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Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

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ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality

Jagriti Saini¹, Maitreyee Dutta¹, Gonçalo Marques²

¹National Institute of Technical Teacher's Training and Research, Chandigarh 160019, India.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

Indoor air pollution significantly impacts public health. This study assesses key pollutants like PM2.5 and CO2 using an IoT system and an Adaptive Dynamic Fuzzy Inference System Tree for accurate indoor air quality monitoring.

Keywords:

fuzzy inference system indoor air quality optimization pollution public health

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Area of Science:

Environmental Science
Public Health
Sensor Technology

Background:

Indoor air pollution is a major cause of increased mortality and morbidity.
Specific pollutants such as PM10, PM2.5, CO2, CO, tVOC, and NO2 pose significant health risks.
These risks include respiratory diseases, central nervous system dysfunction, cardiovascular disease, and cancer.

Purpose of the Study:

To assess indoor air quality by measuring key pollutant concentrations.
To develop and implement an advanced model for processing and predicting indoor air quality data.
To evaluate the model's performance against existing studies and benchmark datasets.

Main Methods:

Utilized an Internet of Things (IoT)-based sensor system for real-time pollutant measurements in a rural Indian site.
Implemented an Adaptive Dynamic Fuzzy Inference System Tree for data processing.
Employed global and local search algorithms for knowledge base design and model tuning, respectively.

Main Results:

Achieved normalized root mean square errors (NRMSE) for pollutants: PM10 (0.6679), PM2.5 (0.6218), CO2 (0.1077), CO (0.2585), tVOC (0.0667), and NO2 (0.0635).
The proposed model demonstrated enhanced prediction performance.
Validation against existing literature and benchmark datasets confirmed the approach's efficacy.

Conclusions:

The developed IoT-based system and Adaptive Dynamic Fuzzy Inference System Tree provide an effective method for indoor air quality assessment.
Accurate monitoring of pollutants like PM2.5 and CO2 is crucial for mitigating public health risks.
The model's performance indicates its potential for real-world application in improving indoor environments.