Assessment Of Pollution Levels Research Articles

Quantification and characterization of municipal solid waste at aler dumpsite, Lira City, Uganda: Assessing pollution levels and health risks

Managing municipal solid waste (MSW) in urban areas of developing countries is a significant challenge due to rapid urbanization and inadequate infrastructure. This study aims to quantify the MSW at Aler dumpsite in Lira City, Uganda, and assess the associated environmental and health risks. Our findings revealed a daily dumping rate of 7.38 tons, predominantly organic waste. Leachate from the site exceeded WHO limits for nutrients and heavy metals, with exceptionally high amounts of cadmium (Cd) found in soil (4.8 mg/kg) and vegetables. The water quality in nearby streams was poor, rendering it unfit for consumption due to elevated ion and nutrient levels. Health risk assessments indicated significant non-carcinogenic risks from heavy metal ingestion, with a hazard index (HI) of 4.6 for adults and 9.6 for children. While dermal exposure risks were generally low, children were at risk from stream 1 (HQderm = 1.165). Vegetable analysis revealed levels of lead (Pb), cadmium (Cd), zinc (Zn), and nickel (Ni) above safe doses, posing significant non-carcinogenic risks. To address these issues, we recommend increasing public awareness to avoid consuming water and vegetables from areas around the dump site. Additionally, we suggest that the responsible authorities improve MSW management by constructing sanitary landfills to minimize water, soil, and plant pollution from landfill leachate.

Beyond samplism: Rethinking the field in exposure science.

Given the many forms of environmental pollution that have accompanied the global spread of industrial capitalism, there is an urgent need to carry out extensive assessments of the potential toxicity of many compounds on human and nonhuman populations. However, the scientific procedures developed to carry out such assessments present several critical shortcomings that greatly diminish their capacity to protect populations at risk. Through an ethnographic analysis of two field campaigns centered on the assessment of polluted sites in northern Chile, this article will reveal that these campaigns are embedded in what we can call samplism. Contrary to approaches that see fieldwork as generative spaces for knowledge creation, samplism radically simplifies the field into a neutral space for the collection of singular and well-defined lab-bound samples. However, samplism entails that multiple field phenomena relevant for assessing pollution levels are left completely unaccounted for, which greatly diminishes exposure science's overall capacity to help improve the lives of those in need. Thus, exposure science urgently needs todevelop more capacious forms of engagement with the field, an ecological fieldwork that truly represents the complexity of forces and processes behind contemporary processes of pollution and damage.

A probabilistic neural network assessment method for insulator pollution level based on discharge noise

The insulators of a transmission line are exposed to air for a long time, a mount of dust particles will be deposited on the surface. When there rain, fog or high humidity, the surface of the insulator will discharge or even flashover to breakdown. For this, in this paper, the characteristic parameters of insulator discharge noise under different pollution levels, ambient temperature and relative humidity are extracted, and the influence of insulator discharge noise are analyzed respectively. Probabilistic neural network (PNN) model training samples are used to propose an insulator pollution level assessment method based on discharge noise and verify it. The results show that the pollution level has obvious influence on the characteristic parameters of insulator discharge noise. The higher the pollution level, the more obvious the discharge phenomenon and the greater the discharge intensity. When the relative humidity ranges from 70 % to 90 %, the greater the relative humidity, the greater the pollution discharge intensity of the insulator. The energy of the noise signal gradually concentrated in the 5.5 kHz to 8.3 kHz frequency band, and the pollution discharge in the 5.5 kHz to 16.5 kHz frequency band reflected in the low-frequency part of the information increased. Ambient temperature has no obvious effect on insulator discharge noise. The precision rate of the assessment method for pollution levels Ⅰ, Ⅱ, Ⅲ and Ⅳ are 91.5 %, 88.2 %, 86.5 % and 94.0 %, respectively.

