Vehicular emissions have become one of the main source of pollution of urban soils; this highlights the need for more detailed research on various traffic-related emissions and related distribution patterns. Since the banning of asbestos in the European Union, its substitution with antimony (Sb) in brake linings has led to increased inputs of this toxic metalloid to environmental compartments. The objective of this study was to provide detailed information about the spatial distribution patterns of Sb and to assess its mobility and bioavailability. Roadside soils along an arterial road (approx. 9000 vehicles per day) in Cologne (Germany) were studied along five transects, at four soil depths and at seven sampling points set at varying distances from the road (n = 140). For all samples, comprehensive soil characterization was performed and inverse aqua regia-extractable trace metal content was determined being pseudo-total contents. Furthermore, for one transect, also total Sb and a chemical sequential extraction procedure was applied (n = 28). Pseudo-total Sb for all transects decreased significantly with soil depth and distance from the road, reflecting a distribution pattern similar to that of other trace metals associated with brake lining emissions. Conversely, metals associated with exhaust emissions showed a convex distribution. The geochemical fractionation of Sb revealed the following trends: i) non-specifically sorbed Sb was <5%; ii) specifically sorbed Sb was only detected within 1 m distance from the road and decreased with depth; iii) Sb associated with poorly-crystalline Fe oxides decreased with distance from the road; and iv) content of Sb bounded to well-crystalline Fe oxides, and Sb present in the residual fraction remained relatively constant at each depth. Consequently, roadside soils appear to inhibit brake lining-related Sb contamination, with significant but rather low ecotoxicological potential for input into surface and groundwater.

The widespread adoption of genetically modified, glyphosate-tolerant corn and soybean varieties in US crop production has led to a dramatic increase in glyphosate usage. Though present at or below regulatory limits currently set for human foodstuffs, the concentration of glyphosate in companion animal feed is currently unknown. In the present study, 18 commercial companion animal feeds from eight manufacturers were analyzed for glyphosate residues using ELISA. Every product contained detectable glyphosate residues in the range of 7.83 × 10¹–2.14 × 10³ μg kg⁻¹ dry weight, with the average and medians being 3.57 × 10² and 1.98 × 10² μg kg⁻¹ respectively. Three products were tested for within-bag variation and six were tested for lot to lot variation. Little within-bag variation was found, but the concentration of glyphosate varied by lot in half of the products tested. Glyphosate concentration was significantly correlated with crude fiber content, but not crude fat or crude protein. Average daily intakes by animals consuming feeds containing the median glyphosate concentration are estimated to result in exposures that are 0.68–2.5% of the Allowable Daily Intake (ADI) for humans in the US and EU, which are 1750 and 500 μg kg⁻¹ respectively. Consumption of the most contaminated feed, however, would result in exposure to 7.3% and 25% of the above ADIs, though the relevance of such an exposure to companion animals is currently unknown.Companion animal feeds contained 7.83 × 10¹–2.14 × 10³ μg kg⁻¹ glyphosate which is likely to result in pet exposure that is 4–12 times higher than that of humans on a per Kg basis.

The concentrations of Cd, Cr and Pb in soil samples and As, Cd, Cr and Pb in plant specimens were analyzed in an arid area in central Iran. Plants were categorized into desert-adapted (Haloxylon ammodendron, Atraphaxis spinosa and Artemisia persica) and non-desert species. It was found that the trace element (TE) accumulating potential of the desert species (Haloxylon ammodendron and Artemisia persica) with a mean value of 0.1 mg kg⁻¹ for Cd was significantly higher than that of the majority of the non-desert species with an average of 0.05 mg kg⁻¹. Artemisia also had a high As accumulating capability with a mean level of 0.8 mg kg⁻¹ in comparison with an average of 0.2 mg kg⁻¹ for most of the other plant species. The mean values of Cr and Pb in Haloxylon ammodendron and Artemisia persica were 5 and 3 mg kg⁻¹, respectively. Among the desert-adapted plants, Atraphaxis proved to be a species with high Cr and Pb accumulating potential, as well. The geoaccumulation index and the overall pollution scores indicated that the highest environmental risk was related to Cd. Different statistical analyses were used to study the spatial patterns of soil Cd and their connections with pollution sources. The variogram was estimated using a classical approach (weighted least squares) and was compared with that of the posterior summaries that resulted from the Bayesian technique, which lay within the 95% Bayesian credible quantile intervals (BIC) of posterior parameter distributions. The prediction of cadmium values at un-sampled locations was implemented by multi-Gaussian kriging and sequential Gaussian simulation methods. The prediction maps showed that the region most contaminated by Cd was the north-eastern part of the study area, which was linked to mining activities, while agricultural influence contributed less in this respect.

