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Increased nitrogen (N) deposition and land-use and land-cover change (LUCC) have influenced the terrestrial ecosystem carbon budget in China over the past few decades. However, the coupling effects of N deposition and LUCC on the carbon cycle remain unclear. This study first evaluated the effects of LUCC on N deposition based on estimated N deposition data from NO column remote sensing data and the GlobeLand30 LUCC dataset, and then assessed the coupling effects of N deposition and LUCC on carbon budgets in China based on a terrestrial ecosystem process-based model. The results showed that the average rate of increase in N deposition in China was 0.35 Tg N yr (Tg = 10 g), which caused net primary production (NPP) and net ecosystem production (NEP) to rise by 92.2 Tg C yr and 46.9 Tg C yr, respectively. The effects of LUCC reduced N deposition by 0.21 Gg N yr (Gg = 10 g). The land changed from forest to cropland had the greatest rate of increase in N deposition among all types of land-cover change. Changes from cropland to forest slowed the rate of N deposition increase the most. Generally, the change in N deposition resulting from LUCC reduced NPP and NEP by 0.7 and 0.4 Gg C yr, respectively. Compared with the total effects of N deposition on NPP and NEP, N deposition changes caused by LUCC had a limited aggregate effect on the C budget. [ABSTRACT FROM AUTHOR]

Observations of carbon monoxide (CO) retrieved from Scanning Imaging Absorption SpectroMeter for Atmospheric Chartography (SCIAMACHY), Measurement of Pollution in the Troposphere (MOPITT), and Atmospheric Infrared Sounder (AIRS) are compared in this article. To better validate the retrieved data from SCIAMCHY, AIRS, and MOPITT, six surface stations at different locations and with various elevations were chosen. The results show these three instruments can all reflect CO spatial distribution well and show same temporal variations of CO concentration as well as six surface station measurements. MOPITT and AIRS have similar retrieval results with correlation coefficients being mostly over 0.70, except for a sixth field station on Crozet Island. The three satellites all have the ability to monitor CO concentration change on land, but SCIAMCHY results show a relatively larger bias than MOPITT and AIRS in low CO concentration areas because of systematic error. [ABSTRACT FROM PUBLISHER]

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO2columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO2columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NOxemission data to expand the density of NO2columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO2columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m–2year–1in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets. [ABSTRACT FROM PUBLISHER]

Particulate matter (PM) with an aerodynamic diameter of <2.5 μm (PM2.5) has become the primary air pollutant in most major cities in China. Some studies have indicated that there is a positive correlation between the aerosol optical thickness (AOT) and surface-level PM2.5concentration. In order to estimate PM2.5concentration over large areas, a model relating the concentration of PM2.5and AOT has been established. The scale height of aerosol and relative humidity as well as the effect of surface temperature and wind velocity were introduced to enhance the model. 2013 full year Moderate Resolution Imaging Spectroradiometer (MODIS) AOT data and ground measurements of the PM2.5concentration in the Beijing–Tianjin–Hebei region were used to fit a seasonal multivariate linear equation relating PM2.5concentration and AOT, and the accuracy of the model has been determined. When comparing MODIS-estimated PM2.5with the measurements from ground monitoring stations during spring, summer, autumn and winter, we found theR2values were 0.45, 0.45, 0.37, and 0.31, respectively. Based on this model, the spatial distribution of PM2.5concentration during four typical haze events sampled by seasons was derived, and displayed with the backward air trajectories calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We undertook a preliminary analysis about the source of surface-level PMs and the process of its accumulation and dispersion during the haze episodes by analysing the effect of terrain and topography in the specific location of the Beijing–Tianjin–Hebei region. The spatial distribution of the PM2.5concentration showed that the high value region was generally in the southeast of the study area, which approximately overlapped an area of lower vegetation coverage, and the temporal variation of PM2.5concentration indicated that the air pollution was more severe during winter and spring than summer and autumn. The results of the analysis of backward air trajectories suggested that the hazy weather in the Beijing–Tianjin–Hebei region was mainly caused by unfavourable terrain and weather conditions. [ABSTRACT FROM PUBLISHER]

