Himalaya, the highest mountain system in the world and house of important biodiversity hotspot, is sensitive to projected warming by climate change. Rhizocarpon geographicum (map lichen), a crustose lichen, grows in high mountain ranges, is a potential indicator species of climate change. In the present study, MaxEnt species distribution modeling algorithm was used to predict the suitable habitat for R. geographicum in current and future climate scenarios. Nineteen bioclimatic variables from WorldClim database, along with elevation, were used to predict the current distribution and three representative concentration pathway (RCP) scenarios by integrating three general circulation models (GCMs) for future distribution of species covering years 2050 and 2070. Furthermore, we performed change analysis to identify the precise difference between the current and future distribution of suitable areas of the species for delineating habitat range expansion (gain), habitat contraction (loss), and stable habitats. The final ensemble model obtained had average test value 0.968, and its predicted ~ 27.5% of the geographical area in the Indian Himalayan Region is presently climatically suitable for the species. The predicted highly suitable area for R. geographicum is observed to be declining in Northwestern Himalaya, and it is shifting towards the higher elevation areas of the Eastern Himalaya. The projected distribution in future under the RCP scenarios (RCP 4.5, 6.0, and 8.5) showed the range expansion towards higher elevations, and it is more pronounced for the extreme future scenarios (RCP 8.5) than for the moderate and intermediate climate scenarios (RCP 4.5 and RCP 6.0). However, assuming that species can migrate to previously unoccupied areas, the model forecasts a habitat loss of 10.86–16.51% for R. geographicum, which is expected due to increase in mean annual temperature by 1.5–3.7 °C. The predictive MaxEnt modeling approach for mapping lichen will contribute significantly to the understanding of the impact of climate change in Himalayan ecosystems with wide implications for drawing suitable conservation plans and to take adaptation and mitigation measures.

Iron oxide nanoparticles (nano-Fe₂O₃) widely distribute in waters with low toxicity to aquatic organisms. But it is unclear for nano-Fe₂O₃ to affect the fate of coexisting arsenic (As) with its bioaccumulation and biotransformation. In this study, we thus mainly investigated arsenate (As(V)) toxicity, uptake kinetics, biotransformation and subcellular distribution in Microcystis aeruginosa influenced by nano-Fe₂O₃. The results showed that M. aeruginosa was more sensitive to As(V) associated with nano-Fe₂O₃. Due to the exaggerated increase of efflux rate constants of As compared with the uptake rate constants in algal cells affected by different levels of nano-Fe₂O₃, the As(V) bioconcentration factor decreased with nano-Fe₂O₃ increasing correspondingly, indicating that As bioaccumulation was diminished by nano-Fe₂O₃. The decreased As accumulation in M. aeruginosa could be supported by the evidential As(V) sequestration through high adsorption of nano-Fe₂O₃, which resulted in decreasing free As level for algae uptake in media. Meanwhile, As subcellular distribution was adjusted by nano-Fe₂O₃ with decreasing in cell walls and rising in cytoplasmic organelles compared with nano-Fe₂O₃ free. As(V) reduction and methylation were enhanced with increasing nano-Fe₂O₃, stimulating by its sensitivity to the interaction of nano-Fe₂O₃ and As(V) as well as the rising level of As in cytoplasmic organelles of this algae. It is confirmed by the higher relative gene expression levels of arsC and arsM in elevated nano-Fe₂O₃. Accordingly, it is highlighted to be deserved more attention that the changing behavior of As(V) by nano-Fe₂O₃ that reduce As bioaccumulation and accelerate its biotransformation in algae in As contaminated water.

Recurring cyanobacterial blooms have seriously hindered the sustainable development of cities. In this study, the variation trend of cyanobacterial blooms was analyzed by taking Lake Chaohu in China as the study area, and the Normalized Difference Vegetation Index (NDVI) derived from Landsat observations combined with the development index of surrounding cities from 2009 to 2019 was used to quantitatively analyze the response of cyanobacterial blooms to urban development and climate change. The results showed that the NDVI of the Northwest Lake region was significantly higher than that of other regions. Summer and autumn were the main seasons for the outbreak of cyanobacterial blooms. The NDVI of Lake Chaohu and Baohe Lake region showed a significant correlation with the gross domestic product (GDP) growth rate of Hefei city (HF), the districts and counties around the lake (DCL), Baohe District (BH), and the population (P). As the economic regions gradually focused on BH rather than on HF and DCL, there was an increasing trend correlation between the NDVI of Baohe Lake region and the GDP growth rate. However, the elimination of GDP in BH did not affect the consistency relationship between the economic growth of other regions and the NDVI of Lake Chaohu on a large scale. In addition, the results of relative importance analysis indicated that the GDP growth rate of BH and the area of Hefei except DCL (HF-DCL) accounted for important contribution to the [Formula: see text] of the regression. This study has momentous reference value for understanding the coupling relationship between urban development and lake environment.

