Arsenic (As) is a hazardous pollutant that negatively impacts the physiological functions of alga. So far, a detailed understanding of algal response to As stress is still lacking. In this study, a transcriptome analysis was performed to illustrate the toxicity response of Caulerpa lentillifera J. Agardh, an edible algae with rich nutrition, to arsenite [As(III)], a toxic form of As. Totally, 1913 differentially expressed genes (DEGs) were screened, of which 642 were up- and 1271 were downregulated in C. lentillifera under As(III) stress (30 mg·L–¹) compared with control. As(III) stress promoted the growth of C. lentillifera at low concentration (0.1 mg·L–¹) and inhibited the growth at high concentration (≥ 0.5 mg·L–¹). Multiple DEGs involved in oxidoreductase activities were significantly affected by As(III), and several DEGs related to antioxidant enzyme activity were downregulated, resulting in suffering from oxidative stress in C. lentillifera. Results also showed that As(III) stress inhibited chlorophyll and carotenoid synthesis, destroyed the integrity of chloroplasts, and interfered with the absorption of light energy, thereby inhibiting photosynthesis in C. lentillifera. The highly enriched ABC transporter-related genes involved in the detoxification process were upregulated under As(III) stress, indicating their critical role in the resistance to As stress in C. lentillifera. The gene expressions for 10 selected DEGs were confirmed by qRT-PCR, showing the reliability of the data revealed by RNA sequencing. Our novel work illustrated the toxicity of C. lentillifera under As(III) stress at the molecular level, serving as a basis for future investigations on the prevention and treatment of such pollutants.

Short-term exposures to air pollution have been associated with various adverse health effects. In this study, we investigated the associations between ambient air pollutants with the number of hospital admissions and mortality from cardiovascular diseases (CVDs). This time series study was conducted in Tehran for the years 2014–2017 (1220 day). We collected the ambient air pollutant concentration data from the regulatory monitoring stations. The health data were obtained from the Ministry of Health and Medical Education. A distributed lag non-linear model (DLNM) was used for the analyses. Total CVDs and ischemic heart disease (IHD) admissions were associated with CO for each 1 mg/m³ increase at lags of 6 and 7 days. Also, there was a positive association between total CVDs (RR 1.01; 1.001 to 1.03), IHD (RR 1.04; 1.006 to 1.07), and cerebrovascular diseases (RR 1.03; 1.005 to 1.07) mortality with SO₂ at a lag of 4 days. PM₂.₅ and PM₁₀ were associated with cerebrovascular disease admissions in females aged 16–65 years and 16 years and younger for each 10 µg/m³ increase, respectively. Short-term exposure to SO₂, NO₂, and CO was associated with hospital admissions and mortality for CVDs, IHD, cerebrovascular diseases, and other cardiovascular diseases at different lags. Moreover, females were more affected by ambient air pollutants than males in terms of their burden of CVDs. Therefore, identifying the likely harmful effects of pollutants given their current concentrations requires the planning and implementation of strategies to reduce air pollution.

A novel adsorbent consisting of tannic acid (TA) immobilized on graphene oxide (GO) was proposed and used to remove Hg²⁺ from desulfurization wastewater. The morphology and physicochemical properties of tannin-immobilized graphene oxide (TAIGO) were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The characterization results showed that TA was successfully immobilized on GO, and that new functional groups were introduced on TAIGO. The effects of contact time, adsorbent dose, pH, and ion components on removal efficiency were evaluated. The adsorption process was found to be complete within 15 min, and the removal efficiency increased with increasing adsorbent dosage. The pH value affected the protonation of TAIGO and the form of Hg²⁺ in wastewater. High concentrations of Cl⁻ and SO₃²⁻ hindered the adsorption performance, whereas SO₄²⁻ and cations had a negligible effect. In addition, the excellent economic benefits of TAIGO were analyzed in an economic evaluation, and the Hg²⁺ removal efficiency remained at 88% after three recycles. A pseudo-second-order kinetic model (R = 0.9995) was used to fit the adsorption process, and the oxygen-containing functional groups and chelation reaction played critical roles in adsorption. TAIGO is a low-cost adsorbent with high Hg²⁺ removal efficiency and could be further used in practical desulfurization wastewater.

