Nevertheless, carrots were richer in Ni and Mn. The applied pollution indices indicated a contamination with heavy metals in 90% of the soil samples, with 9% probability of toxicity, the remaining 10% being classified into the precaution domain category. The plant bioconcentration of heavy metals into the Solanum lycopersicum fruits and Daucus carota roots is characterized using transfer factors. Generally, the results indicate that Daucus carota was the most susceptible to uptake Cu and Mn, while Solanum lycopersicum would rather uptake Cd and Zn. The estimated non-carcinogenic risk, based on the human health risk indices, indicates that the studied vegetables are safe for consumption with no impact on the human health. The results are lower than the critical value. Similarly, the carcinogenic risk indices results showed acceptable risks of cancer developing. It is important to assess and monitor the heavy metals levels in soil and in the vegetables intended to be consumed, in order to prevent contamination and potential negative effects on the environment and implicitly on the human health. The obtained data can be used in remediation techniques, as well as in implementing control measures of heavy metal contamination in soil and vegetables.

Given the extensive impacts of climate change on the agricultural sector and their interactions, the climate change is known as one of the main factors influencing agricultural production. The present study aims to explore the short- and long-term impacts of climate change on the yield of irrigated barley in 28 Iranian provinces over the 1999–2015 period. The research uses panel data and dynamic ordinary least squares (DOLS) method. The study also estimated the threshold levels of temperature and rainfall which confirmed an inverted U-shaped relationship between climate change variables and irrigated barley yield. The threshold levels of temperature and rainfall are estimated to be 15.48 °C and 239 mm, respectively; beyond these threshold levels, the increase in temperature and rainfall have negative impact on barley yield in Iran. The long-term elasticity of temperature shows that the yield will be reduced with the increase in temperature in the long run. Same is the case with the precipitation and barley yield. The findings of the study suggest the need of a comprehensive national climate change policy and alignment of sectoral policies with it mitigate and adapt the climate change and global warming. Moreover, it also provided the guidelines for the government and policy-makers to introduce the use of modern eco-friendly and resource saving technologies such as water-saving methods of irrigation, use of fertilizer in required quantities, and improved seeds use. The government should also introduce the climate change awareness programs especially for farmers.

Heavy metals (As, Pb, Cd, and Cu) were traced in a model of the aqueous food chain in the Aras River, located in northwest of Iran. The selected model included the zander (Sander lucioperca L.) and crustacean species known as amphipods (Gammarus sp.) which belong to the food chain of this ray-finned fish. A total of 172 samples (70 fish and 102 amphipods) were collected randomly and analyzed for heavy metals using atomic absorption spectrophotometry (AAS). The results showed that the accumulation of heavy metals in both taxa are in the order of As > Pb > Cd > Cu, and concentrations of heavy metals in fish muscle are higher than Gammarus sp. in all stations in different seasons. Specimens of station (1) displayed the highest heavy metal content due to local industrial activities. The recorded concentrations of As, Pb, and Cd exceed the permissible limits. There is a close correlation between the concentrations of heavy metals in the amphipods and zanders. Target hazard quotient (THQ), total target hazard quotient (TTHQ), and carcinogenic risk (CR) were calculated to assess risks to human health. The average of THQ for As (1.43) exceeded the international standards and presenting health risks to the consumers of this fish species. The TTHQ for heavy metals was estimated higher than 1. At all stations, the value of CRCd > 1 × 10⁻³ indicating the degree of carcinogenicity of this metal in all parts of the Aras River. Therefore, according to our results, efficient control measures and regular biomonitoring should be established in this region.

The impact of crop peels on reducing pesticide residue levels in crops during household food processing was evaluated in this study. We proposed a series of pesticide fate models to simulate the removal efficiency of residues in crop peels and medullas (i.e., pulps) via soaking and washing. The simulated results indicated that the variation in the peel thickness had a significant impact on residue removal from the peel compartment. However, the peel compartment had a low impact on the removal efficiency of pesticide residues from the medulla compartment, as demonstrated by the simulated results from the non-peel model (i.e., already peeled crops). In addition, we observed that even though systemic pesticides have a higher potential to penetrate from the peel into the medulla, the increasing residue level caused by the mass transfer from the peel into the medulla is too low to cause human health damage, because the absolute mass of residues in the peel is considerably small. Based on the simulation results, we concluded that washing or soaking crops with or without peels using water is not effective in reducing residue levels in crop medullas. Modifying crops into slices, instead of directly washing or soaking crops, could significantly improve the removal efficiency of pesticide residues inside the medulla. The models proposed in this study can improve our understanding on the fate of pesticides in crops during household food processing.

