“…The simulation has been performed for the year 2011, considered as a standard year in terms of wind speed and rainfall phenomena and for this reason comparable in terms of source-apportionment to the average situation. More details about the validation and the source-apportionment results can be found in ( Carnevale et al, 2018 ) and ( Carnevale et al, 2022 ). Fig.…”
“…The simulation has been performed for the year 2011, considered as a standard year in terms of wind speed and rainfall phenomena and for this reason comparable in terms of source-apportionment to the average situation. More details about the validation and the source-apportionment results can be found in ( Carnevale et al, 2018 ) and ( Carnevale et al, 2022 ). Fig.…”
“…• Monitoring instruments, enabling policy makers to assess the current concentration levels within a specified area. They encompass tolls for managing measured data [9], [10] and modeling systems capable of integrating their outputs with virtually any accessible measurement data [11], [12], [13]; • Forecasting tools, providing pollutant predicted concentrations within a specified area over a defined predictive time-frame. These tools comprehend (i) models based on data (data-driven) [14], capable of providing information about the pollutant concentrations at monitoring station locations, (ii) deterministic grid models [15], [16] or (iii) models employing a combined method [17]; • Management/Planning solutions, enabling the definition of air quality control measures in the designated area [18], [19] through cost-effectiveness and/or multiobjective methodologies [20], [21], , [22].…”
This study introduces and evaluates a methodology to define optimal integrated short and long-term air pollution control measures, to support policy formulation by Local Authorities. The approach utilized in this methodology is based on a receding horizon strategy. In this approach, an auto-regressive model provides understanding on the dynamic characteristics of air quality within a designated time period. The model is established using daily observed data on pollutant concentration, meteorological variables, and estimated emission data in the study area. At each time step, the resulting optimization problem is addressed using genetic algorithms. The effectiveness of the overall control has been assessed in the context of controlling NO 2 concentrations within the city of Milan. The outcomes of the study demonstrate that this control system can serve as a valuable tool to assist Local Authorities in making informed decisions regarding appropriate air quality management strategies.
“…Control theory can grant authorities both theoretical and practical tools to find a set of suitable short and long-term strategies complying with these constraints due to the fact that the NO 2 dynamic in the atmosphere is driven by complex and nonlinear phenomena involving chemical reactions, anthropogenic and biogenic emission, and meteorological conditions (in particular solar radiation and wind speed and direction) [5]. The inter-relationship among different areas of interest (i.e., chemistry, meteorology) has led the literature to focus on increasingly complex deterministic systems of systems aimed at the description of the atmospheric phenomena over a predefined geographical domain [6][7][8][9][10][11]. Unfortunately, the complexity of these systems implies computational times that are not compatible with the phase of control system development, often performed solving optimization problems through numerical algorithms.…”
In this work, a data-driven approach for the identification of a piece-wise linear model for nitrogen oxide daily concentration simulation is presented and applied. The model has been identified by using daily measured concentrations, meteorological variables, and emission levels estimated starting from the results contained in suitable emission databases. We propose an innovative methodology that jointly optimizes clustering and parameter identification. The procedure has been applied considering data from the Milan (Italy) metropolitan area. The methodology has been compared with two state-of-the-art approaches based on a two-step, cluster-based algorithm and on Hammerstein–Wiener models. The results show how, in the presented application, the devised approach ensures better performance with respect to the two literature methods, both in terms of statistical indexes (correlation, normalized mean absolute error) and in terms of problem-specific metrics (hit ratio, false alarm). For this reason, the approach can be considered suitable to be used in the definition of optimal emission control strategies.
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