Fine Time Resolution Research Articles

Characterization of a radially emitting spray jet in a cylindrical inertial electrostatic confinement plasma source based on particle in cell simulation

This paper investigates the outflow mechanism of a cylindrical inertial electrostatic confinement plasma source using Particle in Cell plasma simulations. Various phenomena such as ion-wall interactions, impact ionization, excitation, and secondary electron generation are considered. Together with a fine time and cell resolution to reveal relevant phenomena. The results indicate a high electron and ion density in front of the cathode at the widest grid opening, which suggests the formation of a cascade-like charge carrier emission, commonly referred to as the “spray jet”. Initially, a virtual anode is formed within the cathode grid and is shifted toward the grid opening as time elapsed. Ions are accelerated in the plasma sheath around the cathode and collide with the cathode grids or oscillate through the grid opening. Electrons are accelerated and ionize the background gas at the plasma sheath edge. While the electron flow is disordered within the cathode it forms a cascade-like quasi-neutral plasma at the outlet of the plasma source. Through the formation of a virtual anode the discharge becomes self-sustained. With this simulation results the working principle of the inertial electrostatic confinement plasma source can be derived.

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

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.

Correction: Wu et al. Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection. Electronics 2022, 11, 1018

In the original publication [...]

RoPod, a customizable toolkit for non-invasive root imaging, reveals cell type-specific dynamics of plant autophagy

Arabidopsis root is a classic model system in plant cell and molecular biology. The sensitivity of plant roots to local environmental perturbation challenges data reproducibility and incentivizes further optimization of imaging and phenotyping tools. Here we present RoPod, an easy-to-use toolkit for low-stress live time-lapse imaging of Arabidopsis roots. RoPod comprises a dedicated protocol for plant cultivation and a customizable 3D-printed vessel with integrated microscopy-grade glass that serves simultaneously as a growth and imaging chamber. RoPod reduces impact of sample handling, preserves live samples for prolonged imaging sessions, and facilitates application of treatments during image acquisition. We describe a protocol for RoPods fabrication and provide illustrative application pipelines for monitoring root hair growth and autophagic activity. Furthermore, we showcase how the use of RoPods advanced our understanding of plant autophagy, a major catabolic pathway and a key player in plant fitness. Specifically, we obtained fine time resolution for autophagy response to commonly used chemical modulators of the pathway and revealed previously overlooked cell type-specific changes in the autophagy response. These results will aid a deeper understanding of the physiological role of autophagy and provide valuable guidelines for choosing sampling time during end-point assays currently employed in plant autophagy research.

Integration of Microfluidic Chip and Probe with a Dual Pump System for Measurement of Single Cells Transient Response.

The integration of liquid exchange and microfluidic chips plays a critical role in the biomedical and biophysical fields as it enables the control of the extracellular environment and allows for the simultaneous stimulation and detection of single cells. In this study, we present a novel approach for measuring the transient response of single cells using a system integrated with a microfluidic chip and a probe with a dual pump. The system was composed of a probe with a dual pump system, a microfluidic chip, optical tweezers, an external manipulator, an external piezo actuator, etc. Particularly, we incorporated the probe with the dual pump to allow for high-speed liquid change, and the localized flow control enabled a low disturbance contact force detection of single cells on the chip. Using this system, we measured the transient response of the cell swelling against the osmotic shock with a very fine time resolution. To demonstrate the concept, we first designed the double-barreled pipette, which was assembled with two piezo pumps to achieve a probe with the dual pump system, allowing for simultaneous liquid injection and suction. The microfluidic chip with on-chip probes was fabricated, and the integrated force sensor was calibrated. Second, we characterized the performance of the probe with the dual pump system, and the effect of the analysis position and area of the liquid exchange time was investigated. In addition, we optimized the applied injection voltage to achieve a complete concentration change, and the average liquid exchange time was achieved at approximately 3.33 ms. Finally, we demonstrated that the force sensor was only subjected to minor disturbances during the liquid exchange. This system was utilized to measure the deformation and the reactive force of Synechocystis sp. strain PCC 6803 in osmotic shock, with an average response time of approximately 16.33 ms. This system reveals the transient response of compressed single cells under millisecond osmotic shock which has the potential to characterize the accurate physiological function of ion channels.

