Human Impact Assessment Research Articles

Using cumulative human impact assessment to support adaptive management of the threatened species’ habitats in the Yangtze River Estuary

The conflict between multiple functions of human use and habitat conservation represents an intractable challenge for environmental management. While numerous studies on marine conservation prioritize cumulative impact assessments (CIA) to tackle this issue, estuaries — despite their immense conservation significance amidst intense human activities — remain overlooked. Taking the Yangtze River Estuary (YRE) as the case study area, it is one of the most intensely human-utilized estuaries globally, yet it serves as a critical habitat and migratory corridor for the endangered Chinese sturgeon (Acipenser sinensis).We employed ecological indicators and quantitative modeling to assess habitat suitability and cumulative anthropogenic impacts on this endangered species in the YRE. The results demonstrated that suitable habitat distribution and human impact varied for juvenile Chinese sturgeon in the YRE. The suitable habitat distribution for juvenile Chinese sturgeon was concentrated in Chongming Dongtan and the North Channel. Ship density, submarine optical cables, and smooth cord grass invasion were the main anthropogenic factors impacting the Chinese sturgeon's migrating habitat in the YRE. Moreover, existing Chinese sturgeon habitats remain at risk, with 36.24 % of potential habitats and 56.60 % of high human impact areas requiring enhanced protection. Adaptive management strategies were proposed, tailored to the spatial and environmental protection needs across the dynamic habitat demands of aquatic species. Our spatially explicit and indicator-based findings provide key scientific support for enlarging protected areas and implementing adaptive management of human activities to balance conservation and sustainable use in this highly utilized ecosystem.

Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.

A novel framework for assessment of human impact of floods: Demonstrated for the Indian subcontinent

Flood damages are a result of mutual interaction between flood hazard and socio-economic factors. The increase in flood damages over time can be attributed to various enhancements in the magnitude and frequency of floods as well as socio-economic development in the floodplains. The quantification and analysis of the human dimensions of flood damages is challenged due to irreversibility and lack of understanding of complex-human flood interactions, especially in less developed regions. Since flood damage assessments play a critical role in designing effective flood preparedness plans, we suggest including the human dimension in evaluating damages. We present a framework for the assessment of the human impact of floods by incorporating statistics on the number of people exposed, displaced, and fatalities recorded for each flood event. The framework involves categorizing human impact, identifying spatial clusters of different categories, and exploring the role of various hazard and socio-economic characteristics in determining impact. While categorizing and clustering of impact helps to distinguish between regions prone to different impact, understanding the role of socio-economic and demographic factors is expected to help in developing tailor-made strategies to mitigate flood disasters. The methodology broadly consists of categorization using unsupervised learning, spatial clustering using Local Indicators of Spatial Autocorrelation (LISA), and spatial regression. The results indicate that the impact varies from one region to another and so does the role of hazard and socio-demographic variables in determining impact. Moreover, the framework developed may be used to study the relevance of various socio-hydrological theories and their role in determining the human impact of floods.

A Hidden Pathway for Human Exposure to Micro- and Nanoplastics-The Mechanical Fragmentation of Plastic Products during Daily Use.

In numerous environmental compartments around the world, the existence of micro- and nanoplastics (MNPs) in the environment has been verified. A growing number of studies have looked at the interaction between MNPs and human activities due to the risks they may pose to humans. Exposure pathways are key factors in measuring MNPs risks. However, current research largely ignores the contribution of mechanical fragmentation pathways to MNPs exposure during the daily use of plastic products. Our critical review demonstrated the research gap between MNP fragmentation and risk assessments via a network analysis. The release of fragmented MNPs and their properties were also described at various scales, with emphasis on environmental stressors and mechanical fragmentation. In the scenarios of daily use, plastic products such as food packaging and clothing provide acute pathways of MNPs exposure. The release tendency of those products (up to 102 mg MNPs) are several orders of magnitude higher than MNPs abundances in natural compartments. Despite the limited evidence available, waste recycling, landfill and municipal activities represented long-term pathways for MNPs fragmentation and point sources of MNPs pollution in environmental media. Assessing the health effects of the fragmentation process, unfortunately, is further hampered by the current absence of human exposure impact assessments for secondary MNPs. We proposed that future studies should integrate aging evaluation into risk assessment frameworks and establish early warning signs of MNPs released from plastic products.

