Estimating the Additional Greenhouse Gas Emissions in Korea: Focused on Demolition of Asbestos Containing Materials in Building
When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model to predict the total GHG emission produced by ACM removal in the entire country of Korea. First, an input-equipment inventory was established for each step of the ACS removal process. Second, an energy consumption database for each equipment type was established. Third, the total GHG emission contributed by each step of the process was calculated. The GHG emissions generated from the 1,142,688 ACS-containing buildings in Korea was estimated to total 23,778 tonCO2eq to 132,141 tonCO2eq. This study was meaningful in that the emissions generated by ACS removal have not been studied before. Furthermore, the study deals with additional problems that can be triggered by the presence of asbestos in building materials. The method provided in this study is expected to contribute greatly to the calculation of GHG emissions caused by ACM worldwide.
Highlights
greenhouse gas (GHG) emissions occur during the dismantling of general building materials
When asbestos containing materials (ACM) must be removed from the building before demolition, additional GHG
The GHG emission generated by the asbestos cement slates (ACS) removal stage per one day of ACS was found to range from 1.0436 kgCO2 eq to 2.7997 kgCO2 eq, while the GHG emission generated by transporting 1 m2 of ACS for 1 km by a single cargo truck ranged from 0.000646 kgCO2 eq to
Summary
The Intergovernmental Panel on Climate Change (IPCC) has warned that without global efforts to reduce additional greenhouse gas (GHG) emissions, the mean global temperature may rise by up to. 3–5 degrees by 2100 [1,2]. The global society is adopting extensive practices and policies toward reducing GHG emissions [3]. Various studies conducted over the past couple of years have focused on the reduction of GHG emissions from the building sector [4,5,6,7], as this sector accounts for approximately 30% of total global GHG emission [8]. GHGs are generated by buildings directly and indirectly over the course of a building’s life cycle, from the construction stage through the operation. Public Health 2016, 13, 902; doi:10.3390/ijerph13090902 www.mdpi.com/journal/ijerph
50
- 10.1016/j.shaw.2015.12.006
- Jan 2, 2016
- Safety and Health at Work
492
- 10.1164/arrd.1976.114.1.187
- Jul 1, 1976
- The American review of respiratory disease
207
- 10.1016/j.rser.2012.12.037
- Jan 25, 2013
- Renewable and Sustainable Energy Reviews
22
- 10.1016/j.jclepro.2014.08.092
- Sep 6, 2014
- Journal of Cleaner Production
12
- 10.1061/(asce)hz.2153-5515.0000266
- Jan 27, 2015
- Journal of Hazardous, Toxic, and Radioactive Waste
91
- 10.1016/j.enbuild.2012.03.027
- Apr 9, 2012
- Energy and Buildings
614
- 10.1016/j.buildenv.2009.05.001
- May 18, 2009
- Building and Environment
42
- 10.1016/j.scitotenv.2015.10.115
- Oct 27, 2015
- Science of The Total Environment
96
- 10.1016/j.rser.2015.12.164
- Feb 6, 2016
- Renewable and Sustainable Energy Reviews
733
- 10.1001/jama.1964.03060270028006
- Apr 6, 1964
- JAMA
- Research Article
3
- 10.1061/(asce)hz.2153-5515.0000639
- Oct 1, 2021
- Journal of Hazardous, Toxic, and Radioactive Waste
Abstract The aim of this research was to compare the environmental impacts of applying thermal vitrification, recycling in clinker furnace, or final disposal of asbestos in a hazardous waste landfi...
