Extensive green-roof systems are expected to have a synergetic effect in mitigating urban runoff, decreasing temperature and supplying water to a building. Mitigation of runoff through rainwater retention requires the effective design of a green-roof catchment. This study identified how to improve building runoff mitigation through quantitative analysis of an extensive green-roof system. Quantitative analysis of green-roof runoff characteristics indicated that the extensive green roof has a high water-retaining capacity response to rainfall of less than 20 mm/h. As the rainfall intensity increased, the water-retaining capacity decreased. The catchment efficiency of an extensive green roof ranged from 0.44 to 0.52, indicating reduced runoff comparing with efficiency of 0.9 for a concrete roof. Therefore, extensive green roofs are an effective storm water best-management practice and the proposed parameters can be applied to an algorithm for rainwater-harvesting tank design.

This literature review focuses on the prevalence of nitrogen and phosphorus in urban environments and the complex relationships between land use and water quality. Extensive research in urban watersheds has broadened our knowledge about point and non-point pollutant sources, but the fate of nutrients is not completely understood. For example, it is not known how long-term nutrient cycling processes in turfgrass landscapes influence nitrogen retention rates or the relative atmospheric contribution to urban nitrogen exports. The effect of prolonged reclaimed water irrigation is also unknown. Stable isotopes have been used to trace pollutants, but distinguishing sources (e.g., fertilizers, wastewater, etc.) can be difficult. Identifying pollutant sources may aid our understanding of harmful algal blooms because the extent of the relationship between urban nutrient sources and algal blooms is unclear. Further research on the delivery and fate of nutrients within urban watersheds is needed to address manageable water quality impacts.

The objective of this study was to assess the quality of harvested rainwater in the Mekong Delta (MD), Vietnam for local (roof types, storage system and duration) and spatial (proximity of industry, main roads, coastline) conditions. 78 harvested rainwater samples were collected in the MD and analyzed for pH, turbidity, TDS, COD, nutrients (NH4, NO3, NO2, o-PO4), trace metals and coliforms. The results show that thatch roofs lead to an increase of pollutants like COD (max 23.2 mgl−1) and turbidity (max 10.1 mgl−1) whereas galvanized roofs lead to an increase of Zn (max 2.2 mgl−1). The other local and spatial parameters had no or only minor influence on the quality of household harvested rainwater. However, lead (Pb) (max. 16.9 μgl−1) and total coliforms (max. 102 500 CFU100 ml−1) were recorded at high concentrations, probably due to a variety of household-specific conditions such as rainwater storage, collection and handling practices.

Metaldehyde is a selective molluscicide used in the agricultural and residential sector to control slugs and snails for a wide variety of crops. In recent years, some water companies have started monitoring drinking water supply catchments for presence of this compound, with positive and concern results. Conventional techniques are yet to achieve complete efficient and feasible removal of metaldehyde. The aim of this study was to measure the efficiency of nano-sized zinc oxide/laponite composites (NZnC) in the effective removal of metaldehyde (influent concentration of 500 μg dm⁻³) through the interaction of photocatalysis. Reaction time, pH of sample solution and NZnC mass were tested against each other using a rotatable central composite design method of experimentation. Statistical tests showed that linear effects of time, quadratic/linear effects of NZnC mass and the interaction of pH and NZnC mass proved to be the most significant variables for degrading metaldehyde. Optimal values of each variable for the highest removal efficiency were achieved, being pH equal to 10.4 and NZnC mass added equal to 28 g. The rate of reaction was then predicted by non-linear regression of four models. The best fit was provided by the modified first-order with residual kinetic model, with the apparent degradation coefficient k equal to 0.0363 min⁻¹ and the lowest remaining metaldehyde concentration observed among all runs was 278.7 μg dm⁻³. NZnC has shown to be a prominent nanotechnology for metaldehyde removal.