Report on the impact of anthropogenic factors on the properties and functions of soils from a selected area of Central European Lowland province

PurposeThe study aims to assess the impact of human activity on the content and migration of trace elements in soil profiles of various physico-chemical properties. The research hypothesis assumed that human impact related to the functioning of buildings, agriculture, industry and communication negatively affected the trace element contents and physico-chemical properties of soils in a selected urban area (the Grudziądz Basin).Materials and methodsSeven soil exposures were made in areas that differed in land use and soil type over an area totalling approx. 200 ha. In the soil profiles, samples of soil of disturbed structure were taken from each genetic horizon. Selected physico-chemical properties of soils were determined: soil texture, soil pH, TOC, CaCO3 content. The total content of heavy metals in the soils was determined by AAS after digestion in hydrofluoric acid and bioavailable forms in 1 M DTPA. The sources of elements in the studied soils were assessed using the following indices: Enrichment Factor (EF), Geoaccumulation Index (Igeo) and Contamination Factor (CF).Results and discussionThe sand fraction dominated the examined soil profiles, with its content ranging widely and averaging 64.7%. In general, the humus horizons had lower pH values than the deeper layers and parent rock horizons. The organic carbon content ranged from 0.07 to 4.64%. The vertical decomposition of total N was strongly related to the decomposition of organic carbon. The highest metal contents in the soil samples were for zinc, and then for nickel. Conversely, the lowest concentrations were for mercury. Most of the profiles had higher contents of the analysed elements in their upper horizons. The humus horizons were characterised by the accumulation of Cr, Hg, Zn and Fe. The samples from the soil profiles were highest in bioavailable forms of zinc and nickel.ConclusionsThe assessment of human impact on the concentrations and migration of trace elements in profiles of agricultural soils found the contents of heavy metals to be variable. Only in a few profiles were EF values for Cd, Pb and Ni found to indicate the effects of human activity. Likewise, CF and Igeo attest to only slight contamination in the research area.

A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model

Background: This paper proposes a framework to cope with the lack of data at the time of a disaster by employing predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. Methods: A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely the Moving Average (MA). Results: Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. Conclusions: comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) with up to 3% error; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.

Heavy metal pollution levels and health risk assessment of dust storms in Jazmurian region, Iran

The Jazmurian basin in Iran is an area affected by climate change and desertification where aerosols and dust storms are common. The aim of this work was to determine the human and ecological risks from atmospheric particles during dust storms in different cities in the Jazmurian basin. For this purpose, the dust samples were collected from Jiroft, Roodbar Jonoob, Ghaleh Ganj, Kahnooj and Iranshahr cities, which are located around the Jazmurian playa in southeast of Iran. Satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products and the Aerosol Optical Depth (AOD) were used to detect aerosol loading in the atmosphere. Moreover, the trace element composition of the collected particles was determined and used to evaluate human and ecological impact assessment using US EPA human health risk assessment and ReCiPe 2016 endpoint hierarchist impact assessment method incorporated in the OpenLCA 1.10.3 software. The human health risk assessment of the particles revealed high non-carcinogenic risks for children from exposure to nickel and manganese and carcinogenic risks in both adults and children due to hexavalent chromium, arsenic and cobalt during dust storm events. Terrestrial ecotoxicity was found to have the largest ecological impact on ecosystems with copper, nickel and zinc exhibiting the largest contributions.

Геоэкологические аспекты археологических памятников различных типов на северо-западе русской равнины

The study of geo-ecological factors allows not only to determine the nature of human economic activities but also reveals natural influences affecting the placement of archaeologi­cal objects in the past. Special attention is given to «Stone piles» («Lappish cairns») — stone piles widely found within the studied territory. Despite years of archaeological research, the nature of these structures remains enigmatic, though archaeological artifacts are often absent. The application of geo-chemical research methods enables the assessment of ancient human impact on the surrounding environment and also provides insights into their activities and motivations for selecting specific locations. Analysis of geo-chemical indicators such as SiO2, Al2O3, MgO, CaO, Fe2O3 helps reveal distinct microrelief features. The examination of an­thropogenic activity-related geo-chemical modules within the archaeological site, such as P2O5(anthrop), CaO(anthrop), K2O(anthrop), Rb(anthrop), facilitates the reconstruction of functional zones within the site and identifies the type of economic activities conducted there. This study has for the first time identified geo-ecological factors influencing various types of archaeologi­cal sites, including settlements, burial grounds, and ritual objects. Through the analysis of geo-chemical, archaeological, and landscape parameters, it was possible to establish the influ­ence of these factors on the distribution of such objects in the past.

