Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring.

Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent AirTM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent AirTM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R2 = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R2 = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent AirTM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent AirTM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements.Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

A wind-albedo-wind feedback driven by landscape evolution.

The accurate characterization of near-surface winds is critical to our understanding of past and modern climate. Dust lofted by these winds has the potential to modify surface and atmospheric conditions as well as ocean biogeochemistry. Stony deserts, low dust emitting regions today, represent expansive areas where variations in surficial geology through time may drastically impact near-surface conditions. Here we use the Weather Research and Forecasting (WRF) model over the western Gobi Desert to demonstrate a previously undocumented process between wind-driven landscape evolution and boundary layer conditions. Our results show that altered surficial thermal properties through winnowing of fine-grained sediments and formation of low-albedo gravel-mantled surfaces leads to an increase in near-surface winds by up to 25%; paradoxically, wind erosion results in faster winds regionally. This wind-albedo-wind feedback also leads to an increase in the frequency of hours spent at higher wind speeds, which has implications for dust emission potential.

A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds.

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing.

Optical Properties of Wintertime Aerosols from Residential Wood Burning in Fresno, CA: Results from DISCOVER-AQ 2013.

The optical properties, composition and sources of the wintertime aerosols in the San Joaquin Valley (SJV) were characterized through measurements made in Fresno, CA during the 2013 DISCOVER-AQ campaign. PM2.5 extinction and absorption coefficients were measured at 405, 532, and 870 nm along with refractory black carbon (rBC) size distributions and concentrations. BC absorption enhancements (Eabs) were measured using two methods, a thermodenuder and mass absorption coefficient method, which agreed well. Relatively large diurnal variations in the Eabs at 405 nm were observed, likely reflecting substantial nighttime emissions of wood burning organic aerosols (OA) from local residential heating. Comparably small diurnal variations and absolute nighttime values of Eabs were observed at the other wavelengths, suggesting limited mixing-driven enhancement. Positive matrix factorization analysis of OA mass spectra from an aerosol mass spectrometer resolved two types of biomass burning OA, which appeared to have different chemical composition and absorptivity. Brown carbon (BrC) absorption was estimated to contribute up to 30% to the total absorption at 405 nm at night but was negligible (<10%) during the day. Quantitative understanding of retrieved BrC optical properties could be improved with more explicit knowledge of the BC mixing state and the distribution of coating thicknesses.

Interfacial Tensions of Aged Organic Aerosol Particle Mimics Using a Biphasic Microfluidic Platform.

Secondary organic aerosol (SOA) particles are a major component of atmospheric particulate matter, yet their formation processes and ambient properties are not well understood. These complex particles often contain multiple interfaces due to internal aqueous- and organic-phase partitioning. Aerosol interfaces can profoundly affect the fate of condensable organic compounds emitted into the atmosphere by altering the way in which ambient organic vapors interact with suspended particles. To accurately predict the evolution of SOA in the atmosphere, we must improve our understanding of aerosol interfaces. In this work, biphasic microscale flows are used to measure interfacial tension of reacting methylglyoxal, formaldehyde, and ammonium sulfate aqueous mixtures with a surrounding oil phase. Our experiments show a suppression of interfacial tension as a function of organic content that remains constant with reaction time for methylglyoxal-ammonium sulfate systems. We also reveal an unexpected time dependence of interfacial tension over a period of 48 h for ternary solutions of both methylglyoxal and formaldehyde in aqueous ammonium sulfate, indicating a more complicated behavior of surface activity where there is competition among dissolved organics. From these interfacial tension measurements, the morphology of aged atmospheric aerosols with internal liquid-liquid phase separation is inferred.