Life-cycle assessment of current and future electricity supply addressing average and marginal hourly demand: An application to Italy

The increase in overall electricity demand in recent years, together with the rapid and significant changes in electricity generation has motivated increased research addressing environmental impacts of current and future electricity generation and supply systems. This article presents a comprehensive life-cycle assessment (LCA) of electricity generation and supply for the Italian current mix and future scenarios, for a wide set of environmental impacts and indicators, addressing intra-year variations, and including average and marginal demand impact perspectives. The generation mix was modelled for a 3-year period (2018–2020) with hourly and country-specific data, for the main generation technologies; and two future scenarios (for 2030) were modelled applying the EPLANopt energy system model. While future scenarios - with larger renewable energy shares - resulted in reduced environmental impacts in most categories, they were associated with burden shifts. In particular, increased shares of solar photovoltaic generation in future scenarios resulted in significant contributions to mineral and metal resource depletion and to land use impacts. The high penetration of renewable energy sources in future scenarios was also associated with important seasonal and hourly variations, demonstrating the importance of addressing intra-year temporal variations. Moreover, complementing an average with a marginal demand perspective provided insight on potential impacts of demand changes. When considering a marginal demand perspective, future scenarios offered no clear benefits in terms of global warming, for example. The research demonstrated the need for comprehensive and detailed environmental impact assessments with fine time resolution, which can assess seasonal and intra-daily variations, and the evaluation of environmental impacts with both average and marginal demand perspectives highlighted the complementary nature of the two in providing insightful results. The approach is fully replicable to other countries and regions - it can improve LCAs of electricity generation and supply and provide robust results to adequately inform decision-making on electricity generation and demand management, toward more sustainable energy systems.

Comparison of an [formula omitted]- and [formula omitted]-adaptive finite element solver for chemo-mechanically coupled battery electrode particles

Numerical investigations of mechanical stresses for phase transforming battery electrode materials on the particle scale are computationally highly demanding. The limitations are mainly induced by the strongly varying spatial and temporal scales of the underlying phase field model, which require an ultra fine mesh and time resolution, however, solely at specific stages in space and time. To overcome these numerical difficulties we present a general-purpose space and time adaptive solution algorithm based on an hp-adaptive finite element method and a variable-step, variable-order time integrator. At the example of a chemo-mechanical electrode particle model we demonstrate the computational savings gained by the hp-adaptivity. In particular, we compare the results to an h-adaptive finite element method and show the reduction of computational complexity.

Coreflood-on-a-Chip Investigation of Oil Bank Formation in Low Interfacial Tension Chemical Floods at Favorable and Unfavorable Viscosity Ratios

Summary An efficient tertiary chemical flood involves the chemicals displacing the oil in a stable manner, so the initially disconnected trapped oil ganglia coalesce into a bank as the flood progresses. However, the location of surfactants and polymers in the core during a chemical flood is difficult or impossible to determine at very fine time and space resolutions. In this work, we bridge this gap by visualizing saturation profiles in a coreflood-on-a-chip, a micromodel that is the same length as a core, and monitoring fluorescent aqueous injectant as displacements occur. We visualize, for the first time, surfactants in and around oil banks at the pore and Darcy scales during low-tension displacements. We present 12 chemical floods in the coreflood-on-a-chip, imaged at the centimeter scale, including six surfactant and six alkali-surfactant-polymer (ASP) floods at low, intermediate, and high initial water saturations. Additionally, we present a micron-scale visualization of an ASP flood under UV light to observe the distribution of surfactant around an oil bank at the pore scale. We found that oil banks formed even during very unfavorable displacements, when surfactant solution (1 cp) displaced oil (80 cp) in a micromodel at intermediate and residual oil saturations. Although our results show saturation profiles are mostly well described by fractional flow theory, the distribution of injected aqueous phase is not. Significant aqueous injectant, including surfactant, flows within and ahead of oil banks.

A Multi-Time-Gated SPAD Array with Integrated Coarse TDCs

Time-gating of single-photon avalanche diodes (SPADs) was commonly used as a method to reduce dark noise in biomedical imaging applications where photon events are correlated with a reference clock. Time-gating was also used to obtain timing information of photon events by shifting the gate windows applied to a SPAD array. However, in this approach, fine timing resolution comes at the cost of a lengthened measurement time due to the large number of counts required for each shift. As a solution, we present a multi-time-gated SPAD array that simultaneously applies shifted gate windows to an array of SPADs, which has the potential to reduce the measurement time compared to a single time gate window. Compared to similar works, this design has fully integrated the multi-gate generation using shared circuitry which also functions as a coarse time-to-digital converter. The proposed array, fabricated in the TSMC 65 nm standard CMOS process, achieved a median dark count rate (DCR) of 37 kHz, 4.37 ns gate widths, 550 ps timing resolution, and a peak photon detection probability (PDP) of 42.9% at 420 nm, all at a 0.8 V excess bias.