Estimating the extended and hidden species diversity from environmental DNA in hyper‐diverse regions

Species inventories are the building blocks of our assessment of biodiversity patterns and human impact. Yet, historical inventories based on visual observations are often incomplete, impairing subsequent analyses of ecological mechanisms, extinction risk and management success. Environmental DNA (eDNA) metabarcoding is an emerging tool that can provide wider biodiversity assessments than classical visual‐based surveys. However, eDNA‐based inventories remain limited by sampling effort and reference database incompleteness. In this study, we propose a new framework coupling eDNA surveys and sampling‐theory methods to estimate species richness in under‐sampled and hyper‐diverse regions where some species remain absent from the checklist or undetected by visual surveys. We applied this framework to the coastal fish diversity in the heart of the coral triangle, the richest marine biodiversity hotspot worldwide. Combining data from 279 underwater visual censuses, 92 eDNA samples and an extensive custom genetic reference database, we show that eDNA metabarcoding recorded 196 putative species not detected by underwater visual census including 37 species absent from the regional checklist. We provide an updated checklist of marine fishes in the ‘Raja Ampat Bird's Head Peninsula' ecoregion with 2534 species including 1761 confirmed and 773 highly probable presences. The Chao lower‐bound diversity estimator, based on the incidence of rare species, shows that the region potentially hosts an additional 123 fish species, including pelagic, cryptobenthic and vulnerable species. The extended and hidden biodiversity along with their asymptotic estimates highlight the ability of eDNA to expand regional inventories and species distributions to better guide conservation strategies.

Development and Economy in Mongolia through a Human Rights Law Lens

Abstract This article studies the Mongolian economic and development policies implemented in recent years until March 2020, including its revenue matrix sustainability, from an international human rights law perspective. Policy and legal recommendations for discussion are also presented. Based on a United Nations mission the author conducted to Mongolia in 2019, this country study examines the macroeconomic policies, including debt issues, from a human rights perspective; the extent to which mineral rents are translated into inclusive and comprehensive social and environmental policies, focusing on the mining project Oyu Tolgoi; the impact of illicit financial flows on human rights; and the effects of lending for infrastructure and mining projects and other foreign direct investments. The study concludes that economic diversification and conducting effective gender-sensitive, participatory human rights and environmental impact assessments of economic reforms and mining and infrastructure projects are the main challenges Mongolia faces.

Assessment of human impact on flooding for Thai East coast - gulf basin

Due to a rapid increase in urban and built-up areas, the East Coast – Gulf basin of Thailand faces flood hazards more frequently than in the past. In this study, we aim to assess the effects of building construction on flooding, and any link between them. The FloodMap model is used to simulate flooding in the study area in September 2015. Two flooding scenarios were designed; one based only on land surface elevation and the other one with building construction included on the land surface. According to the result, we found that human construction increases flood hazard in the study areas, particularly flood depth. Flood areas are also found to increase if the human factor is added into the model, but in a lesser extent. With human construction, paved road is found to have the highest flood potential compared to other types of road. Built up areas are more flooded, while flooding extent is almost similar to results from the scenario of no human construction in forest and agricultural areas.

Variation partitioning in double-constrained multivariate analyses: linking communities, environment, space, functional traits, and ecological niches

Constrained multivariate analysis is a common tool for linking ecological communities to environment. The follow-up is the development of the double-constrained correspondence analysis (dc-CA), integrating traits as species-related predictors. Further, methods have been proposed to integrate information on phylogenetic relationships and space variability. We expand this framework, proposing a dc-CA-based algorithm for decomposing variation in community structure and testing the simple and conditional effects of four sets of predictors: environment characteristics and space configuration as predictors related to sites, while traits and niche (dis)similarities as species-related predictors. In our approach, ecological niches differ from traits in that the latter are distinguished by and characterize the individual level, while niches are measured on the species level, and when compared, they are characteristics of communities and should be used as separate predictors. The novelties of this approach are the introduction of new niche parameters, niche dissimilarities, synthetic niche-based diversity which we related to environmental features, the development of an algorithm for the full variation decomposition and testing of the community–environment–niche–traits–space (CENTS) space by dc-CAs with and without covariates, and new types of diagrams for the results. Applying these methods to a dataset on freshwater mollusks, we learned that niche predictors may be as important as traits in explaining community structure and are not redundant, overweighting the environmental and spatial predictors. Our algorithm opens new pathways for developing integrative methods linking life, environment, and other predictors, both in theoretical and practical applications, including assessment of human impact on habitats and ecological systems.

