Air quality monitoring and simulation on urban scale over Munich

Khorsandi, Ehsan; Baier, F. W.; Erbertseder, Thilo; Bittner, Michael

doi:10.1117/12.2503969

Cited by 4 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…POLYPHE-MUS/DLR is an air quality modelling platform operated at DLR based on the POLYPHEMUS chemistry transport model (Mallet et al, 2007) coupled to the Weather Research and Forecasting (WRF) model (Skamarock et al, 2008) with the TNO-MACC emission inventory (Denier van der Gon et al, 2010). It has been further developed within the PA-SODOBLE project for sensitivity studies of the mountainous Black Forest region (Bergemann et al, 2012) and to cover urban areas in southern Bavaria (Khorsandi et al, 2018). It uses the TNO-MACC emission inventory (Kuenen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

et al. 2021

Self Cite

View full text Add to dashboard Cite

Abstract. Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The DLR nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2×1014 molec./cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5×1014 molec./cm2 in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured. For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric NO2 data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to ∼ −20 %.

show abstract

Section: Introductionmentioning

confidence: 99%

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The CAMS European Forecasting Ensemble is an ensemble of seven chemical transport models described in more detail by Marécal et al (2015). For air quality forecasting at a higher spatial resolution for Germany and regions therein, the air quality modelling system Polyphemus/DLR operationally provides predictions of up to 72 hours at spatial resolutions of 2 km down to 500 m (Khorsandi et al, 2018;Mallet et al, 2007).…”

Section: State Of the Artmentioning

confidence: 99%

Design and Results of an Ai-Based Forecasting of Air Pollutants for Smart Cities

Petry

Meiers

Reuschenberg

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.

show abstract

“…Among others, core air pollutant species (O3, NO2, SO2, CO, PM10 and PM2.5) are daily forecast by seven European models (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE, SILAM). In 2019 DEHM and GEM-AQ models were also added [11]. These regional CTMs run locally on common input for meteorology, boundary conditions and emissions.…”

Section: 22mentioning

confidence: 99%

Comparing the aggregated health risk calculated from different Earth observation resources

Houdayer

Khorsandi

Handschuh

et al. 2021

Remote Sensing Technologies and Applications in Urban Environments VI

Self Cite

View full text Add to dashboard Cite

Ambient air quality (AQ) is a recurrent issue in cities, exacerbated in low-and middle-income countries. Global AQ impacts on health can be assessed using an aggregated health risk indicator (ƩRIs), derived from in-situ stations, chemical transport models (CTMs) or satellite data.AQ monitoring is well covered in the city of Munich (Germany): in-situ stations, POLYPHEMUS CTM with three domains, CAMS-Reanalysis of the regional model ensemble and satellite data from MODIS. From these data sets, the ƩRIs was calculated considering four major air pollutants (NO2, PM10, PM2.5, O3), using their respective relative risk for the mortality-all causes health end-point. Then, the ƩRIs from the models and the satellite data were compared to the ƩRIs in-situ by means of basic statistics, time series and violin plots and the mean relative difference (MRD).Using the ƩRIs allows to further observe the contribution of individual pollutants to the index. For the mortality all causes health end point between 2017 and 2018, ground observations and CTMs show an increase of ca. 12-13% when exposed to ambient air pollution. The difference between traffic and background stations can be observed: ƩRIs in situ mean is higher at the traffic station than at the background stations. This order is however reversed when considering ƩRIs mean from the models. The four CTMs simulate the ƩRIs well and its seasonality is also represented. Most of the data are spread around the mean and the median for all data sets and stations with an overall distribution skewed towards high values. With 0.5

show abstract

Air quality monitoring and simulation on urban scale over Munich

Cited by 4 publications

References 6 publications

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

Design and Results of an Ai-Based Forecasting of Air Pollutants for Smart Cities

Comparing the aggregated health risk calculated from different Earth observation resources

Contact Info

Product

Resources

About

Air quality monitoring and simulation on urban scale over Munich

Cited by 4 publications

References 6 publications

An improved TROPOMI tropospheric NO&lt;sub&gt;2&lt;/sub&gt; research product over Europe

An improved TROPOMI tropospheric NO&lt;sub&gt;2&lt;/sub&gt; research product over Europe

Design and Results of an Ai-Based Forecasting of Air Pollutants for Smart Cities

Comparing the aggregated health risk calculated from different Earth observation resources

Contact Info

Product

Resources

About

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe

An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe