Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

Abstract

As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution.

Multi-Points Indoor Air Quality Monitoring Based on Internet of Things

“…Monitoring results showed that, during open cooking, the maximum concentrations of PM2.5 reached up to 500 μg/m 3 , which poses a threat to the health of occupants. Svertoka et al [23] combined the three environmental parameters of PM2.5, PM10, and NO2 perceived by the sensor, and proposed a decision support algorithm. Through a graphical interface, ordinary users can observe the measurement results in the client, receive danger warnings and decision support suggestions.…”

Wearables for Industrial Work Safety: A Survey

“…An index shows the degree of air pollution in a certain area [91]. It is calculated based on measured concentrations of pollutants and government-set limits for those concentrations.…”

“…The influence of high AQI levels (101 and above) on the human body varies depending on the predisposition (great age, heart/lung diseases), and could lead to such diseases as lung cancer, stroke, pneumonia, etc. [91] Gas sensors (e.g. CO2 sensor [92])…”

Internet of things based visualisation of effect of air pollution on the lungs using HEPA filters air cleaner

“…Air pollution affects various parts of the body differently, however to the lungs, when the air pollutant particles are too large to be filtered out of the human body, they penetrate the lung alveoli [18] …”