Detrending Research Articles

Trends of Anthropogenic Dissolved Inorganic Carbon in the Northwest Atlantic Ocean Estimated Using a State Space Model

AbstractThe northwest Atlantic Ocean is an important sink for carbon dioxide produced by anthropogenic activities. However the strong seasonal variability in the surface waters paired with the sparse and summer biased observations of ocean carbon makes it difficult to capture a full picture of its temporal variations throughout the water column. We aim to improve the estimation of temporal trends of dissolved inorganic carbon (DIC) due to anthropogenic sources using a new statistical approach: a time series generalization of the extended multiple linear regression (eMLR) method. Anthropogenic increase of northwest Atlantic DIC in the surface waters is hard to quantify due to the strong, natural seasonal variations of DIC. We address this by separating DIC into its seasonal, natural and anthropogenic components. Ocean carbon data is often collected in the summer, creating a summer bias, however using monthly averaged data made our results less susceptible to the strong summer bias in the available data. Variations in waters below 1000m have usually been analyzed on decadal time scales, but our monthly analysis showed the anthropogenic carbon component had a sudden change in 2000 from stationary to an increasing trend at the same rate as the waters above. All depths layers had similar rates of anthropogenic increase of ∼0.57µmol kg−1 year−1, and our uncertainty levels are smaller than with eMLR results. Integration throughout the water column (0–3,500 m) gives an anthropogenic carbon storage rate of 1.37 ± 0.57 mol m−2 year−1, which is consistent with other published estimates.

Combining trends from disparate monitoring programs to inform Red List assessments: The case of the Cape Vulture (Gyps coprotheres)

Conservation decisions are often informed by Red List assessments, which are substantially influenced by estimated population trends. Population trend estimates used by Red List assessments should therefore be as quantitative, comprehensive, and transparent as possible. We combine counts of breeding pairs of the Cape Vulture (Gyps coprotheres) that occur at separate breeding colonies and span differing time periods. The methodological concept is simple: transform time series into interannual rates of change, put those rates into a year-by-series matrix, then average that matrix to estimate the interannual population wide rate of change. Our analysis uses state-space models and basic arithmetic to estimate interannual rates of change per time series. Analyses are performed under a Bayesian framework to ensure that uncertainty is propagated into a composite index that estimates the percent global population reduction over three generations. Our results indicate that the global Cape Vulture population declined by 25 % (95 % CRI = 0.5 %–44 %) from 1977 to 2019. Such a decline suggests the species should be listed as Near Threatened under Criterion A2, instead of Vulnerable, as the species is currently listed. However, we performed a sensitivity analysis that suggested the species might indeed be Vulnerable if unmonitored colonies are in decline. Although the analysis for each species and time series should be customized, we suggest that the general practice of averaging the interannual rates of change for all available time series could improve qualitative estimates of population reduction used in many Red List assessments.

An improved DenseNet model for prediction of stock market using stock technical indicators

Reliable estimation of future cost prices of stocks are a significant and exciting task in both educational and economic research. Current progressions in machine learning (ML), and explicitly, in the area of deep learning (DL) have permitted the research community to utilize the financial information from the social and finance websites to estimate the future prices of products in a viable manner. Besides, the accurate estimation of stock future trends is a complex task due to the unstable nature of the finance data. We have tried to overcome the limitations of existing studies by presenting a more reliable DL-based approach. More clearly, the proposed work is based on estimating the final prices of products by utilizing the 10 years of stock data from the Yahoo Finance website along with the computed Stock Technical Indicators (STIs). The calculated STIs are initially passed as input to the autoencoder to reduce the feature space by eliminating the highly correlated data and providing a more nominative set of STIs. The processed STIs together with the Yahoo finance data are passed as input to the DenseNet-41. The final feature vector computed by the DenseNet-41 is then passed to the SoftMax layer of the network to determine the closing cost prices of products for small, medium, and longtime horizons. Based on the predicted prices of stocks the proposed approach provides three types of signals designated as buy, sell, or hold to assist the investors in their decision-making. Performance evaluation demonstrates that our work outperforms the latest methods by acquiring a minimum MAPE score of 0.32.

