**4. Conclusions**

This study conducted measurements of four IAQ parameters i.e., T, RH, CO, and CO2, in a small room with six occupants and found that the levels of some of the parameters exceeded the recommended levels, particularly the level of CO2, the source of which was human respiration. Therefore, the number of occupants in the room and its ventilation efficiency are the key factors for the CO2 concentration. Poor conditions (i.e., a CO2 concentration of over 1000 ppm) were not detected

after the simple mitigation practices were implemented. However, T is a significant factor that must be taken into consideration in the adoption of measures to reduce electricity consumption. Without reconstruction of the office space and the installation of a ventilation system, Case 3/C was the best option with good IAQ, and it would achieve a reduction of electricity consumption of 24.3% based on the situation without taking any mitigating action. Moreover, this system would be feasible with the agreement of all members of staff working in the office. Another feasible option for CO2 reduction was placing mother-in-law's tongue plants in the office. The average reduction in the CO2 level based on using between two and six plants was almost 16%, which rendered the CO2 concentration within the standard comfortable level of 1000 ppm. However, human activities are the key factor in the CO2 concentration in a room, not the number of plants. The data that were derived from measuring the actual IAQ parameters in various scenarios and the results of the computer simulation are helpful in identifying and promoting simple practices that aimed at achieving good IAQ while reducing electricity costs and, hence, GWP in office situations. Further research should be directed towards measuring other IAQ parameters, and the causes of SBS, which are possibly associated with the CO, particulate matter, and VOCs in car exhaust fumes.

**Author Contributions:** Conceptualization, K.P. (Kanittha Pamonpol); methodology, K.P. (Kanittha Pamonpol) and K.P. (Kritana Prueksakorn); software model K.P. (Kritana Prueksakorn); validation K.P. (Kanittha Pamonpol) and K.P. (Kritana Prueksakorn); formal analysis, K.P. (Kanittha Pamonpol) and K.P. (Kritana Prueksakorn); investigation, K.P. (Kanittha Pamonpol); resources, K.P. (Kanittha Pamonpol); data curation, K.P. (Kanittha Pamonpol) and T.A.; writing-original draft preparation, K.P. (Kanittha Pamonpol) and K.P. (Kritana Prueksakorn); writing-review and editing, T.A.; visualization, K.P. (Kanittha Pamonpol) and K.P. (Kritana Prueksakorn); supervision, K.P. (Kanittha Pamonpol); funding acquisition, K.P. (Kanittha Pamonpol), K.P. (Kritana Prueksakorn), and T.A.; project administration, T.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was performed with the financial support of the Research and Development Institute, Valaya Alongkorn Rajabhat University under the Royal Patronage and the Andaman Environment and Natural Disaster Research Center, Prince of Songkla University, Phuket Campus.

**Acknowledgments:** The authors would like to express our appreciation to the Research and Development Institute, Valaya Alongkorn Rajabhat University under the Royal Patronage, Prince of Songkla University, Phuket Campus for the financial support. The support by assistants, i.e., Tip Sophea and Hong Anh Thi Nguyen are also acknowledged. The authors would like to thank Robert William Larsen for kind advice on English writing.

**Conflicts of Interest:** The authors declare no conflict of interest.
