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Big Data Analysis and Compression for Indoor Air Quality
Khushboo Sansanwal, Gulshan Shrivastava, Rohit Anand, and Kavita Sharma
CONTENTS
1.1 Introduction
1.2 Sources of Indoor Air Pollutants
1.3 Categories of Indoor Air Pollutants
1.4 Factors Affecting Indoor Air Quality
1.5 Materials and Methods
1.6 Big Data Management System
1.7 Big Data Processing Using Distributed Data Storage
1.7.1 Algorithm Process for Data Compression Using the Specified Techniques
1.8 Results
1.9 Conclusion and Future Scope
References
1.1 Introduction
There have been several works around the world to define what constitutes the Quality of the Indoor Air (IAQ). In one such work, “Ventilation for Acceptable Indoor Air Quality,” Quality of the Indoor Air has been defined as “air in which there are no known contaminants at harmful concentrations as determined by cognizant authorities and with which a substantial majority [80% or more] of the people exposed do not express dissatisfaction” (Persily 1997).
Further, the Quality of the Indoor Air has been defined by the World Health Organization (WHO) as “the physical and chemical nature of indoor air, as delivered to the breathing zone of building occupants, which produces a complete state of mental, physical and social well-being of the occupants, and not merely the absence of disease or infirmity” (Heseltine and Rosen 2009).
Over the last few decades, the importance of the quality of indoor air inside buildings, office spaces, schools, and so on has drawn a significant concern, as there has been an undeniable accumulation of evidence that the deterioration or contamination of the air quality inside the buildings is leading to health problems among the occupants (Heinsohn and Cimbala 2003). The researchers have further proved that mitigation of the air quality has brought substantial improvement in the health symptoms, well-being, and comfort of the occupants of the buildings, in turn leading to increased productivity (Alker et al. 2014). This has thus even justified that improving the same makes economic sense by increasing the productivity on one side and decreasing the number of finances spent on health restoration of the occupants arising on account of poor indoor air quality. It has been seen that there is a compelling reason for the managers of affected buildings to address this issue, as air quality deterioration and contamination are occupational hazards also (Kephalopoulos et al. 2007, Solanki et al. 2015, Kadam et al. 2019, Dhall and Solanki 2017, Solanki et al. 2018a, 2018b).
As found by the United States Environmental Protection Agency, substantial evidence exists for showing urgent concern for the care of quality in the indoor air, as serious risks have been found to be there for the inhabitants of such affected buildings whenever quality of the air indoors has deteriorated. Further such deterioration of indoor air has been found to be existing as an essential risk factor among the top five risks for the people’s health (Jones 1999). Thus, immediate and appropriate action is required to ensure the quality maintenance of the indoor air to avoid any catastrophic consequences on the people’s health (Roulet et al. 1994). In 2009, the World Health Organization (WHO) presented their work on the risks faced by the health of the people worldwide. The said extensive work established firmly that deterioration in the quality of the indoor air leads to diseases and hence was found to account for around 3% of the diseases faced by the world, a number which is too vast to be ignored (WHO 2017).
Further research has shown that there has been ample evidence to suggest that the overall productivity of the inhabitants of the buildings possessing contaminated indoor air gets jeopardized. Several effects were found to be taking a toll on such affected inhabitants, causing absence from work, leaves taken for the impact on health, decrease in productivity and efficiency in the work done, and so on (Alker et al. 2014). The Occupational Safety and Health Administration, an organization of the United States, in an another work found the impact on the profitability of business to have suffered at least 15 minutes of loss overall in the business generated by a worker over each day of work, on account of the contaminated air indoors (Occupational Safety and Health Administration 2015). The loss is further compounded by the medical costs of the restoration of the health of such affected employees (Nathanson 1993). When estimates have been tried to be made on the loss suffered in the performance of the work done in another study, a substantial loss on account of performance, to the extent of around 4%, has been found due to the contaminated air indoors (US EPA, I-BEAM 2008).
It is understandable that a considerable investment is required to be made to avoid the contamination/deterioration of the indoor air and thus is a matter of concern for the finances in managing such buildings but for a relief it has been found that the expenditure made on such account, in turn, returns the cost incurred in the form of overall improvement, preservation of the health of workers, further leading to preserving the performance of workers (Occupational Safety and Health Administration 2015).
