Measuring built environment users' response using wearable biosensors could provide a new opportunity for understanding their experience in built environment. Electrodermal activity (EDA) sensors are especially useful in detecting people's stressful interaction with the built environment. Despite this potential advancement, the detection accuracy is still limited because of noises in EDA collected from uncontrolled settings. Alleviating respiration noise is most challenging due to the similarity in signal characteristics between the respiration noise and EDA response to distress. The authors propose an adaptive denoising method that references photoplethysmogram (PPG) to detect and remove respiration noise in EDA. Quality of denoising and quality improvement in stress measurement were measured for validation. The results showed that the proposed method brought better quality of respiration noise removal than previous methods, and therefore improved stress measurement quality. The finding can contribute to improve quality of EDA from the field, which is essential to accurately understand people's stressful interaction with built environment.