Low Temporal Resolution Research Articles

High-resolution precipitation monitoring with a dense seismic nodal array

Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design.

Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease.

Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

Sediment flux and early diagenesis inferred from high-resolution XRF-CS data and iron and arsenic sulfides during the last 30 kyr in Lake Urmia, Iran: Implications for studies of hypersaline lakes

We analyzed by high resolution X-ray fluorescence core scanning (XRF-CS) the elemental composition of a 12.5 m long composite sediment core extracted from the hypersaline Lake Urmia (NW Iran). The main objective of the study was to reconstruct on a high temporal scale, the changes in sediment origin/inflow and water levels of Lake Urmia over the last 30,000 years. Combined with previously obtained results on 14C accelerated mass spectrometry (AMS) chronology, grain size and distribution of major minerals, albeit with a much lower temporal resolution than the XRF-CS data, this multi-proxy approach allowed recognition of specific hydrological changes and associated sedimentary processes in the Lake Urmia basin, including physical and chemical weathering, depositional fluxes and carbonate crystallization processes in lake water, and early diagenesis in anoxic bottom sediments. The high natural logarithm ratios (ln-ratios) of K/Ti, Ca/Ti and Sr/Ca, and low ln-ratios of Rb/Sr reveal increasing chemical weathering in the catchment area in conjunction with increasing authigenic carbonate precipitation in the lake. These findings reflect wet conditions, high lake levels and less saline water during the following intervals (i) 29.8–20.2, (ii) 15.2–13.3, (iii) 11.8–5.6 and (iv) 4.1–2.3 cal kBP. In contrast, the increasing ln-ratios of Rb/Sr associated with decreasing ln-ratios of K/Ti, Ca/Ti and Sr/Ca indicates enhanced erosion in the catchment area linked to drier conditions, lower lake levels and more saline conditions predominating at around 30–29.8 cal kBP, 20.2–15.2, 13.3–11.8, and 5.6–4.1 cal kBP. Based on the As-XRF-CS pattern and magnetic parameters we identified iron and naturally-occurring arsenic sulfides rarely reported from saline lakes, acting as specific indicators of anoxic conditions in the lake bottom environment. Moreover, XRF-CS data revealed short-lived periods with increasing chemical weathering in the catchment area at (i) 28.6–28.3, (ii) 27.3–27.1, (iii) 4.1–3.6 and (iv) 3.4–3.3 cal kBP, as well as increasing erosion at (i) 27.0–26.8, (ii) 26.3–26.0, (iii) 25.6–25.3 and (iv) 24.6–24.4 cal kBP. Overall, this study confirms the previously reported findings concerning the sediments of Lake Urmia as well as completing these, both by providing more details on the environment for the periods already identified and by highlighting events of short duration. In particular, the results reported here permit better definition of the processes of chemical weathering versus erosion in the catchment area, and of the oxygenation levels of the lake bottom. The close correlation of these long- and short-term changes with the North Atlantic climate system clearly indicates that the Lake Urmia area has been affected mainly by this system during the last 30 cal kBP.Over and above their value with regard to Lake Urmia, these new data fill an important gap in eastern Mediterranean/western Asia paleoclimate records as a whole, providing unique and continuous 30 cal kBP information on the possible driving mechanisms of the observed changes. These can be compared to existing data on paleoenvironmental and climatic changes on regional and global scales and can also be used in global circulation models.

Inverse characterization of a material model using an ensemble-based four-dimensional variational method

The identification accuracy of material model parameters is essential for accurately predicting the deformation behavior of metallic materials (e.g., metal forming) using a finite element (FE) simulation. Numerous researchers have studied inverse material model characterization, where parameters are determined by minimizing a cost function that quantifies the difference between experimental data and mechanical test simulation results. However, sensitivity analysis in the optimization process hinders the extensibility of inverse methods due to issues like computational cost and complex numerical implementation. In this study, we developed a novel inverse methodology for material model characterization that improves extensibility by applying an ensemble-based four-dimensional variational method (En4DVar), which has the potential to address the challenges associated with conventional FE-based inverse material model characterization. The developed method was verified through numerical experiments in which En4DVar was applied to an elastoplastic FE simulation of the deformation of an aluminum alloy during a uniaxial tensile test, including diffuse necking. We investigated the estimation accuracy of the strain-hardening parameters in Swift’s hardening law and evaluated the simulation results under various conditions through numerical experiments. We focused on the effect of time and location to incorporate synthetic experimental data into the simulation to examine the quantities of synthetic experimental data required for parameter estimation. The results of the numerical experiments showed that En4DVar is a powerful approach for estimating the parameters and characterizing the deformation behavior of a material. Moreover, it was shown that accurate estimation results can be obtained even using synthetic experimental data with a relatively low temporal resolution or a small field of view. The proposed method's ease of extensibility using En4DVar expands the range of problems solvable in the field of material model characterization.

