We report the field methodology of a 32-month monitoring study with temperature dataloggers as Stove Use Monitors (SUMs) to quantify usage of biomass cookstoves in 80 households of rural Guatemala. The SUMs were deployed in two stoves types: a well-operating chimney cookstove and the traditional open-cookfire. We recorded a total of 31,112 days from all chimney cookstoves, with a 10% data loss rate. To count meals and determine daily use of the stoves we implemented a peak selection algorithm based on the instantaneous derivatives and the statistical long-term behavior of the stove and ambient temperature signals. Positive peaks with onset and decay slopes exceeding predefined thresholds were identified as "fueling events", the minimum unit of stove use. Adjacent fueling events detected within a fixed-time window were clustered in single "cooking events" or "meals". The observed means of the population usage were: 89.4% days in use from all cookstoves and days monitored, 2.44 meals per day and 2.98 fueling events. We found that at this study site a single temperature threshold from the annual distribution of daily ambient temperatures was sufficient to differentiate days of use with 0.97 sensitivity and 0.95 specificity compared to the peak selection algorithm. With adequate placement, standardized data collection protocols and careful data management the SUMs can provide objective stove-use data with resolution, accuracy and level of detail not possible before. The SUMs enable unobtrusive monitoring of stove-use behavior and its systematic evaluation with stove performance parameters of air pollution, fuel consumption and climate-altering emissions.

中文翻译：

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作为炉灶使用监测器 (SUM) 的温度数据记录器：现场方法和信号分析。

我们报告了一项为期 32 个月的监测研究的现场方法，使用温度数据记录器作为炉灶使用监测器 (SUM)，以量化危地马拉农村 80 户家庭的生物质炉灶使用情况。SUM 部署在两种炉灶类型中：运行良好的烟囱炉灶和传统的开放式炉灶。我们从所有烟囱式炉灶中总共记录了 31,112 天，数据丢失率为 10%。为了计算膳食和确定炉子的日常使用，我们实施了一种基于瞬时导数和炉子和环境温度信号的统计长期行为的峰值选择算法。具有超过预定阈值的起始和衰减斜率的正峰值被确定为“燃料事件”，即炉子使用的最小单位。在固定时间窗口内检测到的相邻加油事件聚集在单个“烹饪事件”或“膳食”中。观察到的人口使用平均值是：所有炉灶的使用天数和监测天数的 89.4%，每天 2.44 顿饭和 2.98 次加油事件。我们发现，在该研究地点，与峰值选择算法相比，来自每日环境温度年度分布的单一温度阈值足以区分使用天数，灵敏度为 0.97，特异性为 0.95。通过适当的放置、标准化的数据收集协议和仔细的数据管理，SUM 可以提供客观的炉灶使用数据，其分辨率、准确性和详细程度是前所未有的。