Locating industrial VOC sources with aircraft observations

Environmental Pollution 159 (2011) 1174e1182

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Locating industrial VOC sources with aircraft observations P. Toscano a, *, B. Gioli a, S. Dugheri b, A. Salvini c, A. Matese a, A. Bonacchi b, A. Zaldei a, V. Cupelli b, F. Miglietta a, d a

Institute for Biometeorology (IBIMET e CNR), Via G. Caproni 8, 50145 Firenze, Italy Careggi Hospital-University of Florence, Occupational Health Division, Largo Palagi 1, 50100 Florence, Italy c Department of Organic Chemistry, University of Florence, Via della Lastruccia 13, 50019 Sesto Fiorentino, Florence, Italy d Fondazione Edmund Mach, Via Mach 1, San Michele all’Adige, Trento, Italy b

An integrated strategy based on atmospheric aircraft observations and dispersion modelling was developed, aimed at estimating spatial location and strength of VOC point source emissions in industrial areas.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 October 2010 Received in revised form 25 January 2011 Accepted 2 February 2011

Observation and characterization of environmental pollution, focussing on Volatile Organic Compounds (VOCs), in a high-risk industrial area, are particularly important in order to provide indications on a safe level of exposure, indicate eventual priorities and advise on policy interventions. The aim of this study is to use the Solid Phase Micro Extraction (SPME) method to measure VOCs, directly coupled with atmospheric measurements taken on a small aircraft environmental platform, to evaluate and locate the presence of VOC emission sources in the Marghera industrial area. Lab analysis of collected SPME fibres and subsequent analysis of mass spectrum and chromatograms in Scan Mode allowed the detection of a wide range of VOCs. The combination of this information during the monitoring campaign allowed a model (Gaussian Plume) to be implemented that estimates the localization of emission sources on the ground. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Environmental pollution Volatile organic compounds Gaussian plume model SPME Environmental research aircraft

1. Introduction Investigating relations between environmental air pollution and public health in urban and industrial areas requires the capability to properly sample and model air properties, concentrations and dispersion of the compound of interest, at appropriate spatial and temporal scales. Pollutants do not remain in the medium where they originate but move across environmental phase boundaries, becoming distributed throughout the various environmental compartments as the result of complex physical, chemical, and biological processes. The resulting environmental and human health risks depend upon the degree of exposure of human and ecological receptors, via multiple pathways, to pollutant chemicals (Cohen, 1996). Volatile organic compounds (VOCs), in particular, are highly mobile in the environment; VOCs that are initially present in soil or water can readily volatilize to the atmosphere where they can be transported over significant distances from the source location (Cohen, 1996). Considerable quantities of VOCs are produced in industrialized nations, they are contained in many manufactured products, including paints, adhesives, gasoline,

* Corresponding author. E-mail address: [email protected] (P. Toscano). 0269-7491/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2011.02.013

plastics and many of them are mobile, persistent and toxic. In the atmosphere, many VOCs can have a relatively short half-life of a few hours due to degradation, whereas in other media they can be very persistent and display little degradation over a period of years (Squillace et al., 1999). In most urban areas, VOCs can contribute substantially to the total cancer risk associated with toxic air pollutants (Cohen, 1996; Kampa and Castanas, 2008). The potential health risks associated with exposure to VOCs and their role in the formation of photochemical smog have led to increasing public concern about the presence of VOCs in the environment (Dunovant et al., 1986; Loehr and Ward, 1987; Govind et al., 1991; FinlaysonPitts and Pitts, 2000; Molina and Molina, 2002; Volkamer et al., 2005). Several methods are available for the sampling of VOCs in the air. The most frequently used are adsorption techniques (Pompe et al., 2000; Helmig and Vierling, 1995; Zielinska et al., 1996). The trapped VOCs are subsequently thermally desorbed and analyzed by GCeFID or GCeMS. These measurements are highly sensitive and give very detailed information on the atmospheric composition (Taipale et al., 2008). Another technique for online measurement of atmospheric volume mixing ratios of VOCs is based on Proton Transfer Reaction Mass Spectrometry (PTR-MS) (Lindinger et al., 1998). In the PTR-MS instrument, ambient air is continuously pumped through a drift tube reactor and the VOCs in the sample are