The present work calculates the steelworks increments of particulate matter (PM) mass and chemical composition of Partisol, Filter Dynamic Measurement System (FDMS), and Streaker data using wind sector analysis. Particulate matter sampling took place at the perimeter of a steelworks complex in Port Talbot, Wales, UK, between April 17 and May 16, 2012. Two sampling sites were selected representing the upwind and downwind sectors. Daily Partisol, hourly FDMS, and Streaker PM samples were analysed for mass and chemical composition using standard procedures. Wind-determined analysis was carried out on the daily and hourly PM results by finding the differences between the downwind and upwind PM data that were associated with the steelworks production units. Little Warren (LW) monitoring site located at the coastal site of Port Talbot represents the upwind site when the prevailing wind blows from westerly and south-westerly across the steelworks complex to the fire station (FS), the upwind site. When the prevailing wind blows from the southeast and south, LW represents the downwind sector. Results indicated common episodic days where both the FDMS and Partisol PM₁₀ data exceeded the World Health Organization (WHO) limit of 50 µg m⁻³ (24-h mean). The Partisol PM₂.₅/PM₁₀ ratios revealed elevated coarse particle concentrations, whilst the FDMS PM₂.₅/PM₁₀ ratios showed domination by PM₂.₅ particles. Wind-determined profiles of PM during the 1-month campaign provided useful information about the tracer elements specific to a particular processing unit of the steelworks industry. The annual PM steelworks increments at Port Talbot by Partisol and FDMS are approximately 2.0 µg m⁻³.

Our previous study showed that dithiophenolate (DTP) and its chitosan nanoparticles (DTP-CSNPs) have abilities to bind with DNA helixes. So in this study, their lethal doses (LD₅₀) and therapeutic roles against rat liver injuries induced by carbon tetrachloride (CCl₄) were evaluated. The study focused on the determination of the markers of oxidative stress (OS) and apoptosis and compare the results with those of cisplatin treatment. The results revealed that LD₅₀ values of DTP and DTP-CSNPs are 2187.5 and 1462.5 mg/kg, respectively. Treatment with DPT and DPT-CSNPs after CCl₄ administration reduced liver injuries, induced by CCl₄, and improved liver functions and architecture through the reduction of OS and apoptosis. Where the oxidant marker was decreased with elevations of antioxidant markers. Also, there was an elevation in Bcl-2 value, with decreases in caspase-8, Bax, and Bax/Bcl-2 ratio. DPT-CSNPs treatment gave preferable results than those treated with DPT. Moreover, DTP and DPT-CSNPs treatment gave better results than cisplatin treatment. The administration of healthy rats with low doses of DTP and DTP-CSNPs for 14 days had no effect. Otherwise, the study on HepG2 cell line showed that DTP and DPT-CSNPs inhibited cell growth by arresting cells in the G2/M phase and inducing cell death. In conclusion, DTP and DTP-CSNPs have antiapoptotic and anti-oxidative stress toward hepatotoxicity induced by CCl₄. Moreover, DTP and DTP-CSNPs have anticancer activity against the HepG2 cell line. Generally, DTP-CSNPs are more effective than DTP. So, they can be used in the pharmacological fields, especially DTP-CSNPs.

