Sampling Resolution Research Articles

Source term estimation in the unsteady flow with dynamic mode decomposition

When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE.

Realistic photon-number resolution in Gaussian boson sampling

Gaussian boson sampling (GBS) is a model of nonuniversal quantum computation that claims to demonstrate quantum supremacy with current technologies. This model entails sampling photocounting events from a multimode Gaussian state at the outputs of a linear interferometer. In this scheme, collision events—those with more than one photon for each mode—are infrequent. However, they are still used for validation purposes. Therefore, the limitation of realistic detectors to perfectly resolve adjacent photon numbers becomes pivotal. We derive a photocounting probability distribution in GBS schemes which is applicable for use with general detectors and photocounting techniques. This probability distribution is expressed in terms of functionals of the field-quadrature covariance matrix, e.g., Hafnian and Torontonian in the well-known special cases of photon-number-resolving and on-off detectors, respectively. Based on our results, we consider a GBS validation technique involving detectors with realistic photon-number resolution. Published by the American Physical Society 2024

No universal mathematical model for thermal performance curves across traits and taxonomic groups

In ectotherms, the performance of physiological, ecological and life-history traits universally increases with temperature to a maximum before decreasing again. Identifying the most appropriate thermal performance model for a specific trait type has broad applications, from metabolic modelling at the cellular level to forecasting the effects of climate change on population, ecosystem and disease transmission dynamics. To date, numerous mathematical models have been designed, but a thorough comparison among them is lacking. In particular, we do not know if certain models consistently outperform others and how factors such as sampling resolution and trait or organismal identity influence model performance. To fill this knowledge gap, we compile 2,739 thermal performance datasets from diverse traits and taxa, to which we fit a comprehensive set of 83 existing mathematical models. We detect remarkable variation in model performance that is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results reveal a surprising lack of well-defined scenarios in which certain models are more appropriate than others. To aid researchers in selecting the appropriate set of models for any given dataset or research objective, we derive a classification of the 83 models based on the average similarity of their fits.

Effect of environmental DNA sampling resolution in detecting nearshore fish biodiversity compared to capture surveys.

Sampling and sequencing marine environmental DNA (eDNA) provides a tool that can increase our ability to monitor biodiversity, but movement and mixing of eDNA after release from organisms before collection could affect our inference of species distributions. To assess how conditions at differing spatial scales influence the inferred species richness and compositional turnover, we conducted a paired eDNA metabarcoding and capture (beach seining) survey of fishes on the coast of British Columbia. We found more taxa were typically detected using eDNA compared to beach seining. eDNA identified more taxa with alternative habitat preferences, and this richness difference was greater in areas of high seawater movement, suggesting eDNA has a larger spatial grain influenced by water motion. By contrast, we found that eDNA consistently missed low biomass species present in seining surveys. Spatial turnover of communities surveyed using beach seining differed from that of the eDNA and was better explained by factors that vary at small (10-1000s meters) spatial scales. Specifically, vegetation cover and shoreline exposure explained most species turnover from seining, while eDNA turnover was not explained by those factors and showed a distance decay pattern (a change from 10% to 25% similarity from 2 km to 10 km of distance), suggesting unmeasured environmental variation at larger scales drives its turnover. Our findings indicate that the eDNA sample grain is larger than that of capture surveys. Whereas seining can detect differences in fish distributions at scales of 10s-100s of meters, eDNA can best summarize fish biodiversity at larger scales possibly more relevant to regional biodiversity assessments.

Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Localization of albumin with correlative super resolution light- and electron microscopy in the kidney

The functioning of vertebrate life relies on renal filtration of surplus fluid and elimination of low-molecular-weight waste products, while keeping serum proteins in the blood. In disease, however, there is leak of serum proteins and tracing them to identify the leaking position within tissue with a nanometer resolution poses a significant challenge. Correlative microscopy integrates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Using chemical tagging of albumin with synthetic fluorophores we achieve protein-specific labeling that preserve their post-embedding fluorescence after high-pressure freezing and freeze-substitution of murine kidney tissue. Using advanced registration techniques for super-resolution correlative light and electron microscopy, we can localize the labeled albumin with a high precision in the x-y plane of electron micrographs and cartograph its distribution. Thereby we can quantify the albumin concentration and measure a linear reduction gradient across the kidney filtration barrier. Our study shows the feasibility of combining different microscopy contrasts for tracing fluorescently labeled protein markers with super resolution in various tissue samples and opens new perspectives for correlative imaging in volume electron microscopy.

