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Household air pollution generated from cooking is severe, especially for Chinese-style cooking. We measured the emission rates of multiple air pollutants including fine particles (PM), ultrafine particles (UFPs), and volatile organic compounds (VOCs, including formaldehyde, benzene, and toluene) that were generated from typical Chinese cooking in a residential kitchen. The experiment was designed through five-factor and five-level orthogonal testing. The five key factors were cooking method, ingredient weight, type of meat, type of oil, and meat/vegetable ratio. The measured emission rates (mean value ± standard deviation) of PM, UFPs, formaldehyde, total volatile organic compounds (TVOCs), benzene, and toluene were 2.056 ± 3.034 mg/min, 9.102 ± 6.909 × 10 #/min, 1.273 ± 0.736 mg/min, 1.349 ± 1.376 mg/min, 0.074 ± 0.039 mg/min, and 0.004 ± 0.004 mg/min. Cooking method was the most influencing factor for the emission rates of PM, UFPs, formaldehyde, TVOCs, and benzene but not for toluene. Meanwhile, the emission rate of PM was also significantly influenced by ingredient weight, type of meat, and meat/vegetable ratio. Exhausting the range hood decreased the emission rates by approximately 58%, with a corresponding air change rate of 21.38/h for the kitchen room.

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In this paper, we propose a novel distributed localization scheme LDB, a 3D localization scheme with directional beacons for Underwater Acoustic Sensor Networks (UWA-SNs). LDB localizes sensor nodes using an Autonomous Underwater Vehicle (AUV) as a mobile beacon sender. Mounted with a directional transceiver which creates conical shaped directional acoustic beam, the AUV patrols over the 3D deployment volume with predefined trajectory sending beacons with constant interval towards the sensor nodes. By listening two or more beacons sent from the AUV, the nodes can localize themselves silently. Through theoretical analysis, we provide the upper bound of the estimation error of the scheme. We also evaluate the scheme by simulations and the results show that our scheme can achieve a high localization accuracy, even in sparse networks.

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