Continental United States Research Articles

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023).

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Nā Kānaka Maoli ma nā 'Āina 'Ē: Exploring Place of Residency as a Native Hawaiian Health Predictor During the COVID-19 Pandemic.

Little is known about the impacts of living in diaspora from the Hawaiian Islands on Native Hawaiian health. To address this, the authors conducted an exploratory analysis using cross-sectional data from the 2021 Native American COVID-19 Alliance Needs Assessment. A total of 1418 participants identified as Native Hawaiian (alone or in any combination), of which 1222 reported residency in the continental US and 196 in Hawai'i. Residency status in the continental US vs Hawai'i was evaluated as a predictor of survey outcomes using likelihood ratio tests on linear and logistic regression models for linear and binary outcomes, respectively. Results showed that NH residency in the continental US was significantly associated with increased odds of reporting fair or poor self-rated health; increased odds for screening positive for anxiety, depression, and suicidality; and increased odds of health insurance loss (P's < .05). Residency in the continent was also associated with lower odds of reporting a diagnosed chronic health condition (P < .05). Residency in the continental US had no observed effect on the odds that participants engaged cultural activities or cultural coping strategies. These results support the role of place of residency as an important Native Hawaiian health predictor during and beyond the COVID-19 pandemic.

Access to Dermatological Appointments Based on Insurance Types in Hawai'i.

Although there is evidence that Medicaid beneficiaries in the continental United States experience barriers to accessing dermatological care, limited data exists on whether these same barriers exist in Hawai'i. Using a secret shopper study design, a total of 46 dermatology offices were contacted, 41 (89%) of which were accepting new patients. Thirty (73%) offices were located on O'ahu, and the remaining 11 (27%) were distributed among the neighboring islands (Hawai'i Island, Kaua'i, Maui). Overall, the acceptance rate for Medicaid (n=14) was 34%, which was significantly lower (P<.0001) than private insurance (n=39 (95%)) and Medicare (n=38 (93%)). The acceptance rate for patients with Medicaid insurance was lower for O'ahu offices (27%) than for neighboring islands' offices (55%), but the difference was not statistically significant (P=.095). Differences in average wait times were not statistically significant among insurance types or between O'ahu and neighboring islands. Overall, these results suggest that Medicaid recipients compared to those with private insurance or Medicare might experience difficulty in accessing dermatological care in Hawai'i.

Assessing the Impact of Catastrophic Electricity Loss on the Food Supply Chain

The food supply chain’s heavy reliance on electricity poses significant vulnerabilities in the event of prolonged and widespread power disruptions. This study introduces a system-dynamics model that integrates five critical infrastructures—electric grid, liquid fossil fuels, Internet, transportation, and human workforce—to evaluate the resilience of food supply chains to major power outages. We validated the model using the 2019 Venezuelan blackouts as a case study, demonstrating its predictive validity. We then explored how more extreme electricity losses would disrupt the supply chain. More specifically, we modeled the impact of a large-scale cyberattack on the US electric grid and a high-altitude electromagnetic pulse (HEMP) event. A cyberattack severely damaging the US electric grid and allowing for recovery within a few weeks or months would lead to substantial drops in food consumption. However, it would likely still be possible to provide adequate calories to everyone, assuming that food is equitably distributed. In contrast, a year-long recovery from a HEMP event affecting most of the continental United States could precipitate a state of famine. Our analysis represents a first attempt at quantifying how food availability progressively worsens as power outages extend over time. Our open-source model is made publicly available, and we encourage its application to other catastrophic scenarios beyond those specifically considered in this work (for example, extreme solar storms, high-lethality pandemics).

