First detection of industrial hydrogen emissions using high-precision mobile measurements in ambient air

First detection of industrial hydrogen emissions using high-precision mobile measurements in ambient air | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article First detection of industrial hydrogen emissions using high-precision mobile measurements in ambient air Iris M. Westra, Hubertus A. Scheeren, Firmin T. Stroo, Steven M.A.C. van Heuven, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4618373/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Projections towards 2050 of the global hydrogen (H 2 ) demand indicate an eight-fold increase in present-day hydrogen consumption. Leakage during production, transport, and consumption therefore presents a large potential for increases in the atmospheric hydrogen burden. Although not a greenhouse gas itself, hydrogen has indirect climate effects: through oxidation with the OH radical in the atmosphere the lifetime of methane increases, tropospheric ozone is produced, and the concentration of stratospheric water vapour increases. The Global Warming Potential of H 2 is estimated to be 12.8 times that of CO 2 . Available technologies to detect hydrogen emissions have been limited to risk assessments of industrial facilities, while smaller climate-relevant emissions remain undetected. The latter requires measurement capacity at the parts-per-billion level (ppb). We developed and tested a simple and effective method to detect small hydrogen emissions from industrial installations combining active AirCore sampling with ppb-precision analysis by gas chromatography. We applied our methodology at a chemistry park in the Groningen province, the Netherlands, where several hydrogen production and storage facilities are concentrated. From a car and an unmanned aerial vehicle, we detected for the first time small but consistent industrial emissions from leakage and purging across the hydrogen value chain, which include electrolysers, a hydrogen fuelling station, and chemical production plants. Our emission estimates indicate current loss rates between 1-5% of the estimated production and storage in these facilities. This is sufficiently large to urgently flag the need for monitoring and verification of H 2 emissions for the purpose of understanding our climate change trajectory in the 21 st century. Earth and environmental sciences/Environmental sciences/Environmental impact Physical sciences/Energy science and technology/Renewable energy/Hydrogen energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction To achieve a zero or low-carbon energy economy, an energy carrier capable of zero emissions of air pollutants and greenhouse gases is needed. Molecular hydrogen (H 2 ) emerges as a promising contender for this role in this energy transition 13,31 . Initiatives such as the U.S. National Clean Hydrogen Strategy and Roadmap, Germany’s ‘Energiewende’, and the hydrogen roadmap of the Netherlands (‘Nationaal Waterstof Programma’) alongside numerous other programs, underscore countries' ambitions towards a hydrogen value chain 9,20,24,39 . However, due to hydrogen’s pivotal role in the energy transition, the expected increasing release of anthropogenic H 2 emissions into the atmosphere can result in enhanced global warming from indirect effects. Increased levels of atmospheric H 2 can result in the lengthening of the lifetime of CH 4 and ozone, and higher levels of stratospheric water vapour 4,12,13,27,32,38,42,44 . Adding up to a global warming potential of 12.8 ± 5.2 over 100 years and a perturbation lifetime of 1.9 ± 0.5 years in the atmosphere, H 2 surpasses carbon dioxide (CO 2 ) in terms of greenhouse gas potency 12,14,25,40,41 . The current estimates of the loss rate potential (including venting, purging and uncontrolled leakage) of anthropogenic H 2 emissions, solely based on models, range from 1–10% of the total production 13,35 . So far, however, these estimates have not been validated at all by actual measurements, due to the lack of appropriate measurement techniques. Currently, H 2 detectors utilised in industry are used for safety purposes only. Since the flammability range of H 2 is at 4% volume, handheld detectors with a detection limit starting at 30 µmol mol − 1 or ppm up to 10% volume are used. However, since the atmospheric background concentration (mole fraction) of molecular hydrogen is ~ 0.5 ppm, anthropogenic H 2 from leakages with no flammability risk but a potential impact on the climate remains undetected. Precise atmospheric H 2 measurements within the scientific world started in 1957 with the introduction of the principle of liquefaction of air 11 , followed in the 1970s 33 with a gas chromatographic (GC) method, designed to analyse molecular hydrogen in atmospheric air based