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A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Ecotoxicology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 25133

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Prof. Dr. Alina Barbulescu

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Guest Editor

Department of Civil Engineering, Transilvania University of Brașov, 500036 Brașov, Romania
Interests: hydrology; time series analysis; applied statistics; mathematical modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Lucica Barbes

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Departament of Chemistry and Chemical Engineering, "Ovidius" University of Constanta, B-dul Mamaia 124, 900527 Constanţa, România
Interests: advanced methods and monitoring techniques of water, air and soil; analysis of organic and inorganic compounds in atmosphere, drinking water, wastewater, sewage and soil (including toxic species); bioremediation and ecosystem restoration models
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Special Issue Information

Dear Colleagues,

The pollution of air, water, and soil with different materials, chemical products, and constantly increasing amounts of waste is a global issue. It is also considered that biodegradation in the environment occurs after or simultaneously with physical and chemical degradation, which modifies the pollutants’ structure to the molecular level, generating more and more waste through increasingly complex processes to control. Currently, there are only exceptions regarding, for example, pollution with plastic materials, antibiotics, fertilizers, etc. and the study of colonizing microorganisms, biodegradation processes, and the toxic effects of these materials or chemical products on terrestrial biota and implicitly on health of human being of great interest.

Therefore, the purpose of this special issue on "Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration" is to assess the air, water and soil pollution by different evaluation methods and to apply the findings for possible mitigation measures. The articles selected for this special issue will provide an overview of the actual research stage in the field, aiming to assess the risks and impact on the environment.

Besides the solutions to the practical problems of cleaning the water, air and soil, the selected topics will directly answer questions related to selecting different tools that best emphasize the environmental quality changes and their impact on society's future.

We look forward to receiving your contributions.

Prof. Dr. Alina Barbulescu
Dr. Lucica Barbes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

atmospheric pollution control engineering and human health
water & wastewater treatment engineering and ecotoxicity evaluation
toxicological evaluation of on marine species
pollution impact assessments on costal areas
environmental impact and risk assessement
modeling the pollutants’ dissipation and transport

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Published Papers (12 papers)

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Editorial

Jump to: Research

6 pages, 220 KiB

Open AccessEditorial

Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration

by Alina Bărbulescu, Lucica Barbeş and Cristian Ștefan Dumitriu

Toxics 2024, 12(4), 244; https://doi.org/10.3390/toxics12040244 - 26 Mar 2024

Cited by 2 | Viewed by 1306

Abstract

Global pollution demands continuous attention and concerted efforts to reduce its effects [...] Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

Research

Jump to: Editorial

21 pages, 5513 KiB

Open AccessArticle

Toxicity Risk Assessment Due to Particulate Matter Pollution from Regional Health Data: Case Study from Central Romania

by Carmen Maftei, Ashok Vaseashta and Ionut Poinareanu

Toxics 2024, 12(2), 137; https://doi.org/10.3390/toxics12020137 - 7 Feb 2024

Viewed by 1524

Abstract

Air pollution poses one of the greatest dangers to public well-being. This article outlines a study conducted in the Central Romania Region regarding the health risks associated with particulate matter (PM) of two sizes, viz., PM₁₀ and PM_2.5. The methodology used consists of the following: (i) an analysis of the effects of PM pollutants, (ii) an analysis of total mortality and cardiovascular-related mortality, and (iii) a general health risk assessment. The Central Region of Romania is situated in the Carpathian Mountains’ inner arch (consisting of six counties). The total population of the region under investigation is about 2.6 million inhabitants. Health risk assessment is calculated based on the relative risk (RR) formula. During the study period, our simulations show that reducing these pollutants’ concentrations below the new WHO guidelines (2021) will prevent over 172 total fatalities in Brasov alone, as an example. Furthermore, the potential benefit of reducing annual PM_2.5 levels on total cardiovascular mortality is around 188 persons in Brasov. Although health benefits may also depend upon other physiological parameters, all general health indicators point towards a significant improvement in overall health by a general reduction in particulate matter, as is shown by the toxicity assessment of the particulate matter in the region of interest. The modality can be applied to other locations for similar studies. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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40 pages, 12476 KiB

Open AccessArticle

Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions

by Miljan Kovačević, Bahman Jabbarian Amiri, Silva Lozančić, Marijana Hadzima-Nyarko, Dorin Radu and Emmanuel Karlo Nyarko

