Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution

Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution

Nagarathna Ravi, Vimala Rani P, Rajesh Alias Harinarayan R, Mercy Shalinie S, Karthick Seshadri, Pariventhan P
Copyright: © 2019 |Pages: 32
DOI: 10.4018/IJIIT.2019100105
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Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.
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Contamination of nature’s resources that are very much necessary for the sustenance of flora, fauna and microbes on Earth is termed as pollution. There are several types of pollution namely: air pollution, water pollution, land pollution, sound pollution, light pollution, radiation pollution, thermal pollution etc. Out of these various types of pollution, air pollution leads to deaths of millions of humans each and every year across the world (Donahue, 2017). Air pollution is nothing but degradation of quality of air. Activities of humans are the major reason for the degradation of air quality. Air is said to be polluted when it is adulterated by a mix of particulate matter like particulate matter 2.5 (PM2.5) (particles that have size less than 250nm), particulate matter 10 (PM10) (particles that have size less than 1000nm) and other gases like sulphur dioxide (SO2), nitrogen oxides (NOx), ozone (O3), carbon monoxide (CO), etc. (Babadjouni et al., 2017). The pollutants can be in any of three forms like gaseous, droplets and particle matters. Pollutants are also categorized as primary and secondary based on whether the pollutant is a direct result of human related activities or not emitted directly. Both the type of pollutants is hazardous in their own way. Kids, aged people and people with chronic diseases are affected by acute health issues when exposed to high levels of atmospheric pollutants (Bogdanovic et al., 2017).

Atmospheric pollution is known to create a hike in the rate of morbidity and mortality, thereby reducing the life expectancy (Delpont et al., 2018). There are various studies that substantiate the positive correlation between the exposure to atmospheric pollutants and cancer, death due to stroke and the triggering of stroke (Delpont et al., 2018). Smog has also been evident in playing a role in cardiovascular diseases such as heart failure, atherosclerosis, cardiac arrest and arrhythmias (Mishra, 2017). Also, there are research studies that have found positive correlation between diabetes and NOx (Renzi et al., 2018). There also exists a significant association between the diabetes in women of age less than 50 years and the secondary air pollutant O3 (Renzi et al., 2018). Various classes of diseases that affect the respiratory system, digestive system, blood circulatory system, ophthalmological system, connective tissue, musculoskeletal, system of metabolism and genitourinary system have been associated with the primary air pollutants like PM2.5, PM10 and NO2 (Chen et al., 2017). Even the primary air pollutant SO2 has a strong association with mental disorders in people (Chen et al., 2017). It is also found that seasons play a vital role in diseases because of exposure to air pollutants (Chen et al., 2017). For instance, positive correlations between the mental disorder and the air pollutants was relatively lesser in cold seasons when compared to warm seasons (Chen et al., 2017). Exposure to primary pollutants also leads to cognitive dysfunction in children as well as elderly persons and other neurodegenerative conditions like Parkinson’s and Alzheimer’s diseases (Babadjouni et al., 2017). There has also been evidence that when pregnant ladies are exposed to air pollutants persistently, it leads to miscarriage (Ha et al., 2017). Exposure to PM2.5 not only affects health, but also leads to increase in psychological distress (Sass et al., 2017). Air pollutants also leave its footprints on climatic system by bringing about changes in the pattern of weather and temperature, which ultimately leads to loss in the crops (Ravina et al., 2017). This subsequently leads to a market equilibrium shift in the food supply chain (Sun et al., 2017).

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