Water quality observation is an important task which must performed at every water reservoir, especially at those whose water is used for water supply. In the purpose of easier and faster entering to storied data, for all potential users, and in the purpose of performing faster verification and handling data, two models are designed: 1) data base and 2) information system for dam observation and water quality management. These two models are parts of the Information System of Serbia for Water Resources Management, who is in development. Methodological bases of those two systems are presented in the paper.

Nanobiomaterials (NBMs) are a special category of nanomaterials used in medicine. As applications of NBMs are very similar to pharmaceuticals, their environmental release patterns are likely similar as well. Different pharmaceuticals were detected in surface waters all over the world. Consequently, there exists a need to identify possible NBM exposure routes into the environment. As the application of many NBMs is only carried out at specific locations (hospitals), average predicted environmental concentrations (PECs) may not accurately represent their release to the environment. We estimated the local release of poly(lactic-co-glycolic acid) (PLGA), which is investigated for their use in drug delivery, to Swiss surface waters by using population data as well as type, size and location of hospitals as proxies. The total mean consumption of PGLA in Switzerland using an explorative full-market penetration scenario was calculated to be 770 kg/year. 105 hospitals were considered, which were connected to wastewater treatment plants and the receiving water body using graphic information system (GIS) modelling. The water body dataset contained 20,167 river segments and 210 lake polygons. Using the discharge of the river, we were able to calculate the PECs in different river segments. While we calculated high PLGA releases of 2.24 and 2.03 kg/year in large cities such as Geneva or Zurich, the resulting local PECs of 220 and 660 pg/l, respectively, were low due to the high river discharge (330 and 97 m³/s). High PLGA concentrations (up to 7,900 pg/l) on the other hand were calculated around smaller cities with local hospitals but also smaller receiving rivers (between 0.7 and 1.9 m³/s). Therefore, we conclude that population density does not accurately predict local concentration hotspots of NBMs, such as PLGA, that are administered in a hospital context. In addition, even at the locations with the highest predicted PLGA concentrations, the expected risk is low.

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the ‘Sistema de Información sobre Ocupación del Suelo de España’ (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM₂.₅ can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM₂.₅, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM₂.₅ levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008–2011 and 2014–2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM₂.₅ concentrations varied from 0.98 to 54.2 μg m⁻³, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM₂.₅, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM₂.₅ exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Noise is associated with poor reproductive health. A number of animal studies have suggested the possible effects of exposure to high noise levels on fertility; to date, a little such research has been performed on humans.We examined an association between daytime and nocturnal noise exposures over four years (2002–2005) and subsequent male infertility.We used the National Health Insurance Service-National Sample Cohort (2002–2013), a population-wide health insurance claims dataset. A total of 206,492 males of reproductive age (20–59 years) with no history of congenital malformations were followed up for an 8-year period (2006–2013). Male infertility was defined as per ICD-10 code N46. Data on noise exposure was obtained from the National Noise Information System. Exposure levels of daytime and night time noise were extrapolated using geographic information systems and collated with the subjects' administrative district code, and individual exposure levels assigned.During the study period, 3293 (1.6%) had a diagnosis of infertility. Although there was no association of infertility with 1-dB increments in noise exposure, a non-linear dose-response relationship was observed between infertility and quartiles of daytime and night time noise after adjustment for confounding variables (i.e., age, income, residential area, exercise, smoking, alcohol drinking, blood sugar, body mass index, medical histories, and particulate pollution). Based on WHO criteria, adjusted odds for infertility were significantly increased (OR = 1.14; 95% CI, 1.05–1.23) in males exposed to night time noise ≥ 55 dB.We found a significant association between exposure to environmental noise for four years and the subsequent incidence of male infertility, suggesting long-term exposure to noise has a role in pathogenesis of male infertility.

This study reports a spatiotemporal characterization of formaldehyde and acetaldehyde in the summer and winter of 2017 in the urban area of Shiraz, Iran. Sampling was fulfilled according to EPA Method TO-11 A. The inverse distance weighting (IDW) procedure was used for spatial mapping. Monte Carlo simulations were conducted to evaluate carcinogenic and non-cancer risk owing to formaldehyde and acetaldehyde exposure in 11 age groups. The average concentrations of formaldehyde/acetaldehyde in the summer and winter were 15.07/8.40 μg m⁻³ and 8.57/3.52 μg m⁻³, respectively. The formaldehyde to acetaldehyde ratios in the summer and winter were 1.80 and 2.43, respectively. The main sources of formaldehyde and acetaldehyde were photochemical generation, vehicular traffic, and biogenic emissions (e.g., coniferous and deciduous trees). The mean inhalation lifetime cancer risk (LTCR) values according to the Integrated Risk Information System (IRIS) for formaldehyde and acetaldehyde in summer and winter ranged between 7.55 × 10⁻⁶ and 9.25 × 10⁻⁵, which exceed the recommended value by US EPA. The average LTCR according to the Office of Environmental Health Hazard Assessment (OEHHA) for formaldehyde and acetaldehyde in summer and winter were between 4.82 × 10⁻⁶ and 2.58 × 10⁻⁴, which exceeds recommended values for five different age groups (Birth to <1, 1 to <2, 2 to <3, 3 to <6, and 6 to <11 years). Hazard quotients (HQs) of formaldehyde ranged between 0.04 and 4.18 for both seasons, while the HQs for acetaldehyde were limited between 0.42 and 0.97.

