Pyrolysis is a feasible solution for environmental problems related to the inadequate disposal of waste tires, as it leads to the recovery of pyrolytic products such as carbon black, liquid fuels and gases. The characteristics of pyrolytic carbon black can be enhanced through chemical activation in order to produce the required properties for its application. In the search to make the waste tire pyrolysis process profitable, new applications of the pyrolytic solid products have been explored, such as for the fabrication of energy-storage devices and precursor in the synthesis of nanomaterials. In this study, waste tires powder was chemically activated using acid (H₂SO₄) and/or alkali (KOH) to recover pyrolytic carbon black with different characteristics. H₂SO₄ removed surface impurities more thoroughly, improving the carbon black’s surface area, while KOH increased its oxygen content, which improved the carbon black’s stability in water suspension. Pyrolytic carbon black was fully characterized by elemental analysis, inductively coupled plasma–optical emission spectrometry (ICP-OES), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), N₂ adsorption/desorption, scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDS), dynamic light scattering (DLS), and ζ potential measurement. In addition, the pyrolytic carbon black was used to explore its feasibility as a precursor for the synthesis of carbon dots; synthesized carbon dots were analyzed preliminarily by SEM and with a fluorescence microplate reader, revealing differences in their morphology and fluorescence intensity. The results presented in this study demonstrate the effect of the activating agent on pyrolytic carbon black from waste tires and provide evidence of the feasibility of using waste tires for the synthesis of nanomaterials such as carbon dots.

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies.