Temporal Datasets Research Articles

Short-term prediction of airway congestion index using machine learning methods

This study presents a hybrid spatiotemporal convolutional long short-term memory network with tri-directional temporal features (SCLN-TTF) for airway congestion index prediction. The spatial features, temporal features and spatiotemporal features are taken as input variables, among which the temporal and spatiotemporal input datasets are categorized into forward time series datasets, backward time series datasets and datasets at the target time. The spatial features are extracted using the fast-approximate convolutions on graphs, and the temporal features are extracted using the long short-term memory network. The subsequent congestion index is the output variable. The model parameters are sequentially updated based on the recent collected data and the new predicting results. It is found that, in general, the proposed SCLN-TTF method achieves the most stable and satisfactory performance for all the selected airways. The models exhibit the best performance for the 60 min prediction time horizon in terms of MAPE, while the proposed SCLN-TTF shows superiority compared to the other benchmark methods particularly for the 10 min prediction. Then the graph convolutional network and extreme gradient boosting method are combined for high congestion index identification. The finding further confirms the superiority of the proposed method by considering both the spatial and temporal dependencies and integrating forward and backward features.

High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/−0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Niño event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.

Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning

Knowledge graphs (KGs) are usually incomplete—many new facts can be inferred from KGs with existing information. In some traditional reasoning methods, temporal information is not taken into consideration, meaning that only triplets (head, relation, tail) are trained. In current dynamic knowledge graphs, it is a challenge to consider the temporal aspects of facts. Recent temporal reasoning methods embed temporal information into low-dimensional spaces. These methods mainly support implicitly reasoning, which means they cannot get the specific reasoning paths. These methods limit the accuracy of reasoning paths and ignore multiple explainable reasoning paths in temporal knowledge graphs (TKGs). To overcome this limitation, we propose a multi-hop reasoning model TPath in this paper. It is a reinforcement learning (RL) framework which can learn multi-hop reasoning paths and continuously adjust the reasoning paths in TKGs. More importantly, we add time vectors in reasoning paths, which further improve the accuracy of reasoning paths. Meanwhile, considering the diversity of temporal reasoning paths, we propose a new reward function. In TPath, the agent employs the Long Short-Term Memory networks (LSTM) to capture current observations from the environment, and it outputs action vectors (relation vectors and time vectors) to the environment through activation functions. Experimentally, our model outperforms other state-of-the-art reasoning methods in several aspects over two public temporal knowledge graph datasets.

Modelling Mediterranean heavy precipitation events at climate scale: an object-oriented evaluation of the CNRM-AROME convection-permitting regional climate model

Modelling the rare but high-impact Mediterranean Heavy Precipitation Events (HPEs) at climate scale remains a largely open scientific challenge. The issue is adressed here by running a 38-year-long continuous simulation of the CNRM-AROME Convection-Permitting Regional Climate Model (CP-RCM) at a 2.5 km horizontal resolution and over a large pan-Alpine domain. First, the simulation is evaluated through a basic Eulerian statistical approach via a comparison with selected high spatial and temporal resolution observational datasets. Northwestern Mediterranean fall extreme precipitation is correctly represented by CNRM-AROME at a daily scale and even better at an hourly scale, in terms of location, intensity, frequency and interannual variability, despite an underestimation of daily and hourly highest intensities above 200 mm/day and 40 mm/h, respectively. A comparison of the CP-RCM with its forcing convection-parameterised 12.5 km Regional Climate Model (RCM) demonstrates a clear added value for the CP-RCM, confirming previous studies. Secondly, an object-oriented Lagrangian approach is proposed with the implementation of a precipitating system detection and tracking algorithm, applied to the model and the reference COMEPHORE precipitation dataset for twenty fall seasons. Using French Mediterranean HPEs as objects, CNRM-AROME’s ability to represent the main characteristics of fall convective systems and tracks is highlighted in terms of number, intensity, area, duration, velocity and severity. Further, the model is able to simulate long-lasting and severe extreme fall events similar to observations. However, it fails to reproduce the precipitating systems and tracks with the highest intensities (maximum intensities above 40 mm/h) well, and the model’s tendency to overestimate the cell size increases with intensity.