Natural Forest Protection Project (NFPP) is a national project, with great significance to promote the sustainable development of society and economy. NFPP has driven land use change significantly, altering the provision of ecosystem services (ESs) and influencing human well-being. The selection of priority conservation areas would provide a reference for the further implementation of the NFPP. Here we used the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model to perform a comprehensive spatio-temporal assessment of the impact of NFPP on ESs provision, and ordered weighted averaging (OWA) method to identify priority conservation areas by balancing ESs tradeoffs in Qinghai province, China. The results showed that ESs were changed with the increase in forest land due to the implementation of the NFPP from 2000 to 2020. Soil retention, habitat quality, carbon storage and water purification increased significantly, while water yield showed a downward trend during the 2000–2020. By selecting priority conservation areas based on multi-scenarios analysis, the priority conservation areas were mainly distributed in the southern regions of Qinghai province and eastern regions of the Haibei Prefecture, and the area of forest, shrub and natural grass accounted for 6.10%, 32.30% and 69.54%, respectively. This study provides critical knowledge of the impact of ecological engineering on provision of multiple ESs. Selecting the priority conservation areas will be beneficial to ecosystem management and ecological sustainable development of the regions. [Display omitted] • The Natural Forest Protection Project improved regional ecosystem services. • Priority conservation areas (PCA) were picked rely on ecosystem service tradeoffs. • PCA were mainly distributed in regions that ecosystem services balanced well. • We provided a science-based overview for decision-making. [ABSTRACT FROM AUTHOR]

[Display omitted] Brake emissions from vehicles are increasing as the number of vehicles increases. However, current research on brake emissions, particularly the intensity and characteristics of emissions under real road conditions, is significantly inadequate compared to exhaust emissions. To this end, a dataset of 600 (200 unique real-world braking events simulated using three types of brake pads) real-world braking events (called brake pad segments) was constructed and a mapping function between the average brake emission intensity of PM 2.5 from the segments and the segment features was established by five algorithms (multiple linear regression (MLR) and four machine learning algorithms). Based on the five algorithms, the importance of the different features of the fragments was discussed and brake energy intensity (BEI) and metal content (MC) of the brake pad emissions were identified as the most significant factors affecting brake emissions and used as the final modeling features. Among the five algorithms, categorical boosting (CatBoost) had the best prediction performance, with a mean R2 and RMSE of 0.83 and 0.039 respectively for the tenfold cross-validation. In addition, the CatBoost-based model was further compared with the MOVES model to demonstrate its applicability. The CatBoost-based model has better prediction performance than the MOVES model. The MOVES model overpredicts brake fragment emissions for urban roads and underpredicts brake fragment emissions for motorways. Furthermore, the CatBoost-based model was interpreted and visualized by an individual conditional expectation (ICE) plot to break the machine learning "black box", with BEI and MC showing nonlinear monotonic increasing relationships with braking emissions. ICE plot also provides viable technical solutions for controlling brake emissions in the future. Both avoiding aggressive braking driving behavior (e.g., the application of smart transportation technologies) and using brake pads with less metal content (e.g., using ceramic brake pads) can effectively reduce brake emissions. The construction of a machine learning-based brake emission model and the white-boxing of its model provide excellent insights for the future detailed assessment and control of brake emissions. [ABSTRACT FROM AUTHOR]

Marek's disease (MD) is a neoplastic disease of chickens caused by an avian alphaherpesvirus, Marek's disease virus (MDV, also known as Gallid alphaherpesvirus 2 [GaHV2]). A total of 14 microRNA (miRNA) precursors and 26 mature miRNAs have been identified in MDV genome, which were grouped in three distinct clusters. In recent years, our studies revealed the role of MDV encoded cluster 3 miRNAs (or miR-M8-M10) and the specific function of its three members, miR-M6, miR-M7 and miR-M10, in regulating MDV replication and pathogenesis. In this study, we characterized the unique function of the other two members, miR-M8 and miR-M13, in cluster 3 miRNAs. Our results show that miR-M8 and miR-M13 are not important for MDV plaque formation and genome replication in vitro. Animal experiment results show that deletion of miR-M8–5p and miR-M13–5p eliminates the bursa atrophy, but not thymus atrophy, of MDV inoculated chickens. In addition, we found that the survival curve and MD incidences were not affected by disruption of miR-M8 and miR-M13. Taken together, this study uncovers the unique role of miR-M8 and miR-M13 in MDV replication and pathogenesis, which filled the gap in the research of MDV encoded miRNAs. • Construction of miR-M8-5p and miR-M13-5p deletion and revertant viruses. • Both miR-M8 and miR-M13 are dispensable for MDV replication in cell culture. • Both miR-M8 and miR-M13 are dispensable for mortality and MD incidences in chickens. • Deletion of miR-M8-5p and miR-M13-5p eliminates the bursa atrophy induced by MDV infection. [ABSTRACT FROM AUTHOR]

