Characterization of atmospheric aerosol (PM10 and PM2.5) from a medium sized city in São Paulo state, Brazil.

Air pollution causes deleterious effects on human health with aerosols being among the most polluting agents. The objective of this work is the characterization of the PM2.5 and PM10 aerosol mass in the atmosphere. The methods of analysis include WD-XRF and EDS. Data were correlated with meteorological information and air mass trajectories (model HYSPLIT) by multivariate analysis. A morphological structural analysis was also carried out to identify the probable sources of atmospheric aerosols in the city of São José do Rio Preto, Brazil. The mean mass concentration values obtained were 24.54 µg/m3 for PM10, above the WHO annual standard value of 20 µg/m3 and 10.88 µg/m3 for PM2.5 whose WHO recommended limit is 10 µg/m3. WD-XRF analysis of the samples revealed Si and Al as major components of the coarse fraction. In the fine fraction, the major elements were Al and S. The SEM-FEG characterization allowed identifying the morphology of the particles in agglomerates, ellipsoids and filaments in the PM10, besides spherical in the PM2.5. The analysis by EDS corroborated WD-XRF results, identifying the crustal elements, aluminosilicates and elements of anthropogenic origin in the coarse fraction. For the fine fraction crustal elements were also identified; aluminosilicates, black carbon and spherical particles (C and O) originating from combustion processes were predominant. The use of multivariate analysis to correlate air mass trajectories with the results of the morpho-structural characterization of the particulate matter allowed confirmation of the complex composition of the particles resulting from the combination of both local and long-distance sources.

Standard addition method with cumulative additions: Monte Carlo uncertainty evaluation.

The cumulative standard addition method allows the calibration of an instrument affected by matrix effects when a small sample volume is available. Recently, it was developed and validated a metrologically sound procedure to estimate the uncertainty of these measurements based on the modelling of the uncertainty of the extrapolation of the calibration curve by the linear least squares regression model. However, this procedure is only applicable when the uncertainty of cumulative sample dilutions and analyte mass additions are negligible given the uncertainty of the total solution volume (v) times the instrumental signal (I) (i.e. v∙I). This work developed a measurement uncertainty model, not limited by this assumption of the quality of calibrators preparation, based on Monte Carlo simulations. This method was successfully applied to the voltammetric measurements of uric acid in human serum, using a working nanocarbon electrode modified with Cu-nanocarbon-lignin, since the uncertainty model adapts to the uncertainty of cumulative volume additions. The validated procedure was checked through the analysis of spiked physiological serum samples and human serum samples, by assessing the metrological compatibility between estimated and reference values. The measurements are reported with an expanded uncertainty not larger than a target value of 0.56 mg dL-1. The used spreadsheet is made available as supplementary material.