Application of mobile aerosol and trace gas measurements for the investigation of megacity air pollution emissions: the Paris metropolitan area

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Open Access Atmos. Meas. Tech., 7, 279–299, 2014 doi:10.5194/amt-7-279-2014 © Author(s) 2014. CC Attribution 3.0 License. Atmospheric Measurement Techniques Application of mobile aerosol and trace gas measurements for the investigation of megacity air pollution emissions: the Paris metropolitan area S.-L. von der Weiden-Reinmüller1, F. Drewnick1, M. Crippa2,*, A. S. H. Prévôt2, F. Meleux3, U. Baltensperger2, M. Beekmann4, and S. Borrmann1,5 1Particle Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany 2Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland 3Institut National de l‘Environnement Industriel et des Risques, Verneuil en Halatte, France 4Laboratoire inter-universitaire des Systèmes Atmosphériques, UMR7583 – CNRS, Université Paris Est Créteil et Université Paris Diderot, France 5Institute of Physics of the Atmosphere, Johannes Gutenberg University Mainz, Mainz, Germany *now at: European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy Correspondence to: F. Drewnick ( Received: 6 August 2013 – Published in Atmos. Meas. Tech. Discuss.: 22 August 2013 Revised: 22 November 2013 – Accepted: 9 December 2013 – Published: 28 January 2014 Abstract. For the investigation of megacity emission development and the impact outside the source region, mobile aerosol and trace gas measurements were carried out in the Paris metropolitan area between 1 July and 31 July 2009 (summer conditions) and 15 January and 15 February 2010 (winter conditions) in the framework of the European Union FP7 MEGAPOLI project. Two mobile laboratories, MoLa and MOSQUITA, were deployed, and here an overview of these measurements and an investigation of the applicability of such measurements for the analysis of megacity emissions are presented. Both laboratories measured physical and chemical properties of fine and ultrafine aerosol particles as well as gas phase constituents of relevance for urban pollution scenarios. The applied measurement strategies include cross-section measurements for the investigation of plume structure and quasi-Lagrangian measurements axially along the flow of the city’s pollution plume to study plume aging processes. Results of intercomparison measurements between the two mobile laboratories represent the adopted data quality assurance procedures. Most of the compared measurement devices show sufficient agreement for combined data analysis. For the removal of data contaminated by local pollution emissions a video tape analysis method was applied. Analysis tools like positive matrix factorization and peak integration by key analysis applied to high-resolution time-of-flight aerosol mass spectrometer data are used for in-depth data analysis of the organic particulate matter. Several examples, including a combination of MoLa and MOSQUITA measurements on a cross section through the Paris emission plume, are provided to demonstrate how such mobile measurements can be used to investigate the emissions of a megacity. A critical discussion of advantages and limitations of mobile measurements for the investigation of megacity emissions completes this work. 1 Introduction A growing fraction of the world’s population is living in cities or large urban agglomerations of increasing size. In 2008 more than 50 % of the human beings lived in an urban environment. Considering the actual world’s population of more than 7 billion people (United Nations, 2013), this leads to a huge concentration of activities within a relatively small area. The number of so-called megacities, defined as metropolitan areas with more than 10 million inhabitants (Molina and Molina, 2004), grew from 2 in 1970 to 23 in 2011. It is predicted that in 2025 about 37 cities worldwide Published by Copernicus Publications on behalf of the European Geosciences Union. 280 S.