Special Issue "Air Quality Control and Planning"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 6861

Special Issue Editor

Prof. Dr. Claudio Carnevale
E-Mail Website
Guest Editor
DIMI-Sede Branze via Branze, University of Brescia, 3825121 Brescia, Italy
Interests: control systems; air quality management; air quality planning; nonlinear modelling; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to the World Health Organization (WHO), air pollution has been one of the most important risk factors for all ages in recent years. Moreover, recent trends show how the levels of some pollutants (i.e., particulate matter) are still increasing, or are slowly lowering in some parts of the world.

For this reason, the European Union asks regulatory agencies to define plans assessing population and ecosystem exposure, both in the short (days) and the long (year) term.

Due to the nonlinearity and complexity of air pollution production and accumulation phenomena, and the high number of exogenous variables that can affect it, the design of such plans requires the development and the application of Decision Support Systems (DSSs) to assess both the impact of emission reduction strategies on pollution indices and the costs of such emission reductions. In order to develop and apply this approach, a number of challenges and gaps have to be faced by the scientific community, ranging from the modelling of complex systems, to the definition and numerical solution of the control problem, and to the evaluation of the results and their uncertainties.

This Special Issue will include papers addressing all the scientific challenges related to air quality management and planning. Studies related to advances in air quality modelling, source-apportionment, air quality control, optimal control strategies selection, air pollution impacts and uncertainty evaluation on air quality control are welcome.

Dr. Claudio Carnevale
Guest Editor

Manuscript Submission Information

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Keywords

  • Air pollution
  • Air quality control
  • Air quality planning
  • Ozone
  • Nitrogen Oxides
  • Particulate Matter
  • Nonlinear modelling
  • Uncertainty analysis

Published Papers (4 papers)

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Research

Article
Application of Data Fusion Techniques to Improve Air Quality Forecast: A Case Study in the Northern Italy
Atmosphere 2020, 11(3), 244; https://doi.org/10.3390/atmos11030244 - 29 Feb 2020
Cited by 4 | Viewed by 1124
Abstract
Deterministic air quality forecasting models play a key role for regional and local authorities, being key tools to ensure that timely information about actual or near future exceedances of pollutant threshold values are provided to the public, as stated by the EU directive [...] Read more.
Deterministic air quality forecasting models play a key role for regional and local authorities, being key tools to ensure that timely information about actual or near future exceedances of pollutant threshold values are provided to the public, as stated by the EU directive (2008/50/EC). One of the main problems of these models is that they usually underestimate some important pollutants, like PM10, especially in high-concentration areas. For this reason, the forecast of critical episodes (i.e., exceedance of 50 μ g/m 3 for PM10 concentration daily threshold) has low accuracy. To overcome this issue, several computationally fast techniques have been implemented in the last decade. In this work, two computational fast techniques are introduced, implemented and evaluated. The techniques are based on the off-line correction of the chemical transport model output in the forecasting window, estimated by means of the measurement data up to the beginning of the forecast. In particular, the techniques are based on the estimation of the correction performed as a linear combination of the corrections computed for the days when the measurements are available. The resulting system has been applied to the Lombardy region case (Northern Italy) for daily PM10 forecasting with good results. Full article
(This article belongs to the Special Issue Air Quality Control and Planning)
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Article
Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction
Atmosphere 2019, 10(9), 560; https://doi.org/10.3390/atmos10090560 - 18 Sep 2019
Cited by 2 | Viewed by 1366
Abstract
Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) [...] Read more.
Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions. Full article
(This article belongs to the Special Issue Air Quality Control and Planning)
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Article
Seasonal Levels, Sources, and Health Risks of Heavy Metals in Atmospheric PM2.5 from Four Functional Areas of Nanjing City, Eastern China
Atmosphere 2019, 10(7), 419; https://doi.org/10.3390/atmos10070419 - 21 Jul 2019
Cited by 21 | Viewed by 2442
Abstract
Aerosol pollution is a serious environmental issue, especially in China where there has been rapid urbanization. To identify the intra-annual and regional distributions of health risks and potential sources of heavy metals in atmospheric particles with an aerodynamic diameter less than or equal [...] Read more.
Aerosol pollution is a serious environmental issue, especially in China where there has been rapid urbanization. To identify the intra-annual and regional distributions of health risks and potential sources of heavy metals in atmospheric particles with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), this work collected monthly PM2.5 samples from urban, industrial, suburban, and rural areas in Nanjing city during 2016 and analyzed the heavy metal compositions (Cu, Pb, Cd, Co, V, Sr, Mn, Ti, and Sb). Enrichment factors (EFs) and principal component analysis (PCA) were applied to investigate the sources. The atmospheric PM2.5 pollution level was highest in the industrial area, followed by the urban and suburban areas, and was the lowest in the rural area. Seasonally, the concentrations of PM2.5 and associated heavy metals in spring and winter were higher than those in summer and autumn. Besides natural sources, heavy metal pollution in PM2.5 might come from metallurgical dust in the industrial area, while it mainly comes from automobile exhaust in urban and suburban areas. Health risk assessments revealed that noncancerous hazards of heavy metals in PM2.5 were low, while the lifetime cancer risks obviously exceeded the threshold. The airborne metal pollution in various functional areas of the city impacted human health differently. Full article
(This article belongs to the Special Issue Air Quality Control and Planning)
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Article
Combining a Multi-Objective Approach and Multi-Criteria Decision Analysis to Include the Socio-Economic Dimension in an Air Quality Management Problem
Atmosphere 2019, 10(7), 381; https://doi.org/10.3390/atmos10070381 - 09 Jul 2019
Cited by 4 | Viewed by 1704
Abstract
Due to some harmful effects on humans and the environment, particulate matter (PM) has recently become among the most studied atmospheric pollutants. Given the growing sensitivity to the problem and, since production and accumulation phenomena involving both primary and secondary PM10 [...] Read more.
Due to some harmful effects on humans and the environment, particulate matter (PM) has recently become among the most studied atmospheric pollutants. Given the growing sensitivity to the problem and, since production and accumulation phenomena involving both primary and secondary P M 10 fractions are complex and non-linear, environmental authorities need tools to assess their plans designed to improve the air quality as requested from environmental laws. Multi-criteria decision analysis (MCDA) can be applied to support decision makers, by processing quantitative opinions provided by pools of experts, especially when different views on social aspects should be considered. The results obtained through this approach, however, can be highly dependent on the subjectivity of experts. To partially overcome these challenges, this paper suggests a two-step methodology in which an MCDA is fed with the solution of a multi-objective analysis (MOA). The methodology has been applied to a test case in northern Italy and the results show that this approach is a viable solution for the inclusion of subjective criteria in decision making, while reducing the impact of uncertain expert opinions for data that can be computed through the MOA. Full article
(This article belongs to the Special Issue Air Quality Control and Planning)
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