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1. Design and Development of Surface Based Air Pollution Measurement and Monitoring System for Climate Computing

   https://doi.org/10.1109/I2MTC53148.2023.10175932  

   Gasseller, M.; El Saddik, A.; Brooks, D.; Sunda-Meya, A.; Al Khawli, T.; Islam, S. (May 2023, IEEE)

   — In this paper, we first develop a low-cost surfacebased air pollutants measurement system for the real-time air pollution monitoring and forecasting applications. Then, we compare the performance achieved by the proposed system in real-time urban environment with currently used static monitoring stations by the governmental environmental protection agency (EPA). The proposed design uses particulate matter, humidity, and temperature sensors to measure the values of the air pollutant that determines the value of the Air Quality Index (AQI). The SD storage device is interfaced with the system to store the large amount of data sensed by the system. The Arduino UNO-based processing unit integrates with the sensing units to process and control the sensed air pollutants data. The proposed system is deployed in indoors and outdoor environment in under served minority communities in big cities to illustrate real-time environmental pollution measurement and monitoring applications. The system can measure, monitor and alert the level of PM2.5 and PM10 components of the AQI as they are often the main pollutant that determines the AQI value. The performance of the proposed system compares with the expensive data logger-based EPA-approved LDEQ sensorsbased air quality monitoring system. Our analysis shows that the measurement and monitoring performance of the proposed system is comparable with the EPA-approved LDEQ sensorsbased air quality monitoring system. The analysis also shows that there is a spatial and temporal variation of PM2.5 and PM10 values even for sites that are less than a mile apart. The interaction interphase of the system is simpler and easier to use as compared with bulky display systems in traditional EPA-based monitoring systems. In contrast with the traditional data logger-based system, the proposed system is smaller and quicker to deploy to test specific air pollutants in interested urban and rural locations . 

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2. Data Augmentation Strategies for Improved PM2.5 Forecasting Using Transformer Architectures

   https://doi.org/10.3390/atmos16020127  

   Pan, Phoebe; Malarvizhi, Anusha Srirenganathan; Yang, Chaowei (February 2025, Atmosphere)

   Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. 

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3. Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes

   https://doi.org/10.1021/acs.est.0c02341  

   Kelly, Kerry E.; Xing, Wei W.; Sayahi, Tofigh; Mitchell, Logan; Becnel, Tom; Gaillardon, Pierre-Emmanuel; Meyer, Miriah; Whitaker, Ross T. (December 2020, Environmental Science & Technology)

   null (Ed.)

   Short-term exposure to fine particulate matter (PM2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3−7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3−21.5 μg/m3, n(normalized)-RMSE: 9−24%), the wildfire (n: 46, RMSE: 2.6−4.0 μg/m3; nRMSE: 13.1−22.9%), and the PCAP (n: 96, RMSE: 4.9−5.7 μg/m3; nRMSE: 20.2−21.3%). They also revealed dramatic geospatial differences in PM2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency’s AirNow’s visualizations. Complementing the PM2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users. 

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4. Robust Environmental Sensing Using UAVs

   https://doi.org/10.1145/3464943  

   Boubrima, Ahmed; Knightly, Edward W. (November 2021, ACM Transactions on Internet of Things)

   In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high. 

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5. Trace element chemistry and strontium isotope ratios of atmospheric particulate matter reveal air quality impacts from mineral dust, urban pollution, and fireworks in the Wasatch Front, Utah, USA

   https://doi.org/10.1016/j.apgeochem.2024.105906  

   Marcy, Micah J; Carling, Gregory T; Thompson, Alyssa N; Bickmore, Barry R; Nelson, Stephen T; Rey, Kevin A; Fernandez, Diego P; Heiner, Matthew; Adams, Bradley R (February 2024, Applied Geochemistry)

   Atmospheric particulate matter (PM) in urban areas is derived from natural and anthropogenic sources, but it is difficult to identify how these various sources contribute to air quality. To characterize PM sources in an urban setting, we collected PM in three size fractions (PM2.5, PM10, and total suspended particulates, TSP) for two-week intervals from 2019 through 2021 in the Wasatch Front of northern Utah. The PM samples were analyzed for major and trace element concentrations and 87Sr/86Sr ratios. Using principal components analysis, we identified mineral dust, urban pollution, and fireworks as the primary PM sources affecting Wasatch Front air quality. Dust contributed Al, Be, Ca, Fe, Mg, Rb, Y, and REEs, which are typical components of carbonate and silicate minerals, with highest concentrations in the TSP fraction. Urban sources produced PM that was enriched in As, Cd, Mo, Pb, Sb, Se, and Tl, and fireworks smoke had high concentrations of Ba, Cr, Cu, K, Sr, and V. Dust events dominated PM chemistry during spring through fall, punctuated by fireworks smoke over the Independence Day holiday, while urban pollution dominated PM chemistry from November through February during winter inversions. 87Sr/86Sr ratios revealed that Sr was sourced from regional playas, local sediment, and fireworks. Strontium released from fireworks had relatively low 87Sr/86Sr ratios that dominated the PM isotopic composition during holidays. Sequential leaching showed that potentially harmful elements such as Se, Cd, and Cu were readily removed by weak acids, suggesting that they are readily available in the environment or through human inhalation. This is the first study to describe seasonal variations in PM chemistry in the Wasatch Front and serves as an example of investigating air quality in complex urban areas impacted by desert dust. 

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