on the reduction of mercuric oxide. In 1994, Wentworth et al. 43 designed the pulsed discharge helium ionisation detector (PDHID), for use in a widespread range of applications outside atmospheric science. In 2009, Novelli et al. 23 adopted this method on a GC-system to measure molecular hydrogen in the atmosphere. The GC-PDHID technique showed a stable performance with a linear response over the 0-2000 nmol mol − 1 or ppb range (AGAGE 30 , CSIRO 8 , NOAA 28,29 ). The combination of this lab-based-measurement system, and active AirCore sampling on mobile platforms, is the novel sampling technique designed and tested in this study. The active AirCore is a long thin tube that can preserve the profile of the trace gas of interest during sampling, storage and analysis with minimum diffusive mixing 1,17 . The active AirCore was first designed and used for applications focused on CH 4 from the energy (e.g. coal mines) and the agricultural sector (e.g. farms). In our study, the application of the active AirCore sampling technique is broadened to also include the sampling and analysis of atmospheric H 2 . While the energy transition unrolls, further insights into the hydrogen value chain (production, transport, storage, end-use applications) and the potential risks of H 2 leakage are of great importance 9,19,21,42 . Historically, studies like EUROHYDROS 44 , Harvard Forest (1996–1998 3 , Mace head 1994–1998 34 , focused on the natural hydrogen budget through short-term campaigns. Long-established international networks (AGE-AGAGE, NOAA, more recently ICOS 29,30 ) have been measuring atmospheric H 2 in an accurate and systematic way, but their stations are mostly remote. Until now field campaigns specifically focused on regional and local anthropogenic H 2 emission sources originating from the hydrogen value chain have been absent. In Sun et al. (2024) 35 it is rightly pointed out that: “It is important to note that the rates of hydrogen emissions are currently unknown across the value chain. Empirical measurements are needed to improve our understanding of where emissions are coming from and in what quantities.”. Consequently, to bridge the gap between model predictions and reality, our study offers innovative and versatile sampling techniques combined with a state-of-the-art high-precision hydrogen analysis system to provide empirical data from atmospheric H 2 mole fractions originating from industrial activities. Our study is the first -to our knowledge- that provides such empirical measurements from atmospheric H 2 mole fractions originating from industrial activities. The proof of concept for this study entails detailed measurements of atmospheric H 2 using the active AirCore sampling technique at an industrial site in the province of Groningen (the Netherlands). We first outline our analysis and sampling techniques, after which we discuss the measurement site and necessary a priori information. We then present our observations from two mobile platforms (car and unmanned aerial vehicle (UAV)) before quantifying the emissions from the downwind sources. We use both a mass balance approach and an inverse Gaussian Plume model with multiple source configurations, and we discuss their respective uncertainties. We finish the paper with conclusions and a future outlook for our novel methodology. 2. Methods 2.1 Sampling methods 2.1.1 Active AirCore The AirCore is an atmospheric sampling system that consists of a thin-wall stainless-steel (S.S.) tubing in the shape of a coil with a passivated inner surface, invented and patented by Pieter Tans 17 . The original design is used to obtain a vertical atmospheric profile by filling itself using the air pressure gradient in the atmosphere. Our “active” AirCore (length 245–285 m, 3 / 16 " OD) collects air samples via the use of a micro-pump (KNF NMP015 KPDC-B 6V) and a mass flow controller (Bronkhorst IQFlow-200C-AAD-11-V-S) 2, 36 . Using this technique, the AirCore is filled, through a chemical dryer using magnesium perchlorate located at the inlet of the system, with a pressurised and dried profile of the trace gas of interest along a given measurement trajectory 2,36,37 . For our experiments, a similar AirCore as designed and described by Tong et al. (2023) was used, and for more details the reader is referred to this paper. The active AirCore was used on two mobile platforms: driven with a passenger car and flown with a UAV. For the passenger car, we used an active AirCore with a sample volume of ~ 4.1 L. This AirCore is filled to an end-pressure of up to 1.6 bar over the course of about 2 hours of sampling, resulting in up to 38 useful discrete H 2 samples for the GC-PDHID (described in detail in section 2.2 ). The sampling flow rate was constant and was set to either 45 or 60 ml min − 1 , depending on the desired duration of the sampling. For the active AirCore applied on the UAV the sample volume was 3.7 L, and it was filled with a flow of 200 ml min − 1 at atmospheric pressure, allowing for up to 21 useful discrete H 2 samples over the course of about 15 minutes of flying time. Prior to each field campaign, both AirCores were filled with synthetic air (UN1956; 20.5 Vol. % O 2 , rest N 2 ), to clean the coil and identify a clear starting point of zero mole fraction H 2 during the analysis. 