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Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO

The MODULAIR-PM is a professional-grade particulate matter sensor manufactured by QuantAQ, Inc. (Somerville, MA USA). This application note describes its general operating principle and data corrections.

Low-cost gas and particulate matter sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference-grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k-nearest neighbors (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models were also the most transferable approach when applied to deployment data collected in Malawi. We compared kNN hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, partly due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using colocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH >70 %) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi. Data recovery (30 %–80 %) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure.

Significance Poor air quality is a global public health issue, contributing to millions of premature deaths per year worldwide. Low-cost air quality sensors are a promising tool to improve monitoring capabilities. In this study, we built and deployed a low-cost sensor network for emergency response during an extreme air quality event, the 2018 Kīlauea Lower East Rift Zone eruption. This network was used to estimate fine-scale population exposures to multiple pollutants, to measure the chemical transformation of volcanic emissions, and to provide real-time observations as part of emergency management efforts., Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kīlauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai‘i Island in “vog” (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 μg m−3 and 1,200 parts per billion (ppb), respectively. The LCS network’s high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island’s population was exposed to elevated levels of fine PM than to SO2. Additionally, the spatially distributed network was able to resolve the volcanic plume’s chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

We develop a common path interferometer to measure the refractive index of liquids from the visible to infrared. In this work, we present experimental data and comparison of several organic solvents.

Low-cost sensors (LCS) offer the opportunity to measure urban air quality at a spatiotemporal scale that is finer than what is currently practical with expensive research- or regulatory-grade instruments. Recently, the LCS research community has focused largely on sensor calibration, pollution monitoring, and exposure assessment; here, we investigate the applicability of LCS for characterizing particulate pollution sources in an urban environment. Using an integrated multipollutant LCS system (which measures both gases and particles), we collected air quality data for 6 weeks during the winter at a site in Delhi, India. The results were compared to measurements taken by co-located research-grade particle instruments. Non-negative matrix factorization was used to deconvolve LCS data into unique factors that were then identified by examining the factor composition and comparing them to the research-grade measurements. The data were described well by three factors: a combustion factor characterized by high CO levels and two factors characterized by measured particles. These factors align well with measurements by research-grade instruments, including particle types determined from factor analysis of online particle composition measurements. This work demonstrates that multipollutant LCS measurements, despite their inherent limitations (e.g., calibration challenges and inability to measure smallest particles), can provide insight into sources of fine particulate matter in a complex urban environment.

Chamber oxidation experiments conducted at the Fire Sciences Laboratory in 2016 are evaluated to identify important chemical processes contributing to the OH chemistry of biomass burning non-methane organic gases (NMOG). Based on the decay of primary carbon measured by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS), it is confirmed that furans and oxygenated aromatics are among the NMOG emitted from western United States fuel types with the highest reactivities towards OH. The oxidation processes and formation of secondary NMOG masses measured by PTR-ToF-MS and iodide clustering time-of-flight chemical ionization mass spectrometry (I-CIMS) is interpreted using a box model employing a modified version of the Master Chemical Mechanism (v. 3.3.1) that includes the OH oxidation of furan, 2-methylfuran, 2,5-dimethylfuran, furfural, 5-methylfurfural, and guaiacol. The model supports the assignment of major PTR-ToF-MS and I-CIMS signals to a series of anhydrides and hydroxy furanones formed primarily through furan chemistry. This mechanism is applied to a Lagrangian box model used previously to model a real biomass burning plume. The updated mechanism reproduces the decay of furans and oxygenated aromatics and the formation of secondary NMOG, such as maleic anhydride. Based on model simulations conducted with and without furans, it is estimated that furans contributed up to 10 % of ozone and over 90 % of maleic anhydride formed within the first 4 hours of oxidation. It is shown that maleic anhydride is present in a biomass burning plume transported over several days, which demonstrates the utility of anhydrides as tracers for aged biomass burning plumes.

