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Researchers have shown the effectiveness of ion chromatography (IC) in identifying markers of forest fire emissions in rainfall samples, providing valuable insights into the signature of forest fire emissions and aiding in the development of models to predict and control air pollution resulting from these events.
A recent study published in Chromatographia by Murat Kilic and Serpil Kilic from Isparta University of Applied Sciences in Turkey demonstrates the effectiveness of using ion chromatography (IC) to identify markers of forest fire emissions in rainfall samples (1).
Forest fires are a major source of air pollution, and tracking the chemicals released into the atmosphere is crucial for developing models to predict and control air pollution resulting from these events. In this study, the researchers collected two rainfall events following the Antalya forest fires using a volume-based sequential sampling method. They obtained two fractional samples (S-series) from the first rainfall event and four fractional samples (M-series) from the second rainfall event.
The researchers used IC to determine the concentrations of F−, Br−, NO2−, Cl−, NO3−, SO42−, PO43−, Ca2+, Na+, Mg2+, NH4+, K+ and Li+ ions in the sequential rain samples. They also measured the pH and conductivity of the fractional samples and analyzed them for water-soluble ions. In addition, the water-insoluble particulate matter in the samples was analyzed for its particle size distributions using a particle size analyzer, and the morphologies of the particles were characterized using scanning electron microscopy energy dispersive X-ray spectroscopy (SEM–EDS).
IC is an analytical technique that separates ions based on their charge properties. It uses a high performance liquid chromatography (HPLC) system with a specialized column that contains charged resins that attract and separate ions. The sample is introduced into the column, and the mobile phase (eluent) carries the sample through the column. As the sample passes through the column, the ions are attracted to and retained by the resins, and they are eluted out of the column at different times based on their interaction with the resin. IC can detect and quantify a wide range of ions, including anions (negatively charged) and cations (positively charged) in various sample matrices, including environmental, biological, and industrial samples. The technique has high sensitivity, specificity, and reproducibility.
The results of the study showed that sulfate, chloride, calcium, ammonium, and potassium ions were identified as markers of forest fires using the IC method. The researchers compared these markers with the PM10 and PM2.5 forest fire emission source profiles reported in the US EPA SPECIATE database. They also used upper atmospheric back trajectory analyses and local wind roses to estimate the possible sources and/or source regions of the measured species.
The use of IC to determine the concentrations of ions in the sequential rain samples provided valuable insights into the signature of forest fire emissions. The method proved effective in identifying the markers of forest fire emissions and could help researchers develop better models to predict and control air pollution resulting from these events.
Overall, the study highlights the importance of using IC in environmental research and provides valuable information on the chemical composition of rainfall samples following forest fires. The findings could help improve our understanding of the impact of forest fires on air quality and guide efforts to mitigate their effects.
(1) Kilic, M.; Kilic, S. Ionic Compositions of Sequential Rainfall Samples as Source Signatures of Forest Fire Emissions. Chromatographia 2023, 86, 153–165. DOI: 10.1007/s10337-023-04233-8