Provision of Stroke Care Services by Community Disadvantage Status in the US, 2009-2022

Stroke center certification is granted to facilities that demonstrate distinct capabilities for treating patients with stroke. A thorough understanding of structural discrimination in the provision of stroke centers is critical for identifying and implementing effective interventions to improve health inequities for socioeconomically disadvantaged populations. To determine whether (1) hospitals in socioeconomically disadvantaged communities (defined using the Area Deprivation Index) are less likely to adopt any stroke certification and (2) adoption rates differ between entry-level (acute stroke-ready hospitals) and higher-level certifications (primary, thrombectomy capable, and comprehensive) by community disadvantage status. This cohort study used newly collected stroke center data merged with data from the American Hospital Association, Healthcare Cost Report Information datasets, and the US Census. All general acute hospitals in the continental US between January 1, 2009, and December 31, 2022, were included. Data analysis was conducted from July 2023 to May 2024. The primary outcome was the likelihood of hospitals adopting stroke care certification. Cox proportional hazard and competing risk models were used to estimate the likelihood of a hospital becoming stroke certified based on the socioeconomic disadvantage status of the community. Among the 5055 hospitals studied from 2009 to 2022, 2415 (47.8%) never achieved stroke certification, 602 (11.9%) were certified as acute stroke-ready hospitals, and 2038 (40.3%) were certified as primary stroke centers or higher. When compared with mixed-advantage communities, adoption of any stroke certification was most likely to occur near the most advantaged communities (hazard ratio [HR], 1.24; 95% CI, 1.07-1.44) and least likely near the most disadvantaged communities (HR, 0.43; 95% CI, 0.34-0.55). Adoption of acute stroke-ready certification was most likely in mixed-advantage communities, while adoption of higher-level certification was more likely in the most advantaged communities (HR,1.41; 95% CI, 1.22-1.62) and less likely for the most disadvantaged communities (HR, 0.31; 95% CI, 0.21-0.45). After adjusting for population size and hospital capacity, compared with mixed-advantage communities, stroke certification adoption hazard was still 20% lower for relatively disadvantaged communities (adjusted HR, 0.80; 95% CI, 0.73-0.87) and 42% lower for the most disadvantaged communities (adjusted HR, 0.58; 95% CI, 0.45-0.74). In this cohort study examining hospital adoption of stroke services, when compared with mixed-advantage communities, hospitals located in the most disadvantaged communities had a 42% lower hazard of adopting any stroke certification and relatively disadvantaged communities had a 20% lower hazard of adopting any stroke certification. These findings suggest that there is a need to support hospitals in disadvantaged communities to obtain stroke certification as a way to reduce stroke disparities.

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.

A database of nutritional strategies of nanoplankton genera present in North American lake surface waters

Abstract A database of nutritional strategies of nanoplankton genera present in North American lake surface waters is presented. This work represents an integrated and updated database of nutritional strategies for nanoplankton genera commonly found in surface waters of North American lakes. We tabulate the nutritional strategies (autotroph, phago-mixotroph and phago-heterotroph) for nanoplankton genera identified during several pan-continental lake surveys: the EPA-NLA surveys conducted in 2012 and 2017 across the continental USA and the NSERC Canadian Lake Pulse survey campaign conducted from 2017 to 2019. We expect that this work will serve others in the plankton community interested in assessing nanoplankton feeding strategies.

Evaluating the economic and environmental benefits of deploying a national-scale, thermo-chemical plastic waste upcycling infrastructure in the United States

Emerging chemical technologies can upcycle plastic waste by producing high-value polymers and other products. In this work, we study the economic and environmental benefits of deploying an upcycling infrastructure in the continental United States for producing low-density polyethylene (LDPE) and polypropylene (PP) from post-consumer plastic waste. Our analysis is based on a computational framework that integrates techno-economic analysis, life-cycle assessment, and value chain optimization. Our results demonstrate that the infrastructure could generate a market of nearly 20 billion USD per year and that this market is robust to various externalities. Our analysis also indicates that the infrastructure can achieve a plastic-to-plastic degree of circularity of 34% relative to residential plastic waste production, and leads to significant environmental benefits over alternative waste disposal methods, including 69%–75% lower greenhouse gas emissions than waste-to-energy systems and 38 million tonnes of avoided landfill waste per year.

Identifying priority ecosystem services in tidal wetland restoration.

