Identification Of Pollution Sources Research Articles

Carbon based sensors for air quality monitoring networks; middle east perspective

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region’s ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Source and Migration Pathways of Heavy Metals in Soils from an Iron Mine in Baotou City, China

The exploitation of iron ore could cause heavy metals pollution in the soils, which threatens the ecosystem and human health. In this study, soil, stream sediment, tailings, rock, and atmospheric deposition samples were collected from an iron mine in Baotou City. The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, Zn, Al2O3, CaO, K2O, MgO, Na2O, SiO2, and Fe2O3, as well as the mineral composition and heavy metal speciation of the samples, were analyzed for pollution assessment and source identification of heavy metals. The results reveal that the concentration of Cu in the soils was significantly higher than the background value, and an unpolluted to moderately polluted state was the main pollution level. By analyzing the relationship between Cu/Al2O3 and CaO in different samples, as well as the characteristics of the chemical index of alteration (CIA), mineral composition, and the chemical speciation of Cu in soils and profiles, the results suggest that tailings were the source of Cu pollution in soils. The distribution characteristics of Cu and CaO in stream sediments indicated that hydraulic transport may be one of the main migration pathways. In addition, wind transport may also be a pathway of migration.

Characterization and recognition of three-dimensional excitation-emission matrix spectra of wastewater from six typical categories

In this study, we analyzed the characteristics of three-dimensional excitation-emission matrix spectra (EEMs) of 150 samples from five industrial wastewater types and domestic sewage to track water pollution sources effectively. We then developed a recognition model for wastewater EEMs by establishing a feature dataset containing fluorescence peak values and parameters derived from EEMs, integrated with machine learning techniques. This model enables the rapid and precise identification of pollution sources. Our findings suggest that although the EEMs of the six wastewater categories are distinct, visual differentiation is challenging. This was confirmed by cosine similarity assessments, showing some samples with low within-group (< 0.8) and high between-group (> 0.95) similarities. Despite significant variations in EEMs features across wastewater categories, identifying specific pollutants remains difficult, especially for pulp mills and leather effluents. Among the tested classification algorithms, Support Vector Machine (SVM) achieved the highest performance with 91.7 % accuracy, 94 % precision, 91 % recall, and 92 % F1-score, outperforming K-Nearest Neighbors and Partial Least Squares Discriminant Analysis. The SVM significantly improved identification accuracy for pulp mill and leather processing wastewaters compared to other models. To enhance identification accuracy, further exploration of EEMs features and expanding the training dataset are recommended. Combining EEMs features with machine learning presents a promising method for improving water pollution supervision and source tracing in environmental management practices.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

The comprehensive evaluation of nitrate origin and transformation pathways in the oxic alluvial aquifer in Serbia.

Accurate pollution source identification is essential for establishing adequate water management strategies, particularly in groundwater with slow flow and prolonged recharge process allowing long-term pollution retention. An integrated study based on hydrogeochemical, dual isotopic (δ15NNO3 and δ18ONO3), and microbiological approaches (DN, IRB, and SRB BART tests) along with the statistical data processing was conducted to determine nitrate origin and fate in oxic alluvial groundwater source Ključ in Serbia. The findings from a comprehensive investigation, encompassing 20 groundwater sampling locations during the period 2010-2019, delineated three distinct zones - the hinterland (anthropogenic impact area-untreated sewage inflow), the middle zone (area of mixed influence from fertilizer application, accompanied by a mitigated anthropogenic impact), and the zone of riparian denitrification. Significant linear relationship between anthropogenic impact parameters (Na, Cl, B, NO3-, NH4+, and electrical conductivity) along with the isotopic signatures (δ15N-NO3- ranking from + 10.01 to + 11.18‰ and δ18O-NO3- ranking from + 1.15 to + 6.24‰) and grouped sampling objects by cluster analysis indicated that hinterland is burdened by the nitrates originating from anthropogenic impact. The cross-section of groundwater flow data, concurrent increase of NH4+, and pH levels, along with the highest values of δ15N-NO3- (+ 12.90‰) and δ18O-NO3- (+ 9.70‰), indicated area of fertilizers (urea) impact. BART test results, pH increase, and low oxygen concentration, along with the groundwater flow data in riparian zone, indicated the unfolding of denitrification process. Presented research emphasizes the importance, necessities, and advantages of simultaneous and complementary use of hydrogeochemical, microbiological, and isotopic data.