2.1.2 Flasks samples As a complementary method to validate the AirCore H 2 measurements, vacuumized and dried 2.4 L glass flasks (with two Louwers Hapert Viton sealed valves) were filled at atmospheric pressure in pairs along the measurement trajectory to collect discrete dry air samples, by applying a magnesium perchlorate dryer tube on the flask inlet. All mole fraction analyses of the glass flasks were conducted by both the GC-PDHID for H 2 and a cavity ringdown spectrometer (CRDS) system (Picarro Inc. CA, model G2401) for mole fractions of CO 2 , CH 4 , and CO, the latter to get additional information on the potential emission sources co-located with H 2 (described in more detail in the Supplementary information section 1 and 2 : Methods & Materials and Results & Discussion) 5,22 . 2.2 Analysis methods 2.2.1 GC-PDHID system For the detection of molecular H 2 in the atmosphere we use an Agilent 8890 Gas Chromatograph (GC) equipped with a pulsed discharge helium ionisation detector (PDHID, Agilent) designed after Novelli et al. (2009). In addition, our GC-system was fitted with a separate analysis line for N 2 O and SF 6 equipped with a micro electron capture detector (µECD, Agilent) of which more details are given in the Supplementary information. For the purpose of this paper, we will focus here on the PDHID analysis used for the detection of H 2 . First, sample air is flushed over a 2 ml sample loop for H 2 (and 5 ml for N 2 O/SF 6 ) at a rate of 52 ± 2 ml min − 1 for 1.37 minutes. As such, the average sample size used for one measurement is 137 ± 5 ml which includes the overshoot of the pressure controller and the dead volume of the tubing and valves (using ¹⁄ 16 ” OD and ⅛” OD S.S. Swagelok) between the sample carrier (flask or AirCore) and the loops (2 ml & 5 ml). Secondly, for 30 s the sample loop is equilibrated from 1.5 bar filling pressure to ambient pressure (the exhaust of the loop is equipped with a 1 m x ¹⁄ 16 ” OD coil to prevent back-diffusion of lab air). Then, the sample is injected onto the first packed pre-column (Agilent S.S. packed column, 4.5 m x ⅛” OD x 2 mm, Hayesep-DB, 100–120 mesh) where H 2 is separated from the air matrix. Right after elution of the H 2 onto the second analytical column (Agilent S.S. packed column, 4.5 m x ⅛” OD x 2 mm, Hayesep-DB, 80–100 mesh), the pre-column is set into backflush mode to prevent oxygen and other contaminants from reaching the analytical column and detector. At 5.3 ± 0.3 minutes the H 2 peak reaches the detector, and it shows a 19.5 ± 1 seconds wide chromatogram. The total measurement time for one sample is 7 minutes. Potential drift is corrected by measuring a reference tank every 3 samples. Our GC-PDHID measures H 2 with a precision < 2 ppb and it is calibrated against a suite of in-house made dry whole-air working standards which themselves are calibrated against 3 primary standards linked to the international NOAA-H2-X1996 hydrogen scale (maintained by the Max Planck Institute for Biogeochemistry (MPI-BGC) Jena, Germany) 15,16 . The absolute accuracy of the primary standards is < 1 ppb. For more details about our GC-setup and measurement procedure, we refer to the Supplementary information (section 1: Methods & Materials). AirCore analysis on the GC-PDHID During analysis, the inlet of the active AirCore is connected to a push gas, while the outlet is connected to the sample inlet of the GC. As a push gas, the same H 2 -free synthetic air as for the prefilling (at 1 bar over-pressure) was used, clearly marking the start and end of the atmospheric sampling sequence. The GC-system uses an electronic pressure controller to regulate the flow. Typically, the push gas is set to the same pressure as the AirCore sample being around 1.6 bar absolute to minimise smearing of the sample. However, in our system, a slight overpressure of 0.4 hPa is required to push the sample through our sample loop. Potential drift is corrected for by utilising a bracketing method, wherein up to 3 samples are bracketed by a known low and high standard for calibration. It should be noted that measurement uncertainty cannot be minimised by repetition or duplication, since by the very nature of the AirCore sampling technique, it is considered a series of unique samples. AirCore sample storage time, sample resolution and positioning The