Monitoring indoor air quality is critical because Americans spend 93 of their life indoors, and around 6.3 million children suffer from asthma. We want to passively and unobtrusively monitor the asthma patient's environment to detect the presence of two asthma-exacerbating activities: smoking and cooking using the Foobot sensor. We propose a data-driven approach to develop a continuous monitoring-activity detection system aimed at understanding and improving indoor air quality in asthma management. In this study, we were successfully able to detect a high concentration of particulate matter, volatile organic compounds, and carbon dioxide during cooking and smoking activities. We detected smoking with an error rate of 1; cooking with an error rate of 11; and obtained an overall 95.7 percent accuracy classification across all events (control, cooking and smoking). Such a system will allow doctors and clinicians to correlate potential asthma symptoms and exacerbation reports from patients with environmental factors without having to personally be present.