Observations in the Spanish Mediterranean Waters: A Review and Update of Results of 30-Year Monitoring

The Instituto Español de Oceanografía (IEO, Spanish Institute of Oceanography) has maintained different monitoring programs in the Spanish Mediterranean waters (Western Mediterranean) since 1992. All these monitoring programs were unified in 2007 under the current program RADMED (series temporales de datos oceanográficos en el Mediterráneo), which is devoted to the in situ multidisciplinary sampling of the water column of coastal and open-sea waters by means of periodic oceanographic campaigns. These campaigns, together with a network of tide-gauges, are part of the IEO Observing system (IEOOS). In some cases, the temperature and salinity time series collected in the frame of these monitoring programs are now more than 30 years long, whereas sea level time series date to the beginning of the 1940s. This information has been complemented with international databases and has been analyzed in numerous works by the Grupo mediterráneo de Cambio Climático (GCC; Mediterranean Climate Change Group) for more than 20 years. These works have been devoted to the detection and quantification of the changes that climate change is producing on the physical, chemical, and biological properties of the Spanish Mediterranean waters. In this work, we review the results obtained by the GCC since 2005 in relation to the changes in the physical properties of the sea: water column temperature, salinity, and density, heat content, mixed layer depth, and sea level. Time series and results are updated from the last works, and the reliability of the existing time series for the detection of climatologies and long-term trends are analyzed. Furthermore, the different sources of uncertainty in the estimation of linear trends are considered in the present work. Besides this review and update of the results obtained from the data collected in the frame of the IEOOS, we conduct a review of the existing monitoring capabilities from other institutions in the Spanish Mediterranean waters and a review of results dealing with climate change in the Spanish Mediterranean obtained by such institutions. In particular, we include a review of the results obtained by SOCIB (Servicio de Observación y Predicción Costero de las Islas Baleares; Balearic Islands costal observing and forecasting system) in relation to the study of marine heat waves and the warming of the sea surface, and the results corresponding to the intense warming of the Catalan continental shelf at L’Estartit oceanographic station. All these results evidence that the surface Spanish Mediterranean waters are warming up at a rate higher than that affecting the global ocean (>2 °C/100 years). This warming and a salinity increase are also observed along the whole water column. Marine heat waves are increasing their intensity, frequency, and duration since 1982, and coastal sea level is increasing at a rate of 2.5 mm/yr. The salinity increase seems to have compensated for the warming, at least at surface and intermediate waters where no significant trends have been detected for the density. This could also be the reason for the lack of significant trends in the evolution of the mixed layer depth. All these results highlight the importance of monitoring the water column and the necessity of maintaining in situ sampling programs, which are essential for the study of changes that are occurring throughout the Spanish Mediterranean waters.

Towards reliable estimates of abundance trends using automated non‐lethal moth traps

Abstract Monitoring insect abundance or species richness at high spatial and temporal resolution is difficult due to personnel, maintenance, and post‐processing costs as well as ethical considerations. Non‐invasive automated insect monitoring systems could provide a solution to address these constraints. However, every new insect monitoring design needs to be evaluated with respect to reliability and bias based on comparisons with conventional methods. In this study, we evaluate the effectiveness of an automated moth trap (AMT), built from off‐the‐shelf‐hardware, in capturing declines in moth abundance, by comparing it to a conventional, lethal trap. Both trap types were operated five times on 16 plots from the beginning of July 2021 to the end of August 2021. On average AMTs recorded fewer individuals than conventional traps. However, both trap types depicted the same seasonal decline of approximately 3% per day, which corresponded to a total difference of ~85% over the sampling period. Given our sample size, both trap types had the same limitations in their reliability to detect smaller changes in abundance trends. This first proof of concept demonstrated that AMTs depict large magnitude events such as phenological patterns just as well as conventional, lethal traps. Therefore, AMTs are a promising tool for future autonomous and non‐lethal monitoring, which paves the way for high temporal coverage and resolution in insect monitoring. However, this initial quantitative field test revealed that its long‐term applicability must be preceded by several adjustments to the image quality, power supply and to data transfer.