accuracy and precision of the active AirCore samples are dependent on the storage time following the completion of the field campaign. An AirCore sample is typically measured in the lab directly after a field experiment, to keep the storage time, and thus the smearing effects by molecular diffusion, as short as possible. Extensive laboratory storage tests were done to evaluate the profile loss, i.e. the storability of molecular hydrogen in an active AirCore and determine the necessary maximum time for which an accurate retrieval can be guaranteed. Further details regarding these experiments are available in the Supplementary information (section 1: Methods & Materials). Across all sampling days, the median storage time, calculated from the ending of the sampling time, was for the car AirCore 0.75 h while for the UAV it was 1.41 h, before we started our analysis. Given the continuous air sampling in the AirCore but discrete analyses on the GC, a relation needs to be established between the H 2 mole fractions and the path driven or flown. The spatial distribution is primarily influenced by sample size, sampling flow and mobility. With a constant sampling flow, each discrete sample linearly corresponds to a specific time duration and range of GPS coordinates. Smaller sample sizes increase the resolution of the trace gas profile and decrease the spatial distribution. Considering the spatial distribution, the samples are categorised into stationary and mobile, depending on whether the mobile platform (passenger car or UAV) was stationary or in motion. The analysis of the stationary samples is straightforward, the GPS-coordinates directly pinpoint the representative location. The mobile samples require additional interpretation and assumptions. As the mobile platform (passenger car or UAV) moves during sampling, each discrete sample corresponds to a range of GPS-coordinates. Given the filling mode of the sample loop (see 2.2.1), the actual sampled air of the discrete sample corresponds to the final segment of the loop flush. The assigned location is deducted to be at 83 \(\pm\) 10% of the discrete sample’s transect, based on the loss during equilibration and the flushing time, a more extensive explanation is given in the Supplementary information (section 1: Methods & Materials). Flasks analysis The flasks filled in the field were measured on the GC-PDHID for H 2 and on the CRDS system for CO 2 , CO and CH 4 with well-established methods in our lab 22 , more details in the Supplementary information (section 1: Methods & Materials). 2.2.3 Measurement platforms 2.2.3.1 Passenger car For sampling from a passenger car, the sampler is installed on the backseat next to the operator. The inlet of the AirCore is positioned outside of the car's rear window (Fig. 1 ) with the filter facing backwards and downwards to protect the inlet from potential rainfall and impact from insects. The measurement starts when the AirCore is set to sampling mode and the pump flow is set to either 45 or 60 ml min − 1 . A detailed description of the car AirCore is provided in the Supplementary information (section 1: Methods & Materials). The ambient air is dried at the inlet of the AirCore with magnesium perchlorate. The active AirCore in the passenger car is equipped with a GPS tracker and a logger for timestamp, pump pressure, coil pressure, volume sampled, and volume collected in the coil. After the field campaign is completed, typically 2 hours duration from start sampling, the AirCore is transported immediately back to the laboratory for analysis. 2.2.3.2 Unmanned Aerial Vehicle (UAV) The UAV or drone has a carrier capacity of 8 kg, sufficient for our AirCore system with a payload capacity of 6 kg. Considering the sample volume and flow rate, the maximum flight duration is 15 minutes. Although the UAV can ascend vertically up to 500 m, flight restrictions limited the vertical profile to an altitude of maximally 140 m. The UAV AirCore system has a similar sampling method as the passenger car, only simplified, with a manual pump switch and without parameter registration. GPS tracking, flight speed, altitude, and other technical parameters are recorded by the UAV. Figure 2 shows a simple schematic of the aerial AirCore setup, for more details we refer to the Supplementary information (section 1: Methods & Materials). 