A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China

Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. The existing spatial data of CEs have low resolution and cannot distinguish the distribution characteristics of CEs of different emission sectors. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. The spatial distribution of CEs based on this study has higher spatial resolution and more details than that based on traditional methods, and can distinguish the spatial distribution characteristics of different sectors. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Technological progress has promoted the improvement of energy efficiency, and there has been a decoupling between the economic development and CEs in the city was observed during in 2000–2015.

Mechanisms of change in multicomponent group-based treatment for patients suffering from medically unexplained physical symptoms

Objective: Understanding psychological mechanisms of change is essential to advance treatments for patients suffering from medically unexplained physical symptoms (MUPS). This study aimed to test the role of selected change mechanisms (incl. interoceptive awareness, emotional regulation skills, symptom acceptance, relational needs satisfaction, clarification of meaning, working alliance, and group cohesion) in the modification of patients’ somatic symptom intensity and well-being. Method: N = 290 patients suffering from MUPS participated in a multi-component group-based treatment at seven clinical sites. Data were collected weekly. Multi-level modeling was used to test cross-lagged relationships between the hypothesized mechanisms and outcomes in terms of Granger causality (with lags of 1, 2, and 3 weeks). Results: None of the mechanisms predicted a time-lagged change in outcomes in the expected direction. In fact, there was a consistent pattern of negative time-lagged relationships (i.e., an increase in a mechanism predicted worsening of the outcome). Findings consistent with the hypothesized role of the mechanisms were found only in concurrent relationships between mechanisms and outcomes. Conclusion: This study did not support time-lagged relationships under the condition of weekly measurement and many methodological factors remain to be considered (e.g., a finer time resolution).

Accumulatively Increasing Sensitivity of Ultrawide Instantaneous Bandwidth Digital Receiver with Fine Time and Frequency Resolution for Weak Signal Detection

It is always an interesting research topic for digital receiver (DRX) designers to develop a DRX with (1) ultrawide instantaneous bandwidth (IBW), (2) high sensitivity, (3) fine time-of-arrival-measurement resolution (TMR), and (4) fine frequency-measurement resolution (FMR) for weak signal detection. This is because designers always want their receivers to have the widest possible IBW to detect far away and/or weak signals. As the analog-to-digital converter (ADC) rate increasing, the modern DRX IBW increases continuously. To improve the signal detection based on blocking FFT (BFFT) method, this paper introduces the new concept of accumulatively increasing receiver sensitivity (AIRS) for DRX design. In AIRS, a very large number of frequency-bins can be used for a given IBW in the time-to-frequency transform (TTFT), and the DRX sensitivity is cumulatively increased, when more samples are available from high-speed ADC. Unlike traditional FFT-based TTFT, the AIRS can have both fine TMR and fine FMR simultaneously. It also inherits all the merits of the BFFT, which can be implemented in an embedded system. This study shows that AIRS-based DRX is more efficient than normal FFT-based DRX in terms of using time-domain samples. For example, with a probability of false alarm rate of 10−7, for N=220 frequency-bins with TMR = 50 nSec, FMR = 2.4414 KHz, IBW > 1 GHz and ADC rate at 2.56 GHz, AIRS-based DRX detects narrow-band signals at about −42 dB of input signal-to-noise ratio (Input-SNR), and just uses a little less than N/2 real-samples. However, FFT-based DRX have to use all N samples. Simulation results also show that AIRS-based DRX can detect frequency-modulated continuous wave signals with ±0.1, ±1, ±10 and ±100 MHz bandwidths at about −39.4, −35.1, −30.2, and −25.5 dB of Input-SNR using about 264.6 K, 104.7 K, 40.2 K and 18.3 K real-samples, respectively, in 220 frequency-bins for TTFT.

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level.

Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

Peer-to-Peer (P2P) energy trading has gained much attention recently due to the advanced development of distributed energy resources. P2P enables prosumers to trade their surplus electricity and allows consumers to purchase affordable and locally produced renewable energy. Therefore, it is significant to develop solutions that are able to forecast energy consumption and generation toward better power management, thereby making renewable energy more accessible and empowering prosumers to make an informed decision on their energy management. In this paper, several models for forecasting short-term renewable energy consumption and generating are developed and discussed. Real-time energy datasets were collected from smart meters that were installed in residential premises in Western Australia. These datasets are collected from August 2018 to Apr 2019 at fine time resolution down to 5 s and comprise energy import from the grid, energy export to the grid, energy generation from installed rooftop PV, energy consumption in households, and outdoor temperature. Several models for forecasting short-term renewable energy consumption and generating are developed and discussed. The empirical results demonstrate the superiority of the optimised deep learning-based Long Term Short Memory (LSTM) model in forecasting both energy consumption and generation and outperforms the baseline model as well as the alternative classical and machine learning methods by a substantial margin.

Three-Dimensional Numerical Analysis of Longitudinal Thermoacoustic Instability in a Single-Element Rocket Combustor

This study numerically investigated the thermoacoustic combustion instability characteristics of a scaled rocket combustor based on a hybrid of the Reynolds-averaged Navier–Stokes and large–eddy simulation method. The turbulence–combustion interactions were treated using flamelet generated manifold approach. An unstable case was simulated with detailed reaction mechanisms (GRI-Mech 3.0). The obtained results agree well with experiment data from Purdue University, in terms of pressure oscillations frequency and power spectral density spectrum. The combustion instability mode was identified to be coupled with the first longitudinal acoustic mode of the combustion chamber by dynamic model decomposition method. According to Rayleigh index analysis, the unstable driving source was found to be located near the combustor step, which was further confirmed by time-averaged flow fields. Detailed three-dimensional vortex ring shedding evolutions at the combustor step were tracked with fine time resolution. Results indicate that the combustion instability arises from periodic vortex ring shedding at the combustor step and interacting with the chamber wall. The unburnt reactants were rolled up by the shedding vortex ring, which would not break up until impact with the chamber wall. Therefore, the mixing performance was significantly enhanced, leading to sudden heat release. Consequently, the thermal energy is added to the acoustic field, and the first longitudinal mode is thus reinforced, giving rise to large amplitude axial velocity oscillations which prompt the generation of the new vortex ring. The results of the present investigation will support the design and development of high-performance rocket engines.

Data-driven modeling for long-term electricity price forecasting

Estimating the financial viability of renewable energy investments requires the availability of long-term, finely-resolved electricity prices over the investment lifespan. This entails, however, two major challenges: (i) the combination of extensive time horizons and fine time resolutions, and (ii) the prediction of out-of-sample electricity prices in future energy and market scenarios, or shifts in pricing regime, that were not observed in the past. This paper tackles such challenges by proposing a data-driven model for the long-term prediction of electricity market prices that is based on Fourier analysis. The electricity price is decomposed into components leading to its base evolution, which are described through the amplitudes of the main frequencies of the Fourier series, and components leading to high price volatility, which are described by the residual frequencies. The former are predicted via a regression model that uses as input annual values of relevant energy and market quantities, such as electricity generation, prices and demands. The proposed method shows capable of (i) predicting the most relevant dynamics of the electricity price; (ii) generalization by capturing the market mechanisms of previously unseen electricity markets. These findings support the relevance and validity of data-driven, finely-resolved, long-term predictions and highlight the potential for hybrid data-driven and market-based models.

Ultra-Low Power and High Sensitivity of Joint Clock Gating Based Dual Feedback Edge Triggered Flip Flop for Biomedical Imaging Applications

