The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two hybrid neuro-fuzzy models are implemented for the prediction of groundwater level (GWL) fluctuations, as well as variations of Cl − and HCO3 − in the Karnachi well, Kermanshah, Iran in monthly intervals within a 13-year period from 2005 to 2018. In order to develop AI models, the adaptive neuro-fuzzy inference system (ANFIS), firefly algorithm (FA), and wavelet transform (WT) are used. In other words, two hybrid models including ANFIS-FA (adaptive neuro-fuzzy inference system-firefly algorithm) and WANFIS-FA (wavelet-adaptive neuro-fuzzy inference system-firefly algorithm) are utilized for the estimation of the quantitative and qualitative parameters. Firstly, influencing lags of the time-series of the qualitative and quantitative parameters are identified using the autocorrelation function. Then, four and eight separate models are developed for the approximation of GWLs and qualitative parameters (i.e. Cl − and HCO3 −), respectively. It is worth to mention that about 75% of observed values are assigned to train the hybrid AI models, while the rest (i.e. 25%) to test them. Sensitivity analysis results reveal that the WANFIS-FA models display more acceptable performance than the ANFIS-FA ones. Also, the estimations of MAE, NSC, and SI for the simulation of HCO3 − by the superior model of the WANFIS-FA are obtained to be 0.040, 0.988, and 0.022, respectively. In addition, the lags (t-1), (t-2), (t-3), and (t-4) are ascertained as the most effective time-series lags for the estimation of Cl − .

Dust events impose negative socio-economic, health, and environmental impacts on vulnerable areas and reflect their sources’ physiochemical and biological characteristics. This study aimed to assess the impact of two dust sources on the concentration and diversity of airborne fungi in one of the dustiest areas in the world. This study is the first attempt to investigate the relationship between dust sources fungal community and those in airborne dust. Also, the contribution of dust sources to airborne fungi was estimated. Air masses arriving at the study area were assessed using local wind rose and the HYSPLIT model. Sampling was carried out from airborne dust at the Arvand Free Zone as target areas and soil in the dried parts of the Hor al-Azim and Shadegan wetlands as source areas to explore the relationship between fungi in the dust sources and the downwind area. The samples were analyzed in the lab to extract DNA. The internal transcribed spacer (ITS) regions of the rDNA gene were amplified using the primers ITS1F and ITS4, and then PCR products were sent to the lab for sequencing. The raw DNA data were processed using the QIIME virtual box to pick operational taxonomic units and taxonomy assignments. The most common fungi at the genus level were in the order of Penicillium > Aspergillus > Alternaria > Fusarium > Paradendryphiella > Talaromyces. The similarity between air and soil fungal genera was investigated using richness and diversity indices, the phylogenetic tree, and principal component analysis. The results showed that the community structures of ambient fungi in the Hor al-Azim and Shadegan dust sources were more similar to those on dusty days than non-dusty days. The source tracker model was used to quantify the contributions of known dust sources to airborne fungi. The results showed that the main source of airborne fungi was Hor al-Azim on dusty and non-dusty days. This study’s results can help managers identify and prioritize dust sources regarding fungal species.

Environmental pollutants and lifestyle both contribute to the rapidly increasing prevalence of type 2 diabetes mellitus (T2DM) worldwide. Evidence suggests that exposure to environmental contaminants such as arsenic is associated with impaired glucose metabolism and insulin signaling. In the present study, isolated rat liver mitochondria (1 mg/ml) were co-exposed to low concentration of arsenic trioxide (ATO) (IC₂₅ = 40 µM) and hyperglycemic condition (20, 40, 80, 160 mM glucose or 20, 40, 80, 160 mM pyruvate (PYR)). Mitochondrial dehydrogenase activity (complex II), glutathione content (GSH), reactive oxygen species (ROS), lipid peroxidation, mitochondrial membrane potential (ΔΨ), and mitochondrial swelling were then evaluated in the presence of ATO 40 µM and PYR 40 mM. Unexpectedly, glucose alone (20, 40, 80, 160 mM) had no toxic effect on mitochondria, even at very high concentrations and even when combined with ATO. Interestingly, PYR at low concentrations (≤ 10 mM) has a protective effect on mitochondria, but at higher concentrations (≥ 40 mM) with ATO, it decreased the complex II activity and increased mitochondrial ROS production, lipid peroxidation, GSH depletion, mitochondrial membrane damage, and swelling (p < 0.05). In conclusion, PYR but not glucose increased ATO mitochondrial toxicity even at low concentrations. These results suggest that pre-diabetics with non-clinical hyperglycemia, who are inevitably exposed to low concentrations of arsenic through food and water, may develop mitochondrial dysfunction that accelerates their progression to diabetes over time.