Remote Sensing and GIS in Air Pollution Mitigation: A Bibliometric Review of Chhattisgarh, India

Remote sensing and GIS serve as indispensable tools in the ongoing battle against air pollution and in the quest to improve air quality. These technologies, including satellite imagery and aerial mapping, provide a wealth of data pertaining to atmospheric conditions and the presence of pollutants. Through the integration of this data within Geographic Information Systems (GIS) software, researchers and policymakers gain valuable insights into the distribution and concentration of pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter, all of which have significant implications for human health and the environment. The utilization of remote sensing allows for the monitoring of pollutant movement over large geographical areas, enabling the identification of pollution sources and the assessment of their impact on surrounding regions. GIS, on the other hand, facilitates the visualization of this data through the creation of maps and spatial analyses, offering a comprehensive understanding of air pollution patterns and their correlations with demographic characteristics and land use practices. By leveraging these technologies, decision-makers can pinpoint areas with heightened pollution levels, such as urban centers or regions with extensive industrial activity. Armed with this information, targeted mitigation strategies can be devised and implemented to alleviate pollution burdens and safeguard public health. Furthermore, the integrated approach provided by remote sensing and GIS enables the monitoring of the effectiveness of these strategies over time, allowing for adjustments and refinements as needed. In the context of Chhattisgarh, India, where air pollution poses a significant challenge, the application of remote sensing and GIS holds particular promise. By highlighting the advancements and innovations in these fields within the state, we aim to underscore the potential for informed decision-making and proactive measures to combat air pollution and enhance air quality for the benefit of all residents. In this review paper we aim to highlight the advancements in this field that has occurred in controlling air pollution in the state of Chhattisgarh, India.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

Evaluation of the concentration and human health risk of nitrate and potentially toxic elements (PTEs) in melons from a southern region of Iran: Identification of pollution sources

Decentralized agriculture, improper monitoring of cultivation conditions, and leaching of contaminants into lands led to the contamination of crops with various potentially toxic elements (PTEs). However, it is essential to know more about the profile level and associated risk of these contaminants and their origin, especially in high-water content crops. This study aimed to investigate the concentration of PTEs in melons of one of Iran's southern cities and follow that health risk assessment in the target population for the first time. Results of the present study confirmed that although the mean concentration of some metals was lower than the safety standard (Cr: 4.6 ± 2 mg/kg and Pb: 7.4 ± 4 mg/kg), their nutritional value was unfavorable regarding some micronutrients (Cu: 88.8 ± 27 mg/kg and Zn: 480 ± 275 mg/kg). The highest metal concentration in cantaloupe was iron (1706.47 mg/kg, p-value<0.05), and nitrate concentration in all melon types was 2.59–524.54 mg/kg (p-value<0.05). Principal component analysis (PCA) with K-means clustering and the Positive Matrix Factorization (PMF) model have shown that contaminants in melons originated from human activities. So, excessive use of agricultural fertilizers is a possible source of nitrates in melons, which have 93 % of factor loading values. The health risk assessment also showed that melons' carcinogenic and non-carcinogenic risk using the deterministic method was lower than the permissible limit (HQ < 1, ILCR<E-4). However, the Monte Carlo simulation estimated HQ > 1 in the children group for the 95th percentile. Furthermore, the level of certainty in the carcinogenesis risk for children, women, and men was estimated at 86.48 %, 64.67 %, and 61.30 %, respectively. Also, the consumption rate was determined as the most important parameter in the sensitivity analysis. As a consequence, there is a potential health risk for Iranians after the consumption of melon due to PTEs and nitrate levels that also originated from anthropogenic sources.

