Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 3.761
Filtrar
1.
Artigo em Inglês | MEDLINE | ID: mdl-36673883

RESUMO

Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system's performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.


Assuntos
Aprendizado Profundo , Humanos , Idoso , Redes Neurais de Computação , Radar , Atividades Humanas
2.
Sci Rep ; 13(1): 965, 2023 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-36653370

RESUMO

The Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual's activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person's activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows by leveraging the pre-trained weights of two popular and efficient transfer learning convolutional neural network models. Finally, a wrapper-based feature selection method has been employed for selecting the optimal feature subset that both reduces the training time and improves the final classification performance. The proposed HAR model has been tested on the three benchmark datasets namely, HARTH, KU-HAR and HuGaDB and has achieved 88.89%, 97.97% and 93.82% respectively on these datasets. It is to be noted that the proposed HAR model achieves an improvement of about 21%, 20% and 6% in the overall classification accuracies while utilizing only 52%, 45% and 60% of the original feature set for HuGaDB, KU-HAR and HARTH datasets respectively. This proves the effectiveness of our proposed wrapper-based feature selection HAR methodology.


Assuntos
Atividades Humanas , Dispositivos Eletrônicos Vestíveis , Humanos , Redes Neurais de Computação , Smartphone , Atenção à Saúde
3.
Sensors (Basel) ; 23(1)2023 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-36617075

RESUMO

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.


Assuntos
Atividades Humanas , Redes Neurais de Computação , Humanos , Máquina de Vetores de Suporte , Modelos Logísticos
4.
Huan Jing Ke Xue ; 44(1): 169-179, 2023 Jan 08.
Artigo em Chinês | MEDLINE | ID: mdl-36635805

RESUMO

The widespread use of antibiotics has led to a large number of antibiotics entering the environment, to which microorganisms have become resistant. In recent years, with the intensification of human activities in the plateau region, the occurrence and migration of antibiotic resistance genes (ARGs) in plateau wetlands have attracted considerable attention. Here, we selected the Caohai National Wetland Park, located in the Yunnan-Guizhou Plateau, as our study area. The contents of tetracyclines, sulfonamides, quinolones, and macrolides in sediments from the upstream (the pristine habitat near the spring eye) and downstream (the sewage discharge outlet of residents) areas of the river in the park were analyzed. Among them, the detection content of tetracycline antibiotics was 103.65-2185 µg·kg-1, which was the highest antibiotic detection content. To further investigate the occurrence characteristics and influencing factors of tetracycline resistance genes, the influence of environmental factors, bacterial community structure, and pathogenic bacteria on tetracycline ARGs under the influence of human activities were revealed via correlation analysis and network analysis. The results showed that a total of 15 tetracycline resistance genes were detected in the upstream and downstream sediments. Among them, seven resistance genes including tetPA, tetD, and tetPB were detected in the upstream, and 13 resistance genes such as tetPA, tetE, tetM, and tetX were detected in the downstream. The abundance of eight new resistance genes in the downstream accounted for 43.44% of the downstream genes. The tetracycline-like antibiotics and soil physicochemical indicators (i.e., available phosphorus, total organic carbon, nitrate nitrogen, and total phosphorus) were the main environmental factors affecting the distribution of tetracycline ARGs. Additionally, the bacteria detected in the upstream and downstream sediments belonged to 64 bacterial phyla, among which Proteobacteria, Firmicutes, and Bacteroidota were the main phyla affecting the abundance of tetracycline ARGs; meanwhile, 27 pathogenic bacteria were detected in the upstream and downstream sediments. Network analysis showed that the correlation between the eight new resistance genes and pathogens in the downstream area accounted for 70% of the network connectivity, and Listeria monocytogenes, Enterococcus faecalis, and Bacteroides vulgatus were identified as potential hosts for the transmission of tetracycline ARGs. Compared to the pristine habitat, the discharge of domestic sewage introduced large amounts of antibiotics and also changed the microenvironment and microbial community structure of the river wetland. Additionally, it increased the species of ARGs in sediments, which promoted the spread and transmission of ARGs among microorganisms and even pathogens.


