Ultrafine particles (UFP) are abundant in urban atmospheres. To assess the strength and temporal variation of urban UFP emission sources, information on the surface-atmosphere exchange, i.e. the turbulent vertical flux of particles, is vital. A three-year time series of UFP emission fluxes (FUFP) observed at an urban site in Berlin, Germany, using the eddy covariance technique was utilized to develop and evaluate generalized additive models (GAM) for FUFP. GAM allow to account for non-linear relationships between response and predictor variables. Two separate models for summer and winter were developed. The predictors that most strongly influenced modelled FUFP in the summer model were traffic activity, friction velocity, land use, air temperature and PM10 concentration, whereas the winter model additionally incorporated relative humidity. The GAM were evaluated by ten-fold cross-validation for the first two study years, and by predicting the third year based on the model trained with observational data of the first two years. The coefficients of determination of the two validation methods were R2 = 0.52 (uncertainty of -47 to 88% for FUFP) and R2 = 0.48 (-45 to 82% for FUFP) for the winter model, whereas the summer model yielded R2 = 0.48 and 0.44 (uncertainty of -51 to 102%). GAM were shown to successfully capture the non-linear relationships between predictor variables and FUFP for the three-year data set at this urban site.

To estimate the spatial and temporal variation in urban particle number concentrations (PNCs), e.g., for exposure studies, a better knowledge of the exchange of particles between the urban surface and the atmosphere is important. Size-resolved fluxes of PNCs were quantified in Berlin, Germany, using the micrometeorological eddy covariance technique. The method requires measurements of particle number size distributions (PNSDs) by a fast particle spectrometer. The Engine Exhaust Particle Sizer (EEPS) Spectrometer 3090 (TSI Inc.) is designed for fast (10 Hz), high-concentration measurements of particles in the size range of 5.6–560 nm, e.g., in the exhaust plume of engines. In the urban background environment of Berlin, however, PNCs in some size channels can temporarily fall below the minimum threshold concentration of the analyser, resulting in missing concentrations that lead to gaps in the PNSD. In the present study, three gap-filling methods were applied to derive complete PNSDs: linear interpolation (LI), natural spline interpolation (NSI) and log-normal fitting (LNF). To evaluate the methods, different numbers of artificial gaps were inserted into 105 gapless PNSDs. Using three different data sets, the results demonstrate that LI and NSI (LI: R2 = 0.84–0.94; NSI: R2 = 0.84–0.95) outperform LNF (R2 = 0.78–0.88). With regard to the Berlin data set, NSI is the recommended gap-filling method since it results in a lower average uncertainty of 10.5–21.8

Recent research has demonstrated that airports are important emission sources for ultrafine particles (UFP) which may largely contribute to regional UFP number concentrations. However, in areas where multiple particle sources are present, the differentiation and the quantification of the contribution of aircraft emissions to ambient UFP is challenging. We present the results from a car-based mobile measurement campaign in the vicinity of the recently opened Berlin-Brandenburg airport (BER) in Germany. Total particle number concentrations (TNC), CO2, particle mass (PM) and equivalent black carbon (eBC) concentrations were measured downwind from the airport. TNC roughly followed an exponential decline with increasing distance to the airport, which was more pronounced during the measurement campaign in the fall than in the subsequent summer. Cluster analysis showed that airport emissions are dominated by elevated TNC concentrations (median elevation by 59%), while particle emissions from vehicle traffic coincided with increased CO2 and eBC concentrations. The airport emission cluster could be found up to approx. 7000 m distance to the central terminal building of the airport during the fall campaign. The real-world emission factors derived from the measurements indicate that aircrafts emit roughly eight times the number of particles per kg fuel than vehicle traffic. Overall, our study presents the effects of airport-related emissions onto the regional air quality downwind from the airport. To get further insights into the contribution percentage of airports to ambient UFP, studies on aerosol dispersion and dynamics modelling are needed.

Ultrafine particles (UFP) are quickly transformed within a few 100 m distance to the source because of aerosol dynamic processes. In order to predict the transformation of particle number size distributions (PNSD) near major streets, we developed a sectional model that represents aerosol dynamics within an air parcel that is transported from a major street along a minor street into the urban background. Simplifying assumptions on the transport and transformation due to dilution, coagulation, deposition and condensation were introduced to allow fast prediction with a limited number of model parameters. Model predictions were compared to observed PNSDs from semi-mobile measurement campaigns along 200 m long transects in Berlin, Germany. The total number concentration (TNC) along the transects declined on average by 30% for the evaluated measurement runs. The model agreed well with the observed PNSDs (coefficient of determination, R2 = 0.94). The model was sensitive to the selection of a dilution parameter b (+8/-11% TNC change due to variation by factor 2) and friction velocity u* (up to 3% TNC change by replacing parameterized u* by constant values in a similar range), but almost not sensitive to the other parameters. According to the model, the dilution contributed the most to the TNC decline (approx. 73% after 200 m transport distance), followed by coagulation and deposition (13 and 14% contribution, respectively). Due to the low computational effort of the model, it may contribute to real-time forecasting of PNSDs near major streets and to determining short-term exposure to particles.