Natural sorting of sediments in the wave run-up zone works for microplastics as well

The distribution of plastic pollution in the marine environment is highly variable in time and space, making it difficult to assess pollution levels. This study shows that mixing and natural sorting of material in the wave run-up zone of a sandy beach results in a relatively stable abundance of microplastics in the size range 0.5–2 mm (S-MPs). Based on 175 samples collected over 14 months during 42 monitoring surveys at 6 stations along the shore of the Vistula Spit (Baltic Sea), the mean abundance of S-MPs was found to be 64 ± 36 items/kg DW (98.6 % fibers), with a coefficient of variation of only 56 % over more than one year. Statistical tests confirmed its independence from current wind speed, significant wave height, mean sediment grain size, sediment sorting, percentage of certain sand fractions, month, season, or location along the shore. It can therefore be used as a suitable indicator for long-term monitoring of increasing plastic pollution in the marine environment.

Interplay of natural and anthropogenic factors in sediment dynamics and trace element distribution in Güllük Gulf, western Türkiye: A comprehensive geochemical and hydrodynamic analysis

Güllük Gulf, located in the southeastern Aegean Sea, is significantly impacted by diverse human activities, including urban development, tourism, and intensive aquaculture. This study aims to assess the spatial distribution of trace metals in the Gulf's sediments, identify their sources, and evaluate their environmental impacts to inform effective management strategies. It also examines sediment transport patterns, which are crucial in influencing the distribution and concentration of pollutants. Establishing background concentration levels for various elements provides a baseline to assess pollution levels and identify deviations due to anthropogenic impact. Sediment samples were collected from 45 stations and analyzed for grain size, organic carbon, carbonate content, and concentrations of 26 elements. Contamination was assessed using indices such as Enrichment Factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (Cf), Degree of Contamination (Cdeg), Pollution Load Index (PLI), Modified Pollution Index (MPI), and Toxic Risk Index (TRI). Statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), were used to identify pollution sources. Results indicated that Güllük Gulf's sediments are predominantly sandy, with significant silt and clay fractions in sheltered bays. Organic carbon content averaged 1.68%, with higher values near urban and aquaculture areas. Trace metal concentrations exhibited notable spatial variability, with elevated levels of Pb, Cu and Zn in specific areas, indicating both natural and anthropogenic sources. PCA identified five significant factors contributing to metal presence, linking them to lithogenic origins, aquaculture activities, and industrial inputs. TRI results showed moderate toxic risk in Asin Bay, primarily due to Ni, As and Cr. The sediment transport model of Güllük Gulf, crucial in understanding regional pollution dynamics, revealed a complex interplay of hydrodynamic forces. The study underscores the significant environmental impact of human activities on sediment quality in Güllük Gulf. Elevated trace metal levels near aquaculture sites emphasize the need for stringent environmental management practices. Effective strategies should include regular monitoring, sustainable aquaculture practices, and stakeholder engagement to mitigate environmental impacts and preserve the Gulf's ecological integrity. This comprehensive approach provides crucial insights for future research and environmental management policies aimed at addressing the complex challenges facing Güllük Gulf.

Long-term deployment of commonly used DGT devices for trace element measurement across laboratory and field settings

Diffusive Gradients in Thin-films (DGT) technique is a promising passive sampling technique, which was used for the determination of lots of inorganic and organic pollutants. Although many DGT devices have not been extensively tested and verified in field, DGT device such as LSNP-NP, LSNT-NP, LSNZ-NP, and LSNM-NP DGT were widely employed for the assessment of various trace elements. However, the deployment time of these DGTs were much shorter than the theoretical time in the preexisting literature. Therefore, the performance of DGT for long-term application in different water bodies is not known. This investigation utilized the four DGTs for the assessment of various trace elements across extended periods both in controlled laboratory settings and natural field environments. Synthetic soft, hard, and seawater compositions served as media for laboratory deployments. Most elements can be measured accurately in 4–7 days in soft and hard water. Deployment durations in seawater exhibited a notable reduction compared to those in freshwater matrices. LSNZ-NP DGT excelling in oxyanion determination, while LSNT-NP DGT showcased superior efficacy in phosphorus quantification. Field deployments in rivers and sea affirmed the robustness of LSNZ-NP DGT, evidencing prolonged deployment capabilities for various elements, such as Mo, Se, As, Sb, and P, spanning 7–28 days. Assessment of pollution levels across four sampling sites revealed heightened concentrations of most elements in marine waters relative to riverine environments, except for phosphorus. Notably, all assessed elements, except for phosphorus, conformed to Class I water quality standards. This study demonstrated the difference between the theoretical application time and actual application time for the first time. It raises new questions for the application of DGT in nature water bodies. The mechanization of the difference between the theoretical and actual application time should be studied in the future research. The measures to extend the application time should be studied too.

