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Geo-Information Technology for Air Quality Management. New Trends and Scientific...
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Geo-Information Technology for Air Quality Management. New Trends and Scientific Challenges

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 10210

Special Issue Editors

Dr. Grazia Fattoruso

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Guest Editor
TERIN/FSD/SAFS Lab, Department of Energy Technologies and Renewable Sources, ENEA Research Center Portici, P.le Enrico Fermi, 1, 80055 Portici, Italy
Interests: geomatics; spatial multicritical analysis; spatial statistics; smart water network; site suitability analysis; (Agri-)PV site suitability mapping; solar cadaster; optimal sensor placement; GIS/DSS systems; urban air quality mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Maurizio Pollino

E-Mail Website
Guest Editor
ENEA-Italian National Agency for New Technologies, Energy and Sustainable Economic Development Casaccia Research Centre, Via Anguillarese, 301, 00123 Rome, Italy
Interests: geomatics; GIS; critical Infrastructures; natural hazards; risk assessment; remote sensing; web mapping; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Saverio De Vito

E-Mail Website
Guest Editor
TERIN-FSD Division, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy
Interests: artificial olfaction & vision; smart cyber physical systems & IoT; intelligent sensing; machine learning with application to environmental (air quality) monitoring; energy production; aerospace industry; water management cycle; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Elena Esposito

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Guest Editor
ENEA—Italian National Agency for New Technologies, Energy and Sustainable Economic Development—Research Centre Portici, Sensing and Photovoltaic Systems and Applications Laboratory (DTE/SAFS), 80055 Portici (Naples), Italy
Interests: machine learning; multivariate sensor calibration; distributed chemical sensing; numerical methods; mathematical models

Special Issue Information

Dear Colleagues,

Despite considerable progress in the past decades, ambient air pollution remains the main environmental cause of premature deaths due to elevated levels of fine particles, nitrogen dioxide, and ozone. Air pollution impacts are multiple and complex, significantly affecting human health.

To face this issue, especially at urban scale, in recent years, efforts have been addressed at designing and implementing a (citizen) science-based air quality monitoring and amelioration policy. This has been achieved through research targeting innovation: (i) in pervasive monitoring through high-density networks of new low-cost and small air quality sensors, mobile and fixed; (ii) in high resolution pollutant mapping, obtained through data fusion from heterogeneous sources (e.g., pervasive monitoring network including fixed monitoring stations and portable multisensory devices); and (iii) in DSS for air quality management, based on GIS technology and advanced atmospheric pollutant dispersion modeling along with data assimilation techniques. Further, mobile sensor devices linked to mobile apps have been developed to monitor personal exposure to urban air pollutants.  The present Special Issue intends to outline the current state of the research on urban air quality management and infer advances to be achieved to better safeguard human health and the environment by air pollution. We invite authors to submit their original papers. Potential topics include, but are not limited to the following keywords.

Dr. Grazia Fattoruso
Dr. Maurizio Pollino
Dr. Saverio De Vito
Dr. Elena Esposito
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. ISPRS International Journal of Geo-Information 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 1700 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

  • air pollution and its multiple impacts
  • atmospheric pollutant dispersion modeling
  • wind tunnel experiments: geostatistics for air quality mapping
  • remote sensing for air quality monitoring
  • sensing systems for pervasive air quality monitoring
  • air quality data assimilation and sensor fusion
  • GIS and WebGIS technologies for air quality management
  • citizen science for measuring air pollution DSS for urban air quality management
  • calibration and deployment of air pollution sensor networks

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Published Papers (3 papers)

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Research

18 pages, 8946 KiB  
Article
Urban Air Pollutant Monitoring through a Low-Cost Mobile Device Connected to a Smart Road
by Stefano Chiesa, Antonio Di Pietro, Maurizio Pollino and Sergio Taraglio
ISPRS Int. J. Geo-Inf. 2022, 11(2), 132; https://doi.org/10.3390/ijgi11020132 - 14 Feb 2022
Cited by 5 | Viewed by 3073
Abstract
Air pollutant monitoring is a basic issue in contemporary urban life. This paper describes an approach based on the diffused use of low-cost sensors that can be mounted on board urban vehicles for more abundant and distributed measures. The system exchanges data, exploiting [...] Read more.
Air pollutant monitoring is a basic issue in contemporary urban life. This paper describes an approach based on the diffused use of low-cost sensors that can be mounted on board urban vehicles for more abundant and distributed measures. The system exchanges data, exploiting a “Smart Road” infrastructure, with a central computing facility, the CIPCast platform, a GIS-based Decision Support System designed to perform real-time monitoring and interpolation of data with the aim of possibly issuing alarms with respect to different town areas. Experimental data gathering in the Rome urban area and subsequent processing results are presented. Algorithms for data fusion among different simulated monitoring systems and interpolation of data for a geographically denser map were utilised. Thus, in the framework of the Smart Road, protocols for data exchange were designed. Finally, air pollutant distribution maps were produced and integrated into the CIPCast platform. The feasibility of a full system architecture from the sensors to the real-time pollutant maps is shown. Full article
(This article belongs to the Special Issue Geo-Information Technology for Air Quality Management. New Trends and Scientific Challenges)
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14 pages, 5233 KiB  
Article
Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China
by Junchen He, Zhili Jin, Wei Wang and Yixiao Zhang
ISPRS Int. J. Geo-Inf. 2021, 10(10), 676; https://doi.org/10.3390/ijgi10100676 - 6 Oct 2021
Cited by 4 | Viewed by 2008
Abstract
High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break [...] Read more.
High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research. Full article
(This article belongs to the Special Issue Geo-Information Technology for Air Quality Management. New Trends and Scientific Challenges)
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18 pages, 4908 KiB  
Article
PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China
by Nengcheng Chen, Meijuan Yang, Wenying Du and Min Huang
ISPRS Int. J. Geo-Inf. 2021, 10(1), 31; https://doi.org/10.3390/ijgi10010031 - 13 Jan 2021
Cited by 14 | Viewed by 3205
Abstract
The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air [...] Read more.
The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small. Full article
(This article belongs to the Special Issue Geo-Information Technology for Air Quality Management. New Trends and Scientific Challenges)
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