P. Toscano et al. / Environmental Pollution 159 (2011) 1174e1182

ionized in proton transfer reactions with hydronium ions (H3Oþ) (Taipale et al., 2008). Measurements of VOCs in the earth’s atmosphere using PTR-MS have recently been reviewed by De Gouw and Warneke (2007) and various approaches have been reported to improve the selectivity by combining the PTR-ionization with more powerful separation techniques. The GCePTR-MS approach, i.e., coupling a chromatographic column to a PTR-MS instrument (Fall et al., 2001; Karl et al., 2001; Warneke et al., 2003) allows for greater selectivity than PTR-MS alone, but sample pretreatment steps are necessary and the sampling frequency is strongly reduced to only one per 30 min. Recently, several groups have reported the coupling of a chemical ionization unit to a time of flight (TOF) mass spectrometer (Blake et al., 2004; Ennis et al., 2005; Tanimoto et al., 2007; Wyche et al., 2007) increasing mass resolution (Grauss et al., 2010). All such techniques require instruments located in a controlled environment, with continuous on-site monitoring or mobile monitoring that is expensive and difficult to calibrate (Tumbiolo et al., 2005). By exposing a media to air that is capable of containing or adsorbing certain types of molecules, and then analyzing the media in the Lab with one of the above-cited techniques, it becomes possible to monitor in a wide range of conditions. Examples are canisters, flasks and SPME. Solid Phase Micro Extraction (SPME) is a sample preparation technique used both on-site and in the laboratory based on a simple and inexpensive technique that can be thought of as a very short gas chromatography column turned inside out. SPME involves the use of a fibre coated with an extracting phase, that can be a liquid (polymer) or a solid (sorbent), which extracts different kinds of analytes (including both volatile and non-volatile) from different kinds of media that can be in liquid or gas phase. After extraction, the SPME fibre is transferred to the injection port of separating instruments, such as a Gas Chromatography (GC), where desorption of the analyte takes place and analysis is carried out. The attraction of SPME is that the extraction is fast and simple and can be done without solvents, and detection limits can reach parts per trillion levels for certain compounds. SPME has been widely used for many years in various applications, such as environmental and water samples, food and fragrance analysis, or biological fluids (Khaled and Pawliszyn, 2000; Isetun et al., 2004; Martos and Pawliszyn, 1999; Pacenti et al., 2010). By measuring pollutants concentrations in the atmosphere with one of the above-cited techniques, the level of exposure can be assessed, but it is not generally possible to estimate the location and strength of source areas (Kume et al., 2008), due to turbulent transport and mixing. On the other hand, when air and turbulence properties are sampled at an appropriate temporal and spatial resolution, this combined information can be used to investigate and resolve atmospheric transport and diffusion processes. Aircraft measurements, simultaneously providing the measurement of the wind vector and the compound of interest, have been used to estimate the CO2 source strength of relatively large industrial areas by applying mass budgeting techniques (Alfieri et al., 2010). Such techniques are particularly suitable for diffuse emissions over large areas, while in the presence of strong emissions from industrial plants, behaving like point sources, the strength of the source can be estimated by measuring both turbulent transport and concentration inside the plume downwind of the emission point and applying inverse dispersion modelling techniques (Lushi and Stockie, 2010; Flesch et al., 2005). While continuous measurement of gases like CO2 or pollutants like NOx and O3 is now relatively easy, the determination of VOC compounds remains much more difficult. When dealing with aircraft observations, in-flight real time determination of VOCs is an

