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Cigarette butts are known to contain toxic metals which pose a potential threat to the environment and human health. The seriousness of this threat is largely determined by the leachability of these toxic metals when the butts are exposed to aqueous solutions in the environment. The aims of this study were to determine the presence and mobility of toxic and non-toxic elements found in discarded cigarette butts; to relate this mobility to two different contact situations with leaching liquids: tumbling and trampling (batch test) and percolation in a static position (column test); and finally, to verify possible variations in solubility by simulating different environmental systems. Five leachants with different pH values were used to simulate various environmental conditions The concentrations of the solubilized metals were determined by inductively coupled plasma-atomic emission spectrometry (ICP-AES) and inductively coupled plasma-mass spectrometry (ICP-MS). CH3COOH pH 2.5 showed the greatest capacity to dissolve many elements. On the contrary, weakly acidic or alkaline environments did not favor the leachability of the elements. The best extraction capacity of the column with respect to the batch is statistically significant (p μg/g. However, Hg was < LOD in all different leachants.

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.