Paired passive acoustic and gillnet sampling reveal the utility of bioacoustics for monitoring fish populations in a turbid estuary

Abstract Estuaries worldwide provide critical habitat to many ecologically and socioeconomically important fish species. However, the high turbidity and strong environmental gradients inherent to many estuaries make the monitoring of fish populations a challenging task. To assess the health and diversity of local fish populations, resource managers often employ fisheries-independent sampling methods, which result in valuable population assessments, but suffer from low-temporal resolution and high rates of mortality. Passive acoustic monitoring (PAM) provides a near-continuous, non-invasive means for monitoring soniferous fish activity over extended periods, but its utility for quantifying entire fish communities is largely untested, especially in turbid estuaries. Here, we use synchronized acoustic and gillnet sampling at 51 locations in the Mission-Aransas Estuary (TX), across two sampling seasons to test correlations between acoustic indices and gillnet catch. Positive correlations were observed between gillnet catch and sound pressure levels in both broadband (50–22050 Hz) and low-frequency (50–2500 Hz) bands. Additionally, sciaenid calling activity was positively correlated with sciaenid catch during the spring, when most spawning activity occurs. Our results indicate that PAM can be used to monitor the relative abundances of fish in Gulf of Mexico estuaries and beyond, although this is subject to species identity and seasonal dynamics.

The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams

Abstract Drift or downstream dispersal is a fundamental process in the life cycle of many riverine organisms. In the face of rapidly declining freshwater biodiversity, there is a need to enhance our capacity to study the drift of riverine organisms, by overcoming the limitations of traditional labour‐intensive sampling methods that result in data of low temporal and spatial resolution. To address this need, we developed a new technology, the Riverine Organism Drift Imager (RODI), which combines in situ imaging with machine‐learning classification. This technique expands on the traditional methodology by replacing the collection cup of a drift net with a camera system that continuously images riverine organisms as they drift through the device. After being imaged, organisms are released into the environment unharmed. A machine‐learning classifier is used after field sampling to identify drifting organisms. Therefore, RODI provides a non‐invasive sampling method that can quantify organism drift at unprecedented temporal resolution. Multiple deployments have served to validate the performance of the technology in the field. In its current implementation, images are captured continuously for 1.5 h at 50 frames per second. We demonstrate that the quality of the resulting images enables a convolutional neural network classifier to identify organisms to the family level. The weighted F1 score, a metric for the performance of the classifier, was 94%, based on training and testing on a field‐collected dataset consisting of 4598 images of 285 organisms belonging to seven classes (one species, five families and one order). In conclusion, this work provides a proof of concept, demonstrating the viability of the deployment of RODI as an automated, in situ organism drift sampler. This novel approach offers the possibility to advance our fundamental understanding of the drift of riverine organisms and how this is affected by human impacts in natural streams while, at the same time, can serve as a cost‐effective tool for biodiversity monitoring.

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

An Evaluation Method of Ship-Tracking Algorithms for High-Frequency Surface Wave Radar considering High Maneuvers Generated by the MMG Model

Thelong-distance coverage of high-frequency surface wave radar (HFSWR) has promoted it as an enormous means for ship monitoring on the country’s maritime territory. Since it is a primaryradar, noncooperative targets can also be detected. However, this radar also has a shortcoming of low spatial and temporal resolutions due to the narrow available bandwidth in the HF band. This limitation can reduce the performance of ship detection and tracking, especially for highly maneuvering ships. This paper proposes a new method to assess the tracking algorithm for a high-maneuvering ship. The absence of a high-maneuvering plot in the AIS data and existing analytical models are replaced by the MMG model run on MANSIM software. The linear, turning, and zigzag motions are generated and used to evaluate the tracking algorithms. The Monte Carlo simulation was conducted regarding the degradation of spatial resolution in the higher radial range. The tracking performance was analyzed by calculating the RMSE of four parameters, i.e., absolute position, radial range, bearing angle, and speed. For a trial case, four tracking algorithms were evaluated, i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). The evaluation results showed that the EKF tracker had a minor error for the linear track with RMSE of absolute position, radial range, bearing angle, and speed being 1.368 km, 0.526 km, 1.550°, and 0.005 m/s, respectively. Otherwise, the UKF performed slightly better than EKF for the high maneuver targets. The RMSE of absolute position, radial range, bearing angle, and speed were 1.649 km, 0.639 km, 1.919°, and 0.165 m/s, respectively. The results also ensure the applicability of the MMG model to evaluate the tracking algorithm’s performance in HFSWR.

