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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 MtTop 20% of coal mines contribute to half of the region's total methane emissionsFindings highlight the importance of direct measurements for accurate emission estimates [ABSTRACT FROM AUTHOR]

Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments, however, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using untraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach, by training the model with grids corresponding to ground monitor sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods' performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models, challenging conventional expectations and are not explored in the recent literature. The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with CNN-based deep-learning approaches like the ResNet model, successfully mitigates PM2.5 overestimation (by 5–30 µg m-3) and corresponding exposure (by 3 million people • µg m-3) in the downwind area over the past nine years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground-measurement sites identified through the testbed is essential to achieve a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community. [ABSTRACT FROM AUTHOR]

As a special branch of global trade, the trade of agricultural products has an important impact on food security and the environment. In this paper, we studied international trade network of agricultural products from 2000 to 2016 as a whole and in part. We explored the overall characteristics of the network, analyzed the evolution of communities and identified core countries of the communities. The results show that the structure of the trade network became increasingly complex and the trade relations became closer over time. There were four major communities in the network, whose primary core countries were Germany, the United States, Brazil, and China. Since 2007, the community represented by China has disappeared, and the community pattern of the network has been in a three-pillar state and basically stable. We discuss the actual roles of certain trading countries, the formation of communities and the impact of economic events on agricultural products trade. This paper reveals the underlying patterns of the agricultural products trade and provides a way to track its evolution over time. [ABSTRACT FROM AUTHOR]

The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the MAIAC AOD throughout Hubei Province. The reconstructed AOD dataset, exhibiting an R2/RMSE of 0.76/0.18, compared to AERONET AOD, was subsequently used for PM2.5 estimation. Our research breaks from traditional methodologies that solely depend on latitude and longitude information. Instead, it emphasizes the use of climate feature as an input for estimating PM2.5 concentrations. This strategic approach prevents potential spatial discontinuities triggered by geolocation information (latitude and longitude), thus ensuring the precision of the PM2.5 estimation (sample/spatial CV R2 = 0.91/0.88). Moreover, we proposed a method for identifying the absolute feature importance of machine-learning models. Contrasted with the relative feature-importance property typical of machine-learning models (a minor difference in the order of top three between geolocation-based and climate-feature-based models, and the slight difference in the top three: 0.08%/0.17%), our method provides a more comprehensive explanation of the absolute significance of features to the model (maintaining the same order and a larger difference in the top three: 0.99%/0.72%). Crucially, our findings demonstrated that AOD reconstruction can mitigate the overestimation of annual mean PM2.5 concentrations (ranging from 0.52 to 9.28 µg/m3). In addition, the seamless PM2.5 dataset contributes to reducing the bias in exposure risk assessment (ranging from −0.11 to 9.81 µg/m3). [ABSTRACT FROM AUTHOR]

Background: According to epidemiological studies, gingivitis is a common disease. However, its morbidity, considerably varies among individual. This study aimed to investigate the epidemiological characteristics of gingivitis, including prevalence, severity, intraoral distribution, and associated risk factors, in children aged 6–12 years in Jinzhou, China. Methods: A multistage, whole-group, randomized sample of 2880 children aged 6–12 years in Jinzhou City, China, was selected and clinically examined. Each selected child completed a questionnaire on sociodemographic factors and oral health behaviors in cooperation with the investigator and teacher. Gingival bleeding refers to the bleeding of 10% or more teeth under the condition of ingivitis. Gingivitis was further categorized into localized gingivitis (30% ≥ number of teeth positive for gingival bleeding ≥ 10%) and generalized gingivitis (number of positive for gingival bleeding > 30%). The score of gingival bleeding was recorded using the Gingival Index. Results: The prevalence of gingivitis in children aged 6–12 years in Jinzhou was 28.58%, including 701 cases of localized gingivitis (24.3%) and 122 cases of generalized gingivitis (4.2%). There were 429 cases (28.3%) of gingivitis in males and 394 cases (28.9%) in females, with no statistically significant difference in prevalence between males and females (P > 0.05). Chi-square tests and binary logistic regression analysis showed that aging, dental calculus, plaque, and dental crowding were significantly associated with a high prevalence of gingivitis. Conclusions: Our study showed that dental calculus, large amount dental plaque, poor oral health behavior, and oral health awareness are associated with the prevalence of gingivitis and maintaining children's oral health requires professional guidance and regular preventive care. [ABSTRACT FROM AUTHOR]