-L. von der Weiden-Reinmüller et al.: Mobile urban emission investigations will classify as megacities (United Nations, 2012). Along with major challenges as urban planning, industrial development and transportation these intense pollution hot-spots cause a number of scientific questions concerning their influence on local and regional air quality, with its impact on human health, flora and fauna as well as atmospheric chemistry and climate (e.g. Kunkel et al., 2012). In Europe the metropolitan areas of London, Paris, the Rhine-Ruhr and the Po valley regions, Moscow and Istanbul are classified as megacities. Within the framework of the European Union FP7 MEGAPOLI project (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation; MEGAPOLI Project, 2013) two major field campaigns were carried out in the greater Paris region in summer 2009 and winter 2010. The focus of these measurement campaigns was to characterise the Paris emission plume with respect to trace gases (e.g. O3, SO2, NOx, CO2) and aerosol particles in the size range from a few nanometers to several micrometers including chemical composition. The overall goal was to assess its impact on the local and regional air quality and to investigate aerosol transformation processes within this plume as it travels away from its source. This includes the influence of meteorology on the emission plume due to different environmental conditions in summer and winter. Typically, aerosol and trace gas measurements are performed at stationary sites what leads to a strong spatial limitation of the data and dependency on peculiarities of the chosen location. Especially for the investigation of emissions from spatially extended aerosol sources like cities and formation and transformation of particles during transport, stationary measurements are only suitable to a limited extent. For this purpose, in the past few years several ground-based mobile laboratories equipped with high-time resolution instrumentation have been developed which allow measurements with large spatial flexibility (Bukowiecki et al., 2002; Kolb et al., 2004; Pirjola et al., 2004; Drewnick et al., 2012). These ground-based mobile laboratories installed on vehicles are an expedient addition to research aircraft and laboratories installed on ships. Here we focus on mobile ground-based measurements of aerosol and trace gas characteristics that were carried out during both campaigns by two research groups with two different mobile aerosol laboratories. These mobile measurements were embedded in a network of stationary groundbased, additional mobile ground-based remote sensing and aircraft measurements, satellite observations and local, regional and global modelling (Beekmann et al., 2014). To integrate the presented measurements in a greater context of urban pollution investigations (e.g. in Barcelona, Beijing, Mexico City, Nashville, Paris, West Midlands, UK) we refer to the comprehensive number of publications focused on this topic (e.g. Nunnermacker et al., 1998; McMurry, 2000; Raga et al., 2001; Seakins et al., 2002; Harrison et al., 2006; Gros et al., 2007; Pey et al., 2008; Elanskii et al., 2010; Mohr et al., 2012; Crippa et al., 2013; Freutel et al., 2013). The measured parameters of both mobile laboratories include concentrations of gas phase O3, NOx and CO2, aerosol particle number concentration, size distribution and chemical composition as well as meteorological parameters (wind direction, relative humidity, pressure and temperature). For the measurement of the sub-micron particle chemical composition on-line measurement devices such as the aerosol mass spectrometer (Jayne et al., 2000; DeCarlo et al., 2006; Lanz et al., 2010) have been deployed and adopted for the mobile measurements. In combination with complex analysis tools as positive matrix factorization (PMF) (Paatero and Tapper, 1994; Paatero, 1997; Lanz et al., 2007; Ulbrich et al., 2009) and peak integration by key analysis (PIKA) (ToFAMS Analysis Software Homepage, 2013) a large amount of information can be obtained about aerosol chemical composition (Zhang et al., 2005; Canagaratna et al., 2007; Sun et al., 2010; Zhang et al., 2011). The measurement of carbon dioxide, ozone and nitrogen oxides together with meteorological parameters is needed for numerical simulation of the urban atmospheric chemistry (e.g. Fenger, 1999; Akimoto, 2003; Crutzen, 2004; Gurjar and Lelieveld, 2005). The purpose of this paper is to discuss the general applicability of the developed and deployed measurement and analysis strategies for urban emission investigations using the MEGAPOLI data base as example. In Sect. 2 we provide an overview of the MEGAPOLI field campaigns and describe the two mobile laboratories including specification and intercomparison of the on-board instruments, as well as different measurement strategies. A general overview of the advanced data preparation