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A B S T R A C TSites contaminated by Dense Non-Aqueous Liquid Phases (DNAPLs) containing chlorinated compounds are a ubiquitous problem caused by spills or the dumping of wastes with no concern for the environment. Their migration by gravity through the subsurface and their accumulation far below ground level make in-situ treatments the most appropriate remediation technologies. In this work, an aqueous solution containing a non-ionic and biodegradable surfactant was injected in the Sardas alluvial layer contaminated at some points with DNAPL (formed by a mixture of more than 28 chlorinated compounds) from lindane production. A volume of 5.28 m 3 of an aqueous surfactant emulsion (13 g L À1 ) was injected at 14.5 m b g.l in the permeable layer (gravel-sand), at a flow rate of 0.6 m 3 h À1 and the groundwater was monitored within a test cell (3.5 m radius) built ad hoc. The flow of the injected fluids in the subsurface was also evaluated using a conservative tracer, bromide (130 mg L À1 ), added to the surfactant solution. Concentration of contaminants, chloride, bromide and surfactant, surface tension and conductivity were measured at the injection point and at three monitoring points over time. High radial dispersion was noticed resulting in high dilution of the injected fluids. The surfactant was not adsorbed in the soil during the injection time, the adsorption of the surfactant took place in the meantime (15 h) between its injection and the groundwater (GW) extraction. The concentration of chlorinated compounds dissolved from the soil in the surfactant aqueous phase when equilibrium was reached (about 850 mg L À1 ) is related to the moderate average contamination of the soil in the test cell (about 1230 mg kg À1 ). In contrast, the extraction of the free DNAPL in the altered marls layer was highly enhanced due to the addition of the surfactant. Finally, it was found that the surfactant and the contamination did not migrate from the capture zone.

This letter presents a full computer vision system for the identification of postharvest damages in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.

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