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even a bigger challenge, due to installation and operational constraints. Only a few applications exist implementing a PTR-MS system on aircraft (Hansel et al., 1999; Murphy et al., 2010), but they are confined to the use of large and expensive aircraft facilities. The SPME method can instead provide a simple and easily deployable solution. The aim of this study is to use the SPME method to measure VOCs, directly coupled with atmospheric measurements taken on a small aircraft environmental platform, to evaluate and locate the presence of VOC emission sources on the ground in industrial areas. The aircraft is the Sky Arrow ERA, originally equipped to measure wind speed and direction at high frequency, CO2 and H2O concentrations, temperature, and other variables, and capable of flying at low altitude and reduced ground speed (Gioli et al., 2004). Our experimental flight strategy was to fly repeated transects downwind of an intensive industrial area, at different altitudes and over different horizontal tracks. During these transects fibres were exposed to high CO2 concentrations (i.e., within the plume of an emission area), given that CO2 patterns were available in real time during flights. A Gaussian dispersion model was then run on the flight spatial domain with an unknown emission source strength, and an optimization algorithm was adopted to potentially derive both location and strength of emission sources. The challenges of this method are related to the nature of the SPME technique, which is conditional sampling, i.e., fibres are exposed to air for given time intervals during the flight, in a noncontinuous mode, and to the spatial resolution of the measurements, since the aircraft covers distances of approximately 3.7e4.4 km and the fibres are exposed for 120 s during the flight, so the measurements result as spatially integrated, limiting the detection capability of the method. 2. Materials and methods 2.1. Sky Arrow ERA platform Aircraft measurements were made by a Sky Arrow ERA (Fig. 6), equipped with a Mobile Flux Platform (MFP) which consists of a set of sensors for atmospheric measurements. The platform is described in detail in other works (Dumas et al., 2001; Gioli et al., 2006). In brief, the aircraft uses the Best Atmospheric Turbulence (BAT) probe to measure the velocity of air with respect to the aircraft using a hemispheric nine-hole pressure sphere that records static and dynamic pressures by means of four differential pressure transducers (Crawford and Dobosy, 1992). The actual wind components (U, V and W) relative to the ground are then calculated introducing corrections for three-dimensional velocity, pitch, roll and heading of the aircraft, which are obtained using a combination of GPS velocity measurements and data from two sets of three orthogonal accelerometers mounted at the centre of gravity of the aircraft and in the centre of the pressure sphere. Atmospheric turbulence is measured at a frequency of 50 Hz, which corresponds, given a mean aircraft velocity of 40 m s1, to a spatial resolution of approximately 0.7 m. Atmospheric densities of CO2 and H2O are also sampled and recorded at 50 Hz by a LiCor 7500 (LiCor, Lincoln, NE, USA) open path infrared gas analyzer. Turbulence statistics and covariances are computed using averages calculated over space rather than over time (Gioli et al., 2004). 2.2. SPME sampling and Fast GC/MS analysis Analytical methods using SPME for air sampling, in no-equilibrium conditions, have been developed and validated in recent years to measure different categories of compounds using different types of fibres. Martos and Pawliszyn (1998), used SPME to measure the levels of formaldehyde in air. Koziel et al. (2000), Koziel and Pawliszyn (2001), Koziel et al. (2001) and Kolár et al. (2004) developed SPME methodologies to measure VOCs and PAHs. Similarly, we have applied these analytical SPME methods for the determination of air concentrations of VOCs with sampling periods of less than 15 min. Such “rapid SPME” methodology applied to field atmospheric sampling, is described in detail in (Pozzi et al., 2006; Pieraccini et al., 2002; Pacenti et al., 2009). For the determination of aldehydes we used 65 mm polydimethylsiloxane/divinylbenzene (PDMSeDVB) fibres, with O-(2,3,4,5,6, pentafluorobenzyl)-hydroxylamine as derivatizing agent; for VOCs, we used 85 mm PDMS-carboxen (CAR) (gases and low molecular weight (MW) compounds 30e225), 100 mm PDMS (VOCs, MW 60e275); and 7 mm PDMS (non-polar, MW 125e600). Such combination of fibres (Table 1) was chosen to

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Table 1 Sampling conditions during flight paths: flight altitude, air temperature and carbon dioxide values are averages along the flight path. For each fibre exposed time, compound detected and concentrations are reported. 