for analysis completes this section. In Sect. 3 four examples of mobile measurements are presented that investigate the differences between long-range transported and locally produced pollution. A combination of data from both mobile laboratories allows a detailed view of the spatial structure of the emission plume. Axial measurement trips show the spatial extent of the emission plume, while a final example of stationary measurements at various locations around the city illustrates the influence of the Paris emission plume on local air quality. These measurement examples demonstrate the successful application of the individual measurement strategies. Section 4 provides a critical discussion of possibilities, challenges and limitations of mobile measurements. 2 Methods 2.1 MEGAPOLI field campaigns MEGAPOLI project: the European Union FP7 MEGAPOLI project (MEGAPOLI Project, 2013) is a collaborative project to assess impacts of megacities and large air-pollution hotspots on local, regional, and global air quality and climate. Atmos. Meas. Tech., 7, 279–299, 2014 S.-L. von der Weiden-Reinmüller et al.: Mobile urban emission investigations 281 An additional goal is the quantification of feedbacks between megacity emissions, air quality, local and regional climate, and global climate change. Based on new findings improved, integrated tools are to be developed and implemented in existing air quality models to assess the impacts of air pollution from megacities on regional and global air quality and climate and to evaluate the effectiveness of mitigation strategies. MEGAPOLI field campaigns: two major field campaigns took place in the greater Paris region in France, which is classified as megacity with currently about 10.6 million inhabitants (United Nations, 2012). The greater Paris metropolitan area, defined as the territory with high residential density and additional surrounding areas that are also influenced by the city, for example by frequent transport or road linkages (United Nations, 2012), has actually a population of more than 12 million inhabitants (Aire urbaine, 2013). The summer measurement period was carried out between 1 July and 31 July 2009, the winter field campaign between 15 January and 15 February 2010. The main objective of these field measurements was to characterise and quantify sources of primary and secondary aerosol in and around a large agglomeration and to investigate its evolution and impacts on local and regional air quality as well as atmospheric chemistry in the megacity emission plume. Three groundbased stationary measurement sites were operated – two suburb and one downtown sites (for details see MEGAPOLI project, 2013; Freutel et al., 2013; Crippa et al., 2013). Mobile measurements were carried out by a research aircraft (applying an ATR42 in summer and a Piper Aztec in winter, SAFIRE, 2013), remote-sensing mobile laboratories (DOAS instrument operated on top of a regular passenger vehicle, a LIDAR instrument installed on a pick-up truck, Royer et al., 2011) and the two mobile aerosol research laboratories MoLa and MOSQUITA. These mobile laboratories measured while driving through atmospheric background and emission plume influenced air masses, carried out several stationary measurements at various places in and around Paris and were also used for intercomparison studies with the stationary measurement sites and the research aircraft. 2.2 Mobile laboratories and on-board instrumentation 2.2.1 Mobile laboratory “MoLa” Platform: the Max Planck Institute for Chemistry in Mainz, Germany, developed a compact Mobile aerosol research Laboratory (“MoLa”) based on a Ford Transit delivery vehicle for stationary and mobile measurements of aerosol physical and chemical properties and trace gas concentrations (Drewnick et al., 2012). The main inlet system for mobile measurements is located above the driver’s cabin at a height of approximately 2.2 m and it is equipped with a nozzle optimised for an average driving velocity of about 50 km h−1. Stationary measurements are performed using an extendable inlet (up to 10 m) on the roof of the vehicle for aerosol sampling. A specific feature of the MoLa aerosol inlet system is its optimisation for minimal sampling and transport losses using the Particle Loss Calculator (von der Weiden et al., 2009). The residence time of the aerosol during its way through the inlet system to the individual instruments was measured