Abstract. In aerosol science, there is an increasing interest to perform mobile measurements to obtain number size distribution of ultrafine particles (UFP), using portable instruments based on unipolar charging and size segregation by electrical particle mobility. Applications of such measurements range from ambient and indoor aerosol studies to source identification in work environments. However, knowledge on the actual measurement uncertainties of these portable instruments under various conditions has been limited. This investigation presents results from an intercomparison workshop conducted at the World Calibration Center for Aerosol Physics (WCCAP) in Leipzig, Germany, in January 2020. Manufacturers and users were invited to have their portable instruments tested and compared against reference instrumentation for particle number size distributions (PNSD) and total particle number concentration (PNC). In particular, the performances and uncertainties of the NanoScan SMPS (Scanning Mobility Particle Sizer) Model 3910 (TSI Inc.) and the Mini Wide Range Aerosol Spectrometer (WRAS) Model 1371 (Grimm Aerosol Technik) were investigated extensively against the WCCAP Mobility Particle Size Spectrometers (MPSS) and Condensation Particle Counters (CPC). A total of 11 TSI NanoScan SMPS and 4 GRIMM Mini WRAS instruments were characterized for ambient aerosols as well as lab-generated aerosols. The workshop results affirm that the portable instruments must be serviced and calibrated annually or prior field studies to provide measurements within the given uncertainties. It should be noted that users should carry out timely service, maintenance and calibration of portable instruments at their facilities. During initial inspection, non-serviced NanoScan SMPS instruments overestimated a dominant ultrafine aerosol mode by 120 % at around 80 nm. Maintenance and servicing improved the performance. Overall, the performance of NanoScan SMPS instruments improved for the ultrafine aerosol mode while the PNC in the fine aerosol mode still overestimated by up to 80 %. The latter effect seems to be systematically related to the unipolar charging of particles, and the reduced sensitivity of electrical particle mobility with increasing particle size above 200 nm. Due to shift in the second mode of bimodal distribution, particles are overcounted around 100 nm. With regard to the integral PNC, some of the NanoScan SMPS found to be in good agreement (i.e. within 20 %) compared to the reference CPC. In addition, a reasonably good unit-to-unit agreement within ±20 % was found for NanoScan SMPS instruments. The Mini WRAS instruments, after proper cleaning and servicing, provided improved results within ±15 % deviation in PNC in the ultrafine aerosol mode. Overall, most of the GRIMM Mini WRAS instruments (operating with software version 10.0) agrees well with PNC (i.e. 10–50 %) when the ultrafine mode was dominant. Conversely, PNC of the fine aerosol mode was systematically underestimated by 60 % above 100 nm. Except for one instrument, the integral PNC of the GRIMM Mini WRAS spectrometers were within an uncertainty range of ±20 % compared to the reference CPC. Additionally, it is important for users to note that the Mini WRAS performed significantly better when using software version 10.0 compared to version 8.2. The workshop results suggest that despite the above-mentioned uncertainties, these portable instruments are suited for mobile ultrafine particle measurements to detect relative differences in the PNSD such as source apportionment studies of ultrafine particles at work places or outdoors near sources.

Ultrafine particles (UFP) pose a risk to human health, but due to the multitude of sources and fast transformation in the urban atmosphere, quantifying the exposure is challenging. Furthermore, physical properties of aerosol particles depend on the particle size. Statistical models are used to quantify spatial and temporal variation of UFP, but rarely used for particle number size distribution (PNSD). The aim of the study was to establish an interpretable statistical model capturing spatial and temporal variation of urban PNSDs using generalized additive models (GAM) and multivariate adaptive regression spline models (MARS). These algorithms automatically fit interpretable, non-linear marginal function to represent relationships between explanatory and response variables. Three different approaches were evaluated to cope with the multidimensionality of the PNSD data (20-800 nm, 34 size bins): a generalized additive model for the particle number concentration (PNC) of every individual size bin (GAM(bins)), a generalized additive model for the parameters of the PNSD function (GAM(pams)) and a multivariate adaptive regression spline model for the PNC of every size bin (MARS(bins)). Reanalysis data of meteorological quantities, urban geometry parameters and approximated traffic counts were used as explanatory variables. Marginal functions of the final models could be attributed to major processes that contribute to spatial and temporal variation of the PNSD, i.e. emissions from vehicle traffic, transport, dilution, accumulation, deposition and new particle formation. Cross-validation coefficients of determination ranged between 0.27 and 0.48 for most size bins. Nonetheless, the modelling approaches resulted in similar root mean square errors (RMSE) and mean absolute error (MAE). Though direct spatial transferability of the models is limited, the presented approaches may be useful for estimating ambient exposure to particles.