Building on the resource-based view (RBV) theory, this paper aims to shed light on how does the implementation of green strategies affect enterprises’ performance. To distinguish the evolution of strategy implementation effect, we adopt a panel estimation strategy and gather data from 3869 listed companies in China from 2008 to 2019. Furthermore, we innovatively use the semi-supervised clustering algorithm to classify the companies according to whether they implement green strategies or not and then discuss long-term and short-term financial effects of implementing green strategies. Our study finds that the implementation of green strategy facilitates a company’s long-term performance but hampers its short-term performance. According to the moderating analysis, a green strategy could negatively impact a company’s financial performance by increasing debt ratios. The findings highlight the importance of implementing green strategies and the obstacles in the process of transforming enterprises to be green.

Eco-toxicity profiles for commonly used disinfectants were lacking. Available traditional toxicity techniques have some limitations (assessments and ethical issues). Behaviour toxicology is a promising research area towards early warning and non-invasive approaches. We studied the potential eco-toxic effects of sodium hypochlorite (NaOCl) on the swimming behaviour of zebrafish. Zebrafish were exposed to different concentrations (Treatment I, Treatment II, Treatment III, and Treatment IV) of NaOCl for 360 h. Recovery study (144 h) was conducted for NaOCl treatment groups. The swimming behaviour of zebrafish was quantified efficiently using an online monitoring system (OMS). OMS dataset was processed for determination of behavioural differences by MATLAB and SPSS. Compared to the control group, the swimming strength of zebrafish under NaOCl treatments declined significantly (p < 0.001). Avoidance behaviour has occurred on zebrafish under NaOCl exposure periods. Furthermore, NaOCl toxicity also adjusted circadian rhythms on zebrafish. Zebrafish swimming strength was significantly (p < 0.001) improved under-recovery periods. Moreover, normal diurnal patterns have occurred. NaOCl could cause behavioural abnormalities in non-target organisms. Continuous exposure to common disinfectants could cause external and internal stress on non-target organisms, resulting in behavioural changes and circadian rhythm adjustments. Continuous changes in behavioural and circadian rhythms might reduce organisms’ fitness and adaptation capacity. This study highlights (1) the importance of computer-based toxicity assessments, and (2) swimming behaviour is an early warning biomarker for eco-toxicity studies.

Radiology workers might constantly be exposed to low-dose ionizing radiation due to their profession. Low doses of radiation in a short exposure time have the potential to alter the genome, which might potentially lead to diseases. The main objective of this study was to determine whether the amount of cell-free nucleic acids in plasma samples of radiation-exposed workers was different from the general public, in other words, non-exposed individuals. In this context, we investigated the association between radiation exposure and cell-free nucleic acids concentration by using radiation exposure parameters. The study consisted of 40 radiology workers and 40 individuals who were not exposed to ionizing radiation. The plasma concentrations of cell-free DNA, RNA, and miRNA were measured fluorometrically. We found that the ccfRNA concentration of the radiation-exposed group was significantly different from that of the non-exposed group (p = 0.0001). However, there are no differences between both groups in terms of ccfDNA and ccfmiRNA concentration. The concentration of ccfDNA is significantly correlated with working time in the fluoroscopy field (p < 0.05). We found that the concentration of ccfmiRNA was significantly correlated with working time in plain radiography (p < 0.01) and computed tomography (p < 0.05) and with total working time (p < 0.01). Similarly, the concentrations of ccfRNA were significantly correlated with working time in computed tomography (p < 0.01) and with the total working time (p < 0.05) of the workers. We found that imaging number in computed tomography significantly altered the level of ccfRNA (p = 0.006) and that working time in the computed tomography field significantly affected the ccfRNA concentration (p = 0.03, R² = 0.36 for model). Finally, we determined that total working time was significantly associated with total ccfRNA concentration (p < 0.05, R² = 0.25 for model). In conclusion, total RNA measured in radiation-exposed workers has the potential to predict the radiation exposure risk. Furthermore, total working time and working time in the tomography field significantly alter the level of free nucleic acids.