N Flightetrack

Area

Flight altitude (m)

Air temp. (K)

Carbon dioxide (ppm) Average

Max

F1R1

Marghera

162

285.65

376.43

457.57

F1R2

Marghera

316

285.78

371.61

392.02

F1R3

Paciova

169

287.44

370.23

374.75

F2R1

Marghera

158

288.72

375.95

471.37

F2R2

Marghera

312

287.95

368.70

467.22

F3R3

Paciova

159

290.11

367.19

373.66

maximize the capability to monitor a wide range of air pollutants. All fibres were exposed to air flow for a period of 120 s during flight. Exposure position is located below the aircraft, at a distance of 6 cm from aircraft body (Fig. 6). Full automation of the sampling and analysis procedure was achieved using an SPME Automatic Sampler (SAS) developed by Chromline (Prato, Italy) and Teckna (Florence, Italy), which allows fibre exposure with a programmed time. Fibres are hosted in Fast Fit Assemblies (FFA, Supelco, SigmaeAldrich, Milan, Italy), and a Gerstel MultiPurpose Sampler (SRA Instruments, Milan, Italy) equipped with 32position tray Multi Fibre Exchange (MFX) allows interfacing with GC. FFA is the device that allows SPME to be used within fully automated processes and makes the fibre much more robust and identifiable by its barcode. Using FFA it is possible to analyze and replace SPME fibres in automatic mode by MFX fibres are transported from the 32-position tray to the injector trough an SPME holder equipped with a plunger/magnetic system; at the end of the analysis desorbed fibre is moved back to the tray and the cycle is repeated for a different position. The analysis cycle completes by conditioning and sealing fibres in the Teflon septum inserted in the tray. Fast GC/MS analyses were performed on a Shimadzu GC 2010 with the system acquisition GC Solution software 2.5 SU3 version, using an SLB5-MS customizer column (5 m  0.10 mm  0.4 mm film thickness, Supelco, SigmaeAldrich) with a Shimadzu QP 2010 series mass selective detector (Shimadzu Italia, Milan, Italy) operating in the EI-SCAN mode. Oven settings were 45  C held for 1 min, with a ramp of 150  C/min up to 100  C and 50  C/min up to 320  C. Inlet pressure and average linear velocity were 344 kPa and 100 cm/s, respectively. The injector (320  C) was initially set in splitless mode for 1 min and then switched to 20:1 split mode. The MS detector was a Shimadzu QP 2010 series with the system acquisition GC Solution software 2.5 SU3 version operating in the electron ionization (EI), methane negative chemical ionization (NCI), and methane positive chemical ionization (PCI) mode. Spectra were collect at 6 s and set to 1000 V. The MS detector was auto-timed weekly. MS analysis was operated by the new fast automated scan/ selective ion monitoring (SIM) technique (FASST) acquisition mode with a scan range of m/z 40e550. 2.3. SPME calibration and SETUP A calibration experiment, aimed at investigating the influence of air velocity on measured quantities, was made with the actual SPME setup, using a syringe-pump and permeation tubes (Gorlo et al., 1997; Martos and Pawliszyn, 1997) for organic toxic air contaminants proposed by Office of Environmental Health Hazard Assessment (OEHHA) as Acute Reference Exposure Levels (ARELs). Briefly, the solutions evaporated were carried through a current of nitrogen and blended with a flow of nitrogen (2.5e40 l/min), previously moistened, in a glass thermostat exposure chamber and provided with plugs for the introduction of SPME fibres. The influence of the air velocity on the SPME system was verified using a glass cylinder connected to the exposure chamber. The glass cylinder, with four different

N* fibres

Fibres exposed

Time (UTC)

Compound detected and concentration

2 1 1 1 1 1 1 1 2 2 2 1 2 1 1 1 1 1 1 1 2 2 2 1

7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS 7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS 7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS 7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS 7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS 7 mm PDMS 65 mm PDMS/DVB 85 mm PDMS/CAR 100 mm PDMS

