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Air quality assessment is an important task for local authorities due to several adverse health effects that are associated with exposure to e.g., urban particle concentrations throughout the world. Based on the consumption of costs and time related to the experimental works required for standardized measurements of particle concentration in the atmosphere, other methods such as modelling arise as integrative options, on condition that model performance reaches certain quality standards. This study presents an Artificial Neural Network (ANN) approach to predict atmospheric concentrations of particle mass considering particles with an aerodynamic diameter of 0.25-1 μm (PM(0.25-1)), 0.25-2.5 μm (PM(0.25-2.5)), 0.25-10 μm (PM(0.25-10)) as well as particle number concentrations of particles with an aerodynamic diameter of 0.25-2.5 μm (PNC(0.25-2.5)). ANN model input variables were defined using data of local sound measurements, concentrations of background particle transport and standard meteorological data. A methodology including input variable selection, data splitting and an evaluation of their performance is proposed. The ANN models were developed and tested by the use of a data set that was collected in a street canyon. The ANN models were applied furthermore to a research site featuring an inner-city park to test the ability of the approach to gather spatial information of aerosol concentrations. It was observed that ANN model predictions of PM(0.25-10) and PNC(0.25-2.5) within the street canyon case as well as predictions of PM(0.25-2.5), PM(0.25-10) and PNC(0.25-2.5) within the case study of the park area show good agreement to observations and meet quality standards proposed by the European Commission regarding mean value prediction. Results indicate that the ANN models proposed can be a fairly accurate tool for assessment in predicting particle concentrations not only in time but also in space.

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO 2 , NH 3 , NO,NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO 2 , NO x , and O 3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH 3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.health [12][13][14]. Especially in street canyons, both the noise levels and the NO 2 and PM 10 concentrations are high [15]. As an increased traffic density correlates with a high noise level [16], several studies have shown that urban air pollutant concentrations can be well described by metrics of sound [17,18]. Alternatively, another proxy for the traffic density and, in turn, air pollutant concentrations, can be achieved by combining the time of the day with the day of the week [19].While sources of air pollutants beyond traffic, e.g., industry, are also important for certain cities, the main drivers of local pollutant concentrations in the urban atmospheric boundary layer are traffic emissions, background concentrations and meteorological conditions that control the transport of pollutants [20]. Thus, these drivers should be considered as predictors in air pollution models. Since environmental relationships are nonlinear and reasonably complex, this context is well suited to artificial neural networks (ANNs) [18,[21][22][23]. In particular, the modelling of urban NO 2 /NO x concentrations [24] and PM/PN concentrations [18] using a Multilayer Perceptron (MLP) has shown good prediction results. The basic idea of ANNs is to mimic processes that occur in the human brain. They receive and process information and output results, whereby the network can restructure itself during processing [25]. By recognizing certain patterns within input datasets, they can learn the best way to predict the output [26].This study presents the development and evaluation of ten ANN models for predicting urban air pollutant concentrations (CO 2 , NH 3 , NO, NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) using meteorological data, background concentrations and certain predictors of traffic volume, namely sound, traffic and time, as new input variables. We selected ...

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Abstract. SODAR (SOund Detection And Ranging), eddy-covariance, and tower profile measurements of wind speed and carbon dioxide were performed during 17 consecutive nights in complex terrain in northern Taiwan. The scope of the study was to identify the causes for intermittent turbulence events and to analyse their importance in nocturnal atmosphere–biosphere exchange as quantified with eddy-covariance measurements. If intermittency occurs frequently at a measurement site this process needs to be quantified in order to achieve reliable values for ecosystem characteristics such as net ecosystem exchange or net primary production. Fourteen events of intermittent turbulence were identified and classified into above canopy drainage flows (ACDF) and low-level jets (LLJ) according to the height of the wind speed maximum. Intermittent turbulence periods lasted between 30 min and 110 min. Towards the end of LLJ or ACDF events, positive vertical wind velocities and, in some cases upslope flows occurred, counteracting the general flow regime at night time. The observations suggest that the LLJ and ACDF penetrate deep into the cold air pool in the valley, where they experience strong buoyancy due to density differences, resulting in either upslope flows or upward vertical winds. Turbulence was found to be stronger and better developed during LLJs and ACDFs, with eddy-covariance data presenting higher quality. This was particularly indicated by spectral analysis and stationary tests. Significantly higher fluxes of sensible heat, latent heat and shear stress occurred during these periods. During LLJ and ACDF, fluxes of sensible heat, latent heat, and CO2 were mostly one-directional. For example, exclusively negative sensible heat fluxes occurred while intermittent turbulence was present. Latent heat fluxes were mostly positive during LLJ and ACDF with a median value of 34 W m−2, while outside these periods the median was 2 W m−2. In conclusion, intermittent turbulence periods exhibit a strong impact on nocturnal energy and mass fluxes.

Abstract. Sodar (SOund Detection And Ranging), eddycovariance, and tower profile measurements of wind speed and carbon dioxide were performed during 17 consecutive nights in complex terrain in northern Taiwan. The scope of the study was to identify the causes for intermittent turbulence events and to analyze their importance in nocturnal atmosphere-biosphere exchange as quantified with eddycovariance measurements. If intermittency occurs frequently at a measurement site, then this process needs to be quantified in order to achieve reliable values for ecosystem characteristics such as net ecosystem exchange or net primary production.Fourteen events of intermittent turbulence were identified and classified into above-canopy drainage flows (ACDFs) and low-level jets (LLJs) according to the height of the wind speed maximum. Intermittent turbulence periods lasted between 30 and 110 min. Towards the end of LLJ or ACDF events, positive vertical wind velocities and, in some cases, upslope flows occurred, counteracting the general flow regime at nighttime. The observations suggest that the LLJs and ACDFs penetrate deep into the cold air pool in the valley, where they experience strong buoyancy due to density differences, resulting in either upslope flows or upward vertical winds.Turbulence was found to be stronger and better developed during LLJs and ACDFs, with eddy-covariance data presenting higher quality. This was particularly indicated by spectral analysis of the vertical wind velocity and the steady-state test for the time series of the vertical wind velocity in combination with the horizontal wind component, the temperature, and carbon dioxide.Significantly higher fluxes of sensible heat, latent heat, and shear stress occurred during these periods. During LLJs and ACDFs, fluxes of sensible heat, latent heat, and CO 2 were mostly one-directional. For example, exclusively negative sensible heat fluxes occurred while intermittent turbulence was present. Latent heat fluxes were mostly positive during LLJs and ACDFs, with a median value of 34 W m −2 , while outside these periods the median was 2 W m −2 . In conclusion, intermittent turbulence periods exhibit a strong impact on nocturnal energy and mass fluxes.

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