Comparing Building and Neighborhood-Scale Variability of CO₂ and O₃ to Inform Deployment Considerations for Low-Cost Sensor System Use.

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO₂ (a primary pollutant) and O₃ (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions

Autonomous low-cost ozone sensors: development, calibration, and application to study exposure and spatial gradients

2022 Spring.Includes bibliographical references.Ozone (O3), a criteria pollutant and atmospheric oxidant, is not routinely measured in rural and remote environments and hence exposure to ozone pollution in these regions remains poorly understood. In this work, we built, calibrated, and deployed five low-cost, autonomous ozone sensor systems (called MOOS) in Northern Colorado, a region that is non-compliant for O3 during the summertime. Each MOOS included the following components: (i) an Aeroqual SM50, a heated metal oxide ozone sensor, mounted inside a custom radiation shield, (ii) a power system that consisted of a 30 W solar panel, 108 Wh lithium-ion battery, and charge controller, (iii) a Particle Boron to acquire, process, and transmit data to the Cloud, and (iv) an environmental sensor to measure temperature, relative humidity, and pressure. In a three-week long collocated study, we found that all MOOS, calibrated using 48 hours of reference data, compared well against reference monitors with a measurement error between 4-6 parts per billion by volume (ppbv). Manufacturer- and laboratory-based calibrations over- and under-estimated ozone levels at higher and lower ozone mixing ratios, respectively. When deployed in Northern Colorado for an additional three weeks to measure O3 exposure and study O3 trends across an urban-rural gradient, we found that the MOOS, calibrated using data from the collocated study and calibrated using 48 hours of reference data in the field, demonstrated good sensor performance (RMSE of 3.98 - 8.80 ppbv and MBE of 0.22 - 3.82 ppbv). Compared to the collocated study, the field study resulted in larger measurement errors for all five MOOS (RMSE of 3.66 - 4.00 versus RMSE of 3.98 - 8.80). Furthermore, there was modest variability in the field performance across the different MOOS (RMSE < 5 ppbv) that could not be explained by environmental differences between the different sites (e.g., proximity of the MOOS to the reference monitor, land use type, temperature). We found that MOOS were able to capture 100% of non-compliant O3 days during the collocated study and between 25-87% of non-compliant O3 days during the field study depending on the calibration approach used. Furthermore, both reference monitors and MOOS deployed along the east-west corridor in Northern Colorado were able to capture the negative, west-east O3 gradients observed in previous aircraft and modeling studies. Overall, our study indicates that the MOOS shows promise as a low-cost O3 sensor that could be used to supplement routine ambient monitoring and characterize regional ozone pollution

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a 1-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesburg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing and explore cross sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks

Understanding the ability of low-cost MOx sensors to quantify ambient VOCs

Volatile organic compounds (VOCs) present a unique challenge in air quality research given their importance to human and environmental health, and their complexity to monitor resulting from the number of possible sources and mixtures. New technologies, such as low-cost air quality sensors, have the potential to support existing air quality measurement methods by providing data in high time and spatial resolution. These higher-resolution data could provide greater insight into specific events, sources, and local variability. Furthermore, given the potential for differences in selectivities for sensors, leveraging multiple sensors in an array format may even be able to provide insight into which VOCs or types of VOCs are present. During the FRAPPE and DISCOVER-AQ monitoring campaigns, our team was able to co-locate two sensor systems, using metal oxide (MOx) VOC sensors, with a proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS) providing speciated VOC data. This dataset provided the opportunity to explore the ability of sensors to estimate specific VOCs and groups of VOCs in real-world conditions, e.g., dynamic temperature and humidity. Moreover, we were able to explore the impact of changing VOC compositions on sensor performance as well as the difference in selectivities of sensors in order to consider how this could be utilized. From this analysis, it seems that systems using multiple VOC sensors are able to provide VOC estimates at ambient levels for specific VOCs or groups of VOCs. It also seems that this performance is fairly robust in changing VOC mixtures, and it was confirmed that there are consistent and useful differences in selectivities between the two MOx sensors studied. While this study was fairly limited in scope, the results suggest that there is the potential for low-cost VOC sensors to support highly resolved ambient hydrocarbon measurements. The availability of this technology could enhance research and monitoring for public health and communities impacted by air toxics, which in turn could support a better understanding of exposure and actions to reduce harmful exposure.</p

Improving Low-Cost Measurement Techniques to Investigate the Connections Between Fossil Fuels and Air Quality: from Oil and Gas Production to Home Heating

