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.

Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO₂ and O₃ for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

The optical properties of ambient black carbon‐containing particles and the composition of their associated coatings were investigated at a downtown site in Toronto, Canada, for 2 weeks in June 2013. The objective was to assess the relationship between black carbon (BC) coating composition/thickness and absorption. The site was influenced by emissions from local vehicular traffic, wildfires in Quebec, and transboundary fossil fuel combustion emissions in the United States. Mass concentrations of BC and associated nonrefractory coatings were measured using a soot particle–aerosol mass spectrometer (SP‐AMS), while aerosol absorption and scattering were measured using a photoacoustic soot spectrometer (PASS). Absorption enhancement was investigated both by comparing ambient and thermally denuded PASS absorption data and by relating absorption data to BC mass concentrations measured using the SP‐AMS. Minimal absorption enhancement attributable to lensing at 781 nm was observed for BC using both approaches. However, brown carbon was detected when the site was influenced by wildfire emissions originating in Quebec. BC coating to core mass ratios were highest during this period (~7), and while direct absorption by brown carbon resulted in an absorption enhancement at 405 nm (>2.0), no enhancement attributable to lensing at 781 nm was observed. The efficiency of BC coating removal in the denuder decreased substantially when wildfire‐related organics were present and may represent an obstacle for future similar studies. These findings indicate that BC absorption enhancement due to lensing is minimal for downtown Toronto, and potentially other urban locations, even when impacted by long‐range transport events.

We used site-level methane (CH₄) emissions data from over 1,000 natural gas (NG) production sites in eight basins, including 92 new site-level CH₄ measurements in the Uinta, northeastern Marcellus, and Denver-Julesburg basins, to investigate CH₄ emissions characteristics and develop a new national CH₄ emission estimate for the NG production sector. The distribution of site-level emissions is highly skewed, with the top 5% of sites accounting for 50% of cumulative emissions. High emitting sites are predominantly also high producing (>10 Mcfd). However, low NG production sites emit a comparably larger fraction of their CH₄ production. When combined with activity data, we predict that this creates substantial variability in the basin-level CH4 emissions which, as a fraction of basin-level CH4 production, range from 0.90% for the Appalachian and Greater Green River to > 4.5% in the San Juan and San Joaquin. This suggests that much of the basin-level differences in production-normalized emissions reported by aircraft studies can be explained by differences in site size and distribution of site-level production rates. We estimate that NG production sites emit total CH₄ emissions of 830 Mg/h (95% CI: 530—1,200), 63% of which come from the sites producing <100 Mcfd that account for only 10% of total NG production. Our total CH₄ emissions estimate is 2.3 times higher than the U.S. EPA’s estimate and likely attributable to the disproportionate influence of high emitting sites.

Previous studies have characterized spatial patterns of pollution with land use regression (LUR) models from distributed passive or filter samplers at low temporal resolution. Large-scale deployment of low-cost sensors (LCS), which typically sample in real time, may enable time-resolved or real-time modeling of concentration surfaces. The aim of this study was to develop spatiotemporal models of PM_2.5, NO₂, and CO using an LCS network in Pittsburgh, Pennsylvania. We modeled daily average concentrations in August 2016–December 2017 across 50 sites. Land use variables included 13 time-independent (e.g., elevation) and time-dependent (e.g., temperature) predictors. We examined two models: LUR and a machine-learning-enabled land use model (land use random forest, LURF). The LURF models outperformed LUR models, with increase in the average externally cross-validated R² of 0.10–0.19. Using wavelet decomposition to separate short-lived events from the regional background, we also created time-decomposed LUR and LURF models. Compared to the standard model, this resulted in improvement in R² of up to 0.14. The time-decomposed models were more influenced by spatial parameters. Mapping our models across Allegheny County, we observed that time-decomposed LURF models created robust PM_2.5 predictions, suggesting that this approach may improve our ability to map air pollutants at high spatiotemporal resolution.