Impact Assessment Studies Research Articles

Reducing the Uncertainty in the Tropical Precipitation through a Multi‐Criteria Decision‐Making Approach

ABSTRACTThe inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers.

Greenness and mortality in an industrialized area in Southern Italy: a health impact assessment study

Abstract Background In Taranto the effects of industrial installations have been studied, but less attention has been placed on the impacts of green-oriented policies. The “3-30-300” rule states that every citizen should have 30% tree-canopy-cover in their neighborhood and not live more than 300m away from the nearest green space. In this Health Impact Assessment (HIA) study, we aimed to quantify the deaths that could be prevented in population aged ≥20 years by implementing this policy in Taranto. Methods Population weighted exposures (PWE) to green spaces (10m) were estimated using the 2022 greenest season NDVI from Sentinel-2 and the 2021 ESA WorldCover within a 300m (WHO 2016) buffer. We set a tree-cover optimal target of 30% and interim targets from 10% to 25%. For each tree-cover target, we set the counterfactual exposure (CE) as the corresponding PWE to NDVI. Age-specific preventable deaths (PD) were calculated by multiplying the baseline deaths (ISTAT) by the preventable fractions (PF). Preventable years of life lost (PYLL) were calculated by multiplying the PD by the specific life expectancy. To compute the 95%CI, we ran 100,000 Monte Carlo simulations to propagate the uncertainties of both CE and ERF to the final estimates. Results The PWEs to NDVI and tree-cover-proportion were 0.19 and 8.0%, respectively. For the 30% tree-cover target (currently achieved by the 1.5% of population), the CE (NDVI) and PF (%) were 0.33 (0.32-0.33) and 5.4% (4.1%-6.2%). A total of 126 (97-146) PD and 1270 (976-1474) PYLL were estimated. Three-cover, shrubland and grassland were associated to higher NDVI values, while built-up and deprivation-index were associated to lower greenness. NDVI and summer temperature showed an inverse relationship. Conclusions This study as a pioneering effort in Southern Italy. Providing more green space could prevent a substantial number of premature deaths in Taranto, potentially up to 5.4% by achieving a 30% tree-cover. Key messages • Providing more green space could prevent a substantial number of premature deaths in Taranto, potentially up to 5.4% by achieving a 30% tree-cover. • Built-up and deprivation-index were associated to lower greenness. NDVI and summer temperature showed an inverse relationship.

Prevalence and Risk Factors of HIV Drug Resistance in Zimbabwe: Evidence from Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA) 2020 Survey

(1) Background: HIV drug resistance (HIVDR) poses a significant challenge to the effectiveness of antiretroviral therapy and the overall management of HIVand AIDS. Understanding the predictors of HIVDR is critical for developing strategies to mitigate its impact. The objectives of this study were to identify the predictors of HIVDR among Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA 2020) study participants, a national population-based survey. (2) Methods: Data from people living with HIV who participated in the ZIMPHIA 2020 were used to determine the predictors of HIVDR. (3) Results: The prevalence of HIVDR was 44.9%. Acquired HIVDR was present in 76.1% of people with a virological failure and transmitted resistance is 22.6% in naïve individuals. Factors associated with HIVDR in adjusted analysis were the number of lifetime sexual partners (aOR = 1.03, 95% CI: 1.01–1.06, p = 0.017), each additional year since the first HIV positive result (aOR = 1.17, 95% CI: 1.09–1.25, p < 0.01), each additional year on ART (aOR = 1.14, 95% CI: 1.06–1.23, p = 0.001), initiating ART before 2014 (aOR = 3.08, 95% CI: 1.72–5.49, p = 0.020), ever had switched antiretrovirals (aOR = 2.47, 95% CI: 1.15–5.29, p = 0.020) or had ever had a viral load test (aOR = 2.54, 95% CI: 1.54–4.17, p < 0.001) and a CD4 count < 350 (aOR = 2.04, 95% CI: 1.48–2.83, p < 0.01), while age ≥ 50 (aOR = 0.56, 95% CI: 0.32–0.98, 32 p = 0.04), condom use at last encounter (OR: 0.49, 95%CI: 0.33–0.73, p < 0.001), and not being on ART (aOR = 0.09, 95% CI: 0.06–0.13, p < 0.01) were associated with reduced odds of HIVDR. Conclusions: HIVDR was high among the participants. There is a need to address HIVDR and enhance the mechanisms already in place. This study introduces more information that would help in developing targeted interventions to prevent HIVDR and improve patient outcomes.