The complete genome sequence of a double-stranded RNA (dsRNA) mycovirus that was isolated from Alternaria solani strain DT-10 causing potato foliar disease was determined. The virus, designated as "Alternaria solani chrysovirus 1" (AsCV1), has four dsRNA segments (dsRNA 1-4) with a length of 3600 bp, 3128 bp, 2996 bp, and 2714 bp, respectively. The RNA-dependent RNA polymerase (RdRp, 1084 amino acids [aa]), putative capsid protein (905 aa), alphachryso-P3 (835 aa), and alphachryso-P4 (729 aa) were encoded by dsRNA1, dsRNA2, dsRNA3, and dsRNA4, respectively, which had the highest sequence identity of 41.77%-72.38% to their counterparts in Helminthosporium victoriae virus 145S (HvV145S) of the genus Alphachrysovirus, family Chrysoviridae. Moreover, the 5′-untranslated regions (UTRs) of AsCV1 dsRNA 1-4, which contained several unique inserts (3-37 bp) and deletions (5-64 bp), shared 51.65%-68.01% identity with those of HvV145S. Phylogenetic analysis based on RdRp sequences showed that AsCV1 clustered the most closely with HvV145S. Considering its distinct host specificity, the low sequence similarity of its encoded proteins to those of other viruses, the unusual features of the 5′-UTRs of its dsRNA 1-4, and the phylogenetic position of its RdRp gene, AsCV1 should be considered a member of a new species in the genus Alphachrysovirus. To the best of our knowledge, this is the first alphachrysovirus identified from phytopathogenic A. solani. [ABSTRACT FROM AUTHOR]

The inaccuracy of anthropogenic emission inventories on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, which is a typical emission inventory estimation method, requires a lot of human labor and material resources, whereas a top-down approach focuses on individual pollutants that can be measured directly as well as relying heavily on traditional numerical modeling. Lately, the deep neural network approach has achieved rapid development due to its high efficiency and nonlinear expression ability. In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation. Specifically, we utilized a neural-network-based comprehensive chemical transport model (NN-CTM) to explore the complex correlation between emission and air pollution. We further updated the emission inventory based on back-propagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NNs) and achieved a relatively good representation of the CTM, with similarity reaching 95 %. To reduce the gap between the CTM and observations, the NN model suggests updated emissions of NO x , NH 3 , SO 2 , volatile organic compounds (VOCs) and primary PM 2.5 changing, on average, by - 1.34 %, - 2.65 %, - 11.66 %, - 19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NO x and PM 2.5 are even higher (∼ 10 %) in regions that suffer from large uncertainties in original emissions, such as Northwest China. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO 2 , SO 2 , O 3 and PM 2.5 concentrations are reduced significantly (by about 10 %–20 %), indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of the emission inventory and the performance of the air quality model. [ABSTRACT FROM AUTHOR]

Background: Yiganmingmu oral liquid (YGMM), a well known over-the-counter (OTC) drug in China, is composed of 12 types of valuable herbal medicines and has been widely used in clinical for the treatment of soreness and weakness of waist and knees, dizziness, memory loss, and fatigue. However, the chemical compositions of YGMM and its absorbed compounds in plasma remain unclear. Methods: Since chemical investigation is the first important step to reveal effects and action mechanisms of traditional Chinese medicine (TCM), in this study, based on the self built components database, systematic characterization of the chemical profile of YGMM in vitro was carried out by using a reliable UPLC-Q-TOF-MS method. Moreover, to obtain better understanding of the absorbed prototypes in plasma, serum pharmacochemistry analysis of YGMM after oral administration was conducted by using cynomolgus monkeys as animal model. Results: A total of 667 constituents from the 12 single herbal medicines were collected in the self built components database by searching the reported literatures, and 415 of them were initially screened as candidate compounds in YGMM by comparison of their experimental accurate mass measurements with those theoretical values. After that, 117 compounds including 17 phenolic acids, 25 flavonoids, 4 alkaloids, 10 phthalides, 5 monoterpenes, 8 triterpenoid saponins, 9 anthraquinones, and 39 other compounds, were unambiguously identified or tentatively characterized by analysing their MS/MS fragmentation patterns, and also by comparison with reference standards and those data reported in the literatures. 61 prototypes absorbed in plasma of cynomolgus monkey, including 13 phenolic acids, 21 flavonoids, 8 phthalides, 3 monoterpenes, 4 triterpenoid saponins, and 12 other compounds were tentatively assigned by serum pharmacochemistry analysis after oral administration. Conclusion: It was the first comprehensive analysis of chemical constituents of YGMM and prototypes in plasma, and the data analysis strategy developed in this study showed high efficiency in the structural elucidations. The results might provide scientific evidence for further research on material basis of YGMM. [ABSTRACT FROM AUTHOR]

