Repetitive extragenic palindromic PCR (REP-PCR) as a method used for bulking process detection in activated sludge

Bulking of activated sludge is a world-widely prevalent problem and can lead to loss of bio-oxidation, further deterioration of effluent quality, and even to a complete breakdown of the entire treatment process. Most common reasons of bulking are bacterial community changes, especially excessive growth of filamentous bacteria or excess of biopolymers on surface of non-filamentous microbes. Because of complex nature of the bulking phenomenon, the successful bulking control strategy finding is still a very important need awaiting new options and advices. The repetitive extragenic palindromic PCR (REP-PCR) fingerprinting method has been applied to distinguish bacterial community in non-bulking and bulking activated sludge. The characteristic REP-PCR fingerprinting patterns, using the Ward??s clustering method, have been analyzed to determine homology/similarity relation between particular non-bulking and bulking sludge sampling. The received clustering results were in high concordance with activated sludge typing done based on physicochemical sludge analysis. The choice and application of molecular typing method in sludge analysis will depend upon the needs, skill level, and resources of the laboratory. The proposed REP-PCR method and statistical analysis of fingerprinting patterns seems to be simple, rapid, and effective methods to show differences between population in non-bulking and bulking activated sludge. It is easy to implement, and it may be useful for routinely activated sludge monitoring as well as may be helpful in early detection of bulking process.     

Spatial distribution and human contamination quantification of trace metals and phosphorus in the sediments of Chaohu Lake,a eutrophic shallow lake,China

Distinguishing and quantifying anthropogenic trace metals and phosphorus accumulated in sediment is important for the protection of our aquatic ecosystems. Here, anthropogenic proportion and potential sources of trace metals and phosphorus in surface sediments of Chaohu Lake were evaluated based on the exhaustive geochemical data. The analysis shows that concentrations of major and trace metals, and phosphorus, displayed significant spatial diversity and almost all elements were over the pre-industrial background value, which should be related to the variations of sediment composition partially. Therefore, conservative element normalization was introduced and calculated enrichment factors (EFs) of the elements were referenced highlighting the human contamination. EFs of the major and trace metals, except Zn, Pb, and Cu, were all nearly 1.0, indicating the detrital origin. The EFs of Zn, Pb, Cu and phosphorus were 1.0–10.4, 1.0–3.8, 1.0–4.9, and 1.0–7.6, respectively, showing moderate to significant contamination. Higher EFs of Zn, Pb and Cu occurred in the mouth areas of Nanfei River and Zhegao River, and they decreased to the lake center in the northwest and northeast lake areas, respectively. We deduced that anthropogenic Zn, Pb, and Cu were mainly from urban and industrial point sources and the non-point sources of atmospheric deposition contributed little to their contamination. The EFs of phosphorus showed similar spatial degradation with that of Zn, Pb, and Cu. Moreover, higher EFs (>1) of phosphorus also occurred in other areas adjacent to the river mouths besides Nanfei River and Zhegao River. This indicated that the non-point agricultural source may also be responsible for the contamination of phosphorus in Chaohu Lake in addition to the urban sewage sources. Anthropogenic phosphorus was mainly concentrated in the speciation of NaOH-P, which had higher potential biological effects than the detrital proportion. Concentrations of Zn, Pb and Cu surpassed the threshold effect concentrations (TEC) of consensus-based sediment quality guidelines of freshwater ecosystems, especially in the contaminated northwest area of Chaohu Lake. This highlighted the contributions of anthropogenic contamination to the elevated potential biological effects of trace metals. Though there had been no obvious human contamination of Cr and Ni in Chaohu Lake, concentrations were all over the TECs, which may be due to higher background levels in the parent materials of soils and bedrocks in Chaohu Lake catchment.     

Using the analytical hierarchy process to assess the environmental vulnerabilities of basins in Taiwan

Every year, Taiwan endures typhoons and earthquakes; these natural hazards often induce landslides and debris flows. Therefore, watershed management strategies must consider the environmental vulnerabilities of local basins. Because many factors affect basin ecosystems, this study applied multiple criteria analysis and the analytical hierarchy process (AHP) to evaluate seven criteria in three phases (geographic phase, hydrologic phase, and societal phase). This study focused on five major basins in Taiwan: the Tan-Shui River Basin, the Ta-Chia River Basin, the Cho-Shui River Basin, the Tseng-Wen River Basin, and the Kao-Ping River Basin. The objectives were a comprehensive examination of the environmental characteristics of these basins and a comprehensive assessment of their environmental vulnerabilities. The results of a survey and AHP analysis showed that landslide area is the most important factor for basin environmental vulnerability. Of all these basins, the Cho-Shui River Basin in central Taiwan has the greatest environmental vulnerability.     