GatorByte – An Internet of Things-Based Low-Cost, Compact, and Real-Time Water Resource Monitoring Buoy

Conventional water resource monitoring systems are usually expensive, have a low-temporal resolution, and lack spatial dimension entirely. These systems are typically available as stations or handheld devices. Pinpointing sources of pollution using these systems can be difficult. This project involves developing a high-resolution free-flowing monitoring buoy that records spatiotemporal water-quality data in flowing stream environments. The system is highly customizable, and even users with limited experience in programming or electronics can tailor GatorByte to their needs. The platform includes a data logger, a cloud-based server, and visualization tools. The data logger uses low-cost sensors, electronic peripherals, a 3D-printed enclosure, and printed circuit boards, with a total cost per unit under $1,000 USD. The data logger uses an NB-IoT-capable Arduino for real-time reporting and visualizing sensor data. The GatorByte records physiochemical water metrics – pH, temperature, dissolved oxygen, electroconductivity, and the current location of the buoy using a GPS module. The data logger also includes micro-SD storage and a Bluetooth module for on-field diagnostics. Using the GatorByte buoy, the collection of variations in water quality data in temporal as well as spatial dimensions can be achieved cost-effectively and reliably, enabling quick detection and resolution of pollution events.

An analytical method to evaluate curtailment of hydro–photovoltaic hybrid energy systems and its implication under climate change

Identifying power curtailment is essential for system reliability, especially in hydro–photovoltaic (PV) hybrid energy systems (HPESs) exposed to future climate variability. However, the relatively low temporal resolution of climate projections challenges the effectiveness of current numerical simulation methods for evaluating the curtailment rate of HPESs. This study derives an analytical method based on daily hydropower and PV power rather than the commonly used hourly data in numerical methods, to reduce reliance on the temporal resolution of climate inputs. A short-term operation model is constructed to verify the effectiveness of the derived power curtailment function. A long-term operation model incorporating the analytical power curtailment function is then established to analyze variations in future curtailment rates using climate information projections. Results of a case study in the Yalong River Basin validate the accuracy of the derived PV curtailment function. Compared with the reference period, future hydropower and PV power will increase in most climate scenarios with substantial variances, whereas the PV curtailment rate exhibits an overall increase in the near future (+2.49%) and a larger increase in the far future (+5.53%). The most noticeable increases in future PV curtailment rates occur in June, July, and October (+3.15%, +5.47%, and +3.11%). The proposed analytical method provides valuable insights for the risk evaluation of HPESs by coupling the short-term power curtailment rate with mid- and long-term climate variations.

DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes

The event camera, a new bio-inspired vision sensor with low latency and high temporal resolution, has brought great potential and demonstrated a promising application in machine vision and artificial intelligence. Corner detection is a key step of object motion estimation and tracking. However, most existing event-based corner detectors, such as G-eHarris and Arc*, lead to a huge number of redundant or wrong corners, and cannot strike a balance between the accuracy and real-time performance, especially in complex scenes with high texture that require higher computational costs. To address these issues, we propose an asynchronous corner detection method: a double threshold filter with Sigmoid eHarris (DTFS-eHarris) and an asynchronous corner tracker. The main contributions are that a double threshold filter is designed to reduce the redundant events and the improved Sigmoid function is utilized to represent the Surface of Active Events (Sigmoid*-SAE). We selected four scenes—shapes, dynamic, poster and boxes—from the public event camera dataset DAVIS240C to compare with the existing state-of-the-art hybrid method; our method has shown more than a 10% reduction in false positive rate and a 5% and 20% improvement in accuracy and throughput, respectively. The evaluations indicate that DTFS-eHarris shows a significant improvement, especially in complex scenes. Thus, it is anticipated to enhance the real-time performance and feature detection accuracy for future robotic applications.