9:08:26 j 3:08:26 j 9:08:26 j 3:08:26 j 9:47:46 j 9:47:46 j 9:47:46 j 9:47:46 j 10:22:25 10:22:25 10:22:25 10:22:25 12:47:50 12:47:50 12:47:50 12:47:50 13:27:00 13:27:00 13:27:00 13:27:00 14:05:10 14:05:10 14:05:10 14:05:10

e e e e e e e GBL 0.6 mg/mc e e e e e e e GBL 80 mg/mc e e e e e e e e

9:10:28 9:10:26 9:10:26 9:10:26 9:49:46 9:49:46 9:49:46 9:49:46 j 10:24:25 j 10:24:25 j 10:24:25 j 10:24:25 j 12:49:50 j 12:49:50 j 12:49:50 j 12:49:50 j 13:29:00 j 13:29:00 j 13:29:00 j 13:29:00 j 14:07:10 j 14:07:10 j 14:07:10 j 14:07:10

internal diameters, each provided with a plug for the introduction of the fibre, allows 4e200 cm/s air speed to be obtained, dividing the air flow rate by the crosssectional area of each stage of the sampling cylinder. There was no significant difference found between the sampling rate experimental values at 4 cm/s compared with 200 cm/s. The detection limits for all toxic air contaminants were lower than 50% with respect to ARELs, for 120 s sampling. The resulting calibration curves were linear, in the investigated range for all the considered VOCs, with correlation coefficients >0.998. The precision of the assay (reported as a coefficient of variation, C.V.%), estimated both as within session and as intersession repeatability resulted in the range 9.73e13.58% and 11.97e14.21%, respectively. Accuracy was within 15% of the theoretical concentration.

2.4. Study area The industrial study site is located in Marghera, 5 km NW of the historical centre of Venice (Italy), between the urban mainland (Mestre, Marghera and Malcontenta) and the coastal lagoon. It spans an area of 2000 ha: 1400 ha for industries; 340 ha of water channels; 120 ha for the commercial harbour; 80 ha for roads and railway; 60 ha are State-owned land. The main activities are: coke-derived production, petrochemical production, refining, aluminium and semi-finished material production, shipyards, chemicals, fertilizer production, waste and wastewater treatment, coastal oil storage, and energy production. The local topography and climate of the Po Valley, as well as natural and anthropogenic emissions, make this one of the most polluted areas in Europe (Climate, 03/2009 EvK2cnr). The Veneto region covers the eastern part of the Po Valley and is bounded by the Alps to the north and by the Adriatic Sea on the east. The air quality in the Venice Lagoon is threatened by the presence of Porto Marghera, one of the most industrialized areas in Italy and a busy commercial port. The presence of high concentrations of dioxins and other carcinogenic compounds have been reported here, mainly originating from the Enichem petrochemical plant and other industries (Greenpeace Press, 3 May 1995). This study was conducted over a period of one day (13 March 2007) over an area of 4040 km between Padova (725080 E 5032400 N UTM32) and Marghera (266860 E 5034410 N UTM 33), during which the aircraft made two flights for a total of 5 h of measurements. Flights were planned to sample urban and industrial areas (Fig. 1), focussing on Marghera. The airborne measurements consist of three vertical profiles per flight collected near the coastline (B, C) and inland (A) (Fig. 1), to characterize the atmospheric boundary conditions near the sea and over the land in order to select horizontal transects for SPME exposures.

2.5. Gaussian modelling framework A Gaussian Dispersion Plume Model (Arya, 1999) was implemented to analyze the local transport and dispersion of compounds downwind of an emission area. The

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Fig. 1. Land cover map of the study area in the North-Eastern Italy (Veneto Region, grey urban area, black industrial area) with location of cities (white circle and black dot), aircraft tracks (line) and vertical profiles (A, B, C).

general relationship between the source strength S, and the perturbation in concentration, (ci  ci,b), of air passing over the source is