Seamless hourly surface fine particulate matters (PM 2.5) datasets are urgently needed for environmental and epidemiologic studies. Most existing models rely on satellite AOD with spatial gaps and the time-geolocation information not directly physically related to ground-level PM 2.5 as input, leading to several problems, e.g., for daytime only, spatial discontinuity. By exploring spatiotemporal connection, here we propose a novel framework for improving reanalysis AOD with satellite AOD and retrieving seamless PM 2.5 concentration. With the wavelet decomposition on the meteorological / AOD time-series and the machine learning algorithm 'CatBoost', the grid cells are well connected based on the meteorological/AOD similarity (quasi-Lagrangian description), rather than the spatial distance (Eulerian description). Our new AOD dataset (compared with AERONET AOD, R2 = 0.60, RMSE = 0.19) combines the advantages of reanalysis AOD (all-day hourly; seamless) and satellite AOD (high accuracy and spatial resolution). The new PM 2.5 estimation strategy performed excellent in cross-validation (spatial CV R2/RMSE = 0.84/14.57 μg/m3) and mapping (no spatial discontinuity). With the help of new AOD dataset, the PM 2.5 estimation exhibits significant improvement (R2: increased from 0.60 to 0.65, RMSE: reduced from 21.52 to 20.10 μg/m3) on retrieving PM 2.5 at ground monitoring site sparse area (the spatial distance to the closest site is over 20 km). Such high temporal-resolved PM 2.5 prediction improved daily estimates of PM 2.5 as it leveraged the additional hourly information to well capture the transport pattern, with the increased R2 (from 0.72 to 0.77) and decreased RMSE (from 15.54 to 14.30 μg/m3) at ground monitoring site sparse area. The annual averaged PM 2.5 estimated by hourly model also exhibit noticeable differences (∼6%) from that estimated with daily model, implying the latter might suffer overestimation (∼3%) in population-weighted PM 2.5 exposure across China. In addition, our new PM 2.5 dataset shows the Chinese average PM 2.5 is highest around 24:00, lowest around 17:00. Synopsis: By performing the wavelet decomposition on meteorological factors/AOD (independent variables), the hourly seamless PM 2.5 concentration across China is well estimated with improved spatiotemporal connection. • The model accurately estimates seamless hourly PM 2.5 (0.01° × 0.01°) across China • Exceptional performance noted for daytime/nighttime estimations • Using AOD improves PM 2.5 estimation in regions with sparse monitoring sites • Hourly-revolved prediction enhances the accuracy in daily/annually estimates [ABSTRACT FROM AUTHOR]

• A high-precision algorithm to retrieve AOD over the entire land is developed for MERSI-II. • A new cost function is proposed for fast AOD retrieval. • The land surface reflectance/albedo is determined using BRDF database. • Eight candidate aerosol models are assumed based on long-term AERONET measurements. • Comprehensive evaluation is conducted for the two-years MERSI-II AOD retrievals over China. The Medium Resolution Spectral Imager-II (MERSI-II) onboard the recently launched Chinese Fengyun-3D (FY-3D) satellite has great capability in detecting global aerosols as it includes aerosol bands similar to Moderate Resolution Imaging Spectroradiometer (MODIS). However, to date, aerosol retrieval based on MERSI-II is still limited to dark target regions and there is no official aerosol products for the MERSI-II. This study focuses on developing a high-precision algorithm to retrieve aerosol optical depth (AOD) suitable for entire land areas (except snow/ice and inland waters) based on MERSI-II measurements. Considering both the accuracy and retrieval efficiency, a new cost function is constructed based on (1) the fact that the AOD (550 nm) retrieved independently from different bands should be the same with the correct aerosol model, and (2) the assumption that the aerosol types are the same in the 5 × 5 km spatial range. The retrieval method based on the new cost function is nearly 50 times faster than most current methods using iterative calculations. To extend the application adaption of the FY-3D MERSI-II AOD retrieval and avoid the errors caused by the surface Lambertian hypothesis, a bidirectional reflectance distribution function (BRDF) database is built using MODIS products. Eight candidate aerosol models in different natural zones of China are constructed based on AERONET aerosol products from 2010 − 2021. The new method is applied to MERSI-II images over China and validated against ground-based measurements at 14 stations from 2020 to 2021. MODIS aerosol products from three operational algorithms are also used for comparison purposes. The results show that MERSI-II AOD retrievals agree well with the ground-based measurements with correlation coefficient (R), root mean square error (RMSE), and relative mean bias (RMB) of 0.913, 0.123, and 0.955, respectively. In addition, 72.19 % of AOD matchups fall within the expected error (EE) envelopes. The MERSI-II retrievals show higher accuracy than that of MODIS dark target (DT) and deep blue (DB) products and comparable accuracy of the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. MERSI-II AOD also shows higher stability in terms of spatial and temporal and better performance under heavy aerosol loading conditions than MODIS products. A good AOD agreement with R from 0.777 to 0.863 between MERSI-II and MODIS products is found over the land of China. The new method showing high retrieval efficiency and accuracy has great potential to be operationally applied on AOD retrieval for MERSI-II. [ABSTRACT FROM AUTHOR]