Ultra-High Field Imaging of Human Visual Cognition.

Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.

Laboratory development of TDR automatic distributed settlement sensing for land subsidence monitoring

Recognizing land subsidence to reduce the risk is critical in complex alluvial-basin aquifers and the subsidence profiling requires multi-leveling techniques. However, the manual magnetic ring sampling is limited with low temporal resolution. This study adopted Time Domain Reflectometry (TDR) for automatic and high-accuracy instrumentation with designs of subsidence sensor and data reductions. A physical model was included to examine measurement accuracy and standard deviation due to the groundwater variation. The water fluctuates in the unconfined aquifer scenario affected the measurement accuracy to −1.52 cm/m by the Maximum point method. Additionally, the Maximum point accuracy is 0.84 mm/m with a stable water level as the confined aquifer case, proofing the measurement feasibility but limited measurement range due to signal attenuation. Thus, a novel TDR multi-sections schema was provided to solve the signal attenuation, resulting in the measurement range extending to 150 m with a standard deviation ≤ 1 mm.

Unmanned aerial vehicles assisted rice seedling detection using shark smell optimization with deep learning model

Rice seedling classification using an unmanned aerial vehicle (UAV) images remains a challenging problem that needs to be addressed. It is still a difficult task because it is prone to low temporal and spatial resolution images. Recently, machine learning (ML) and deep learning (DL) models can be employed for several image preprocessing tasks such as classification, object detection, and segmentation. Therefore, this study focuses on the design of shark smell optimization with deep learning based rice seedling detection (SSODL-RSD) on UAV imagery. The presented SSODL-RSD technique recognizes the UAV images into arable land and rice seedlings. To achieve this, the SSODL-RSD technique employs the adaptive Wiener filtering (AWF) technique for the noise removal procedure. In addition, the SSODL-RSD technique exploits the NestNet feature extractor model. Moreover, the SSO algorithm is used for the hyperparameter tuning of the NestNet model. Finally, the long short term memory-recurrent neural network (LSTM-RNN) model is employed for the classification of rice seedlings. The extensive comparative study highlighted the improved outcomes of the SSODL-RSD technique over other existing models.

Compressed sensing cine imaging with higher temporal resolution for analysis of left atrial strain and strain rate by cardiac magnetic resonance feature tracking.

Cardiac magnetic resonance (CMR) feature tracking (FT) is more widely used in the measurement of left atrial (LA) strain and strain rate (SR). However, in recent years, researchers have attempted to improve the low temporal resolution of CMR-FT to better capture the subtle deformations of the myocardium. The technique of compressed sensing (CS) has been applied clinically, reducing scan time while increasing temporal resolution. The purpose of this study was to explore the effect of the increased temporal resolution of CS cine sequences on the analysis of LA longitudinal strain and SR. Twenty-nine healthy subjects were included in the study. They underwent CMR with a reference steady-state free precession cine sequence of conventional temporal resolution (standard SSFP sequence), a cine sequence of higher temporal resolution (HT sequence), and an HT cine sequence with CS (CS HT sequence) (temporal resolution: 22.1-44.3/24.9-47.1ms, 11.1-19.4ms, and 8.3-19.4ms, respectively). The standard SSFP sequence, HT sequence, and CS HT sequence were acquired in all subjects during the same scanning session. LA longitudinal strain and SR, reflecting LA reservoir, conduit, and contraction booster-pump function, were measured by CMR-FT and compared among the three sequences. The measurements of LASR reservoir, conduit, and booster-pump were significantly higher on the HT and CS HT sequences than on the standard SSFP sequence. The standard SSFP sequence was correlated significantly with the HT and CS HT sequences in terms of LA strain and SR analysis, respectively. The LA strain and SR measurements also showed excellent agreement between the HT and CS HT sequences. Higher temporal resolution led to significantly higher measured LASR values in CMR-FT. Furthermore, the addition of CS reduced scan time and did not affect LA longitudinal strain or SR analysis.