- Research Article
4
- 10.1016/j.jclepro.2022.132032
- Jul 1, 2022
- Journal of Cleaner Production
Methods for assessing asbestos-containing roofing slate distribution in an area with poor dwelling conditions
- Preprint Article
- 10.1101/2025.06.04.25328959
- Jun 5, 2025
Abstract ObjectivesArtificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims to develop and evaluate a Smart User Interface (Smart UI) framework that enhances fairness in diabetes prediction systems by operationalizing fairness at the human-computer interaction level, a dimension frequently overlooked in AI fairness research.Materials and MethodsWe employed a nine-metric fairness evaluation framework across four demographically diverse diabetes datasets (Kaggle, Pima Indian, Azure Open, CDC Health Indicators). The Smart UI integrates contextual adjustment tools, dynamic visualizations, real-time alerts, and transparent reporting, combining structured EHR data, wearable sensor inputs, and unstructured clinical notes via natural language processing. The framework was evaluated on a clinical dataset to assess fairness and performance improvements.ResultsThe Smart UI significantly reduced disparities: for age, the equal opportunity difference (EOD) improved from 0.35 to 0.25, with accuracy rising from 90.52% to 91.83%; for BMI, EOD decreased from 0.56 to 0.38, with the F1-score increasing from 83.89% to 86.37%. These outcomes highlight the framework’s ability to enhance fairness without altering underlying algorithms.DiscussionWhile the Smart UI demonstrates promise as a model-agnostic, scalable solution for equitable AI deployment, challenges such as data privacy, usability, and real-time processing persist. The framework’s reliance on diverse data sources and user-centered design underscores its potential, though validation in broader clinical settings is needed.ConclusionThe Smart UI offers a replicable blueprint for embedding fairness in healthcare AI through interface design. Future research should focus on multicenter trials and applications to other chronic diseases to advance inclusive digital health solutions.
- Research Article
4
- 10.3390/s23198021
- Sep 22, 2023
- Sensors (Basel, Switzerland)
The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.
- Research Article
18
- 10.1016/j.wasman.2017.03.042
- Mar 31, 2017
- Waste Management
Optimal management program for asbestos containing building materials to be available in the event of a disaster
- Research Article
45
- 10.1186/s12711-019-0459-5
- Apr 29, 2019
- Genetics, Selection, Evolution : GSE
BackgroundSocietal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitations of previous studies on GHG emissions are that they generally describe feed intake inadequately, assess the consequences of selection on particular traits only, or examine consequences for only part of the production chain. Here, we examine GHG emissions for the whole production chain, with the estimated cost of carbon included as an extra cost on traits in the breeding objective of the production system.MethodsWe examined an example beef production system where economic merit was measured from weaning to slaughter. The estimated cost of the carbon dioxide equivalent (CO2-e) associated with feed intake change is included in the economic values calculated for the breeding objective traits and comes in addition to the cost of the feed associated with trait change. GHG emission effects on the production system are accumulated over the breeding objective traits, and the reduction in GHG emissions is evaluated, for different carbon prices, both for the individual animal and the production system.ResultsMultiple-trait selection in beef cattle can reduce total GHG and GHG emissions per unit of product while increasing economic performance if the cost of feed in the breeding objective is high. When carbon price was $10, $20, $30 and $40/ton CO2-e, selection decreased total GHG emissions by 1.1, 1.6, 2.1 and 2.6% per generation, respectively. When the cost of feed for the breeding objective was low, selection reduced total GHG emissions only if carbon price was high (~ $80/ton CO2-e). Ignoring the costs of GHG emissions when feed cost was low substantially increased emissions (e.g. 4.4% per generation or ~ 8.8% in 10 years).ConclusionsThe ability to reduce GHG emissions in beef cattle depends on the cost of feed in the breeding objective of the production system. Multiple-trait selection will reduce emissions, while improving economic performance, if the cost of feed in the breeding objective is high. If it is low, greater growth will be favoured, leading to an increase in GHG emissions that may be undesirable.
- Research Article
70
- 10.1016/j.joule.2020.08.001
- Aug 25, 2020
- Joule
Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers
- Research Article
1
- 10.1016/j.oneear.2021.11.008
- Dec 1, 2021
- One Earth
Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third
- Research Article
14
- 10.1007/s10668-020-00879-8
- Jul 16, 2020
- Environment, Development and Sustainability
In this study, a comparative analysis was presented to detect the quota of urban and rural areas from total greenhouse gas (GHG) emissions in 26 selected countries of the Middle East and Central Asia (MECA) during 1994–2014. For this purpose, 18 independent variables such as land area, population characteristics, energy use and consumption, gross domestic product (GDP), CO2 emissions, etc., were considered in addition to one dependent variable of total GHG emissions. Statistical modeling to investigate GHG emissions was constructed comprising the quantitative procedures of the correlation test and clustering analysis, which can be considered as the fundamental basis of each econometric analysis. The GHG emissions from the urban (rural) sector of total countries in 2014 were obtained as 3313.4 (1135.6) Mt of CO2 equivalents, which is about 74.5% (25.5%) of the total GHG emissions (4449.1 Mt of CO2 equivalents) in the MECA region. The correlation test between GHG emissions and urban indicators revealed the significant records (R from 0.745 to 0.981) compared with rural indicators (R from 0.337 to 0.890). Based on the clustering analysis of the countries, Cluster A, comprised of three countries of Iran, Saudi Arabia, and Turkey, was categorized as countries with very high contributing to the total GHG emissions in the MECA region (~ 43.3%). The quotas of emissions from urban and rural sectors in the Cluster A were estimated as 83.1% and 16.9% from the total GHG emissions in 2014 (1921.3 Mt of CO2), while the same quotas were predicted as 73.1% and 26.9% from the total GHG emissions in 2030 (1921.3 Mt of CO2). This study carried out comprehensive research on the GHG emissions from the urban and rural areas in a crucial region of the world, which is faced with the rising growth of population, urbanization, globalization, high-energy use, and fuel consumption.
- Research Article
79
- 10.1016/j.agee.2011.05.010
- Jun 8, 2011
- Agriculture, Ecosystems & Environment
Whole-farm systems modelling of greenhouse gas emissions from pastoral suckler beef cow production systems
- Conference Article
- 10.1109/icast1.2018.8751267
- Oct 1, 2018
Kupang city is growth rapidly and located in a strategic position between Australia and Timor Leste. A sharp increase of GHG emission along with environmental pollution, contamination of water, air and improper waste disposal practices as its consequence to the global environment. The city’s government ambition to evaluate impact of economic activity on greenhouse gases (GHG) emission contribution. This paper outlined pollutant sectors that contribute substantially to GHG emission in Kupang along with its structure, and count an estimated amount of emission coefficients for 27 economy sectors. More in-depth explanation about indirect coefficient pollutant emission which beneficial not only for calculation of the emission amount but more as inventory data for LCA. The paper is investigated review the trends of some priority sectors, then introduction of indirect coefficients of pollutant sectors, and showed the Pollutant Emission Structure for Kupang. After that, an estimated amount of Kupang GHG emission under BAU is also counted and confirmed. The paper only considers GHG emission issues while air pollutant emission only be provided as inventory data but will not be used as exogenous data for this paper. In the final part a brief explanation and implications of GHG emission policy in Kupang are identified. A detailed of input-output data for individual process are provided includes all groups of processes or industry sectors relevant to economy activities in Kupang City. A time period for Global Warming Potential (GWP) 20 year and 100 years are used to forecasted amounts share of total GHG emission in Kupang and Indonesia by 2020 compared to 2010. As results first, the GHG emission and air pollutant coefficients for 27 sectors in Kupang based on method is presented in NIES which use to count the GHG emission. These also become an Inventory data for researchers of regional science in Indonesia, however, geography and socioeconomic conditions in every region is different, so that some criteria will be applied. Second, found total GHG emission in Kupang is $1.0164\mathrm{x} 10^{-3}$ Gt or around 0.047% compared to total GHG emission by 2010 and 0.034% compared to total GHG emission by 2020 in Indonesia. The study suggests to government consider a proper method in decide a reliable environmental policy and technical measures to reach GHG emission targets by 2020. Third, total share of CO 2 e in Indonesia emitted from Kupang for GWP 20 years and 100 years respectively were came out as follow.
- Research Article
111
- 10.1007/s10705-012-9522-0
- Aug 18, 2012
- Nutrient Cycling in Agroecosystems
Studies on the sustainability of crop production systems should consider both the carbon (C) footprint and the crop yield. Knowledge is urgently needed to estimate the C cost of maize (Zea mays L.) production in a continuous monoculture or in rotation with a leguminous crop, the popular rotation system in North America. In this study, we used a 19-year field experiment with maize under different levels of synthetic N treatments in a continuous culture or rotation with forage legume (Alfalfa or red clover; Medicago sativa L./Trifolium pratense L.) or soybean (Glycine max L. Merr) to assess the sustainability of maize production systems by estimating total greenhouse gas (GHG) emissions (kg CO2 eq ha−1) and the equivalent C cost of yield or C footprint (kg CO2 eq kg−1 grain). High N application increased both total GHG emissions and the C footprint across all the rotation systems. Compared to continuous maize monoculture (MM), maize following forage (alfalfa or red clover; FM) or grain (soybean; SM) legumes was estimated to generate greater total GHG emissions, however both FM and SM had a lower C footprint across all N levels due to increased productivity. When compared to MM treated with 100 kg N ha−1, maize treated with 100 kg N ha−1, following a forage legume resulted in a 5 % increase in total GHG emissions while reducing the C footprint by 17 %. Similarly, in 18 out of the 19-year period, maize treated with 100 kg N ha−1, following soybean (SM) had a minimal effect on total GHG emissions (1 %), but reduced the C footprint by 8 %. Compared to the conventional MM with the 200 kg N ha−1 treatment, FM with the 100 kg N ha−1 treatment had 40 % lower total GHG emissions and 46 % lower C footprint. Maize with 100 kg N ha−1 following soybean had a 42 % lower total GHG emissions and 41 % lower C footprint than MM treated with 200 kg N ha−1. Clearly, there was a trade-off among total GHG emissions, C footprint and yield, and yield and GHG emissions or C footprint not linearly related. Our data indicate that maize production with 100 kg N ha−1 in rotation with forage or grain legumes can maintain high productivity while reducing GHG emissions and the C footprint when compared to a continuous maize cropping system with 200 kg N ha−1.
- Research Article
2
- 10.3390/su15118531
- May 24, 2023
- Sustainability
Food losses and waste (FLW) reduction and mitigating climate impact in food chains are priorities in achieving sustainable development goals. However, many FLW-reducing interventions induce additional greenhouse gas (GHG) emissions, for example, from energy, fuel, or packaging. The net effect of such interventions (expressed in GHG emissions per unit of food available for consumption) is not obvious, as is illustrated in a number of case studies. We recommend that in the decision to take on FLW-reducing interventions, the trade-offs on sustainability impacts (such as GHG emissions) are taken into consideration. Since FLW induce demand and extra operations in all stages along a supply chain, adequate representation of cumulative GHG emissions along the production and supply chain, including ‘hidden parts’ of the chain, is required, which is challenging in full LCA studies. As a workaround, the case studies in this paper are based on a generic tool, the Agro-Chain greenhouse gas Emission (ACE) calculator that includes metrics and data for common food product categories and supply chain typologies. The calculator represents the structure of a generic (fresh food) supply chain and offers data sets for, amongst others, crop GHG emission factors and FLW in different stages of the production and distribution chain. Through scenario calculations with different chain parameters (describing pre and post-intervention scenarios), the net effects of an intervention on GHG emissions and FLW per unit of food sold to the consumer can be compared with little effort. In the case studies, interventions at the production stage as well as in post-harvest operations, are analyzed. Results show that post-harvest activities (especially FLW) contribute substantially to the carbon footprint of supplied food products. The FLW-reducing interventions are considered to induce additional GHG emissions. In most case studies, FLW-reducing interventions lower total GHG associated with a unit of food supplied to a client or consumer. However, in one case study, the extra emissions due to the intervention were higher than the prevented emission from lowering food losses. Consequently, in the latter case, the intervention is not an effective GHG emission reduction intervention.
