MEASUREMENT AND EVALUATION OF AIR QUALITY MONITORING TOOL FOR PASSENGER VEHICLES

One of the major health environmental issues is air quality. Polluted air harms human health and contributes to environmental changes such as global warming and weather fluctuations. Outdoor air quality pollution has been one of the world's biggest problems for decades. This problem is closely related to the vehicle, which is the most significant source of pollutants. Nowadays, there is a lack of technology available to the public to provide a monitoring tool for users in the vehicle context. People cannot trace or monitor the air quality their vehicle emits and the air quality within the vehicle itself. Existing projects have been studied, and improvements have been made to ensure that the study is aligned and attainable within the scope. This study developed an air quality monitoring tool based on six types of pollutants, i.e., carbon monoxide, particle matters, ground-level ozone, nitrogen dioxide, and sulfur dioxide, which can be used in a vehicle context. The tool's main purpose is to provide vehicle users with real-time readings of the amount of pollutants from their vehicle and transmit the data to a dashboard for visualization. If the pollutant level exceeds the safe limit, a notification will be sent to the user. Experiments revealed that the air quality in the vehicle might be attributed to the vehicle's current state (idle or moving), duration of the running engine, prolonged exposure to the weather, and outdoor air quality.

A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings

The 24 h and 14-day relationship between indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (p < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization.