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This is the first study demonstrating how an ecosystem of Internet of Things or IoT-enabled devices and sensors can effectively control cooking-emitted air pollution energy-efficiently. The study was conducted in the simulated fully furnished residential modules in the Well Living Lab in Rochester, MN. IoT sensor consisted of particulate matter (PM2. 5) sensors and a circuit monitor for tracking the stove on/off status. In this study, we developed and tested seven air pollution control algorithms and benchmarked them against the residential temperature setpoint-controlled constant air volume (CAV) air supply. Results show that, compared to the air temperature setpoint controlled CAV air supply, the ecosystem of IoT sensors and IoT-enabled stove hood, portable air cleaners (PAC), and bathroom exhaust operated by air pollution control algorithms produce significant improvement in indoor air quality during cooking. Reduction of integrated PM2. 5 concentration ranged from 81% to 94% compared to the CAV air supply. Cooking-emitted pollution was effectively controlled by activating a stove hood or a combination of stove hood and other interventions, producing a PM2. 5 integrated concentration reduction of ∼90% compared to the CAV baseline. On an annual level, the electrical energy consumed to mitigate cooking emitted particles ranges from 39. 4 kWh to 265 kWh translating to an additional cost of 5. 6 to 42. 5. From the energy standpoint, PM-activated PACs are the most efficient, producing a PM2. 5 integrated concentration reduction of ∼80% compared to the CAV baseline. When considering exposure reduction and electrical energy consumption, PM-activated Stove Hood is the most effective intervention consuming an additional 86. 9 kWh of electrical energy annually, costing 12. 2. Sensors for PM monitoring must be mounted on the stove hood to produce timely control action. Using circuit monitors for intervention activation did not significantly improve the exposure.
Pantelic et al. (Mon,) studied this question.