A novel adsorbent based on metal sulfide nanoparticles (MeSNPs) was biologically synthesized from metallic wastewater and examined for azo dyes removal from aqueous solution in batch and continuous systems. The size of the MeSNPs was in the range of 8–10 nm, with an average specific surface area of 120.4 m²/g. Batch adsorption study was then carried out using Direct Red 80 (DR 80) and Mordant Blue 9 (MB 9) as the model azo dyes by varying MeSNPs dosage, contact time, pH, and initial dye concentration. More than 99% removal efficiency of both the dyes was achieved by using MeSNPs at the following optimum conditions: 200 mg dosage, pH 2, 6 min contact time, and 100 mg L⁻¹ initial dye concentration. The batch sorption isotherm results were described using the Sips model, with the maximum predicted capacity values of 143.7 and 198.3 mg of dye per gram of adsorbent for DR 80 and MB 9, respectively. Besides, the sorption kinetic data for both the dyes followed the pseudo-second-order rate. Furthermore, maximum desorption efficiency values of 93% for DR 80 and 97% for MB 9 were achieved using an aqueous solution of pH 12, thus indicating that the adsorbent can be regenerated and reused further. Dynamic adsorption of the dyes was studied using a fixed-bed column with the MeSNPs as a function of liquid flow rates. The results showed an increase in breakthrough time with a decline in the flow rates for both DR 80 and MB 9 and the breakthrough behavior was explained using Thomas, Clark, and Yoon-Nelson models.

In recent years, suppliers in developing countries face the challenge of their low technological and knowledge-related capabilities to enhance sustainability. In addition, buying firms cannot optimize the performance of the whole supply chain because the supplier sustainability practices are unobservable to them and to the public. These two challenges lead to the occurrence of several environmental and social scandals. These scandals pose serious economic and reputational consequences to supply chain members, especially buying firms. Given these challenges and using a sequential game-theoretic methodology, our contribution to the literature on supplier sustainability is to address the effects of supplier code of conduct (SCC) programs on different performance indicators of supply chains. Our analyses indicated that (a) an SCC program can mitigate the supply chain performance deficiencies created by the lack of the integrated optimization of the whole supply chain. Moreover, an SCC program can be superior to some common auditing programs initiated by buying firms. (b) In cases that the supplier’s capability is low or moderate, an SCC program provides a Pareto improvement in its sustainability level and the buying firm’s profit. (c) High consumer awareness threatens the buying firm’s survival in the market. Given this threat, an SCC program indirectly enhances the buying firm’s potential to survive. (d) Buying firms should lower their suppliers’ wholesale prices in exchange for a substantial investment in their suppliers’ capabilities, irrespective of whether or not an SCC program is initiated.

The rapid growth of environmental pollution and the destruction of eco-systems force every individual economy to focus on environmentally friendly economic development. This research explores economic growth, energy use, foreign direct investment, agriculture, industrialization, and urban population growth with environmental sustainability proxied as CO₂ emissions from 1971 to 2018. Econometric methods are employed for different purposes as the unit root for the stationary check. ARDL determines the long-run relationship, while the Decoupling Index examines the growing speed of variables and CO₂ emissions, and VECM has been used for short- and long-run causalities. The study’s findings confirm the long-run impact of all environmental pollution variables as ECM-1 is negatively significant. The short-run causality test shows CO₂ emissions because of economic growth (GDP = > CO₂ ≠ GDP), energy use (ENU = > CO₂ ≠ ENU), and foreign direct investment (FDI = > CO₂ ≠ FDI) at a 1% level. In contrast, CO₂ emissions are not the Granger cause of GDP, ENU, and FDI. Economic growth, energy use, and foreign direct investment will increase CO₂ emission, not vice versa. The study findings suggest that governments should move toward adopting green technology by implementation of green fiscal policies.

The effects of air pollution on sleep and dementia remain unclear. The objective of this study was to investigate the effects of air pollution on cognitive function as mediated by the sleep cycle. A cross-sectional study design was conducted to recruit 4866 subjects on which PSG had been performed. Fifty of them were further given a cognitive function evaluation by the MMSE and CASI as well as brain images by CT and MRI. Associations of 1-year air pollution parameters with sleep parameters, cognitive function, and brain structure were examined. We observed that O₃ was associated with a decrease in arousal, an increase in the N1 stage, and a decrease in the N2 stage of sleep. NO₂ was associated with an increase in the N1 stage, a decrease in the N2 stage, and an increase in REM. PM₂.₅ was associated with a decrease in the N1 stage, increases in the N2 and N3 stages, and a decrease in REM. The N1 and N2 stages were associated with cognitive decline, but REM was associated with an increase in cognitive function. The N1 stage was a mediator of the effects of PM₂.₅ on the concentration domain of the MMSE. O₃ was associated with an increase in the pars orbitalis volume of the left brain. NO₂ was associated with increases in the rostral middle frontal volume, supramarginal gyrus volume, and transverse temporal volume of the left brain, and the pars opercularis volume of the right brain. PM₂.₅ was associated with increases in the pars triangularis volume of the left brain and the fusiform thickness of the right brain. In conclusion, we observed that air pollution was associated with cognitive decline by mediating effects on the sleep cycle with changes in the brain structure in controlling executive, learning, and language functions in adults.

