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Sustainability
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Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment
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Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 9681

Special Issue Editors

Dr. Alessandro Zaldei

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Guest Editor
National Research Council-Institute of Biometeorology (CNR-IBIMET), Via Caproni 8, 50145 Firenze, Italy
Interests: environmental monitoring
Dr. Eng. Giovanni Gualtieri

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Guest Editor
National Research Council, Institute of Biometeorology (CNR-IBIMET), Via Caproni 8, 50145 Firenze, Italy
Interests: air quality; air pollutant dispersion models; road transportation; wind energy; wind resource assessment; boundary layer meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Regulatory air quality monitoring is generally performed using complicated, bulky, and expensive analytical instruments, the cost of which is remarkable not only in the initial investment and operation, but also in the resources required to routinely maintain and calibrate them. Significant investments are also required to set up and manage networks of fixed air quality stations. Spread sparsely within a city, these stations deliver detailed and accurate measurements, but at a limited number of point-based locations, thus, making it difficult to compile representative and reliable information for a whole urban area and to convey a clearer picture of air pollution pattern.

Towards achieving a sustainable city, innovative non-regulatory air quality sensors are receiving greater and greater attention for air quality monitoring and assessment. Due to their low cost, small size, and low power consumption, these new technologies are very appealing in situations where traditional monitors are impractical. Their ability to provide highly resolved air quality data makes them an efficient supplementary and/or alternative monitoring option. In addition to supplying a more widespread network that can effectively capture the spatio-temporal variability of air pollution, the deployment of a significant number of these innovative sensors can also assist in creating pollutant emission inventories, detecting pollution hotspots, or allowing real-time exposure assessment for designing mitigation strategies. As they are easily portable, these devices could also be particularly helpful in those cases where traffic restriction actions at an urban-scale are adopted, in order to assess how the latter actually mitigate air pollution issues.

Innovative air quality monitoring paves the way for a sustainable approach towards a novel generation of smart mobility solutions, and for implementing air quality citizen science, i.e., all those research activities by which citizens could be more directly involved in participatory environment monitoring and city management.

This Special Issue welcomes original research articles significantly contributing to issues related (but not limited to) the following topics:

  • Application of innovative non-regulatory air quality sensors
  • Development of high spatio-temporal resolution air quality monitoring networks
  • Development of real-time air quality information technologies
  • Real-time mobile air quality monitoring and assessment
  • High-resolution air pollutant dispersion modeling
  • Effectiveness assessment of road traffic restrictions
  • Air quality mitigation strategies
  • Air quality citizen science

Dr. Alessandro Zaldei
Dr. Eng. Giovanni Gualtieri
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. 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

  • Sustainable city
  • Air quality
  • Innovative technologies
  • Non-regulatory air quality sensors
  • Air quality monitoring networks
  • Real-time air quality information technologies
  • Air pollutant dispersion modeling
  • Mitigation strategies
  • Road traffic restrictions
  • Citizen science

Published Papers (3 papers)

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Research

26 pages, 6262 KiB  
Article
Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities
by José Adães and José C. M. Pires
Sustainability 2019, 11(21), 6019; https://0-doi-org.brum.beds.ac.uk/10.3390/su11216019 - 29 Oct 2019
Cited by 18 | Viewed by 3154
Abstract
Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate [...] Read more.
Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed. Full article
(This article belongs to the Special Issue Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment)
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18 pages, 1885 KiB  
Article
Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies
by Ana Belen Vicente, Pablo Juan, Sergi Meseguer, Laura Serra and Sergio Trilles
Sustainability 2019, 11(20), 5857; https://0-doi-org.brum.beds.ac.uk/10.3390/su11205857 - 22 Oct 2019
Cited by 5 | Viewed by 2791
Abstract
A statistical modelling of PM10 concentration (2006–2015) is applied to understand the behaviour, to know the influence of the variables to exposure risk, to treat the missing data to evaluate air quality, and to estimate data for those sites where they are not [...] Read more.
A statistical modelling of PM10 concentration (2006–2015) is applied to understand the behaviour, to know the influence of the variables to exposure risk, to treat the missing data to evaluate air quality, and to estimate data for those sites where they are not available. The study area, Castellón region (Spain), is a strategic area in the framework of EU pollution control. A decrease of PM10 is observed for industrial and urban stations. In the case of rural stations, the levels remain constant throughout the study period. The contribution of anthropogenic sources has been estimated through the PM10 background of the study area. The behaviour of PM10 annual trend is tri-modal for industrial and urban stations and bi-modal in the case of rural stations. The EU Normative suggests that 90% of the data per year are necessary to control air quality. Thus, interpolation statistical methods are presented to fill missing data: Linear Interpolation, Exponential Interpolation, and Kalman Smoothing. This study also focuses on testing the goodness of these methods in order to find the ones that better approach the gaps. After analyzing graphically and using the RMSE the last method is confirmed to be the best option. Full article
(This article belongs to the Special Issue Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment)
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19 pages, 3211 KiB  
Article
Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China
by Xinghan Xu and Weijie Ren
Sustainability 2019, 11(11), 3096; https://0-doi-org.brum.beds.ac.uk/10.3390/su11113096 - 31 May 2019
Cited by 25 | Viewed by 3050
Abstract
With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a [...] Read more.
With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring. Full article
(This article belongs to the Special Issue Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment)
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