ANALYSIS OF PHYSIOLOGICAL SIGNALS ON THE WEARABLE ASSIST SUIT FOR REPETITIVE AGRICULTURAL TASK

Musculoskeletal disorders are the most common among farmers and farming constitutes the highest industrial accident rate. To prevent these, we developed a smart auxiliary workwear and investigated its effectiveness through human impact assessment experiments. In our method, the actuator of the waist elastic band was released when there was no need to directly adjust the length with the strength support of the waist, and the elastic band was held once the motion using the waist strength disappeared. To examine the performance, 15 workers were recruited; before the experiment, they were subjected to a basic fitness evaluation to examine their general characteristics. The lumbar peak torques before and after wearing the designed work clothes were measured using the Biodex System III to determine their lumbar assistance power, which was confirmed to have increased by approximately 17%. In addition, electromyographic comparison of the amount of muscle used before and after the wearing revealed that the muscle use was reduced by 26.41% and 19.38% after wearing the work clothes when the weight was lifted in the stooping and squatting postures, respectively. Based on these results, it can be stated that the proposed smart assistive work clothes could contribute to reduced muscle usage required for work and lessen related weariness by supporting the waist, and thus, would greatly help farmers in preventing musculoskeletal disorders.

Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models.

Sediment metal enrichment and ecological risk assessment of ten ports and estuaries in the World Harbours Project

Ten global harbours were assessed for sediment quality by quantifying the magnitude of anthropogenic change and ecological risk. Anthropogenic change (enrichment) was high for Derwent River and Sydney estuary, moderate for Santander Harbour, Rio de Janeiro and Dublin Port, slight for Hong Kong, minimal for Darwin. All 10 enrichment indices used showed similar results. Derwent River sediment was rated at high ecological risk, followed by Sydney and Santander estuaries with moderate risk. Auckland and Darwin sediments exhibited minimal ecological risk and sediment in the remaining harbours (Dublin, Hong Kong, Ravenna, Ria de Vigo and Rio de Janeiro) were assessed at slight ecological risk.The extraordinary variety of environments and types/quantities/qualities of data investigated resulted in as much a critique and development of methodology, as an assessment of human impact, including unique techniques for elemental normalisation and contaminant classification. Recommendations for an improved technical framework for sediment quality assessment are provided.

Using green to cool the grey: Modelling the cooling effect of green spaces with a high spatial resolution

The urban heat island effect creates warmer and drier conditions in urban areas than in their surrounding rural areas. This effect is predicted to be exacerbated in the future, under a climate change scenario. One way to mitigate this effect is to use the urban green infrastructure as a way to promote the cooling island effect. In this study we aimed to model, with a high spatial resolution, how Mediterranean urban parks can be maximized to be used as cooling islands, by answering the following questions: i) which factors influence the cooling effect and when?; ii) what type of green spaces contributes the most to the cooling effect?; iii) what is the cooling distance of influence? To answer these questions we established a sampling design where temperature and relative humidity were measured in different seasons, in locations with contrasting characteristics of green and grey cover. We were able to model the effect of green and grey spaces in the cooling island effect and build high spatial resolution predicting maps for temperature and relative humidity. Our study showed that even green spaces with reduced areas can regulate microclimate, alleviating temperature by 1–3 °C and increasing moisture by 2–8%, on average. Green spaces with a higher density of trees were more efficient in delivering the cooling effect. The morphology, aspect and level of exposure of grey surfaces to the solar radiation were also important features included in the models. Green spaces influenced temperature and relative humidity up to 60 m away from the parks' limits, whereas grey areas influenced in a much lesser range, from 5 m up to 10 m. These models can now be used by citizens and stakeholders for green spaces management and human well-being impact assessment.