Sources, Distribution, and Health Implications of Heavy Metals in Street Dust across Industrial, Capital City, and Peri-Urban Areas of Bangladesh

Heavy metals in road dusts can directly pose significant health risks through ingestion, inhalation, and dermal contact. This study investigated the pollution, distribution, and health effect of heavy metals in street dust from industrial, capital city, and peri-urban areas of Bangladesh. Inductively coupled plasma mass spectrometry (ICP-MS) examined eight hazardous heavy metals such as Zn, Cu, Pb, Ni, Mn, Cr, Cd, and Co. Results revealed that industrial areas showed the highest metal concentrations, following the order Mn > Zn > Cr > Pb > Ni > Co > Cd, with an average level of 444.35, 299.25, 238.31, 54.22, 52.78, 45.66, and 2.73 mg/kg, respectively, for fine particles (≤20 μm). Conversely, multivariate statistical analyses were conducted to assess pollution levels and sources. Anthropogenic activities like traffic emissions, construction, and industrial processing were the main pollution sources. A pollution load index revealed that industrial areas had significantly higher pollution (PLI of 2.45), while the capital city and peri-urban areas experienced moderate pollution (PLI of 1.54 and 1.59). Hazard index values were below the safety level of 1, but health risk evaluations revealed increased non-carcinogenic risks for children, especially from Cr, Ni, Cd, and Pb where Cr poses the highest cancer risk via inhalation, with values reaching 1.13 × 10−4–5.96 × 10−4 falling within the threshold level (10−4 to 10−6). These results underline the need for continuous environmental monitoring and pollution control in order to lower health hazards.

Air Quality Analysis through IoT Device and Risk Prediction of Asthma Attack using ML Techniques

Background: The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning [ML] models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol- Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning [SML] models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article. Methods: The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability. Results: The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters. method: In this research work the researcher considered 9 different ML models and used them as supervised as well as semi supervised model to determine the pollution level of the certain area. In this research work the researcher also selected the most relevant features and discarded the less relevant features. In case of SML algorithm, the accuracy level of the different ML algorithm has been determined and depicted in the result analysis section. The most effective ML model has been chosen for the proposed embedded system so that accuracy could be achieved at most. In case of semi supervised algorithm the feature selection is done as per the supervised algorithm. In this case the training is done same as the SML algorithm, but the testing phase is done like unsupervised machine learning algorithm where the decision parameter is predicted and ultimately matched with the previously achieved data of SML algorithm. The reliability of this approach is much more effective than simple SML algorithm. Conclusion: This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.

Environmental Changes of the Temirnik River in Rostov-on-Don

General Background: The increasing anthropogenic impact on river ecosystems has raised concerns about water quality and its effects on surrounding communities. Specific Background: This study focuses on the Temirnik River, located in the northern part of the city, particularly assessing pollution levels in the beaches of "Druzhba" and "Surp Khash." Historical data from 2000-2001 and recent analyses from 2017-2019 reveal significant fluctuations in chemical and biological toxicity. Knowledge Gap: While previous studies have documented pollution levels, a comprehensive comparison of historical and recent data to understand long-term changes and community impacts remains insufficiently explored. Aims: This study aims to track changes in pollution levels over time, evaluate the chemical and biological toxicity, and assess the anthropogenic impacts on the Temirnik River. Results: Chemical analysis revealed increased hardness of water due to the accumulation of calcium and magnesium sulfates and chlorides. The river, heavily impacted by untreated industrial and municipal waste, showed high toxicity levels, especially in the years 2017-2019. Bioassays using Chlorella vulgaris, Raphanus sativus, and Daphnia magna confirmed these findings. Novelty: This study provides a detailed comparative analysis of historical and contemporary pollution data, highlighting the persistent and worsening impact of anthropogenic activities on the Temirnik River. Implications: The findings underscore the critical need for ongoing monitoring and intervention to mitigate pollution. They advocate for implementing effective cleaning operations and regulatory measures to protect water quality and community health. Highlights: Increased water hardness from industrial and municipal waste. Persistent high toxicity levels impacting ecosystems. Urgent need for continuous monitoring and pollution control. Keywords: Temirnik River, chemical pollution, biological toxicity, historical data, anthropogenic impact