Worldwide use of glyphosate is constantly increasing and its residues are detected in drinking water, agriculture, and food products. There are controversial data regarding the potential reproductive adverse effects of glyphosate herbicide. Therefore, we conducted a systematic review and meta-analysis on the studies in which the alteration of at least one sexual hormone including testosterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol was reported as a measured outcome in rats. In November 2020, 284 articles were screened, of which eight were eligible for the meta-analysis. An overall considerable effect of glyphosate exposure was found on decreasing of testosterone (7 studies, WMD = − 1.48 ng/mL; 95% CI, − 2.34 to − 0.61; P = 0.001), LH (3 studies, WMD = − 2.03 mIu/mL; 95% CI, − 3.34 to − 0.71; P = 0.003), and FSH (3 studies, WMD = − 2.28 mIu/mL; 95% CI, − 5.12 to 0.55; P = 0.115). According to our results, glyphosate intake could have major effects on the health of reproductive system. Consequently, strict monitoring of the residual glyphosate content in the drinking water, agricultural crops, and food products is necessary.

Titanium-based nanomaterials co-doped with terbium (Tb) and sulfur (S) were synthesized by sol–gel method via a facile step. Physicochemical properties of the resulting composites were characterized by X-ray diffraction (XRD), Scanning electron microscope (SEM), X-ray photo-electron spectroscopy (XPS) and UV–Vis diffused reflectance spectroscopy (DRS). Methylene blue (MB) was used as a degradation target for evaluating the photocatalytic performance. The factors which influence the photocatalytic activity were investigated, including calcined temperatures and S doping amount. Tb, S (2 wt%) co-doped TiO₂ composite calcined at 500 °C exhibited the highest photocatalytic activity with a degradation rate of 72.4% in 3 h. The reaction constant was 0.11529, 0.26025, 0.35038 and 0.41462 h⁻¹ for undoped TiO₂, Tb-doped TiO₂, S-doped TiO₂ and Tb, S co-doped TiO₂, respectively. Importantly, the synergistic effect of terbium and sulfur dopants was profoundly discussed. Furthermore, recycling tests and acute toxicity experiments were carried out to confirm the reusability and biosafety of Tb, S co-doped TiO₂.

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST₅), 15-cm (ST₁₅), and 30-cm (ST₃₀) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash–Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.

Submerged macrophytes and microbial communities are important parts of lake ecosystems. In this study, the bacterial community composition in rhizosphere sediments and water from areas cultivated with (PL) and without (CK) shining pondweed (Potamogeton lucens Linn.) was investigated to determine the effects of P. lucens Linn. on the structure of the bacterial communities in Nansi Lake, China. Molecular techniques, including Illumina MiSeq and qPCR targeting of the 16S rRNA gene, were used to analyze the composition and abundance of the bacterial community. We found that bacterial alpha diversity was higher in PL water than in CK water, and the opposite trend was observed in sediment. In addition, 16S rRNA gene copy number in sediment was lower in PL than in CK. We found 30 (e.g., Desulfatiglans) and 29 (e.g., Limnohabitans) significantly different genera in sediment and water, respectively. P. lucens Linn. can change chemical properties in sediment and water and thereby affect the bacterial community. At the genus level, members of bacterial community clustered according to source (water/sediment) and area (PL/CK). Our study demonstrated that submerged macrophytes can affect the bacterial community composition in both sediment and water, suggesting that submerged macrophytes affect the transportation and cycling of nutrients in lake ecosystems.

The mosquito vectors of the genera Aedes and Anopheles present resistance to several commercial insecticides, which are also toxic to non-predator targets. On the other hand, essential oils are a promising source of insecticides. Thus, in this work, the essential oil from the leaves of Piper purusanum was characterized by gas chromatography–based approaches and evaluated as biodefensive against malaria and dengue vectors. The main compounds of P. purusanum essential oil were β-caryophyllene (57.05%), α-humulene (14.50%), and germacrene D (8.20%). The essential oil inhibited egg hatching (7.6 ± 1.5 to 95.6 ± 4.5%), caused larval death (LC₅₀ from 49.84 to 51.60 ppm), and inhibited the action of acetylcholinesterase (IC₅₀ of 2.29 µg/mL), which can be related to the mechanisms of action. On the other hand, the biological activities of β-caryophyllene, α-humulene, and germacrene D were higher than that of essential oil. In addition, these sesquiterpenes and essential oil did not show a lethal effect on Toxorhynchites splendens, Anisops bouvieri, Gambusia affinis, and Diplonychus indicus (LC₅₀ from 2098.80 to 7707.13 ppm), although D. indicus is more sensitive (SI/PSF from 48.56 to 252.02 ppm) to essential oil, representing a natural alternative against these relevant vectors.