3. Results and discussion

ci  ci;b ¼ JA Di ðxÞSðxÞdðxÞ

Weather conditions for the measurement period showed a high pressure system centred over North-western Europe in EAWR regime (East Atlantic/West Russia high pressure pattern), with higher than average stability conditions and temperature for the season. Vertical profiles were measured by the Sky Arrow aircraft between 100 m and 1200 m in the morning flight and between 100 m and 2000 m in the afternoon flight. Fig. 2 shows profiles of potential temperature, relative humidity and wind speed and direction. Morning profiles flown (Fig. 2a) on point C (Marghera) at 09:25 UTC indicate a homogeneous low regime wind field, while the temperature profile shows a stratified boundary layer structure, with a well-mixed layer extending up to 420 m, and a residual mixed layer up to 750 m, topped by free troposphere. Humidity profiles confirm this complex stratified structure, typical of early PBL development stages. In the afternoon (Fig. 2b), the profile was measured at 13:06 UTC on the same point, showing a sea breeze development from the east-south-east, confined beneath 600e800 m. Mixed layer depth can be clearly derived from both potential temperature and wind profiles, and is equal to 1050 m; a transition entrainment zone extends up to 1200 m before the free troposphere. The relative humidity profile shows, as in the morning, the presence of different layers: a jump is present at 800 m, so within the mixed layer, possibly still related to the presence of the residual layer observed in the morning. These observations justify, overall, the adopted measurement strategy: SPME samples were in fact collected at about 160 m above ground level (AGL) and about 320 m AGL, thus in both flights, samples were taken well within the mixed layer, also excluding the residual layers. Along the flight paths above the Marghera area, CO2 concentration showed a peak during the morning flight at about 450 ppm, while in the afternoon flight more peaks were detected, up to 600 ppm, over diffuse source (Fig. 4).

where integration is over the source area A and subscript b is for background concentrations upwind of the source. Atmospheric dispersion is represented by Di and is proportional to the probability of a particle released at position x reaching position i, where the concentration is measured in the downwind plume. Gaussian plume models are widely used in atmospheric pollution studies to evaluate the Di term. The accuracy of such steady-state models depends on stability classification schemes as well as plume rise equations. A general plume dispersion model (GPDM) for a point source emission, based on the Gaussian plume dispersion equation, was adopted here in order to estimate the emitter location of pollutant compounds as precisely as possible. It has the flexibility of using five kinds of stability classification schemes, i.e., Lapse Rate, PasquilleGifford (PG), Turner, seq and Richardson number. It also has the option of using two types of plume rise formulations e Briggs’ and Holland’s. The model, applicable for both rural and urban roughness conditions, uses meteorological and emission data as its input parameters, and calculates pollutant concentrations at the centre of each cell in a predefined grid area with respect to the given source location (http://www.mathworks.com/matlabcentral/fileexchange). The output of model run is a 3-D matrix containing the concentrations of the emitted substance over a field with the first dimension (y) representing the crosswind axis, the second dimension (x) the downwind distance, and the third dimension (z) the vertical axis, with the origin set at the base of the stack. The model simulates transport and turbulent diffusion of a species s within a well-mixed PBL based on input information on wind speed and direction, atmospheric stability, surface roughness, and s source flow rate. Wind speed and direction measured at Venezia Tessera Airport Weather Station during flights were used as inputs; atmospheric stability classes were assigned equal to 2 (moderately unstable), based on overall synoptic conditions, on the presence of a growing well-mixed layer, and strong irradiance in the absence of cloud cover; surface roughness was assumed as “urban”, as from the model classification. A geographical grid was adopted with spatial resolution of 500  500 m (xy) and the model was run in iterative mode assigning the source location to each grid node, and evaluating the matches between observed concentration (Cs) and modelled concentration (Cm) derived by the interception of aircraft track and 3-D plume fields. Error (e) between concentration measurements (Cm) and concentrations simulated (Cs) was calculated for each grid node (90 nodes). Cm  Cs e ¼ Cm i;j

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Fig. 2. Profiles of potential temperature, relative humidity and wind speed and direction. Morning profiles (a) flown on point C (Marghera) at 09:25 UTC indicate a homogeneus low regime wind field, while temperature profile shows a stratified boundary layer structure, with a well-mixed layer extending up to 420 m (dots line), and a residual mixed layer up to 750 m, topped by free troposphere. Humidity profiles confirm this complex stratified structure, typical of early PBL development stages. In the afternoon (b) profile was measured at 13:06 UTC on the same point, showing a sea breeze development from the east-south-east, confined below 600e800 m. Mixed layer depth can be clearly derived both from potential temperature and wind profiles, and is equal to 1050 m; a transition entrainment zone extends up to 1200 m before the free troposphere. Relative humidity profile shows, as in the morning, the presence of different layers: a jump is present at 800 m, so within the mixed layer, possibly still related to the presence of the residual layer observed in the morning.

