Severe eutrophication of surface water has been a major problem of increasing environmental concern worldwide. In the present study, economic plant annual ryegrass (Lolium multiflorum) was grown in floating mats as an economic plant-based treatment system to evaluate its potential after ion implantation for removing nutrients in simulated eutrophic water. The specific weight growth rate of L. multiflorum with ion implantation was significantly greater than that of the control, and the peroxidase, nitrate reductase, and acid phosphatase activities of the irradiated L. multiflorum were found to be greater than those plants without ion implantation. Higher total nitrogen (TN) and total phosphorus (TP) removal efficiencies were obtained for the L. multiflorum irradiated with 25 keV 5.2 × 10¹⁶ N⁺ions/cm²and 30 keV 4.16 × 10¹⁶ N⁺ions/cm², respectively (p < 0.05). Furthermore, the nitrogen and phosphorus contents in the plant biomass with ion implantation were also greater than those in the control and were positively correlated with TN and TP supplied. L. multiflorum itself was directly responsible for 39–49 and 47–58 % of the overall N and P removal in the experiment, respectively. The research results suggested that ion implantation could become a promising approach for increasing phytoremediation efficiency of nutrients from eutrophic water by L. multiflorum.

The main objective of this work was the development of a new modelling tool for quantification of human exposure to traffic-related air pollution within distinct microenvironments by using a novel approach for trajectory analysis of the individuals. For this purpose, mobile phones with Global Positioning System technology have been used to collect daily trajectories of the individuals with higher temporal resolution and a trajectory data mining, and geo-spatial analysis algorithm was developed and implemented within a Geographical Information System to obtain time–activity patterns. These data were combined with air pollutant concentrations estimated for several microenvironments. In addition to outdoor, pollutant concentrations in distinct indoor microenvironments are characterised using a probabilistic approach. An example of the application for PM2.5 is presented and discussed. The results obtained for daily average individual exposure correspond to a mean value of 10.6 and 6.0–16.4 μg m⁻³in terms of 5th–95th percentiles. Analysis of the results shows that the use of point air quality measurements for exposure assessment will not explain the intra- and inter-variability of individuals’ exposure levels. The methodology developed and implemented in this work provides time-sequence of the exposure events thus making possible association of the exposure with the individual activities and delivers main statistics on individual’s air pollution exposure with high spatio-temporal resolution.

To investigate the spatial and seasonal variations of nitrous oxide (N₂O) fluxes and understand the key controlling factors, we explored N₂O fluxes and environmental variables in high marsh (HM), middle marsh (MM), low marsh (LM), and mudflat (MF) in the Yellow River estuary throughout a year. Fluxes of N₂O differed significantly between sampling periods as well as between sampling positions. During all times of day and the seasons measured, N₂O fluxes ranged from −0.0051 to 0.0805 mg N₂O m⁻² h⁻¹, and high N₂O emissions occurred during spring (0.0278 mg N₂O m⁻² h⁻¹) and winter (0.0139 mg N₂O m⁻² h⁻¹) while low fluxes were observed during summer (0.0065 mg N₂O m⁻² h⁻¹) and autumn (0.0060 mg N₂O m⁻² h⁻¹). The annual average N₂O flux from the intertidal zone was 0.0117 mg N₂O m⁻² h⁻¹, and the cumulative N₂O emission throughout a year was 113.03 mg N₂O m⁻², indicating that coastal marsh acted as N₂O source. Over all seasons, N₂O fluxes from the four marshes were significantly different (p < 0.05), in the order of HM (0.0256 ± 0.0040 mg N₂O m⁻² h⁻¹) > MF (0.0107 ± 0.0027 mg N₂O m⁻² h⁻¹) > LM (0.0073 ± 0.0020 mg N₂O m⁻² h⁻¹) > MM (0.0026 ± 0.0011 mg N₂O m⁻² h⁻¹). Temporal variations of N₂O emissions were related to the vegetations (Suaeda salsa, Phragmites australis, and Tamarix chinensis) and the limited C and mineral N in soils during summer and autumn and the frequent freeze/thaw cycles in soils during spring and winter, while spatial variations were mainly affected by tidal fluctuation and plant composition at spatial scale. This study indicated the importance of seasonal N₂O contributions (particularly during non-growing season) to the estimation of local N₂O inventory, and highlighted both the large spatial variation of N₂O fluxes across the coastal marsh (CV = 158.31 %) and the potential effect of exogenous nitrogen loading to the Yellow River estuary on N₂O emission should be considered before the annual or local N₂O inventory was evaluated accurately.