2.3 Measurement site 2.3.1 Site description Located on the northeast side of Groningen (the Netherlands), the chemistry park Delfzijl (53.3105 N, 6.9752 E) (Fig. 3 ) offers an ideal site for the field campaigns outlined for this study. With the Wadden Sea located north of the park and rural areas extending at least 18 km southward (SLD: Straight-line distance), minimum external influences on the atmospheric H 2 mole fractions are ensured. The chemistry park is a confined piece of land surrounded by a rural environment with large-scale and fully operational chemical processes in which molecular hydrogen is either produced, transported, stored or consumed within the park. With predominantly north-westerly winds, the emissions from the park itself are easily distinguishable downwind of the park, while easterly winds allow for observing background conditions from the surrounding agricultural area. The closest potential polluter outside of the park, is the seaport Eemshaven, 17 km SLD away to the North-West, hosting coal-and gas-fired power plants but so-far no known H 2 emitting processes. Its emissions are potentially only visible (co-emitted with CO 2 and CO) with strong north-western winds. In the WSW at 28 km SLD a potential H 2 emitter is the urban area of the city of Groningen, with e.g. a H 2 fuelling station and H 2 buses. The optimal accessibility to the chemistry park, without the need for permits or registration for all mobile platforms, facilitates the field campaigns. Furthermore, the cooperation with the industry stakeholders strengthens the eventual emission estimates and loss rate predictions, because of the specifications given on the processes and production rates (through personal communication). Considering the sampling equipment, the proximity of Delfzijl to our laboratory ensured the profile accuracy by maintaining a minimum storage time. Even though the primary processes in the chemistry park in Delfzijl are not entirely representative of the complete hydrogen value chain, it is crucial to identify potential H 2 leakages or emissions from real facilities, before a widespread implementation is completed. 2.3.2 Industrial hydrogen production and emission estimates For our purpose, the chemistry park Delfzijl houses four relevant industries, based on a priori information about production and emission processes, to ensure confidentiality the industries in question will be referred to by an arbitrary numbering. At industry N1 (Industry N1 consists of two processes), NaOH, Cl and H 2 are produced from brine via the process of electrolysis (1a). The surplus H 2 is transported over the park to a nearby power plant for combustion (1b) and to industry F3 for direct usage at a hydrogen fuelling station. Any remaining H 2 is directly vented. At industry E2 hydrogen peroxide is produced, for which in-house H 2 is produced from steam-methane reformation. For industries N1 and E2, hydrogen emissions from purging and venting on sampling days are known (personal communication). Since no emission data for F3 are available, a loss rate estimation of 0.5% (~ 0.24% high-pressure storage, ~ 0.25% compressor leakage) is used from the known storage present 9 . Industry R4 encompasses all remaining factories/companies for which no a priori information is available regarding H 2 production, consumption, purging or venting. The daily production is calculated using the average annual production rate for industry N1 and industry E2. Combined with the H 2 consumption of industry F3, the estimated production and emission rates per day are summarised in Table 1 , where only for day 1 the H 2 emissions differ from the rest of the measurement days. A detailed description of the daily production and emission estimates per category is provided in the Supplementary information (section 1: Methods & Materials). Table 1 Production and emission estimates, as provided via personal communication. Date (dd-mm-yy) Production per day (10 7 g) Emission per day (10 3 g) 04-08-23 2.05 ± 0.33 7.3 ± 0.7 (*10 2 ) 06-09-23 to 20-12-23 2.05 ± 0.33 8.3 ± 0.8 2.4 Emission estimate methods and uncertainties 2.4.1 Mass Balance approach We apply a Mass Balance approach to estimate the amount of H 2 emitted in g s − 1 by the Delfzijl chemistry park industries, by using the enhancement of atmospheric H 2 in an assumedly homogenous distributed 2D plane downwind of the park 1,7,18,26,36 . We use the following simple Mass Balance equation to derive the H 2 flux (Q in g s − 1 ) across the transect within the downwind plane of the chemistry park: $$Q = C\cdot \frac{u\cdot cos\left(\theta \right)\cdot \varDelta A\cdot {M}_{H2}\cdot P}{R\cdot T}$$ 1 In the Mass Balance Eq. ( 1 ), C is the enhancement of the H 2 mole fraction [mol H 2 /mol air] over background values. The average wind speed is denoted by ū [m s − 1 ], and the area of a vertical grid box perpendicular to the wind direction is given by ΔA [m 2 ]. To account for uncertainty in the mean wind direction, a deviation to the wind angle (assumed perpendicular to the grid box) is represented by θ [degrees]. Finally, the molar mass of H 2 is given by M H2 , P is the air pressure [Pa], R is the universal gas constant [m 3 ⋅Pa⋅K − 1 ⋅mol − 1 ] and T is the mean atmospheric temperature [K]. We assume a standard atmospheric air pressure of 101325 Pa and an air temperature of 288.15 K. We run the Mass Balance in a Monte Carlo approach (N = 500 simulations) to account for parameter uncertainties. The determination and uncertainties for the parameters used in the Mass Balance approach are discussed in more detail in the Supplementary information (section 1: Methods & Materials). 