Associations between personal apparent temperature exposures and asthma symptoms in children with asthma

Ambient temperature and relative humidity can affect asthma symptoms. Apparent temperature is a measure of temperature perceived by humans that takes into account the effect of humidity. However, the potential link between personal exposures to apparent temperature and asthma symptoms has not been investigated. We conducted a panel study of 37 asthmatic children, aged 5–11 years, during an early spring season (average daily ambient temperature: 14C, range: 7–18C). Asthma symptoms were measured 4 times for each participant with a 2-week interval between consecutive measurements using the Childhood Asthma-Control Test (C-ACT). Average, minimum, and maximum personal apparent temperature exposures, apparent temperature exposure variability (TV), and average ambient temperature were calculated for the 12 hours, 24 hours, week, and 2 weeks prior to each visit. We found that a 10C lower in 1-week and 2-week average & minimum personal apparent temperature exposures, TV, and average ambient temperature exposures were significantly associated with lower total C-ACT scores by up to 2.2, 1.4, 3.3, and 1.4 points, respectively, indicating worsened asthma symptoms. Our results support that personal apparent temperature exposure is potentially a stronger driver than ambient temperature exposures for the variability in asthma symptom scores. Maintaining a proper personal apparent temperature exposure could be an effective strategy for personalized asthma management. Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose

Development and application of a United States-wide correction for PM_2.5 data collected with the PurpleAir sensor

PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10 000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12 000 24 h averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (US), including 16 states. Consistent with previous evaluations, under typical ambient and smoke-impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the US. A simple linear regression reduces much of this bias across most US regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 to 3 µg m−3, with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.</p

Correction and Accuracy of PurpleAir PM_2.5 Measurements for Extreme Wildfire Smoke

PurpleAir particulate matter (PM) sensors are increasingly used in the United States and other countries for real-time air quality information, particularly during wildfire smoke episodes. Uncorrected PurpleAir data can be biased and may exhibit a nonlinear response at extreme smoke concentrations (>300 µg/m3). This bias and nonlinearity result in a disagreement with the traditional ambient monitoring network, leading to the public’s confusion during smoke episodes. These sensors must be evaluated during smoke-impacted times and then corrected for bias, to ensure that accurate data are reported. The nearby public PurpleAir sensor and monitor pairs were identified during the summer of 2020 and were used to supplement the data from collocated pairs to develop an extended U.S.-wide correction for high concentrations. We evaluated several correction schemes to identify an optimal correction, using the previously developed U.S.-wide correction, up to 300 µg/m3, transitioning to a quadradic fit above 400 µg/m3. The correction reduces the bias at each air quality index (AQI) breakpoint; most ambient collocations that were studied met the Environmental Protection Agency’s (EPA) performance targets (twelve of the thirteen ambient sensors met the EPA’s targets) and some smoke-impacted sites (5 out of 15 met the EPA’s performance targets in terms of the 1-h averages). This correction can also be used to improve the comparability of PurpleAir sensor data with regulatory-grade monitors when they are collectively analyzed or shown together on public information websites; the methods developed in this paper can also be used to correct future air-sensor types. The PurpleAir network is already filling in spatial and temporal gaps in the regulatory monitoring network and providing valuable air-quality information during smoke episodes