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.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high-performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. In a proof-of-concept integration with the particle-resolved model PartMC, TChem-atm reproduces the existing PartMC–CAMP implementation within solver tolerances and delivers substantial GPU speedups, especially for large particle populations. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. TChem-atm enables performance-portable execution across CPUs and GPUs, though optimal efficiency may require modest architecture-specific tuning (e.g., team and vector sizes), with up to a twofold improvement on the NVIDIA H100. It directly supports sectional and particle-resolved host models, while modal aerosol schemes require minor adaptation to provide particle-scale quantities such as representative diameters. By enabling chemically detailed, multiphase simulations with performance portability and host-model flexibility, TChem-atm facilitates the incorporation of advanced chemistry into atmospheric models.

Possessing the strongest magnetic fields in the Universe, magnetars mark an extremum of physical phenomena. The strength of their magnetic fields is sufficient to deform the shape of the stellar body, and when the rotational and magnetic axes are not aligned, these deformations lead to the production of gravitational waves (GWs) via a time-varying quadrupole moment. Such gravitational radiation differs from signals presently detectable by the Laser Interferometer Gravitational-Wave Observatory. These signals are continuous rather than the momentary ‘chirp’ waveforms produced by binary systems during the phases of inspiral, merger, and ringdown. Here, we construct a computational model for magnetar stellar structure with strong internal magnetic fields. We implement an n = 1 polytropic equation of state (EOS) and adopt a mixed poloidal and toroidal magnetic field model constrained by the choice of EOS. We utilize fiducial values for magnetar magnetic field strength and various stellar physical attributes. Via computational simulation, we measure the deformation of magnetar stellar structure to determine upper bounds on the strength of continuous GWs formed as a result of these deformations inducing non-axisymmetric rotation. We compute predictions of upper limit GW strain values for sources in the McGill Magnetar Catalog, an index of all detected magnetars.

Open-source software tools designed specifically for evaluating and reporting air sensor performance are limited. Available tools do not provide a means for summarizing sensor performance using common statistical metrics and figures nor are they suited for handling the wide variety of data formats currently used by air sensors. We developed sensortoolkit as a free, open-source Python library to encourage use of the U.S. Environmental Protection Agency’s (U.S. EPA) recommended performance evaluation protocols for air sensors measuring particulate matter and gases. The library compares collocated air sensor against reference monitor data and includes procedures to reformat both datasets into a standardized format using an interactive setup module. Library modules calculate performance metrics (e.g., coefficient of determination (R2), slope, intercept, root mean square error (RMSE)) and make plots to visualize the data. These metrics and plots can be used to better understand sensor accuracy, precision between sensors of the same make and model, and the influence of meteorological parameters at 1-hour and 24-hour averages. Results can be compiled into a reporting template allowing for easier comparison of sensor performance results generated by different organizations. This paper provides a summary of the sensortoolkit and a case study to demonstrate its utility.

Open-source software tools designed specifically for evaluating and reporting air sensor performance are limited. The available tools do not provide a means for summarizing the sensor performance using common statistical metrics and figures, nor are they suited for handling the wide variety of data formats currently used by air sensors. We developed sensortoolkit v1.1.0 as a free, open-source Python v3.8.20 library to encourage the use of the U.S. Environmental Protection Agency’s (U.S. EPA) recommended performance evaluation protocols for air sensors measuring particulate matter and gases. The library compares the collocated air sensor against reference monitor data and includes procedures to reformat both datasets into a standardized format using an interactive setup module. Library modules calculate performance metrics (e.g., the coefficient of determination (R²), slope, intercept, and root mean square error (RMSE)) and make plots to visualize the data. These metrics and plots can be used to better understand sensor accuracy, the precision between sensors of the same make and model, and the influence of meteorological parameters at 1 h and 24 h averages. The results can be compiled into a reporting template allowing for the easier comparison of sensor performance results generated by different organizations. This paper provides a summary of the sensortoolkit and a case study to demonstrate its utility.