Forecasting smog-related health hazard based on social media and physical sensor

Abstract

a b s t r a c tSmog disasters are becoming more and more frequent and may cause severe consequences on the environment and public health, especially in urban areas. Social media as a real-time urban data source has become an increasingly effective channel to observe people's reactions on smog-related health hazard. It can be used to capture possible smogrelated public health disasters in its early stage. We then propose a predictive analytic approach that utilizes both social media and physical sensor data to forecast the next day smog-related health hazard. First, we model smog-related health hazards and smog severity through mining raw microblogging text and network information diffusion data. Second, we developed an artificial neural network (ANN)-based model to forecast smogrelated health hazard with the current health hazard and smog severity observations. We evaluate the performance of the approach with other alternative machine learning methods. To the best of our knowledge, we are the first to integrate social media and physical sensor data for smog-related health hazard forecasting. The empirical findings can help researchers to better understand the non-linear relationships between the current smog observations and the next day health hazard. In addition, this forecasting approach can provide decision support for smog-related health hazard management through functions like early warning.

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

“…Furthermore, we intend to improve our CGM and use it to classify outliers and find their cause. Considering the diverse machine learning models used in air quality prediction, such as Neural Network [13][14][15], regression [18], decision trees, and Support Vector Machine [17], we applied and tested most of these classifiers in this study. Alternative approaches to improve the accuracy of our model would consist of performing a prediction based on an ensemble of different algorithms of data processing and modeling [16,17,22].…”

“…Unlike a pure statistical method, a machine learning approach can consider several parameters in a single model. The most popular classifiers to forecast pollution from meteorological data are artificial Neural Networks [13][14][15]. Other successful studies use hybrid or mixed models that combine several artificial intelligence algorithms, such as fuzzy logic and Neural Network [16], or Principal Component Analysis and Support Vector Machine [17], or numerical methods and machine learning [10].…”

“…However, the data mining does not only differ from one study to another, in terms of classification algorithms, but also regarding the used features. Some of them consider a quite exhaustive list of meteorological factors [15,16], whereas others proceed with a careful selection [13,14,17,22] or do not even use climatic parameters at all [18]. Since machine learning is a very promising method to forecast pollution, we propose applying this approach to predict PM 2.5 concentration in Quito.…”

Smog episodes in the Lodz agglomeration in the years 2014-17

“…Unfortunately, apart from the above mentioned cities, also in other regions of Poland there are numerous places with exceeded permissible pollutant concentrations in the atmospheric air [7][8][9][10]. In summer it is usually tropospheric ozone, while in winter this are mainly particulate matter PM10 [27] and carcinogenic benzo(a)pyrene. Elevated levels of these substances are primarily caused by low emissions, mainly road transport and municipal sector (individual heating systems).…”

“…Large amounts of suspended particulate matter can cause atherosclerosis and respiratory system diseases (children and the elderly are particularly vulnerable) [26][27][28][29][30].…”