Obtaining High‐Resolution Magnetic Records From Speleothems Using Magnetic Microscopy

AbstractSpeleothems are mineral deposits capable of recording detrital and/or chemical remanent magnetization at annual timescales. They can offer high‐resolution paleomagnetic records of short‐term variations in Earth's magnetic field, crucial for understanding the evolution of the dynamo. Owing to limitations on the magnetic moment sensitivity of commercial cryogenic rock magnetometers (∼10−11 Am2), paleomagnetic studies of speleothems have been limited to samples with volumes of several hundreds of mm3, averaging tens to hundreds of years of magnetic variation. Nonetheless, smaller samples (∼1–10 mm3) can be measured using superconducting quantum interference device (SQUID) microscopy, with a sensitivity better than ∼10−15 Am2. To determine the application of SQUID microscopy for obtaining robust high‐resolution records from small‐volume speleothem samples, we analyzed three different stalagmites collected from Lapa dos Morcegos Cave (Portugal), Pau d'Alho Cave (Brazil), and Crevice Cave (United States). These stalagmites are representative of a range of magnetic properties and have been previously studied with conventional rock magnetometers. We show that by using SQUID microscopy we can achieve a five‐fold improvement in temporal resolution for samples with higher abundances of magnetic carriers (e.g., Pau d'Alho Cave and Lapa dos Morcegos Cave). In contrast, speleothems with low abundances of magnetic carriers (e.g., Crevice Cave) do not benefit from higher resolution analysis and are best analyzed using conventional rock magnetometers. Overall, by targeting speleothem samples with high concentrations of magnetic carriers we can increase the temporal resolution of magnetic records, setting the stage for resolving geomagnetic variations at short time scales.

Exploring the utility of complementary separations in liquid chromatography

An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in-silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ~ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 lags of observation. However, we found that diel patterns in fish detection strongly affected the abundance estimates due to diel vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs even further. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Sensitive response of erosion and weathering to the Indian Summer Monsoon changes in South Asia during Dansgaard-Oeschger oscillations

Continental weathering plays a key role in regulating the long-term climate stability via negative feedback. However, whether and how erosion and weathering respond to rapid climate change (e.g millennial scale) in large river basins remains unclear, partly due to the lack of archives with robust age models and high sampling resolution. As one of the largest sediment source-to-sink systems, the Himalaya-Ganga/Brahmaputra River-Bay of Bengal system is considered as an ideal laboratory for examining the weathering-climate relationships over the past. Here we present the elemental and mineralogical data from well age-constrained core sediments in the Bay of Bengal, which document the erosion and weathering conditions in South Asia during the last glacial period. All proxies generally show larger values during glacial interstadials than the stadials, consistent with the millennial variabilities of regional sea surface temperature (SST) and the Indian Summer Monsoon (ISM) strength. We confirm that the erosion and chemical weathering signals in Ganga/Brahmaputra River basins, which sensitively respond to the ISM strength and/or temperature change, could be propagated through the large river systems and faithfully preserved in the South Asian marginal seas, without noticeable time lag on a millennial scale. Our findings provide valuable data for response of erosion and weathering dynamics in a large river basin to rapid climate changes, and highlight the immense potential of monsoon-driven continental silicate weathering in floodplains as a substantial short-term carbon sink, thus underscoring a pivotal natural carbon sequestration mechanism amidst the escalating threat of global warming.

Demonstration and optimization of coherent Doppler wind LiDAR with low sampling resolution

A low sampling resolution scheme for coherent Doppler wind LiDAR (CDWL) is proposed. The CDWL offers advantages in precision and detection resolution but suffers from the requirement of high-speed data acquisition (DAQ) with high sampling resolution, such as 12- or 14-bit, which leads to an increase of the computational complexity and the system cost. The use of a DAQ system with lower sampling resolution can provide a solution to mitigate this problem. The feasibility of the proposed scheme is validated by simulations and experiments. The detection performance can be greatly affected by the quantization interval selected during sampling. It is shown that the optimal quantization interval exists and only depends on the carrier-to-noise ratio (CNR), and the optimal quantization intervals of several sampling resolutions are given at different CNRs. With the given optimal quantization configuration, the low sampling resolution data can be used for reliable wind field measurements. For long-distance detection with a CNR lower than −13dB, the CNR deterioration of 1-bit, 2-bit, 3-bit, and 4-bit signals can be as low as 2, 0.5, 0.2, and 0.1 dB.

