How to compute long-term averages for air quality assessment at urban areas?

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance.Implications: At present, China's air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.

High Spatial Resolution Assessment of the Effect of the Spanish National Air Pollution Control Programme on Street-Level NO2 Concentrations in Three Neighborhoods of Madrid (Spain) Using Mesoscale and CFD Modelling

Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on Computational Fluid Dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality.