AI & IOT enabled Eco-Friendly Proactive GRS System for People Sustainable Health Management (Smart City)

computer engineering

Deteriorating levels of in/outdoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. In recent times indoor/outdoor Environment quality has attracted the attention of policymakers and researchers as an important similarity to that of external air pollution for smart cities’ wealth management. In a certain sense, indoor air quality must be paid more attention than outdoor air quality as people spends more time indoors than outdoors. The indoor environments are confined and closed compared to external environments providing less opportunity for the pollutants to dilute. A concurrent multi-parameter monitoring approach accounting for the most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployment ability, and detectability of only select pollutants. The aim of this project is to present a comprehensive indoor Environment quality monitoring system using a low-cost Raspberry Pi with Seed studio grove pi Hat-based GPS air quality sensor modules. The custom-built system measures 10 indoor environmental conditions including pollutants: Air Quality (AQI), Temperature, Relative Humidity, Pressure, Altitude, Radiation level, Light intensity, Carbon monoxide (CO), Oxygen(O2), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). Residential units, educational offices, and Traffic signal points were selected and monitored over a span of several months. The recorded mean Air Quality concentrations were significantly higher in the residential unit compared to the office building. The mean O2, CO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and Air Quality for both residential units and office buildings while the latter depicted strong Temperature and Humidity, Pressure, Altitude, correlation with sound noise levels, O2, CO2, Air Quality, Radiation level, Ambient light levels, IR levels, pm 2.5 air concentration when compared to the former for Smart city sustainable health management.

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