Toxics 2023, 11(12), 996; https://doi.org/10.3390/toxics11120996 - 7 Dec 2023

Viewed by 1768

Abstract

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998–2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na⁺, Mg²⁺, Ca²⁺, SO₄²⁻, Cl⁻, HCO³⁻, K⁺, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson’s Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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16 pages, 2426 KiB

Open AccessArticle

Assessing the Efficiency of a Drinking Water Treatment Plant Using Statistical Methods and Quality Indices

by Alina Bărbulescu and Lucica Barbeș

Toxics 2023, 11(12), 988; https://doi.org/10.3390/toxics11120988 - 5 Dec 2023

Cited by 1 | Viewed by 1592

Abstract

This study presents the efficiency of a drinking water treatment plant from Constanța, Romania. Individual and aggregated indices are proposed and built using nine water parameters for this aim. The analysis of individual indices permits the detection of the period of malfunctioning of the water treatment plant with respect to various parameters at various sampling points. In contrast, the cumulated indices indicate the overall performance of the treatment plant during the study period, considering all water parameters. It was shown that the outliers significantly impact the values of some indices. Comparisons between the simple average and weighted average indices (built taking into account the importance of each parameter) better reflect the impact on the water quality of some chemical elements that might harm people’s health when improperly removed. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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20 pages, 3216 KiB

Open AccessArticle

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

by Jin Hwi Kim, Hankyu Lee, Seohyun Byeon, Jae-Ki Shin, Dong Hoon Lee, Jiyi Jang, Kangmin Chon and Yongeun Park

Toxics 2023, 11(12), 955; https://doi.org/10.3390/toxics11120955 - 23 Nov 2023

Cited by 5 | Viewed by 1338

Abstract

Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique–Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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19 pages, 4684 KiB

Open AccessArticle

Evaluating the Contamination by Indoor Dust in Dubai

by Yousef Nazzal, Alina Bărbulescu, Manish Sharma, Fares Howari and Muhammad Naseem

Toxics 2023, 11(11), 933; https://doi.org/10.3390/toxics11110933 - 17 Nov 2023

Viewed by 2529

Abstract

Nowadays, people spend most of their time indoors. Despite constantly cleaning these spaces, dust apparition cannot be avoided. Since dust can contain chemical elements that negatively impact people’s health, we propose the analysis of the metals from the indoor dust component collected in different locations in Dubai, UAE. Multivariate statistics (correlation matrix, clustering) and quality indicators (QI)—I_geo, PI, EF, PLI, Nemerow—were used to assess the contamination level with different metals in the dust. We proposed two new QIs (CPI and AQI) and compared the results with those provided by the most used indices—PLI and Nemerow. It is shown that high concentrations of some elements (Ca in this case) can significantly increase the values of the Nemerow index, CPI, and AQI. In contrast, the existence of low concentrations leads to the decrement of the PLI. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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15 pages, 3532 KiB

Open AccessFeature PaperArticle

Modeling the Chlorine Series from the Treatment Plant of Drinking Water in Constanta, Romania

by Alina Bărbulescu and Lucica Barbeș

Toxics 2023, 11(8), 699; https://doi.org/10.3390/toxics11080699 - 13 Aug 2023

Cited by 1 | Viewed by 1429

Abstract

Ensuring good drinking water quality, which does not damage the population’s health, should be a priority of decision factors. Therefore, water treatment must be carried out to remove the contaminants. Chlorination is one of the most used treatment procedures. Modeling the free chlorine residual concentration series in the water distribution network provides the water supply managers with a tool for predicting residual chlorine concentration in the networks. With regard to this idea, this article proposes alternative models for the monthly free chlorine residual concentration series collected at the Palas Constanta Water Treatment Plant, in Romania, from January 2013 to December 2018. The forecasts based on the determined models are provided, and the best results are highlighted. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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16 pages, 4607 KiB

Open AccessEditor’s ChoiceArticle

A New Method for Ecological Risk Assessment of Combined Contaminated Soil

by Qiaoping Wang, Junhuan Wang, Jiaqi Cheng, Yingying Zhu, Jian Geng, Xin Wang, Xianjie Feng and Hong Hou

