Dark Forcing: Soot, or black carbon, is a ubiquitous atmospheric pollutant whose warming effect might be second only to carbon dioxide. When black carbon is emitted, it combines with other aerosols to form heterogeneous mixtures. Models have predicted that internal mixing of black carbon with other materials can double the amount of radiation absorbed. Cappa et al. (p. 1078 ) report that in situ measurements of the enhancement of radiation absorption by these mixed black carbon–containing particles in the atmosphere show a much smaller effect. Thus, many climate models may be overestimating the amount of warming caused by black carbon emissions.

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO₂, O₃, and CO₂. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16–19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO₂, CO₂, O₃) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO₂ (2 % relative error), 3.5 ppb for NO₂ (29 % relative error), and 3.4 ppb for O₃ (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO₂ and CO₂. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO₂ concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Low-cost sensors for the measurement of fine particulate matter mass (PM_2.5) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM_2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m³) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m³. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.

Measurement of ambient particulate organic carbon (POC) with quartz filters is prone to positive and negative sampling artifacts. One approach for estimating these artifacts is to sample with a backup quartz filter placed behind either the main quartz filter or a Teflon filter in a parallel line. Another approach is to use a denuder to reduce the positive artifact in combination with a highly adsorbent backup filter to capture any negative artifact. Results obtained using both of these approaches in parallel for over one year in Pittsburgh, PA are presented in this article. A sampler using an activated carbon monolith denuder has been developed and tested extensively. Transmission losses were found to be negligible, and the denuder is on average 94% efficient at removing gas-phase organics. Denuder breakthrough is corrected for each run using a dynamic blank in parallel with the sample line. Comparisons with the dynamic blank indicate that the denuder almost eliminates the positive artifact on the quartz filter. Negative artifact from the denuded quartz filter is quantified using a carbon-impregnated glass fiber (CIG) backup filter and was found to be small, typically less than 10% of the ambient POC. Compared to the denuded sampler POC, 24 h bare quartz samples showed an almost constant positive artifact of 0.5 μg-C/m³ for samples taken throughout the year-long study period. Sampling for shorter durations (4–6 h) resulted in a larger positive artifact. A quartz filter behind a Teflon filter (QBT) provides a consistent estimate of the positive artifact on the bare quartz filter irrespective of sample duration, though it overcorrects for the positive artifact by 16–20% (attributed to particulate matter volatilizing off the upstream Teflon filter). The quartz behind quartz (QBQ) approach provides a reasonable estimate of the positive artifact on the bare quartz filter for the 24 h samples but not for the shorter samples. A slight seasonal variation is observed in the absolute value of the positive artifact, with higher values observed during the summer months.

An inter-comparison study of instruments designed to measure the microphysical and optical properties of soot particles was completed. The following mass-based instruments were tested: Couette Centrifugal Particle Mass Analyzer (CPMA), Time-of-Flight Aerosol Mass Spectrometer—Scanning Mobility Particle Sizer (AMS-SMPS), Single Particle Soot Photometer (SP2), Soot Particle-Aerosol Mass Spectrometer (SP-AMS) and Photoelectric Aerosol Sensor (PAS2000CE). Optical instruments measured absorption (photoacoustic, interferometric, and filter-based), scattering (in situ), and extinction (light attenuation within an optical cavity). The study covered an experimental matrix consisting of 318 runs that systematically tested the performance of instruments across a range of parameters including: fuel equivalence ratio (1.8 ≤ φ ≤ 5), particle shape (mass-mobility exponent ( D _fm ), 2.0 ≤ D _fm ≤ 3.0), particle mobility size (30 ≤ d _m ≤ 300 nm), black carbon mass (0.07 ≤ m _BC ≤ 4.2 fg) and particle chemical composition. In selected runs, particles were coated with sulfuric acid or dioctyl sebacate (DOS) (0.5 ≤ Δ r _ve ≤ 201 nm) where Δ r _ve is the change in the volume equivalent radius due to the coating material. The effect of non-absorbing coatings on instrument response was determined. Changes in the morphology of fractal soot particles were monitored during coating and denuding processes and the effect of particle shape on instrument response was determined. The combination of optical and mass based measurements was used to determine the mass specific absorption coefficient for denuded soot particles. The single scattering albedo of the particles was also measured. An overview of the experiments and sample results are presented.