Natural gas, as an energy resource can exert both positive and negative influences on air quality where people live, work, and go to school. Air quality in basins where oil and gas are produced from geologic formations can be potentially degraded by industry activities, but using natural gas in place of solid fuels like wood and coal for home heating and in other applications can potentially result in improved air quality. Low- cost gas sensors have emerged recently with great potential to help inform air quality on the scales that people live in ways that traditional instrumentation is not well suited, though the usefulness of these tools is complicated by cross sensitivity to environmental variables like temperature and humidity, as well as potentially confounding gas species. The ability of low-cost gas sensors to yield meaningful information about air quality, with relevance to human and environmental health, is therefore contingent on progress in terms of sensor signal quantification methods, best practices for experimental design and deployment, along with data quality assessment and interpretation.In this dissertation, such methods are developed and applied, employing low-cost gas sensors to characterize air quality in both indoor and ambient environments, in the context of natural gas production and end use as a home heating fuel. Carbon monoxide (CO) measurements are used to characterize how home heating fuels can differentially influence air quality in homes on the Navajo Nation. CO levels in homes are quantified with uncertainty estimation and are employed to estimate air exchange rates in homes and CO emission rates. Methods to measure air quality in oil and gas production basins using arrays of low-cost gas sensors are also developed and analyzed. Field normalization sensor signal quantification methods employing both artificial neural networks and multiple linear regressions are compared. The sensitivity and robustness of each quantification method is explored for each gas species. To further understand how distributed grids of sensor measurements can inform spatial and temporal patterns of air quality in oil and gas production basins, the performance of these sensor quantification methods are assessed when extended to new sampling locations

Hyperspectral Observations for Atmospheric Remote Sensing: Instrumentation, Atmospheric Correction, and Spectral Unmixing

Hyperspectral instruments expand the spectral dimension of remote sensing measurements by collecting data in hundreds of contiguous wavelength channels. Spectrally resolved measurements can be used to derive science products for a diverse range of fields such as atmospheric science, geology, oceanography, ecology, climate monitoring, and agricultural science, to name a few. The spectral information collected by hyperspectral instruments enables more accurate retrievals of physical properties and detection of temporal changes. These advantages have led to an increasing number of active and planned hyperspectral instruments. This thesis describes methods for attributing hyperspectral radiation observations to physical sources.We developed, validated and characterized improvements to a hyperspectral instrument, the Solar Spectral Irradiance Monitor (SSIM), built at the University of Colorado Boulder’s Laboratory for Atmospheric and Space Physics. Contributions include the characterization of the optics’ angular response, testing of an optics stabilizing platform and the development and testing of a spectrometer thermal control system. This instrument was then deployed on an aircraft for a field study with the National Ecological Observatory Network (NEON). SSIM measurements of upwelling and downwelling irradiance were used in conjunction with NEON’s Imaging Spectrometer to enable atmospheric correction of imagery collected below cloud layers.We developed a numerical spectral unmixing algorithm, Informed Non-Negative Matrix Factorization (INMF), to separate contributions to hyperspectral imagery from distinct physical sources such as surface reflectance, atmospheric absorption, molecular scattering, and aerosol scattering. INMF was tailored for hyperspectral applications by introducing algorithmic constraints based on the physics of radiative transfer. INMF was tested using imagery collected by the Hyperspectral Imager for the Coastal Ocean (HICO). To validate the method INMF results were compared to model-based atmospheric correction results. We demonstrate possible applications of INMF by presenting the retrieval of two physical properties, aerosol attributed radiance and seafloor depth. The retrievals were evaluated by comparing INMF output to independent retrievals of aerosol properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ seafloor depth measurements from the U.S. Coastal Relief Model. In these comparisons INMF shows promise for retrieving both physical properties, and may be improved with physics-based constraints on the seafloor and aerosol source spectra

Impacts of oil and natural gas development and other sources on volatile organic compound concentrations in Broomfield, Colorado

2022 Summer.Includes bibliographical references.In 2017 substantial new oil and natural gas (ONG) extraction was approved by the City and County of Broomfield (CCOB). A monitoring program was established by CCOB to determine how new ONG extraction impacted local air quality. Multiple instruments were utilized to monitor air quality in the county including weekly volatile organic carbon (VOC) sampling canisters deployed across CCOB by Colorado State University and Ajax Analytics and hourly VOC, methane, and criteria pollutant measurements taken by the Colorado Air Monitoring Mobile Lab (CAMML) deployed near an ONG well-pad by the Colorado Department of Public Health and Environment (CDPHE). Weekly samples, collected from October 2018 through December 2020 were analyzed for 52 VOCs using a 5-channel gas chromatograph. The CAMML reported 20 VOCs, methane, PM2.5, PM10, nitrogen oxides (NOx), and ozone. Positive Matrix Factorization (PMF) was applied to both datasets to characterize key air pollution sources and their impacts in space and time. Six factors were found to describe the weekly data best: Background (biogenic), Combustion, Light Alkane, Complex Alkane, a Drilling factor, and an Ethyne factor. Contributions of the ONG-related PMF factors increased most strongly near well-pads during particular ONG pre-production activities. The Light Alkane factor was most active during production and coiled tubing operations, and flowback at one or more of the new well-pads. The Complex Alkane factor iii was strongly associated with drilling and coiled tubing operations and flowback at one of two well-pads. The Drilling factor contained a VOC profile that closely matched volatiles released from a drilling mud (lubricant for the drill bit) used at two of the three sites. The Ethyne profile represents an unknown and previously undocumented source composition originating from a well-pad. This ethyne and benzene-rich emission was independently observed in other CCOB air monitoring efforts. Five factors best explained the hourly CAMML data; these factors resembled those derived from PMF analysis of the weekly data set. Three factors, Combustion, Ozone background, and Particulate Matter, were not found to be related to local ONG extraction while the profiles containing many of the alkane species (Light Alkane factor and Complex Alkane factor) showed correlation with pad activities. Wind direction analysis suggests emissions associated with these factors were transported from the pad. Benzene was a particular focus of the study given its potential health effects at modest concentration levels. On average, the source factors contributing most to benzene were combustion (38%), longer-lived alkanes from ONG production (22%), and shorter-lived alkanes from ONG production (16%). ONG activities contributed more strongly to benzene levels during pre-production and production phases