Characterization and assessment of a large-scale domestic advanced wastewater treatment plant in Turkey

The paper presents new studies about the removal efficiencies and characterization of the Municipal Wastewater Treatment Plant (MWWTP) in Kayseri, Turkey, which is serving for 800,000 population equivalents with the capacity of 110,000 m(3)/day, between 2006 and 2009. Kayseri discharges its effluents to Karasu Creek that downstream joins the Kizilirmak river, the longest river in Turkey discharging into the Black Sea. Four years of operation data between 2006 and 2009 calendar years were obtained from KASKI General Directorate. Influent and effluent samples were collected daily and suspended solids (SS), chemical oxygen demand (COD), biochemical oxygen demand in 5 days (BOD(5)), total nitrogen (TN), and total phosphorus (TP) were measured, aiming to study the evolution of the removal efficiencies for each year. The following yearly mean removal efficiencies were performed in the treatment plant: 94%, 97%, 97%, 95% for SS; 94%, 96%, 95%, 95% for COD; 98%, 98%, 98%, 98% for BOD(5); 84%, 87%, 89%, 82% for TN; and 71%, 86%, 80%, 86% for TP, in 2006, 2007, 2008, and 2009, respectively. The performance of Kayseri MWWTP was given both in terms of influent and effluent quality and in comparison with the current legislation on discharge limits to the receiving body. During the studied period, mean concentrations in the effluent did not exceed the imposed limits, but sometimes, higher values were noticed for SS, TN, and TP. In conclusion, the plant performances concerning these parameters were excellent and Kayseri MWWTP was operating efficiently according to the conventional control parameters.     

Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks

Suspended sediment concentration (SSC) is generally determined from the direct measurement of sediment concentration of river or from sediment transport equations. Direct measurement is very costly and cannot be conducted for all river gauge stations. Therefore, correct estimation of suspended sediment amount carried by a river is very important in terms of water pollution, channel navigability, reservoir filling, fish habitat, river aesthetics and scientific interests. This study investigates the feasibility of using turbidity as a surrogate for SSC as in situ turbidity meters are being increasingly used to generate continuous records of SSC in rivers. For this reason, regression analysis (RA) and artificial neural networks (ANNs) were employed to estimate SSC based on in situ turbidity measurements. The SSC was firstly experimentally determined for the surface water samples collected from the six monitoring stations along the main branch of the stream Harsit, Eastern Black Sea Basin, Turkey. There were 144 data for each variable obtained on a fortnightly basis during March 2009 and February 2010. In the ANN method, the used data for training, testing and validation sets are 108, 24 and 12 of total 144 data, respectively. As the results of analyses, the smallest mean absolute error (MAE) and root mean square error (RMSE) values for validation set were obtained from the ANN method with 11.40 and 17.87, respectively. However these were 19.12 and 25.09 for RA. It was concluded that turbidity could be a surrogate for SSC in the streams, and the ANNs method used for the estimation of SSC provided acceptable results.     

Implementing In-Stream Nutrient Processes in Large-Scale Landscape Modeling for the Impact Assessment on Water Quality

For a long time, watershed models focused on the transport of chemicals from the terrestrial part of the watershed to the surface water bodies by leaching and erosion. After the substances had reached the surface water, they were routed through the channel network often without any further transformation. Today, there is a need to extend watershed models with in-stream processes to bring them closer to natural conditions and to enhance their usability as support tools for water management and water quality policies. This paper presents experience with implementing in-stream processes in a ecohydrological dynamic watershed model and its application on the large scale in the Saale River basin in Germany. Results demonstrate that new implemented water quality parameters like chlorophyll a concentrations or oxygen amount in the reach can be reproduced quite well, although the model results, compared with results achieved without taking into account algal and transformation processes in the river, show obvious improvement only for some of the examined nutrients. Finally, some climate and water management scenarios expected to impact in-stream processes in the Saale basin were run. Their results illustrate the relative importance of physical boundary conditions on the amount and concentration of the phytoplankton, which leads to the conclusion that measures to improve water quality should not only take nutrient inputs into account but also climate influences and river morphology.     

Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster

The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha^?1 h^?1 year^?1. Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3 % of the cluster has soil loss below 20 t ha^?1 year^?1. The soil loss from crop land varied from 2.9 to 3.6 t ha^?1 year^?1 in low rainfall years to 31.8 to 34.7 t ha^?1 year^?1 in high rainfall years with a mean annual soil loss of 12.2 t ha^?1 year^?1. The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha^?1 year^?1 in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3 % of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water using farm ponds and percolation tanks. This methodology can be adopted for estimating the soil loss from similar ungauged watersheds with deficient data and for planning suitable soil and water conservation interventions for the sustainable management of the watersheds.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

Implications of differing input data sources and approaches upon forest carbon stock estimation

Site index is an important forest inventory attribute that relates productivity and growth expectation of forests over time. In forest inventory programs, site index is used in conjunction with other forest inventory attributes (i.e., height, age) for the estimation of stand volume. In turn, stand volumes are used to estimate biomass (and biomass components) and enable conversion to carbon. In this research, we explore the implications and consequences of different estimates of site index on carbon stock characterization for a 2,500-ha Douglas-fir-dominated landscape located on Eastern Vancouver Island, British Columbia, Canada. We compared site index estimates from an existing forest inventory to estimates generated from a combination of forest inventory and light detection and ranging (LIDAR)-derived attributes and then examined the resultant differences in biomass estimates generated from a carbon budget model (Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3)). Significant differences were found between the original and LIDAR-derived site indices for all species types and for the resulting 5-m site classes (p?<?0.001). The LIDAR-derived site class was greater than the original site class for 42% of stands; however, 77% of stands were within ±1 site class of the original class. Differences in biomass estimates between the model scenarios were significant for both total stand biomass and biomass per hectare (p?<?0.001); differences for Douglas-fir-dominated stands (representing 85% of all stands) were not significant (p?=?0.288). Overall, the relationship between the two biomass estimates was strong (R ²?=?0.92, p?<?0.001), suggesting that in certain circumstances, LIDAR may have a role to play in site index estimation and biomass mapping.