An economic passive sampling method to detect particulate pollutants using magnetic measurements.

Identifying particulate matter (PM) emitted from industrial processes into the atmosphere is an important issue in environmental research. This paper presents a passive sampling method using simple artificial samplers that maintains the advantage of bio-monitoring, but overcomes some of its disadvantages. The samplers were tested in a heavily polluted area (Linfen, China) and compared to results from leaf samples. Spatial variations of magnetic susceptibility from artificial passive samplers and leaf samples show very similar patterns. Scanning electron microscopy suggests that the collected PM are mostly in the range of 2-25 µm; frequent occurrence of spherical shape indicates industrial combustion dominates PM emission. Magnetic properties around power plants show different features than other plants. This sampling method provides a suitable and economic tool for semi-quantifying temporal and spatial distribution of air quality; they can be installed in a regular grid and calibrate the weight of PM.

Atmospheric pollution history at Linfen (China) uncovered by magnetic and chemical parameters of sediments from a water reservoir.

We studied magnetic and chemical parameters of sediments from sediments of a water reservoir at Linfen (China) in order to quantitatively reconstruct the atmospheric pollution history in this region. The results show that the main magnetic phases are magnetite and maghemite originating from the surrounding catchment and from anthropogenic activities, and there is a significant positive relationship between magnetic concentration parameters and heavy metals concentrations, indicating that magnetic proxies can be used to monitor the anthropogenic pollution. In order to uncover the atmospheric pollution history, we combined the known events of environmental improvement with variations of magnetic susceptibility (χ) and heavy metals along the cores to obtain a detailed chronological framework. In addition, air comprehensive pollution index (ACPI) was reconstructed from regression equation among magnetic and chemical parameters as well as atmospheric monitoring data. Based on these results, the atmospheric pollution history was successfully reconstructed.