Snow depth retrieval in North-Western Himalayan region using pursuit-monostatic TanDEM-X datasets applying polarimetric synthetic aperture radar interferometry based inversion Modelling

ABSTRACT Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.

Optimal Routing Design of Feeder Transit With Stop Selection Using Aggregated Cell Phone Data and Open Source GIS Tool

A novel bilevel programming model for designing feeder bus routes to simultaneously consider bus stop selection, bus routing, and passenger route/trip choice behavior is developed in this work. The objective of the upper-level model is to minimize the total in-vehicle travel time of passengers. The objective of the lower-level model is to determine bus stop locations and assign passenger demand to stops by minimizing the total passenger walking time. An ant colony optimization-based two-stage heuristic algorithm is developed to solve the bilevel programming model within an acceptance computation time. Different from conventional methods, the proposed methodology benefits from using real temporal and spatial characteristic datasets of travel demand aggregated from cellular data and traveling time and distance matrices under actual traffic conditions resulted from an open geographic information system (GIS) tool. Results of a real-life case study in Chongqing municipality, China, show that the proposed methodology could achieve a significant reduction of 2.9% total passenger travel time compared with a traditional method. Finally, sensitivity analyses are conducted to further understand the performance of the model. It is anticipated that the proposed methodology could be very useful in designing attractive and cost-effective feeder transit service in practice.

Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah

Economic conditions in Indonesia are still unstable, causing the US dollar exchange rate to increase. This is because most international transactions in Indonesia use US dollars. Prediction or forecasting is chosen as one of the important things in choosing a market to invest in buying and selling. This research will focus on making forecasting applications and analyzing the exchange rate of USD against rupiah based on time series data or temporal datasets from the Investing.com site using machine learning methods, namely Extreme Gradient Boosting (XGBoost). Applications created using the python programming language and streamlit framework. Modeling is carried out using the Knowledge Discovery in Database (KDD) methodology with the stages of dividing the dataset with a 50:50 percentage share into test and train data. The modeling uses hyperparameter tuning values, namely n_estimators = 1000, max_depth = 1, x_colsample_bytree = 0.9894, x_gamma = 0.9989, x_min_child = 1.0, x_reg_lamda = 0.2381, and x_subsample = 0.7063 with best loss or RMSE 451.4151. The values of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when making the model were 6.61374% and 3.95485%. Meanwhile, when testing the model, the RMSE is 0.23577% and MAPE is 0.11643%.

Determination of Cochlear Duct Length With 3D Versus Two-dimensional Methods: A Retrospective Clinical Study of Imaging by Computed Tomography and Cone Beam Computed Tomography.

The aim of this study was to compare three different methods for measurement of cochlear duct length (CDL) in the clinical setting for two different imaging modalities, namely computed tomography (CT) and cone-beam computed tomography (CBCT). One hundred temporal bone data sets (CT: n=50; CBCT: n=50) of non-malformed cochleae were retrospectively analyzed using three different CDL estimation techniques: 3D curved multiplanar reconstruction (cMPR), 2D cMPR and the A-value formula. The data sets belonged to 60 patients (34 males, 26 females; mean age=50.28±18.58 years). For both imaging modalities, application of the 3D cMPR estimation technique led to significantly greater mean CDL values than the two-dimensional methods (p<0.0083). The CDL measurements viewed in CT imaging software were significantly shorter than the corresponding CBCT measurements (p<0.05). Using a linear mixed model, differences in CDL by sex (p=0.796), age (p=0.377) and side of ear (p=0.690) were not significant. The 3D cMPR technique was found to provide the most accurate in vivo CDL measurement in non-malformed cochlea in both CT and CBCT imaging compared to 2D methods. The study results also suggest that the higher spatial resolution in CBCT imaging results in more precise CDL determination than in CT.

HVAQ: A High-Resolution Vision-Based Air Quality Dataset

Air pollutants, such as particulate matter, negatively impact human health. Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. However, real-world pollution distributions vary rapidly with position and the visual effects of air pollution can be used to estimate concentration, potentially at high spatial resolution. Accurate pollution monitoring requires either densely deployed conventional point sensors, at-a-distance vision-based pollution monitoring, or a combination of both. The main contribution of this paper is that to the best of our knowledge, it is the first publicly available, high temporal and spatial resolution air quality dataset containing simultaneous point sensor measurements and corresponding images. The dataset enables, for the first time, high spatial resolution evaluation of image-based air pollution estimation algorithms. It contains PM2.5, PM10, temperature, and humidity data. We evaluate several state-of-art vision-based PM concentration estimation algorithms on our dataset and quantify the increase in accuracy resulting from higher point sensor density and the use of images. It is our intent and belief that this dataset can enable advances by other research teams working on air quality estimation. Our dataset is available at https://github.com/implicitDeclaration/HVAQ-dataset/tree/master.

