The construction of synthetic gene circuits in plants has been limited by a lack of orthogonal and modular parts. Here, we implement a CRISPR (clustered regularly interspaced short palindromic repeats) interference (CRISPRi)-based reversible gene circuit platform in plants. We create a toolkit of engineered repressible promoters of different strengths and construct NOT and NOR gates in Arabidopsis thaliana protoplasts. We determine the optimal processing system to express single guide RNAs from RNA Pol II promoters to introduce NOR gate programmability for interfacing with host regulatory sequences. The performance of a NOR gate in stably transformed Arabidopsis plants demonstrates the system's programmability and reversibility in a complex multicellular organism. Furthermore, cross-species activity of CRISPRi-based logic gates is shown in Physcomitrium patens, Triticum aestivum and Brassica napus protoplasts. Layering multiple NOR gates together creates OR, NIMPLY and AND logic functions, highlighting the modularity of our system. Our CRISPRi circuits are orthogonal, compact, reversible, programmable and modular and provide a platform for sophisticated spatiotemporal control of gene expression in plants.
Objective: To compare the diagnostic efficacy of 11C-choline PET/CT, neck ultrasonography, 99mTc-MIBI dual-phase planar scintigraphy, and 99mTc-MIBI SPECT/CT imaging in the diagnosis of primary hyperparathyroidism (PHPT). Methods: We conducted a retrospective analysis of 32 patients with PHPT who visited the Nuclear Medicine Department of Jilin University China-Japan Union Hospital between January 2019 and December 2022. All patients underwent 11C-choline PET/CT, neck ultrasonography, 99mTc-MIBI dual-phase planar scintigraphy, and 99mTc-MIBI SPECT/CT examinations within two months before surgery. Sensitivity, specificity, positive predictive value, and negative predictive value of each imaging study were compared using postoperative pathology and follow-up results. Diagnostic efficacy was further analyzed using ROC curve analysis. Factors influencing on 99mTc-MIBI imaging were also investigated. Results: A total of 35 lesions were resected in the 32 patients. The diagnostic sensitivity of 11C-choline PET/CT, neck ultrasonography, 99mTc-MIBI dual-phase planar scintigraphy, and 99mTc-MIBI SPECT/CT was 88.2%, 52.9%, 58.8%, and 67.6%, respectively. Specificity was 96.8%, 95.7%, 96.8%, and 95.7%, respectively. Positive predictive values were 90.9%, 81.8%, 86.9%, and 85.2%, respectively, and negative predictive values were 95.7%, 84.9%, 86.7%, and 89.1%, respectively. The areas under the ROC curve (AUC) were 0.925, 0.743, 0.778, and 0.817, respectively. Among them, 11C-choline PET/CT had higher sensitivity and AUC than other imaging studies (p<0.05), while specificity, positive predictive value, and negative predictive value were similar (p>0.05). The positive group in 99mTc-MIBI SPECT/CT imaging had significantly larger lesion diameters than the negative group (P<0.05), while preoperative blood calcium and PTH showed no statistical differences (P>0.05). Conclusion: 11C-choline PET/CT demonstrates superior preoperative diagnostic efficacy for PHPT compared to neck ultrasonography, 99mTc-MIBI dual-phase planar scintigraphy, and 99mTc-MIBI SPECT/CT. Lesion size may be the primary factor affecting the sensitivity of 99mTc-MIBI imaging.
How did the motorcycle emissions evolve during the economic development in China? To address data gaps, this study firstly measured the volatile organic compound (VOC) and intermediate-volatility organic compound (IVOC) emissions from motorcycles. The results confirmed that the emission control of motorcycles, especially small-displacement motorcycles, significantly lagged behind other gasoline-powered vehicles. For the China IV motorcycles, the average VOC and IVOC emission factors (EFs) were 2.74 and 7.78 times higher than the China V-VI light-duty gasoline vehicles, respectively. The notable high IVOC emissions were attributed to a dual influence from gasoline and lubricating oil. Furthermore, based on the complete EF dataset and economy-related activity data, a county-level emission inventory was developed in China. Motorcycle VOC and IVOC emissions changed from 2536.48 Gg and 197.19 Gg in 2006 to 594.21 Gg and 12.66 Gg in 2020, respectively. The absence of motorcycle IVOC emissions in the existed vehicular inventories led to an underestimation of up to 20%. Across the 15 years, the motorcycle VOC and IVOC emission hotspots were concentrated in the undeveloped regions, with the rural emissions reaching 5.81-10.14 times those of the urban emissions. This study provides the first-hand and close-to-realistic data to support motorcycle emission management and accurate air quality simulations.