Classification systems can be an important tool for identifying and quantifying the importance of relationships, assessing spatial patterns in a standardized way, and forecasting alternative decision scenarios to characterize the potential benefits (e.g., ecosystem services) from ecosystem restoration that improve human health and well-being. We present a top-down approach that systematically leverages ecosystem services classification systems to identify potential services relevant for ecosystem restoration decisions. We demonstrate this approach using the U.S. Environmental Protection Agency's National Ecosystem Service Classification System Plus (NESCS Plus) to identify those ecosystem services that are relevant to restoration of tidal wetlands. We selected tidal wetland management documents from federal agencies, state agencies, wetland conservation organizations, and land stewards across three regions of the continental United States (northern Gulf of Mexico, Mid-Atlantic, and Pacific Northwest) to examine regional and organizational differences in identified potential benefits of tidal wetland restoration activities and the potential user groups who may benefit. We used an automated document analysis to quantify the frequencies at which different wetland types were mentioned in the management documents along with their associated beneficiary groups and the ecological end products (EEPs) those beneficiaries care about, as defined by NESCS Plus. Results showed that a top combination across all three regions, all four organizations, and all four tidal wetland types was the EEP naturalness paired with the beneficiary people who care (existence). Overall, the Mid-Atlantic region and the land steward organizations mentioned ecosystem services more than the others, and EEPs were mentioned in combination with tidal wetlands as a high-level, more general category than the other more specific tidal wetland types. Certain regional and organizations differences were statistically significant. Those results may be useful in identifying ecosystem services-related goals for tidal wetland restoration. This approach for identifying and comparing ecosystem service priorities is broadly transferrable to other ecosystems or decision-making contexts.

Geographic Access to Urogynecology Care in the United States.

Although access to urogynecologic care is known to influence patient outcomes, less is known regarding geographic access to care and how it may vary by population characteristics. The primary objective of this study was to estimate geographic accessibility of urogynecologic services in terms of drive time and by population demographics. We performed a descriptive study using practice location data by zip code for all board-certified urogynecologists who are American Urogynecologic Society members (n = 497) and pelvic floor physical therapists (n = 985). Drive times from each zip code to the nearest health care professional zip code were calculated. These data were then overlaid onto a map of the continental United States. Race/ethnicity, age, education, poverty status, disability status, health insurance coverage, and rurality were compared across travel times. Of the 31,754 zip codes of the continental United States, 389 (1.23%) had at least 1 urogynecologist, and 785 (2.47%) had at least 1 pelvic floor physical therapist; 92.29 million women older than 35 years were represented in the demographic analyses. Seventy-nine percent of the studied population live within 1 hour of a urogynecologist, and 85% live within 1 hour of a pelvic floor physical therapist. Seven percent and 3% live >2 hours from urogynecologic services, respectively. Values for drive times to all health care professionals indicate that American Indian/Alaska Native individuals have a much greater travel burden than other racial/ethnic groups. There are population groups with limited geographic access to urogynecologic services. Pelvic floor physical therapists are more geographically accessible to the population studied than urogynecologists.

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

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/m 3, 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/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.

Defoliator outbreaks track with warming across the Pacific coastal temperate rainforest of North America

The biogeography of irruptive insect herbivores is determined by host availability and climate conditions. As such, outbreak distributions are sensitive to climatic change, especially across large latitudinal gradients. Here, we investigate the outbreak distributions of two understudied defoliators, hemlock sawfly Neodiprion tsugae (Hymenoptera) and western blackheaded budworm Acleris gloverana (Lepidoptera), that have both recently impacted the greatest land area recorded across the Pacific coastal temperate rainforest since the establishment of aerial survey programs. We compiled polygon‐based estimates of insect damage collected by aerial observers, forest inventory, and downscaled climatic data to develop gridded estimates of bioclimatic conditions across the extent of the Pacific coastal temperate rainforest, including the continental United States, British Columbia and Alaska. We leveraged these data to develop ensemble machine learning models with the goal of predicting the outbreak distribution of each insect. In this manuscript we: 1) describe the historical patterns of defoliator outbreaks, 2) identify and describe climatic conditions associated with outbreaks in both species and 3) assess whether historic outbreaks have tracked geographic shifts in climate conditions across the region. We demonstrate that outbreaks of hemlock sawfly and western blackheaded budworm have been observed across the Pacific coastal temperature rainforests of North America in each decade since the establishment of the Canadian and United States aerial survey programs. The distribution of outbreaks by both insects were best explained by host availability, a limited range of spring, summer, and winter temperatures, and minimum precipitation. Finally, we demonstrate that outbreaks have tracked the poleward shift in suitable climate over the last century. This study establishes a baseline understanding of the climatic constraints and biogeographic patterns of historic sawfly and budworm outbreaks across the Pacific coastal temperate rainforest and emphasizes the overarching importance of climate in driving the irruptive dynamics of these defoliator species.Keywords: climate envelope, defoliators, hemlock sawfly, Pacific coastal temperate rainforest, population dynamics, western blackheaded budworm