Biomass burning is an important source of aerosol and trace gases to the atmosphere, but how these emissions change chemically during their lifetimes is not fully understood. As part of the Fire Influence on Regional and Global Environments Experiment (FIREX 2016), we investigated the effect of photochemical aging on biomass burning organic aerosol (BBOA), with a focus on fuels from the western United States. Emissions were sampled into a small (150 L) environmental chamber and photochemically aged via the addition of ozone and irradiation by 254 nm light. While some fraction of species undergoes photolysis, the vast majority of aging occurs via reaction with OH radicals, with total OH exposures corresponding to the equivalent of up to 10 days of atmospheric oxidation. For all fuels burned, large and rapid changes are seen in the ensemble chemical composition of BBOA, as measured by an aerosol mass spectrometer (AMS). Secondary organic aerosol (SOA) formation is seen for all aging experiments and continues to grow with increasing OH exposure, but the magnitude of the SOA formation is highly variable between experiments. This variability can be explained well by a combination of experiment-to-experiment differences in OH exposure and the total concentration of non-methane organic gases (NMOGs) in the chamber before oxidation, measured by PTR-ToF-MS (r2 values from 0.64 to 0.83). From this relationship, we calculate the fraction of carbon from biomass burning NMOGs that is converted to SOA as a function of equivalent atmospheric aging time, with carbon yields ranging from 24 ± 4 % after 6 hours to 56 ± 9 % after 4 days.

The use of low-cost air quality sensors for air pollution research has outpaced our understanding of their capabilities and limitations under real-world conditions, and there is thus a critical need for understanding and optimizing the performance of such sensors in the field. Here we describe the deployment, calibration, and evaluation of electrochemical sensors on the island of Hawai`i, which is an ideal test bed for characterizing such sensors due to its large and variable sulfur dioxide (SO₂) levels and lack of other co-pollutants. Nine custom-built SO₂ sensors were co-located with two Hawaii Department of Health Air Quality stations over the course of 5 months, enabling comparison of sensor output with regulatory-grade instruments under a range of realistic environmental conditions. Calibration using a nonparametric algorithm (k nearest neighbors) was found to have excellent performance (RMSE 0.997) across a wide dynamic range in SO₂ ( 2 ppm). However, since nonparametric algorithms generally cannot extrapolate to conditions beyond those outside the training set, we introduce a new hybrid linear–nonparametric algorithm, enabling accurate measurements even when pollutant levels are higher than encountered during calibration. We find no significant change in instrument sensitivity toward SO₂ after 18 weeks and demonstrate that calibration accuracy remains high when a sensor is calibrated at one location and then moved to another. The performance of electrochemical SO₂ sensors is also strong at lower SO₂ mixing ratios (

Discrepancies between modeled and measured atmospheric organic aerosol (OA) have highlighted the need for in situ instrumentation to better characterize the sources, formation mechanisms, and atmospheric evolution of ambient OA. We have developed the Volatility and Polarity Separator (VAPS) for hourly measurements of volatility- and polarity-resolved OA detected using high-resolution time-of-flight mass spectrometry (HR-ToF-MS). Here, atmospheric OA is inertially impacted onto a collection cell, material is transferred onto a short transfer line located inside a gas chromatography (GC) oven, the oven is heated to provide a first-dimension separation of volatility, then thermally pulsed through a short polar GC column for a second-dimension polarity separation, and finally detected by HR-ToF-MS. This novel instrument increases the mass throughput of ambient OA in comparison to traditional GC due to shorter transfer paths and passivated coatings. Molecular separation resolution is partially sacrificed for this increased mass recovery, but the high-resolution mass spectral data recovers information such as chemical classes and even some individual compounds along with elemental composition to determine aerosol oxidation states. Different techniques for interpreting and representing VAPS data are considered and its applicability to positive matrix factorization (PMF) analysis is demonstrated. Copyright © 2016 American Association for Aerosol Research