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1. Community-based participatory research for low-cost air pollution monitoring in the wake of unconventional oil and gas development in the Ohio River Valley: Empowering impacted residents through community science

   https://doi.org/10.1088/1748-9326/ac6ad6  

   Raheja, Garima; Harper, Leatra; Hoffman, Ana; Gorby, Yuri; Freese, Lyssa; O’Leary, Brendan; Deron, Nathan; Smith, Shannon; Auch, Ted; Goodwin, Melissa; et al (May 2022, Environmental Research Letters)

   Abstract Belmont County, Ohio is heavily dominated by unconventional oil and gas development that results in high levels of ambient air pollution. Residents here chose to work with a national volunteer network to develop a method of participatory science to answer questions about the association between impact on the health of their community and pollution exposure from the many industrial point sources in the county and surrounding area and river valley. After first directing their questions to the government agencies responsible for permitting and protecting public health, residents noted the lack of detailed data and understanding of the impact of these industries. These residents and environmental advocates are using the resulting science to open a dialogue with the EPA in hopes to ultimately collaboratively develop air quality standards that better protect public health. Results from comparing measurements from a citizen-led participatory low-cost, high-density air pollution sensor network of 35 particulate matter and 25 volatile organic compound sensors against regulatory monitors show low correlations (consistently R 2 < 0.55). This network analysis combined with complementary models of emission plumes are revealing the inadequacy of the sparse regulatory air pollution monitoring network in the area, and opening many avenues for public health officials to further verify people’s experiences and act in the interest of residents’ health with enforcement and informed permitting practices. Further, the collaborative best practices developed by this study serve as a launchpad for other community science efforts looking to monitor local air quality in response to industrial growth. 

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2. A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

   https://doi.org/10.1109/CISS.2019.8693023  

   Islam, Shafkat; Razi, Abolfazl (March 2019, 53rd Annual Conference on Information Sciences and Systems (CISS))

   This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for firefighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider a more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that the success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB). 

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3. Model-Driven Requirements for Humans-on-the-Loop Multi-UAV Missions

   https://doi.org/10.1109/MoDRE51215.2020.00007  

   Agrawal, Ankit; Cleland-Huang, Jane; Steghofer, Jan-Philipp (August 2020, Model-Driven Requirements Engineering)

   null (Ed.)

   The use of semi-autonomous Unmanned Aerial Vehicles (UAVs or drones) to support emergency response scenarios, such as fire surveillance and search-and-rescue, has the potential for huge societal benefits. Onboard sensors and artificial intelligence (AI) allow these UAVs to operate autonomously in the environment. However, human intelligence and domain expertise are crucial in planning and guiding UAVs to accomplish the mission. Therefore, humans and multiple UAVs need to collaborate as a team to conduct a time-critical mission successfully. We propose a meta-model to describe interactions among the human operators and the autonomous swarm of UAVs. The meta-model also provides a language to describe the roles of UAVs and humans and the autonomous decisions. We complement the meta-model with a template of requirements elicitation questions to derive models for specific missions. We also identify common scenarios where humans should collaborate with UAVs to augment the autonomy of the UAVs. We introduce the meta-model and the requirements elicitation process with examples drawn from a search-and-rescue mission in which multiple UAVs collaborate with humans to respond to the emergency. We then apply it to a second scenario in which UAVs support first responders in fighting a structural fire. Our results show that the meta-model and the template of questions support the modeling of the human-on-the-loop human interactions for these complex missions, suggesting that it is a useful tool for modeling the human-on-the-loop interactions for multi-UAVs missions. 

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4. Laboratory Chamber Evaluation of Flow Air Quality Sensor PM2.5 and PM10 Measurements

   https://doi.org/10.3390/ijerph19127340  

   Crnosija, Natalie; Levy Zamora, Misti; Rule, Ana M.; Payne-Sturges, Devon (June 2022, International Journal of Environmental Research and Public Health)

   The emergence of low-cost air quality sensors as viable tools for the monitoring of air quality at population and individual levels necessitates the evaluation of these instruments. The Flow air quality tracker, a product of Plume Labs, is one such sensor. To evaluate these sensors, we assessed 34 of them in a controlled laboratory setting by exposing them to PM10 and PM2.5 and compared the response with Plantower A003 measurements. The overall coefficient of determination (R2) of measured PM2.5 was 0.76 and of PM10 it was 0.73, but the Flows’ accuracy improved after each introduction of incense. Overall, these findings suggest that the Flow can be a useful air quality monitoring tool in air pollution areas with higher concentrations, when incorporated into other monitoring frameworks and when used in aggregate. The broader environmental implications of this work are that it is possible for individuals and groups to monitor their individual exposure to particulate matter pollution. 

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5. High-resolution urban air quality monitoring from citizen science data with echo-state transformer networks

   https://doi.org/10.1093/jrsssc/qlaf007  

   Bonas, Matthew; Castruccio, Stefano (February 2025, Journal of the Royal Statistical Society Series C: Applied Statistics)

   Abstract Citizen science data for monitoring air pollution have recently emerged as a powerful yet under-explored resource to complement expensive and sparse national air quality monitors. In urban environments, these new data have the potential to allow for high-resolution and high-frequency forecasts, and thereby to provide an assessment of population exposure at neighbourhood level. The complex spatio-temporal structure of these data, however, requires new flexible methods that are also able to provide timely forecasts. In this work, we propose a novel method that first provides forecasts with a reservoir computing approach, an echo-state network, adjusts the forecast with a transformer network with attention mechanism and then merges the echo-state and transformer forecast into a combined network. The stochastic nature of the method allows for a fast and more accurate forecast then individual predictors as well as standard statistical methods. Simulation and application to San Francisco air pollution show how the proposed method is able to produce high-resolution urban maps of air quality. Additionally, we show how these forecasts can be used to provide neighbour-level exposure assessment using population data, a task that would not be achievable with sparse government-sponsored air quality networks. 

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