The concentration of a range of endocrine disruptors: 17-β-estradiol, estrone, 17-α-ethinylestradiol, bisphenol-A, pentachlorophenol, triclosan, and butylbenzylphthalate, was analyzed by gas chromatography/mass spectrometry in the Wetland zone of Xochimilco, a periurban area of Mexico City, during an annual cycle. Samples were taken based on their level of use and by selecting sampling points related with activities such as agriculture, livestock, and urban, as well as their potential presence in water at the Cerro de la Estrella Wastewater Treatment Plant (WWTP) which supplies the majority of water (>90 %) to the study area. The compounds analyzed are present in a wide range of products from cosmetics to home care, pharmaceuticals, and subproducts of the food industry. The importance of identifying these compounds lies in the fact that they can disrupt the endocrine system of vertebrates, in particular reproductive gland function, affecting the development of organisms and their offspring. Pentachlorophenol, triclosan, bisphenol-A, butylbenzylphthalate, estrone, and 17-β-estradiol were detected in concentrations in nanogram-per-liter levels; 17-α-ethinylestradiol was always below the detection limit. The compounds showed a trend toward greater concentrations in the rainy season, probably due to the runoff that carries these compounds into the system.

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH⁴⁺–N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH⁴⁺–N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing–refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH⁴⁺–N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering “real” data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.

Following the technical closure of the brown lignite Meirama mine (NW Spain) in April 2008, the reclamation of the mined area is being accomplished with the controlled flooding of its large pit. During the first 7 months of flooding, the sequential arrest of the ground water dewatering system led to the growth of an acidic water body of about 2 hm(3). Since October 2008, the surface waters from some local streams have been diverted towards the pit so that these have become the major water input in the flooding process. Surface water has promoted a major change in the chemical composition of the lake water so that, at present, its surface has a circum neutral pH, net alkalinity, and low conductivity. At present, the lake has slightly more than one half of its final volume, and it is expected the overflow in 3 to 3.5 years. The lake is meromictic, with a sharp chemocline separating the acidic monimolimnion (pH ≈ 3.2, acidity ≈ 150 mg CaCO3/L, κ 25 ≈ 2.4 mS/cm) from the main water body (pH ≈ 6.5, alkalinity ≈ 15 mg CaCO3/L, κ 25 ≈ 0.3 mS/cm). Oxygen is being depleted at the bottom of the lake so that the monimolimnion became anoxic in January 2011. Above the chemocline, the composition of the lake is similar, but not identical, to that of the flooding stream waters. Close to the surface, some constituents (pH, metals) show strong seasonal variations in coincidence with the phytoplankton growing periods. Those parameters whose limits are legally prescribed comply with the corresponding water quality standards, and they are also consistent with the forecasting results obtained in early modeling. At present, a project considering the construction of an uptake tunnel to exploit the lake is being developed for the emergency water supply of the metropolitan area of A Coruña.

Bioavailable concentrations of polycyclic aromatic hydrocarbons (PAHs) were investigated in water of Three Gorges Reservoir (TGR) using semipermeable membrane devices during the period of completely impounding water. ∑PAH concentrations in water of TGR in the period of completely impounding water were 15-381 ng L(-1). ∑PAH concentrations increased from town or counties to big industrialized cities in TGR, indicating urbanization effects on PAH pollution in the water. Tributaries in TGR have a certain contribution of PAH pollution to the mainstream of Yangtze River and their pollution could not be neglected. An obvious decrease of PAH concentration was observed after 175-m water impounding in 2011 in TGR. Several factors may account for this decrease, including execution of comprehensive treatment and management measures in TGR, less rainfall in 2011, and sedimentation effect caused by the dam. Passive sampling method has been successfully applied in the investigation of trace PAH in water of TGR and proved to be a useful and efficient tool for the management and sustainable development of the big reservoir. The results of the study provide valuable information about PAH pollution in the whole reservoir including some tributaries, and the pollution status is dynamically related with human activities. Therefore, PAH could be used as a marker compound or indicator in the network monitoring system to surveil and trace the pollution status in TGR.

An event-driven, urban, drinking water quality early warning and control system (DEWS) is proposed to cope with China’s urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality early warning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality early warning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score—multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.