Indicator Compounds Representative of Contaminants of Emerging Concern (CECs) Found in the Water Cycle in the United States

The presence of contaminants of emerging concern (CECs) in the aquatic environment has recently become a global issue. The very large number of CECs reported in the literature makes it difficult to interpret potential risks as well as the removal efficiencies, especially for the more recalcitrant compounds. As such, there is a need for indicator compounds that are representative of CECs detected in systems worldwide. In an effort to develop such a list, five criteria were used to address the potential for applying indicator compounds; these criteria include usage, occurrence, resistance to treatment, persistence, and physicochemical properties that shed light on the potential degradability of a class of compounds. Additional constraints applied included the feasibility of procuring and analyzing compounds. In total, 22 CECs belonging to 13 groups were selected as indicator compounds. These compounds include acetaminophen and ibuprofen (analgesic); erythromycin, sulfamethoxazole, and trimethoprim (antibiotics); diazepam and fluoxetine (antidepressants); carbamazepine (antiepileptic); atenolol and propranolol (β-blockers); gemfibrozil (blood lipid regulator); tris(2-chloroethyl)phosphate (TCEP) (fire retardant); cotinine (nicotine metabolite); atrazine, metolachlor, and N,N-diethyl-meta-toluamide (DEET) (pesticides); 17β-estradiol and cholesterol (steroids); caffeine (psychomotor stimulant); perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) (surfactants); and iopromide (X-ray contrast agent). These thirteen groups of compounds represent CECs with the greatest resistance to treatment processes, most persistent in surface waters, and detected with significant frequency throughout the water cycle. Among the important implications of using indicator compounds are the ability to better understand the efficacy of treatment processes as well as the transport and fate of these compounds in the environment

Identification of tree species in Mt Chojnik (Karkonoski National Park) forest using airborne hyperspectal APEX data

We used hyperspectral data from APEX scanner (288 spectral bands in 380−2500 nm spectral range; 3,5 m spatial resolution) to classify five tree species occurring in the area of Mt. Chojnik in the Karkonoski National Park (south−western Poland). Data used to delimit learning and verification polygons were acquired during field research in August 2013, when ground truth polygons were acquired using device equipped with GPS receiver. Raw APEX data went through radiometric and geometric correction at VITO office. To reduce processing time, 40 most informative bands were selected using information content analysis. The Support Vector Machines (SVM) algorithm was used for classification of the following tree species: Fagus sylvatica L., Betula pendula Roth, Pinus sylvestris L., Picea alba L. Karst and Larix decidua Mill. Final classification had 78.66% overall accuracy with Kappa coefficient equal to 0.71. The best classified species included beech (87.09%) and pine (83.96%), while the worst results were obtained for larch (60.29%). Low accuracy for larch could be caused by the fact that most of larch trees in the research area grow in small patches, which made it hard to specify large enough sample of training data. All classified tree species had producer's accuracy of at least 60%, with the highest value reaching 87%. User's accuracies were from 53% for pine to 85% for beech. It is possible to classify tree species using hyperspectral data with moderate to high accuracy even if the data used lacked atmospheric correction. Further work will focus on improving the classification accuracy and use of neural networks based classification methods. Results from this paper will serve as basis for tree species map of the Karkonoski National Park