Satellite observations of carbon dioxide (CO2) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO2(denoted XCO2) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO2have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO2is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO2and surface CO2were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO2seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO2we found that the increase of CO2concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO2improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO2. [ABSTRACT FROM PUBLISHER]

Global nitrogen deposition has increased over the past 100 years. Monitoring and simulation studies of  nitrogen deposition have evaluated nitrogen deposition at both the global and regional scale. With the development of remote-sensing instruments, tropospheric NO2column density retrieved from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) sensors now provides us with a new opportunity to understand changes in reactive nitrogen in the atmosphere. The concentration of NO2in the atmosphere has a significant effect on atmospheric nitrogen deposition. According to the general nitrogen deposition calculation method, we use the principal component regression method to evaluate global nitrogen deposition based on global NO2column density and meteorological data. From the accuracy of the simulation, about 70% of the land area of the Earth passed a significance test of regression. In addition, NO2column density has a significant influence on regression results over 44% of global land. The simulated results show that global average nitrogen deposition was 0.34 g m−2yr−1from 1996 to 2009 and is increasing at about 1% per year. Our simulated results show that China, Europe, and the USA are the three hotspots of nitrogen deposition according to previous research findings. In this study, Southern Asia was found to be another hotspot of nitrogen deposition (about 1.58 g m−2yr−1and maintaining a high growth rate). As nitrogen deposition increases, the number of regions threatened by high nitrogen deposits is also increasing. With N emissions continuing to increase in the future, areas whose ecosystem is affected by high level nitrogen deposition will increase. [ABSTRACT FROM PUBLISHER]

[Display omitted] • Machine learning is used to build vehicle CO 2 and NO X transient models. • The models are trained on multiple vehicles and verified by a separate vehicle. • The input features of the models are more comprehensive than MOVES. • For CO 2 and NO X , how different model inputs affect the model results is analyzed. • Different models are constructed according to different pollutants. The transient simulation of CO 2 and NO X from motor vehicles has essential applications in evaluating vehicular greenhouse gas emissions and pollutant emissions. However, accurately estimating vehicular transient emissions is challenging due to the heterogeneity between different vehicles and the continuous upgrading of vehicle exhaust purification technology. To accurately characterize the transient emissions of motor vehicles, a Super-learner model is used to build CO 2 and NOx transient emission models. The actual onboard test data of 9 China VI N 2 vehicles were used to train the model, and the test data of another China VI N 2 vehicle were selected for further robustness verification. There were significant differences in the emissions between the vehicles, but the constructed transient model could capture the common law of transient emissions from China VI N 2 vehicles. The R2 values of CO 2 and NOx emission in the test data of the validation vehicle were 0.71 and 0.82, respectively. In addition, to further prove the model's robustness, the training data were synchronously modelled based on the Moves-method. The Super-learner model has a smaller RMSE on the validation set than the model based on the Moves-method, indicating that the Super-learner model has more transient simulation advantages. The marginal contributions of the model characteristics to the model results were analysed by SHapley Additive exPlanation (SHAP) value interpretation, and the marginal contributions of different pollutant characteristic parameters varied. Therefore, when establishing transient models of different pollutants, the selection of the model parameters demands considering the generation and purification process of different pollutants. The present work provides novel insights into the parameter selection, construction, and interpretation of the transient vehicle emission model. [ABSTRACT FROM AUTHOR]

