Human Activities Research Articles

Circular–Sustainable–Reliable Waste Management System Design: A Possibilistic Multi-Objective Mixed-Integer Linear Programming Model

Waste management involves the systematic collection, transportation, processing, and treatment of waste materials generated by human activities. It entails a variety of strategies and technologies to diminish environmental impacts, protect public health, and conserve resources. Consequently, providing an effective and comprehensive optimization approach plays a critical role in minimizing waste generation, maximizing recycling and reuse, and safely disposing of waste. This work develops a novel Possibilistic Multi-Objective Mixed-Integer Linear Programming (PMOMILP) model in order to formulate the problem and design a circular–sustainable–reliable waste management network, under uncertainty. The possibility of recycling and recovery are considered across incineration and disposal processes to address the main circular-economy principles. The objectives are to address sustainable development throughout minimizing the total cost, minimizing the environmental impact, and maximizing the reliability of the Waste Management System (WMS). The Lp-metric technique is then implemented into the model to tackle the multi-objectiveness. Several benchmarks are adapted from the literature in order to validate the efficacy of the proposed methodology, and are treated by CPLEX solver/GAMS software in less than 174.70 s, on average. Moreover, a set of sensitivity analyses is performed to appraise different scenarios and explore utilitarian managerial implications and decision aids. It is demonstrated that the configured WMS network is highly sensitive to the specific time period wherein the WMS does not fail.

Coupled effects of climate change and human activities on vegetation dynamics in the Southwestern Alpine Canyon Region of China

Coupled effects of climate change and human activities on vegetation dynamics in the Southwestern Alpine Canyon Region of China

PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks

Abstract. Effective observation of the ocean is vital for studying and assessing the state and evolution of the marine ecosystem and for evaluating the impact of human activities. However, obtaining comprehensive oceanic measurements across temporal and spatial scales and for different biogeochemical variables remains challenging. Autonomous oceanographic instruments, such as Biogeochemical (BGC)-Argo profiling floats, have helped expand our ability to obtain subsurface and deep-ocean measurements, but measuring biogeochemical variables, such as nutrient concentration, still remains more demanding and expensive than measuring physical variables. Therefore, developing methods to estimate marine biogeochemical variables from high-frequency measurements is very much needed. Current neural network (NN) models developed for this task are based on a multilayer perceptron (MLP) architecture, trained over point-wise pairs of input–output features. Although MLPs can produce smooth outputs if the inputs change smoothly, convolutional neural networks (CNNs) are inherently designed to handle profile data effectively. In this study, we present a novel one-dimensional (1D) CNN model to predict profiles leveraging the typical shape of vertical profiles of a variable as a prior constraint during training. In particular, the Predict Profiles Convolutional (PPCon) model predicts nitrate, chlorophyll, and backscattering (bbp700) starting from the date and geolocation and from temperature, salinity, and oxygen profiles. Its effectiveness is demonstrated using a robust BGC-Argo dataset collected in the Mediterranean Sea for training and validation. Results, which include quantitative metrics and visual representations, prove the capability of PPCon to produce smooth and accurate profile predictions improving upon previous MLP applications.

Identification of subsurface archaeology at Mutaredh, United Arab Emirates, using ground penetrating radar

The United Arab Emirates holds great historical importance, as evidenced by many archaeological sites, such as the Jebel Hafit Tombs and the Hili Archeological Park in Al Ain. At the western edge of Mutaredh Oasis, a major new archaeological site was discovered in 2023 during a construction project. Several important archeological features have been documented, including an earthen mosque and boundary walls, Iron Age irrigation systems, and a circular stone tomb dating to the Bronze Age. However, the eastern edge of the Mutaredh site has remained unexplored to date. Ground Penetrating Radar (GPR) has been proven to be a successful method in mapping archaeological remains. Accordingly, a high-resolution GPR survey was carried out to identify the continuity of the unearthed archeological elements and delineate new unexplored features. A comprehensive 3D model of buried archaeological features was constructed using the acquired high-resolution GPR data in the eastern part of the Mutaredh site. Several selected anomalies have been observed, similar to the uncovered features in the western part of the Mutaredh site and structures documented in other nearby archaeological sites. The geometry and extension of these anomalies have enabled the possible identification of a further two Bronze Age circular tombs, as well as the delineation of a system of water channels (falaj), and irrigation networks with tree pits from the Iron Age. Moreover, walls probably belonging to the Late Islamic Age are identified. These findings suggest that Mutaredh has been a site of intensive human activity from the Bronze Age through to the Late Islamic period. Given the density of identified anomalies, further significant features are anticipated to lie buried in the immediate surrounding areas, promising continued insights into the area’s rich archaeological heritage. The findings of this study may guide archaeologists to specific locations and assist in selecting the most appropriate excavation techniques for the verification stage.

