Variable Emission Research Articles

Variation and Spatial Distribution of Emissions from Livestock Manure Management in Iran: An Evaluation and Location Analysis

Variation and Spatial Distribution of Emissions from Livestock Manure Management in Iran: An Evaluation and Location Analysis

Disentangling the X-ray variability in the Lyman continuum emitter Haro 11

Context. Lyman break analogs in the local Universe serve as counterparts to Lyman break galaxies (LBGs) at high redshifts, which are widely regarded as major contributors to cosmic reionization in the early stages of the Universe. Aims. We studied XMM-Newton and Chandra observations of the nearby LBG analog Haro 11, which contains two X-ray-bright sources, X1 and X2. Both sources exhibit Lyman continuum (LyC) leakage, particularly X2. Methods. We analyzed the X-ray variability using principal component analysis (PCA) and performed spectral modeling of the X1 and X2 observations made with the Chandra ACIS-S instrument. Results. The PCA component, which contributes to the X-ray variability, is apparently associated with variable emission features, likely from ionized superwinds. Our spectral analysis of the Chandra data indicates that the fainter X-ray source, X2 (X-ray luminosity LX ∼ 4 × 1040 erg s−1), the one with higher LyC leakage, has a much lower absorbing column (NH ∼ 1.2 × 1021 cm−2) than the heavily absorbed luminous source X1 (LX ∼ 9 × 1040 erg s−1 and NH ∼ 11.5 × 1021 cm−2). Conclusions. We conclude that X2 is likely less covered by absorbing material, which may be a result of powerful superwinds clearing galactic channels and facilitating the escape of LyC radiation. Much deeper X-ray observations are required to validate the presence of potential superwinds and determine their implications for the LyC escape.

Straw type and nitrogen-water management balance rice yield and methane emissions by regulating rhizosphere microenvironment

Context or problemStraw incorporation improves soil fertility but also poses environmental challenges due to increasing methane (CH4) emissions in paddy fields. Whether nitrogen (N) and water management can balance rice yield and CH4 emissions under different crop straw incorporation is still not well-documented. ObjectiveA three-year field experiment was conducted to probe the comprehensive effects of N application ratios and irrigation regimes on rice yield, rhizosphere soil properties, and CH4 emissions, along with the underlying mechanisms of CH4 emission variations among different straw types. MethodsA two-factor randomized block design was used with two Japonica rice cultivars as materials in 2020 and 2021. The straw incorporation treatment included no straw incorporation (NS), wheat straw incorporation (WS), and rape straw incorporation (RS). The N fertilizer application treatments included local farmers' fertilizer practice (LFP) and increasing basal fertilizer rate (IBF). Two irrigation practices, continuously-flooded irrigation (CF) and alternate wetting and drying irrigation (AWD), were designed under the WS and RS treatments in 2022. Results1) WS-IBF and RS-IBF enhanced yield by 6.70∼9.03 % and 8.13∼9.50 % compared to WS-LFP and RS-LFP, respectively. AWD further increased yield by 6.28∼7.76 % compared to CF. 2) WS-IBF and RS-IBF enhanced dissolved organic carbon (DOC) content, synchronously boosted the methanogens (mcrA) and methanotrophs (pmoA) abundances, but decreased the pmoA/mcrA ratio, which significantly promoted CH4 emission flux in early growth stage. This resulted in a 5.04∼8.01 % and 4.60∼7.88 % increase in CH4 emissions compared to WS-LFP and RS-LFP, respectively, but a decrease in yield-scaled CH4 emissions. AWD reduced DOC content, facilitated the conversion of ammonium N to nitrate N, increased dissolved oxygen content, and hence decreased CH4 emissions by 23.41∼24.38 % compared to CF. 3) RS significantly increased microbial biomass C, N, and related metabolites, leading to a 1.29∼2.73 % increase in yield compared to WS. Meanwhile, RS promoted Nitrospira abundance as well as pterin and flavonoid metabolites associated with mcrA inhibition, while decreasing Anaeromyxobacter abundance, ammonium N, and DOC content, resulting in an increase in the pmoA/mcrA ratio and a noticeable drop in CH4 emissions compared to WS. ConclusionsRS combined with IBF and AWD is a more sustainable integrated practice in light of the synergistic improvement in rice production and environmental benefits. ImplicationsThe results reveal that optimizing N and water management can synergize high-yield and low-carbon by regulating rhizosphere microenvironment in rice production under crop straw incorporation.