2.4.2 Inverse Gaussian dispersion model approach As a control and substantiation to the Mass Balance approach, we derive emission rates using the inverse Gaussian approach. For this, we used a standard point source Gaussian dispersion model (Eq. 2 ) in combination with the three-dimensional mole fraction data from the active AirCore measurements. $$C(x,y,z) = \frac{Q}{2\pi {\sigma }_{y}{\left(x\right)\sigma }_{z}\left(x\right)ū}exp(-\frac{(y-{y}_{s}{)}^{2}}{2{{\sigma }_{y}}^{2}\left(x\right)})\left[exp\right(\frac{(z-{z}_{s}{)}^{2}}{2{{\sigma }_{z}}^{2}\left(x\right)})+exp(\frac{(z+{z}_{s}{)}^{2}}{2{{\sigma }_{z}}^{2}\left(x\right)}\left)\right]$$ 2 In the Gaussian dispersion model, C(x, y, z) are the enhanced H 2 mole fractions [mol H 2 mol air − 1 ] inside the plume at specific coordinates (x, y, z in metres) downwind from a source in the Delfzijl chemistry park. Q is the emission rate given in [g s − 1 ] and ū is the wind speed along the plume direction in [m s − 1 ]. The stability parameters 𝜎 𝑦 and 𝜎 𝑧 with units [m] (Eq. 3 ) describe horizontal and vertical mixing, they depend on atmospheric stability and can be calculated using the Pasquill Gifford parameters found in the Supplementary information (section 1: Methods & Materials). The last exponential term in the equation represents the reflection of plumes from the surface 6 . $${\sigma }_{y} = \frac{r\cdot x}{(1+\frac{x}{a}{)}^{P}} {\sigma }_{z} = \frac{s\cdot x}{(1+\frac{x}{a}{)}^{q}}$$ 3 We identify 5 point sources for H 2 in the Delfzijl chemistry park that can be linked to activities involving significant production, storage or usage of H 2 (see section 2.3.2 ). Point source locations (surface coordinates and emission height) are chosen based on process type and personal communication. Similar to the Mass Balance approach, a Monte Carlo approach is run 500 times to ensure not only local minima are found and parameter ranges are significantly explored. For more detailed explanations of the determination of uncertainties and parameters, see the Supplementary information (Section 1: Methods & Materials). 2.5 Field experiments A total of 7 sampling transects on 7 separate days have been made with the active AirCore system aboard the passenger car. For 3 out of 7 transects driven on 4th Augustus, 11th September and 6th December, in the afternoon between 14h:00m and 16h:00m local time (UTC + 1), the wind direction spanned West to North (270–350 degrees), as shown in Fig. 4 . The UAV flights included four transects that lasted between 10h:30m to 13h:13m minutes carried out on 5th, 12th, 17th October and the 6th December of 2023 in the afternoon between 14h:00m and 16h:00m local time, of which two were flown successfully downwind and one upwind of the chemistry park. The height of the vertical profiles spans from 0 m (ground level) to ~ 140 m in altitude. Both the passenger car and UAV flight details are summarised in the Supplementary information (section 1: Methods & Materials). 3. Results & discussion 3.1 Active AirCore passenger car results We measured strong enhancements of H 2 mole fractions with values up to 1346 ± 2 ppb downwind of the chemistry park, relative to 530 ± 11 ppb upwind. We summarise this data in Fig. 5 with a 3D visualisation of individual samples’ enhancement over the background, displayed along the north-west angle of incidence, corresponding to the wind direction. Out of 53 downwind samples analysed, 35 (66%) had > 100 ppb enhancements, a number that is much larger than the typical seasonal changes, trends, and enhancements found at long-term monitoring sites that are situated away from local sources 29 . With no atmospheric chemical pathway to produce this hydrogen in-situ, this unequivocally points towards substantial emissions of H 2 in the Delfzijl park. These emissions are not incidental but systematic (Fig. 6 ), as we find them across all sampling days and also under different wind directions and atmospheric conditions. Compared to the samples collected upwind of the park, the downwind data shows consistent enhanced atmospheric H 2 mole fractions, averaging 30 up to 280 ppb each day. Furthermore, due to an inhomogeneous release of anthropogenic H 2 emissions from the chemistry park, there is high variability (± 92 ppb) in the enhancement of individual samples collected on the same day. In contrast, the upwind sampled data remains consistently close to the atmospheric background mole fraction of H 2 (530 ± 10 ppb). Moreover, the very low variability of the upwind data emphasises the stability of the atmospheric background mole fraction as well as that of our sampling and analysis system, as expected when no nearby sources have influenced the samples. 