Solving East Nile Delta's imaging challenges through new ocean-bottom-node acquisition and advanced imaging

The East Nile Delta, located offshore Egypt, is a challenging area for seismic imaging due to the presence of substantial velocity heterogeneity created by the complex Messinian unconformity and overlaying fault structures. Existing towed-streamer (TS) seismic data have been pushed to their technical limit but remain insufficient for adequate representation of the subsurface. Previous studies have shown that a step change in imaging is possible if advanced acquisition, processing, and velocity model building techniques are employed. Atoll Field is a candidate to benefit from such improvements in seismic data. Results show that ocean-bottom-node acquisition provides improved spatial sampling and higher resolution. Acquiring seismic data with offsets of more than 30 km facilitated the generation of a robust high-frequency velocity model that better captures subsurface complexity. This paper outlines the acquisition and processing, with a focus on the velocity model building process. The generated subsurface model is effectively used for imaging with conventional migration algorithms and full-waveform-inversion-derived reflectivity. Results show a significant step change from previous TS images at the Oligocene target, with channel features clearly imaged. This provides an improved understanding of the depositional systems and reduced depth uncertainty.

High frequency monitoring of carbon (iv) oxide evolution from a tropical soil amended with organomineral fertilizer in a screenhouse Ile-Ife, Nigeria

The study compared responses of carbon (iv) oxide evolved from an Ultisol sampled at different time scales and the soil properties when amended with organomineral fertilizer (OMF); thus tracking the carbon footprint of the amendment in ambient air. The carbon (iv) oxide efflux was measured titrimetrically in a static chamber set up in a screenhouse at sub-daily, daily and weekly time scales. At the same time, the antecedent soil properties were determined using standard methods. The treatments involved OMF addition at three rates (0 kg OMF, 40 kg Urea-N + 2.5 tons/ha organic fertilizer and 40 kg Urea-N + 5 tons/ha organic fertilizer) based on some Nigerian indigenous vegetable nutrient requirements. The result showed that a more detailed and accurate understanding of CO2 evolution processes was revealed at daily sampling resolution while sub-daily variation occurred in response to sub-daily variation in ambient temperature. Organomineral fertilizer at 40 kg/ha Urea + 2.5 tons/ha OF rate was comparatively safer in C management through the reduction in the CO2 released thus constituting a better alternative in terms of the greenhouse effect. Thus, the study recommends at least daily monitoring for a detailed understanding of CO2 evolution dynamics and management and identifies OMF rate with less CO2 release as a comparatively better alternative in the production of those vegetables in the environmental context.

A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records

Tridacna shells have become valuable archives for high-resolution paleoclimate reconstructions due to their daily-to-annual bands. The δ18O, influenced by temperature and salinity, serves as a key geochemical proxy for Tridacna. Traditionally, linear interpolation has been the go-to method for creating evenly spaced Tridacna δ18O timeseries. However, the accuracy of this method can be influenced by the prominent negative shell growth trends. Given that Tridacna shells exhibit significantly faster growth rates during the juvenile stage than in adulthood, constant sampling intervals may lead to overly dense sampling during the juvenile phase, which could potentially introduce errors in the linearly interpolated Tridacna δ18O record. In this study, we proposed a time window averaging method to mitigate the influence of growth trends in Tridacna δ18O records from the northern South China Sea (SCS). The time window averaging method calculates the average of measured Tridacna δ18O data within a time window, whose length is determined by the sampling resolution for each year and the expected temporal resolution. Pseudo-Tridacna experiments, using the newly developed proxy system model for Tridacna δ18O, were conducted to assess the accuracy of both interpolation and averaging methods. Results indicate that the averaging method outperforms the interpolation method, especially in seasonality estimation. The pseudo-Tridacna experiments recommend employing the averaging method for monthly Tridacna δ18O records when sampling density exceeds ∼20 samples/year. Two real Tridacna δ18O records from the northern SCS demonstrate consistency with pseudo-Tridacna experiments, supporting the efficacy of the averaging method in reducing bias in Tridacna δ18O records associated with growth trends. Our results show the potential of the time window averaging method to improve Tridacna-based paleoclimate reconstructions.