Effect of uncorrelated sources assumption on railway vibration modelling

In classical railway vibrations studies, wheel-rail contact points are considered as point sources uncorrelated from each other. This approach gives a more reliable source for vibration impact assessment studies along railways which can be based on in situ experimental measurements or on numerical models. This is also related to the fact that the combined wheel-rail roughness that cannot be perfectly known. This postulation allows using a line spectral force density combined with a line transfer mobility. However, all sources are generated by the same system (train, railway and ground) and are in reality correlated in some way. This paper investigates the effect of sources decorrelation on ground vibration estimation. Numerical models in time and frequency domain are used allowing comparisons for several identical configurations. Results are discussed in detail to understand the effect of sources uncorrelation approach.

Pieces of an Inter‐Disciplinary Puzzle: Connecting Environmental Impact Assessment and Environmental Disaster Studies

ABSTRACTThe field of environmental studies has great, but largely under‐realized, potential to play an integral role in confronting its raison d'etre—the ecological and climate crisis. Realization of this potential depends on the prospect of stronger connections being made across the wide and eclectic spectrum of its sub‐fields. This article explores two sub‐fields and streams of literature that have remained mostly unconnected—environmental impact assessment and environmental disaster studies—and identifies cross‐cutting concepts and themes. It is argued that greater integration of the two sub‐fields may help generate new insights and approaches in the complicated challenge of preventing of environmental disasters.

Modeling of an advanced renewable energy system coupled with hydrogen production and liquefaction for sustainable communities

The primary objective of this study is to develop a sustainable and efficient renewable energy-based multigeneration system that is specifically designed to meet diverse energy demands, such as heat, cooling, electricity and hydrogen. The system comprises several subsystems: a digester for biomass processing, a solar energy collection system, a thermal energy storage system, a multistage Brayton cycle, a steam Rankine cycle with reheat, a double-effect absorption system, a sonic hydrogen production unit, and a multistage hydrogen liquefaction system. The methodology involves conducting energy and exergy analyses, economic evaluation, and environmental impact assessment to determine the system's performance and viability. The energy efficiency results show that the Brayton cycle, steam Rankine cycle and liquefaction system have efficiencies of 30.92 %, 30.18 %, and 64.53 %, respectively. The exergy efficiencies for these subsystems are 50.10 %, 67.21 %, and 11.29 %, respectively. The double-effect absorption system exhibits energetic and exergetic coefficients of performance of 1.68 and 0.64, respectively. The overall energy and exergy efficiencies of the system are 36.41 % and 55.74 %, respectively. The economic analysis indicates a significant revenue potential, with the project reaching its break-even point in 2031 and achieving a net present value of $6.60 million. The results of an environmental impact assessment study highlight a reduction in CO2 emissions compared to conventional systems, emphasizing the system's contribution to sustainable energy solutions.

Agricultural and rural development interventions and poverty reduction: Global evidence from 16 impact assessment studies

Agricultural and rural development interventions can reduce global poverty by providing growth-oriented tools, including access to finance, training, and markets. While such interventions effectively reduce monetary poverty (e.g., $1 a day poverty line), there is increasing interest in incorporating non-monetary poverty indicators, such as education, health, and living standards, to capture inherent multidimensionality in poverty. This study analyzes data from 16 impact evaluation studies conducted between 2019 and 2023 to examine whether and to what extent agricultural and rural development interventions affect multidimensional poverty of small-scale producers. Our analysis shows a 4 percent reduction in multidimensional poverty for treatment households compared to comparison households. Our findings suggest that agricultural and rural development interventions play a positive role in reducing poverty and have the potential to improve the long-term well-being of poor households.

Balancing Environmental Concerns with Infrastructure Growth: A Study of Environment Impact Assessment in Infrastructure Development

Balancing Environmental Concerns with Infrastructure Growth: A Study of Environment Impact Assessment in Infrastructure Development