Ammonia, a significant precursor for secondary inorganic aerosols, plays a pivotal role in new particle formation. Inventories and source apportionment studies have identified vehicular exhaust as a primary source of atmospheric ammonia in urban regions. Existing research on the factors influencing ammonia emissions from gasoline vehicles exhibits substantial inconsistencies in both test results and analyses. The lack of a uniform pattern in ammonia emissions across different standard vehicles and the significant overlap in test results across diverse operational conditions highlight the complexities in this field of study. While individual results can be interpreted through a mechanistic lens, disparate studies often lack a common explanatory framework. To address this gap, our study leverages the robust and comprehensive approach of meta-analysis to reconcile these inconsistencies and provide a more precise understanding of the factors influencing ammonia emissions from gasoline vehicles. A large number (N = 537) of ammonia emission factors were extracted after screening >1628 publications. The combined ammonia emission factor was 23.57 ± 24.94 mg/km. Emission standards, engine type, ambient temperatures, mileage, vehicle speed, and engine displacement have a significant impact on ammonia emission factors, explaining the ammonia emission factor by up to 50.63 %, with speed being the most significant factor. All these factors are attributed to the interplay of catalyst properties, lambda, and residence time (space velocity). In the current fleet, ammonia emission control is relatively insufficient under low-speed and ultra-high speed, low temperature, and ultra-high mileage conditions. Since ammonia emission factors do not monotonically decrease with the upgrading of motor vehicle emission standards, it is called for the addition of ammonia emission factors indicators in motor vehicle emission standards, and stipulation of targeted testing procedures and testing instruments.
Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.
Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom-up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes â¼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.
The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO2) tropospheric vertical column density (TVCD) in 2020. The NO2 TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO2 TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO2 TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO2 TVCD continues to rise. This revenge bounce of NO2 indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development.
Air Pollutants , Air Pollution , COVID-19 , Humans , Nitrogen Dioxide/analysis , Air Pollutants/analysis , Air Pollution/analysis , Communicable Disease Control , Nitrogen Oxides/analysis , China/epidemiology , Environmental Monitoring
How would the organic gas emission inventories of future urban vehicles change with new features of advanced technology cars? Here, volatile organic compounds (VOCs) and intermediate volatile organic compounds (IVOCs) from a fleet of Chinese light-duty gasoline vehicles (LDGVs) were characterized by chassis dynamometer experiments to grasp the key factors affecting future inventory accuracy. Subsequently, the VOC and IVOC emissions of LDGVs in Beijing, China, from 2020 to 2035, were calculated and the spatial-temporal variations were recognized under a scenario of fleet renewal. With the tightening of emission standards (ESs), cold start contributed a larger fraction of the total unified cycle VOC emissions due to the imbalanced emission reductions between operating conditions. It took 757.47 ± 337.75 km of hot running to equal one cold-start VOC emission for the latest certified vehicles. Therefore, the future tailpipe VOC emissions would be highly dependent on discrete cold start events rather than traffic flows. By contrast, the equivalent distance was shorter and more stable for IVOCs, with an average of 8.69 ± 4.59 km across the ESs, suggesting insufficient controls. Furthermore, there were log-linear relationships between temperatures and cold-start emissions, and the gasoline direct-injection vehicles performed better adaptability under low temperatures. In the updated emission inventories, the VOC emissions were more effectively reduced than the IVOC emissions. The start emissions of VOCs were estimated to be increasingly dominant, especially in wintertime. By winter 2035, the contribution of VOC start emissions could reach 98.98 % in Beijing, while the fraction of IVOC start emissions would decrease to 59.23 %. Spatially allocation showed that the high emission regions of tailpipe organic gases from LDGVs have transferred from road networks to regions of intense human activities. Our results provide new insights into tailpipe organic gas emissions of gasoline vehicles, and can support future emission inventories and refined assessment of air quality and human health risk.