Analysing Urban Air Pollution Using Low-Cost Methods and Community Science

Indiana University-Purdue University Indianapolis (IUPUI)Rise in air pollution resulting in negative health externalities for humans has created an urgent need for cities and communities to monitor it regularly. At present we have insufficient ground passive and active monitoring networks in place which presents a huge challenge. Satellite imagery has been used extensively for such analysis, but its resolution and methodology present other challenges in estimating pollution burden. The objective of this study was to propose three low-cost methods to fill in the gaps that exist currently. First, EPA grade sensors were used in 11 cities across the U.S. to examine NO2. This is a simplistic way to assess the burden of air pollution in a region. However, this technique cannot be applied to fine scale analysis, which resulted in the next two components of this research study. Second, a citizen science network was established on the east side of Indianapolis, IN who hosted 32 Ogawa passive sensors to examine NO2 and O3 at a finer scale. These low-cost passive sensors, not requiring power, and very little maintenance, have historically tracked very closely with Federal Reference Monitors. Third, a low-cost PurpleAir PA-II-SD active sensors measuring PM2.5 were housed with the citizen scientists identified above. This data was uploaded via Wi-Fi and available via a crowd sourced site established by PurpleAir. These data sets were analyzed to examine the burden of air pollution. The second and third research studies enabled granular analyses utilizing citizen science, tree canopy data, and traffic data, thus accommodating some of the present limitations. Advancement in low-cost sensor technology, along with ease of use and maintenance, presents an opportunity for not just communities, but cities to take charge of some of these analyses to help them examine health equity impacts on their citizens because of air pollution

Unconventional Oil and Gas Development: Evaluation of selected hydrocarbons in the ambient air of three basins in the United States by means of diffusive sampling measurements

The impact of emissions associated with the extraction of crude oil and natural gas upon air quality in the United States (US) is widely recognised to have an impact on climate change, human health and ground-level ozone formation. A number of measurement approaches are being applied to evaluate the environmental impact of the oil and gas (O&G) sector, including satellite, airborne and ground-based platforms. Measurement based studies, in particular those that estimate flux rates, are critical for the validation of emission inventories that often under-report actual emissions of methane and volatile organic compounds (VOC) from the O&G sector. On-going research projects in the US are investigating the consistency of emission rates from O&G emission sources associated with extraction, transmission and distribution activities. The leakage rates of methane, as related to production levels, in the US for O&G developments varies from less than 1% (e.g. Upper Green River Basin, Wyoming) to over 6% (Uintah Basin, Utah). European research and policy approaches can learn from efforts in the US that are improving the accuracy of reporting emissions from O&G sources, enhancing our understanding of air quality impacts, and reducing emissions through regulatory controls. The Joint Research Centre (JRC) of the European Commission performed a diffusive sampling project, with the collaboration of the University of Wyoming, in conjunction with the SONGNEX (Studying the Atmospheric Effects of Changing Energy Use in the US at the Nexus of Air Quality and Climate Change) project led by the US National Oceanic and Atmospheric Administration. The SONGNEX project is an airborne measurement campaign supported by a number of associated ground based studies. The applicability of the Pocket Diffusive (PoD) sampler, for measurement of VOC (C4-C10), heavy hydrocarbons and volatile polycyclic aromatic hydrocarbons (PAHs) in areas heavily influenced by O&G development, is evaluated. Three sampling surveys were performed to assess three basins (Upper Green River, Uintah and North Platte) characterised by different management regimes, meteorology and hydrocarbon products. This first extensive field deployment of the PoD sampler demonstrates the effectiveness of the sampler for time-integrated measurements of targeted pollutants over wide spatial areas. The ambient air at these basins reveal different compositional profiles of hydrocarbons (C4-C10). Analysis of aromatics supports a finding of relatively elevated levels in the Pinedale Anticline (Upper Green River). From an evaluation of the behaviour of alkanes, it is evident that there is a relatively high leakage rate in the Uintah Basin. Heavy hydrocarbons (C11-C22) and PAHs are measured at relatively low levels. Despite low concentrations, analysis of these compounds improves the accuracy of source identification. A comparison of ground based PoD data and airborne SONGNEX data showed good agreement for commonly reported VOCs. The utility of the PoD sampler for analysis of emission sources was enhanced with reporting of a wide range of compounds. Spatial Positive Matrix Factorization analysis showed the possibility of using PoD samplers for differentiating emission sources, characterizing different areas and estimating the relative contribution of different emission sources.JRC.C.5-Air and Climat