Travel for medical or dental care by race/ethnicity and rurality in the U.S.: Findings from the 2001, 2009 and 2017 National Household Travel Surveys

The travel burden for medical or dental care is a well-documented barrier to healthcare access, particularly in rural areas. There is limited research providing national estimates of the travel trends for medical/dental care, particularly among racial/ethnic groups, and among rural and urban populations. We analyzed data from the 2001, 2009, and 2017 National Household Travel Surveys. Main outcomes were the average travel distance (in miles), average travel time (in minutes), and travel burden, characterized as the percentage of trips lasting ≥ 30 miles or minutes for medical/dental care. We used ordinary least squares and multivariable logistic regressions to examine trends in the travel time/distance and travel burden, controlling for socio-demographic and travel dynamics. Among rural residents, the average travel distance for medical/dental care increased by 17.8% between 2001 and 2017, while no increase was observed among urban residents. Thirty-six percent of trips among rural residents lasted ≥ 30 minutes in 2001 but increased to 47.4% in 2017. Logistic regression estimates show that though Blacks experienced higher odds of a travel time burden compared to Whites, the burden lessened over time. In 2017, urban Blacks (OR = 0.41, 95% C.I. = 0.26,0.66), and rural Blacks (OR = 0.16, 95% C.I. = 0.05,0.55) were less likely to spend ≥ 30 minutes traveling for medical/dental care compared to Whites, using the year 2001 as the baseline. The travel distance and time for medical/dental care have increased in rural areas. However, the travel burden among rural and urban Black residents has decreased. Continuing to alleviate excess burdens of transportation may be beneficial.

Large uncertainty in observed estimates of tropical width from the meridional stream function

Abstract. Recent Hadley cell expansion rate estimates vary substantially, as a multitude of methods and reanalysis datasets yield conflicting results. Among the many methods of estimating the Hadley cell width, the meridional-stream-function 500 hPa zero crossing is the most widely used, as it is directly related to the poleward edge of the Hadley cell (HC). Other common metrics use atmospheric phenomena associated with the HC as a proxy, for instance the zonal-surface-wind zero crossing. As each of these metrics requires different reanalysis data, each with varying error, the level of data-driven uncertainty differs between each metric. While previous work has analyzed the statistical and dynamical relationships between metrics, to date no study has quantified and compared the uncertainty due to reanalysis data error in different HC metrics. In this study, we use ERA5 ensemble members, which include small perturbations in atmospheric variables based on the data error, to quantify the uncertainty associated with six commonly used HC metrics as well as the range of their trend estimates. In the Northern Hemisphere, the tropical expansion rate calculated by the stream function is roughly 0.05∘ per decade, while the Southern Hemisphere rate is 0.2∘ per decade over the period from 1979–2022. Of the six metrics, only the meridional stream function and precipitation minus evaporation have substantial uncertainties. The stream function errors are large due to uncertainty in the underlying meridional-wind data and the presence of large regions of near-neutral circulation at the poleward edge of the tropics. These errors have decreased in recent decades because of improvements in the assimilated observations. Despite these improvements, metrics which use well-observed and constrained quantities such as the zonal-surface-wind zero crossing have lower uncertainty, particularly in summer and fall in the Northern Hemisphere.

Trends of inorganic sulfur and nitrogen species at an urban site in western Canada (2004–2018)