Delayed Emission from Luminous Blue Optical Transients in Black Hole Binary Systems

At least three members of the recently identified class of fast luminous blue optical transients show evidence of late-time electromagnetic activity in great excess of what was predicted by an extrapolation of the early time emission. In particular, AT2022tsd displays fast, bright optical fluctuations approximately a month after the initial detection. Here we propose that these transients are produced by exploding stars in black hole binary systems and that the late-time activity is due to the accretion of clumpy ejecta onto the companion black hole. We derive the energetics and timescales involved, compute the emission spectrum, and discuss whether the ensuing emission is diffused or not in the remnant. We find that this model can explain the observed range of behaviors for reasonable ranges of the orbital separation and the ejecta velocity and clumpiness. Close separation and clumpy, high-velocity ejecta result in bright variable emission, as seen in AT2022tsd. A wider separation and smaller ejecta velocity, conversely, give rise to fairly constant emission at a lower luminosity. We suggest that high-cadence, simultaneous, panchromatic monitoring of future transients should be carried out to better understand the origin of the late emission and the role of binarity in the diversity of explosive stellar transients.

Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

The precise non-contact temperature measurement method is essential for studying gas turbine component heat transfer. While infrared thermal imaging provides high-resolution two-dimensional temperature fields, errors arise from the measurement environment's complexity, emissivity variations, and reflection radiation effects. This study utilizes BP neural networks to correct infrared radiation on gas film cooled plates. By modeling background radiation and analyzing infrared emission processes, factors influencing temperature distribution are identified. Experimental data from uncooled plate are used for network training. Experimental measurements were conducted on uncooled plates to obtain training data. A BP neural network was established based on Planck's law and the radiation transmission process to relate the mainstream temperature to the background radiation. Calibration analysis was performed based on experimental data from the cooled plate, achieving the restoration of the two-dimensional temperature field under multiple operating conditions and significantly reducing measurement errors. This method comprehensively considers the entire process of radiation transmission, thereby eliminating background radiation embedded in the infrared images. The obtained measurement results vividly depict the real temperature distribution, offered significant support for gas turbine engineering applications.

Trends in Emissions from Road Traffic in Rapidly Urbanizing Areas

The process of urbanization has facilitated the exponential growth in demand for road traffic, consequently leading to substantial emissions of CO2 and pollutants. However, with the development of urbanization and the expansion of the road network, the distribution and emission characteristics of CO2 and pollutant emissions are still unclear. In this study, a bottom-up approach was initially employed to develop high-resolution emission inventories for CO2 and pollutant emissions (NOx, CO, and HC) from primary, secondary, trunk, and tertiary roads in rapidly urbanizing regions of China based on localized emission factor data. Subsequently, the standard road length method was utilized to analyze the spatiotemporal distribution of CO2 emissions and pollutant emissions across different road networks while exploring their spatiotemporal heterogeneity. Finally, the influence of elevation and surface vegetation cover on traffic-related CO2 and pollutant emissions was taken into consideration. The results indicated that CO2, CO, HC, and NOx emissions increased significantly in 2020 compared to those in 2017 on trunk roads, and the distribution of CO2 and pollutant emissions in Fuzhou was uneven; in 2017, areas of high emissions were predominantly concentrated in the central regions with low vegetation coverage levels and low topography but expanded significantly in 2020. This study enhances our comprehension of the spatiotemporal variations in carbon and pollutant emissions resulting from regional road network expansion, offering valuable insights and case studies for regions worldwide undergoing similar infrastructure development.

Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China

With the acceleration of urbanization and the improvement of people's living standards, the carbon emissions of residential buildings (BCE) in China have been increasing, hindering the achievement of carbon peaking and carbon neutrality goals. It has become increasingly urgent and important to analyze and grasp the dynamic trends of BCE. Given the notable regional variations in carbon emissions, it is necessary to delve into the distribution characteristics of BCE and the underlying factors influencing them. First, based on the emission factor method, the energy consumption and carbon emissions calculation models are established. Next, the main influencing factors are obtained by improving Kaya's constant equation. Meanwhile, the GA-PPC-Kmeans model is established to divide BCE into different regions. Then, the STIRPAT-Ridge model is built to predict the peak carbon emissions. Finally, a combination of static and dynamic methods is used to forecast peaks under three scenarios, which are low-carbon (LC) scenario, baseline (BA) scenario, and high-carbon (HC) scenario. The results show that five pivotal factors significantly impact BCE in China, which are energy carbon emission intensity (ECEI), energy intensity (EI), economic density (ED), residential housing level (RHL) and population size(P). Furthermore, BCE is categorized into five distinct regions: low-carbon demonstration area, energy efficiency improvement area, carbon concentration and innovation area, high-carbon optimization area, and carbon poverty development area. Under BA scenario, the peaks for BCE in these regions converge primarily around 2040 and 2045. This study provides a regional research perspective for China's residential buildings to reach the peak of carbon emissions, and serves as a reference for formulating carbon emission reduction plans in different regions.

X-Ray Variability in the Symbiotic Binary RT Cru: Principal Component Analysis

Hard X-ray-emitting (δ-type) symbiotic binaries, which exhibit a strong hard X-ray excess, have posed a challenge to our understanding of accretion physics in degenerate dwarfs. RT Cru, which is a member of the δ-type symbiotics, shows stochastic X-ray variability. Timing analyses of X-ray observations from XMM-Newton and NuSTAR, which we consider here, indicate hourly fluctuations, in addition to a spectral transition from 2007 to a harder state in 2012 seen with Suzaku observations. To trace the nature of X-ray variability, we analyze the multimission X-ray data using principal component analysis (PCA), which determines the spectral components that contribute most to the flickering behavior and the hardness transition. The Chandra HRC-S/LETG and XMM-Newton EPIC-pn data provide the primary PCA components, which may contain some variable emission features, especially in the soft excess. Additionally, the absorbing column (first order with 50%), along with the source continuum (20%), and a third component (9%)—which likely accounts for thermal emission in the soft band—are the three principal components found in the Suzaku XIS1 observations. The PCA components of the NuSTAR data also correspond to the continuum and possibly emission features. Our findings suggest that the spectral hardness transition between the two Suzaku observations is mainly due to changes in the absorbing material and X-ray continuum, while some changes in the thermal plasma emission may result in flickering-type variations.

Oversimplification and misestimation of nitrous oxide emissions from wastewater treatment plants

AbstractWastewater treatment is a major source of anthropogenic nitrous oxide (N2O) emissions. However, the current emission estimations rely on a uniform emission factor (EF) proposed by the Intergovernmental Panel on Climate Change based on a limited database suffering from large uncertainties and inaccuracies. To address this limitation, this study expands the database 12-fold and develops a tier-based approach. Our method considers emission variations across spatial scales, treatment processes and monitoring techniques, enabling more-precise estimations. Here, applying this approach to the US database, we highlight the limitations of current estimations based on uniform EFs and quantified the mean wastewater N2O emission in the United States to be 11.6 MMT CO2-eq. The results also reveal the diverse nature of wastewater N2O emissions and underscore the need for a customized approach to inform facility-level N2O emission estimation as well as inform national- and sector-wide greenhouse gases inventories with emphasis on site-specific considerations. Overall, this study provides a tool to recalibrate the estimations of wastewater N2O emissions, which form the foundation of carbon footprint reduction in wastewater treatment.

