Weather and climate have a double-edged effect on tourism. It can be considered both as a limiting and a developing factor for tourism. In this regard, having access to precise bio-climatic information can be of high use to enhance the quality of tourism services. This study has evaluated the Bio-climatic conditions of the tourists in Mashhad, a North-Eastern city in Iran, through the use of thermo-physiological indicator of physiological equivalent temperature (PET). Studies have been done for the hours of 9:30, 12:30 and 21:30 of the local time using the statistical data of the period between 1978 and 2007. According to the results, the longest period of climatic-comfort-hours is around 21:30 (mainly in June, July, and August), and the shortest duration of climatic-comfort-hours is around 9:30 (sporadically assessed in April, May and October). The Bio-climatic conditions during the Nowruz Holidays are only comfortable in the evenings. Especially, there is the challenge of cold stress in the mornings and more specifically, at nights. However, the best condition of the whole assessed times in summer holidays (that is June 22nd to September 23rd) is at 21:30. Summer days are not ideal for tourism due to the heat stress. The daily change of PET index shows a Gaussian curve, the peak of which (indicative of too much heat stress) shows July, and non-stress condition can be seen on both sides of this curve.

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In this paper, we aim at developing a model to predict the daily average concentration of particulate matters with a diameter of less than 2.5 micrometers (PM 2.5 ). In the introduced model, we incorporate Weather Research and Forecasting (WRF) meteorological model, Monte Carlo simulation, wavelet transform, and multilayer perceptron (MLP) neural networks. In particular, the MLP and wavelet transformation are combined for prediction. In order to predict the model's input parameters, including wind speed, wind direction, temperature, rainfall, and temperature inversion, the WRF meteorological model is used. Finally, according to the available uncertainty in the input data and in order to achieve a more accurate prediction, the Monte Carlo simulation is utilized. In order to assess the effectiveness of the model in the real world, it has been conducted in an online mode for 35 days. Numerical results give an acceptable accuracy in terms of some widely used measures. In particular, taking into account the R measurements, it is equal to 0.831 over the set of test instances.