An Analysis of the Spatial and Temporal Synergy between Profiles of Aircraft-Based Observations and Operational Radiosonde Reports over the United States

Abstract Aircraft-based observations (ABOs) are an important component of the global observation system. Observations of pressure, temperature, and wind are obtained from thousands of routine commercial flights daily via the Aircraft Meteorological Data Relay (AMDAR) program, while a subset of approximately 145 aircraft globally (and 135 within the conterminous United States) also produces observations of water vapor from the Water Vapor Sensing System–II (WVSS–II). Aircraft equipped with WVSS–II provide the basic parameters as radiosonde observations throughout most of the troposphere, often at higher temporal and spatial frequency. Since these aircraft are operated according to the demands of passenger and cargo, the availability of aircraft profiles varies significantly in space and time, with more profiles during daytime and early evening than overnight, more profiles on weekdays than weekends, and more during the summer months. The number of available profiles was significantly impacted by reductions in travel during the COVID-19 pandemic but has recovered substantially. The potential for aircraft profiles to support the operational radiosonde network is explored, including the effect of various spatial and temporal matching criteria. Radiosonde launches at 0000 UTC that are well aligned with aircraft profiles are found across the conterminous United States, but well-covered 1200 UTC launches are strongly biased to the east. ABO coverage of asynoptic launch times is also explored. The busiest sites usually have multiple compatible aircraft profiles at both synoptic and asynoptic times. This redundancy lends robustness to the observation network and enables forecasters to monitor atmospheric evolution more continuously throughout the day. Significance Statement Some commercial aircraft make the same observations as weather balloons, but there is not a good record of how frequently these observations are made at specific locations. This paper does a census of where the airplane profile observations are most likely to be found and shows where they are duplicating weather balloon observations and where they are filling in the gaps in the weather balloon network.

One Health, many perspectives: Exploring Indigenous and Western epistemologies

Abstract The One Health approach, which assesses the interconnectedness of animal, human, and environmental health, fails to include and amplify Indigenous knowledge and Indigenous scientists. To effectively center Indigenous knowledge next to and within the One Health approach, which is historically based in Western science, the similarities and differences between Indigenous science, specifically Traditional Ecological Knowledge (TEK), and One Health must be explored. The objective of this project is to identify values in Indigenous science that are unsupported or underrepresented in Western science and then collaboratively ideate recommendations that Western allies can take to center and support Indigenous scientists and elevate Indigenous knowledge. From January to March 2023, the study team conducted semi-structured interviews with Indigenous knowledge keepers via Zoom video calls. American Indian, Alaska Native, Native Hawaiian (Kānaka Maoli), and other Indigenous participants from the global diaspora residing in the continental United States and Hawaiian Islands were recruited through social media and referrals from Indigenous leaders who had participated in formative (phase 1) interviews completed in 2022. In those formative interviews, four themes emerged and called for further exploration of Indigenous perspectives on (1) the natural world, (2) cultural heritage, (3) value expression, and (4) reflection. These ideas were reframed into questions asked in subsequent (phase 2) interviews in 2023. Thematic identification methods were used to highlight key ideas throughout the interviews. Participants noted gaps between their Indigenous worldviews and the One Health model, particularly relating to contrasts with Western culture, holistic expression, power in action, identity and belonging, maintaining community and cultural practices, and sharing information/education. It was also noted that One Health must do more than just uplift Indigenous values to support Indigenous scientists and should instead collaborate with TEK keepers and foster practices that are inclusive and validating of other knowledge systems. Phase 2 interview responses highlighted Indigenous principles that are underrepresented in One Health practice. These principles were transformed into considerations specifically for Western-rooted scientists to improve allyship to Indigenous science and scientists. Adoption of the considerations would strengthen partnership between One Health practitioners and Indigenous scientists and promote collaboration across many ways of knowing. One Health impact statement The One Health theory encourages transdisciplinary collaboration to break down siloing and innovate solutions. Yet, One Health practice, and the Western scientific approach it takes, often excludes involvement with Indigenous ways of knowing and disclaims Indigenous knowledge keepers who have been conducting “One Health” work since time immemorial. Indigenous knowledge keepers who self-identified as cultural bridges between Native communities and Western communities were interviewed and asked to speak to values found in Indigenous science and research. The featured principles highlight some of the similarities and differences between Indigenous and Western scientific approaches. The identified values informed recommendations for Western scientists to be better allies to Indigenous scientists and to amplify Indigenous voices and science. One Health practitioners will benefit from these guidelines by navigating research in a more equitable way and by creating sustainable reciprocal partnerships with Indigenous scientists. This makes One Health more open and accessible to Indigenous and Western ways of knowing.