China’s rapid economic growth has been accompanied by increasing environmental pollution. Mangrove ecosystems are now facing greater pollution pressures due to elevated chemical discharges from various land-based sources. Data on the levels of heavy metals and organic pollutants in mangrove compartments (sediments, plants, zoobenthos, and fish) in China over the past 20 years have been summarized to evaluate the current pollution status of the mangrove ecosystem. Overall, the Pearl River and Jiulong River estuaries were severely polluted spots. Concentrations of Cu, Zn, Cd, and Pb in mangrove sediments of Guangdong, Fujian, and Hong Kong were higher than those from Guangxi and Hainan. The pollution status was closely linked to industrialization and urbanization. The highest concentrations of polycyclic aromatic hydrocarbons (PAHs) were found in mangrove sediments from Hong Kong, followed by Fujian and Guangdong. Mangrove plants tend to have low-enriched ability for heavy metals and organic pollutants. Much higher levels of Pb, Cd, and Hg were observed in mollusks.

Twenty-nine sediment samples were collected from Lake Chaohu, a shallow eutrophic lake in Eastern China, and were analyzed for 15 priority polycyclic aromatic hydrocarbons (PAHs) to determine the spatial distribution and exposure risks of PAHs. Three receptor models, the principal component analysis-multiple linear regression (PCA-MLR) model, the positive matrix factorization (PMF) model, and the Unmix model, were used in combination with the PAHs diagnostic ratios to investigate the potential source apportionment of PAHs. A clear gradient in the spatial distribution and the potential toxicity of PAHs was observed from west to east in the sediments of Lake Chaohu. ∑₁₅PAH concentrations and the TEQ were in the range of 80.82-30 365.01 ng g⁻¹ d.w. and 40.77-614.03, respectively. The highest values of the aforementioned variables were attributed to urban–industrial pollution sources in the west lake region, and the levels decreased away from the river inlets. The three different models yielded excellent correlation coefficients between the predicted and measured levels of the 15 PAH compounds. Similarly, source apportionment results were derived from the three receptor models and the PAH diagnostic ratios, suggesting that the highest contribution to the PAHs was from coal combustion and wood combustion, followed by vehicular emissions. The PMF model yielded the following contributions to the PAHs from gasoline combustion, diesel combustion, unburned petroleum emissions, and wood combustion: 34.49, 24.61, 16.11, 13.01, and 11.78 %, respectively. The PMF model produced more detailed source apportionment results for the PAHs than the PCA-MLR and Unmix models.

In a research project on risk management of harmful substances in water cycles, clindamycin and 12 further antibiotics were determined in different sewage samples. In contrast to other antibiotics, an increase of the clindamycin concentration in the final effluent in comparison to the influent of the sewage treatment plant (STP) was observed. A back transformation from the main metabolite clindamycin sulfoxide to clindamycin during the denitrification process has been discussed. Therefore, the concentration of this metabolite was measured additionally. Clindamycin sulfoxide was stable in the STP and the assumption of back transformation of the metabolite to clindamycin was confuted. To explain the increasing clindamycin concentration in the STP, the ratio of clindamycin sulfoxide to clindamycin was observed. The ratio increased in dry spells with concentrated samples and with long dwell time in the sewer system. A short hydraulic retention in waste water system and diluted samples in periods of extreme rainfall lead to a lower ratio of clindamycin sulfoxide to clindamycin concentration. A plausible explanation of this behavior could be that clindamycin was adsorbed strongly to a component of the sewage during this long residence time and in the STP, clindamycin was released. In the common sample preparation in the lab, clindamycin was not released. Measurements of clindamycin and clindamycin sulfoxide in the influent and effluent of STP is advised for sewage monitoring.

