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BackgroundThere is considerable evidence that exposure to fine particulate matter (PM2.5) air pollution is associated with a variety of adverse health outcomes. However, true exposure-outcome associations are hampered by measurement issues, including compliance and exposure misclassification. ObjectiveThis paper describes the use of the design-feedback iterative cycle to improve the design and usability of a new portable PM2.5 monitor for use in an epidemiologic study of personal air pollution measures. MethodsIn total, 10 adults carried on their person a prefabricated PM2.5 monitor for 1 week over 3 waves of the iterative cycle. At the end of each wave, they participated in a 30-minute moderated focus group and completed 2 validated questionnaires on usability and views on research. The topics addressed included positives and negatives of the monitor, charging and battery life, desired features, and changes to the monitor from each previous wave. They also completed a log to record device wear time each day. The log also provided space to record any issues that may have arisen with the device or for general comments during the week of collection. ResultsThe major focus group topics included device size, noise, battery and charge time, and method for carrying the device. These topics formed the basis of iterative design changes; by the final cycle, the device was reasonably smaller, quieter, held a longer charge, and was more convenient to carry. System usability scores improved systematically across each wave (median scores of 50-66 on a 100-point scale), as did median daily wear time (approximately 749-789 minutes). ConclusionsBoth qualitative and quantitative measures showed an improvement in device usability over the 3 waves. This study demonstrates how the design-feedback iterative cycle can be used to improve the usability of devices manufactured for use in large epidemiologic studies on personal air pollution exposures.

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m-3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

In the United States, the dairy industry has a greater-than-average injury rate on farms. Most injuries are caused by cattle during interactions with dairy workers. Researchers have shown that cow behavior is altered when a cow is unhealthy; however, no studies examined whether these altered behaviors are exhibited during human-cow interactions and how they may pose a risk to workers. The purpose of this study was to evaluate how the health status of dairy cows impacts the safety risk for dairy workers during human-dairy cow interactions. This study was conducted at Washington State University’s Knott Dairy Center in Pullman, WA. Data were collected during Fall 2023 and Winter 2024. Lactating, multiparous Holstein cows (n = 58) were enrolled in the study. Cow health status was determined using CowManager, an electronic ear tag that records activity and ear temperature and uses a proprietary algorithm to alert producers to sick cows. Unhealthy cows (n = 29) enrolled in the study were those whose health status was labelled as ‘suspicious’ by CowManager. A healthy counterpart, not labelled unhealthy but matched with a similar lactation number, reproductive status, and days in milk, was selected for each unhealthy cow. Two Brinno time-lapse video cameras were installed in the holding pen area, and video footage was continuously recorded during morning milkings. Enrolled cows and milkers were fitted with Polar H10 heart rate monitors, and heart rate data were collected while cows and milkers were in the holding pens. Video footage was observed, and behaviors were recorded according to an ethogram. The ethogram included cow behaviors and human behaviors related to low-risk or high-risk potential for human injury. Data were analyzed in SAS (SAS 9.4) using PROC GENMOD, PROC MIXED, and PROC CORR. Unhealthy cows tended to spend a greater percentage of time in the holding pen without a milker present (88 ± 4%) than healthy cows (75 ± 6%, P = 0.09), and milkers spent a greater percentage of their time engaging in high-force tactile (such as pushing) interactions with healthy cows (12 ± 4%) than with unhealthy cows (2 ± 1%, P = 0.08). These results indicate that there are possible risk level differences in interactions with healthy versus unhealthy dairy cows. This information will be used to educate dairy farm employees about these risk differences, with the intention of reducing the number of human injuries caused by interactions with unhealthy dairy cows. [ABSTRACT FROM AUTHOR]