The study was conducted to assess surface water quality in Ca Mau peninsula using multivariate statistical analysis. Fifty-one water samples with the parameters of pH, dissolved oxygen (DO), total suspended solids (TSS), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonium (N-NH4+), orthophosphate (P-PO43-) and total coliform were used in the evaluation. Water quality is assessed using national standard and water quality index (WQI). The methods of cluster (CA), discriminant (DA), principal component analysis (PCA) were used to analyze the variation patterns of water quality. The surface water was contaminated with organic matters, suspended solids, nutrients, and microorganisms. DA revealed that DO, TSS, BOD5 and pH contributed 76.91% to the seasonal variation of water quality. Water quality is classified from bad to heavily polluted. CA grouped water quality into 7 clusters and DO, TSS, BOD5, COD and coliform of the clusters 1-3 were significantly higher than those of the clusters 4-7. PCA presented that PC1-PC3 was the main sources affecting water quality, explaining 85.45% of the variation in water quality. The sources of pollution can be human (domestic wastewater, waste from agriculture, fisheries, industry, landfills), natural (hydrological regime, rainwater overflow, river bank erosion). pH, DO, BOD5, COD, TSS, N-NH4+, P-PO43- and coliform have an impact on water quality and need to be continuously monitored. However, for the multivariate statistical method to be more effective, an initial data set with several water quality parameters sampling locations is needed. The current results provide scientific information and support local water quality monitoring activities.

Surface water contains a large number of pollutants, particularly human pathogens, organic toxicants and heavy metals. Due to the toxic nature of heavy metals towards marine organisms, its removal from the environment has been a growing issue. The biosorption of heavy metal ions from surface water using fish scales has emerged as an environmentally friendly technique. This study assessed the degree of heavy metals accumulation in the scales of Oreochromis niloticus and determining its efficiency as a bioindicator for Cu, Mn and Fe ions removal in the environment of Wewe and Owabi rivers. This study shows that the levels of Cu, Mn, Fe adsorbed from the Owabi river were 685.70 ± 16.51, 247.06 ± 50.46 and 892.90 ± 96.29 mg/kg, respectively. Moreover, the levels of Cu, Mn and Fe adsorbed from Wewe river were 501.60 ± 77.78, 300.89 ± 54.61     and 413.04 ± 9.92 mg/kg, respectively. Under best optimum adsorption conditions, Cu was the best removed heavy metal ions in both surface water reservoirs. Multivariate analysis showed that Cu and Mn showed association in Owabi river, while Mn and Fe were correlated in Wewe river signifying their similarities to a common anthropogenic activity. The Fourier–transform infrared spectrum revealed the existence of a nitro, amine, and carbonyl groups in the biosorption process. This study highlighted that Oreochromis niloticus scales was an efficient bio–sorbent in removing Cu, Mn and Fe ions from Owabi and Wewe rivers.

International audience | Polycyclic aromatic hydrocarbon (PAH) concentrations and N, C stable isotope signatures were determined in mosses Hypnum cupressiforme Hedw. from 61 sites of 3 European regions: Île-de-France (France); Navarra (Spain); the Swiss Plateau and Basel area (Switzerland). Total PAH concentrations of 100-700 ng g-1, as well as δ13C values of -32 to -29‰ and δ15N values of -11 to -3‰ were measured. Pearson correlation tests revealed opposite trends between high molecular weight PAH (4-6 aromatic rings) content and δ13C values. Partial Least Square regressions explained the very significant correlations (r > 0.91, p < 0.001) between high molecular weight PAH concentrations by local urban land use (<10 km) and environmental factors such as elevation and pluviometry. Finally, specific correlations between heavy metal and PAH concentrations were attributed to industrial emissions in Switzerland and road traffic emissions in Spain.

Bacterial communities in antimony (Sb) polluted soils have been well addressed, whereas the important players fungal communities are far less studied to date. Here, we report different responses of bacterial and fungal communities to Sb contamination and the ecological processes controlling their community assembly. Soil samples in the Xikuangshan mining area were collected and subjected to high through-put sequencing of 16S rRNA and ITS1 to investigate bacterial and fungal communities, respectively, along an Sb gradient. Sb speciation in the soil samples and other physicochemical parameters were analyzed as well. Bacterial communities were dominated by Deltaproteobacteria in the soil with highest Sb concentration, whereas Chloroflexi were dominant in the soil with lowest Sb concentration. Fungal communities in high-Sb soils were predominated by unclassified Fungi, whilst Leotiomycetes were dominant in low-Sb soil samples. Multivariate analysis indicated that Sb, pH and soil texture were the main drivers to strongly impact microbial communities. We further identified Sb-resistant microbial groups via correlation analysis. In total, 18 bacterial amplicon sequence variants (ASVs) were found to potentially involve in biogeochemical cycles such as Sb oxidation, sulfur oxidation or nitrate reduction, whereas 12 fungal ASVs were singled out for potential heavy metal resistance and plant growth promotion. Community assembly analysis revealed that variable selection contributed 100% to bacterial community assembly under acidic or high Sb concentration conditions, whereas homogeneous selection dominated fungal community assembly with a contribution over 78.9%. The community assembly of Sb-resistant microorganisms was mainly controlled by stochastic process. The results offer new insights into microbial ecology in Sb-contaminated soils, especially on the different responses of microbial communities under identical environmental stress and the different ecological processes underlining bacterial and fungal community assembly.