Using Advanced InSAR Techniques and Machine Learning in Google Earth Engine (GEE) to Monitor Regional Black Soil Erosion—A Case Study of Yanshou County, Heilongjiang Province, Northeastern China

The black soil region experiences complex erosion due to natural processes and intense human activities, leading to soil degradation and adverse ecological and agricultural impacts. However, the complexities involved in quantifying regional erosion poses remarkable challenges in accurately assessing the current status of regional soil erosion for effective soil conservation. To solve this issue, we proposed a new method for monitoring soil erosion using Interferometric synthetic aperture radar (InSAR) technology and machine learning algorithms within the Google Earth Engine platform. The new method not only enables regional-scale monitoring, but also ensures high accuracy in measurement (millimeter-level). The erosion susceptibility of the study area (Yanshou County, Heilongjiang Province, Northeastern China) was also classified using random forest algorithms to refine the monitored and predicted soil erosion. The results indicate that the five-year (2016–2021) deformation in Yanshou County was −11.08 mm, with a significant mean cumulative deformation of −8.08 mm yr−1 occurring in 2017. The driving factor analysis shows that the region was subject to the compound effect of water and freeze–thaw erosion, closely related to crop phenological stages. The susceptibility analysis indicates that 73.3% of the region was susceptible to erosion, with a higher probability in river areas, at high altitudes, and on steep slopes. However, good vegetation cover can reduce the risk of soil erosion to some extent. This study offers a new perspective on monitoring regional soil erosion in the black soil region of China. The proposed method holds potential for future expansion to monitor soil erosion in a larger areas, thereby guiding the strategies development for protection of the agriculturally important black soil.

Effects of Urbanization on Community Composition and Conditions of Birds in Wetland Ecosystems

Urbanization in wetlands has negatively impacted bird species diversity and populations. It is important to be able to predict the consequences for avian biodiversity and to identify wetlands of special value to the maintenance of local wetland communities. This study involved conducting point counts across four permanent urban wetlands at the University of Florida and three retention ponds in the Gainesville, FL vicinity to observe patterns in wetland bird species diversity and populations. This study also utilized data from the Florida Museum of Natural History (FLMNH) to explore differences in phenotypes of wetland bird specimens. The local wetlands showed trends where specific species thrived in one type of area versus the other. Egrets were frequently observed in larger wetlands, and double-crested cormorants were observed more frequently in smaller ones. Additionally, retention ponds showed promise as a conservation method, dependent on their depth and size. The largest retention pond attracted many bird species in great numbers, showing promise for methods to negate negative the impact of human activity and climate change. Characterization for data collected at the FLMNH on both weight and species analyzed across numerous regional locations showed a negative trend between specimen weight and time progression.

Association of anthropogenic heat with asthma and related symptoms among children in China: A novel index reflecting climate change

BackgroundAnthropogenic heat (AH) is defined as the significant release of waste heat into the environment due to human activities, serving as a controllable heat source contributing to global climate change. However, epidemiological evidence establishing a clear association between AH and childhood asthma is currently lacking. ObjectivesTo explore the relationship between children's exposure to AH and asthma, as well as its related symptoms. MethodsThis population-based cross-sectional study, part of the National Chinese Children Health Study from 2012 to 2018, involved 188,145 children aged 6 to 18 years. We used multisource remote sensing images and ancillary data to estimate AH exposure. Data on asthma symptoms were collected through validated self-reported questionnaires. A generalized linear mixed model was applied to determine the associations. ResultsOur findings indicate a positive correlation between AH exposure and asthma risk in children. An interquartile range (IQR) increase in total AH was linked to higher odds of current asthma (OR: 1.15, 95 % CI: 1.10, 1.20) after adjusting for covariates. Categorizing AH by source, industrial AH exhibited the strongest effect, with an increased risk of current asthma (OR: 1.16, 95 % CI: 1.11, 1.22). Notably, younger children exhibited stronger associations between AH exposure and asthma-related symptoms, with boys showing heightened susceptibility, particularly for persistent cough. ConclusionThis study suggests that exposure to AH may elevate the risk of asthma and related symptoms, particularly in boys and younger children. Providing a foundation for developing practical strategies to mitigate the adverse impacts of global warming on respiratory health, while also guiding the formulation and evaluation of climate action and public health policies, and supporting sustainable urban development.

CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition

Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision. This results in small datasets with a limited number of activities and heterogeneity, lacking the mixed and nuanced movements normally found in free-living scenarios. As such, models trained on laboratory-style datasets may not generalise out of sample. To address this problem, we introduce a new dataset involving wrist-worn accelerometers, wearable cameras, and sleep diaries, enabling data collection for over 24 hours in a free-living setting. The result is CAPTURE-24, a large activity tracker dataset collected in the wild from 151 participants, amounting to 3883 hours of accelerometer data, of which 2562 hours are annotated. CAPTURE-24 is two to three orders of magnitude larger than existing publicly available datasets, which is critical to developing accurate human activity recognition models.

Groundwater dynamics clustering and prediction based on grey relational analysis and LSTM model: A case study in Beijing Plain, China

Study regionBeijing Plain, China Study focusThe traditional zoning of groundwater systems relies on lithological characteristics and hydraulic connections. However, the influence of climate change and human activities has led to the emergence of diverse dynamic patterns within these systems. Additionally, the differences in groundwater dynamic characteristics are often ignored when modeling with machine learning methods. In this study, a new clustering method named GRA-CLU was proposed to classify dynamic types of groundwater. Then, regional models are built using LSTM based on the dynamic zoning, aiming to incorporate hydrogeological significance into the pure data-driven model.New hydrological insights for the region: The GRA-CLU method classified the study area into six groundwater dynamic types. Compared with the traditional hydrogeological units, the new zoning result identified two dynamic types that have never been considered: urban influence zone and mountain-plain junction zone where surface water interacts with groundwater. Through analysis, the significant rise of groundwater levels in 2021 was more influenced by heavy rainfall events rather than human activities. This study further explored the performance of LSTM regional models based on the six dynamic zones. The results indicated that the NSE of models increased by 5.7–50.0 %, the improvement was more obvious in zones with irregular fluctuations. The RMSE decreased by 31.5–59.8 %, particularly noticeable in zones with regular annual fluctuations and large samples.

Impact of COVID-19 lockdown on water quality of major rivers in Sri Lanka

ABSTRACT The COVID-19 lockdown resulted in a reduction in human activities, lowering pollution levels in these rivers. Nevertheless, limited research can be found on the impact of COVID-19 on river water quality in Sri Lankan rivers. Therefore, this research was carried out to investigate the water quality index (WQI) of four major rivers (Mahaweli, Nilwala, Kelani, and Kalu) in Sri Lanka in three consecutive time slots spanning from 2019 to 2022 (before, during, and after the lockdown). In-depth WQI analysis has unveiled nuanced dynamics in river health. While the lockdown period contributed to positive alterations in key parameters, subsequent periods demonstrated declining water quality post-lockdown. This decline underscores the direct correlation between human activity and river water quality deterioration. Several parameters showed positive changes during the lockdown, indicating an improvement in river water quality. When comparing the periods before, during, and after the lockdown, pH, colour, turbidity, total iron, sulphate, chloride, total coliform, and E. coli all showed a significant positive impact. However, some parameters, such as fluoride and electrical conductivity (EC), had a neutral effect. This study sheds light on the positive effects of the COVID-19 lockdown on reducing river pollution in Sri Lanka.

Quantifying global warming potential variations from greenhouse gas emission sources in forest ecosystems

Forest ecosystems play a crucial role in regulating greenhouse gas (GHG) emissions and mitigating climate change. This research aimed to evaluate the GHG emissions of various sources within forested ecosystems and assess their respective contributions to global warming potential (GWP), vital for developing more targeted strategies to mitigate climate change, shaping climate policies, carbon accounting, sustainable forest management, and advancing scientific comprehension of ecosystem-climate dynamics. The study comprehensively analysed carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions EDGAR data of deforestation, fires, and natural processes such as organic soil decomposition within forested ecosystems. The assessment quantified the CO2 equivalent emissions for each category from 1990 to 2022 and forecasted till 2030. Our forecast shows that CO2 emissions from deforestation could reach between 3,990 and 4,529 metric ton (Mt) by 2030, with forest fires contributing an additional 750 Mt. Forestland CO2 absorption is expected to decline to -5134.80 Mt by 2030. There is uncertainty surrounding the forecasts for Organic soil CO2 (829.78 Mt) and Other land CO2 (-764.53 Mt). In addition, deforestation was a significant contributor to CO2 emissions, with a GWP ranging from 4000 to 4500, highlighting the complex interplay between natural processes and human activities in shaping atmospheric warming patterns. Additionally, forest fires emit a complex mix of GHGs. The potency of these gases in warming the planet varies considerably, with CH4 exhibiting a GWP range of 500 to 700 Mt CO2 equivalent, and CO2 ranging from 900 and 1350 Mt. These variations depend on fire intensity and its overall impact on the climate system. Forestland acts as powerful carbon sink, capturing atmospheric CO2 with negative GWP values between -7000 and -6000. Researchers suggest a multifaceted strategy such as stricter enforcement of sustainable forestry regulations, investing in projects that promote carbon sequestration, and reforestation. Additionally, advancements in drone technology, satellite imagery, remote sensing and advanced data analytics can aid in detecting and mitigating climate change impacts, ultimately paving the way for carbon neutrality.Graphical