A 15-year (2004–2018) record of measurement data of gaseous and particulate sulfur and nitrogen pollutants in air collected at an urban site in Burnaby in western Canada was analyzed for generating their decadal trends using three different methods, including linear regression, Mann-Kendall test and Theil-Sen trend estimator (MK-TS), and ensemble empirical mode decomposition (EEMD). Annual mean concentration of SO2 and SO42− decreased by about 59% and 42%, respectively, during the 15-year period. The slower decreases of SO42− than SO2 were mainly caused by the increased O3 concentration and temperature in spring and summer, which promoted conversion of SO2 to SO42− through gas-phase reaction, and by the increased aerosol pH value and availability of H2O2 in winter, which enhanced aqueous-phase SO42− formation. Accordingly, the sulfur oxidation ratio (SOR) increased by 23% or more in spring, summer, and winter during the 15-year period. Annual mean concentrations of NO2 and NO3− declined by 36% and 38%, respectively, during this period. On seasonal basis, NO3− decreased faster than NO2 in autumn and slower in winter. The non-linear responses of NO3− to NO2 concentration decreases were more evident in winter than the other seasons, partly due to the increased particulate NO3− fraction caused by decreased temperature, increased aerosol pH value, and enhanced NO3− formation caused by increased O3 concentrations. Annual mean concentration of NH3 showed small increases due to stable NH3 emission and reduced conversion of NH3 to NH4+. NH4+ concentration decreased by 51% during the 15-year period. These results suggest that reduced oxidants levels are likely responsible for weakened formation of secondary inorganic aerosols, besides emission reductions for SO2 and NO2.

Multi-parameter dynamical diagnostics for upper tropospheric and lower stratospheric studies

Abstract. Ozone trend estimates have shown large uncertainties in the upper troposphere–lower stratosphere (UTLS) region despite multi-decadal observations available from ground-based, balloon, aircraft, and satellite platforms. These uncertainties arise from large natural variability driven by dynamics (reflected in tropopause and jet variations) as well as the strength in constituent transport and mixing. Additionally, despite all the community efforts there is still a lack of representative high-quality global UTLS measurements to capture this variability. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Observed Composition Trends and Variability in the UTLS (OCTAV-UTLS) activity aims to reduce uncertainties in UTLS composition trend estimates by accounting for this dynamically induced variability. In this paper, we describe the production of dynamical diagnostics using meteorological information from reanalysis fields that facilitate mapping observations from several platforms into numerous geophysically based coordinates (including tropopause and upper tropospheric jet relative coordinates). Suitable coordinates should increase the homogeneity of the air masses analyzed together, thus reducing the uncertainty caused by spatiotemporal sampling biases in the quantification of UTLS composition trends. This approach thus provides a framework for comparing measurements with diverse sampling patterns and leverages the meteorological context to derive maximum information on UTLS composition and trends and its relationships to dynamical variability. The dynamical diagnostics presented here are the first comprehensive set describing the meteorological context for multi-decadal observations by ozonesondes, lidar, aircraft, and satellite measurements in order to study the impact of dynamical processes on observed UTLS trends by different sensors on different platforms. Examples using these diagnostics to map multi-platform datasets into different geophysically based coordinate systems are provided. The diagnostics presented can also be applied to analysis of greenhouse gases other than ozone that are relevant to surface climate and UTLS chemistry.

Increased fishery-independent sampling effort results in improved population estimates for multiple target species

The power of fishery-independent surveys for stock assessments and management decisions is in their consistency over time and space. Although the preference is to limit change to survey execution, such changes may be necessary. In multi-species surveys, changes that improve metrics for one species may be a detriment to survey performance for others. In 2010, the Southeast Reef Fish Survey (SERFS) was formed to better address sampling needs off the U.S. Southeast Atlantic coast by intensifying a historical chevron trap survey (MARMAP), especially at the northern and southern extent of the sampling range. We used several performance metrics (encounter rate, annual coefficient of variability, standard error, and relative abundance index values) to determine the impact of this change in survey coverage on trend estimates for three commonly encountered species with varying centers of distribution in the survey region. Gray Triggerfish (Balistes capriscus) is found throughout the range of both surveys (i.e. centrally-distributed), while White Grunt (Haemulon plumierii) and Red Snapper (Lutjanus campechanus) are centered near the northern and southern extent of the sampling range, respectively. For Gray Triggerfish, the survey intensification had no effect on encounter rate, but reduced the coefficient of variation and indicated that the historical index of relative abundance may have been overestimated. For White Grunt, the survey intensification slightly improved CV but did not affect the index of relative abundance value or encounter rate. For Red Snapper, SERFS increased encounter rates, reduced CV overall, and detected a population increase 5 years earlier than MARMAP. Overall, the intensification of the survey improved at least one performance metric for each species and showed few deleterious effects on performance, suggesting that intensification of the survey was a net-positive for the accurate estimation of population trends in several species of interest.