Response of CH4 and N2O emissions to the feeding rates in a pond rice-fish co-culture system.

Feeding rate is an important factor influencing the carbon and nitrogen input and greenhouse gas emission from aquaculture systems. However, the quantitative relationship between feeding rates and GHG emissions is still poorly understood. In this study, we conducted a laboratory-scale experiment to examine the impact of feeding rate (0%, 2%, 4%, 6%, and 8%) on the CH4 and N2O emissions from a pond rice-fish co-culture system. The results showed that the total amount of CH4 emission did not significantly differ when the feeding rate was no more than 6%, but increased more than four times when the feeding rate reach to 8%. The amount of N2O emission showed a linearly increasing trend with the feeding rate. The emission factors of CH4 and N2O was significantly higher for 8% feeding rate than other feeding rates. The variation of CH4 emission was primarily attributed to the ratio of mcrA/pmoA in the sediment and the contents of biological oxygen demand (COD) and dissolved oxygen (DO) in the water; and the variation of N2O was primarily affected by the available nitrogen in the water and sediment and the content of DO in the water. The overall emission of CH4 and N2O showed an exponential relationship with feeding rate. The total yields of fish and rice did not continuously increase when the feeding rate exceeded 4%. The lowest emission intensity per unit yield was reached at the feeding rate of 2.99%. These results can provide a reference for the determination of low-carbon feeding strategy for pond rice-fish co-culture system.

Properties of V-defect injectors in long wavelength GaN LEDs studied by near-field electro- and photoluminescence

The efficiency of multiple quantum well (QW) light emitting diodes (LEDs) to a large degree depends on uniformity of hole distribution between the QWs. Typically, transport between the QWs takes place via carrier capture into and thermionic emission out of the QWs. In InGaN/GaN QWs, the thermionic hole transport is hindered by the high quantum confinement and polarization barriers. To overcome this drawback, hole injection through semipolar QWs located at sidewalls of V-defects had been proposed. However, in the case of the V-defect injection, strong lateral emission variations take place. In this work, we explore the nature of these variations and the impact of the V-defects on the emission spectra and carrier dynamics. The study was performed by mapping electroluminescence (EL) and photoluminescence (PL) with a scanning near-field optical microscope in LEDs that contain a deeper well that can only be populated by holes through the V-defects. Applying different excitation schemes (electrical injection and optical excitation in the far- and near-field), we have shown that the EL intensity variations are caused by the lateral nonuniformity of the hole injection. We have also found that, in biased structures, the PL intensity and decay time in the V-defect regions are only moderately lower that in the V-defect-free regions thus showing no evidence of an efficient Shockley-–Read–Hall recombination. In the V-defect regions, the emission spectra experience a red shift and increased broadening, which suggests an increase of the In content and well width in the polar QWs close to the V-defects.

Temporal Variations in Methane Emissions from a Restored Mangrove Ecosystem in Southern China

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide has been increasingly investigated in recent years. While studies have shown that mangroves are weak sources of methane (CH4) emissions, measurements of CH4 fluxes from these ecosystems remain scarce. In this study, we examined the temporal variation and biophysical drivers of ecosystem-scale CH4 fluxes in China’s northernmost mangrove ecosystem based on eddy covariance measurements obtained over a 3-year period. In this mangrove, the annual CH4 emissions ranged from 6.15 to 9.07 g C m−2 year−1. The daily CH4 flux reached a peak of over 0.07 g C m−2 day−1 during the summer, while the winter CH4 flux was negligible. Latent heat, soil temperature, photosynthetically active radiation, and tide water level were the primary factors controlling CH4 emissions. This study not only elucidates the mechanisms influencing CH4 emissions from mangroves, strengthening the understanding of these processes but also provides a valuable benchmark dataset to validate the model-derived carbon budget estimates for these ecosystems.