Since China has entered the economic “new normal,” China’s industries pay more attention to green and low-carbon development. However, the transportation industry is still one of the three industries with high carbon emissions at present. Based on this, this paper first constructed two scenarios for the early and late stages of economic “new normal.” Furthermore, using the extended structural decomposition model, input–output method, and energy consumption method, this paper studied the carbon emissions status and emissions reduction effect of China’s transportation industries in the early and late stages of economic “new normal.” The results showed as follows. (1) Compared with the early stage of economic “new normal,” the energy intensity of transportation industries and optimization of energy consumption structure played a better role in emissions reduction after entering the economic “new normal.” However, the input structure effect reflecting generalized technological progress did not play a significant role. (2) Compared with the early stage of economic “new normal,” low-carbon energy such as liquefied petroleum gas, natural gas, and liquefied natural gas played a more significant role in the energy structure effect with the emissions reduction effect showing an obvious enhancement trend over time after entering the economic “new normal.” (3) In the early or late stage of economic “new normal,” the final demand effect was the main driving factor for the growth of CO₂ emissions in the transportation industry. Meanwhile, compared with the early stage of economic “new normal,” the final demand effect had a stronger driving effect in the late stage of economic “new normal.” In these two periods, the second industry and the third industry were the main contributors to the final demand effect. This paper provided a basic theoretical analysis basis for carbon emission control of transportation industry under the “new normal” of the Chinese economy, and also provided a realistic guidance path for the transportation industry to carry out more accurate emission reduction from the level of energy varieties on the demand side and industry on the demand side.

This study examined the influence of tail risks on global financial markets, which aids in better understanding of the emergence of COVID-19. This study looks at the global and Vietnamese stock markets impacted by the COVID-19 pandemic to identify systemic emergencies. Risk dependent value (CoVaR) and Delta link VaR are two important tail-related risk indicators used in Conditional Bivariate Dynamic Correlation (DCC) (CoVaR). The empirical findings demonstrate that when COVID-19's worldwide spread widens, the volatility transmission of systemic risks across the global stock market and multiple exchanges shifts and becomes more relevant over time. At the time of COVID-19, the world industrial market was larger than the Vietnamese stock market, and the Vietnamese stock market posed a lesser danger to the global market. A closer examination of the link between the Vietnam value-at-risk (VaR) range index sample and the world stock index indicates a significant degree of downside risk integration in key monetary systems, particularly during the COVID-19 era. Our study findings may help regulators, politicians, and portfolio risk managers in Vietnam and worldwide during the unique moment of uncertainty created by the COVID-19 epidemic.

In this text, a laboratory-scale A²O was performed in Linzhi City at a 3000-m altitude. During the test operation, the UV irradiation was carried out in oxic tank for 0, 5, 10, 30, and 180 min. The 16SrRNA gene sequencing was performed on the activated sludge in anaerobic, anoxic, and oxic tanks, and the colony structure characteristics of phyla, genera, and species classification levels in the sludge were analyzed. There were significant differences in the numbers of genera and species (p ≤ 0.05). The community richness, uniformity, diversity, and other indicators differed to some degree compared with those of other regions. The analysis of composition of bacterial colonies revealed different levels. The significance test of the difference between the groups, the significance of the dominant species, and the mechanism of UV was analyzed. A CCA diagram was used to verify that UV is an important factor in the colony structure composition, and the correlation heatmap diagram was used to analyze the microorganisms that are significantly related to UV. A sample hierarchical cluster analysis showed that the time of UV exposure can be divided into two categories, and the effects of UV exposure increase sequentially as the time of exposure increases. A comprehensive analysis found that the enhancing and inhibitory effects of UV affect the composition of the colony structure in the sample, and the time of irradiation will affect the enhancing or inhibitory effect, that is, the colony structure from the samples that were irradiated for different amounts of time differs greatly.