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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: closed (15 June 2023) | Viewed by 10014

Special Issue 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 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. 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 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

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

Published Papers (5 papers)

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Research

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14 pages, 2985 KiB  
Article
Converting Seasonal Measurements to Monthly Groundwater Levels through GRACE Data Fusion
by Muhammad Zeeshan Ali, Hone-Jay Chu and Tatas
Sustainability 2023, 15(10), 8295; https://doi.org/10.3390/su15108295 - 19 May 2023
Viewed by 1170
Abstract
Groundwater depletion occurs when the extraction exceeds its recharge and further impacts water resource management around the world, especially in developing countries. In India, most groundwater level observations are only available on a seasonal scale, i.e., January (late post-monsoon), May (pre-monsoon), August (monsoon), [...] Read more.
Groundwater depletion occurs when the extraction exceeds its recharge and further impacts water resource management around the world, especially in developing countries. In India, most groundwater level observations are only available on a seasonal scale, i.e., January (late post-monsoon), May (pre-monsoon), August (monsoon), and November (early post-monsoon). The Gravity Recovery and Climate Experiment (GRACE) data are available to estimate the monthly variation in groundwater storage (GWS) by subtracting precipitation runoff, canopy water, soil moisture, and solid water (snow and ice) from the GLDAS model. Considering GRACE-based GWS data, the data fusion is further used to estimate monthly spatial maps of groundwater levels using time-varying spatial regression. Seasonal groundwater monitoring data are used in the training stage to identify spatial relations between groundwater level and GWS changes. Estimation of unknown groundwater levels through data fusion is accomplished by utilizing spatial coefficients that remain consistent with the nearest observed months. Monthly groundwater level maps show that the lowest groundwater level is 50 to 55 m below the earth’s surface in the state of Rajasthan. The accuracy of the estimated groundwater level is validated against observations, yielding an average RMSE of 2.37 m. The use of the GWS information enables identification of monthly spatial patterns of groundwater levels. The results will be employed to identify hotspots of groundwater depletion in India, facilitating efforts to mitigate the adverse effects of excessive groundwater extraction. Full article
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22 pages, 6740 KiB  
Article
Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas
by Muhammed A. Hassan, Hindawi Salem, Nadjem Bailek and Ozgur Kisi
Sustainability 2023, 15(2), 1503; https://doi.org/10.3390/su15021503 - 12 Jan 2023
Cited by 2 | Viewed by 1242
Abstract
The transportation sector is one of the primary sources of air pollutants in megacities. Strict regulations of newly added vehicles to the local market require precise prediction models of their fuel consumption (FC) and emission rates (ERs). Simple empirical and complex analytical models [...] Read more.
The transportation sector is one of the primary sources of air pollutants in megacities. Strict regulations of newly added vehicles to the local market require precise prediction models of their fuel consumption (FC) and emission rates (ERs). Simple empirical and complex analytical models are widely used in the literature, but they are limited due to their low prediction accuracy and high computational costs. The public literature shows a significant lack of machine learning applications related to onboard vehicular emissions under real-world driving conditions due to the immense costs of required measurements, especially in developing countries. This work introduces random forest (RF) ensemble models, for the urban areas of Greater Cairo, a metropolitan city in Egypt, based on large datasets of precise measurements using 87 representative passenger cars and 10 typical driving routes. Five RF models are developed for predicting FC, as well as CO2, CO, NOx, and hydrocarbon (HC) ERs. The results demonstrate the reliability of RF models in predicting the first four variables, with up to 97% of the data variance being explained. Only the HC model is found less reliable due to the diversity of considered vehicle models. The relative influences of different model inputs are demonstrated. The FC is the most influential input (relative importance of >23%) for CO2, CO, and NOx predictions, followed by the engine speed and the vehicle category. Finally, it is demonstrated that the prediction accuracy of all models can be further improved by up to 97.8% by limiting the training dataset to a single-vehicle category. Full article
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13 pages, 2852 KiB  
Article
Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression
by Hone-Jay Chu, Yu-Chen He, Wachidatin Nisa’ul Chusnah, Lalu Muhamad Jaelani and Chih-Hua Chang
Sustainability 2021, 13(11), 6416; https://doi.org/10.3390/su13116416 - 04 Jun 2021
Cited by 13 | Viewed by 2215
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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12 pages, 2244 KiB  
Article
The Occurrence of Potentially Pathogenic and Antibiotic Resistant Gram-Negative Bacteria Isolated from the Danube Delta Ecosystem
by Alina R. Banciu, Daniela L. Ionica, Monica A. Vaideanu, Dragos M. Radulescu, Mihai Nita-Lazar and Cristina I. Covaliu
Sustainability 2021, 13(7), 3955; https://doi.org/10.3390/su13073955 - 02 Apr 2021
Cited by 4 | Viewed by 1760
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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Other

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15 pages, 3700 KiB  
Case Report
The Impact of Urbanization on Water Quality: Case Study on the Alto Atoyac Basin in Puebla, Mexico
by Andrés Estrada-Rivera, Alfonso Díaz Fonseca, Samuel Treviño Mora, Wendy Argelia García Suastegui, Edith Chávez Bravo, Rosalía Castelán Vega, José Luis Morán Perales and Anabella Handal-Silva
Sustainability 2022, 14(2), 667; https://doi.org/10.3390/su14020667 - 07 Jan 2022
Cited by 8 | Viewed by 2010
Abstract
Population growth, poorly planned industrial development and uncontrolled production processes have left a significant footprint of environmental deterioration in the Alto Atoyac watershed. In this study, we propose using the integrated pollution index (PI) to characterize the temporary variations in surface water quality [...] Read more.
Population growth, poorly planned industrial development and uncontrolled production processes have left a significant footprint of environmental deterioration in the Alto Atoyac watershed. In this study, we propose using the integrated pollution index (PI) to characterize the temporary variations in surface water quality during the rapid urbanization process in the municipalities of San Martín Texmelucán (SMT) and Tepetitla de Lardizabal (TL), in the states of Puebla and Tlaxcala, between 1985 and 2020. We assessed the correlation between the population growth rate and the water quality parameters according to the Water Quality Index (ICA). The contribution of each polluting substance to the PI was determined. The industry database was created and the increase in population and industry, and their densities, were estimated. The results indicated that the temporal pattern of surface water quality is determined by the level of urbanization. The water integrated pollution index (WPI) increased with the passage of time in all the localities: SLG 0.0 to 25.0; SMTL 25.0 to 29.0; SRT 4.0 to 29.0; VA 6.0 to 30.0; T 3.5 to 24.0 and SMA 4.0 to 27.0 from 2010 to 2020, respectively. The correlation coefficients between the five parameters (BOD5, COD, CF, TU and TSS) in the six localities were positive with the population. The values that showed a higher correlation with the population were: SLG (FC 0.86), SMTL (BOD5 0.61, COD 0.89, TSS 0.64) and SRT (TU 0.83), corresponding to highly polluted localities, which generates complex and severe environmental implications due to the unsustainable management of water resources. Achieving the sustainability of water in the watershed is a challenge that should be shared between society and state. This type of research can be a useful tool in making environmental management decisions. Full article
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