The Berlin-Brandenburg Air Study-A Methodological Study Paper of a Natural Experiment Investigating Health Effects Related to Changes in Airport-Related Exposures.

Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health.

Optimized microplastic analysis based on size fractionation, density separation and µ-FTIR.

Microplastic particles have been recognized as global hazardous pollutants in the last few decades pointing to the importance of analyzing and monitoring microplastics, especially in soils and sediments. This study focused on a multi-step approach for microplastic analysis combining grain size fractionation, density separation and identification by µ-FTIR-spectroscopy. Eight widely used polymers (polyethylene (PE), polypropylene (PP), polyvinylchloride (PVC), polystyrol (PS), polyethylenterephthalate (PET), polymethylmethacrylate (PMMA), polyurethane (PU) and polyamide (PA)) were fractionated into four groups of grain sizes (0.1-5 mm). Thereafter, sea sand was spiked with these particles to test a ZnCl2-based density separation for the polymer types and the various grain sizes. The obtained recovery rates were close to 100% showing that ZnCl2-based density separation is suitable to separate the polymer particles from a sandy matrix. This approach was extended on three further environmental matrices and recovery rates for two of them (sandy-silty and fine-grained sediment) also provided reliable values (94-106%). Lastly, the developed multi-step approach was verified by analyzing an environmental sample (sediment from river Tiranë, Albania) characterized by smaller grain size and moderate organic matter content. Identification of two polymer types in different grain size classes verified the suitability of the developed approach for microplastic analyses on particulate matter such as soils and sediments.