This is a multicenter prospective cohort study to analyze the correlation of traditional Chinese medicine (TCM) syndrome evolvement and cardiovascular events in patients with stable coronary heart disease (CHD). The impact of syndrome evolvement on cardiovascular events during the 6-month and 12-month follow-up was analyzed using complex networks approach. Results of verification using Chi-square test showed that the occurrence of cardiovascular events was positively correlated with syndrome evolvement when it evolved from toxic syndrome to Qi deficiency, blood stasis, or sustained toxic syndrome, when it evolved from Qi deficiency to blood stasis, toxic syndrome, or sustained Qi deficiency, and when it evolved from blood stasis to Qi deficiency. Blood stasis, Qi deficiency, and toxic syndrome are important syndrome factors for stable CHD. There are positive correlations between cardiovascular events and syndrome evolution from toxic syndrome to Qi deficiency or blood stasis, from Qi deficiency to blood stasis, or toxic syndrome and from blood stasis to Qi deficiency. These results indicate that stable CHD patients with pathogenesis of toxin consuming Qi, toxin leading to blood stasis, and mutual transformation of Qi deficiency and blood stasis are prone to recurrent cardiovascular events. [ABSTRACT FROM AUTHOR]

Inter-provincial trade may cause transfer of air pollutants and associated health risks, leading to the issue of environmental inequality. Strengthened air pollution control in China over the past years has resulted in improvements in air quality and public health, while its effect in modulating the trade-associated environmental inequality is still unknown. Here we explore the impact of air pollution control on the trade-associated environmental inequality in China not only in terms of traditional emissions-based index, but also in terms of exposure-based index which is more related to health risks. Although the inter-provincial transfer of pollutant emissions were substantially reduced due to air pollution control, the emissions-based index implied that the imbalance of pollutant emissions and value added among the 31 provinces aggravated from 2012 to 2017 in China. Moreover, the exposure-based index suggested that air pollution control reduced the absolute and relative environmental inequality associated with human health risks due to inter-provincial transfer. The cost related to inter-provincial trade was about 5.46 premature deaths per 100 million RMB value added in 2012, which fell to 3.91 deaths in 2017. The average percentage of economic losses to value added of all transfers was 5% in 2012 and dropped to 3.6% in 2017. This study reflects inter-provincial trade-associated transfer and environmental inequality in a more refined and comprehensive way, with a view to providing a reference for inter-provincial economic cooperation and financial compensation. [Display omitted] • Exposure-based index is used to quantify environmental inequality on health risk. • Air pollution control reduced trade-associated transfers of pollutant emissions. • The imbalance of emissions and value added aggravated during 2012–2017 in China. • Air pollution control reduced environmental inequality associated with human health. [ABSTRACT FROM AUTHOR]

Ambient PM 2.5 exerts strong regional pattern for its ability for long-range transport, implying that including the features of surrounding stations may improve the accuracy of machine-learning based model to estimate surface PM 2.5 from the satellite-retrieved aerosol optical depth (AOD). However, most of current models either just use single point features, or simply average the observed surface PM 2.5 from adjacent stations based on a fixed spatial proportional relationship. The question that how to properly take advantage of the features of surrounding stations for retrieving PM 2.5 is still not well addressed. Here we propose an integrated algorithm called joint features random forest (JFRF) model which includes complex feature differences with surrounding stations and the observation of stations to learn the dynamic relations with the PM 2.5 of target pixel, rather than the weighted average feature (WAF) only by surface PM 2.5 as traditional models (with WAF) used. Results of cross validation suggest better performance of JFRF (R2 = 0.61–0.8; RMSE = 15.97–20.91 μg/m3) than single point feature model (ΔR2 = 0.09–0.3). JFRF also exhibits better performance than traditional models (with WAF) (ΔR2 = 0.05–0.11), particularly in regions with large AOD gradient (accounts for 33% of the total test set), which is of great significance for accurately representing the spatial heterogeneity of PM 2.5 (e.g., pollution edging and hot spots areas). And the exclusion of AOD from the features significantly reduced the model performance (ΔR2 = −0.07 ∼ −0.1). Therefore, our study demonstrates the important of the feature differences of surrounding stations and satellite-retrieved AOD in representing the regional pattern of PM 2.5 and further helping the machine-learning based model to improve the accuracy in estimating surface PM 2.5. • A novel PM 2.5 retrieval method using feature of surrounding stations was developed. • Including feature of surrounding stations significantly improves model performance. • New method performs better than method with WAF for areas with large AOD gradient. • The importance of AOD is greater in the new method than traditional methods with WAF. • New method can successfully capture the spatial heterogeneity of PM 2.5 [ABSTRACT FROM AUTHOR]

