Group-8-Summary

Group-8-Summary

• 1.
  Air Quality Monitoringusing Soft Computing Tools Clean air is considered to be a basic requirement to human health and well-being. However, air pollution continues to pose a significant threat to health worldwide and is a critical environmental issue that cannot be ignored. According to a WHO assessment about the burden of disease due to air pollution, more than two million premature deaths each year can be attributed to the effects of air pollution. More than half of this disease burden is from the populations of developing countries. The increasing trend of the global urbanization results in high levels of air pollutants in urban areas and megacities, decreasing air quality. Transportation, road traffic, home heating, industrial emissions, and other local anthropic actions are the major emission sources of air pollutants to the atmosphere. Air pollution control and air quality monitoring is needed to implement abatement strategies and stimulate environmental awareness among citizens. For this purpose, there are several techniques and technologies that can be used to monitor air pollution. Many countries are suffering from air pollutions. Many cities have built a few air quality monitoring stations to inform people urban air quality every hour. Influenced by multiple complex factors, however, urban air quality is highly skewed in a city, varying by locations significantly and changing over time differently in different places. Thus, we do not know the air quality of a location without a monitoring station. We do not what the air quality at a place will be tomorrow either, let alone the root cause the air pollution. This project aims to predict the air quality at any location throughout a city. Information about urban air quality, e.g., the concentration of PM10, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this project, we infer the real-time air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor
• 2.
  stations and avariety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). Infer Air Quality at a location Real-time air quality information, such as the concentration of NO2, CO, and PM10, is of great importance to support air pollution control and protect humans from damage by air pollution. A fuzzy logic model is to calculate the AQI using various concentrations of NO2, CO, PM10, SO2 and O3. In reality, however, there are insufficient air quality measurement stations in a city due to the expensive cost of building and maintaining such a station. Forecast Air Quality at Each Station We predict a real-valued AQI for each kind of air pollutant, at each hour, in each station. Our predictive model is comprised mainly of a neural network-based spatial predictor modeling the global factors. The information inferred from the above prediction can further be used for the following purposes: Identify the Root Cause of Air Pollution 1) Study the correlation between vehicular emission and air quality 2) Identify the spatio-temporal causality between air pollutants of different cities. 3) Suggesting the locations for building additional monitoring stations; Study the Impact of Air Pollution to People's Health Air pollution causes health problems like respiratory diseases, asthama, cancer etc. A lot of these health hazards can be prevented using the platform.
• 3.
  References: J. Yuan, Y.Zheng, X. Xie. Discovering regions of different functions in a city using human mobility and POIs. In Proc. of KDD 2012. K. Nigam, R. Ghani. Analyzing the Effectiveness and Applicability of Co-Training. In Proc. of CIKM 2000. S. Vardoulakis, B. E. A. Fisher, K. Pericleous, N. Gonzalez-Flesca. Modelling air quality in street canyons: a review. Atmospheric Environment 37 (2003) 155-182. Y. Jiang, K. Li, L. Tian, R. Piedrahita, X. Yun, O. Mansata, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. Maqs: A personalized mobile sensing system for indoor air quality. In Proc. of UbiComp 2011. Group Members Rahul Kumar Thakur 13CE10031 Soumen Saha 13CE10044 Vipul Bhola 13CE10057 VishalGidwani 13CE10058 Yogesh Jakhar 13CE10062