The availability of marine habitats maps remains limited due to difficulty and cost of working at sea. Reduced light penetration in the water hampers the use of optical imagery, and acoustic methods require extensive sea-truth activities. Predictive spatial modelling may offer an alternative to produce benthic habitat maps based on complete acoustic coverage of the seafloor together with a comparatively low number of sea truths. This approach was applied to the coralligenous reefs of the Marine Protected Area of Tavolara - Punta Coda Cavallo (NE Sardinia, Italy). Fuzzy clustering, applied to a set of observations made by scuba diving and used as sea truth, allowed recognising five coralligenous habitats, all but one existing within EUNIS (European Nature Information System) types. Variable importance plots showed that the distribution of habitats was driven by distance from coast, depth, and lithotype, and allowed mapping their distribution over the MPA. Congruence between observed and predicted distributions and accuracy of the classification was high. Results allowed calculating the occurrence of the distinct coralligenous habitats in zones with different protection level. The five habitats are unequally protected since the protection regime was established when detailed marine habitat maps were not available. A SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis was performed to identify critical points and potentialities of the method. The method developed proved to be reliable and the results obtained will be useful when modulating on-going and future management actions in the studied area and in other Mediterranean MPAs to develop conservation efforts at basin scale.

The depollution of the Mediterranean Sea by 2020 is the overarching goal of Horizon 2020 Initiative (H2020). The first phase (2007–2013) tackled the main land-based pollution sources; municipal waste, urban wastewater and industrial emissions. As part of the H2020 roadmap implementation, the ENPI-SEIS project focused on the first H2020 review mechanism that included a) establishing a process for regular environmental reporting in the European Neighbourhood Policy South countries, and b) generating the knowledge necessary to assess the progress in depolluting the Mediterranean Sea. This paper reviews the process in line with the “Shared Environmental Information System” principles and its three pillars (content, cooperation, infrastructure). The main outcomes of the thematic assessment, based on a set of regional H2020 indicators, are also presented. This assessment highlights the major drivers and their implications on marine protection. It also identifies areas that require further attention in the next phase of H2020 (2014–2020).

The EUNIS (European Union Nature Information System) habitat classification system aims to provide a common European reference set of habitat types within a hierarchical classification, and to cover all terrestrial, freshwater and marine habitats of Europe. The classification facilitates reporting of habitat data in a comparable manner, for use in nature conservation (e.g. inventories, monitoring and assessments), habitat mapping and environmental management. For the marine environment the importance of a univocal habitat classification system is confirmed by the fact that many European initiatives, aimed at marine mapping, assessment and reporting, are increasingly using EUNIS habitat categories and respective codes. For this reason substantial efforts have been made to include information on marine benthic habitats from different regions, aiming to provide a comprehensive geographical coverage of European seas. However, there still remain many concerns on its applicability as only a small fraction of Europe’s seas are fully mapped and increasing knowledge and application raise further issues to be resolved. This paper presents an overview of the main discussion and conclusions of a workshop, organised by the MeshAtlantic project, focusing upon the experience in using the EUNIS habitats classification across different countries and seas, together with case studies. The aims of the meeting were to: (i) bring together scientists with experience in the use of the EUNIS marine classification and representatives from the European Environment Agency (EEA); (ii) agree on enhancements to EUNIS that ensure an improved representation of the European marine habitats; and (iii) establish practices that make marine habitat maps produced by scientists more consistent with the needs of managers and decision-makers. During the workshop challenges for the future development of EUNIS were identified, which have been classified into five categories: (1) structure and hierarchy; (2) biology; (3) terminology; (4) mapping; and (5) future development. The workshop ended with a declaration from the attendees, with recommendations to the EEA and European Topic Centre on Biological Diversity, to take into account the outputs of the workshop, which identify weaknesses in the current classification and include proposals for its modification, and to devise a process to further develop the marine component of the EUNIS habitat classification.

Indoor hazardous airborne carbonyls were quantified in five natural-ventilated roadside dwellings in Colombo, Sri Lanka. The total concentrations of all targeted carbonyls ranged from 13.6 to 18.6 μg/m3. Formaldehyde (C1) was the most abundant carbonyl, followed by acetaldehyde (C2) and acetone (C3K). The concentrations of C1 and C2 ranged from 3.3 to 8.5 μg/m3 and 2.3 to 4.4 μg/m3, respectively, which accounted for 23 to 42% and 18 to 26% respectively, to the total quantified carbonyls. The highest carbonyls levels were obtained in the dwelling located in an urban district with a mixture of industrial, commercial and residential areas. Much lower concentrations of carbonyls were measured in a light local traffic value was counted. Moderate correlations between individual combustion markers from vehicular emissions suggest the strong impacts from traffics to the indoor airs. The concentrations of C1 and C2 were compared with international indoor guidelines established by different authorities. A health assessment was conducted by estimation of inhalation cancer risk, implementing the inhalation unit risk values provided by Integrated Risk Information System (IRIS), associated with C1 and C2, which were 6.2 × 10−5 and 7.7 × 10−6, respectively. Even though the risks did not reach the action level (1 × 10−4), their health impact should not be overlooked. This kick-off indoor monitoring study provides valuable scientific data to the environmental science community since only limit data is available in Sri Lanka.