Caves are generally very stable spaces, but easily alterable from the environmental point of view. In show caves, the geochemical rock-atmosphere interface in carbonate karst caves can be affected by tourism. In this regard, the influence of tourism on the increase of CO2 concentration is particularly important. Therefore, knowing the visitor influx that guarantees the maintenance of the natural balance of show caves is of special interest from the point of view of management and conservation. In this paper, a novel method to determine the maximum recommended number of visitors in periods of a massive influx of tourists in show caves is proposed. This method consists of relating the CO2 level, its derivative and the presence of visitors by means of a linear model. Afterwards, an initial value problem to predict the CO2 concentration throughout the day is established, using the expected pattern of visits and the CO2 concentration at the beginning of the day as inputs.
The results show an excellent performance when used as a descriptive model (correlation coefficient R ≈ 0.99 with respect to the actual values), and as a predictive model (R ≈ 0.95). Once the global predictive model has been learned, it can be used to make predictions about the CO2 levels in any other future events. Thus, the proposed method allows running simulations of different regimes of visits (for instance, limiting the desired maximum level of CO2), allowing cave managers to determine the best regime for a specific cave. Therefore, this method can be useful to minimize the impact of tourism on show caves while maximizing its visitor capacity, helping to protect underground ecosystems.