To evaluate the used and End-of-Life tyre management chains, both direct observation and interviews with approximately 200 used-tyre dealers were conducted. The study revealed that while the used tyre business is well-organized, End-of-Life tyre treatment is nearly non-existent, often involving dumping, open burning for metal recovery, or use in meat processing, causing environmental and health risks. To address these challenges, an 8 t/hr capacity tyre recycling plant is planned, based on estimated tyre availability and development opportunities for sustainable recycling in Togo.

Non-exhaust road transport emissions in cities contribute to poor air quality and create health impacts. This paper presents a new study of the emitted particles due to the tyres-road wear ‘TRWP’ in real driving and gives their emission factors EF. The most frequently observed particles were <1 µm; followed by particles size between [1µm, 4µm] for urban, suburban and highway areas. These particle sizes are well-known to have a higher degree of toxicity. An overall analysis of all the measured pollutants gave EF equal to 30 10+12 #/km and 35 10+12 #/km, respectively for urban and sub-urban areas; and 5 10+12 #/km on highway. The analysis of the 19 most emitted pollutants, taken individually, showed that their EFs ranged from 0.003 to 18.142 10+12#/km. The obtained EF are above the value of the Euro standard for vehicles, but are below the values for vehicles unequipped with a particle filter. Significant test analysis confirmed that inertia of chemical pollutants is homogeneous. EF assessment of the PM10 and PM2.5 emitted is equal to 1.45 mg/km and 0.35 mg/km respectively. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship to TRWP.

This paper presents the results of investigations on the pyrolysis of tyres waste in a laboratory fixed bed batch reactor. The results regarding the influence of either the reaction temperature (425, 450, 475, and 500 °C) and the flow of the inert gas (0, 100, 300, and 500 mL/min) on products yield (referred to pyrolysis of waste tyres) are also considered and discussed. On the basis of the above mentioned findings, the most appropriate experimental conditions were selected to contribute to a higher yield of pyrolysis oil. The sample of pyrolysis oil obtained from the experiments carried out in the selected optimal conditions (reaction time 120 min, temperature 450 °C and the inert gas flow of 100 mL/min) was subjected to a calorimetric and infrared spectroscopy analysis. The results of IR spectroscopy analysis on this oil sample showed the following content in percentages (by mass): 32.59 % of aromatics, 51.06 % of paraffins and 16.35 % of naphthenes. The pyrolysis oil so obtained has a high calorific value (42 MJ/kg) and a low sulfur content (0.41 % by mass), which makes it an excellent raw material for energy production. The solid product, i.e. pyrolysis char or carbon black, has the potential to be used as an adsorbent or catalyst support after an activation process, which changes its irregular pore structure to a more voluminous one, making the overall structure more crystalline and symmetrical. Pyrolysis coal has a fairly high calorific value of 31 MJ/kg, when compared to typical solid fuels, so it can be used in energy production or as a feedstock for the gasification process.

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.