Search

PM2.5 exposure is associated with significant health risk. Exposures in homes derive from both outdoor and indoor sources, with emissions occurring primarily in discrete events. Data on emission event magnitudes and schedules are needed to support simulation-based studies of exposures and mitigations. This study applied an identification and characterization algorithm to quantify time-resolved PM2.5 emission events from data collected during 224 days of monitoring in 18 California apartments with low-income residents. We identified and characterized 836 distinct events with median and mean values of 12 and 30 mg emitted mass, 16 and 23 minutes emission duration, 37 and 103 mg/h emission rates, and pseudo-first-order decay rates of 1.3 and 2.0/h. Mean event-averaged concentrations calculated using the determined event characteristics agreed to within 6% of measured values for 14 of the apartments. There were variations in event schedules and emitted mass across homes, with few events overnight and most emissions occurring during late afternoons and evenings. Event characteristics were similar during weekdays and weekends. Emitted mass was positively correlated with number of residents (Spearman coefficient, ρ=.10), bedrooms (ρ=.08), house volume (ρ=.29), and indoor-outdoor CO2 difference (ρ=.27). The event schedules can be used in probabilistic modeling of PM2.5 in low-income apartments. [ABSTRACT FROM AUTHOR]

The article examines the tensions one superintendent in the USA experienced as he evaluated principals in a high-stakes environment that had undergone numerous transformations at the central office. Using qualitative methods, primarily, shadowing techniques, observations and debriefing, the following tensions emerged and were examined in light of the work of the superintendent evaluating principal performance: (1) discrepancies between principal performance when compared to performance data, (2) length of time in the principalship compared to results, (3) finding the right balance between student achievement data and other indicators of principal performance, (4) what types of achievement data are important and when these data are made available, (5) credence paid to complaints about structural changes implemented by the principal, (6) balancing the principal self-evaluation rating scores with the final evaluator scores and (7) accounting for personal factors such as relationship to principals and knowledge about principal capabilities. Each of these tensions contributes to the difficulty a superintendent may feel when conducting the principal evaluation process. [ABSTRACT FROM PUBLISHER]