To adapt to varied working situations, the latest biomedical imaging applications require low energy consumption, high performance, and extensive energy-performance scalability. State-of-the-art electronics with higher sensitivity, higher counting rate, and finer time resolution are required to create higher precision, higher temporal resolution, and maximum contrast biomedical images. In recent days, the system’s power consumption is important critically in modern VLSI circuits particularly for the low power application. In order to decrease the power, a power optimization technique must be used at various design levels. The low power use of logic cells is a proficient technique for decreasing the circuit level power. Dual Feedback edge triggered Flip Flop (DFETFF) is considered for biomedical imaging applications in the proposed system. Initially, the high dynamic range voltage is given as input signal. The comparator output is then retried at the comparator end. The integration capacitor is employed for storing remaining voltage signal. The comparator voltage is then given to the capacitor reset block. In the proposed work, a capacitor-reset block that employs clock signal takes up a dual-feedbackedge-triggered Flip-flop as an alternative of a conventional type for reducing the final output signals errors. Dual feedback loops assure that feedback loops do not tri-state at the time of SET restoration, a scheme that could lead to SEUs in latches if a single delay component and a single feedback loop are used. In digital system, Clock gating is a competent method of lessening the overall consumption of power along with deactivating the clock signal selectively and is useful for controlling the usage of clock signal asynchronously in reference to input-signal current. The integration-control (Vint) signal is employed in controlling the integration time. On the termination of integration, the signal level phase is kept, also similar one is send to arrangement all through read period. As a result, the simulation was carried out after the design layout and the estimations of performance were made and are compared with traditional approaches to prove the proposed mechanism effectiveness for future biomedical applications.

A Multiplexed, Time-Resolved Assay of Root Gravitropic Bending on Agar Plates.

The ability of roots to orient their growth relative to the vector of gravity, root gravitropism (positive gravitropism), is observed in root systems of higher plants and is an essential part of plant growth and development. While there are various methods for quantifying root gravitropism, many methods that can efficiently measure gravitropism at a reasonable throughput do not yield temporal resolution of the process, while methods that allow for high-temporal resolution are often not suitable for an efficient measurement of multiple roots. Here, we describe a method to analyze the root gravitropism activity at an increased throughput with a fine time-resolution using Arabidopsis thaliana plants.

Predicting supersonic jet mode-switching using non-negative matrix factorization

Imperfectly expanded supersonic jets contain a shock-cell structure in the jet plume which plays a role in generating screech tones along with other jet noise sources. Depending on the Mach number of the jet, two types of jet motion or “jet modes”—a toroidal mode and flapping mode may occur, resulting in the generation of different screech tones that are not harmonics of each other. At a given jet condition, these jet modes (and the corresponding screech tones) can switch between each other based on environmental conditions in a seemingly non-deterministic manner. Understanding when mode-switching occurs at a fine time resolution can provide better understanding of the sensitivity of the jet structure dynamics. This work presents the preliminary results of a novel method that predicts when a particular screech tone is active using non-negative matrix factorization. Using this technique, spectrograms of jet noise are decomposed into a set of basis vectors corresponding to the screech tones present in the jet, and a set of activation functions that indicate when each screech basis is dominant. An activation function comparison technique is then used to identify activations corresponding to mode-switching.

Contributions of internal emissions to peaks and incremental indoor PM2.5 in rural coal use households

Indoor air quality is critically important to the human as people spend most time indoors. Indoor PM2.5 is related to the outdoor levels, but more directly influenced by internal sources. Severe household air pollution from solid fuel use has been recognized as one major risk for human health especailly in rural area, however, the issue is significantly overlooked in most national air quality controls and intervention policies. Here, by using low-cost sensors, indoor PM2.5 in rural homes burning coals was monitored for ~4 months and analyzed for its temporal dynamics, distributions, relationship with outdoor PM2.5, and quantitative contributions of internal sources. A bimodal distribution of indoor PM2.5 was identified and the bimodal characteristic was more significant at the finer time resolution. The bimodal distribution maxima were corresponding to the emissions from strong internal sources and the influence of outdoor PM2.5, respectively. Indoor PM2.5 was found to be correlated with the outdoor PM2.5, even though indoor coal combustion for heating was thought to be predominant source of indoor PM2.5. The indoor-outdoor relationship differed significantly between the heating and non-heating seasons. Impacts of typical indoor sources like cooking, heating associated with coal use, and smoking were quantitatively analyzed based on the highly time-resolved PM2.5. Estimated contribution of outdoor PM2.5 to the indoor PM2.5 was ~48% during the non-heating period, but decreased to about 32% during the heating period. The contribution of indoor heating burning coals comprised up to 47% of the indoor PM2.5 during the heating period, while the other indoor sources contributed to ~20%. The study, based on a relatively long-term timely resolved PM2.5 data from a large number of rural households, provided informative results on temporal dynamics of indoor PM2.5 and quantitative contributions of internal sources, promoting scientific understanding on sources and impacts of household air pollution.