Increased virulence of Puccinia coronata f. sp.avenae populations through allele frequency changes at multiple putative Avr loci.

Pathogen populations are expected to evolve virulence traits in response to resistance deployed in agricultural settings. However, few temporal datasets have been available to characterize this process at the population level. Here, we examined two temporally separated populations of Puccinia coronata f. sp. avenae (Pca), which causes crown rust disease in oat (Avena sativa) sampled from 1990 to 2015. We show that a substantial increase in virulence occurred from 1990 to 2015 and this was associated with a genetic differentiation between populations detected by genome-wide sequencing. We found strong evidence for genetic recombination in these populations, showing the importance of the alternate host in generating genotypic variation through sexual reproduction. However, asexual expansion of some clonal lineages was also observed within years. Genome-wide association analysis identified seven Avr loci associated with virulence towards fifteen Pc resistance genes in oat and suggests that some groups of Pc genes recognize the same pathogen effectors. The temporal shift in virulence patterns in the Pca populations between 1990 and 2015 is associated with changes in allele frequency in these genomic regions. Nucleotide diversity patterns at a single Avr locus corresponding to Pc38, Pc39, Pc55, Pc63, Pc70, and Pc71 showed evidence of a selective sweep associated with the shift to virulence towards these resistance genes in all 2015 collected isolates.

Программа сравнения изображений вегетационного индекса по данным, полученным со спутников и беспилотного летательного аппарата

At present, the practice of supporting many types of human activities requires the use of the spatial data infrastructure. Such an infrastructure integrates spatio-temporal sets from many sources of information within itself, providing the user with various types of processing, analysis and visualization methods. This article describes the architecture of the software system and the processes for managing sets of spatio-temporal data to solve agricultural problems. Measurement data using multispectral satellite systems, unmanned aerial vehicles (UAVs), as well as a priori information (meteorology, agrochemical information, etc.) are taken as input information. The User of the Software System is provided with the opportunity to control the spatial information of the territory of agricultural fields, sets of temporal data from various spatial data. An important achievement of the work is the combination of the results of satellite and UAV images according to the controlled parameters, that makes possible to expand the area of use of UAVs and verify them. The results of real data processing are presented.

Distinct ecotypes within a natural haloarchaeal population enable adaptation to changing environmental conditions without causing population sweeps.

Microbial communities thriving in hypersaline brines of solar salterns are highly resistant and resilient to environmental changes, and salinity is a major factor that deterministically influences community structure. Here, we demonstrate that this resilience occurs even after rapid osmotic shocks caused by a threefold change in salinity (a reduction from 34 to 12% salts) leading to massive amounts of archaeal cell lysis. Specifically, our temporal metagenomic datasets identified two co-occurring ecotypes within the most dominant archaeal population of the brines Haloquadratum walsbyi that exhibited different salt concentration preferences. The dominant ecotype was generally more abundant and occurred in high-salt conditions (34%); the low abundance ecotype always co-occurred but was enriched at salinities around 20% or lower and carried unique gene content related to solute transport and gene regulation. Despite their apparent distinct ecological preferences, the ecotypes did not outcompete each other presumably due to weak functional differentiation between them. Further, the osmotic shock selected for a temporal increase in taxonomic and functional diversity at both the Hqr. walsbyi population and whole-community levels supporting the specialization-disturbance hypothesis, that is, the expectation that disturbance favors generalists. Altogether, our results provide new insights into how intraspecies diversity is maintained in light of substantial gene-content differences and major environmental perturbations.