- Research Article
- 10.3389/frsc.2024.1418214
- Jun 25, 2024
- Frontiers in Sustainable Cities
Understanding urban spatial heterogeneity of greenhouse gas (GHG) emissions from sectoral household consumption is crucial to facilitate moves towards low-carbon cities. In this study, we use Xiamen city of China as a case study to reveal the emission characteristics of household GHG as well as spatial heterogeneity. We conducted a face-to-face questionnaire survey and calculated GHG emissions of districts from household energy consumption, food consumption, transportation, housing, household waste and wastewater treatment. The GHG emissions and the amount of urban residential household consumption shows obvious spatial heterogeneity across districts. Total GHG emissions of Xiamen city were 8.39 Mt. CO2e, and average household and per capita of GHG emissions were 8.11 and 2.72 tCO2e, respectively. While total GHG emissions vary from 0.41 to 2.45 Mt. CO2e across six districts and range from 0.16 to 3.39 Mt. CO2e among six sectors. Household GHG emissions differ from 7.08 to 9.40 tCO2e, while the per capita emissions range between 2.41 to 3.14 tCO2e among districts. Results also showed that more urbanized areas with higher population density have larger total urban residential GHG emissions, whereas household emissions were comparatively lower in these areas. In contrast, our study did not show an (inverted-) U relationship or linear relationship between emissions and population, nor between emissions and income level. Household energy use is the largest sector emitting GHGs. These findings will be useful to underpin policy making towards low-carbon cities.
- Preprint Article
- 10.5194/egusphere-egu23-10836
- May 15, 2023
The Republic of Korea submitted its updated Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change (UNFCCC) Secretariat in December 2021. The updated NDC target is to reduce total national greenhouse gas (GHG) emissions by 40% from the 2018 level, which is 727.6 Mt CO2eq, by 2030. According to the updated NDC, local governments are also required to revise their GHG reduction plans. In addition, local governments should self-inspect the progress and major achievements of the GHG reduction plan every year in accordance with the evaluation guideline of the Ministry of Environment. Of 6 metropolitan cities, Gyeonggi Province shows the highest GHG emissions in the country, which accounts for about 17% of the total national GHG emissions in 2021. Ironically, Goyang City, a basic local government of Gyeonggi Province, was selected as one of the seven best local governments for carbon neutrality in 2021. The City has set a reduction target of 32.8% below BAU by 2030 and prepared a plan to implement reduction targets by sector. Over the last decade, building and transportation sectors have been the major sources of GHG emissions in Goyang City, accounting for approx. 70% of the city’s total GHG emissions. The city promotes zero-energy building (ZEB) for newly constructed buildings and encourages green remodeling for existing buildings in order to reduce GHG emissions in the building sector. It is essential to introduce renewable energy such as solar, geothermal, hydrothermal, etc. for ZEB and green remodeling. In this study, therefore, the potential for solar power generation, which is most easily applicable to the building sector, and GHG reduction were calculated for residential buildings in Goyang City. To calculate the available area for solar power on the roof of residential buildings, spatial data was constructed using high-resolution aerial photographs and the outline of the building roof was extracted through AI training data. AcknowledgementsThis research was carried out as a part of KICT Research Program (Data-Centric Checkup Technique of Building Energy Performance) funded by the Ministry of Science and ICT.