A decomposition of the effects of the COVID-19 pandemic on changes in the motor vehicle collision related mortality in Alabama

Background/ObjectiveMotor vehicle collisions are the leading cause of unintentional injury death in Alabama and at various points during the COVID-19 pandemic there were documented increases in the following risk driving behaviors: speeding, driving under the influence, and seat belt citations. Thus, the objective was to characterize the overall motor vehicle collision (MVC)-related mortality rate in Alabama and the contribution of each component over the first two years of the pandemic compared to before the pandemic by three different road classes: urban arterials, rural arterials, and all other road classes. MethodsMVC data were derived from the Alabama eCrash database, an electronic crash reporting system used by police officers across the state. Data on vehicle miles traveled each year were collected from the U.S. Department of Transportation’s Federal Highway Administration estimates of traffic volume trends. MVC-related mortality in Alabama was the primary outcome and year of MVC was the exposure. The novel decomposition method broke down population mortality rate into four parts: deaths per MVC injury, injury per MVC, MVC per vehicle miles traveled (VMT), and VMT per population. Poisson models with scaled deviance were used to estimate rate ratios of each component. Relative contribution (RC) of each component was calculated by taking the absolute value of the component’s beta coefficient and dividing by the sum of the absolute values of all components' beta coefficients. Models were stratified by road class. ResultsAcross all road classes combined, there were no significant changes to the overall MVC-related mortality rate (per population) and its components when comparing 2020–2022 to 2017–2019; this was due to the increased case fatality rate (CFR) being offset by decreases in the VMT rate and MVC injury rate. In 2020, among rural arterials a non-significant increased mortality rate was offset by a decreased VMT rate (RR 0.91, 95% CI 0.84–0.98, RC 19.2%) and MVC injury rate (RR: 0.89, 95% CI: 0.82–0.97, RC: 22.2%) when compared to 2017–2019. For non-arterials, a non-significant decreased MVC mortality rate was observed in 2020 when compared to 2017–2019 (RR 0.86, 95% CI 0.71–1.03). When considering 2021–2022 versus 2020, the only significant component for any road class was a decreased MVC injury rate for non-arterials (RR: 0.90,95% CI: 0.89–0.93) but this was offset by an increased MVC rate and CFR, resulting in no significant change to the mortality rate (per population). ConclusionsIn a state with one of the highest MVC-related mortality rates in the country, despite decreases in VMTs per population and injuries per MVC, the MVC mortality rate per population did not change during the pandemic due in part to the contributions of an increase in the case fatality rate. Future research should determine whether the increase in CFR was associated with risky driving behaviors during the pandemic.

The epidemiological patterns of non-Hodgkin lymphoma: global estimates of disease burden, risk factors, and temporal trends.

The incidence of non-Hodgkin's lymphoma (NHL) has increased steadily over the past few decades. Elucidating its global burden will facilitate more effective disease management and improve patient outcomes. We explored the disease burden, risk factors, and trends in incidence and mortality in NHL globally. The up-to-date data on age-standardized incidence and mortality rates of NHL were retrieved from the GLOBOCAN 2020, CI5 volumes I-XI, WHO mortality database, and Global Burden of Disease (GBD) 2019, focusing on geographic disparities worldwide. We reported incidence and mortality by sex and age, along with corresponding age-standardized rates (ASRs), the average annual percentage change (AAPC), and future burden estimates to 2040. In 2020, there were an estimated 545,000 new cases and 260,000 deaths of NHL globally. In addition, NHL resulted in 8,650,352 age-standardized DALYs in 2019 worldwide. The age-specific incidence rates varied drastically across world areas, at least 10-fold in both sexes, with the most pronounced increase trend found in Australia and New Zealand. By contrast, North African countries faced a more significant mortality burden (ASR, 3.7 per 100,000) than highly developed countries. In the past decades, the pace of increase in incidence and mortality accelerated, with the highest AAPC of 4.9 (95%CI: 3.6-6.2) and 6.8 (95%CI: 4.3-9.2) in the elderly population, respectively. Considering risk factors, obesity was positively correlated with age-standardized incidence rates (P< 0.001). And North America was the high-risk region for DALYs due to the high body mass index in 2019. Regarding demographic change, NHL incident cases are projected to rise to approximately 778,000 by 2040. In this pooled analysis, we provided evidence for the growing incidence trends in NHL, particularly among women, older adults, obese populations, and HIV-infected people. And the marked increase in the older population is still a public health issue that requires more attention. Future efforts should be directed at cultivating health awareness and formulating effective and locally tailored cancer prevention strategies, especially in most developing countries.