Mobile genetic elements (MGEs) are key agents in the spread of antibiotic resistance genes (ARGs) across environments. Here we used metagenomics to compare the river resistome (collection of all ARGs) and mobilome (e.g., integrases, transposases, integron integrases and insertion sequence common region “ISCR” elements) between samples collected upstream (n = 6) and downstream (n = 6) of an urban wastewater treatment plant (UWWTP). In comparison to upstream metagenomes, downstream metagenomes showed a drastic increase in the abundance of ARGs, as well as markers of MGEs, particularly integron integrases and ISCR elements. These changes were accompanied by a concomitant prevalence of 16S rRNA gene signatures of bacteria affiliated to families encompassing well-known human and animal pathogens. Our results confirm that chronic discharges of treated wastewater severely impact the river resistome affecting not only the abundance and diversity of ARGs but also their potential spread by enriching the river mobilome in a wide variety of MGEs.

Understanding the effect of natural colloidal particles (NCPs) on the photochemistry of organic pollutants is crucial to predict the environmental persistence and fate of them in surface waters, and it is, yet, scarcely elucidated. In this study, the pre-filtered surface water (through a 1 μm capsule filter) from Baiyangdian Lake was further separated into four different size NCPs: F1 (0.65–1.0 μm), F2 (100 kD-0.65 μm), F3 (10–100 kD) and F4 (1–10 kD) by cross-flow ultrafiltration (CFUF), and the photochemical kinetics and mechanisms of ofloxacin (OFL) and enrofloxacin (ENR) were investigated in the presence of those particles under simulated sunlight. Results showed that OFL and ENR underwent both direct and indirect photolysis in F1-F4 solutions, and the observed pseudo first-order rate constants (kobs) for target compounds differed depending on the size of NCPs. Direct photolysis accounted for >50% of the degradation in all cases and was the dominant degradation pathway for the two target antibiotics with the exception of OFL in F1 solution. Except for ENR in both F3 and F4 solutions, nearly all NCPs enhanced the degradation of both target compounds by indirect photolytic pathways, especially in F1 solution that showed the largest reactivity for OFL and ENR, promoting the reactions by 63% and 41%, respectively. The excited state colloidal organic matter (3COM∗) plays a significant role in the indirect photolysis, and the adsorptions of OFL and ENR to NCPs were likely to have a pronounced effect in the photochemistry process. Pearson's correlations analysis showed that the kobs(OFL) was significant positive correlated with binding of Fe (r = 0.963, P < 0.05), and the kobs(ENR) was significant positive correlated with the adsorption percentage of OFL (r = 0.999, P < 0.01).This paper has demonstrated that different size NCPs showed the different photochemical contribution to the reaction rate for OFL and ENR.

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.