A spatiotemporal object-oriented data model for landslides (LOOM)

LOOM (landslide object-oriented model) is here presented as a data structure for landslide inventories based on the object-oriented paradigm. It aims at the effective storage, in a single dataset, of the complex spatial and temporal relations between landslides recorded and mapped in an area and at their manipulation. Spatial relations are handled through a hierarchical classification based on topological rules and two levels of aggregation are defined: (i) landslide complexes, grouping spatially connected landslides of the same type, and (ii) landslide systems, merging landslides of any type sharing a spatial connection. For the aggregation procedure, a minimal functional interaction between landslide objects has been defined as a spatial overlap between objects. Temporal characterization of landslides is achieved by assigning to each object an exact date or a time range for its occurrence, integrating both the time frame and the event-based approaches. The sum of spatial integrity and temporal characterization ensures the storage of vertical relations between landslides, so that the superimposition of events can be easily retrieved querying the temporal dataset. The here proposed methodology for landslides inventorying has been tested on selected case studies in the Cilento UNESCO Global Geopark (Italy). We demonstrate that the proposed LOOM model avoids data fragmentation or redundancy and topological inconsistency between the digital data and the real-world features. This application revealed to be powerful for the reconstruction of the gravity-induced deformation history of hillslopes, thus for the prediction of their evolution.

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

Comparison of community illicit drug use in 11 cities of Turkey through wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) is an ever-increasing discipline that enables researchers to track near-real-time data concerning the recreational use of illicit drugs. Community illicit drug use was estimated in eleven Turkish cities, using eighteen wastewater treatment plants, representing the metropolitan cities and rural areas with different socio-demographic characteristics. In this study, 24-h composite influent wastewater samples were collected for 1 week per season between March 2019 and December 2019. Heroin, amphetamine, methamphetamine, ecstasy, cocaine, and marijuana were covered as illicit drugs in this study. Solid-phase extraction, LC-MS/MS separation, identification, and quantification were used as the analytical methods. Overall results indicate that both cocaine and MDMA usage increased in all cities on weekends. More specifically, it was observed that the average amount of cocaine use regarding 11 cities was 14.7 mg/1000 person/day, while figures for other substances are as follows: 9.5 for amphetamine, 34.5 for methamphetamine, 38.4 for MDMA, 42.2 for heroin, and lastly 5412 mg/1000p/day for marijuana. This study holds the position of being the most comprehensive one conducted, considering spatial and temporal datasets on illicit drug consumption obtained via WBE in Turkey.

Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information

Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems. The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.

Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks

Complex networks, such as transportation networks, social networks, or biological networks, capture the complex system they model by often representing only one type of interactions. In real world systems, there may be many different aspects that connect entities together. These can be captured using multilayer networks, which combine different modalities of interactions in a single model. Coupling in multilayer networks may exhibit different properties which can be related to the very nature of the data they model (or to events in time-dependent data). We hypothesise that such properties may be reflected in the way layers are intertwined. In this paper, we investigated these through the prism of layer entanglement in coupled multilayer networks. We test over 30 real-life networks in 6 different disciplines (social, genetic, transport, co-authorship, trade, and neuronal networks). We further propose a random generator, displaying comparable patterns of elementary layer entanglement and transition coupling entanglement across 1,329,696 synthetic coupled multilayer networks. Our experiments demonstrate difference of layer entanglement across disciplines, and even suggest a link between entanglement intensity and homophily. We additionally study entanglement in 3 real world temporal datasets displaying a potential rise in entanglement activity prior to other network activity.

Combining in-situ fluorometry and distributed rainfall data provides new insights into natural organic matter transport dynamics in an urban river