Introduction: Despite the growing attention paid to the research of translation technology teaching (TTT), there is still a lack of studies on students' attitudes and the motivational factors in relation to it. To this end, the paper reports on a questionnaire-based study that describes students' attitudes towards translation technology (in the Chinese MTI context) and explores its structural relations with translation mindsets and future work self. Methods: Data were collected from 108 grade 2021 MTI students of three selected Chinese universities and analyzed using descriptive statistics and structural equation modeling (SEM). Results: The results demonstrate that Chinese MTI students' overall attitudes towards translation technology are slightly positive. So far, they perceive translation technology to be slightly effective for translation and are slightly mindful of it. They are slightly influenced by teachers and still feel inhibited when learning and using it. Furthermore, the results also indicate that growth translation mindsets positively influence students' attitudes towards the effectiveness of translation technology, teacher influence, exhibition to translation technology, and mindfulness about translation technology, whereas fixed translation mindsets only negatively predict students' teacher influence. Likewise, future work self-salience positively associates with students' attitudes towards the effectiveness of translation technology and mindfulness about translation technology, while future work self-elaboration positively relates to students' exhibition to translation technology. Among them, growth translation mindsets are the strongest predictor for all attitudes components. Discussion: Theoretical and pedagogical implications are also discussed.
Glioblastoma is the most common primary cranial malignancy, and chemotherapy remains an important tool for its treatment. Sanggenon C(San C), a class of natural flavonoids extracted from Morus plants, is a potential antitumor herbal monomer. In this study, the effect of San C on the growth and proliferation of glioblastoma cells was examined by methyl thiazolyl tetrazolium(MTT) assay and 5-bromodeoxyuridinc(BrdU) labeling assay. The effect of San C on the tumor cell cycle was examined by flow cytometry, and the effect of San C on clone formation and self-renewal ability of tumor cells was examined by soft agar assay. Western blot and bioinformatics analysis were used to investigate the mechanism of the antitumor activity of San C. In the presence of San C, the MTT assay showed that San C significantly inhibited the growth and proliferation of tumor cells in a dose and time-dependent manner. BrdU labeling assay showed that San C significantly attenuated the DNA replication activity in the nucleus of tumor cells. Flow cytometry confirmed that San C blocked the cell cycle of tumor cells in G_0/G_1 phase. The soft agar clone formation assay revealed that San C significantly attenuated the clone formation and self-renewal ability of tumor cells. The gene set enrichment analysis(GSEA) implied that San C inhibited the tumor cell division cycle by affecting the myelocytomatosis viral oncogene(MYC) signaling pathway. Western blot assay revealed that San C inhibited the expression of cyclin through the regulation of the MYC signaling pathway by lysine demethylase 4B(KDM4B), which ultimately inhibited the growth and proliferation of glioblastoma cells and self-renewal. In conclusion, San C exhibits the potential antitumor activity by targeting the KDM4B-MYC axis to inhibit glioblastoma cell growth, proliferation, and self-renewal.
Glioblastoma , Humans , Glioblastoma/drug therapy , Glioblastoma/genetics , Bromodeoxyuridine/pharmacology , Bromodeoxyuridine/therapeutic use , Signal Transduction , Proto-Oncogene Proteins c-myc/metabolism , Agar , Cell Proliferation , Cell Line, Tumor , Apoptosis , Jumonji Domain-Containing Histone Demethylases/metabolism
Perfluoroalkyl acids (PFAAs) are proteinophilic pollutants. We hypothesized that fractionation of PFAAs may occur along a food chain. To testify this hypothesis, we investigated the bioconcentration, bioaccumulation, and fractionation of 11 kinds of PFAAs (C-F = 3-11) along an aquatic food chain consisting of D. magna, zebrafish, and cichlid. The results showed that the proportions of PFNA, PFOA, and all shorter chain PFAAs in the D. magna and fish tissues were lower than the ones in exposure water, opposing to the other longer chain PFAAs. Predation promoted such fractionation differences, and the proportions of PFNA, PFOA, and all shorter chain PFAAs in organisms decreased while those of the other longer chain PFAAs increased along the food chain. The results of isothermal titration calorimetry and molecular docking experiments showed that binding affinities of PFAAs and fish proteins increased with the number of perfluorinated carbons, resulting in a substitution of shorter chain PFAAs by their longer chain analogues. It also triggered the differences in the uptake and elimination of PFFAs and competitive bioaccumulation between longer and shorter chain PFAAs. This study suggests that fractionation should be considered in studying environmental behaviors and evaluating ecological risks of multiple PFAAs.
Alkanesulfonic Acids , Fluorocarbons , Water Pollutants, Chemical , Animals , Zebrafish/metabolism , Food Chain , Molecular Docking Simulation , Fluorocarbons/analysis , Fatty Acids , Alkanesulfonic Acids/metabolism , Water Pollutants, Chemical/analysis
Recent increases in emissions from freight transport have caused strong concerns about air quality in Pakistan, following the rapid development of projects related to the China-Pakistan Economic Corridor (CPEC). This study reported the first measurements of on-road truck emissions in Pakistan and investigated their dependence on altitude along CPEC routes. Emissions from 70 trucks were measured on CPEC highways located in Islamabad (540 m above sea level), Sost (2800 m above sea level), and at the Khunjerab Pass (4693 m above sea level). Calculated emission factors for carbon monoxide, hydrocarbons, and nitrogen oxides from heavy-duty trucks in Islamabad were 12.94 ± 1.46, 15.21 ± 1.67, and 10.69 ± 1.34 g km-1 (95% confidence level), respectively, for pre-Pak-II trucks, and 12.75 ± 2.80, 14.24 ± 3.53, and 10.24 ± 2.34 g km-1 (95% confidence level), respectively, for Pak-II trucks, representing 2-20 times higher values than the emission standards in Pakistan and India. An altitude increase of approximately 4000 m, with the associated changes in meteorology and fleet characteristics, induced an average increase of 103.6%, 86.3%, 124.5%, and 133.6% in the emission factors of carbon monoxide, hydrocarbons, nitrogen oxides, and carbon dioxide, respectively. Moreover, on-road emissions along the CPEC were mainly influenced by truck types. This study will support the budget evaluation of transport emissions from the CPEC trade fleet.
Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method's F1 - score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method's F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.
Algorithms , Computer Security
Semivolatile/intermediate-volatility organic compounds (S/IVOCs) from mobile sources are essential SOA contributors. However, few studies have comprehensively evaluated the SOA contributions of S/IVOCs by simultaneously comparing different parameterization schemes. This study used three SOA schemes in the CMAQ model with a measurement-based emission inventory to quantify the mobile source S/IVOC-induced SOA (MS-SI-SOA) for 2018 in China. Among different SOA schemes, SOA predicted by the 2D-VBS scheme was in the best agreement with observations, but there were still large deviations in a few regions. Three SOA schemes showed the peak value of annual average MS-SI-SOA was up to 0.6 ± 0.3 µg/m3. High concentrations of MS-SI-SOA were detected in autumn, while the notable relative contribution of MS-SI-SOA to total SOA was predicted in the coastal areas in summer, with a regional average contribution up to 20 ± 10% in Shanghai. MS-SI-SOA concentrations varied by up to 2 times among three SOA schemes, mainly due to the discrepancy in SOA precursor emissions and chemical reactions, suggesting that the differences between SOA schemes should also be considered in modeling studies. These findings identify the hotspot areas and periods for MS-SI-SOA, highlighting the importance of S/IVOC emission control in the future upgrading of emission standards.