Toxics 2023, 11(5), 411; https://doi.org/10.3390/toxics11050411 - 26 Apr 2023

Cited by 3 | Viewed by 2150

Abstract

Ecological risk assessment of combined polluted soil has been conducted mostly on the basis of the risk screening value (RSV) of a single pollutant. However, due to its defects, this method is not accurate enough. Not only were the effects of soil properties neglected, but the interactions among different pollutants were also overlooked. In this study, the ecological risks of 22 soils collected from four smelting sites were assessed by toxicity tests using soil invertebrates (Eisenia fetida, Folsomia candida, Caenorhabditis elegans) as subjects. Besides a risk assessment based on RSVs, a new method was developed and applied. A toxicity effect index (EI) was introduced to normalize the toxicity effects of different toxicity endpoints, rendering assessments comparable based on different toxicity endpoints. Additionally, an assessment method of ecological risk probability (RP), based on the cumulative probability distribution of EI, was established. Significant correlation was found between EI−based RP and the RSV−based Nemerow ecological risk index (NRI) (p < 0.05). In addition, the new method can visually present the probability distribution of different toxicity endpoints, which is conducive to aiding risk managers in establishing more reasonable risk management plans to protect key species. The new method is expected to be combined with a complex dose–effect relationship prediction model constructed by machine learning algorithm, providing a new method and idea for the ecological risk assessment of combined contaminated soil. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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12 pages, 293 KiB

Open AccessEditor’s ChoiceArticle

Determinants Analysis Regarding Household Chemical Indoor Pollution

by Paolo Montuori, Mariagiovanna Gioia, Michele Sorrentino, Fabiana Di Duca, Francesca Pennino, Giuseppe Messineo, Maria Luisa Maccauro, Simonetta Riello, Ugo Trama, Maria Triassi and Antonio Nardone

Toxics 2023, 11(3), 264; https://doi.org/10.3390/toxics11030264 - 13 Mar 2023

Cited by 4 | Viewed by 2124

Abstract

Indoor household pollution is not yet sufficiently studied in the general population. Over 4 million people die prematurely every year due to air pollution in households. This study aimed to propose quantitative data research through the administration of a KAP (Knowledge, Attitudes, and Practices) Survey Questionnaire. This cross-sectional study administered questionnaires to adults from the metropolitan city of Naples (Italy). Three Multiple Linear Regression Analyses (MLRA) were developed, including Knowledge, Attitudes, and Behavior regarding household chemical air pollution and the related risks. One thousand six hundred seventy subjects received a questionnaire to be filled out and collected anonymously. The mean age of the sample was 44.68 years, ranging from 21–78 years. Most of the people interviewed (76.13%) had good attitudes toward house cleaning, and 56.69% stated paying attention to cleaning products. Results of the regression analysis indicated that positive attitudes were significantly higher among subjects who graduated, with older age, male and non-smokers, but they were correlated with lower knowledge. In conclusion, a behavioral and attitudinal program targeted those with knowledge, such as younger subjects with high educational levels, but do not engage in correct practices towards household indoor chemical pollution. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

17 pages, 5647 KiB

Open AccessFeature PaperArticle

Health Risk Assessment of PAHs from Estuarine Sediments in the South of Italy

by Fabiana Di Duca, Paolo Montuori, Ugo Trama, Armando Masucci, Gennaro Maria Borrelli and Maria Triassi

Toxics 2023, 11(2), 172; https://doi.org/10.3390/toxics11020172 - 13 Feb 2023

Cited by 10 | Viewed by 2243

Abstract

Increased concerns about the toxicities of Polycyclic Aromatic Hydrocarbons (PAHs), ubiquitous and persistent compounds, as well as the associated ecotoxicology issue in estuarine sediments, have drawn attention worldwide in the last few years. The levels of PAHs in the Sele, Sarno, and Volturno Rivers sediments were evaluated. Moreover, the cancerogenic risk resulting from dermal and ingestion exposure to PAHs was estimated using the incremental lifetime cancer risk (ILCR) assessment and the toxic equivalent concentration (TEQ_BaP). For Sele River, the results showed that the total PAH concentration ranged from 632.42 to 844.93 ng g⁻¹ dw, with an average value of 738.68 ng g⁻¹ dw. ∑PAHs were in the range of 5.2–678.6 ng g⁻¹ dw and 434.8–872.1 ng g⁻¹ dw for the Sarno and Volturno River sediments, respectively. The cancerogenic risk from the accidental ingestion of PAHs in estuarine sediments was low at all sampling sites. However, based on the ILCR_dermal values obtained, the risk of cancer associated with exposure by dermal contact with the PAHs present in the sediments was moderate, with a mean ILCR_dermal value of 2.77 × 10⁻⁶. This study revealed the pollution levels of PAHs across the South of Italy and provided a scientific basis for PAH pollution control and environmental protection. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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17 pages, 2283 KiB