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor. [ABSTRACT FROM AUTHOR]

The satellite-retrieved aerosol optical depth (AOD) provides unique estimation of aerosol loading across a continuous space. However, current AOD products with a relatively coarse resolution (≥1 km) can hardly capture the details in urban areas with large spatial AOD gradients. To address this issue, here we developed a novel retrieval algorithm for retrieving AOD with extra fine spatial resolution (30 m) from Landsat-8 satellite OLI images. In the algorithm, the three land surface reflectance (LSR) estimation schemes and four aerosol types are adopted to improve the retrieval accuracy. The algorithm is applied on Beijing and Wuhan city in China during 2014–2019. Results suggest that the retrieved AOD with the new algorithm exhibits good agreement with the ground-based measured AOD (R 2 = 0.920), and 81.63% of the AODs fall within the expected error line with a root-mean-square error of 0.112. Moreover, the high-resolution AOD products also successfully identified polluted sources and discrepancy of aerosol loading over different land cover types in two megacities in China, implying its great potential on detecting fine aerosol emission sources over the complex urban area. Such improvement of the new algorithm for retrieving 30 m AOD developed in this study demonstrates its substantial potentials in supporting further studies of air pollution management and human exposure at extra-fine spatial scale. [Display omitted] ● Urban AOD at 30 m spatial resolution are retrieved from Landsat-8 OLI images. ● Surface anisotropy and aerosol type variation are represented in retrieval. ● Detailed AOD gradient in urban area could be represented. ● Aerosol emission sources are identified with high resolution AOD dataset. [ABSTRACT FROM AUTHOR]

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure, and related health impacts, especially in China, which experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, sulfur dioxide = SO2, ammonia = NH3, VOC, and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results in small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April, and October, and SO2 in April, but overestimate NH3 in April, and VOC in January and October. Primary PM2.5 emissions were also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories. [ABSTRACT FROM AUTHOR]

The geostationary satellite Himawari-8, possessing the Advanced Himawari Imager (AHI), which features 16 spectral bands from the visible to infrared range, is suitable for aerosol observations. In this study, a new algorithm is introduced to retrieve aerosol optical depth (AOD) over land at a resolution of 2 km from the AHI level 1 data. Considering the anisotropic effects of complex surface structures over land, Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) model parameters product (MCD19A3) is used to calculate the surface reflectance for Himawari-8's view angle and band. In addition, daily BRDF model parameters are calculated in areas with dense vegetation, considering the rapid variation of surface reflectance caused by vegetation growth. Moreover, aerosol models are constructed based on long duration Aerosol Robotic Network (AERONET) single scattering albedo (SSA) values to stand for aerosol types in the retrieval algorithm. The new algorithm is applied to AHI images over Bohai Rim region from 2018 and is evaluated using the newest AERONET version 3 AOD measurements and the latest MODIS collection 6.1 AOD products. The AOD retrievals from the new algorithm show good agreement with the AERONET AOD measurements, with a correlation coefficient of 0.93 and root mean square error (RMSE) of 0.12. In addition, the new algorithm increases AOD retrievals and retrieval accuracy compared to the Japan Aerospace Exploration Agency (JAXA) aerosol products. The algorithm shows stable performance during different seasons and times, which makes it possible for use in climate or diurnal aerosol variation studies. [ABSTRACT FROM AUTHOR]