Large-scale land acquisition and human rights at the crossroads: quest for a rights-based approach to land administration in Ethiopia

Large-scale land investment through a form of forceful acquisition, dubbed “land grabbing”, is a perennial concern for local community and indigenous peoples in Africa, particularly in Ethiopia. This article explores the human rights impacts of such large-scale land acquisition in Ethiopia and discusses ways of ameliorating its effects. It demonstrates how largescale land acquisition in the country undermines substantive rights of the local and indigenous peoples, including land rights, right to food, development, culture, self-determination, labour rights, environmental rights, as well as their procedural right to public participation, prior informed consent, access to information and justice, all of which are recognized under international instruments, and the constitution of the Federal Democratic Republic of Ethiopia (FDRE). The article advocates for a human rights-based approach to large-scale land acquisition regime that respects and integrates the aforementioned substantive and procedural rights of local and indigenous peoples. This also involves government’s obligation to implement effective human right impact assessment, efficient monitoring, evaluation, dispute resolution mechanism, and empowering local community and other stakeholders to safeguard the rights of affected community. By so doing, the country can mobilize the large-scale land investment for sustainable development without compromising the rights of local and indigenous peoples.Keywords: Ethiopia, Human Rights, Large-Scale Land Acquisition

Advancing impact assessment for intelligent systems

With the rise of AI technologies in society, we need a human impact assessment for technology.

The evolution and research framework of carbon footprint: Based on the perspective of knowledge mapping

Abstract Carbon footprint can monitor the degree of human stress on the ecological environment and has been widely used to measure the level of climate change and sustainable development. To systematically sort out and analyse the development history of carbon footprint research, a keyword co-appearance and literature co-citation knowledge mapping has been drawn, the carbon footprint research hotspots, knowledge bases, research frontiers, and research features have been analysed, from Web of Science core database since 2008 as a data source. From the review, by establishing a carbon footprint research framework from the perspectives of both government and market, the problems of regional environmental governance efficiency, fairness mechanisms, and carbon emission rights are analysed under the government-led and market-led approaches and reviewed the development of carbon footprint research methods. In addition, the boundary and the applicability of the carbon footprint accounting methods has been discussed. The results show: (1) China is increasingly becoming an ideal for carbon footprint research due to its varied and complicated problem of climate and environment. (2) Future research hotspots will focus more on carbon sinks, land use changes, energy consumption, industrial ecology, buildings, livestock, and international trade. (3) Half-life, burst, and centrality indicate that the knowledge bases of carbon footprint are the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios, ISO14040 (2006) and Livestock’s long shadow. (4) The latest international fronts of carbon footprint research focus on the carbon footprint generated in international trade, innovations of methods and discrimination and definition of the theory concept, for example, the expression of carbon footprint means the use of either ecological land area units or physical weight units. (4) The research features are mainly concerned with the carbon footprint application of the multi-region input-output model in international trade, multi-scale assessment of human impact on the environment and sustainable development, and resource-environment effects associated with food loss and waste.

Using depression deposits to reconstruct human impact on sediment yields from a small karst catchment over the past 600 years

Assessment of long-term human impact on sediment yields from karst settings can improve our understanding of the pattern of soil erosion causing rocky desertification in the historical context of environmental change influenced by human activity. Few previous investigations have estimated this impact over time-scales longer than 50 years. This study used dated depression deposits to reconstruct human impact on sediment yields from a small karst catchment in the Three Gorges Reservoir Region, China, over the past 600 years. 137Cs, 210Pbex, and 14C techniques were used to determine short-term (~50 yr), medium-term (~100 yr), and long-term (~600 yr) sedimentation in the karst depression, respectively. Sedimentation rates and specific sediment yields in the catchment during six distinct stages (1351–1462, 1463–1701, 1702–1809, 1810–1916, 1917–1962, and 1963–2017) were determined from core samples. The results indicate that soil loss during the period 1351–1962 was more intensive than that since 1963, which reveals changing sediment yields impacted by human activity over the past 600 years. The high values during the three stages before 1810 can be attributed to the impacts of large-scale migration of people from Huguang to Sichuan during the Ming and Qing dynasties; the higher values during 1810–1916 might reflect increasing disturbance related to rapid population expansion; the highest values (1917–1962) were caused by large-scale deforestation in 1958 and a consistently increasing population; and low values since 1963 reflect constraints on the supply of sediment source materials. These results suggest that rocky desertification might be a long-term land-surface process induced by human activity over timescales of >100 years rather than a short-term modern process occurring over a number of decades. This is the first attempt to examine the long-term history of human impact on sediment yields from a karst catchment using depression deposits. This work improves our understanding of the influence of human activities on soil loss at a depression-catchment scale, and of the evolution and dynamics of rocky desertification in karst areas.