{'fraction_non_alphanumeric': 0.024489795918367346, 'fraction_numerical': 0.006122448979591836, 'mean_word_length': 5.0184049079754605, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Regional-level prediction model with advection PDE model and fine particulate matter (PM 2.5) concentration data

Real-time and geo-tagged data on PM 2.5 enable researchers to model and predict the trends of air pollution effectively. On the basis of network and clustering, a specific advection partial differential equation (PDE) model is proposed to forecast the spatial-temporal dynamics of PM 2.5 concentration at large scale of city-cluster. The proposed PDE model incorporates the effects of advection, local emission and dispersion. The prediction is performed in real-time with varying model parameters for assessing the current situation. Good simulation results not only demonstrate the proposed PDE has good prediction ability, but also show that the model can quantify the advection and local effects for the air pollution of each city-cluster to some extent. Moreover, the methodology can be extended to other types of air pollution provided that data are available.