- Research Article
16
- 10.1007/s11367-017-1288-9
- Mar 3, 2017
- The International Journal of Life Cycle Assessment
The aim of this study was to estimate the total greenhouse gas (GHG) emissions generated from whole life cycle stages of a sewer pipeline system and suggest the strategies to mitigate GHG emissions from the system. The process-based life cycle assessment (LCA) with a city-scale inventory database of a sewer pipeline system was conducted. The GHG emissions (direct, indirect, and embodied) generated from a sewer pipeline system in Daejeon Metropolitan City (DMC), South Korea, were estimated for a case study. The potential improvement actions which can mitigate GHG emissions were evaluated through a scenario analysis based on a sensitivity analysis. The amount of GHG emissions varied with the size (150, 300, 450, 700, and 900 mm) and materials (polyvinyl chloride (PVC), polyethylene (PE), concrete, and cast iron) of the pipeline. Pipes with smaller diameter emitted less GHG, and the concrete pipe generated lower amount of GHG than pipes made from other materials. The case study demonstrated that the operation (OP) stage (3.67 × 104 t CO2eq year−1, 64.9%) is the most significant for total GHG emissions (5.65 × 104 t CO2eq year−1) because a huge amount of CH4 (3.51 × 104 t CO2eq year−1) can be generated at the stage due to biofilm reaction in the inner surface of pipeline. Mitigation of CH4 emissions by reducing hydraulic retention time (HRT), optimizing surface area-to-volume (A/V) ratio of pipes, and lowering biofilm reaction during the OP stage could be effective ways to reduce total GHG emissions from the sewer pipeline system. For the rehabilitation of sewer pipeline system in DMC, the use of small diameter pipe, combination of pipe materials, and periodic maintenance activities are suggested as suitable strategies that could mitigate GHG emissions. This study demonstrated the usability and appropriateness of the process-based LCA providing effective GHG mitigation strategies at a city-scale sewer pipeline system. The results obtained from this study could be applied to the development of comprehensive models which can precisely estimate all GHG emissions generated from sewer pipeline and other urban environmental systems.
- Research Article
42
- 10.1016/j.jclepro.2014.07.016
- Jul 15, 2014
- Journal of Cleaner Production
Comparison of greenhouse gas emission accounting methods for steel production in China
- Research Article
20
- 10.3390/agriculture8090133
- Sep 1, 2018
- Agriculture
The increasing global demand for vegetable oils has resulted in a significant increase in the area under oil palm in the tropics during the last couple of decades, and this is projected to increase further. The Roundtable on Sustainable Palm Oil discourages the conversion of peatlands to oil palm and rubber plantations. However, our understanding of the effects on soil organic carbon (SOC) stocks and associated greenhouse gas (GHG) emissions of land use conversion is incomplete, especially for mineral soils under primary forests, secondary forests, rubber and other perennial plantations in the tropics. In this review we synthesised information on SOC stocks and GHG emissions from tropical mineral soils under forest, oil palm and rubber plantations and other agroecosystems across the tropical regions. We found that the largest SOC losses occurred after land use conversion from primary forest to oil palm and rubber plantations. Secondary forest and pasture lands showed lower SOC losses as well as total GHG (CO2, N2O and CH4) emissions when converted to oil palm and rubber plantations. However, due to the limited data available on all three GHG emissions, there remains high uncertainty in GHG emissions estimates, and regional GHG accounting is more reliable. We recommend long-term monitoring of oil palm and other perennial plantations established on tropical mineral soils on different soil types and regions on SOC stock changes and total GHG emissions and evaluate appropriate management practices to optimise production and sustainable economic returns, and minimise environmental impact.
- Research Article
99
- 10.1016/j.resconrec.2020.105303
- Dec 10, 2020
- Resources, Conservation and Recycling
The influence of crop and chemical fertilizer combinations on greenhouse gas emissions: A partial life-cycle assessment of fertilizer production and use in China
- Research Article
5
- 10.1016/j.clet.2021.100325
- Dec 1, 2021
- Cleaner Engineering and Technology
Routing on-road heavy vehicles for alleviating greenhouse gas emissions
- New
- Research Article
- 10.3390/ijerph22111684
- Nov 6, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111675
- Nov 4, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111674
- Nov 4, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111672
- Nov 4, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111673
- Nov 4, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111669
- Nov 3, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111665
- Nov 3, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111666
- Nov 3, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111671
- Nov 3, 2025
- International Journal of Environmental Research and Public Health
- New
- Research Article
- 10.3390/ijerph22111668
- Nov 3, 2025
- International Journal of Environmental Research and Public Health
- Ask R Discovery
- Chat PDF