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We present the Mathematical Modelling Learning strategy in which students create a model that will predict behaviour of existing phenomena using real data. In our implementation students create models from atmospheric data and solve them to determine which weather conditions favour high levels of pollutants in the atmosphere of Monterrey metropolitan area in Mexico. To carry out the strategy we structure course topics around this single comprehensive and integrative project. Students follow a procedure consisting of 4 stages. In the first stage they do statistical analysis of the data. In the second stage, students interpolate missing data and project component data to a 2D map of the metro area. In the third stage students create the mathematical models by carrying out curve fitting through least squares technique. In the third stage, students solve the models by finding roots, solving systems of equations, solving differential equations or integrating. The final deliverable is to determine under which weather conditions there can be an environmental situation that put people's health in danger. Analysis of the strategy is presented as well as statistical results.