These CO2 patterns were available in real time during flights, and provided information that has been used to drive the SPME sampling: fibres were in fact exposed in correspondence to high CO2 concentrations above Marghera area (F1R1, F1R2, F2R1 and F2R2), i.e., within the plume of an emission area and above Padova area (F1R3, F2R3). Of course this strategy has some limitations, because CO2 sources are not necessarily the same as VOC sources, and since in any case there is some delay between the CO2 increase detection and fibre exposure. Nevertheless, since SPME is a conditional sampling and not a continuous sampling technique, it is worth testing such a measurement approach, which is aimed at exposing fibres near areas that could be candidate VOC emitters. CO2 concentration during SPME exposure windows of 120 s is reported in Fig. 4. Overall, the match between exposure times and presence of high CO2 (i.e., inside plume conditions) is acceptable, limited by a delay of around 15e20 s between exposure start and initial high concentrations. Considering also the sampling technique, at the moment, the only way to reduce delays would be to introduce an automatic trigger, so that in the presence of CO2 concentrations over a certain threshold fibres will automatically be exposed. Overall, the adopted protocol ensured that sampling conditions were mostly associated to the presence of industrial plumes that we wanted to monitor, even though we cannot rule out that additional interesting industrial areas, with no significant CO2 emission, were present and not sampled. Lab analysis of collected SPME fibres (Table 1), performed within 24 h after the flight, was compared with OHHEA ARELs. All concentrations obtained were lower than the limit values indicated by OEHHA (2008), except for g-butyrolactone (GBL, CAS n. 96-4800). Since not all compounds of interest have a standard reference available, it was also necessary to specifically synthesize the GBL

compound, which was found in considerable amounts in the atmosphere during this study. GBL was synthesized in accordance with the procedure reported in the literature (Nwaukwa and Keehn, 1982). GBL, is a colourless liquid, hygroscopic, water soluble and with a weak characteristic smell. It is a solvent or reagent usually used for industrial production of plastics, pesticides, herbicides, pharmaceutical compounds, aromatic compounds, polyurethanes and pyrrolidone derivatives. It is also used for varnish production, and can also derive from the decomposition of higher lactones, which are used in various industrial sectors. GBL has adverse effects on human health, is a strong irritant of the skin and respiratory tract. Its use has consequently been regulated and monitored in specific industrial processes. GBL concentrations were found to be above zero in two samples, flight F1R2 fibre 100 mm PDMS and flight F2R2 fibre 100 mm PDMS (Table 1), being equal to 0.6 and 80 mg/m3 respectively. Details on exposure times, CO2 and GBL concentration, and sampling conditions are reported in Table 1 and Fig. 3. Whereas the only value above the threshold has been sampled over the industrial area of Marghera, we have narrowed the area of analysis for the four flights on Marghera and analyzing the four flight paths, the detected presence of GBL in two of them, and the wind fields during measurements (Fig. 3), it is possible to speculate on where the emission source is located. During the morning flight, wind was from NeNE, and GBL was detected during Flight F1R2 at 320 m, suggesting the source possibly being located between the two flight paths exposure sampling (Fig. 3). During the afternoon flight, wind was blowing from ESE, and a much larger amount of GBL was detected during Flight F2R2 at 160 m, suggesting again the same source area location (Fig. 3). Such a speculative analysis, however, is not sufficient to provide a robust estimate of where the surface

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Fig. 3. CO2 concentration trend for each flight path during black box event (Fig. 4) above Marghera Area. Flights paths (black line), wind intensity and direction (yellow arrow) and CO2 concentration during SPME sampling are showed. Flight F1R1 without detect GBL (flight altitude about 160 m, length 3.7 km) as Flight F2R2 (flight altitude about 320 m, length 4.4 km); Flight F1R2 where has detected GBL (flight altitude about 320 m, length 4.4 km) as Flight F2R1 (flight altitude about 160 m, length 4.4 km). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

emission is located, since this location depends on wind direction, wind speed, PBL structure, surface roughness, and atmospheric stability, all these affecting transport and turbulent diffusion. The results of the modelling framework, adopted for GBL compound, are shown in Fig. 5, where the flight paths grid model

(UTM 33), the contour of error (%) and highlights the node (source position) for which the value of error (e) is minimum are plotted. The source location estimated with the Gaussian model falls within a heavily industrialized area, where a refinery (ENI RM e ex AGIP), a processed oil storage and a company that produces acrylic and

Fig. 4. Exposition time interval for SPME sampling (black box), CO2 sampling along the track line.