Comparing roles of multiple contamination indicators in tracing groundwater pollution nearby a typical municipal solid waste (MSW) landfill

Groundwater pollution resulting from leachate leakage at landfill sites has garnered significant attention. Investigating the migration of pollutants from these landfills to adjacent groundwater is crucial for understanding the diffusion patterns and extent of contamination. It is imperative to develop cost-effective yet highly efficient tracer techniques to aid landfill operators in monitoring groundwater contamination stemming from their operations. The primary objective of this research was to compare the roles of conservative tracers sodium (Na+) and chloride (Cl−), and conventional pollutants permanganate oxidation (CODMn), ammonium nitrogen (NH4+-N), lead (Pb), and zinc (Zn) in assessing pollution levels from municipal solid waste landfills to groundwater. For this purpose, a typical municipal solid landfill was selected to investigate the origin of Cl−, groundwater quality, and spatiotemporal variations of multiple contaminations. Geochemistry analyses revealed that Na–Cl and Ca–HCO3 were the dominant groundwater type in this study and landfill was the primary source of Cl− in groundwater, with an average contribution of 78 %. Groundwater in proximity to the landfill (5#, 2#, 22#, 23#) exhibited elevated concentrations of Na+ (15.6–914.0 mg/L), Cl− (8.9–1352.0 mg/L), CODMn (0.54–95.9 mg/L), and NH4+-N (0.33–49.0 mg/L), yet demonstrated reduced levels of Pb (0.2–391.0 μg/L) and Zn (2.0–112.8 μg/L). In contrast, groundwater located at a considerable distance from the landfill (13#, 18#, 15#, 26#) displayed the inverse trend, with relatively low concentration of Na+ (3.2–8.5 mg/L), Cl− (0.1–0.7 mg/L), CODMn (0.28–4.78 mg/L), and NH4+-N (0.03–0.52 mg/L), but increased levels of Pb (1.2–483.0 μg/L) and Zn (1.6–357.0 μg/L). The primary determinant of groundwater quality near the landfill was NH4+-N, with the highest pollution index (Pi) of 492.85, whereas Pb was the predominant factor affecting water quality in areas distant from the landfill, with the highest pollution index (Pi) of 10.9. While no discernible seasonal variation was detected for all pollutants, spatial variation can be observed that pollution levels decreased progressively with increasing distance from the landfill, a trend particularly corroborated by the conservative Cl− and Na+ measurements. This research suggests that conservative ions, such as Cl− and Na+, exhibit superior efficacy in tracing the pollution range from municipal solid landfills to groundwater. Therefore, monitoring these conservative ions in groundwater can yield a more precise understanding of the extent of groundwater contamination originated from landfills.

Assessing Ecosystem Health: A Comparative Study Using Water Quality Index Analysis across Ten Lotic System in the Cross River System

This review synthesizes findings from 10 scientific publications utilizing the weighted arithmetic Water Quality Index (WQI) to assess the status of diverse water bodies (Ikpa River, Lower Enyong Creek, Nwaniba River, Imo River, Etim Ekpo River, Lower Qua Iboe River, Ibeno Estuary, Cross River, Ikot Ebak River and Oboroenyin River) all within the Cross River System. The Analysis reveals that 6 out of 10 sampled water bodies are moderately polluted, while the remaining 4 exhibit slight pollution levels. These findings underscore significant concerns regarding water quality degradation, predominantly attributed to industrial effluents, agricultural runoff and urban contaminants. The WQI proves effective in consolidating multiple water quality parameters into a comprehensive index, facilitating a holistic assessment of pollution levels and guiding management strategies. Moving forward, concerted efforts are imperative to enhance monitoring efforts, implement stringent regulatory measures, and adopt sustainable practices to safeguard freshwater resources globally. This review underscores the urgency of integrated approaches to mitigate pollution and ensure resilience of aquatic ecosystems and public health.

