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              <text>Air pollution is a growing concern in urban environments as it can heavily impact public health, especially in relation to respiratory diseases. This has led to a need for real-time monitoring of pollution levels to quickly identify any significant deviations and inform authorities, allowing for necessary interventions. Here, we propose a dynamic monitoring system that uses Kalman filters and multivariate statistical methods to track pollution trends and seasonality. By analysing the residuals of the model with non- parametric methods, we can detect changes in pollution levels and predict future readings. This gives rise to the anticipation of dangerous pollutant levels, enabling us to forewarn authorities. The approach is demonstrated using data from Athens, focusing on key pollutants such as ozone (O3), nitrogen dioxide (NO2), nitrogen oxide (NO), carbon monoxide (CO) and sulphur dioxide (SO2).</text>
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              <text>Investigating a taphonomy model by analysing damage to SedaDNA, changes in proportion of taxa in the sediment and changes to sediment type, in response to large changes in climate. Using sediment samples from the North Sea and OSL and carbon dating techniques to make a timeline of data with respect to sedaDNA damage, rate of change in sediment or rate of change in taxa, the timeline is compared against known large changes of climate.</text>
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