A comparative chemometric study for water quality expertise of the Athenian water reservoirs

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.     

Reproductive endocrine disruption in smallmouth bass (Micropterus dolomieu) in the Potomac River basin: spatial and temporal comparisons of biological effects

A high prevalence of intersex or testicular oocytes (TO) in male smallmouth bass within the Potomac River drainage has raised concerns as to the health of the river. Studies were conducted to document biomarker responses both temporally and spatially to better understand the influence of normal physiological cycles, as well as water quality and land-use influences. Smallmouth bass were collected over a 2-year period from three tributaries of the Potomac River: the Shenandoah River, the South Branch Potomac and Conococheague Creek, and an out-of-basin reference site on the Gauley River. The prevalence of TO varied seasonally with the lowest prevalence observed in July, post-spawn. Reproductive maturity and/or lack of spawning the previous spring, as well as land-use practices such as application of manure and pesticides, may influence the seasonal observations. Annual, seasonal, and site differences were also observed in the percentage of males with measurable concentrations of plasma vitellogenin, mean concentration of plasma vitellogenin in females, and plasma concentrations of 17β-estradiol and testosterone in both sexes. Bass collected in the South Branch Potomac (moderate to high prevalence of TO) had less sperm per testes mass with a lower percentage of those sperm being motile when compared to those from the Gauley River (low prevalence of TO). An inverse relationship was noted between TO severity and sperm motility. An association between TO severity and wastewater treatment plant flow, percent of agriculture, total number of animal feeding operations, the number of poultry houses, and animal density within the catchment was observed.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.