A review of the knowledge structure and trends in research on the interlinkages between the rule of law and environmental sustainability

AbstractThe interplay between nature and human activities is important for both human well‐being and the health of our planet. The rule of law is central to maintaining harmony between environmental needs and human endeavors. Since the early 1990s, particularly after the Earth Summit in 1992, there has been an increased interest in the relationship between environmental sustainability and legal frameworks. This has led to a significant expansion in the field, encompassing a wide array of research. Despite the field's growth, there is still a lack of review papers that explore its knowledge structure and evolution. This study aims to conduct a comprehensive bibliometric analysis of the literature by employing the Visualization of Similarities (VOS) viewer software. We identify key thematic areas within the field, highlight influential authors and journals, and specify major contributing countries and institutions. The findings show that the initial focus was on limited themes, which later gave way to an acknowledgment of the multidimensional nature of the subject. Certain themes, such as environmental emissions and governance, retain prominence. Among the focal themes, sustainability, climate change, and democracy are particularly influential in shaping the field's trajectory. Despite this evolution, certain aspects of both concepts, such as waste and water management, fundamental rights and justice system, and the importance of socio‐economic factors, remain underrepresented. This study offers valuable insights into the linkage between the rule of law and environmental sustainability themes and the associated themes.

Marine protected areas as socio-economic systems: a method for defining socio-economic criteria in marine planning

This research aims to define socio-economic criteria for prioritizing proposals related to new marine protected areas, boundary adjustments, area relocations, and network corridors within marine management approaches. The study also focuses on identifying ecosystem services (ES) that address the social dimensions of various spatial management approaches in the marine realm. The presented method quantifies nature’s significance to human communities through stakeholder perceptions, bridging the gap between human activities and ecosystem services. The research defines essential socio-economic criteria, identifies the corresponding ecosystem services, and assesses their societal values within the socio-ecological system of a specific area, thereby enhancing the effectiveness of marine management processes such as marine spatial planning and marine protected areas.

The construction of international wetland urban ecological security pattern coupled with MSPA and ESF.

Creating an ecological security pattern is crucial for balancing the sustainable development of areas where human activities and the natural environment intersect. Using Hefei, an internationally recognized Wetland City, as a case study, we extracted ecological sources through ecological service function (ESF) analysis and morphological spatial pattern analysis (MSPA) core area connectivity analysis. Based on these ecological sources, we developed an ecological resistance surface system and identified ecological corridors and nodes using circuit theory. The findings are as follows: (1) Ecological source areas: The primary ecological sources in Hefei are located in the water bodies, forested areas, and scattered grasslands in the central and eastern parts of the city. This ecological source area covers 978.96 km2, which constitutes 8.55% of the city's total area. (2) Ecological corridors: Hefei contains 43 ecological corridors with a total length of 940.3km, averaging 21.87km each. These corridors are crucial for maintaining ecological connectivity and facilitating species movement. (3) Ecological nodes: There are 13 significant ecological nodes in Hefei, including 6 ecological obstacle points and 7 ecological pinpoints. These nodes play a vital role in supporting ecological processes and ensuring habitat connectivity. (4) Evaluation metrics: The α and β values for the source identification method that integrates MSPA with ecological service functions were 2.15 and 0.8, respectively, which are higher than those of the control group. Conversely, the γ-value was 0.18, lower than that of the control group. These results indicate that the combined ecological source extraction method provides significant advantages in terms of ecological corridor integrity, connectivity, and ecological flow management.