In yet another study being done by the EPA, the loss on productivity was estimated, and it was found that productivity can suffer a decrease of around 5% on account of the deterioration of the quality of air indoors (NEMI 1994). It was again shown that the cost incurred on account of actions made about the indoor air was far less than the cost incurred for the preservation of the health of the inhabitants of such buildings. Further, it has been found that the cost incurred on the buildings was far less, in fact, less than one fourth to that which was spent on a worker of such buildings every year. It clearly showed the loss suffered on account of productivity: a loss as meager as 1% leads to a cost far more than what would have been suffered on account of maintenance of such buildings (Spengler 2001). About the buildings and their internal environments’ effect on the health of the occupants of the same, there are two types of illness-related effects. The first is building-related disease, and the second is a nonspecific building-related illness that is universally acknowledged with a nomenclature of “Sick Building Syndrome.” This disease has a syndromal presentation whereby the occupants of the building are presented with the development of symptoms such as irritation in the eyes, stuffiness of the nose, respiratory symptoms and illnesses, running nose, headache, and alteration in awareness (Jansz 2011). The symptoms occur in the affected occupants either due to the introduction of noxious chemicals or due to an accumulation of noxious chemicals on account of poor ventilation. Also, these symptoms may occur due to the growth of the disease-producing organisms in the dampness on the walls, such as the growth of mold or fungus. As may be seen in the preceding discussion, these offending substances may be either nonbiological (e.g., chemical) or biological. These illnesses sometimes may be transient, recovering in due time with the rectification of the individual causes. In worse cases, due to long-term exposure to molds and fungus, severe respiratory diseases may occur such as allergic pneumonitis (Sahlberg 2012). At times, such a disease may cause irreversible damage, in which the withdrawal of the causative agent may no longer lead to the recovery of the body. This leads to the importance of the rectifications in the quality of the air indoors and also the importance related to the implementation of rectifying measures in time to avoid such irreversible damage. Building-related disease, by comparison, is a dangerous and severe condition. In building-related illnesses, there are established clear diagnostic criteria in which recognized and research-proven path-physiological characteristics and parameters are used to identify the health problems. Such health illnesses occur due to development of the lesions due to the exposure to the biological; that is. infective, immunologic agents or chemicals such as toxins and irritants. The examples of some of the diseases are asthma and pneumonitis due to hypersensitivities, and inflammation of the airways such as laryngitis, bronchitis, sarcoidosis, and dermatitis. Further, there may be poisonings due to gases such as carbon monoxide and radon (Apter et al. 1994).
Maintenance and preservation of excellent indoor air quality can effectively contribute to the positive health outcomes of the occupants of such buildings. The positive change is brought by the favorable health promoting environment thereby increasing the comfort of the occupants and in turn, increasing the overall productivity of the people (Nathanson 1993).
The increase in the people’s productivity is a significant increase which has been well supported by the available research work. Further, the research data has substantially shown that the buildings where the air quality has been better are concerned with the fewer workers being seen with evidence of the syndromal presentation of the disease called “sick building syndrome” and thus savings on the expenditure of health makes the investment in the better indoor air quality measures a prudent one (Apter et al. 1994; Occupational Safety and Health Administration 2015).
This research study aims to investigate the understanding of the big data related to the quality of indoor air of buildings as well as its management through compression techniques. There are numerous studies on indoor air quality, big data analysis, and big data compression. Still, only a very few researchers combined the concept of indoor air quality analysis and its compression. For example, Maitrey and Jha (2015) investigated a cloud technology, MapReduce, for the big data analysis. Cociorva and Iftene (2017) presented the evaluation of indoor air quality for the smart control of the different kinds of intelligent devices. Yang et al. (2014) suggested an individual compression and scheduling based approach for the excellent processing of big data in the cloud. In this research study, the indoor air quality big data analysis has been done with a suitable compression technique called Snappy. The second contribution of this chapter is that it helps to validate the Snappy compression technique against the other well-known compression techniques with the help of the parameters like time taken and compression ratio. In addition, it also aims to provide useful insights helpful for managing, storing, processing, and analyzing the big data.
1.2 Sources of Indoor Air Pollutants
Various contaminants and pollutants can lead to a fall in the quality of the indoor air. The hazardous nature of indivi...
