The Atmospheric Lifetime (ALT) of a compound represents the potential for the atmospheric accumulation of chemicals. Chemicals with a long lifetime are more resistant to natural decomposition and remain in the environment for a longer period. Minimum Ignition Energy (MIE) is one of the most important properties when evaluating hazardous chemicals. Despite the significance of these environment and safety-related properties, currently, there are no group contribution (GC) models that enable their predictive modeling. The present research aims at filling this gap. To this end, experimental data were collected from literature and the GC model parameters were estimated using the weighted nonlinear least-squares regression. Two approaches were applied; in the step-wise approach the parameters of the first, second and third order GC models were estimated sequentially. By comparison, all these parameters were optimized simultaneously in the second approach. The estimated average relative error and correlation coefficient for ALT model were 45.28% and 0.9999 for the step-wise approach, and 26.16% and 0.9999 for the simultaneous approach, respectively. In the case of MIE, the average relative error and correlation coefficient were 16.70% and 0.9964 for the step-wise approach and 11.48% and 0.9999 for the simultaneous approach, respectively. The proposed models not only provide novel tools for the environmental/safety analysis of the common chemicals when experimental values are unavailable, but they could also be applied to computer-aided product design problems; thus contributing towards the development of improved and more sustainable industrial processes.