Nonparametric Estimation of Trend for Stochastic Processes Driven by G-Brownian Motion with Small Noise

Nonparametric Estimation of Trend for Stochastic Processes Driven by G-Brownian Motion with Small Noise

Trends in Northern Hemispheric Snow Presence

Abstract This paper develops a mathematical model and statistical methods to quantify trends in presence/absence observations of snow cover (not depths) and applies these in an analysis of Northern Hemispheric observations extracted from satellite flyovers during 1967–2021. A two-state Markov chain model with periodic dynamics is introduced to analyze changes in the data in a cell by cell fashion. Trends, converted to the number of weeks of snow cover lost/gained per century, are estimated for each study cell. Uncertainty margins for these trends are developed from the model and used to assess the significance of the trend estimates. Cells with questionable data quality are explicitly identified. Among trustworthy cells, snow presence is seen to be declining in almost twice as many cells as it is advancing. While Arctic and southern latitude snow presence is found to be rapidly receding, other locations, such as eastern Canada, are experiencing advancing snow cover. Significance Statement This project quantifies how the Northern Hemisphere’s snow cover has recently changed. Snow cover plays a critical role in the global energy balance due to its high albedo and insulating characteristics and is therefore a prominent indicator of climate change. On a regional scale, the spatial consistency of snow cover influences surface temperatures via variations in absorbed solar radiation, while continental-scale snow cover acts to maintain thermal stability in the Arctic and subarctic regions, leading to spatial and temporal impacts on global circulation patterns. Changing snow presence in Arctic regions could influence large-scale releases of carbon and methane gas. Given the importance of snow cover, understanding its trends enhances our understanding of climate change.

Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement

AbstractWater is vital to human life and healthy ecosystems. Here we outline the current state of national‐scale water resources trend assessments, identify key gaps, and suggest advancements to better address critical issues related to changes in water resources that may threaten human development or the environment. Questions like, “Do we have less suitable drinking water now than we had 20 years ago?” or “Are flood events more common now than they were in the past?” prompted improvements in data, trend estimation methods, and modeling frameworks to track changes in, and better understand how land use and climate influence four water resources domains: surface and groundwater quantity and quality. However, continued advancement in trend assessments to better address issues related to changes in water availability is needed. Areas of need include more timely and efficient delivery of water resources trend results and improved capacity to estimate trends at unmonitored locations. Additional integration pieces include increased understanding of groundwater–surface water interactions, incorporation of both quantity and quality trends into water availability estimates, and the refinement of trend metrics to account for the competing needs of society and ecological integrity. Coupled with improved driver attribution studies, these components will better inform current and future water resources management.

Assigning trend-based conservation status despite high uncertainty.

Estimates of temporal trends in species' occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision-making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.

Interpreting cancer incidence trends: challenges due to the COVID-19 pandemic.

The considerable deficit in cancer diagnoses in 2020 due to COVID-19 pandemic disruptions in health care can pose challenges in the estimation and interpretation of long-term cancer trends. Using Surveillance, Epidemiology, and End Results (SEER) (2000-2020) data, we demonstrate that inclusion of the 2020 incidence rates in joinpoint models to estimate trends can result in a poorer fit to the data and less accurate or less precise trend estimates, providing challenges in the interpretation of the estimates as a cancer control measure. To measure the decline in 2020 relative to 2019 cancer incidence rates, we used the percent change of rates in 2020 compared with 2019. Overall, SEER cancer incidence rates dropped approximately 10% in 2020, but for thyroid cancer the decrease was as large as 18% after adjusting for reporting delay. The 2020 SEER incidence data are available in all SEER released products, except for joinpoint estimates of trends and lifetime risk of developing cancer.