Part I: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range using the U-Net 3+ machine learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model and how input variables are codified for the model. By combining the capabilities of the U-Net 3+ model with a neighborhood loss function, fractions skill score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridded surface meteorological (gridMET) dataset, a daily, CONUS-wide, high-spatial-resolution dataset of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS. Significance Statement Our work seeks to explore the limits to which deep learning can predict wildfire occurrence in CONUS with the ultimate goal of providing decision support to those allocating fire resources during high fire seasons. By exploring with what accuracy and lead time we can provide insights to these persons, we hope to reduce loss of life, reduce damage to property, and improve future event preparedness. We compare two models, one trained on all fires in the continental United States and the other on only large lightning fires. We found that a model trained on all fires produced a higher probability of fire.

Not just a pretty picture: Mapping Leaf Area Index at 10 m resolution using Sentinel-2

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.

A quest for precipitation attractors in weather radar archives

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km2, 21 years) and the Swiss Alpine region (≈500×500 km2, 6 years).

Spatiotemporal heterogeneity of the association between short-term exposure to carbon monoxide and COVID-19 incidence: A multistage time-series study in the continental United States

BackgroundPrevious research has established carbon monoxide (CO) as a significant air pollutant contributing to coronavirus disease 2019 (COVID-19) transmission. The spatiotemporal heterogeneity in the relationship between short-duration CO exposure and COVID-19 incidence remain underexplored. Investigating such heterogeneity plays a crucial role in designing region-specific cost-effective public health policies, exploring the reasons for heterogeneity, and understanding the temporal trends in the association between CO and an emerging infectious disease such as COVID-19. MethodsThe 49 states of the continental United States (U.S.) were examined in this study. Initially, we developed time-series generalized additive models (GAMs) for each state to assess the preliminary correlation between daily COVID-19 cases and short-term CO exposure from April 1, 2020, to December 31, 2021. Subsequently, the correlations were compiled utilizing Leroux-prior-based conditional autoregression (LCAR) to achieve a smoothed spatial distribution. Finally, we integrated a time-varying component into the GAM and LCAR to analyze temporal correlations and illuminate the factors contributing to spatiotemporal heterogeneity. ResultsOur analysis revealed that, across the 49 states, a 10-ppb increase in CO concentration was associated with a 1.33 % (95%CI: 0.86%–1.81 %) increase in COVID-19 cases on average. Furthermore, spatial variability was noted, with weaker correlations observed in the central and southeastern regions, stronger associations in the northeastern regions, and negligible associations in the western regions. Temporally, the correlation was not significant from April 2020 to June 2021, but began to increase steadily thereafter until the end of 2021. Additionally, vaccination and temperature were determined to be potential causes contributing to the heterogeneity, indicating stronger positive associations in areas with higher vaccination rates and temperatures. ConclusionThe findings of this study underscore the importance of monitoring CO pollution in the central and northeastern US, especially in the aftermath of the pandemic.

Matching Spring Phenology Indicators in Ground Observations and Remote-Sensing Metrics

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.

Accurate Quantification of 0–30 cm Soil Organic Carbon in Croplands over the Continental United States Using Machine Learning

Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r2 (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r2 of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).