The Krakow Receptor Modelling Inter-Comparison Exercise

Second to oil, coal is globally the biggest energy source. Coal combustion is utilized mainly for power generation in industry, but in many metropolitan areas in East Europe and Asia also for residential heating in small stoves and boilers. The present investigation, carried out as a case study in a typical major city situated in a European coal combustion region (Krakow, Poland), aims at quantifying the impact on the urban air quality of residential heating by coal combustion in comparison with other potential pollution sources such as power plants, industry and traffic. For that purpose, gaseous emissions (NOx, SO2) were measured for 20 major sources, including small stoves and boilers, and the emissions of particulate matter (PM) was chemically analyzed for 52 individual compounds together with outdoor and indoor PM10 collected during typical winter pollution episodes. The data was analyzed using multivariate receptor modeling yielding source apportionments for PM10, B(a)P and other regulated air pollutants associated with PM10, namely Cd, Ni, As, and Pb. The source apportionment was accomplished using the chemical mass balance modeling (CMB) and constrained positive matrix factorization (CMF) and compared to five other multivariate receptor models (PMF, PCA-MLRA, UNMIX, SOM, CA). The results are potentially very useful for planning abatement strategies in all areas of the world, where coal combustion in small appliances is significant. During the pollution episodes under investigation the PM10 and B(a)P concentrations were up to 8-200times higher than the European limit values. The major culprit for these extreme pollution levels was shown to be residential heating by coal combustion in small stoves and boilers (>50% for PM10 and >90% B(a)P), whereas road transport (<10% for PM10 and <3% for B(a)P), and industry (4-15% for PM10 and <6% for B(a)P) played a lesser role. The indoor PM10 and B(a)P concentrations were not much lower than the outdoor concentrations and were found to have the same sources as outdoor PM10 and B(a)P The inorganic secondary aerosol component of PM10 amounted to around 30%, which may be attributed for a large part to the industrial emission of the precursors SO2 and NOX.JRC.H.4-Transport and air qualit

Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets

Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named \u2018\u2018tributary\u2019\u2019, \u2018\u2018urban\u2019\u2019, \u2018\u2018rural\u2019\u2019 or \u2018\u2018background\u2019\u2019. This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream

Implementation of neural networks for classification of moss and lichen samples on the basis of gamma-ray spectrometric analysis

Mosses and lichens have an important role in biomonitoring. The objective of this study is to develop a neural network model to classify these plants according to geographical origin. A three-layer feed-forward neural network was used. The activities of radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137 and Be-7) detected in plant samples by gamma-ray spectrometry were used as inputs for neural network. Five different training algorithms with different number of samples in training sets were tested and compared, in order to find the one with the minimum root mean square error. The best predictive power for the classification of plants from 12 regions was achieved using a network with 5 hidden layer nodes and 3,000 training epochs, using the online back-propagation randomized training algorithm. Implementation of this model to experimental data resulted in satisfactory classification of moss and lichen samples in terms of their geographical origin. The average classification rate obtained in this study was (90.7 +/- 4.8)%