Urbanization alters the quality and quantity of Dissolved Organic Matter (DOM) fluxes to rivers potentially leading to water quality problems and impaired ecosystem function. Traditional synoptic and point sampling approaches are generally inadequate for monitoring DOM source dynamics. To identify links between spatial heterogeneity in precipitation and DOM dynamics, we used a unique approach combining high spatial and temporal resolution precipitation datasets featuring point, catchment, and land-cover weighted precipitation to characterise catchment transport dynamics. These datasets were linked to fluorescence records from an urban stream (Bourn Brook, Birmingham, UK). Humic-like fluorescence (HLF: Ex. 365 nm, Em. 490 nm) and Tryptophan-like fluorescence (TLF: Ex. 285 nm, Em. 340 nm) were measured, (plus river flow and turbidity) at 5 min intervals for 10 weeks during Autumn 2017. The relationship between discharge (Q) and concentration (C) for TLF and HLF were strongly chemodynamic at low Q (<Q50) but TLF was chemostatic when Q exceeded this threshold. Figure of eight hysteresis was the most common response type for both HLF and TLF, indicating that DOM sources shift within and between events. Key drivers of DOM dynamics were identified using regression analysis and model outputs using point, catchment-averaged, and land-use weighted precipitation were compared. Antecedent rainfall was identified as the most important predictor (negative relationship) of TLF and HLF change suggesting DOM source exhaustion. Precipitation weighted by land cover showed that urbanization metrics were linked to increased TLF:HLF ratios and changes in hysteresis index. This study presents a novel approach of using land-cover weighted rainfall to enhance mechanistic understanding of DOM controls and sources. In contrast, catchment-average rainfall data have the potential to yield stronger understanding of TLF dynamics. This technique could be integrated with existing high resolution in-situ datasets to enhance our understanding of DOM dynamics in urban rivers.

Impact on China&amp;rsquo;s CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions from COVID-19 pandemic

<p indent="0mm">Assessing the impact of the COVID-19 pandemic on China’s carbon emissions is crucial for China to lead global climate change mitigation. However, thorough quantitative analyses of the pandemic’s effects on energy use and emissions are still lacking. This is largely because well-known published datasets of greenhouse gas emissions are based on annual statistics that commonly become available one or more years after they are gathered. A high temporal resolution emission dataset is critical to capture the immediate effects of an event such as the COVID-19 pandemic. Such an improved resolution might also allow policy makers to more quickly observe the effects of policies aimed at decreasing CO₂ emissions, facilitating rapid adjustments. Here, based on a newly developed, near real-time global emission dataset, Carbon Monitor, we estimate China’s daily energy consumption and associated CO₂ emissions and use this dataset to estimate the impact of COVID-19 on CO₂ emission trends. Such a near-real-time CO₂ emission database based on activity data quantifies both anthropogenic CO₂ emissions from fossil fuel combustion and cement production. We show that the higher-resolution emission dataset can be used to assess the impact of COVID-19 (and similar future disruptions) within a reasonable range of uncertainty. This study finds that China’s CO₂ emissions in the first four months of 2020 decreased by 6.9% compared to the same period in 2019, with a total emission reduction of 234.5 million tons of CO₂. The provinces of Jiangsu, Hubei, and Zhejiang were most severely affected by COVID-19, accounting for 19.4%, 17.0% and 12.5% of the total reduction in CO₂ emissions, respectively. The reduction of CO₂ emissions from Shandong, Hebei, Anhui, Henan and Chongqing provinces is more than 10 Mt CO₂, and the sum of the reduction from these five provinces accounts for 28.8% of the national total reductions. CO₂ emissions in Yunnan Province, Gansu Province, Guangxi Zhuang Autonomous Region, Ningxia Hui Autonomous Region, Shaanxi Province and Xinjiang Uygur Autonomous Region increased slightly compared to the same period in 2019. In addition, COVID-19 has a little effect on CO₂ emissions from Qinghai Province and Tibet Autonomous Region. This decrease in atmospheric pollutants is the largest decrease recorded and is consistent with the decrease in air pollutants observed by ground observation stations. This study finds that China’s national economy has recovered rapidly. In April 2020, China’s carbon emissions returned to the same level observed last year. This study predicts that China’s full-year CO₂ in 2020 will be 2% lower than in 2019. This will be the first time China has experienced a decline since 1997. China’s carbon emissions have not rebound significantly, and the dynamic changes in China’s CO₂ indicate a rapid recovery of the Chinese economy. Regarding long-term trends, it is still unclear how much China’s CO₂ emissions will change at the end of this year and how fast the economy and industry will return to normal. With policy support, the economy will be stimulated as the pandemic fades. The IMF predicts that the global annual economic output will decrease sharply by −3.0% in 2020, which is worse than the financial crisis in 2008, based on the assumption that COVID-19 will fade globally in the second half of this year. Based on current emission dynamics, China’s emission decline is estimated to be less than 5%, and the future trend will be affected by whether there will be another pandemic in the future. Current statistics are still not capable of comprehensively capturing the dynamics of CO₂ emissions during the COVID-19 pandemic, and further monitoring, observation and data collection are urgently needed.

Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.