Silvia Reich

Monitoring trace metals in urban aerosols from Buenos Aires city. Determination by plasma-based techniques

A study was undertaken, within the framework of a 3 years national project, to assess the content of 13 elements in airborne particulate matter collected in representative zones of the metropolitan area of Buenos Aires. The sampling strategy followed consisted in collecting simultaneously 67 samples of PM10 particulate matter in 9 sampling sites covering an area of about 30 km2 during one week. The collection was performed on ash-free fibre-glass filters using high volume samplers. A combination of aqua regia and perchloric acid was used for leaching metals from filters. Key elements, namely Al, Ca, Cu, Fe, Mn, Mo, Ni, Pb, S, Sb, Sn, Zn and Zr, were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS) at micro g g(-1) and ng g(-1) levels. Analyte concentration varied from 130 ng g(-1)(Mo) to over 30%(Ca). Multivariate statistical analysis was performed on the data set including the measured elemental compositions for the monitored period. The atmospheric concentration found for Pb confirms the decreasing levels of this element since the introduction of unleaded gasoline in 1995: 88 ng m(-3)(2001) < 220 ng m(-3)(1997) < 3900 ng m(-3)(1994). The average S concentration above 3 microg m(-3) is somehow unexpectedly high for Buenos Aires since the relatively low S content of liquid fuels and the massive usage of natural gas imply low emissions of this element from combustion activities. To the best of our knowledge, S concentrations are reported for the first time for this city.

Air pollution sources of PM10 in Buenos Aires City

To elucidate the sources of PM10 air pollution from the experimental information collected in a local air quality monitoring campaign we have applied two methods, effective variance and genetic algorithms, in the solution of the chemical mass balance. The comparison of these two mathematical approaches show that the identification of the possible sources and the evaluation of its contributions are quite independent of them. The role of possible different sources for major and trace elements and the significance of standardizing available data is also addressed. We also present a simple method for identifying the number of candidate sources, a key element defining the dimension of the search space.

The mean angular distance among objects and its relationships with Kohonen artificial neural networks.

This job refers to classification of multidimensional objects and Kohonen artificial neural networks. A new concept is introduced, called the mean angular distance among objects (MADO). Its value can be calculated as the cosine of the mean centered vectors between objects. It can be expressed in matrix form for any number of objects. The MADO allows us to interpret the final organization of the objects in a Kohonen map. Simulated examples demonstrate the relationship between MADO and Kohonen maps and show a way to take advantage of the information present in both of them. Finally, a real analytical chemistry case is analyzed as an application on a big data set of an air quality monitoring campaign. It is possible to discover in it a subgroup of objects with different characteristics than those of the general trend. This subgroup is linked to the existence of an unidentified SO(2) source that, a priori, has not been taken into account.

2D mapping by Kohonen networks of the air quality data from a large city.

The 15-variable environmental data (7 concentrations: CO, SO2, O3, NOx, NO, NO2, particulate matter smaller than 10 micron (PM10), and 8 weather data: cloudiness, rainfall, insolation factor (Isfi), temperature, pressure at two locations, and wind intensity with direction) in a period of 45 days with 1-h intervals were extracted from a larger database of concentrations recorded in minute intervals for the same time period. The monitoring site was located in the City of Buenos Aires in a relatively heavy traffic crossroad of two avenues. The data required special pretreatment where the hourly content of rain, wind intensity, wind velocity, and cloudiness were concerned. The new variable named insolation factor (relative UV radiation) calculated on the basis of the general meteorological data, the geographic position of the monitoring site, cloudiness, date, and the time of the recording was composed. The relative intensity of UV radiation was modeled by a Gaussian function, multiplied by a cloudiness factor. Based on the 14-variable input and the 1-variable output (ozone) data, first, the clustering of all 980 data records was made. The top map clustering showing the ozone concentration was related to the maps of all 14 variables. The link between O3 clusters, NO2, and Isfi weight levels is shown and discussed. As a preliminary result of this study some of the most interesting correlations between the maps and remaining variables are given.

Source Extraction Information from Air Quality Data Monitored in an Argentinean Steel Mill

Abstract A statistical analysis of a series of ambient air concentrations of suspended particulate matter (SPM) and NO2 is presented. Measurements were taken at four sites that belong to an Argentinean steel mill and in another site located in its vicinity. The air pollutants were measured during a three-week exploratory sampling. The monitoring sites were selected on the basis of relevant characteristics of the emission sources and the corresponding climatological statistics of the last decade. Suspended particulate matter with aerodynamic diameter of less than 10 μm (PM10) and NO2 were continuously measured at only one site, while 1-hr samples of NO2 and 24-hr samples of total SPM and SO2 were collected at the other sites. The registered concentrations show that SPM was the pollutant of major concern. A first estimate about the nature of the contribution of the different sources of particles and NO2 present in the area was obtained through the statistical analysis of measured concentration data coupled with prevalent meteorological variables.