3.2 Active AirCore UAV results UAV-based sampling confirms the existence of substantial H 2 sources in the Delfzijl industrial park, although downwind measured mole fraction enhancements at the UAV flight level were lower (40–100 ppb of H 2 ) than we measured at the surface. However, the small variability (± 5 ppb) within daily flights relative to those in Fig. 6 makes these signals very robust indicators of enhanced H 2 . Figure 7 summarises this by comparing two downwind-sampling days with one where we sampled upwind vertical profiles. The vertical profiles show that H 2 for all measurement days was relatively well mixed over the vertical plane, and the average enhancements over the background exceed the vertical H 2 gradients and variability. Local wind-shear effects near the surface could possibly have impacted data points below 20 metres in both downwind profiles; such near surface effects would also contribute to the higher variability found for our car samples. 3.3 Flasks sampling results Our flask-derived data confirms the variability in enhanced H 2 mole fractions found at the chemistry park in Delfzijl. The H 2 mole fraction measured from the car and in the flasks for similar GPS-coordinates show for upwind locations a clear background signal while over the span of the park enhanced H 2 mole fractions up to 950 ppb were found, see the Supplementary information for more details (section 2 : Results & Discussion). Next to this, our flask-derived data gives insight into co-located GHG emissions associated with atmospheric H 2 for three categories at the park: chemical processes from industry, microbial processes from biomass, and puff emissions from a hydrogen fuelling station. From Fig. 8 , it is evident that general chemical processes from industry exhibit no clear co-location with CO and CH 4 . Furthermore, the most enhanced atmospheric H 2 mole fractions, with no co-located GHGs, originate from the hydrogen fuelling station situated at the start of the park. Only at the rear of the park, near the biomass waste incinerator, a clear co-location between atmospheric H 2 and microbial processes was found based on observations of CO and CH 4 . 3.4 Emission estimates We calculate loss rates (Fig. 9 ) between 0.2 and 4.6% for H 2 -related activities at the chemistry park Delfzijl, relative to estimated daily production. On a day-to-day basis we find variations in emission rates originating from measured mole fractions and/or meteorological conditions, with median values ranging from 0.9–5.0 g s − 1 (Mass Balance approach) to 0.1–2.8 g s − 1 (inverse Gaussian). This range partly shows the significant impact of the environmental conditions on the model’s outcome, but especially the variability in the observed H 2 mole fraction enhancements originating from the park contributes strongly to the quoted range. The variation in the enhanced H 2 mole fraction suggests consistent but not uniform emissions over time, leading to diverse emission estimates. Furthermore, the relative homogeneity of the flown UAV profiles substantiates the continuous wide spread of emissions. On the days when both a car and UAV enhancements were sampled and measured, the emission estimates are highly similar. This similarity in the determined leak rate by the UAV flight and the car data enhances the robustness of the outcome. We find that, on average, the inverse Mass Balance approach produces median emission rates 2.6 times as high as those by the inverse Gaussian approach for all days. We expect this is due to the presence of high mole fraction samples near sources in the passenger car dataset, which in the Mass Balance approach causes an overestimation of emissions due to an unrealistically large grid box area given the proximity to the source and the resulting short mixing time. Contrarily, the Inverse Gaussian approach can explain these high mole fraction samples using relatively low emission rates given the proximity to the source. 