Ziel des BMBF-Programms Stadtklima im Wandel war die Entwicklung, Validierung und Anwendung eines gebaudeauflosenden Stadtklimamodells fur ganze Stadte. Das Verbundprojekt 3DO ubernahm die dem Modul B zugeordneten Forschungsaufgaben: Aufbereitung vorhandener Daten aus der Langzeitbeobachtung (LTO), Aufbau neuer Messstationen, Gewinnung neuer dreidimensionaler atmospharischer Daten und die Entwicklung neuer Konzepte z.B. zur Modellevaluation. Untersucht wurden der Aufbau der atmospharischen Grenzschicht, die Charakteristik der meteorologischen Parameter und deren Einfluss auf das thermische Empfinden des Menschen. Ein einheitlicher UC2-Datenstandard sowie Analysewerkzeuge wurden entwickelt und in ein Datenmanagementsystem und eine Wissensplattform fur den modulubergreifenden Austausch integriert.

BMBF, 01LP1602A, Verbundprojekt Stadtklima: Evaluierung von Stadtklimamodellen (Modul B), 3DO Teilprojekt 1: Dreidimensionales Monitoring atmospharischer Prozesse in Berlin

In the soil–plant–atmosphere continuum, fluxes of water, energy, and carbon determine the water and carbon balance of peat bogs. We used eddy covariance (EC) measurements to study surface atmosphere exchange and its drivers above an ombrotrophic peat bog (Odersprungmoor) in the Harz Mountains, Germany, with nonideal measurement conditions during the growing season in 2013. For montane peatlands, only very few EC flux measurements exist due to site constraints, for example, surface slope, limited fetch, and frequent dew formation on open path sensors. The measured data were carefully filtered resulting in valid and representative fluxes for the bog. The evapotranspiration (ET) was further characterized by determining the adjusted crop coefficient ( K c* ) for July and August and comparing it with K c* values from 7 years of the FLUXNET site Mer Bleue bog, Ontario, Canada. While soil moisture was taken into consideration, the adjustment was nevertheless necessary as plant health and nutrient supply were not evaluated as required by FAO guidelines. Actual ET at OM was well described by the K c* model ( K c* = 0.85, R 2 = 0.85). The primary control on ET was available energy and atmospheric conditions and, marginally, the soil moisture conditions. This K c* value is comparable to the calculated K c* values for MB, which ranged between 0.82 and 0.86 ( R 2 between 0.84 and 0.97). Since these K c* ranges are narrow for the different sites and years, we hypothesize that these values are good estimates for the true crop coefficients of Sphagnum ‐dominated peat bogs.

Quantification of the exposure of urban residents to ultrafine particle number concentrations (UFP) is challenging due to its high spatial and temporal variability. Hence, statistical models, e.g. generalized additive models (GAM), may be used to estimate time series or spatial characteristics of UFP. The GAM approach allows the representation of non-linear relations of a response variable with explanatory variables without the need to pre-define model functions. Up to now, GAMs were usually fitted to UFP data from a single site or from mobile measurement campaigns with limited temporal coverage. In this study, GAMs were used to determine UFP, accumulation mode particle (ACC) and total number concentration (TNC) at five urban sites in the cities of Leipzig and Dresden, Germany for the period 2011-2013. As explanatory variables, reanalysis data sets of meteorological quantities, urban geometry and traffic volume data were evaluated. Variables causing concurvity, which is the equivalent to collinearity in non-linear model approaches, were neglected to guarantee the interpretability of the final models. The models were then validated in a ten-fold cross-validation approach. The final models contained smooth functions for the building surface fraction, planetary boundary layer height, traffic volume, air temperature, wind direction, atmospheric pressure, relative humidity, global radiation and precipitation. Adjusted coefficients of determination (R2adj.) for the final models were R2adj. = 0.44 for UFP, R2adj. = 0.51 for ACC and R2adj. = 0.48 for TNC. Coefficients of determination of the cross-validation were in a similar range (0.44 for UFP, 0.51 for ACC, 0.49 for TNC). Finally, our study shows that GAMs are able to represent important processes that contribute to the particle number concentration from the smooth functions, i.e. emission, dilution, nucleation, deposition and long-range transport.

Abstract While boreal lowland bogs have been extensively studied using the eddy‐covariance (EC) technique, less knowledge exists on mountainous peatlands. Hence, half‐hourly CO2 fluxes of an ombrotrophic peat bog in the Harz Mountains, Germany, were measured with the EC technique during a growing season with exceptionally dry weather spells. A common biophysical process model for net ecosystem exchange was used to describe measured CO2 fluxes and to fill data gaps. Model parameters and uncertainties were estimated by robust inverse modelling in a Bayesian framework using a population‐based Markov Chain Monte Carlo sampler. The focus of this study was on the correct statistical description of error, i.e. the differences between the measured and simulated carbon fluxes, and the influence of distributional assumptions on parameter estimates, cumulative carbon fluxes, and uncertainties. We tested the Gaussian, Laplace, and Student's t distribution as error models. The t‐distribution was identified as best error model by the deviance information criterion. Its use led to markedly different parameter estimates, a reduction of parameter uncertainty by about 40%, and, most importantly, to a 5% higher estimated cumulative CO2 uptake as compared to the commonly assumed Gaussian error distribution. As open‐path measurement systems have larger measurement error at high humidity, the standard deviation of the error was modeled as a function of measured vapor pressure deficit. Overall, this paper demonstrates the importance of critically assessing the influence of distributional assumptions on estimated model parameters and cumulative carbon fluxes between the land surface and the atmosphere.