Heavy Metal Risk Assessment from Agriculture in Shallow Tropical Karst Lake, Gunungsewu Karst Area, Java Island, Indonesia

This research investigates the heavy metal pollution in karst lakes, crucial for fisheries, agriculture, and domestic activities. Target metals include Pb, Cd, Cr, Fe, Zn, and Cu. Objectives comprise assessing pollution levels via the Heavy Metal Pollution Index (HPI) and conducting an ecological risk assessment, incorporating the Enrichment Factor, Geoaccumulation Index, and Ecological Risk Index. The Pearson’s correlation and cluster analysis were performed to analyze the connection between samples. Results reveal strong correlations between metals in soil and sediment, such as Pb in soil correlating with Cu in sediment (r = 0.88), Zn in soil with Cu in sediment (r = 0.75), and Zn in soil with Pb in sediment (r = 0.81). Kedokan Lake emerges as the most representative observation (distance 13.1), while Kepleng Lake is the least representative (distance 535). Cluster analysis identifies two distinct clusters: Cluster 1 encompasses Ngrejek Lake, Kedokan Lake, Sodong Lake, Kanigoro Lake, and Ngampelombo Lake. HPI calculations reveal varying contamination levels in lake water and underground river samples within the Bribin Underground River Catchment Area. Notably, one location in Kedokan Lake is categorized as contaminated (HPI value of 188.05). Mean Enrichment Factor values range from no enrichment factor (<1) to low enrichment factor (1–3). Geoaccumulation Index (Igeo) indicates an overall unpolluted classification (Igeo<0) across all sampling sites. These findings underscore the nuanced pollution status in the studied area, emphasizing the importance of continued monitoring and intervention to ensure the sustainability of karst lakes in the face of heavy metal contamination.

Utilization of trace elements and radioactivity in fly ash to assess pollution levels around coal-fired power plants

In this study, the results showed that the soil radioactivity concentrations of 232Th and 40K were higher than the reference values given by UNSCEAR, but still within radiologically safe limits. Heavy metal concentrations were within allowable limit according to Vietnamese standards. Radioactivity and metal concentrations in the topsoil did not increase compared with the 30 cm depth after a period of operation. After one year of operation, the gamma dose rates of the area around the plant showed no difference according to the one-way ANOVA test (p = 0.944). The results indicated that after a period of operation, the coal-fired thermal power plant did not shown any significant impact of radioactivity and metals from the fly ash on the surrounding environment.

Assessment of Pollution Level of Soil Contaminated by Fertilizer Industry Waste

Assessment of Pollution Level of Soil Contaminated by Fertilizer Industry Waste

Case Study on the Application of New Technologies for Construction Waste Management in a Historic Building in Jalisco, Mexico