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Fig. 5. Flight paths grid model (UTM 33) are showed (dot for Flight F1R1, circle for Flight F1R2, square for Flight F2R1, cross for Flight F2R2) with the contour of error (%) between concentration measurements (Cm) and concentrations simulated (Cs). Source positions are highlighted with dark blue, for which is minimum the error matching concentration measurements concentrations simulated and wind fields. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. The Sky Arrow Era platform showing the location of SPME Automatic Sampler (SAS) mounted on board. A) Fast Fit Assembled (FFA)-SPME fibres, B) 32-position tray, C) sealing PTFE septum, D) mounting plate to the fuselage. The pressure sphere, Novatel GPS and LI-7500 Open Path CO2/H2O analyzer of the MFP system are visible on the aircraft front probe.

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polyester fibres for a range of textile and technical applications (Montefibre SpA) (http://simage.arpa.veneto.it) are located. Although it was not possible to directly validate our results by comparing them with real emission data from industries in this area, which are mostly not available to the public, overall the presence of several industrial sites in a very limited area provides indirect proof that the method was successful in identifying the presence of a significant VOC source to the atmosphere, with this choice of flight tracks and exposure times, and with the atmospheric conditions encountered. The exposure time that was adopted in this study (120 s), is a compromise between two contrasting requirements: enlarging the exposure time would lead to lower detection limits for all compounds, since more compounds will be present on SPME fibres, thus enhancing the capability of detecting pollutants, but on the other hand would decrease the spatial resolution by increasing distance covered by aircraft during sampling, making challenging the association of measured pollutants with ground facilities. As improved GC will be available on the market, capable of higher measurement resolution and lower detection limits, our setup will benefit by applying lower exposure times (maintaining actual detection limits) thus enhancing SPME measurements spatial resolution and representativeness. 4. Conclusions This paper presents the first reported instance of sampling and high-throughput automated analysis of airborne pollutants using the SAS/MFX Fast GCeMS robotic system, able to process great quantities of samples in a very short time, so reducing the costs of the monitoring campaigns. The attained sensitivity permits the evaluation of airborne concentrations with extremely reduced sampling periods, producing an instantaneous measurement of air pollution levels. An additional advantage of SPME that is worth noting, is that, in comparison to the other methods, it has reduced analytical costs and is easily applicable. Additionally, the absence of any solvents in the analytical procedure reduces health risks and environmental contamination. The use of an aerial platform for pollution monitoring and sampling for environmental and health protection could be a new tool to support monitoring networks on the ground, and also to provide information at regional scale. Combined monitoring and modelling activities could also reduce costs and allow for future projections of potential impacts. The results of the application described in this paper highlight a wide range of potential research applications for the Sky Arrow ERA and SPME sampling. Together with the modelling framework, this study demonstrates that airborne measured fluxes, direct measurement of wind speed and direction and atmospheric turbulence and SPME sampling are excellent tools capable of discriminating VOCs in high-risk industrial areas. Limitations of the proposed methodology are due to the sampling environment, because outdoor SPME measurements are affected by a wide range of wind, sunlight, temperature, and humidity conditions even considering that dynamic samplings are done by fibre inside a needle. Moreover the sampling strategy adopted has resulted in spatially integrated measurements and the sampling conditions were mostly associated to the presence of industrial plumes that we wanted to monitor, even if we cannot rule out that other interesting industrial areas, with no significant CO2 emissions, were present and not sampled. Our setup is aimed at localizing emission sources on the ground, but this method can be applied for other purposes using larger fibre exposure times: this would increase our capability to detect pollutants, and decrease measurements spatial resolution, thus could be successfully used for large areas pollution

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