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China is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH4/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate. Plain Language Summary This study examines methane emissions from coal mines in Shanxi, China, identified as a significant source of global methane, a potent greenhouse gas. Using satellite data over 2 years, we found 138 detected episodic events at 82 facilities in Shanxi emitting an estimated 1.2 million tons of methane annually, far exceeding emissions from known global hotspots in the oil and gas sectors. Our analysis shows that a small percentage of these facilities contribute to the majority of emissions. The results, which challenge previous estimates from environmental monitoring organizations, underscore the need for direct, observational methods to accurately assess and address methane emissions. This work aims to guide efforts in mitigating climate change by identifying key areas for reducing methane release. Key Points Satellite data identify 82 major methane emitters in Shanxi, China, with high annual emissions of up to 1.2 Mt Top 20% of coal mines contribute to half of the region's total methane emissions Findings highlight the importance of direct measurements for accurate emission estimates

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

Development of the measurement-based carbon accounting means is of great importance to supplement the traditional inventory compilation. Mobile CO2/CH4 measurement provides a flexible way to inspect plant-scale CO2/CH4 emissions without the need to notify factories. In 2021, our team used a vehicle-based monitor system to conduct field campaigns in two cities and one industrial park in China, totaling 1143 km. Furthermore, we designed a model based on sample concentrations to evaluate CO2/CH4 emissions, EMISSION-PARTITION, which can be used to determine global optimal emission intensity and related dispersion parameters via intelligent algorithm (particle swarm optimization) and interior point penalty function. We evaluated the performance of EMISSION-PARTITION in chemical, coal washing, and waste incineration plants. The correlations between measured samples and rebuilt simulated ones were larger than 0.76, and RMSE was less than 11.7 mg m−3, even with much fewer samples (25). This study demonstrated the wide applications of a vehicle-based monitoring system in detecting greenhouse gas emission sources.

Background Pre-harvest sprouting (PHS) significantly reduces the yield and quality of Chenopodium quinoa (quinoa). A key determinant of PHS resistance is the balance between seed dormancy and germination, a process primarily regulated by phytohormones. Results To elucidate the molecular mechanisms underlying hormone-mediated germination regulation, we performed transcriptome sequencing on Jingli 1 quinoa seeds 6 h after treatment with six phytohormones: abscisic acid (ABA), indole-3-acetic acid (IAA), jasmonic acid (JA), gibberellic acid (GA₃), brassinolide (BR), and 6-benzylaminopurine (6-BA). The results showed that ABA, IAA, and JA significantly inhibited germination, with a maximum inhibition rate of 36.73%. In contrast, optimal concentrations of GA₃ (45 µM), BR (20 µM), and 6-BA (4.44 µM) promoted germination, with a maximum promotion rate of 22.67%. Transcriptome analysis identified 4,738 differentially expressed genes (DEGs), which were significantly enriched in pathways such as plant hormone signal transduction, starch and sucrose metabolism, and terpenoid backbone biosynthesis. Furthermore, we identified 102 coregulated DEGs, revealing intricate hormone signaling networks (such as ABA-GA-JA and JA-IAA-6-BA). Importantly, we pinpointed 20 core regulatory genes (including CqWRKY33 , CqAnxD3 , CqbHLH18L , CqMYB-V , CqSES , CqHMGCS , CqMVK1/2 , CqCKX7-1/-2 , CqWAT1-1/-2 , CqORR9/10 , CqBNM2AL , CqSAUR72L , CqRAV1L , CqABCG31L , Cq2-ODD , and CqBG7Sg2 ) that showed antagonistic expression patterns in response to promotive versus inhibitory hormones. Conclusions This study systematically elucidates the multi-hormone regulatory network underlying quinoa seed germination, thereby enhancing our understanding of phytohormone-mediated regulatory mechanisms in quinoa. It also identifies promising candidate genes for breeding pre-harvest sprouting (PHS)-resistant quinoa varieties.

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.