Accurate estimation of surface PM 2.5 concentration is critical for the assessment of PM 2.5 exposure and associated health impacts. Due to the limited spatial coverage of ground monitoring stations, most studies often use the satellite products to estimate surface PM 2.5 concentration by constructing a comprehensive relationship between satellite-retrieved aerosol optical depth (AOD) and ground-based measured PM 2.5 concentration with machine learning (ML) technologies. However, uncertainties of ML-based models may lead to considerable biases in PM 2.5 estimation, which need carefully examined. Here we evaluate the accuracy of estimated PM 2.5 concentration from two popular ML-models (i.e., Random Forest and the BP Neural Network) which were trained and tested using hourly data of satellite-retrieved AOD from HIMAWARI, ground-based measured PM 2.5 from China National Environmental Monitoring Center, ERA5 meteorological conditions, and other auxiliary variables for a whole year of 2017 over China. We propose a new validation method considering the spatial pattern of the data during the validation. The results suggest that the traditional validation methods may overestimate the performance of the models on estimating the PM 2.5 at the area with sparse in-situ measurements. Moreover, the spatial distribution pattern of the training data will largely affect the evaluation of models performance, which should be carefully considered. For future study, at least a site-specifically validation is needed rather than only using random sampling validation. • Spatial distribution in training and test set is important for model validation. • Sample-based CV could overestimate the performance at sparse training site areas. • New spatial pattern-based CV can evaluate models performance at different situations. • Model performance for extrapolation is much worse than that for interpolation. • Increase of the shortest pattern distance could decrease the performance of models. [ABSTRACT FROM AUTHOR]

Non-Ferrous Metallurgical Wastes, including copper slag, red mud, jarosite residue, zinc-bearing dust, pyrite cinder, and nickel slag, are hazardous waste products released during the metal extraction process. Metallurgical solid wastes pose significant risks to the environment due to their content of hazardous elements, which may impact the quality of surface water and groundwater. However, the iron content in non-ferrous metallurgical solid wastes such as copper slag, red mud and zinc-containing dust can often reach 30–50%, which is higher than the average iron grade (27%) of the iron ores found in China and has a high utility value. In this manuscript, we summarize the primary optimization process of the coal-based direct reduction-magnetic separation method for metallurgical recovery and further purification of metallurgical solid waste. We discuss technological advancements, including rotary kiln, rotary bottom furnace, and tunnel kiln methods, and address environmental and economic aspects, especially in coal-rich regions with limited natural gas resources. Additionally, we have highlighted the technical challenges and developmental bottlenecks of the direct reduction-magnetic separation process; our findings reveal that the improved processes, characterized by high recycling efficiency and low energy consumption and secondary environmental pollution, continue to be the focus of research and development for the sustainable utilization of metallurgical solid waste. [Display omitted] [ABSTRACT FROM AUTHOR]

With the increasing demand for chemical products in China, how to manage the risks associated with hazardous freight transportation has attracted arising attentions from both government and industry practitioners. From the perspective of carrier companies, transportation modal choice and routing for hazardous freight transportation need to be carefully designed, which motivates current research. This paper aims to optimize the service mode and routing for hazardous freight transportation in a rail-truck network with bi-objectives optimization approach. Several practical concerns are addressed, including the coordination of rail transportation which has fixed train running plan and road transportation which is flexible and mobile, and the consideration of alternative path when the expressway is temporally blocked for hazardous freight due to the traffic restriction measurements on inter-city road segments. Furthermore, the time-varying transportation risks and costs associated with accidents of hazardous freight transportation are investigated. The Max-Min Ant-colony Algorithm (MMAS) algorithm is adopted for solution. For model validations and conclusion generalization, this paper implements a real-life case study in Beijing-Tianjin-Hebei region in China with the objective of minimizing transportation risks and costs. The solutions show that traffic restrictions have an adverse impact on the carrier and alternative paths are able to help the carriers further optimize their routing strategy. A careful consideration and appropriate processing of differences in operational characteristics of two transportation modes, is able to provide a better and more realistic understanding of rail-truck hazardous freight transportation problem. [ABSTRACT FROM AUTHOR]

Fast and accurate prediction of ambient ozone (O 3) formed from atmospheric photochemical processes is crucial for designing effective O 3 pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O 3 prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O 3 concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O 3 formation and quantifying the O 3 response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O 3 concentrations are strongly influenced by the initialized O 3 concentration, as well as emission and meteorological factors during daytime when O 3 is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O 3 chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O 3 pollution. [Display omitted] • A deep learning method (DeepCTM) is proposed to mimic the O 3 simulations of CMAQ • DeepCTM shows great potentials in 7-day continual forecast of O 3 concentrations • DeepCTM exhibits high efficiency in identifying dominant contributors to O 3 formation • DeepCTM efficiently predicts O 3 response to variations in emissions and meteorology [ABSTRACT FROM AUTHOR]

