The development of advanced membrane materials is crucial for removing hazardous pollutants from contaminated water. In this study, a nickel-based metal–organic framework (Ni–BTC) was combined with graphene oxide (GO) to produce a Ni–BTC/GO nanocomposite with superior removal performance for heavy metals and pesticides. GO was synthesized using a rapid and environmentally friendly electrochemical exfoliation of graphite, followed by in situ growth of Ni–BTC on the GO scaffold through a solvothermal method. The oxygen-containing functional groups on GO interacted strongly with the nickel centers, leading to a homogeneous dispersion of Ni–BTC particles and the formation of a hierarchically porous hybrid structure. The successful formation of the composite was confirmed by XRD, FTIR, SEM, BET, Zeta potential, and TGA analyses. The Ni–BTC/GO composite membrane was evaluated using a dead‑end filtration cell. The membrane achieved removal efficiencies (rejection rates) of 92.4
Heavy metal water pollution in agricultural ecosystems is a major threat to ecosystem functioning and human health; however, rapid and cost-effective monitoring tools are limited. In this study, freshwater algae, Spirogyra sp., were evaluated as bioindicators of heavy metal contamination using Visible-Near Infrared (VNIR) spectral indices and algal bioaccumulation data from canal and rice paddy systems at Mae Hia Agricultural Research Centre, Chiang Mai, Thailand. Water quality varied among habitats, with pH ranging from 6.8 to 8.8. Rice paddy systems supported higher algal biomass, reaching up to 17.62 g dry weight, compared with canal environments, likely reflecting greater nutrient availability and hydrological stability. Spirogyra sp. bioaccumulated all seven metals tested lead (Pb), copper (Cu), zinc (Zn), aluminium (Al), nickel (Ni), cadmium (Cd), and mercury (Hg) with bioaccumulation factors (BAFs) exceeding 1 at every site; Al reached the highest BAF of 2.35×106 L/kg in canal samples, followed by Zn, Pb and Cu (2.2–2.5×104 L/kg), confirming strong and active metal uptake from the surrounding water. Strong positive correlations among Pb, Cu, Zn, Al, and Ni in algal tissues (Pearson r = 0.95–1.00) indicated shared non-specific biosorption pathways, while mercury exhibited a distinct accumulation pattern. VNIR spectral indices reliably differentiated Spirogyra sp. from surrounding water. Vegetation- and water-related indices were both negatively correlated with Pb and several co-accumulating metals in algal tissue (Kernel Normalised Difference Vegetation Index, KNDVI r = − 0.88 to − 0.95; Modified Normalized Difference Water Index, MNDWI r = − 0.76 to − 0.85). For log-transformed BAFs, the KNDVI gave the clearest spectral response, decreasing as the bioaccumulation of Al (r = − 0.97), Zn (r = − 0.95), Pb (r = − 0.94), Cu (r = − 0.92) and Ni (r = − 0.92) increased, whereas the Normalised Difference Water Index (NDWI) increased with Hg bioaccumulation (r = 0.87). VNIR spectroscopy cannot directly measure heavy metal concentrations in water, but Spirogyra sp. showed excellent correlations with several heavy metals, mainly Pb, Cu, Zn, Al, Ni, Cd and Hg, suggesting its potential as bioindicator and bioaccumulative organism in freshwater ecosystems. The biomass of algae was highly related to the increasing concentrations of heavy metals in water. Additionally, the use of VNIR spectroscopy for algae has shown potential for estimating heavy metal concentrations from spectral analysis. As far as we are aware, this is the first report combining VNIR spectroscopy with Spirogyra sp. for heavy metal assessment in agricultural freshwater.
This study examines the effects of green budgeting and environmental fiscal instruments on environmental sustainability in 22 European Union (EU-22) countries over the period 2000–2023. The environmental sustainability index, used as the dependent variable, was constructed from the scores for SDG 13 (Climate Action), SDG 14 (Life Below Water), and SDG 15 (Life on Land) using Principal Component Analysis (PCA), with the first and second principal components (PC1 and PC2) jointly explaining 82.89
Heavy metals in aquaculture water from industrial effluent, mining, and agricultural runoff pose risks to ecosystems and human health. Graphene oxide (GO) effectively adsorbs heavy metal ions but can be toxic to fish. A composite of PVA hydrogel with GO enhances water quality maintenance in aquaculture. While PVA-GO hydrogel's adsorption capacity has been studied, its environmental and toxicological impacts are unclear. This study examines the biocompatibility of low-concentration PVA-GO hydrogel composites for water treatment, comparing untreated, metal-contaminated, and treated water samples in Oreochromis niloticus aquaculture. The synthesis of GO and PVA-GO hydrogel was analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and FTIR spectra. The results indicated that heavy metal-contaminated water caused significant anemia and altered plasma biochemistry, while the biomarkers in treated groups returned to levels similar to the control. Additionally, GO and PVA-GO hydrogels exhibited intestinal antibacterial activity. This research suggests that combining GO with PVA hydrogel reduces toxicity to fish, improves heavy metal removal, and enhances their health in aquaculture environments.
The process of rapid urbanization in arid megacities may result in heterogeneous thermal and atmospheric environmental conditions, which may be influenced by the road-network morphology. This study provides a spatially explicit framework to investigate the seasonal relationship between road-network morphology and environmental stress in Makkah, Saudi Arabia. Seasonal daytime land surface temperature (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD11A1.061 and Aqua MYD11A1.061, along with four Sentinel-5 Precursor (Sentinel-5P) atmospheric constituents, namely nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2) and formaldehyde (HCHO), were used for four seasons of 2024. The year of 2024 was selected based on the assessment of the representativeness of data of 2019–2025 and harmonized into a 1-km common analytical grid. Environmental Stress Index (ESI) was calculated based on the LST and pollutants data of four seasons of 2024 using the equal-weight approach where each of the five input variables has a weight of 0.20. The principal component analysis (PCA) was applied to diagnose the multivariate structure and assess its sensitivity rather than determining the ESI weights. The obtained index demonstrated similar coverage in all seasons with 1,454–1,458 valid grid cells and the seasonal standard deviation from 0.396 to 0.552. Graph-theoretical indices describing road-network morphology were assessed using seven regression algorithms and a blocked nested cross-validation scheme. Models considering only road networks lacked spatial generalizability, with pooled out-of-fold R2 varying between − 0.25 and − 0.03 among road types. Inclusion of the spatial context calculated based on a regular grid resulted in an increase in predictive performance of the models for several road types, with pooled R2 for Living Streets, Primary roads, Secondary roads, and Unclassified roads equal to 0.53, 0.46, 0.35 and 0.20 respectively. Predictive performance of the models remained heterogeneous among seasons, with both positive and negative predictive associations were observed across road types and seasons. SHapley Additive exPlanations (SHAP) analysis was applied to assess the importance of road-network variables included in the models, with an attempt to separate predictive associations from causal effects. The study covers a single arid city and one year, so the findings are case-specific and require testing in other cities.