and calculated for sampling time correction and the occurring – but negligible – particle losses were quantified (see Sect. 2.3 and Drewnick et al., 2012). Instrumentation: Table 1 provides detailed information on the deployed instruments. During both field campaigns MoLa was equipped with a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS; DeCarlo et al., 2006) to measure size-resolved mass concentrations of nonrefractory species approximately in the PM1 size range. Additionally, black carbon and particle-bound polycyclic aromatic hydrocarbon (PAH) mass concentrations were measured to achieve detailed chemical information about total PM1 particle composition, including most species found in the sub-micron aerosol. The aerosol total number concentration was measured as well as aerosol particle size distributions applying three different techniques (electrical mobility, aerodynamic sizing, light scattering). The detected trace gases include O3, SO2, NO, NO2, CO, CO2 and H2O. Meteorological parameters like wind direction and speed, ambient pressure, temperature, relative humidity and precipitation were recorded as well as GPS vehicle position and environmental conditions (e.g. traffic situation) using a webcam. 2.2.2 Mobile laboratory “MOSQUITA” Platform: the Paul Scherrer Institute in Villigen, Switzerland, developed a mobile aerosol and trace gas laboratory (Measurements Of Spatial QUantitative Imissions of Trace gases and Aerosols: “MOSQUITA”; Bukowiecki et al., 2002; Weimer et al., 2009; Mohr et al., 2011; Mobile Laboratory MOSQUITA, 2013), which utilises an IVECO Turbo Daily Transporter as platform. The main aerosol inlet is located at a height of 3.2 m in the front part of the vehicle allowing for isokinetic sampling at a driving velocity of 50 km h−1. Instrumentation: Table 2 shows the equipment used in MOSQUITA. PM1 particle chemical composition was obtained from a HR-ToF-AMS and additional black carbon mass concentration measurements. Aerosol particle concentrations were measured in total as well as size-resolved applying different techniques in summer (electrical mobility) and winter (light scattering). The recorded trace gases are O3, NO, NO2, CO, CO2 and H2O. Meteorological parameters including wind direction, pressure, temperature, relative humidity and global radiation as well as GPS information and webcam videos complete the MOSQUITA data set. Atmos. Meas. Tech., 7, 279–299, 2014 282 S.-L. von der Weiden-Reinmüller et al.: Mobile urban emission investigations Table 1. Summary of measurement devices – including information about measured variables, size range, time resolution and detection limit – installed in the mobile laboratory MoLa (Max Planck Institute for Chemistry) during the summer and winter MEGAPOLI field campaigns. AMS detection limits (using the method described in Drewnick et al., 2009) were calculated for summer (S) and winter field campaign (W) separately, because the AMS detection limit can change over time. Measurement device Measured variable Size range Time resolution Detection limit AMSa Size-resolved aerosol chemical composition 50 nm–∼ 1 µm 1 min MAAPb PASc CPCd FMPSe APSf OPCg Airpointerh Black carbon mass concentration PAH mass concentration Particle number concentration Particle size distribution based on electrical mobility Particle size distribution based on aerodynamic sizing Particle size distribution based on light scattering O3, SO2, CO, NO, NO2 mixing ratio 10 nm–1 µm 10 nm–1 µm 2.5nm–3 µm 5.6 nm–560 nm (32 channels) 0.5 µm–20 µm (52 channels) 0.25 µm–32 µm (31 channels) N/A 1 min 12 s 1s 1s 1s 6s 1 min LI-840i CO2, H2O mixing ratio N/A Meteorological Stationj Wind speed, wind direction, temperature, precipitation, N/A pressure, relative humidity 1s 1s GPSk Webcaml Vehicle location and speed Driver’s view through windshield N/A N/A 1s 1s organics: 0.03 (S)/0.01 (W) µg m−3 nitrate: 0.05 (S)/< 0.01 (W) µg m−3 sulfate: < 0.01 (S)/0.01 (W) µg m−3 ammonium: 0.06 (S)/0.03 (W) µg m−3 0.1 µg m−3 1 ng m−3 N/A N/A N/A N/A O3: < 1.0 ppbV SO2: < 1.0 ppbV CO: < 0.2 ppmV NOx: < 1.0 ppbV CO2: < 1 ppmV (RMS noise) H2O: < 10 ppmV (RMS noise) Accuracy: WindSp: 0.3 m s−1 WindDir: 3 Temp: 0.3 C RH: 3 % Precipitation: 5 % Pressure: 0.5 hPa N/A N/A a Aerosol Mass Spectrometer (AMS), HR-ToF-AMS, Aerodyne Research, Inc., USA, (, last access: 15 equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. 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