P043 PREACH IMPLEMENTATION PROJECT A SOCIO-ECOLOGICAL FRAMEWORK FOR HYPERTENSION IN PAKISTAN

Background: Worldwide hypertension prevalence is projected to reach 1.56 billion by 2025, affecting 29% of the global population according to the WHO1. It contributes to 7.5 million deaths, 3.7% of total disability-adjusted life years (DALYs) and is responsible for causing stroke in 54% of hypertensives2,3. Pakistan has a prevalence of 43% hypertension with merely 12.5% of controlled hypertensives4,5. There is a pressing need for a national level intervention through uniform clinical approaches for optimum hypertension prevention, treatment and control6, 7. Through PREACH project, we prioritize primary care physicians as first-line responders and are training them on standardized hypertension guidelines to alleviate the burden from tertiary care hospitals. To detect new onset hypertension the project also incorporates early surveillance and community level screenings. Objectives: • To build capacity of practitioners on standardized guidelines endorsed by health ministry, PCS (Pakistan Cardiac Society) & PHL (Pakistan Hypertension League) • To raise community awareness, to screen and develop self-referral pathways to specialty care • To develop a robust national hypertension registry • To include hypertension in essential primary healthcare packages Methodology: Hypertension guidelines are developed. Accredited national certified workforce of more than 14,000 general practitioners (GPs) will be built through guidelines trainings via train the trainer model. Impact assessment studies will be done to assess training effectiveness and implementation at clinics. Community mobilization and awareness campaigns will be run to identify and direct self-referral pathway to an accredited GP. Development of national hypertension registry as a centralized system for data collection to report actual disease burden. Ministry of health will be advocated to prioritize hypertension management via multi-sectoral collaboration i.e. medical societies, academic, non-profit and private-sector for incorporations into annual development programs. Results \Conclusion: We aim to create an impact in the lives of 4.3 million beneficiaries (anti-hypertensive naïve hypertensive patients of Pakistan) by increasing self-referral of hypertensive cases to specialists through PREACH implementation project at all healthcare service delivery levels.

Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables.

Background: Parkinson's Disease significantly impacts health-related quality of life, with the Parkinson's Disease Questionnaire-39 extensively used for its assessment. However, predicting such outcomes remains a challenge due to the subjective nature and variability in patient experiences. This study develops a machine learning model using accessible clinical data to enable predictions of life-quality outcomes in Parkinson's Disease and utilizes explainable machine learning techniques to identify key influencing factors, offering actionable insights for clinicians. Methods: Data from the Parkinson's Real-world Impact Assessment study (PRISM), involving 861 patients across six European countries, were analyzed. After excluding incomplete data, 627 complete observations were used for the analysis. An ensemble machine learning model was developed with a 90% training and 10% validation split. Results: The model demonstrated a Mean Absolute Error of 4.82, a Root Mean Squared Error of 8.09, and an R2 of 0.75 in the training set, indicating a strong model fit. In the validation set, the model achieved a Mean Absolute Error of 11.22, a Root Mean Squared Error of 13.99, and an R2 of 0.36, showcasing moderate variation. Key predictors such as age at diagnosis, patient's country, dementia, and patient's age were identified, providing insights into the model's decision-making process. Conclusions: This study presents a robust model capable of predicting the impact of Parkinson's Disease on patients' quality of life using common clinical variables. These results demonstrate the potential of machine learning to enhance clinical decision-making and patient care, suggesting directions for future research to improve model generalizability and applicability.

Chemical exposome and children health: Identification of dose-response relationships from meta-analyses and epidemiological studies

BackgroundHealth impact assessment studies quantifying the impact of the chemical exposome on children's health generally consider a small fraction of the exposome. Synthetizing available dose-response relationships is an essential step to fill this gap. We reviewed the literature for dose-response relationships relating the chemical exposome with children health. MethodWe focused on 78 substance-outcome pairs for which the level of evidence had previously been classified as ‘likely’ or ‘very likely’. We searched for dose-response relationships for these pairs from meta-analyses and, if none was available, from single epidemiological studies, from which we conducted meta-analyses whenever possible. ResultsWe identified dose-response relationships for 50 of the 78 prioritized substance-outcome pairs (64%). Dose-response relationships stemmed from meta-analyses for 21 pairs, from de novo meta-analyses for 1 pair and single studies for 28 pairs. Dose-response relationships were available for tobacco (fetal and infant death, congenital heart defects, birth outcomes, orofacial clefts, respiratory health), lead (asthma, cognition, delayed puberty onset and iron deficiency anaemia), polychlorobiphenyls (PCBs) (cognition, respiratory infections and birth outcomes), bisphenol A (cognition), hexachlorobenzene (HCB) (respiratory health), Polybrominated diphenyl ethers (neurodevelopment), DDT (hypospadias, cryptorchidism, miscarriage), pesticides (neurodevelopment), methylmercury (cognition), PFAS (immune system, birth weight, behavior, miscarriage), arsenic (cognition, birth weight, death, respiratory health), cadmium (cognition, birth weight), manganese (behavior), sodium (blood pressure) and thallium (birth weight). For 28 of the 78 substance-outcome pairs (36%), no dose-response relationship was available from epidemiological studies in children. ConclusionsWe identified dose-response relationships for 50 substance-outcome pairs, corresponding to 20 chemicals and 17 health outcomes. These can be used to perform more comprehensive quantitative health impact assessment of the exposome on child health. We also identified 28 substance-outcome pairs corresponding to ‘likely’ or ‘very likely’ effects for which research generating dose-response functions in children would be relevant.