Analysis of Sulphur Dioxide Concentration in Central University of Jammu (Rahya Suchani) Campus

One major air pollutant that has a negative impact on both the environment and human health is sulfur dioxide (SO2). An analysis of the sulphur dioxide levels on the Central University of Jammu (Rahya Suchani) campus is presented in this abstract. The study's objectives are to measure the amount of SO2 pollution, locate probable sources, and analyze the effects of the pollution on the campus environment and the residents' health. In order to gather information on SO2 concentrations at different points on the university campus, the research makes use of quantitative measurements and environmental monitoring techniques. Data is gathered using specialized equipment that can measure SO2 in ambient air precisely. The study aims to investigate the temporal and spatial fluctuations in SO2 concentration, accounting for variables such as local pollution sources, automobile traffic, industrial processes, and weather patterns. The distribution patterns and locations of elevated SO2 pollution on campus are visualized by statistical analysis and spatial mapping approaches. The goal of this study's findings is to increase university stakeholders' knowledge of the level of SO2 pollution on the Central University of Jammu campus. Through the identification of pollutant sources and patterns, the study offers significant insights for the implementation of mitigation techniques and the promotion of a healthier campus environment.

A Novel Approach to Pollution Source Identification in Groundwater Using Hybrid Ensemble of Wavelet Neural Network (WNN) and ARIMA

The groundwater is a significant source of readily available freshwater. However, these groundwater aquifers are susceptible to contamination by the presence of many potential pollution sources. These potential sources may be active at any location for any duration with an unknown magnitude of source fluxes. The identification of these potential sources is necessary for remediation and management. In this work, the groundwater contamination sources are identified for two probable situations, i.e., Error-free concentration measurements and erroneous concentration measurements. The proposed methodology exploits the capability of the multistage approach of recent hybrid soft computing models (ensemble of Wavelet Neural Network [WNN]; Autoregressive Integrated Moving Average [ARIMA]) to identify potential pollution sources effectively. The models are trained by the simulated concentrations for randomly generated source fluxes by Groundwater Modelling System (GMS). The multistage approach has been adopted here, which includes three-stage models. To exploit the effectiveness of WNN and ARIMA, they are used at different stages in the multistage models, thereby developing various hybrid ensembles. Total of four hybrid models are developed utilising the multi-staging approach of WNN and ARIMA models, Namely Hybrid-II, III, IVA, and IVB. Predicted results from these models were compared on the same data sets. They are compared using various performance indices, such as RMSE, NE, correlation coefficient, etc. Error values of Hybrid II ensemble (employing WNN at first stage and ARIMA at second stage) are lowest as compared to only WNN and other Hybrid models. Hence, the Hybrid-II model can be utilised to accurately predict potential contamination sources in any sample area in groundwater.

Design of Non-Structural Practices for Sustainable Water Quality Improvement in an Urban River: A Case Study of South Korea

Urban rivers exhibit characteristics of low flow and significant water quality fluctuations, making them susceptible to pollution from various sources such as untreated sewage, non-point pollution within the urban area, and unknown inflows. To address water quality management in urban rivers, precise investigations into background water quality, pollution levels, and the characteristics of pollution sources are essential. Following the identification of pollution sources, sustainable river management strategies, incorporating both structural and non-structural measures, are crucial. This study aims to develop continuous and long-term river management strategies, considering the characteristics of urban river basins, through citizen participation governance and non-structural approaches. Citizen networks were formed for each target urban river, and activities for water quality improvement were proposed and implemented. This study provides phased approaches to citizen participation governance, and activities include citizen-led water quality monitoring, the purification and monitoring of riverbank pollution sources, and water-related education. It emphasizes the importance of local residents’ interest in urban river water quality improvement and underscores the need for sustained activities through local citizen networks. Additionally, active participation and investments from the local government, government agencies, and various experts are deemed essential.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China.