Printing resolution effect on mechanical properties of porous boehmite direct ink 3D printed structures

Direct ink writing (DIW) is one of the facile and versatile manufacturing techniques for additive manufacturing of ceramics. 3D printed porous boehmite structures fabricated via DIW can offer significant benefits in terms of their high specific surface area and robust mechanical properties, rendering them suitable for applications like catalysis, adsorption, etc. The characteristics of the boehmite structures produced through 3D printing are significantly influenced by several parameters associated with the DIW process, including but not limited to solids concentration in the ink, resolution, and drying time. In this work, for the first time, we try to optimize the DIW parameters for 3D printing of porous boehmite structures to achieve better shape stability, resolution, and mechanical properties. A shear-thinning boehmite ink was prepared by peptization of boehmite powder in nitric acid and the rheological properties were optimized by varying concentrations of boehmite and nitric acid. Boehmite ink with a solid loading of 37 % and a pH of 4 was found to have the optimal rheological properties and printability. A Menger sponge porous structure was printed by DIW using the optimized ink with different sample sizes and resolutions. The resolution and size were optimized for better shape accuracy of printing and structural integrity. Crack-free boehmite structures were obtained by optimizing the drying time. Boehmite structures printed with higher resolution resulted in enhanced compressive strength and energy absorption, i.e., compressive strength and energy absorption of 0.5 mm resolution boehmite structure is enhanced by 208 % and 144 %, respectively, compared to the 1 mm resolution structure.

Computational optical sectioning via near-field multi-slice ptychography.

We introduce a method for the computational sectioning of optically thick samples based on a combination of near-field and multi-slice ptychography. The method enables a large field-of-view 3D phase imaging of samples that is an order of magnitude thicker than the depth of field of bright-field microscopy. An axial resolution for these thick samples is maintained in the presence of multiple scattering, revealing a complex structure beyond the depth of the field limit. In this Letter, we describe the new, to the best of our knowledge, approach and demonstrate its effectiveness using a range of samples with diverse thicknesses and optical properties.

Sensitivity of model-based leakage localisation in water distribution networks to water demand sampling rates and spatio-temporal data gaps

ABSTRACT Model-based leakage localisation in water distribution networks requires accurate estimates of nodal demands to correctly simulate hydraulic conditions. While digital water meters installed at household premises can be used to provide high-resolution information on water demands, questions arise regarding the necessary temporal resolution of water demand data for effective leak localisation. In addition, how do temporal and spatial data gaps affect leak localisation performance? To address these research gaps, a real-world water distribution network is first extended with the stochastic water end-use model PySIMDEUM. Then, more than 700 scenarios for leak localisation assessment characterised by different water demand sampling resolutions, data gap rates, leak size, time of day for analysis, and data imputation methods are investigated. Numerical results indicate that during periods with high/peak demand, a fine temporal resolution (e.g., 15 min or lower) is required for the successful localisation of leakages. However, regardless of the sampling frequency, leak localisation with a sensitivity analysis achieves a good performance during periods with low water demand (localisation success is on average 95%). Moreover, improvements in leakage localisation might occur depending on the data imputation method selected for data gap management, as they can mitigate random/sudden temporal and spatial fluctuations of water demands.

Spatiotemporal heterogeneity of lake carbon dioxide flux leads to substantial uncertainties in regional upscaling estimates

Limited field samplings result in significant uncertainties in regional and global estimates of lake carbon dioxide (CO2) emissions. However, quantitative analysis of uncertainty in regional lake CO2 emission estimates remains unclear. In this study, we utilized satellite data to estimate carbon dioxide flux from 113 eastern China lakes, revealing substantial spatial and temporal variations in flux, averaging 18.07 ± 81.83 mg m−2 d−1. Additionally, satellite-estimated total CO2 effluxes indicated previous upscaling studies had overestimated total CO2 effluxes from these studied lakes by approximately 3–11 times, primarily due to substantial variations in lake CO2 fluxes. Insufficient sampling resolution resulted in considerable uncertainty in upscaling estimations. Temporal variations in carbon dioxide contributed greater upscaling uncertainties than spatial variations in carbon dioxide. To capture the dynamics of lake CO2, increasing the number of sampling points and events is necessary as lake size decreases and trophic state increases. Finally, we propose a prediction for the optimal sampling resolution based on lake area and trophic state, recommending an average of 5 points per lake and bi-monthly sampling as the ideal resolution for similar shallow eutrophic lakes. This approach has been validated as effective in lakes across North America and Europe. We believe that future global-scale lake carbon budget estimates would benefit from field observations conducted at more reasonable sampling points and frequency.