Salmonella, a major global foodborne pathogen, is a leading cause of salmonellosis. Quantitative detection of Salmonella provides a scientific basis for establishing microbiological criteria and conducting risk assessments. The plate count method remains the primary approach for bacterial quantification, whereas the most probable number (MPN) method is commonly used for detecting low levels of bacterial contamination. However, both methods are time-consuming and labor-intensive. Validated digital polymerase chain reaction (dPCR) techniques are emerging as promising alternatives because they enable rapid, absolute quantification with high specificity and sensitivity. Herein, we developed a novel droplet dPCR (ddPCR) assay for identifying and quantifying Salmonella using invA as the target. The assay demonstrated high specificity and sensitivity, with a limit of quantification of 1.1 × 102 colony-forming units/mL in meat samples. Furthermore, the log10 values obtained via ddPCR and plate counting exhibited a strong linear relationship (R2 > 0.99). Mathematical modeling of growth kinetics further confirmed a high correlation between plate count and ddPCR measurements (Pearson correlation coefficient: 0.996; calculated bias factor: 0.88). Collectively, these results indicate that ddPCR is a viable alternative to the MPN method and represents a powerful tool for the quantitative risk assessment of food safety.

Listeria monocytogenes is a Gram-positive bacterial species that causes listeriosis, a major foodborne disease worldwide. The virulence factor inlA facilitates the invasion of L. monocytogenes into intestinal epithelial cells expressing E-cadherin receptors. Naturally occurring premature stop codon (PMSC) mutations in inlA have been shown to result in the production of truncated proteins associated with attenuated virulence. Moreover, different L. monocytogenes strains contain distinct inlA variants. In this study, we first characterized inlA in 546 L. monocytogenes strains isolated from various foods in Shanghai. The results showed that 36.1% (95% Confidence Interval: 32.0~40.2%) of the food isolates harbored inlA with PMSC, which was found to be associated with clonal complex (CC) types, with the highest proportions observed in CC9 and CC121. To investigate the function of inlA, we first used the dominant CC87 isolated from patients as the test strain and constructed an inlA-deleted strain via homologous recombination. Resistance tests and virulence tests showed that while inlA did not affect the resistance of L. monocytogenes, it significantly influenced cell adhesion and invasiveness. To further explore the function of inlA, we performed virulence tests on five CC-type strains carrying inlA with PMSC and their corresponding strains with intact inlA. We found that the virulence of L. monocytogenes strains carrying inlA or inlA with PMSC was associated with their CC type. Our preliminary results showed that premature termination of inlA did not significantly affect the adhesion and invasion abilities of low-virulence CC-type L. monocytogenes strains in Caco-2 cells, but substantially promoted those of high-virulence strains such as CC8 and CC7. In summary, this study preliminarily evaluated the effects of inlA integrity and PMSC mutation variation on the virulence of L. monocytogenes, providing a foundation for further research on inlA-related pathogenic mechanisms.

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction.

Integrated-path differential absorption (IPDA) LiDAR is a promising means of measuring the global distributions of the column weighted xCO2 (dry-air mixing ratio of CO2) with adequate accuracy and precision. Most IPDA LiDARs are incapable of discerning the vertical information of CO2 diffusion, which is of great significance for studies on the carbon cycle and climate change. Hence, we developed an inversion method using the constrained linear least-squares technique for a pulsed direct-detection multi-wavelength IPDA LiDAR to obtain sliced xCO2. In the proposed inversion method, the atmosphere is sliced into three different layers, and the xCO2 of those layers is then retrieved using the constrained linear least-squares technique. Assuming complete knowledge of the water vapor content, the accuracy of the retrieved sliced xCO2 could be as high as 99.85% when the signal-to-noise ratio of central wavelength retrievals is higher than 25 (with a log scale). Further experiments demonstrated that different carbon characteristics can be identified by the sign of the carbon gradient of the retrieved xCO2 between the ABL (atmospheric boundary layer) and FT (free troposphere). These results highlight the potential applications of multiple wavelength IPDA LiDAR.

For high-precision measurements of the CO2 column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg–Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement. Further, the effect of atmospheric delay on distance measurements is considered, leading to further correction of the inversion results. At last, an airborne experiment was carried out in a sea area near Qinhuangdao, China on 14 March 2019. The results showed that the ranging accuracy can reach 0.9066 m, which achieved an excellent ranging accuracy on 1.57-μm IPDA Lidar and met the requirement for high-precision CO2 column length inversion.