PCSboost: A Multi-Model Machine Learning Framework for Key Fragments Selection of Channelrhodopsins Achieving Optogenetics

Optogenetics combines optical and genetic methods to modulate light-controlled gene expression, protein localization, signal transduction and protein interactions to achieve precise control of specific neuronal activity, with the advantages of low tissue damage, high spatial and temporal resolution, and genetic specificity. It provides a cutting-edge approach to establishing a causal relationship between brain activity and behaviors associated with health and disease. Channelrhodopsin (ChR) functions as a photogenic activator for the control of neurons. As a result, ChR and its variants are more widely used in the realization of optogenetics. To enable effective optogenetics, we propose a novel multi-model machine learning framework, i.e., PCSboost, to accurately assist key fragments selection of ChRs segments that realize optogenetics from protein sequence structure and information dataset. We investigate the key regions of the ChR variant protein fragments that impact photocurrent properties of interest and automatically screen important fragments that realize optogenetics. To address the issue of the dataset containing a limited quantity of data but a high feature dimension, we employ principal component analysis (PCA) to reduce the dimensionality of the data and perform feature extraction, followed by the XGBoost model to classify the ChRs based on their kinetics, photocurrent and spectral properties. Simultaneously, we employ the SHAP interpretability analysis to perform an interpretability analysis of the ChR variant protein for pointwise, characteristic similarities to identify key regions of the protein fragment structure that contribute to the regulation of photocurrent intensity, photocurrent wavelength sensitivity and nonkinetic properties. Experimental findings demonstrate that our proposed PCSboost approach can speed up genetic and protein engineering investigations, simplify the screening of important protein fragment sections, and potentially be used to advance research in the areas of optogenetics, genetic engineering and protein engineering.

Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models

Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained in a monitoring program of around 4800 Swedish lakes observed once every 6 years between 2008 and 2021. On applying the methods developed here, we identified nonlinear changes in TOC from consistent negative trends over most of Sweden around 2010 to positive trends during later years in parts of the country.

Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km2) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.

Traffic-Related Air Pollution and Ultrasound Parameters of Fetal Growth in Eastern Massachusetts.

Previous studies have examined the association between prenatal nitrogen dioxide (NO2)-a traffic emissions tracer-and fetal growth based on ultrasound measures. Yet, most have used exposure assessment methods with low temporal resolution, which limits the identification of critical exposure windows given that pregnancy is relatively short. Here, we used NO2 data from an ensemble model linked to residential addresses at birth to fit distributed lag models that estimated the association between NO2 exposure (resolved weekly) and ultrasound biometric parameters in a Massachusetts-based cohort of 9,446 singleton births from 2011-2016. Ultrasound biometric parameters examined included biparietal diameter (BPD), head circumference, femur length, and abdominal circumference. All models adjusted for sociodemographic characteristics, time trends, and temperature. We found that higher NO2 was negatively associated with all ultrasound parameters. The critical window differed depending on the parameter and when it was assessed. For example, for BPD measured after week 31, the critical exposure window appeared to be weeks 15-25; 10-parts-per-billion higher NO2 sustained from conception to the time of measurement was associated with a lower mean z score of -0.11 (95% CI: -0.17, -0.05). Our findings indicate that reducing traffic emissions is one potential avenue to improving fetal and offspring health.

Machine-learning-estimation of high-spatiotemporal-resolution chlorophyll-a concentration using multi-satellite imagery

Chlorophyll-a concentration for quantifying phytoplankton biomass is commonly used as an indicator for evaluating the trophic level of lakes and water quality. This research aimed to develop a high spatiotemporal-resolution model for the retrieval of chlorophyll-a in inland water. Firstly, the machine learning based models considering Sentinel-2 Multispectral Instrument and Sentinel-3 Ocean and Land Color Instrument (OLCI) images were applied to estimate chlorophyll-a concentrations (R2 = 0.873 and 0.822, respectively). The spatiotemporal fusion was performed to fuse the OLCI and MSI chlorophyll-a images with low temporal resolution but fine spatial-resolution, and with high temporal resolution but coarse spatial-resolution. The random forest was applied to fuse images from two distinct sensors, and to refine the spatial resolution of OLCI estimations to be the same as those of Sentinel-2 MSI. Results showed that the spatiotemporal fusion can estimate dense-temporal 10 m spatial resolution chlorophyll-a concentration in the Tsengwen Reservoir (Root-Mean-Square Error, RMSE = 1.25–1.47 μg L−1). The spatiotemporal fusion model was effectively applied to determine high spatiotemporal-resolution chlorophyll-a measurements in the aquatic system.