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Pollution Assessment for Environmental Sustainability in Applied Sciences and Engineering
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Pollution Assessment for Environmental Sustainability in Applied Sciences and Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 7853

Special Issue Editor

Dr. Vincent Perrot

E-Mail Website
Guest Editor
Department of Analytical and Environmental Chemistry (ANCH), Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
Interests: mercury biogeochemistry; analytical chemistry; stable isotope geochemistry; trace metal pollution; aquatic ecosystems; paleoenvironments

Special Issue Information

Dear Colleagues,

Despite the rapidly increasing level of scientific and technological developments, environmental pollution continues to be a major issue of concern for both developed and under-developed countries. Monitoring and assessment of the environmental pollution is a challenging task today. It covers separate environments: Geosphere, Atmosphere, and Hydrosphere. Ground assessment, contamination, geo-statistics, remote sensing, GIS, risk assessment and management, and environmental impact assessment. Atmospheric assessment topics, including the dynamics of contaminant transport, impacts of global warming, indoor and outdoor techniques and practice. The hydrosphere including both the marine and fresh water environments. 

This Special Issue welcomes submissions about integrated approach to pollution assessment across multiple spheres, and aim to provide an integrated reference for academics and professionals working on land, air, and water pollution.

Subject areas include but are not limited to:

Land, air, and water pollution;

Ground assessment, contamination, geo-statistics, remote sensing, GIS, risk assessment and management, and environmental impact assessment;

Dynamics of contaminant transport, impacts of global warming, indoor and outdoor techniques and practice;
Marine and fresh water environments;

Modeling of pollution processes in environment;

Waste generation, treatment and management;

Sources, levels, and distribution of pollutants in the environment, food, and human bodies.

Dr. Vincent Perrot
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • land, air, and water pollution
  • waste generation, treatment and management
  • mercury
  • methylmercury
  • bioavailability
  • geochemistry
  • aquatic environment
  • speciation
  • analytical technique
  • sampling strategie
  • dissolved organic matter
  • diffusion
  • uptake
  • active transport
  • complexation

Published Papers (3 papers)

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Research

16 pages, 2758 KiB  
Article
Development of a Prediction Model for Daily PM2.5 in Republic of Korea by Using an Artificial Neutral Network
by Jin-Woo Huh, Jong-Sang Youn, Poong-Mo Park, Ki-Joon Jeon and Sejoon Park
Appl. Sci. 2023, 13(6), 3575; https://doi.org/10.3390/app13063575 - 10 Mar 2023
Cited by 2 | Viewed by 1164
Abstract
This study aims to develop PM2.5 prediction models using air pollutant data (PM10, NO2, SO2, O3, CO, and PM2.5) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured [...] Read more.
This study aims to develop PM2.5 prediction models using air pollutant data (PM10, NO2, SO2, O3, CO, and PM2.5) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured in South Korea from 2015 to 2019. Two prediction models were developed using an artificial neural network (ANN): a nationwide (NW) model and administrative districts (AD) model. To develop the prediction models, the independent variables daily averages and variances of air pollutant data and meteorological data (independent variables) were used as independent variables, and daily average PM2.5 concentration set as a dependent variable. First, the correlations between independent and dependent variables were analyzed. Second, prediction models were developed using an ANN to predict next-day PM2.5 daily average concentration, both NW and in 16 AD. The ANN models were optimized using a factorial design to determine the hidden layer layout and threshold, and a seasonal (monthly) factor was also considered. In the optimal prediction model, the absolute error in 1 σ was 91% (in-sample 91%, out-of-sample 91%) for the NW model, and the absolute error in 1 σ was 86% (in-sample 88%, out-of-sample 84%) for AD model. The accuracy of these prediction models increases further when they are developed using the next-day weather data, assuming that the weather prediction is accurate. Full article
(This article belongs to the Special Issue Pollution Assessment for Environmental Sustainability in Applied Sciences and Engineering)
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21 pages, 6371 KiB  
Article
Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
by Muhammad Awais, Bilal Aslam, Ahsen Maqsoom, Umer Khalil, Fahim Ullah, Sheheryar Azam and Muhammad Imran
Appl. Sci. 2021, 11(21), 10034; https://doi.org/10.3390/app112110034 - 26 Oct 2021
Cited by 21 | Viewed by 3673
Abstract
Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are [...] Read more.
Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. Full article
(This article belongs to the Special Issue Pollution Assessment for Environmental Sustainability in Applied Sciences and Engineering)
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15 pages, 2444 KiB  
Article
Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
by Yuanzhang Ma, Guoyu Wang, Yuanyuan Wang, Wei Dai and Yaning Luan
Appl. Sci. 2021, 11(19), 8829; https://doi.org/10.3390/app11198829 - 23 Sep 2021
Cited by 4 | Viewed by 2264
Abstract
The use of phytoremediation technology to remove heavy metal ions from aquatic environments or reduce their toxicity offers the possibility of restoring the ecological environment of polluted water bodies. Based on available literature on heavy metal absorption by aquatic plants, we conducted a [...] Read more.
The use of phytoremediation technology to remove heavy metal ions from aquatic environments or reduce their toxicity offers the possibility of restoring the ecological environment of polluted water bodies. Based on available literature on heavy metal absorption by aquatic plants, we conducted a meta-analysis to study the absorptive capacities of different plants as well as the factors that influence their Hg-absorption performance. Seventeen plant families, including Araceae, Haloragaceae, Hydrocharitaceae, and Poaceae, have a strong Hg-absorption capacity. The root systems of aquatic plants belonging to these families are primarily responsible for this remediation function, and only a limited proportion of Hg+ that enters a plant via the root system is transferred to other plant organs. Additionally, the diversity of plant life habits (e.g., floating, submerged, and emergent) and the water pH significantly influence the ability of plants to absorb Hg. It is expected that this study will provide a reference for the cultivation of aquatic plants for restoring the ecological environment of Hg-polluted water bodies. Full article
(This article belongs to the Special Issue Pollution Assessment for Environmental Sustainability in Applied Sciences and Engineering)
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