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.

Two-step AI-aided Bayesian source identification of urban-scale pollution

Poor air quality produces detrimental effects worldwide; hence, it is vital to thoroughly characterize air pollution sources and effectively address and mitigate such effects. Due to the complexity of the underlying physical processes, and uncertainties in the available observations, the air pollution source identification problem is typically cast within a Bayesian inversion framework. The latter incorporates prior knowledge and observations to characterize a release event through the posterior distribution of the source parameters. In this study, we rely on two-dimensional (2D) pollutant concentration distributions as observations, and adopt the Wasserstein (W2) distance to model the likelihood probability distribution for given emission parameters. Since the posterior distribution is estimated via random sampling that involves many forward model runs, the Bayesian framework can be computationally prohibitive for realistic urban air pollution problems that are driven by computationally demanding micro-scale flow simulations. Furthermore, computing the W2 distance is resource-intensive . In this context, we develop a computationally efficient Bayesian framework by following (i) a two-stage approach that reduces the cost of the Bayesian inversion, and (ii) an artificial intelligence (AI) approximation of the W2 distance. In the two-stage approach, a low-resolution dispersion model is run in the first stage to propose representative samples of emission parameters for final selection by the original high-resolution model in the second stage. In addition, we approximate the W2 distance using a deep neural network (DNN) to achieve an appreciable reduction in the computational cost with negligible loss in the inversion performance. We design numerical experiments to test the sensitivity of the inverse solution to the characteristics of the approximative model. The results indicate that pairing the two-stage approach with the DNN approximation of the W2 distance preserves the quality of the inverse solution, while achieving at least threefold reductions in the computational cost.

Identification of water pollution sources and analysis of pollution trigger conditions in Jiuqu River, Luxian County, China.

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

Identification of groundwater pollution sources and health risk assessment in the Songnen Plain based on PCA-APCS-MLR and trapezoidal fuzzy number-Monte Carlo stochastic simulation model

The source of inorganic pollutants in the groundwater of the Songnen Plain are complex; therefore, it is urgent to analyze the pollution sources and their contribution rates, and to determine the effects of pollutants on human health. In this study, the solution for the insufficient extraction of characteristic information and subjective weight in principal component analysis (PCA) was found using log-centralization and the entropy weight method. Based on 896 groundwater samples, the types and distribution characteristics of pollution sources were identified, and the APCS-MLR receptor model was used to quantitatively characterize the contribution of pollution sources to water quality. Considering the uncertainty of the health risk assessment process, a trapezoidal fuzzy number-Monte Carlo stochastic simulation model (TFN-MCS) was constructed. Overcoming the inapplicability of triangular fuzzy numbers in dealing with highly discrete water quality data. The results showed that: (1) The five potential pollution sources in the Songnen Plain were salinity factor (34.086 %), agricultural pollution factor (15.854 %), geological background factor (13.11 %), industrial pollution factor (10.382 %) and organic pollution factor (7.382 %). The degree of groundwater pollution in the study area is controlled by the terrain, hydrogeological conditions, land use type, and industrial layout. (2) The APCS-MLR receptor model was suitable for the allocation of potential sources of groundwater pollution. The average contributions rate of the five common factors to groundwater pollution were 38.86, 14.12, 15.73, 14.6, and 6.77 %. Salinity factor plays a major role in groundwater quality. (3) The THI of children and adults were 2.15–15.03 and 0.86–7.261, respectively, much higher than the safety threshold of one. In general, the THI of children from ingesting contaminated groundwater is twice that to adults. NO3– and F- are the primary risk control indicators for groundwater in the study area. These results support the formulation of effective groundwater pollution control strategies.