Assuntos
Microbiota , Tetraciclina , Humanos , Tetraciclina/farmacologia , Tetraciclina/análise , Áreas Alagadas , Esgotos/microbiologia , Genes Bacterianos , China , Antibacterianos/farmacologia , Antibacterianos/análise , Resistência Microbiana a Medicamentos/genética , Bactérias/genética , Tetraciclinas/farmacologia , Tetraciclinas/análise , Atividades Humanas
5.
Huan Jing Ke Xue ; 44(1): 323-335, 2023 Jan 08.
Artigo em Chinês | MEDLINE | ID: mdl-36635820

RESUMO

Using the MOD13A3 NDVI time series from 2000 to 2020, climate date from 1999 to 2020, and land use type data in 2000 and 2020, the spatio-temporal variation in vegetation cover and the driving mechanisms of climate change and human activities to vegetation variation were analyzed based on Theil-Sen Median analysis, the Mann-Kendall significance test, the multi-collinearity test, residual analysis, and relative analysis. The results showed that the vegetation cover exhibited a fluctuating and increasing trend with a magnitude of 0.0016 a-1 in southwest China from 2000 to 2020. The increasing trend of vegetation cover was mostly significant in the Guangxi Hills and Yunnan-Guizhou Plateau and slightly significant in the Tibet Plateau. The vegetation cover had increased in the context of climate change and human activities, with an increasing rate of 0.0010 a-1 and 0.0006 a-1, respectively. The vegetation improvement was mostly dominated by the combination effects of climate change and human activities. The vegetation improvement was dominated by climate change, and the relative role of climate change reached 61.86%. What is more, the vegetation degradation was dominated by human activities, and the relative role of human activities reached 58.39%. Vegetation cover was positively related to minimum temperature, precipitation, maximum temperature, potential evapotranspiration rate, and relative humidity and negatively related to mean temperature, atmosphere pressure, sunshine duration, warmth index, and humidity index. As a whole, the minimum temperature, sunshine duration, and precipitation were the dominant climate factors affecting the vegetation variation in southwest China. Furthermore, the land use and land cover change were significantly related to vegetation variation in southwest China. The implementation of ecological afforestation projects could be beneficial to regional vegetation improvement, whereas the vegetation degradation was mostly conducted by the built-up land expansion.


Assuntos
Condução de Veículo , Humanos , China , Tibet , Atividades Humanas , Mudança Climática , Temperatura , Ecossistema
6.
Sensors (Basel) ; 23(2)2023 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-36679478

RESUMO

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.


Assuntos
Algoritmos , Atividades Humanas , Humanos , Postura
7.
Sensors (Basel) ; 23(2)2023 Jan 10.
Artigo em Inglês | MEDLINE | ID: mdl-36679576

RESUMO

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.


Assuntos
Algoritmos , Reconhecimento Automatizado de Padrão , Humanos , Reconhecimento Automatizado de Padrão/métodos , Redes Neurais de Computação , Esqueleto , Atividades Humanas
8.
Nat Commun ; 14(1): 262, 2023 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-36650141

RESUMO

Species' life histories determine population demographics and thus the probability that introduced populations establish and spread. Life histories also influence which species are most likely to be introduced, but how such 'introduction biases' arise remains unclear. Here, we investigate how life histories affect the probability of trade and introduction in phylogenetic comparative analyses across three vertebrate classes: mammals, reptiles and amphibians. We find that traded species have relatively high reproductive rates and long reproductive lifespans. Within traded species, introduced species have a more extreme version of this same life history profile. Species in the pet trade also have long reproductive lifespans but lack 'fast' traits, likely reflecting demand for rare species which tend to have slow life histories. We identify multiple species not yet traded or introduced but with life histories indicative of high risk of future trade, introduction and potentially invasion. Our findings suggest that species with high invasion potential are favoured in the wildlife trade and therefore that trade regulation is crucial for preventing future invasions.