Air Pollutants , Volatile Organic Compounds , Vehicle Emissions/analysis , Aerosols/analysis , China , Seasons , Air Pollutants/analysis
The issue of employability has already become a well-delineated topic of study among interpreting educators. However, the current literature still lacks descriptive research on interpreting students' employability development and ignores the developmental effects of interpreter competences in this process. Moreover, the advantage of using career adaptability for measurement is also under-researched. This exploratory case study aims at taking an initial step forward, surveying interpreting students' career adaptability development and the developmental effects of different interpreter competences on major adaptability resources, and ultimately diagnosing curriculum problems and making modifications accordingly. Thirty grade 2019 interpreting students from three Chinese universities contributed to data collection, through six questionnaires in a two-wave survey. The results highlight that, throughout the Chinese MTI program, interpreting students could become more concerned and well prepared for their future (concern), more curious to explore themselves and their surroundings (curiosity), and more capable of solving problems (confidence). The results also indicate that students' knowledge and language competence serve as the major facilitators in this process, and that other interpreter competences, such as psychological competence, transfer competence, professionalism, and cross-cultural competence, are also instrumental. In order to further boost their adaptability constructs, the results suggest that students' language and knowledge competence, professionalism, and cross-cultural and mental agility still need to be improved. Five suggestions for curriculum revision have been raised accordingly. As an initial effort, the current study will hopefully inspire further studies on interpreting students' career adaptability and add more knowledge to the curriculum design from this viewpoint.
Low-level features contain spatial detail information, and high-level features contain rich semantic information. Semantic segmentation research focuses on fully acquiring and effectively fusing spatial detail with semantic information. This paper proposes a multiscale feature-enhanced adaptive fusion network named MFEAFN to improve semantic segmentation performance. First, we designed a Double Spatial Pyramid Module named DSPM to extract more high-level semantic information. Second, we designed a Focusing Selective Fusion Module named FSFM to fuse different scales and levels of feature maps. Specifically, the feature maps are enhanced to adaptively fuse these features by generating attention weights through a spatial attention mechanism and a two-dimensional discrete cosine transform, respectively. To validate the effectiveness of FSFM, we designed different fusion modules for comparison and ablation experiments. MFEAFN achieved 82.64% and 78.46% mIoU on the PASCAL VOC2012 and Cityscapes datasets. In addition, our method has better segmentation results than state-of-the-art methods.
Image Processing, Computer-Assisted , Neural Networks, Computer , Gene Fusion , Image Processing, Computer-Assisted/methods , Semantics
Intermediate volatility organic compounds (IVOCs) from mobile sources contribute significantly to secondary organic aerosol (SOA) formation. However, the assessments of IVOC emissions remain considerably uncertain due to the lack of localized measured data and detailed emission source classifications. This study established a comprehensive database of IVOC emission factors (EFs) for mobile sources based on the diversified measured EFs and correlations with hydrocarbons. The provincial-level IVOC emission inventories over China were further established by integrating activity data of various mobile sources. The national mobile source IVOC emissions were 507.5 Gg in 2017. The IVOC emissions of on-road and non-road mobile sources were roughly the same. Trucks and non-road construction machineries were the major contributors to IVOC emissions, accounting for >66 % of the total. The IVOC emission characteristics and spatial distributions from various mobile sources varied significantly with different types and usages. The IVOC emission inventories with detailed classifications can be used to evaluate emission control policies for mobile sources. Incorporating localized measured data would be beneficial for a better understanding for the atmospheric impacts of mobile source IVOC emissions.