Assessment of Ecological Hazards in the Inaouen Wadi and Its Tributaries Using the Presence of Potentially Toxic Elements in Its Sediments

Inaouen wadi is the second largest tributary of the Sebou river, one of Morocco’s major rivers, which holds significant economic and social importance. Unfortunately, this watercourse is severely impacted by pollution from various human activities, particularly industrial sources. However, available data on the presence of potentially toxic elements (PTEs) that could harm human health in this region remain limited. PTEs pose major environmental risks due to their toxicity, persistence, and bioaccumulation. This study aimed to assess the concentrations of PTEs in the sediments of Inaouen wadi and its main tributaries based on sediment samples collected from 12 locations in 2019. The concentrations of Cd, Pb, Cr, Ag, Al, Cu, Fe, and Zn were measured using inductively coupled plasma atomic emission spectroscopy (ICP–AES), and sediment contamination levels were evaluated using multiple indices: the enrichment factor (EF), the geo-accumulation index (Igeo), the potential ecological hazard index (RI), and the modified ecological risk index (MRI). The results indicate that concentrations of Pb, Cd, Cr, Cu, Fe, and Zn are significantly influenced by urban discharges, particularly at sites S1, S3, and S5 near the cities of Taza and Oued-Amlil. The maximum values recorded were 7.01 g/kg for Pb, 0.9 g/kg for Cd, 0.1 g/kg for Cr, 19.9 g/kg for Fe and 1.9 g/kg for Zn. The enrichment factor (EF) revealed anthropogenic sources of Fe and Pb, confirming the human origin of these elements. The geo-accumulation index (Igeo) showed that the areas around stations S1, S3, and S5 are highly contaminated by Pb, Cd, and Fe, a finding also supported by the MRI. The study identified potential ecological risks at stations S1, S3, and S5, highlighting the urgent need for improved pollution management practices to mitigate environmental risks.

Environmental Effects of Natural Processes and Human Activities on the Water Environment in Watershed

The Special Issue focused on the water environment changes within watersheds due to its critical role in sustaining ecosystems, supporting biodiversity, and providing essential resources for human life [...]

Increased adult movements and decreased juvenile apparent survival of urban crows during COVID-19 lockdowns

The increasing abundance of animal species thriving in urban environments is a source of conflicts with managers and users of public spaces. Although opportunistic urban species often use resources originating from human food leftovers, the potential impact of a reduction in these resources on their demography is hard to quantify. The COVID-19 epidemic, which led many countries to set up lockdowns, gave us the opportunity to estimate the impact of a drastic reduction in such food resources and human activities on the demography of an urban bird population. Based on 7 years (2015–2021) of capture-mark-recapture of carrion crows (Corvus corone) in the city of Paris, France, we used multi-state models to examine the intra-annual (3-month time steps) apparent survival and movement patterns of crows during and outside COVID-19 lockdowns. We showed that the apparent survival of juvenile carrion crows decreased down during lockdown, while adult movements increased during this period, with more adult crows moving out of the urban district. Lockdown modified the demography of this urban crow population, suggesting that the reduction in food resources was sufficient to affect fitness and reduce carrying capacity.

Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management

River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality and their potential mechanisms in changing environments remain incomprehensively understood through available ML-based researches. Here, we developed a ML-based framework integrating a self-organizing map (SOM) model with a random forest (RF) model. This framework was applied to simultaneously investigate the spatiotemporal patterns and potential drivers of river permanganate (CODMn), ammonia nitrogen (NH3-N), and total phosphorus (TP) dynamics across 34 sites from 2010 to 2020 in a coastal city threatened by deteriorating water environment in southeastern China. The sites were divided into two clusters in the spatial context with different water quality conditions. The year of 2015 for NH3-N and 2018 for CODMn and TP were identified as the key turning points of water quality variations. Features including sewage discharge, population dynamics, percentage of cultivated land, and fertilizer application contributed greatly to water quality deterioration. The increase in forest vegetation reflected by percentage of forest and leaf area index may improve water quality. The ML-based modeling framework demonstrated a promising way to address the spatiotemporal dynamics of river water quality, and provided insights for water management in a coastal city with intensifying human-nature interactions.

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014–2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄2⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

Features of Definitions of Modern Terminology of Ecological and Economic Processes

The article considers approaches to definitions of terms used to analyze the impact of production and human activity on the environment. Understanding the essence of the term allows you to correctly assess and build actions to manage processes affecting environmental violations. The purpose of the article is to define a category reflecting the integration of economic and environmental goals of human activity. Definitions of the most common ecological and economic terms are considered. The author's approach to the definition of the term environmental friendliness of production is proposed and its significance is substantiated.