SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors

The increasing attention paid to urban air quality modeling places higher requirements on urban air quality datasets. This article introduces a new urban air quality dataset—the SmartAQnet2020 dataset—which has a large span and high resolution in both time and space dimensions. The dataset contains 248,572,003 observations recorded by over 180 individual measurement devices, including ceilometers, Radio Acoustic Sounding System (RASS), mid- and low-cost stationary measuring equipment equipped with meteorological sensors and particle counters, and low-weight portable measuring equipment mounted on different platforms such as trolley, bike, and UAV

Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII Phase-2

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).This study reviews the top ranked meteorology and chemistry interactions in online coupled models recommended by an experts’ survey conducted in COST Action EuMetChem and examines the sensitivity of those interactions during two pollution episodes: the Russian forest fires 25 Jul -15 Aug 2010 and a Saharan dust transport event from 1 Oct -31 Oct 2010 as a part of the AQMEII phase-2 exercise. Three WRF-Chem model simulations were performed for the forest fire case for a baseline without any aerosol feedback on meteorology, a simulation with aerosol direct effects only and a simulation including both direct and indirect effects. For the dust case study, eight WRF-Chem and one WRF-CMAQ simulations were selected from the set of simulations conducted in the framework of AQMEII. Of these two simulations considered no feedbacks, two included direct effects only and five simulations included both direct and indirect effects. The results from both episodes demonstrate that it is important to include the meteorology and chemistry interactions in online-coupled models. Model evaluations using routine observations collected in AQMEII phase-2 and observations from a station in Moscow show that for the fire case the simulation including only aerosol direct effects has better performance than the simulations with no aerosol feedbacks or including both direct and indirect effects. The normalized mean biases are significantly reduced by 10-20% for PM10 when including aerosol direct effects. The analysis for the dust case confirms that models perform better when including aerosol direct effects, but worse when including both aerosol direct and indirect effects, which suggests that the representation of aerosol indirect effects needs to be improved in the model.Peer reviewedFinal Published versio

Improving the deterministic skill of air quality ensembles

<p><strong>Abstract.</strong> Forecasts from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as the model itself (e.g. physical parameterization, chemical mechanism). Multi-model ensemble forecasts can improve the forecast skill provided that certain mathematical conditions are fulfilled. We demonstrate through an intercomparison of two dissimilar air quality ensembles that unconditional raw forecast averaging, although generally successful, is far from optimum. One way to achieve an optimum ensemble is also presented. The basic idea is to either add optimum weights to members or constrain the ensemble to those members that meet certain conditions in time or frequency domain. The methods are evaluated against ground level observations collected from the EMEP and Airbase databases.<br><br> The two ensembles were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Verification statistics shows that the deterministic models simulate better O<sub>3</sub> than NO<sub>2</sub> and PM<sub>10</sub>, linked to different levels of complexity in the represented processes. The ensemble mean achieves higher skill compared to each station's best deterministic model at 39 %–63 % of the sites. The skill gained from the favourable ensemble averaging has at least double the forecast skill compared to using the full ensemble. The method proved robust for the 3-monthly examined time-series if the training phase comprises 60 days. Further development of the method is discussed in the conclusion.</p&gt

Kosten von Brennstoffzellensystemen auf Massenbasis in Abhängigkeit von der Absatzmenge

The currently high cost of fuel cells is determined by expensive materials and low production volume. A detailed understanding of the cost structures reveals unexploited potential that can reduce costs in future. However, this requires a method of predicting costs that can be applied with little effort and which offers both a sufficient degree of detail and also good accuracy. Existing forecasting methods do not, however, fulfil these requirements. The major objective of the present work was to apply mass-specific cost forecasting to fuel cell systems for the first time and to modify the approach for this application. In this method, the cost of an object is estimated solely by means of the object mass with the aid of empirical values (€/kg). The advantages of the method are its simple application and the accuracy of the forecast. Due to the considerable complexity of the fuel cell and the heterogeneity of the materials used, the application of mass-specific cost forecasting does not provide the desired benefits on the level of the aggregated system. The mass-specific cost forecast approach was therefore expanded and optimized. Instead of determining costs on the level of the aggregated system, the cost forecast was applied directly to the individual components. Cost parameters were also embedded in the method in order to include component-internal cost-relevant differences. Due to the great influence of the production rate on the manufacturing costs, an additional dependence on number of units was also integrated. Expanding the empirical values from discrete values to distribution functions enabled a detailed error analysis to be performed and also a statistical localization of the predicted production costs. Empirical values are necessary in order to implement the modified method and therefore an extensive data search was performed. To this end, a methodology was developed which comprehensively described the data acquisition and the required data evaluation on the basis of decision trees. A computer model was created for the mass-specific cost forecast and complemented by a function for assembly costs. The model was successfully applied and validated as part of work on various fuel cell systems. The work showed that the production costs of a fuel cell system can be determined with little effort, good modelling accuracy and high degree of detail by applying modified mass-specific cost forecasting. However, there is a restriction on applications in the light mass range. Apart from the dominant costs of the catalyst and the membrane, system components such as the pumps, compressors, heat exchangers and batteries are responsible for a large proportion of the production costs