Particulate matter with an aerodynamic diameter of less than 1 μm (PM 1.0) can be extremely hazardous to human health, so it is imperative to accurately estimate the spatial and temporal distribution of PM 1.0 and analyze the impact of related policies on it. In this study, a stacking generalization model was trained based on aerosol optical depth (AOD) data from satellite observations, combined with related data affecting aerosol concentration such as meteorological data and geographic data. Using this model, the PM 1.0 concentration distribution in China during 2016–2019 was estimated, and verified by comparison with ground-based stations. The coefficient of determination (R2) of the model is 0.94, and the root-mean-square error (RMSE) is 8.49 μg/m3, mean absolute error (MAE) is 4.10 μg/m3, proving that the model has a very high performance. Based on the model, this study analyzed the PM 1.0 concentration changes during the heating period (November and December) in the regions where the "coal-to-gas" policy was implemented in China, and found that the proposed "coal-to-gas" policy did reduce the PM 1.0 concentration in the implemented regions. However, the lack of natural gas due to the unreasonable deployment of the policy in the early stage caused the increase of PM 1.0 concentration. This study can provide a reference for the next step of urban air pollution policy development. • Use stacking machine learning algorithm and satellite data to reconstruct PM 1.0. • "Coal-to-gas" policy led to PM 1.0 concentration changes, notably in 2017 and 2019. • Provides valuable insights for mitigate air pollution in Chinese cities. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the prevalence, severity, oral distribution, and associated risk factors of carious lesions in the pri- mary teeth in children in Jinzhou, China, aged 7-9 years., Materials and Methods: A total of 1603 primary school students aged 7-9 years old from public and private schools in Jinzhou were recruited using multi-stage, stratified, and random sampling methods for cross-sectional studies. Carious lesions in the primary teeth of school-age children were detected and recorded according to the World Health Organiza- tion standard, and a questionnaire was collected from a parent or guardian with information on the relevant risk factors for the child. Odds ratios and 95% confidence intervals of factors related to carious lesions were estimated using binary logistic regression analysis (p<0.05)., Results: The prevalence of carious lesions in the primary teeth was 74.5%, the average number of carious lesions was 3.02, and dmft was 4.08 ± 2.74. There were 655 cases (77.1%) of dental carious lesions in boys and 546 cases (72.5%) in girls, and the difference between them was statistically significant (p<0.05). Binary logistic regression analysis showed that the mother's educational level, brushing frequency, brushing time, and consumption of soft drinks, desserts, and sweets were all associated with a higher prevalence of carious lesions (p<0.05)., Conclusions: The children in our sample had a high incidence of carious lesions of the primary teeth, especially the man- dibular primary molars. Social demographic factors, oral hygiene habits, and dietary habits all play an important role in the occurrence of carious lesions.

Purpose: To investigate the prevalence, clinical manifestations and related risk factors of malocclusion in schoolchildren of Jinzhou City, China., Materials and Methods: A total of 2162 children aged 6-12 years were randomly selected from various districts of Jinzhou. Conventional clinical examination was performed by stomatologists, and the results were described based on different clinical manifestations of malocclusion and individual normal occlusion. Further, a questionnaire survey completed by children's parents or guardians provided the demographic data, lifestyle, and oral habits. The distribution of individual normal occlusion and malocclusion was documented in percentage, and Pearson's Χ2 was used for two-factor analysis. The data were statistically analysed using SPSS software (version 25.0) with a significance level of α = 0.05., Results: A total of 1129 boys and 1033 girls were included in this study, i.e. 52.2% and 47.8% of the total number of children, respectively. The prevalence of malocclusion in children aged 6-12 years old in Jinzhou was 67.9%, of which crowded dentition was the most common form, with a prevalence of 71.8%, followed by deep overbite, anterior crossbite, dental spacing, deep overjet, anterior edge-to-edge occlusion, and anterior open bite. In the logistic regression model, the results showed that BMI index had little effect on the occurrence of malocclusion (p > 0.05), while dental caries, bad oral habits, retained primary teeth, and a low labial frenum were all related to the occurrence of malocclusion (p < 0.05). Moreover, the higher frequency and duration of bad oral habits were associated with a higher likelihood of malocclusion., Conclusions: The prevalence of malocclusion in children aged 6-12 years in Jinzhou is high. In addition, bad oral habits (such as lip biting, tongue thrusting, biting/gnawing objects, unilateral chin supporting, and unilateral mastication) and other related risk factors (such as dental caries, mouth breathing, retention of primary teeth, and low labial frenum, etc) were associated with malocclusion.