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing’s pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China’s carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China.

The purification of micro-polluted water for drinking water can play an important role in solving water crisis. To investigate the effects of spectral composition on nutrient removal and biofuel feedstock production using duckweed, Landoltia punctata was cultivated in different spectral compositions in micro-polluted water. Results showed that the nitrogen and phosphorus removal efficiency were 99.4% and 93.5% at an recommended red and blue light photon intensity mixture ratio of 2:1. Meanwhile, maximum growth rate of duckweed (11.37 g/m²/day) was observed at red/blue = 2:1. In addition, maximum starch accumulation rate of duckweed was found to be 6.12 g/m²/day, with starch content of 36.63% at red/blue = 4:1, which was three times higher when compared to that of white light. Moreover, the recommended ratio of red and blue light was validated by economic efficiency analysis of energy consumptions. These findings provide a sustainable environmental restoration method to transform water micro-pollutants to available substances.

The authors investigate how artificial intelligence modifies a huge piece of the energy area, the oil and gas industry. This paper attempts to evaluate technical and non-technical factors affecting the adoption of machine learning technologies. The study includes machine learning development platforms, network architecture, and opportunities and challenges of adopting machine learning technologies in the oil and gas industry. The authors elaborate on the three different sectors in this industry namely upstream, midstream, and downstream. Herein, a review is presented to evaluate the applications and scope of machine learning in the oil and gas industry to optimize the upstream operations (including exploration, drilling, reservoir, and production), midstream operations (including transportation using pipelines, ships, and road vehicles), and downstream operations (including production of refinery products like fuels, lubricants, and plastics). Enhanced processing of seismic data is illustrated which provides the industry with a better understanding of machine learning applications. Basing on the investigation of AI implementation prospects and the survey of subsisting implementations, they diagram the latest patterns in creating AI-based instruments and distinguish their impacts on speeding up and de-gambling measures in the business. They examine AI proposition and calculations, just as the job and accessibility of information in the portion. Furthermore, they examine the principal non-specialized difficulties that forestall the concentrated use of man-made brainpower in the oil and gas industry (OGI), identified with information, individuals, and new types of joint effort. They additionally diagram potential situations of how man-made reasoning will create in the OGI and how it might transform it later on (in 5, 10, and 20 years).

Coal mine and coal-fired power plant are the main sources of selenium (Se) and mercury (Hg) for the collapsed lakes derived from mining subsidence. However, whether and how Se and Hg impact the bacterial communities in the artificial aquatic ecosystems is barely known. The bacterial communities, physicochemical properties, and Se/Hg concentrations of the overlying water and surface sediment from Nanhu Lake, a typical collapsed lake in Huaibei city, Anhui province, were characterized through high-throughput sequencing and chemical analysis. The lake was enriched with N and P, and the surface sediment contained extremely high Se (6.90–30.37 mg/kg). The composition and structure of bacterial communities in the overlying water were different from that in the surface sediment. Alpha diversity indices in the surface sediment were higher than that in the overlying water, and they increased with TP, TN, and NH₃-N. Overall, redundancy analysis indicated that Se, Hg, and physicochemical properties co-mediated the bacterial community in Nanhu Lake. The results highlighted the necessity to reduce the input of exogenous nutrients and to reconsider the environmental and health risks of receiving Se- and Hg-containing mine water.