Pollution status, spatiotemporal variations, and source identification of potentially toxic elements (PTEs) in street dust, the case of Hamedan metropolis, west of Iran

ABSTRACT In the current study, a total of 162 street dust specimens were collected from 18 sites of Hamedan Megacity in 2023 and the contents of tested potentially toxic elements (PTEs) were determined by ICP-OES. The average contents of As, Cd, Cr, Cu, Ni, Pb, and Zn with 3.91, 0.609, 53.9, 70.1, 94.1, 82.4, and 308 mg kg-1, respectively, were found to be greater than those in the background samples reported for Iran, implying the anthropogenic sources of these elements. The greatest concentrations of the analyzed PTEs were observed in the commercial or industrial areas and their highest amount were related the samples obtained in fall, indicating the impact of human entries and seasonal variations of emission sources. The cumulative mean EF, I-geo, and PI values for the analyzed PTEs decreased in the order of Zn > Cd > Cr> Cu > Pb > Ni > As. Computed cumulative values of EF, I-geo, and PI ranged from 13.8 to 53.9, 0.382 to 2.35, and 1.95 to 7.66, respectively, indicating ‘severe to very severe’, ‘unpolluted to moderately polluted’ to ‘moderately to heavily polluted’, and ‘moderate to very high pollution’ contamination levels, respectively. The PLI with accumulative mean value of 2.77 reflected high pollution levels in the entire study area. Additionally, the cumulative mean values of RI with 146, demonstrated considerable ecological risks across the investigated area. The results of PCC, PCA and HCA showed that, traffic, fossil fuel combustion and industrial activities could be considered as the most important anthropogenic factors having a significant impact on the content of PTEs in the street dust. In conclusion, an accurate investigation of probability of the environmental risks due to the dust pollution by other PTEs and persistent organic pollutants (POPs) was recommended for future works.

Chemical Characteristics of Shallow Groundwater in the Yellow River Diversion Area of Henan Province and Identification of Main Control Pollution Sources

The northern plain of Henan in the lower reaches of the Yellow River is an area where the Yellow River is frequently diverted. The shallow groundwater quality in this area is poor， and many types of components have been found to be exceeding the limit value； however， the contribution of various environmental factors to water quality needs to be further quantified. In order to clarify the genesis of water quality of shallow groundwater in the study area， 330 groups of shallow groundwater samples were collected via a regional water quality survey. The evolution of shallow groundwater quality in the Yellow River diversion area of northern Henan was revealed using the principal component-absolute principal component score-multiple linear regression （PCA-APCS-MLR） model. The results showed that the components with a shallow groundwater excess rate greater than 10% in descending order were manganese， iron， total hardness， total dissolved solids， sodium， fluoride， arsenic， chloride ions， sulfate， and ammonium. In particular， the excess rate of manganese reached 76%. The four factors of dissolution enrichment， native origin of soil， redox conditions， and agricultural activities were identified as the main reasons for poor groundwater quality， which accounted for 71.24% of the cumulative interpretation rate of variance. In addition， the recharge from the surface water also influenced the groundwater quality. The effects of dissolution between the water and aquifer matrix and redox condition in the aquifer of the Yellow River dried-riverway like Xinxiang were significantly enhanced， resulting in the increasing concentration of iron， arsenic， total hardness， TDS， and other components in groundwater. Fluoride enrichment was caused by dissolution enrichment， the origin of the soil， and lateral replenishment of the Yellow River. Groundwater with high manganese concentration was widely affected by the soil matrix. Nitrate pollution of the groundwater was caused by the extensive use of chemical fertilizers in agricultural activities in individual areas.

A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

Erratum to: Soil Pollution Source Identification in Southern Iran Using Geochemical Data as a Global Study Model

Erratum to: Soil Pollution Source Identification in Southern Iran Using Geochemical Data as a Global Study Model

Soil Pollution Source Identification in Southern Iran Using Geochemical Data as a Global Study Model

Soil Pollution Source Identification in Southern Iran Using Geochemical Data as a Global Study Model