Using gas-phase air quality sensors to disentangle potential sources in a Los Angeles neighborhood

Collier-Oxandale, Ashley; Wong, Nicole J.; Navarro, Sandy; Johnston, Jill; Hannigan, Michael P.

doi:10.1016/j.atmosenv.2020.117519

Cited by 16 publications

(18 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the results of this mathematical model are divided into two parts to fulfill the study's aims, as follows: (i) determining the total concentration of VOCs with the resistance values obtained from original and diluted samples and (ii) estimating individual VOC composition in the original sampled air. Once a VOC composition is known, it is then possible to calculate an estimated AQI VOC value, according to Equation (4).…”

Section: Discussionmentioning

confidence: 99%

“…The high concentration of VOCs in air excessing limitations can cause many health issues [2], and some can even contribute to damage to the Earth's ozone layer [3]. Hence, monitoring the emissions (point source) and immissions (ambient concentration) of VOCs is essential, enabling detected values to be compared to limiting safe values [4] and thus encouraging the implementation of more effective emission control and personal protection strategies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Thakor

Zhang

2021

Clean Technol.

View full text Add to dashboard Cite

Monitoring volatile organic compounds (VOCs) places a crucial role in environmental pollutants control and indoor air quality. In this study, a metal-oxide (MOx) sensor detector (used in a commercially available monitor) was employed to delineate the composition of air containing three common VOCs (ethanol, acetone, and hexane) under various concentrations. Experiments with a single component and double components were conducted to investigate how the solvents interact with the metal oxide sensor. The experimental results revealed that the affinity between VOC and sensor was in the following order: acetone > ethanol > n-hexane. A mathematical model was developed, based on the experimental findings and data analysis, to convert the output resistance value of the sensor into concentration values, which, in turn, can be used to calculate a VOC-based air quality index. Empirical equations were established based on inferences of vapour composition versus resistance trends, and on an approach of using original and diluted air samples to generate two sets of resistance data per sample. The calibration of numerous model parameters allowed matching simulated curves to measured data. Therefore, the predictive mathematical model enabled quantifying the total concentration of sensed VOCs, in addition to estimating the VOC composition. This first attempt to obtain semiquantitative data from a single MOx sensor, despite the remaining selectivity challenges, is aimed at expanding the capability of mobile air pollutants monitoring devices.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Thakor

Zhang

2021

Clean Technol.

View full text Add to dashboard Cite

show abstract

“…One previous study used the same two generalized VOC sensors-the Figaro TGS 2600 and 2602-to quantify speciated VOC data [21]. As multivariate linear regression has been frequently used to match low-cost sensor signals to reference concentrations [1][2][3]18,[21][22][23][24][25], Collier-Oxandale and co-workers used this method to quantify benzene as an individual hydrocarbon as well as aromatics as a group (benzene, toluene, C 8 , and C 9 ). Using one of the pod datasets explored in this work, R 2 values between the reference and sensor benzene signals reached 0.72 for calibration and 0.67 for validation, explaining nearly three-fourths of the variability in the species.…”

Section: Voc Quantification Using Low-cost Sensorsmentioning

confidence: 99%

“…Based on previous success in using ANNs to quantify pollutants with low-cost sensors [26], we focused our efforts on this machine learning technique, which has not been used previously for this specific hydrocarbon application. When using multivariate linear regression to quantify pollutants such as methane and generalized VOCs previously, interaction terms among sensors proved beneficial [1,2,21]. For instance, the ratio or product of the two VOC sensors has been added as an additional term to better quantify methane.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…While numerous studies have utilized low-cost sensors to study generalized VOCs or TNMHCs in addition to methane [1][2][3][4], few have attempted to characterize specific hydrocarbons other than methane in outdoor monitoring experiments. These current characterizations have limited utility; it is helpful to know when and where hydrocarbon emissions are significant, but not knowing the exact chemical composition leaves lingering questions about source attribution as well as impacts on climate and human health, since some hydrocarbons are more damaging to people and the environment than others [5,6].…”

Section: Introduction 1the Need For Low-cost Speciated Hydrocarbon Datamentioning

confidence: 99%

See 1 more Smart Citation

Applications and Limitations of Quantifying Speciated and Source-Apportioned VOCs with Metal Oxide Sensors

Okorn

Hannigan

2021

Atmosphere

Self Cite

View full text Add to dashboard Cite

While low-cost air quality sensor quantification has improved tremendously in recent years, speciated hydrocarbons have received little attention beyond total lumped volatile organic compounds (VOCs) or total non-methane hydrocarbons (TNMHCs). In this work, we attempt to use two broad response metal oxide VOC sensors to quantify a host of speciated hydrocarbons as well as smaller groups of hydrocarbons thought to be emanating from the same source or sources. For sensors deployed near oil and gas facilities, we utilize artificial neural networks (ANNs) to calibrate our low-cost sensor signals to regulatory-grade measurements of benzene, toluene, and formaldehyde. We also use positive matrix factorization (PMF) to group these hydrocarbons along with others by source, such as wet and dry components of oil and gas operations. The two locations studied here had different sets of reference hydrocarbon species measurements available, helping us determine which specific hydrocarbons and VOC mixtures are best suited for this approach. Calibration fits on the upper end reach above R2 values of 0.6 despite the parts per billion (ppb) concentration ranges of each, which are magnitudes below the manufacturer’s prescribed detection limits for the sensors. The sensors generally captured the baseline trends in the data, but failed to quantitatively estimate larger spikes that occurred intermittently. While compounds with high variability were not suited for this method, its success with several of the compounds studied represents a crucial first step in low-cost VOC speciation. This work has important implications in improving our understanding of the links between health and environment, as different hydrocarbons will have varied consequences in the human body and atmosphere.

show abstract

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation

Samuyelu

M²,

Babburu³

et al. 2022

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.

show abstract

Using gas-phase air quality sensors to disentangle potential sources in a Los Angeles neighborhood

Cited by 16 publications

References 45 publications

Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Sensing and Delineating Mixed-VOC Composition in the Air Using a Single Metal Oxide Sensor

Applications and Limitations of Quantifying Speciated and Source-Apportioned VOCs with Metal Oxide Sensors

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation

Contact Info

Product

Resources

About