Category: News

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM_2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM_2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM_2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM_2.5 to 9 μg/m³, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM_2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM_2.5 levels that exceeded 9 μg/m³ for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m³ for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM_2.5 concentrations below 9 μg/m³.

Keywords: particulate matter; remote sensing; IoT sensor; aerosol optical depth; machine learning

Dewage PMH, Wijeratne LOH, Yu X, Iqbal M, Balagopal G, Waczak J, Fernando A, Lary MD, Ruwali S, Lary DJ. Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches. Remote Sensing. 2024; 16(13):2454. https://doi.org/10.3390/rs16132454

Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how Generative Topographic Mapping (GTM), a probabilistic realization of the self-organizing map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply GTM to visualize the distribution of captured reflectance spectra, revealing the small-scale spatial variability of the water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of nearshore algae, as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

Keywords: hyperspectral imaging; remote sensing; unsupervised classification; endmember extraction; generative topographic mapping

Waczak J, Aker A, Wijeratne LOH, Talebi S, Fernando A, Dewage PMH, Iqbal M, Lary M, Schaefer D, Balagopal G, et al. Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping. Remote Sensing. 2024; 16(13):2430. https://doi.org/10.3390/rs16132430

Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM₁ and PM_2.5, carbon dioxide (CO₂), nitrogen dioxide (NO₂), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM₁, PM_2.5, CO₂, NO₂, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM₁, PM_2.5, and CO₂ using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R²) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO₂ and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM₁, PM_2.5, and CO₂with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO₂ and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.

BioMedInformatics 2024, 4(2), 1019-1046; https://doi.org/10.3390/biomedinformatics4020057

Keywords: 

autonomic response; exposome; microenvironment; air pollution; biometric observations; machine learning; particulate matter; CO₂; NO₂; NO

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition.

Remote Sens. 2024, 16(6), 996; https://doi.org/10.3390/rs16060996

Human physiology is known to react to various environmental stimuli over different time frames. Prolonged exposure to elements such as heat, air pollution, and volatile organic compounds negatively affects health, as established in previous research. Our earlier work demonstrated that autonomic responses of the human body, recorded through biometric sensors on a single individual, could empirically predict levels of inhalable particulate matter in their immediate environment. This current study extends this finding to observations from multiple participants. Subjects cycled on stationary bikes outdoors, equipped with a range of biometric sensors, while environmental sensors simultaneously captured data on their surroundings. Using this expanded data set, machine learning models achieved a high degree of accuracy (R²=0.97) in predicting concentrations of particulate matter (PM_2.5) using a few readily available biometric features, including skin temperature, heart rate, and respiration rate. This research underscores the importance of physiological responses as markers of exposure to particulate matter, laying the foundation for the use of biometric data in environmental health surveillance and real-time pollution assessment.

FERNANDO, Bharana Ashen et al. Data-Driven Environmental Health: Unraveling Particulate Matter Trends with Biometric Signals. Medical Research Archives, [S.l.], v. 12, n. 1, feb. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4899>. Date accessed: 03 may 2024. doi: https://doi.org/10.18103/mra.v12i1.4899.

Respiration is vital for human function. Inhaling specific gases can have specific physiological and cognitive impacts. Using a suite of sensors, we can collect detailed information on a range of both physiological and environmental factors. This study builds on previous research exploring how particulate matter affects physiological and cognitive responses, now expanded to include CO₂. We tracked the biometric variables of a cyclist, analyzing 329 specific variables. Simultaneously, an electric vehicle following the cyclist measured CO₂ and other environmental factors. After data collection, we used machine learning models to decipher the interactions between the human body and its surroundings. We found that biometric data alone could be used to accurately estimate the amount of CO₂ inhaled, achieving a good level of precision (r²=0.98) when comparing the estimated CO₂ based on biometrics and the actual observed CO₂ levels. In addition, we developed a ranking system to identify the biometric variables that most significantly predict environmental CO₂ inhalation.

RUWALI, Shisir et al. Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach. Medical Research Archives, [S.l.], v. 12, n. 1, jan. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4890>. Date accessed: 03 may 2024. doi: https://doi.org/10.18103/mra.v12i1.4890.

Climate change, driven by increases in human-produced greenhouse gases and particles (collectively referred to as GHGs), is the most serious environmental issue facing society. The need to reduce GHGs has become urgent as heat waves, heavy rain events, and other impacts of climate change have become more frequent and severe. Since the Paris Agreement was adopted in 2015, more than 136 countries, accounting for about 80% of total global GHG emissions, have committed to achieving net-zero emissions by 2050. A growing number of cities, regional governments, and industries have also made pledges to reduce emissions. Providing decision makers with useful, accurate, and trusted GHG emissions information is a crucial part of this effort.

National Academies of Sciences, Engineering, and Medicine. 2022. Greenhouse Gas Emissions Information for Decision Making: A Framework Going Forward. Washington, DC: The National Academies Press. https://doi.org/10.17226/26641.

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically capture and evaluate the interactions between an environmental context and physiological markers of an individual operating that environment. A cyclist equipped with a biometric sensing suite is followed by an environmental survey vehicle during outdoor bike rides. The interactions between environment and physiology are then evaluated though the development of empirical machine learning models, which estimate particulate matter concentrations from biometric variables alone. Here, we show biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. This work sets the stage for future investigations of these interactions for a larger number of factors, e.g., black carbon, CO₂, NO/NO₂/NO_x, and ozone. By tapping into our body’s ‘built-in’ sensing abilities, we can gain insights into how our environment influences our physical health and cognitive performance.

Sensors 2022, 22(11), 4240; https://doi.org/10.3390/s22114240

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. The goal of this work is to outline an objective strategy for discovering optimal EEG bands based on signal power spectra. A two-step data-driven methodology is presented for objectively determining the best EEG bands for a given dataset. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. Additionally, key spectral components were isolated in dedicated frequency bands. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.

Sensors 2022, 22(8), 3048; https://doi.org/10.3390/s22083048

Public voting ended Sunday for the 7th annual Green Source DFW Sustainable Leadership Awards! The contest has been narrowed down to the top three finalists in each category. A panel of judges from the North Texas green community will be tasked with the tough job of deciding winners in each category.

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