This article presents a comprehensive examination of a case study conducted at the “Catedral de Santa María de la Asunción” in Jalisco, Mexico. The focus is on innovative strategies for the management of construction waste in historic buildings. In this context, the scope of the research, the purpose of exploring new methodologies, the existing gap in waste management practices, the implications of implementing advanced technologies, and the need to adopt sustainable approaches in the preservation of architectural heritage are discussed. The analysis examines the deterioration and degradation of construction materials, emphasizing the significance of advanced methodologies in waste management to mitigate these effects. A comprehensive two‐ and three‐dimensional study has been carried out using Revit, a state‐of‐the‐art technology, to improve precision and accuracy in quantifying waste. The intervention parameters are quantified and classified according to the codification of the European Waste List (LER), thus facilitating the implementation of efficient waste management practices and closing the identified gap in current practices. Subsequently, the sorted materials are integrated into the toolkit, which allows for the assessment of pollution levels and the assessment of the feasibility of reusing construction and demolition waste (CDW). This toolkit offers practical solutions for sustainable waste management. The recovery process, which encompasses recycling, recycling, or landfilling, ensures a nuanced and sustainable approach to CDW management within the study area. This approach has positive implications for the conservation of historical heritage and the reduction of environmental impact. The necessity for the design and implementation of innovative sustainable construction strategies becomes evident, and each project benefits from the use of advanced design and modeling software, such as Revit and its toolkit. These indispensable tools facilitate effective sorting based on LER coding, thereby streamlining waste management practices and helping to bridge the gap between current practices and sustainable best practices. Furthermore, the study concludes that materials from demolished architectural heritage can be reused in the creative design of new sustainable buildings, underscoring the importance of adopting integrated approaches to heritage conservation and sustainable urban development. This is consistent with contemporary ethical frameworks that emphasize conservation and sustainability, advocating for the reduction of resource consumption and demolition waste. This approach effectively balances the production of materials, consumption during construction and restoration, and the utilization of essential natural resources for both historic and new buildings, driving a positive transformation in the construction industry toward environmental sustainability and cultural heritage conservation.

Spatialized PM2.5 during COVID-19 pandemic in Brazil's most populous southern city: implications for post-pandemic era.

Brazil has experienced one of the highest COVID-19 fatality rates globally. While numerous studies have explored the potential connection between air pollution, specifically fine particulate matter (PM2.5), and the exacerbation of SARS-CoV-2 infection, the majority of this research has been conducted in foreign regions-Europe, the United States, and China-correlating generalized pollution levels with health-related scopes. In this study, our objective is to investigate the localized connection between exposure to air pollution exposure and its health implications within a specific Brazilian municipality, focusing on COVID-19 susceptibility. Our investigation involves assessing pollution levels through spatial interpolation of in situ PM2.5 measurements. A network of affordable sensors collected data across 9 regions in Curitiba, as well as its metropolitan counterpart, Araucaria. Our findings distinctly reveal a significant positive correlation (with r-values reaching up to 0.36, p-value < 0.01) between regions characterized by higher levels of pollution, particularly during the winter months (with r-values peaking at 0.40, p-value < 0.05), with both COVID-19 mortality and incidence rates. This correlation gains added significance due to the intricate interplay between urban atmospheric pollution and regional human development indices. Notably, heightened pollution aligns with industrial hubs and intensified vehicular activity. The spatial analysis performed in this study assumes a pivotal role by identifying priority regions that require targeted action post-COVID. By comprehending the localized dynamics between air pollution and its health repercussions, tailored strategies can be implemented to alleviate these effects and ensure the well-being of the public.

Health Risk Appraisal of Trace Elements in Groundwater in an Urban Area: A Case Study of Sichuan Basin, Southwest China

Intense anthropogenic activities pose a serious threat to groundwater quality in urban areas. Assessing pollution levels and the health risks of trace elements within urban groundwater is crucial for protecting the groundwater environment. In this study, the heavy metal pollution index (HPI) and health risk assessment were conducted to analyze trace element pollution levels and the non-carcinogenic and carcinogenic risks of groundwater resources in Sichuan Basin, SW China, based on the hydrochemical results of 114 groundwater samples. The HPI results displayed that 14.92% of groundwater samples were contaminated, primarily attributed to anthropogenic influence. The health risk assessment indicated that children faced the highest non-carcinogenic risk while adults had the highest carcinogenic risk. The Monte Carlo simulation further enhanced the reliability of the health risk model. A sensitivity analysis indicated that Pb was the most sensitive element affecting both non-carcinogenic and carcinogenic risks. The achievements of this research would provide a basis for groundwater management in urban areas.