Size resolved particle contribution to vehicle induced ultrafine particle number concentration in a metropolitan curbside region

The concentrations and behavior of nano particles (10–1000 nm) in Delhi, a densely populated megacity, in different seasons (winter, spring, summer, monsoon, and autumn) are examined, for the first time. The concentration of particles is classified into four different sizes as Nnuc (10–30 nm, nucleation), Nsatk (30–50 nm, small Aitken), Nlatk (50–100 nm, large Aitken), and Nacc (100–1000 nm, accumulation mode), and the total (10–1000 nm) particle number concentration (PNC) as Ntotal. PNC ranges between 104 cm−3 and 106 cm−3 over Delhi during the year, and the highest concentration occurs in winter. Winter concentration is 2, 1.6 and 1.3 times higher than monsoon, summer, autumn and spring concentrations, respectively. Nnuc, Nsatk, Nlatk and Nacc and their respective contributions to total PNC exhibit significant seasonal variations. During winter Nlatk and Nacc contribute more to total due to coagulation, with Nacc alone contributing >40% to total PNC. Nnuc, Nsatk, and Nlatk are higher in spring and summer during mid-day due to nucleation and/or ultrafine particle burst events. The direct primary emissions from engine exhaust produce a prominent double hump structure during morning and evening peak hours in winter and autumn. PNC and their contributions exhibit day-night variations as they are influenced by variations in emission sources and meteorological parameters (wind speed, relative humidity, temperature, solar radiation and boundary layer height) between day and night. Carbon monoxide correlates positively with Nacc in all seasons (R2 ≥ 0.5) as fossil fuel emission is a predominant source for gases and particles in the study environment. These quantitative results on seasonal variations of nano particles and air pollutants together with the knowledge on seasonal variations in meteorological parameters and atmospheric dynamics provide a foundation which can positively contribute better to the urban planning and devising mitigation measures aimed at improving air quality and public health.

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

Multiple-model based simulation of urban atmospheric methane concentration and the attributions to its seasonal variations: A case study in Hangzhou megacity, China

Cities are treated as global methane (CH4) emission hotspots and the monitoring of atmospheric CH4 concentration in cities is necessary to evaluate anthropogenic CH4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH4 emitter as of China, and although obvious seasonal variations of atmospheric CH4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH4 concentrations, year-round CH4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH4 background contributed by −8.1 ppb, −44.6 ppb, and −1.0 ppb, respectively, and the CH4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from −13.4 to −6.3 ppb. And the temperature induced seasonal CH4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.

Tillage Practices Effect on Root Distribution and Variation of Soil CO2 Emission under Different Cropping Strategies

Conservation soil management strategies, particularly no-tillage and cropping strategies, have become an effective and widely adopted practice that has a direct influence on root parameters and mitigation of greenhouse gasses. However, the effect of different tillage and cropping strategies on root growth in field conditions is rarely studied. The study aimed to quantify and characterise the relationship between root network development and CO2 emission and how these parameters are affected by different cropping and tillage strategies. Five different crop rotations were tested, with or without the inclusion of catch crops (CC), by growing them in the soil where different tillage practices were applied. Selected cropping strategies differed among themselves in terms of the frequency of CC grown per rotation. The data revealed that in NT treatments, the CO2 emission (both autotrophic and heterotrophic simultaneously) was 25% higher than in CT. The cropping strategies were identified as an important factor influencing CO2 emissions. An increase in CO2 emission varied between 30 and 35% depending on the share of legume and CC inclusion. The frequency of CC grown per rotation has had an effect on the rate of CO2 emission. The cropping strategy when CC was grown every year showed the lowest amount of CO2 (by 26%), while in other cropping strategies, when CC was grown once or twice per rotation, significantly higher CO2 emissions were observed. Root growth and their development were significantly affected (p < 0.05) by soil depth and cropping strategies concerning root length and root volume changes. The inclusion of CC into the rotations led towards a decrease in root volume (by 21%). Root length (R2 = 0.45; p < 0.05) and root volume (R2 = 0.82; p < 0.05) had a significant impact on soil CO2 emissions. The results collected from 2021 to 2023 experiments indicated that cropping strategies and CC management areas are important tools not only for the improvement of root parameters but also for understanding how they affect CO2 emissions. The main message for stakeholders is that the cropping strategies diversification with the inclusion of CC every year in a winter oil-seed rape, spring wheat and pea crop rotation (R/W/P + CC) had demonstrated the possibilities to reduce CO2 emission and improve the root network parameters as compared to the monoculture strategy.