The response of maize (Zea mays L.) to inorganic arsenic exposure was studied, at the seedling stage under hydroponic conditions, preliminarily in sixteen lines (fourteen hybrids and two inbred lines) and then, more deeply, in six of these lines, selected by showing contrasting differences in their sensitivity to the metalloid. The results indicated that (i) maize is rather tolerant to arsenic toxicity, (ii) arsenite is more phytotoxic than arsenate, (iii) roots are less sensitive than shoots to the metalloid, (iv) a great accumulation of non-protein thiols (probably phytochelatins), without substantial effect on the glutathione content, is produced in roots but not in shoots of arsenic-exposed plants and (v) maize is able to accumulate high levels of arsenic in roots with very low translocation to shoots. The study, thus, suggests that maize, for its very low rate of acropetal transport of arsenic from roots to shoots, may be a safe crop in relation to the risk of entry of metalloid in the food chain and, for being an important bioenergy crop capable of expressing high levels of arsenic tolerance and accumulation in roots, may represent an interesting opportunity for the exploitation of agricultural useless arsenic contaminated lands.

Water pollution caused by organic matter is a major global problem which requires continuous evaluation. Multivariate statistical analysis was applied to assess spatial and temporal changes caused by natural and anthropogenic phenomena along Potrero de los Funes River. Cluster analysis (CA), principal component analysis (PCA) and analysis of variance (ANOVA) were applied to a data set collected throughout a period of 3 years (2010–2012), which monitored 22 physical, chemical and biological parameters. Content of dissolved oxygen in water and biochemical oxygen demand in a watercourse are indicators of pollution caused by organic matter. For this reason, the Streeter-Phelps model was used to evaluate the water self-purification capacity. Hierarchical cluster analysis grouped the sampling sites based on the similarity of water quality characteristics. PCA resulted in two latent factors explaining 75.2 and 17.6 % of the total variance in water quality data sets. Multidimensional ANOVA suggested that organic pollution is mainly due to domestic wastewater run-offs and anthropogenic influence as a consequence of increasing urbanization and tourist influx over the last years. Besides, Streeter-Phelps parameters showed a low reaeration capacity before dam with low concentration of dissolved oxygen. Furthermore, self-purification capacity loss was correlated with the decrease of the Benthic Index. This measurement suggested that biological samplings complement the physical-chemical analysis of water quality.

This paper gives an overview of management considerations required for better control of deicing chemicals in the unsaturated zone at sites with winter maintenance operations in cold regions. Degradable organic deicing chemicals are the main focus. The importance of the heterogeneity of both the infiltration process, due to frozen ground and snow melt including the contact between the melting snow cover and the soil, and unsaturated flow is emphasised. In this paper, the applicability of geophysical methods for characterising soil heterogeneity is considered, aimed at modelling and monitoring changes in contamination. To deal with heterogeneity, a stochastic modelling framework may be appropriate, emphasizing the more robust spatial and temporal moments. Examples of a combination of different field techniques for measuring subsoil properties and monitoring contaminants and integration through transport modelling are provided by the SoilCAM project and previous work. Commonly, the results of flow and contaminant fate modelling are quite detailed and complex and require post-processing before communication and advising stakeholders. The managers’ perspectives with respect to monitoring strategies and challenges still unresolved have been analysed with basis in experience with research collaboration with one of the case study sites, Oslo airport, Gardermoen, Norway. Both scientific challenges of monitoring subsoil contaminants in cold regions and the effective interaction between investigators and management are illustrated.