Open AccessArticle

Occurrence and Distribution of Persistent Organic Pollutants (POPs) from Sele River, Southern Italy: Analysis of Polychlorinated Biphenyls and Organochlorine Pesticides in a Water–Sediment System

by Elvira De Rosa, Paolo Montuori, Maria Triassi, Armando Masucci and Antonio Nardone

Toxics 2022, 10(11), 662; https://doi.org/10.3390/toxics10110662 - 4 Nov 2022

Cited by 15 | Viewed by 2572

Abstract

The concentrations, possible sources, and ecological risk of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) were studied by analyzing water column (DP), suspended particulate matter (SPM) and sediment samples from 10 sites on the Sele River. Total PCBs concentration ranged from 2.94 to 54.4 ng/L and 5.01 to 79.3 ng/g in the seawater and sediment samples, with OCPs concentration in the range of 0.51 to 8.76 ng/L and 0.50 to 10.2 ng/g, respectively. Pollutants loads in the seaside were measured in approximately 89.7 kg/year (73.2 kg/year of PCBs and 16.5 kg/year of OCPs), indicating that the watercourse could be an important cause of contamination to the Tyrrhenian Sea. Statistical analysis indicates that all polychlorinated biphenyls analytes are more probable to derive from surface runoff than an atmospheric deposition. The results explain that higher concentrations of these pollutants were built in sediment samples rather than in the other two phases, which are evidence of historical loads of PCBs and OCPs contaminants. The Sediment Quality Guidelines (SQGs), the Ecological Risk Index (ERI) and the Risk Quotient (RQ) show that the Sele river and its estuary would reputedly be a zone possibly at risk. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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18 pages, 4564 KiB

Open AccessEditor’s ChoiceArticle

Polycyclic Aromatic Hydrocarbons (PAHs) in the Dissolved Phase, Particulate Matter, and Sediment of the Sele River, Southern Italy: A Focus on Distribution, Risk Assessment, and Sources

by Paolo Montuori, Elvira De Rosa, Fabiana Di Duca, Bruna De Simone, Stefano Scippa, Immacolata Russo, Pasquale Sarnacchiaro and Maria Triassi

Toxics 2022, 10(7), 401; https://doi.org/10.3390/toxics10070401 - 19 Jul 2022

Cited by 22 | Viewed by 2957

Abstract

The Sele River, located in the Campania Region (southern Italy), is one of the most important rivers and the second in the region by average water volume, behind the Volturno River. To understand the distribution and sources of polycyclic aromatic hydrocarbons (PAHs) in the Sele River, water sediment samples were collected from areas around the Sele plain at 10 sites in four seasons. In addition, the ecosystem health risk and the seasonal and spatial distribution of PAHs in samples of water and sediment were assessed. Contaminant discharges of PAHs into the sea were calculated at about 1807.9 kg/year. The concentration ranges of 16 PAHs in surface water (DP), suspended particulate matter (SPM), and sediment were 10.1–567.23 ng/L, 121.23–654.36 ng/L, and 331.75–871.96 ng/g, respectively. Isomeric ratio and principal component analyses indicated that the PAH concentrations in the water and sediment near the Sele River were influenced by industrial wastewater and vehicle emissions. The fugacity fraction approach was applied to determine the trends for the water-sediment exchange of 16 priority PAHs; the results indicated that fluxes, for the most part, were from the water into the sediment. The toxic equivalent concentration (TEQ) of carcinogenic PAHs ranged from 137.3 to 292.6 ngTEQ g⁻¹, suggesting that the Sele River basin presents a definite carcinogenic risk. Full article

(This article belongs to the Special Issue Advances in Water, Air and Soil Pollution Monitoring, Modeling and Restoration)

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