Multiple contaminants can affect plant-microbial remediation processes because of their interactive effects on environmental behaviour, bioavailability and plant growth. Recent studies have suggested that arbuscular mycorrhizal fungi (AMF) can facilitate the revegetation of soils co-contaminated with rare earth elements (REEs) and heavy metals. However, little is known regarding the role of AMF in the interaction of REEs and heavy metals. A pot experiment was conducted to evaluate the effects of Claroideoglomus etunicatum on the biomass, nutrient uptake, metal uptake and translocation of maize grown in soils spiked with Lanthanum (La) and Cadmium (Cd). The results indicated that individual and combined applications of La (100 mg kg−1) and Cd (5 mg kg−1) significantly decreased root colonization rates by 22.0%–35.0%. With AMF inoculation, dual-metal treatment significantly increased maize biomass by 26.2% compared to single-metal treatment. Dual-metal treatment significantly increased N, P and K uptake by 20.1%–76.8% compared to single-metal treatment. Dual-metal treatment significantly decreased shoot La concentration by 52.9% compared to single La treatment, whereas AM symbiosis caused a greater decrease of 87.8%. Dual-metal treatment significantly increased shoot and root Cd concentrations by 65.5% and 58.7% compared to single Cd treatment and the La translocation rate by 142.0% compared to single La treatment, whereas no difference was observed between their corresponding treatments with AMF inoculation. Furthermore, AMF had differential effects on the interaction of La and Cd on metal uptake and translocation under the background concentrations of soil metals. Taken together, these results indicated that AMF significantly affected the interaction between La and Cd, depending on metal types and concentrations in soils. These findings promote a further understanding of the contributions of AMF to the phytoremediation of co-contaminated soil.

The extensive use of reclaimed wastewater (RW) as a source of urban landscape pond replenishment, stimulated by the lack of surface water (SW) resources, has raised public concern. Greater attention should be paid to pond sediments, which act as ‘sinks’ and ‘sources’ of contaminants to the overlying pond water. Three ponds replenished with RW (RW ponds) in three Chinese cities were chosen to investigate 22 indices of sediment quality in four categories: eutrophication, heavy metal, ecotoxicity and pathogens risk. RW ponds were compared with other ponds of similar characteristics in the same cities that were replenished with SW (SW ponds). Our results show a strong impact of RW to the eutrophication and pathogenic risks, which are represented by organic matter, water content, total nitrogen, total phosphorus and phosphorus fractions, and pathogens. In particular, total phosphorus concentrations in the RW pond sediments were, on average, 50% higher than those of SW ponds. Moreover, the content of phosphorus, extracted by bicarbonate/dithionite (normally represented by BD-P) and NaOH (NaOH-P), were 2.0- and 2.83-times higher in RW ponds, respectively. For pathogens, the concentrations of norovirus and rotavirus in RW pond sediments were, on average, 0.52 and 0.30- log times those of SW ponds. The duration of RW replenishment was proved to have a marked impact on the eutrophication and pathogens risks from sediments. The continued use of RW for replenishment increases the eutrophication risk, and the pathogens risk, especially by viral pathogens, becomes greater.

In parallel to technological advances and ever-increasing use of nanoparticles in industry, agriculture and consumer products, the potential ecotoxicity of nanoparticles and their potential accumulation in ecosystems is of increasing concern. Because scientific reports raise a concern regarding nanoparticle toxicity to plants, understanding of their bioaccumulation has become critical and demands more research. Here, the synthesis of isotopically-labeled nanoparticles of silver, copper and zinc oxide is reported; it is demonstrated that while maintaining the basic properties of the same unlabeled (“regular”) nanoparticles, labeled nanoparticles enable more sensitive tracing of nanoparticles within plants that have background elemental levels. This technique is particularly useful for working with elements that are present in high abundance in natural environments. As a benchmark, labeled and unlabeled metal nanoparticles (Ag-NP, Cu-NP, ZnO-NP) were synthesized and compared, and then exposed in a series of growth experiments to Arabidopsis thaliana; the NPs were traced in different parts of the plant. All of the synthesized nanoparticles were characterized by TEM, EDS, DLS, ζ-potential and single particle ICP-MS, which provided essential information regarding size, composition, morphology and surface charge of nanoparticles, as well as their stability in suspensions. Tracing studies with A. thaliana showed uptake/retention of nanoparticles that is more significant in roots than in shoots. Single particle ICP-MS, and scanning electron micrographs and EDS of plant roots showed presence of Ag-NPs in particular, localized areas, whereas copper and zinc were found to be distributed over the root tissues, but not as nanoparticles. Thus, nanoparticles in any natural matrix can be replaced easily by their labeled counterparts to trace the accumulation or retention of NPs. Isotopically-labeled nanoparticles enable acquisition of specific results, even if there are some concentrations of the same elements that originate from other (natural or anthropogenic) sources.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.