Special Issue "Modern Water/Air Quality Monitoring and Mapping for Sustainable Management"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Dr. Hone-Jay Chu
E-Mail Website
Guest Editor
Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Interests: space-time insights and data mining from remote sensing; big data; open data for environmental management and social sensing; environmental resilience; water and air quality mapping; groundwater; land cover and land use change; ISO metadata standards
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Special Issue Information

Dear Colleagues,

Water/air quality is a critical environmental problem. However, observation data are generally limited in water/air quality monitoring. Traditional water/air sampling methods are reliable but are ineffective in identifying detailed spatiotemporal variations of water/air quality, which renders comprehensive management infeasible.

Water/air quality monitoring can be conducted efficiently through the application of low-cost sensors, various unmanned aerial vehicle (UAV) platforms, and satellites.

To develop high-resolution spatiotemporal water/air quality monitoring, low-cost water quality sensors are promising supplements to regulatory monitors. Low-cost sensors have been developed using Internet of Things (IoT) technology. Low-cost sensors have been used to collect real-time high-density water/air quality data. Investigators can deploy more sensors to increase the spatial coverage of a water/air quality monitoring network. Low-cost sensors can gather more information for the community in real time at any location. The sensors are potentially easy to use and maintain because they require less energy and space to operate. Moreover, estimates of water/air quality have the potential to vastly expand our ability to observe the dynamics of water/air bodies. Furthermore, the visualization of monitoring data provides an accessible way to see and understand the trends, process, and patterns in water/air quality.

This Special Issue of Sustainability offers an opportunity to publish high-quality multi-disciplinary water/air quality monitoring and mapping research. We welcome papers related to new monitoring and mapping technologies in the following areas:

  • Monitoring technology: remote sensing, UAV, IoT and low-cost sensors;
  • Mapping algorithm: interpolation, data integration, data visualization and big data;
  • Water quality monitoring and mapping from oceans and seas, rivers, reservoirs, lakes, and groundwater.
  • Air quality monitoring and mapping

Prof. Dr. Hone-Jay Chu
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 papers will be 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. Sustainability 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 1900 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

  • water quality
  • air quality
  • mapping, remote sensing
  • low-cost sensors
  • interpolation
  • data integration
  • data visualization
  • big data
  • UAV
  • IoT

Published Papers (2 papers)

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Research

Article
Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression
Sustainability 2021, 13(11), 6416; https://doi.org/10.3390/su13116416 - 04 Jun 2021
Viewed by 539
Abstract
Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in [...] Read more.
Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs. Full article
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Article
The Occurrence of Potentially Pathogenic and Antibiotic Resistant Gram-Negative Bacteria Isolated from the Danube Delta Ecosystem
Sustainability 2021, 13(7), 3955; https://doi.org/10.3390/su13073955 - 02 Apr 2021
Viewed by 466
Abstract
The spread of a growing number of antibiotic-resistant bacteria (ARB) outside the clinical setting into the environment has been observed. The surface water plays an important role in ARB dissemination by being both habitats and transport systems for microorganisms. The ecological and touristic [...] Read more.
The spread of a growing number of antibiotic-resistant bacteria (ARB) outside the clinical setting into the environment has been observed. The surface water plays an important role in ARB dissemination by being both habitats and transport systems for microorganisms. The ecological and touristic importance of the Danube Delta make it a European priority for close monitoring of its freshwater system. The main goal of this paper was to analyze how the St. Gheorghe branch of the Danube Delta microbiological contamination and their antibiotic-resistant profile were influenced by climate change, especially the global warming from 2013 up to 2019. In the surface water from all sampling points, total and fecal coliform bacteria showed a constant colony forming units (CFU) increase tendency during the years, with a sharp rise from 1500 CFU/mL in 2015 to more than 20,000 CFU/mL in 2019. The bacterial population’s analyses revealed an indirect proportionality between coliform bacteria density in water and sediment during the years in accordance with global warming. The most commonly identified bacterial strains such as Escherichia coli, Klebsiella oxytoca, Citrobacter freundii and Proteus mirabilis have been shown a resistance rate of approximatively 70% to beta-lactam antibiotics, especially to ampicillin and amoxicillin-clavulanate. Full article
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