Paleo-temperature inferred from brGDGTs over the past 18 cal ka BP from Lake Barrine, tropical NE Australia

Terrestrial temperature exerts a major influence on vegetation distribution, the hydrologic cycle and many biogeochemical cycles. Yet little research into terrestrial paleo-temperature reconstruction has been conducted covering the last deglaciation in northern Australia, and recent global surface temperature reconstructions include no data from this area. Here, for the first time, we reconstructed the mean annual air temperatures (MAAT) at Lake Barrine in this region from 18.3 to 1.7 cal ka BP at a relatively high resolution (averaged 400 yr). This temperature reconstruction was based on separated 5- and 6-methyl brGDGTs (branched glycerol dialkyl glycerol tetraethers) that are increasingly used to reconstruct terrestrial temperature because of their ubiquity and a preservation window of at least tens of millions of years. A 7.2 m master core from the center of the lake was extracted, along with five surface lake sediment and seven catchment soil samples. The results indicated that the fractional abundances of all the brGDGTs in the catchment soils, surface and downcore sediments in Lake Barrine were closely correlated, with dominant tetramethylated brGDGTs, especially Ia. We applied a global calibration to reconstruct the temperature using the lake sediment samples and a binary mixing model to correct temperature bias caused by soil-derived brGDGT input. The corrected temperature trend estimate compares favorably with the temperature anomaly recorded in the EPICA ice core record, except during the Younger Dryas. The corrected MAAT estimates at Lake Barrine indicated an increase across Heinrich Stadial 1 (HS 1; 18–14.9 cal ka BP) from 19.4 °C to 21.9 °C, a stable temperature during the Antarctic Cold Reversal (ACR; 14.7–13 cal ka BP) of ∼21.6 °C, followed by a slight decrease during the Younger Dryas (YD; 12.8–11.6 cal ka BP). After a further 1.6 °C increase into the early Holocene, the temperature decreased by 1.8 °C through the Holocene until a recent rise by 1.6 °C after 1.7 cal ka BP.

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.

Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine

Open pit coal mining affects surrounding populated areas, resulting in terrain surface deformation. Surface deformation should be monitored as often as possible to control deformations and prevent potential incidents. This paper analyzes time series deformation estimated from the Sentinel-1 satellite images using the Persistent Scatterer Interferometry method to monitor subsidence rates caused by open pit mining activities. It is possible to measure deformations using classical geodetic methods, but those are rarely used in practice because they are time-consuming and expensive for application in large areas. Using the open access radar images from the Sentinel-1 mission, 513 images from the repository were downloaded between October 2016 and the end of December 2020. We present the processing steps in detail in order to establish a workflow for the automated processing of vertical displacement estimation using open source tools; a total of 402 images were processed: 215 images belonged to the ascending satellite orbit, 187 images belonged to the descending orbit, and 111 images were rejected because of adverse weather conditions. The PS InSAR technique has never been used for the mines of the Republic of Serbia or for land surveying practices related to deformation monitoring. The results based on the Sentinel-1 images were compared with results from geodetic leveling and with neotectonic uplift trends. The trend lines of vertical displacement obtained from PS and corresponding leveling are significantly similar (a Pearson correlation of 85% with a p-value of 0.015). The final evaluation reported results of vertical displacements at the leveling benchmark of −3.4 mm/year with the PS InSAR method and −2.7 mm/year with the leveling method. A comparison of the PS vertical displacements with a settlement model fits reasonably, suggesting that the measurements are valid. As four years of PS time series data is insufficient to establish undisputable conclusions on the neotectonics uplift, extending the time series (covering at least a decade) implies that this approach will become attractive in future neotectonic uplift trend estimations. This study illustrates not only the ability of Sentinel-1 data in mapping vertical deformations, but the obtained results could also be used for geohazard monitoring and land monitoring in general for the area of interest.