Assuntos
Répteis , Vertebrados , Animais , Humanos , Filogenia , Anfíbios , Mamíferos , Espécies Introduzidas , Atividades Humanas
9.
Nature ; 613(7944): 503-507, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36653569

RESUMO

The Greenland Ice Sheet has a central role in the global climate system owing to its size, radiative effects and freshwater storage, and as a potential tipping point1. Weather stations show that the coastal regions are warming2, but the imprint of global warming in the central part of the ice sheet is unclear, owing to missing long-term observations. Current ice-core-based temperature reconstructions3-5 are ambiguous with respect to isolating global warming signatures from natural variability, because they are too noisy and do not include the most recent decades. By systematically redrilling ice cores, we created a high-quality reconstruction of central and north Greenland temperatures from AD 1000 until 2011. Here we show that the warming in the recent reconstructed decade exceeds the range of the pre-industrial temperature variability in the past millennium with virtual certainty (P < 0.001) and is on average 1.5 ± 0.4 degrees Celsius (1 standard error) warmer than the twentieth century. Our findings suggest that these exceptional temperatures arise from the superposition of natural variability with a long-term warming trend, apparent since AD 1800. The disproportionate warming is accompanied by enhanced Greenland meltwater run-off, implying that anthropogenic influence has also arrived in central and north Greenland, which might further accelerate the overall Greenland mass loss.


Assuntos
Clima , Aquecimento Global , Temperatura , Aquecimento Global/estatística & dados numéricos , Groenlândia , Camada de Gelo , Atividades Humanas/tendências , Movimentos da Água , Congelamento
10.
PLoS One ; 18(1): e0277913, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36662785

RESUMO

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.


Assuntos
COVID-19 , Pandemias , Humanos , Londres/epidemiologia , Big Data , Controle de Doenças Transmissíveis , COVID-19/epidemiologia , Atividades Humanas
11.
Mar Pollut Bull ; 186: 114473, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36512859

RESUMO

The marine coast is an important ecological transitional boundary but easily suffers from human intervention. Total petroleum hydrocarbons (TPHs) are ubiquitous along the coast. However, the influence of anthropogenic and natural factors on TPHs distribution remains unclear. This study sampled surficial sediment (N = 243) from the coasts of the largest peninsula-Leizhou Peninsula, in Southern China. We found that land-based discharge, sea traffic, and sediment type significantly (p < 0.05) drive the accumulation of TPHs. We observed that TPHs increased by 1.036 µg · g-1 (exp[αi] = exp. [0.0355]) of its original value with the addition of one more boat on the wharf. Although the average TPHs were at a moderate level (124.68, ND-1536.14, µg · g-1) and risk, 'Blue Carbon' ecosystems, i.e., mangroves (224.84, ND - 1441.13, µg · g-1, p < 0.001) were more severely polluted. Cleaner production policy should be applied to mitigate TPHs discharging trend from coastal areas.


Assuntos
Petróleo , Poluentes Químicos da Água , Humanos , Ecossistema , Petróleo/análise , Hidrocarbonetos/análise , Atividades Humanas , China , Sedimentos Geológicos , Monitoramento Ambiental , Poluentes Químicos da Água/análise
13.
Int J Neural Syst ; 33(1): 2350002, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36573880

RESUMO

The problem of human activity recognition (HAR) has been increasingly attracting the efforts of the research community, having several applications. It consists of recognizing human motion and/or behavior within a given image or a video sequence, using as input raw sensor measurements. In this paper, a multimodal approach addressing the task of video-based HAR is proposed. It is based on 3D visual data that are collected using an RGB + depth camera, resulting to both raw video and 3D skeletal sequences. These data are transformed into six different 2D image representations; four of them are in the spectral domain, another is a pseudo-colored image. The aforementioned representations are based on skeletal data. The last representation is a "dynamic" image which is actually an artificially created image that summarizes RGB data of the whole video sequence, in a visually comprehensible way. In order to classify a given activity video, first, all the aforementioned 2D images are extracted and then six trained convolutional neural networks are used so as to extract visual features. The latter are fused so as to form a single feature vector and are fed into a support vector machine for classification into human activities. For evaluation purposes, a challenging motion activity recognition dataset is used, while single-view, cross-view and cross-subject experiments are performed. Moreover, the proposed approach is compared to three other state-of-the-art methods, demonstrating superior performance in most experiments.