• Construction of miR-M6−5p or miR-M10−5p deletion Marek's disease virus. • Deletion of miR-M6−5p significantly enhances the replication of Marek's disease virus in cell culture, but not in chickens. • Deletion of miR-M10−5p does not affect growth of Marek's disease virus in vitro and in vivo. • Deletion of miR-M6−5p or miR-M10−5p does not alter the pathogenesis of Marek's disease virus. MicroRNAs (miRNAs) are a class of approximately 22 nucleotides long non-coding RNAs, and virus-encoded miRNAs play an important role in pathogenesis. Marek's disease virus (MDV) is an oncogenic avian alphaherpesvirus that causes immunosuppression and tumors in its natural host, chicken. In the MDV genome, 14 miRNA precursors and 26 mature miRNAs were identified, thus MDV has been used as a model to study the function of viral miRNAs in vivo. Recently, a cluster of miRNAs encoded by MDV, Cluster 3 miRNAs (miR-M8-M10), has been shown to restrict early cytolytic replication and pathogenesis of MDV. In this study, we further analyzed the role of miR-M6 and miR-M10, members of cluster miR-M8-M10, in MDV replication and pathogenicity. We found that, compared to parental MDV, deletion of miR-M6−5p significantly enhanced the replication of MDV in cell culture, but not in chickens. The replication of miR-M6−5p deletion MDV was restored once the deleted sequences were re-inserted. Our results also showed that deletion of miR-M10−5p did not affect the replication of MDV in vitro and in vivo. In addition, our animal study results showed that deletion of miR-M6−5p or miR-M10−5p did not alter the pathogenesis of MDV. In conclusion, our study shows that both miR-M6 and miR-M10 are dispensable for MDV replication and pathogenesis in chickens, while also suggests a repressive role of miR-M6 in MDV replication in cell culture. [ABSTRACT FROM AUTHOR]

Urbanization and the resulting increase in development areas and populations cause micro-climate changes such as the urban heat island (UHI) effect. This micro-climate change can affect vegetation phenology. It can advance leaf unfolding and flowering and delay the timing of fallen leaves. This study was carried out to clarify the impact of urbanization on the leaf unfolding of Mongolian oak. The survey sites for this study were established in the urban center (Mts. Nam, Mido, and Umyeon in Seoul), suburbs (Mts. Cheonggye and Buram in Seoul), a rural area (Gwangneung, Mt. Sori in Gyeonggi-do), and a natural area (Mt. Jeombong in Gangwon-do). Green-up dates derived from the analyses of digital camera images and MODIS satellite images were the earliest in the urban center and delayed through the suburbs and rural area to the natural area. The difference in the observed green-up date compared to the expected one, which was determined by regarding the Mt. Jeombong site located in the natural area as the reference site, was the biggest in the urban center and decreased through the suburbs and rural area to the natural area. Green-up dates in the rural area, suburbs, and urban center were earlier by 11.0, 14.5, and 16.3 days than the expected ones. If these results are transformed into the air temperature based on previous research results, it could be deduced that the air temperature in the urban center, suburbs, and rural area rose by 3.8 to 4.6 °C, 3.3 to 4.1 °C, and 2.5 to 3.1 °C, respectively. Green-up dates derived based on the accumulated growing degree days (AGDD) showed the same trend as those derived from the image interpretation. Green-up dates derived from the change in sap flow as a physiological response of the plant showed a difference within one day from the green-up dates derived from digital camera and MODIS satellite image analyses. The change trajectory of the curvature K value derived from the sap flow also showed a very similar trend to that of the curvature K value derived from the vegetation phenology. From these results, we confirm the availability of AGDD and sap flow as tools predicting changes in ecosystems due to climate change including phenology. Meanwhile, the green-up dates in survey sites were advanced in proportion to the land use intensity of each survey site. Green-up dates derived based on AGDD were also negatively correlated with the land use intensity of the survey site. This result implies that differences in green-up dates among the survey sites and between the expected and observed green-up dates in the urban center, suburbs, and rural area were due to the increased temperature due to land use in the survey sites. Based on these results, we propose conservation and restoration of nature as measures to reduce the impact of climate change. [ABSTRACT FROM AUTHOR]

Over the past 100 years, human activity has greatly changed the rate of atmospheric N (nitrogen) deposition in terrestrial ecosystems, resulting in N saturation in some regions of the world. The contribution of N saturation to the global carbon budget remains uncertain due to the complicated nature of C-N (carbon-nitrogen) interactions and diverse geography. Although N deposition is included in most terrestrial ecosystem models, the effect of N saturation is frequently overlooked. In this study, the IBIS (Integrated BIosphere Simulator) was used to simulate the global-scale effects of N saturation during the period 1961-2009. The results of this model indicate that N saturation reduced global NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) by 0.26 and 0.03 Pg C yr−1, respectively. The negative effects of N saturation on carbon sequestration occurred primarily in temperate forests and grasslands. In response to elevated CO2 levels, global N turnover slowed due to increased biomass growth, resulting in a decline in soil mineral N. These changes in N cycling reduced the impact of N saturation on the global carbon budget. However, elevated N deposition in certain regions may further alter N saturation and C-N coupling. [ABSTRACT FROM AUTHOR]