Evaluating high-resolution aviation emissions using real-time flight data

Amidst robust global economic growth and advancing globalization, the aviation market is poised for significant expansion. Consequently, the environmental impact of aviation emissions is growing in significance. However, due to limitations in real flight data and aviation emissions index models, further clarification of the emission characteristics throughout entire flights is necessary. To better assess the emission characteristics of entire flights, this study employs real Quick Access Recorder (QAR) data and a high-precision aviation emissions index model, yielding four-dimensional emission data (time, longitude, latitude, altitude) from flights. The analysis compares QAR data with emissions from scheduled flight data (SFD) and Broadcast Automatic Correlation Monitoring (ADS-B) projections, explores seasonal variations in aviation emissions, and assesses the impact of sustainable aviation fuels (SAFs) on emissions reductions. For both number and mass of nvPM emissions, as well as nitrogen oxide emissions, the rankings are: ADS-B-E > SFD-E > QAR-E; for CO, SFD-E > ADS-B-E > QAR-E, particularly during the climb-cruise-descent (CCD) cycle. There are significant differences in the emission of aviation pollutants in airport area and high-altitude area in different seasons. Employing four types of Sustainable Aviation Fuels (SAFs) significantly reduces both the mass and the number of nvPM emissions. Therefore, it is recommended to utilize more QAR data to refine the assessment of the environmental impact of aviation emissions.

Spatio-temporal Heterogeneity of Factors Influencing Transportation Carbon Emissions in Provinces Along the Belt and Road

The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

Exceptional point enhanced nanoparticle detection in deformed Reuleaux-triangle microcavity

In this paper, we propose a deformed Reuleaux-triangle resonator (RTR) to form exceptional point (EP) which results in the detection sensitivity enhancement of nanoparticle. After introducing single nanoparticle to the deformed RTR at EP, frequency splitting obtains an enhancement of more than 6 times compared with non-deformed RTR. In addition, EP induced a result that the far field pattern of chiral mode responses significantly to external perturbation, corresponding to the change in internal chirality. Therefore, single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far field directional emission. Compared to traditional frequency splitting, the far field pattern produced in deformed RTR provides a cost-effective and convenient path to detect single nanoparticle at a long distance, without using tunable laser and external coupler. Our structure indicates great potential in high sensitivity sensor and label-free detector.Graphical

The excellent infrared radiation regulation ability of VO2 nanorods

Dynamic infrared radiation has significantly contributed to thermal comfort by adaptively radiating heat into space without necessitating external energy consumption. This study introduces a novel approach to fabricate dynamic infrared radiation-regulated materials using VO2 nanorods. The VO2 nanorods were synthesized via a conventional hydrothermal method. XRD and SEM analyses confirmed the high crystallinity and uniform size of the VO2 nanorods. HRTEM measurements revealed that the nanorods had an approximate length of 1 μm and a width of 100 nm. These nanorods were combined with KBr and pressed into a tablet form. Subsequent thermal imaging was conducted to assess their dynamic infrared radiation regulation capability. Remarkably, the optimized variation in emissivity for the VO2-based tablets reached up to 0.46. And the Mie scattering theory was used to interprete the insight mechanism. It seems that the Qabs of VO2 nanorod before and after phase transition dominate the dynamic infrared radiation regulation ability at different temperatures. This finding offers a novel, straightforward, and highly efficient energy-saving solution for thermal management systems.