Previous studies have reported potential adverse effects of exposure to ambient air pollutants on semen quality in infertile men, but studies on the general population have been limited and inconsistent, and the pollutants that play a major role remain unclear. This study aimed to explore the potential association between exposure to six air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , O 3 and CO) during different sperm development periods and semen quality among the general population, and to explore the interaction between different air pollutant exposures. We included 1515 semen samples collected from the Human Sperm Bank. We improved individuals' exposure level estimation by combining inverse distance weighting (IDW) interpolation with satellite remote sensing data. Multivariate linear regression models, restricted cubic spline functions and double-pollutant models were used to assess the relationship between exposure to six air pollutants and sperm volume, concentration, total sperm number and sperm motility. A negative association was found between SO 2 exposure and progressive motility and total motility during 0-90 lag days and 70-90 lag days, and SO 2 exposure during 10-14 lag days adversely affected sperm concentration and total sperm number. Sensitive analyses for qualified sperm donors and the double-pollutant models obtained similar results. Additionally, there were nonlinear relationships between exposure to PM, NO 2 , O 3 , CO and a few semen parameters, with NO 2 and O 3 exposure above the threshold showing negative correlations with total motility and progressive motility, respectively. Our study suggested that SO 2 may play a dominant role in the adverse effects of ambient air pollutants on semen quality in the general population by decreasing sperm motility, sperm concentration and total sperm number. Also, even SO 2 exposure lower than the recommended standards of the World Health Organization (WHO) could still cause male reproductive toxicity, which deserves attention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The "Beautiful China" strategy, launched by the Chinese government in 2020, requires that all cities in China attain 35 μg/m 3 or below for annual mean concentration of PM 2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) by 2035. Meanwhile, China adopts a portfolio of low-carbon policies to meet its Nationally Determined Contribution (NDC) pledged in the Paris Agreement. Previous studies demonstrated the cobenefits to air pollution reduction from implementing low-carbon energy policies. Pathways for China to achieve dual targets of both air quality and CO 2 mitigation, however, have not been comprehensively explored. Here, we couple an integrated assessment model and an air quality model to evaluate air quality in China through 2035 under the NDC scenario and an alternative scenario (Co-Benefit Energy [CBE]) with enhanced low-carbon policies. Results indicate that some Chinese cities cannot meet the PM 2.5 target under the NDC scenario by 2035, even with the strictest end-of-pipe controls. Achieving the air quality target would require further reduction in emissions of multiple air pollutants by 6 to 32%, driving additional 22% reduction in CO 2 emissions relative to the NDC scenario. Results show that the incremental health benefit from improved air quality of CBE exceeds 8 times the additional costs of CO 2 mitigation, attributed particularly to the cost-effective reduction in household PM 2.5 exposure. The additional low-carbon energy polices required for China's air quality targets would lay an important foundation for its deep decarbonization aligned with the 2 °C global temperature target., Competing Interests: The authors declare no competing interest.

Background: Apolipoprotein E (ApoE) plays an important role in lipid metabolism and clearance. Statins are the most common drugs used to modulate the lipid profile in the clinic therapy; the associations between ApoE polymorphisms and statin response to lipids were inconsistent in previous studies among different ethnicities. Our study aimed to demonstrate the relationships among the statins response and the ApoE gene common polymorphisms and lifestyle risk factors in Chinese arteriosclerotic cardiovascular disease (ASCVD) patients with dyslipidemia., Methods: A total of 1002 dyslipidemia ASCVD patients were recruited in this study, including 311 patients with a history of type 2 diabetes mellitus (T2DM). These patients were all treated with drugs atorvastatin (10 mg/d) or rosuvastatin (5 mg/d) for at least 4 weeks and genotyped for ApoE e2/e3/e4 alleles, using Kompetitive Allele Specific PCR (KASP) and Sanger sequencing. The plasma lipids levels were determined before and after statins treatment., Results: The results of ApoE genotyping with KASP method were consistent with the sequencing analysis. In the total 1002 patients, the E2 phenotypes (e2/e3, e2/e2) had significant lower low-density lipoprotein cholesterol (LDL-C) baseline levels than subjects with E3 (e3/e3, e2/e4) and E4 (e3/e4, e4/e4) phenotypes (P = 0.007, 0.005, respectively), and E2 phenotypes had the highest triglyceride (TG) baseline levels. To statins treatment, E2 phenotypes had a better response in TG, Total cholesterol (TC) and LDL-C reduction percentage compared with other phenotypes, and smoking/alcohol drinking status also had a significant influence on statins response of LDL-C lowering. No significant difference was found in the effects of lipids decreasing between atorvastatin and rosuvastatin drugs in all patients., Conclusions: We developed the KASP technique for the ApoE genotyping, and demonstrated ApoE polymorphisms interacted with smoking/drinking to influence the declining extent of TG, TC and LDL-C levels after statins therapy in Chinese dyslipidemia ASCVD patients. These discoveries developed our cognition with the genetic polymorphisms effects on statin response, which should be taken more seriously in smoking/drinking E4 amino acid isoform carriers.