Assuntos
Atividades Humanas , Redes Neurais de Computação , Humanos , Máquina de Vetores de Suporte
14.
PLoS Comput Biol ; 18(12): e1010725, 2022 12.
Artigo em Inglês | MEDLINE | ID: mdl-36520687

RESUMO

Cities are growing in density and coverage globally, increasing the value of green spaces for human health and well-being. Understanding the interactions between people and green spaces is also critical for biological conservation and sustainable development. However, quantifying green space use is particularly challenging. We used an activity index of anonymized GPS data from smart devices provided by Mapbox (www.mapbox.com) to characterize human activity in green spaces in the Greater Toronto Area, Canada. The goals of our study were to describe i) a methodological example of how anonymized GPS data could be used for human-nature research and ii) associations between park features and human activity. We describe some of the challenges and solutions with using this activity index, especially in the context of green spaces and biodiversity monitoring. We found the activity index was strongly correlated with visitation records (i.e., park reservations) and that these data are useful to identify high or low-usage areas within green spaces. Parks with a more extensive trail network typically experienced higher visitation rates and a substantial proportion of activity remained on trails. We identified certain land covers that were more frequently associated with human presence, such as rock formations, and find a relationship between human activity and tree composition. Our study demonstrates that anonymized GPS data from smart devices are a powerful tool for spatially quantifying human activity in green spaces. These could help to minimize trade-offs in the management of green spaces for human use and biological conservation will continue to be a significant challenge over the coming decades because of accelerating urbanization coupled with population growth. Importantly, we include a series of recommendations when using activity indexes for managing green spaces that can assist with biomonitoring and supporting sustainable human use.


Assuntos
Parques Recreativos , Smartphone , Humanos , Urbanização , Cidades , Atividades Humanas
15.
Sensors (Basel) ; 22(24)2022 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-36560059

RESUMO

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.


Assuntos
Aprendizado Profundo , Exoesqueleto Energizado , Robótica , Dispositivos Eletrônicos Vestíveis , Humanos , Atividades Humanas
16.
Sensors (Basel) ; 22(24)2022 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-36560259

RESUMO

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson's disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Humanos , Aprendizado de Máquina , Atividades Humanas , Reconhecimento Psicológico
17.
Nature ; 612(7940): 477-482, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36517714

RESUMO

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.


Assuntos
Atmosfera , Metano , Áreas Alagadas , Humanos , Controle de Doenças Transmissíveis/estatística & dados numéricos , COVID-19/epidemiologia , Metano/análise , Ozônio/análise , Atmosfera/química , Atividades Humanas/estatística & dados numéricos , Fatores de Tempo , História do Século XXI , Temperatura , Umidade , Óxidos de Nitrogênio/análise
18.
Artigo em Inglês | MEDLINE | ID: mdl-36554615

RESUMO

Agricultural sustainability is the foundation and a guarantee of sustainable human reproduction. The scientific assessment of China's agricultural sustainability is a prerequisite for properly resolving the conflict between short-term economic interests and long-term ecological security. This paper uses the emergy analysis method to estimate agricultural sustainability in China and further calculates the agricultural environmental cost and green GDP. The results show that China's agricultural emergy yield rate (EYR) is generally greater than 1. This means that more emergy is obtained in relation to renewable and non-renewable inputs from human activity, which also indicates that China's agricultural agroecosystem is characteristic of a profound transition from a self-supporting tradition to a modern industry based on external economic resource consumption. In contrast, China's agricultural growth is mainly driven by the input of a large amount of non-renewable resources, which makes the environmental loading rate (ELR) increase year by year, resulting in the deterioration of China's agricultural emergy sustainability index (ESI). China's agricultural green GDP accounts for about 94.4% of traditional GDP, which means that the average agricultural environmental cost is about 5.6%, mainly from land loss, accounting for 48.23% of the environmental cost.