This current study review provides a brief review of a natural bee product known as propolis and its relevance toward combating SARS-CoV viruses. Propolis has been utilized in medicinal products for centuries due to its excellent biological properties. These include anti-oxidant, immunomodulatory, anti-inflammatory, anti-viral, anti-fungal, and bactericidal activities. Furthermore, studies on molecular simulations show that flavonoids in propolis may reduce viral replication. While further research is needed to validate this theory, it has been observed that COVID-19 patients receiving propolis show earlier viral clearance, enhanced symptom recovery, quicker discharge from hospitals, and a reduced mortality rate relative to other patients. As a result, it appears that propolis could probably be useful in the treatment of SARS-CoV-2-infected patients. Therefore, this review sought to explore the natural properties of propolis and further evaluated past studies that investigated propolis as an alternative product for the treatment of COVID-19 symptoms. In addition, the review also highlights the possible mode of propolis action as well as molecular simulations of propolis compounds that may interact with the SARS-CoV-2 virus. The activity of propolis compounds in decreasing the impact of COVID-19-related comorbidities, the possible roles of such compounds as COVID-19 vaccine adjuvants, and the use of nutraceuticals in COVID-19 treatment, instead of pharmaceuticals, has also been discussed.

Reported evidence has increasingly indicated that exposure to phthalates can cause adverse pregnancy outcomes. However, phthalate exposure levels among pregnant women remains unclear. We aimed to evaluate the concentrations and predictors of phthalate metabolites in urine samples of the ongoing Zunyi cohort of pregnant women from Southwest China. The urine samples were collected from 1003 pregnant women during their third trimester of pregnancy. The concentrations of nine phthalate metabolites in urine samples were then determined. Data on socio-demographic profiles of the participants, lifestyle during pregnancy, parity, and sampling season were collected using questionnaires. The detectable rate of phthalate metabolites ranged from 76 to 100%. On average, mono-butyl phthalate exhibited the highest median concentration (62.45 μg/L), while mono-benzyl phthalate exhibited the lowest median concentration (0.04 μg/L). Urine concentrations of phthalate metabolites were significantly higher in older, multiparous, higher body mass index, higher income, and passive smoking during pregnancy participants. The levels of low-molecular-weight phthalate metabolites were highest during the summer. The findings indicate the health of pregnant women and fetuses in Zunyi may be generally harmed by the high exposure of phthalate metabolites, especially by mono-n-butyl phthalate. In addition, phthalate metabolites present a demographic and seasonal differential distribution among the study population. Targeted measures to reduce phthalate exposure for high-risk pregnant women and during high-exposure seasons may have potential benefits for maternal and fetal health protection.

The rapid economic development and climate change have accelerated the changes in China’s food production and have a potential impact on food security. In this paper, the grain sown area from 2001 to 2019 was selected to analyze the spatio-temporal evolution and driving factors of China’s grain production through spatial autocorrelation analysis and geographically weighted regression. Our findings were as follows: (1) From the perspective of time characteristics, China’s grain production from 2001 to 2019 experienced four stages, rapid decline, rapid growth, steady growth, and slow decline, although with an overall upward trend. (2) From the perspective of spatial characteristics, the overall spatial pattern had a significant positive correlation. The high values were mainly concentrated in Shandong, Anhui, and Jilin and moved to the northeast China as time went on. (3) In terms of influencing factors, the positive impact of agricultural labor force on the grain production gradually decreased, showing a decreasing trend from southwest to northeast. The promotion of agricultural mechanization on the grain production increased year by year, with the spatial distribution characteristics of high in the northeast and low in the southwest. Besides, the coefficient of water resources endowment was negative, showing a spatial distribution pattern of high north and low south.