A methodology for evaluating the relative pollution level of metal pollution in surface sediments of rivers based on the statistical results of relevant literatures covering world-wide rivers

Due to the intervention of human activities, the background values of riverbed sediment exhibit spatiotemporal variability, which can affect the accuracy of risk assessment results. Using risk assessment that do not rely on background values is an executable alternative to avoid such problems. In this study, a relative pollution level assessment (RPLA) method which was based on the statistical results of relevant literatures was proposed. This method includes a four-step data processing procedure to extract the evaluation indexes of relative pollution degree of pollutants in environment and a series of relative pollution status assessment methods to evaluate the overall relative pollution level and regional difference of world-wide rivers. To demonstrate how to use RPLA method, 310 relevant literatures covering world-wide rivers were selected. And the ambient background value (x̅), the world-wide threshold values (WWTV) and the relative pollution grades (LEVEL I ∼ IV) of 9 target metals (Cr, Ni, Cu, Zn, As, Cd, Pb, Sb and Tl) in riverbed surface sediments of world-wide rivers were extracted and used for evaluation. Moreover, the stability and applicability of RPLA method were evaluated. Results show that the evaluation results of RPLA method are robust and comparable with traditional evaluation method.

Seasonal effects on hydrochemistry, microbial diversity, and human health risks in radon-contaminated groundwater areas

Groundwater is an important human resource. Daejeon in South Korea faces severe water quality issues, including radon, uranium, and fluoride pollution, all of which pose health risks to humans. With climate change, threats to potable water, such as heavy rain and typhoons, have become common. Therefore, examining the seasonal effects on groundwater quality and resultant health risks is important for understanding the mechanisms of different hydroclimatological conditions to enable the implementation of sustainable management plans in radon-contaminated groundwater areas. However, this issue has not yet been studied. To bridge this gap, in this study, major ions and microbial community structures were employed and groundwater quality index (GWQI) were calculated with hazard index based on limits set by the World Health Organization (WHO) to investigate the hydrochemical characterization and to assess pollution levels. The results showed that the rainy season had distinct hydrochemical characteristics with high correlations between radon and fluoride, and most groundwater samples collected after the typhoon had characteristics similar to those collected during the dry season, owing to the flow path. Furthermore, the microbial diversity and hazard quotient (HQ) values of fluoride revealed that pollution worsened during the dry season. All of the calculated effective dose values of radon exceeded the threshold limit set by the WHO, despite the low GWQI. Infants and children were particularly susceptible to radon-contaminated groundwater. The statistical results of self-organizing map (SOM) suggested that radon analysis was sufficient for public health intervention in the rainy season; however, in the dry season, combined analyses of radon, fluoride, and microbial diversity played important roles in health risk assessment. Our study presents a comprehensive understanding of radon-contaminated groundwater characteristics under seasonal effects and can serve as a reference for other similar zones to provide significant insights into the effective management of radon contamination.

Uncertainties in Pollution and Risk Assessments of Heavy Metals in Lake Sediments Using Regional Background Soils in China.

Background soils are frequently utilized as a surrogate to assess pollution levels and environmental risks of heavy metals in Chinese lakes. However, there remains a lack of understanding regarding the reliability and uncertainty of such assessments. Here, we determined heavy metals (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment cores from five rural lakes in North China to evaluate the reliability and uncertainty of the assessments using background soils by comparing them with assessments based on background sediments. Comparative studies reveal large uncertainties in the assessments using background soils. Among these metals, uncertainties for Hg and Cd are relatively large, whereas those for the other metals are minor. This discrepancy is due to the considerably higher natural variability of Hg and Cd in soils and sediments in comparison to the other metals. Generally, assessments utilizing background soils underestimate pollution levels and risks of Hg but overestimate those of Cd in these lakes. Despite limited human activities around the lakes, they still received a considerable influx of heavy metals via regional atmospheric transport. Assessments of the nine metals indicate moderate to considerable ecological risks in these lakes. The risks are contributed primarily (78-89%) by Hg and Cd. This study underscores the substantial uncertainties in assessing heavy metal pollution and risks using regional background soils and emphasizes the importance of controlling atmospheric emissions of Hg and Cd to mitigate pollution in rural and remote water bodies in China.