Air Pollutants , Volatile Organic Compounds , Vehicle Emissions/analysis , Aerosols/analysis , Volatile Organic Compounds/analysis , Motor Vehicles , Hydrocarbons , China , Air Pollutants/analysis
Identification of air toxics emitted from light-duty gasoline vehicles (LDGVs) is expected to better protect human health. Here, the volatile organic compound (VOC) and intermediate VOC (IVOC) emissions in the high-emitted start stages were measured on a chassis dynamometer under normal and extreme temperatures for China 6 LDGVs. Low temperature enhanced the emission rates (ERs) of both VOCs and IVOCs. The VOC ERs were averaged 5.19 ± 2.74 times higher when the temperature dropped from 23 °C to 0 °C, and IVOCs were less sensitive to temperature change with an enlargement of 2.27 ± 0.19 times. Aromatics (46.75 ± 2.83%) and alkanes (18.46 ± 1.21%) dominated the cold start VOC emissions under normal temperature, which was quite different from hot running emission profiles. From the perspective of emission inventories, changes in the speciated composition of VOCs and IVOCs were less important than that in the actual magnitude of ERs under cold conditions. However, changes in the ERs and emission profiles were equally important at high temperatures. Furthermore, high time-resolved measurements revealed that low temperature enhanced both the emission peak and peak duration of fuel components and incomplete combustion products during cold start, while high temperature only increased the peak concentration of fuel components.
Air Pollutants , Volatile Organic Compounds , Air Pollutants/analysis , Environmental Monitoring , Gasoline/analysis , Humans , Motor Vehicles , Temperature , Vehicle Emissions/analysis , Volatile Organic Compounds/analysis
The growing of vehicle population aggravates air pollution and threatens human health. In this study, based on the refined whole-process vehicle emission inventory considering volatile organic compounds (VOCs) evaporation emission, the CAMx model was applied to comprehensively quantify the impacts of the vehicle sector on the annual and seasonal concentrations of PM2.5 and O3 in China. Also, the health risks caused by long-term exposure to PM2.5 and O3 were evaluated. The model results showed that vehicle emission was an important source of severe O3 pollution in summer, with a contribution of more than 30% in most parts of China, but not an important source of serious PM2.5 pollution in winter, with a contribution of less than 20% in heavily polluted regions in China. Compared to tailpipe emission, vehicle VOCs evaporation emission led to increases of 25% and 47% to sectoral contribution to PM2.5 and O3. Health risk assessment results showed that attributable deaths caused by long-term exposure to PM2.5 and O3 were 975,029 and 46,043 in 2018, to which vehicle emission contributed approximately 12.5% and 22.2%, respectively.
Air Pollutants , Air Pollution , Ozone , Air Pollutants/analysis , Air Pollution/analysis , China , Environmental Monitoring , Humans , Particulate Matter/analysis , Vehicle Emissions/analysis
In this study, real-time measurement of Volatile Organic Compounds (VOCs) was conducted at an urban site in Changzhou, a typical corridor city in the Yangtze River Delta (YRD) region in China, by Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) during 2019 China International Import Expo (CIIE) episode. An improved method based on Air Quality Index (AQI) value is applied to identify polluted and clean periods. Diurnal pattern of VOC levels revealed elevated photochemical reactivity during polluted periods. Five VOC sources were identified by Positive Matrix Factorization (PMF) model, including secondary formation (22.71 ± 12.33%), biogenic (21.50% ± 11.76%), solvent usage (20.50 ± 10.07%), vehicle exhaust (18.32 ± 8.32%), and industrial process and fuel usage (16.96 ± 13.21%). The mean contribution of vehicular exhaust was 10.84% higher during the nighttime than the daytime under polluted days. The biogenic source contributed more during clean periods, while the secondary formation presented the opposite. Spatial analysis displayed that the VOC concentration was higher in the S and SSE. In terms of the regional transport, short-distance air masses from the northeast and the south within the YRD region led to high VOC levels and biogenic VOC derived from the ocean might affect the entire region. Stringent emission control policies enforced over the YRD for 2019 CIIE provided an excellent opportunity to determine the source-receptor response. As joint control area, the VOC level of Changzhou exhibited a substantial reduction and the VOC amounts emitted by solvent usage showed the biggest decrease (-58%). The findings of this study highlight the superiority of high time-resolved data in identifying the dynamic variation pattern (with the change of time and wind) of VOC levels and emission intensities.
Air Pollutants , Ozone , Volatile Organic Compounds , Air Pollutants/analysis , China , Environmental Monitoring , Ozone/analysis , Volatile Organic Compounds/analysis