Green space, air pollution and mortality in a metropolitan area in Southern Italy: a health impact assessment study

Green space, air pollution and mortality in a metropolitan area in Southern Italy: a health impact assessment study

Hierarchy of environmental impacts in hydroelectric projects established in mountain ecosystems

In Bolivia, hydroelectric projects are gaining relevance as a source of renewable energy, which is why there is little record of specific experiences on the implementation of hydroelectric projects in mountain areas that include an analysis of the environment within the framework of sustainability energetic. The present study takes a hydroelectric reservoir project established in a mountain ecosystem as a unit of analysis to demonstrate that a hierarchy of environmental impacts responds more precisely to the environmental dimension, according to its technical and ecosystem characteristics. The development of the work starts with the analysis of the information recorded in the Environmental Impact Assessment Study - EEIA and based on this information, a procedure is carried out to rank the environmental impacts with the use of matrix, qualitative and quantitative methods, in addition to the use of specialized software. Finally, it is concluded that there are no standard evaluations, and it is affirmed that the EIA of any hydro-energy project, whether established or not, in a mountain ecosystem, is a complex and unique problem. Therefore, a hierarchical ranking of environmental impacts in an initial and particular way to the project site area contributes in a real and effective way to an environmental sustainability of the intervened ecosystem, within the framework of Sustainable Energy Development.

Evaluation of the effectiveness and performance of environmental impact assessment studies in Greece

As environmental management is becoming increasingly complex, adequate assessment techniques must be utilized to forecast environmental impact trends. The current study aims to investigate the effectiveness of the evaluation system in Greece regarding environmental impact assessment (EIA) concerning projects. Data were extracted from literature and a questionnaire, which was distributed electronically on a Pan-Hellenic scale. Results indicated that the EIA system in Greece has solid foundations with a weak point regarding implementation. Strengths, weaknesses, and threats of EIA institution were analyzed through SWOT-AHP (Strength, Weaknesses, Opportunities, Threats and Analytical Hierarchy Process) analysis. A dominant criterion in the analysis due to the dynamic of the deep economic recession was found to be the economic crisis.

Different pathways toward net-zero emissions imply diverging health impacts: a health impact assessment study for France

In the transport sector, efforts to achieve carbon neutrality may generate public health co-benefits by promoting physical activity. This study aims to quantify the health impacts related to active transport based on four different scenarios leading France toward carbon neutrality in 2050. The French Agency for Ecological Transition developed four consistent and contrasting scenarios (S1 to S4) achieving carbon neutrality by 2050 as well as a business-as-usual (BAU) scenario that extends our current lifestyles until 2050, without reaching net-zero. For each of these Transitions2050 scenarios, we distributed the mobility demand for walking, cycling and e-cycling across age groups. Relying on the health impact assessment framework, we quantified the impacts of the corresponding physical activity on all-cause mortality. The impact of each of the carbon neutrality scenarios was determined by comparison with estimates from the BAU scenario. In S1 and S2 scenarios, volumes of active transport are projected to increase to fulfill the World Health Organizations (WHO) recommendations by 2050, while they increase slightly in S3 and decrease in S4. S2 scenario reaches the highest levels of health co-benefits, with 494 000 deaths prevented between 2021 and 2050. This would translate into a life expectancy gain of 3.0 months for the general population in 2050, mainly driven by e-bikes. S1 would provide smaller but important health benefits, while these benefits would be modest for S3. On the contrary, S4 implies 52 000 additional deaths as compared to the BAU scenario and a loss of 0.2 month in life expectancy. Different ways to decarbonize mobility in a net-zero perspective may achieve very contrasting public health co-benefits. This study illustrates how the public health dimension may provide a relevant insight in choices of collective transformation toward net-zero societies.

Performance evaluation of CMIP6 models for application to hydrological modelling studies – A case study of Australia

Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.

