Optical Sensing Approach to the Recognition of Different Types of Particulate Matters for Sustainable Indoor Environment Management

Abstract

The indoor environment is a crucial part of the built environment where our daily time is mostly spent. It is governed not only by indoor activities, but also affected by interconnected activities such as door opening, walking and routine tasks throughout the inside and outside of buildings and houses. Pollutant control is one of the major concerns for maintaining a sustainable indoor environment, and finding the source of pollutants is a relatively hard part of that task. Pollutants are emitted from various sources, transformed by sunlight, react with vapor in ozone and are transported into cities and from country to country. Due to these reasons, there has been high demand to monitor the transportation of particulate matters and improve air quality. The monitoring of pollutants and identification of their type and concentration enables us to track and control their generation and consequently discover reliable suitable mitigation measures to control air quality at regulated levels by contaminant source removal. However, the monitoring of pollutants, especially particulate matter generation and its transportation, is still not fully operated in atmospheric air due to its open nature and meteorological factors. Even though indoor air is relatively easier to monitor and control than outdoor air in the aspect of specific volume and contaminant source, meteorological parameters still need to be considered because indoor air is not fully separated from outdoor air flow and contaminants’ transportation. In this study, an optical approach using a spectral sensor was attempted to reveal the feasibility of wavelength and chromaticity values of reflected light from specific particles. From the analysis of reflected light of various particulate matters according to different liquid additives, parameter studies were performed to investigate which experimental conditions can contribute to the enhanced selective sensing of particulate matter. Five different particulate matters such as household dust, soil, talc powder, gypsum powder and yellow pine tree pollen were utilized. White samples were selectively identified by the peak at 720 nm for talc and 433 nm and 690 nm in wavelength for gypsum under chemical additives. Other grey household dust and yellowish soil and pine tree pollen revealed a distinct chromaticity x, y coordinates shift in vector within the maximum range from (0.22, 0.19) to (0.55, 0.48). Applicable approaches to assist current particle matter sensors and improve the selective sensing were suggested.