The impact of land use type on the content of potentially toxic elements (PTEs) in the soils of the Qinghai-Tibet Plateau (QTP) and the associated ecological and human health risks has drawn great attention. Consequently, in this study, top- and subsurface soil samples were collected from areas with four different land uses (i.e., cropland, forest, grassland, and developed area) and the total contents of Cr, Cd, Cu, Pb and Zn were determined. Geostatistical analysis, self-organizing map (SOM), and positive matrix factorization (PMF), ecological risk assessment (ERA) and human health risk assessment (HRA) were applied and used to classify and identify the contamination sources and assess the potential risk. Partial least squares path modeling (PLS-PM) was applied to clarify the relationship of land use with PTE contents and risk. The PTE contents in all topsoil samples surpassed the respective background concentrations of China and corresponding subsurface concentrations. However, the ecological risk of all soil samples remained at a moderate or considerable level across the four land use types. Developed area and cropland showed a higher ecological risk than the other two land use types. Industrial discharges (32.8%), agricultural inputs (22.6%), natural sources (23.7%), and traffic emissions (20.9%) were the primary PTE sources in the tested soils, which indicate that anthropogenic activities have significantly affected soil PTE contents to a greater extent than other sources. Industrial discharge was the most prominent source of non-carcinogenic health risk, contributing 37.7% for adults and 35.2% for children of the total risk. The results of PLS-PM revealed that land use change associated with intensive human activities such as industrial activities and agricultural practices distinctly affected the PTE contents in soils of the Qinghai-Tibet Plateau.

The contamination status of heavy metals in urban environment changes frequently with the industrial structure adjustment, energy conservation and emission reduction and thus requires timely investigation. Based on enrichment factor, multivariate statistical analysis and isotope fingerprinting, we assessed comprehensively the inputs and sources of heavy metals in different samples from an urban area that was less impacted by leaded gasoline exhaust. The road dust contained relatively high levels of Cr, Pb and Zn (with enrichment factor >2) that originated from both exhaust and non-exhaust traffic emissions, while the moss plants could accumulate high levels of Pb and Zn from the deposition of traffic exhaust emission. This suggest that the traffic emission is still an important source of metals in the urban area although gasoline is currently lead free. On the contrary, the occurrences of metals in the urban soils were controlled by natural sources and non-traffic anthropogenic emission. These findings revealed that different samples would receive different inputs of metals from different sources in the urban area, and the responsiveness and sensitiveness of these urban samples to metal inputs can be ranked as moss ≥ dust > soil. Taken together, our results suggested that in order to avoid generalizing and get detail source information, multi-samples and multi-measures must be adopted in the assessment of integrated urban environmental quality.

Passive biomonitoring was applied in four Atlantic forest plots in southeast Brazil, affected by different levels of trace metal pollution (OP site located in Minas Gerais State and PEFI, PP and STG located in São Paulo State). Native tree species were selected as biomonitors according to their abundance in each plot and successional classification. Current trace metal concentrations in total suspended particles, leaves of non-pioneer (NPi) and pioneer (Pi) species, topsoil (0–20 cm) and litter and concentration ratios at the plant/soil interface were analyzed to verify the atmosphere-plant-soil interactions, basal concentrations, spatial variations and metal accumulation at the ecosystem level. Redundant analysis helped to identify similar characteristics of metal concentrations in PP and PEFI, which can be influenced by the high concentrations of elements related to anthropogenic inputs. Analysis of variance and multivariate statistics indicated that the trees of OP presented higher concentrations of Cr, Fe, Mn and Ni than those in the other sites. High enrichment of Cd, Fe, Ni in non-pioneer plants indicated that the PP forest (initially considered as the least polluted) has still been affected by metal pollution. Soil collected in STG was enriched by all elements, however these elements were low available for plant uptake. Metal deposited in leaves and litter was an important sink for soil cycling, nevertheless, these metals are not bioavailable in most cases. Non-pioneer tree species revealed to be more appropriate than pioneer species to indicate the current panorama of the contamination and bioavailability levels of trace metals in the tree community-litter-soil interface of the Atlantic forest remnants included in this study.

Elevated concentrations of heavy metals in agricultural soils threatening ecological security and the quality of agricultural products, and apportion their sources accurately is still a challenging task. Multivariate statistical analysis, GIS mapping, Pb isotopic ratio analysis (IRA), and positive matrix factorization (PMF) were integrated to apportion the potential sources of heavy metal(loid)s of orchard soil in Karst-regions. Study region soils were moderately contaminated by Cd. Obvious enrichment and moderate contamination level of Cd were found in study region surface soils, followed by As, Zn, and Pb. Correlation analysis (CA) and principal component analysis (PCA) indicated Ba, Co, Cr, Ni, V were mainly from natural sources, while As, Cd, Cu, Pb, Zn were derived from two kinds of anthropogenic sources. Based on Pb isotope composition, atmospheric deposition and livestock manure were the main sources of soil Pb accumulation. Further source identification and quantification results with PMF model and GIS mapping revealed that soil parent materials (46.44%) accounted for largest contribution to the soil heavy metal(loid)s, followed by fertilizer application (31.37%) and mixed source (industrial activity and manure, 22.19%). Uncertainty analysis indicated that the three-factors solution of PMF model was an optimal explanation and the heavy metal(loid) with lower percentage contributions had higher uncertainty. This study results can help to illustrate the sources of heavy metals more accurately in orchard agricultural soils with a clear expected future for further applications.

The increasing salinization of groundwater renders it challenging to maintain the water quality. Moreover, knowledge regarding the characteristics and mechanism of groundwater salinization in mining areas remains limited. This study represents the first attempt of combining the hydrochemical, isotope (δD, δ¹⁸O, δ³⁷Cl, and ⁸⁷Sr/⁸⁶Sr) and multivariate statistical analysis methods to explore the origin, control, and influence of fluoride enrichment in mining cities. The TDS content of groundwater ranged from 275.9 mg/L to 2452.0 mg/L, and 54% of the groundwater samples were classified as class IV water according to China's groundwater quality standards (GB/T 14848–2017), indicating a decline in the water quality of the study area. The results of the groundwater ion ratio and isotope discrimination analysis showed that dissolution and evaporation involving water-rock interactions and halite were the main driving processes for groundwater salinization in the study area. In addition to the hydrogeological and climatic conditions, mine drainage inputs exacerbated the increasing salinity of the groundwater in local areas. The mineral dissolution, cation exchange, and evaporation promoted the F⁻ enrichment, while excessive evaporation and salinity inhibited the F⁻ enrichment. Gangue accumulation and infiltration likely led to considerable F⁻ enrichment in individual groundwater regions. Extensive changes in the groundwater salinity indicated differences in the geochemical processes that controlled the groundwater salinization. Given the particularity of the study area, the enrichment of salinization and fluoride triggered by mining activities cannot be ignored.

Soil contamination in urban environment by trace metals is of public concerns. For better risk assessment, it is important to determine their background concentrations in urban soils. For this study, we determined the background concentrations of 9 trace metals including As, Ba, Cd, Co, Cu, Ni, Pb, Se, and Zn in 214 urban soils in Florida from two large cities (Orlando and Tampa) and 4 small cities (Clay County, Ocala, Pensacola and West Palm Beach). The objectives were to determine: 1) total concentrations of trace metals in urban soils in cities of different size; 2) compare background concentrations to Florida Soil Cleanup Target Levels (FSCTLs); and 3) determine their distribution and variability in urban soils via multivariate statistical analysis. Elemental concentrations in urban soils were variable, with Pb being the highest in 5 cities (165–552 mg kg⁻¹) and Zn being the highest concentration in Tampa (1,000 mg kg⁻¹). Besides, the As and Pb concentrations in some soils exceeded the FSCTL for residential sites at 2.1 mg kg⁻¹ As and 400 mg kg⁻¹ Pb. Among the cities, Clay County and Orlando had the lowest concentrations for most elements, with Cd, Co, and As being the lowest while Ba, Pb and Zn being the highest. Among all values, geometric means were the lowest while 95th percentile was the highest for all metals. Most 95th percentile values were 2–3 folds higher than the GM data, with Pb presenting the greatest difference, being 4 times greater than GM value (58.9 vs. 13.6 mg kg⁻¹). Still they were lower than FSCTL, with As exceeding FSCTL for residential sites at 2.1 mg kg⁻¹. In addition, the linear discriminate analysis showed distinct separation among the cities: Ocala (Ba & Ni) and Pensacola (As & Pb) were distinctly different from each other and from other cities with higher metal concentrations. The large variations among elemental concentrations showed the importance to establish proper background concentrations of trace metals in urban soils.