“Let It Go”: Comparative Study on Zombie Water Projects in Europe and Asia

To date, there can be found numerous controversial large-scale water projects in Asia and Europe. The so-called “zombie water projects” are associated with insufficient communication, massive investments and prolonged marginalisation of the negative impacts. Traditional impact assessment studies backed by water engineers and other water professionals are struggling to accountably address the political issues. Hence, in the presented study, a new schematic political impact assessment was proposed to evaluate meaningful water dialogue. To dynamics of water dialogue over the zombie water projects was investigated upon the VUDU analytical framework at four micro-case studies (Navigational Channel Improvement Project, Myitsone dam, Oder-Danube-Elbe Canal and Gabčíkovo-Nagymaros dam). These studies were selected according to the extensive literature review to illustrate different development pathways and long-term behavioural changes. The results show that despite the conflict of interests and prolonged distrust towards zombie water projects, the justification of these projects were often driven by inconsistent empirical data, (i)rational justification and changing political environments. In addition, by comparing various four micro-case studies, we noticed that the meaningful water dialogue was more influenced by the degree of transparency and communication rather than the actual decision whenever to build such water project or not. By showing contemporary dilemmas towards the large-scale water project development, the presented study provides valuable feedback on the previous interdisciplinary studies regarding the transboundary water governance.

Rare Earth Element Accumulation in Fiddler Crabs (Minuca rapax) from the Rio Doce Tropical Estuary Strongly Affected by Mine Tailings Following the Fundão Disaster.

A mining tailing dam rupture in Brazil in November 2015 released millions of tons of mining waste into the Rio Doce ecosystem, leading to long-term aquatic ecosystem impacts. Although multiple lines of evidence indicate tailings associations with potentially toxic elements in estuarine sediments and biological impact and bioaccumulation pathways in fishes, the extent of contamination in base benthic species is still largely unknown. Moreover, Rare Earth Elements (REE) have not received any attention in this regard. This study assessed REE in fiddler crabs (Minuca rapax) sampled from the Rio Doce estuary in 2017, nearly 2years after the disaster. The ΣREE in crab hepatopancreas and muscle were high (327.83mgkg-1 w.w. and 33.84mgkg-1 w.w., respectively, compared to other assessments in crabs, indicating a preference for REE bioaccumulation in the hepatopancreas compared to muscle. Neodimium, La, and Ce were detected at the highest concentrations. The REE from the Rio Doce Basin were, thus, transported and deposited in the estuary with the mine tailings slurry, leading to bioaccumulation in crabs. This may lead to trophic effects and other ecological impacts not readily measured by typical impact assessment studies, revealing an invisible and not typically acknowledged damage to the Rio Doce estuary.

Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003–2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 94.4 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

Enhancing recruitment of individuals living with frailty, multimorbidity and cognitive impairment to Parkinson's research: experiences from the PRIME-UK cross-sectional study.

People with parkinsonism who are older, living in a care home, with frailty, multimorbidity or impaired capacity to consent are under-represented in research, limiting its generalisability. We aimed to evaluate more inclusive recruitment strategies. From one UK centre, we invited people with parkinsonism to participate in a cross-sectional study. Postal invitations were followed by telephone reminders and additional support to facilitate participation. Personal consultees provided information on the views regarding research participation of adults with impaired capacity. These approaches were evaluated: (i) using external data from the Parkinson's Real World Impact assesSMent (PRISM) study and Clinical Practice Research Datalink (CPRD), a sample of all cases in UK primary care, and (ii) comparing those recruited with or without intensive engagement. We approached 1,032 eligible patients, of whom 542 (53%) consented and 477 (46%) returned questionnaires. The gender ratio in PRIME-UK (65% male) closely matched CPRD (61% male), unlike in the PRISM sample (46%). Mean age of PRIME participants was 75.9 (SD 8.5) years, compared to 75.3 (9.5) and 65.4 (8.9) years for CPRD and PRISM, respectively. More intensive engagement enhanced recruitment of women (13.3%; 95% CI 3.8, 22.9%; P= 0.005), care home residents (6.2%; 1.1, 11.2%; P = 0.004), patients diagnosed with atypical parkinsonism (13.7%; 5.4, 19.9%; P < 0.001), and those with a higher frailty score (mean score 0.2, 0.1, 0.2; P < 0.001). These recruitment strategies resulted in a less biased and more representative sample, with greater inclusion of older people with more complex parkinsonism.