Assuntos
Agricultura , Conservação dos Recursos Naturais , Humanos , Conservação dos Recursos Naturais/métodos , Agricultura/métodos , Indústrias , China , Atividades Humanas
19.
Artigo em Inglês | MEDLINE | ID: mdl-36498177

RESUMO

The aim of this study was to reveal the spatiotemporal pattern of the supply and demand of ecosystem services (ESs), as well as the significant driving factors for understanding the impact of human activities on the natural ecosystem. To provide a scientific basis for formulating regional sustainable development strategies that enhance human well-being, resource-based cities in the Yellow River Basin (YRB) were selected as the case study. The supply and demand of ecosystem services in these cities from 2000 to 2020 were measured. The spatiotemporal evolution of the supply-demand relationship was illustrated by taking its coordination degree. In addition, geographical detector and geographically weighted regression (GWR) models were applied to quantify the spatiotemporally varying effects of natural and socioeconomic factors on the ES supply--demand relationship. The results showed that resource-based cities in the YRB were experiencing expansion in supply and demand overall, but the supply-demand relationship tended to be tense. The northwest YRB had higher coordination values of supply-demand, while lower values were found in the southeast YRB. Moreover, the relationship between supply and demand was significantly affected by natural and socioeconomic factors, such as elevation, slope, precipitation, land-use type, population density, and gross domestic product (GDP) per land. Furthermore, the GWR model suggested that the effects of driving factors on the supply-demand relationship had notable spatial heterogeneity. The coordination of ES supply-demand in the resource-based cities of southeast YRB was mainly influenced by socioeconomic factors, while that of the west YRB was mainly influenced by natural factors. Our study suggested that it is necessary to enhance the awareness of environmental protection, pay attention to ecological restoration, and avoid unreasonable human disturbance to the ecosystem.


Assuntos
Conservação dos Recursos Naturais , Ecossistema , Humanos , Conservação dos Recursos Naturais/métodos , Rios , Cidades , Atividades Humanas , China
20.
Artigo em Inglês | MEDLINE | ID: mdl-36361170

RESUMO

The acceleration of the urbanization process brings about the expansion of urban land use, while changes in land-use transformation affect the urban habitat quality, and land-use change brings a threat to regional sustainable development. Against such a backdrop, the assessment of land use on the habitat quality and the relationship between the intensity of human activities is becoming a hot spot in terms of the current land use coordinated with habitat quality. Based on the land-use data of Guiyang in 2000, 2005, 2010, 2015 and 2020, the spatial-temporal evolution characteristics of habitat quality in the study area, combined with the spatial correlation between human activity intensity and habitat quality, were hereby analyzed using the InVEST model. The impact of human activity intensity on habitat quality was correspondingly analyzed. The results show that: (1) From 2000 to 2020, the habitat quality level in Guiyang remained stable without drastic changes, but the changes showed hierarchical distribution and were scattered, mainly reflected in the urban expansion areas of the urban-rural fringe and the key areas of industrial development, and the ecological environment quality fluctuated in a small range. (2) From 2000 to 2020, the intensity of human activities in Guiyang was mainly affected by the relatively concentrated distribution, featuring obvious and significant changes. From 2010 to 2015, the high-impact area surrounded the Guanshan Lake New Area, and the regional habitat quality presented a downward trend. In 2020, the high-impact area of the main urban area and the key industrial development zone was expected to be formed, while the low-impact area was still distributed in forest areas with complex natural conditions. (3) From 2000 to 2020, there was a significant positive correlation between human activity intensity and habitat quality in Guiyang, and such a spatial correlation was weak from 2000 to 2005. The period from 2015 to 2020 witnessed the rapid development of urban construction in Guiyang, human construction activities continue to affect the urban habitat quality. The results show that the intensity of human activities on the promoting function of land use, and the dependencies between them should be considered at the same time, and that explorations on the influence of human activities on land-use intensity and habitat quality of space link are crucial to improving the efficiency of urban land use and ecological environment protection, as well as the coordination between land use and the sustainability of urban development.


Assuntos
Conservação dos Recursos Naturais , Ecossistema , Humanos , Cidades , China , Atividades Humanas , Urbanização
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...