4. Conclusions & Outlook We developed a measurement method for low-level in-situ (semi)-continuous (industrial) H 2 emissions using an active AirCore sampler and a GC-PDHID analysis system. During a number of field experiments significantly enhanced atmospheric H 2 mole fractions were detected at the industrial chemistry park in Delfzijl (Groningen province, the Netherlands), ranging from downwind mole fractions of 580 ppb up to 1500 ppb. The consistency in the enhanced atmospheric H 2 mole fraction during every experiment indicated a continuity in H 2 emissions from the chemistry park. In addition to the AirCore samples, we analysed grab samples from flasks collected during the field experiments. These flasks were intercompared on H 2 with the AirCore data (at similar GPS-coordinates), and besides H 2 , analysed on mole fractions of CO 2 , CH 4 , and CO to gather additional information on co-located processes. From the flask data, the enhanced H 2 mole fractions found at the chemistry park could be divided into three sections: microbial production, chemical production and H 2 purge emissions. The flask results indicated that most of the enhanced H 2 mole fractions were substantiating the AirCore data and did not correlate with enhanced mole fractions of CO 2, CO and CH 4 except for CH 4 and CO emissions in the proximity of a biomass waste incinerator at the park. The UAV-based AirCore downwind vertical profiles were relatively well mixed showing some plume variability in H 2 mole fraction along the 140 m altitude. The background upwind profile showed a nearly constant continuous background signal throughout the profile. Our first empirically determined emission estimates showed percentages (0.2–5%) well within the range of model predictions. Our results represent a stepping stone in the development of an easy-to-use and highly accurate sampling technique to detect and quantify in situ H 2 emissions from leakage, purging and storage, pivotal for the development of the energy transition. With the data obtained from this study, we hope to inform industry and policy makers to not oversee the environmental impact of current small H 2 emissions, considering the plans for significant upscaling of sectors along the hydrogen value chain. For future work, data gathered with our sampling and analysis method has the potential to substantiate model emission estimates across the hydrogen value chain. Further work will focus on increasing the number of transects for the further expansion of data sets, not only for the chemical industry but also focused on other parts of the hydrogen value chain. Declarations Acknowledgements We are grateful for the technical support of Marc O. Bleeker. Author contributions W.P. and H.A.J.M. initiated the study. I.M.W. and H.A.S. contributed to the study concept and design. Material preparation and all experiments were conducted by I.M.W., H.A.S. and S.M.A.C.. Data analysis was done by I.M.W. and H.A.S. The system setup was supported by B.A.M.K.. The model development was done by F.T.S. and W.P.. The draft of the manuscript was written by I.M.W. with support of H.A.S. and F.T.S.. All authors read, commented, and approved the final manuscript. Data availability All data and analysis software used in this manuscript is accessible from an open access data archive under ‘https://github.com/IrisMWestra/Atmospheric-hydrogen-Delfzijl-.git’. For more information correspondence should be addressed to I.M.W. Funding The project has received financial support from the Nationaal Programma Groningen and the European Union via the subsidy ‘Waterstof Werkt: Train and Learn Hub ’ and by the Gas & Hydrogen Partnerships Shell Nederland. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to I.M.W.. References Andersen, T. (2021). Quantifying Local to Regional Emissions of Methane Using UAV-based Atmospheric Concentration Measurements (Vols. 978-94-6416-887–7). Andersen, T., Scheeren, B., Peters, W., & Chen, H. (2018). A UAV-based active AirCore system for measurements of greenhouse gases. Atmospheric Measurement Techniques , 11 (5), 2683–2699. https://doi.org/10.5194/amt-11-2683-2018 Barnes, D. H., Wofsy, S. C., Fehlau, B. P., Gottleib, E. W., Elkins, J. W., Dutton, G. S., & Novelli, P. C. (2003). Hydrogen in the atmosphere: Observations above a forest canopy in a polluted environment. Journal of Geophysical Research: Atmospheres , 108 (6). https://doi.org/10.1029/2001jd001199 Bertagni, M. B., Pacala, S. W., Paulot, F., & Porporato, A. (2022). Risk of the hydrogen economy for atmospheric methane. Nature Communications , 13 (1), 7706. https://doi.org/10.1038/s41467-022-35419-7 Crosson, E. R. (2008). A cavity ring-down analyzer for measuring atmospheric levels of methane, carbon dioxide, and water vapor. Applied Physics B: Lasers and Optics , 92 (3 SPECIAL ISSUE), 403–408. https://doi.org/10.1007/s00340-008-3135-y Csanady, G. T. (1973). Turbulent diffusion in the environment (Vol. 3). Boston, D. Reidel Pub. Co. Fiehn, A., Kostinek, J., Eckl, M., Klausner, T., Galkowski, M., Chen, J., Gerbig, C., Röckmann, T., Maazallahi, H., Schmidt, M., Korbeń, P., Neçki, J., Jagoda, P., Wildmann, N., Mallaun, C., Bun, R., Nickl, A. L., Jöckel, P., Fix, A., & Roiger, A. (2020). Estimating CH4, CO2and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach. Atmospheric Chemistry and Physics , 20 (21), 12675–12695. https://doi.org/10.5194/acp-20-12675-2020 Francey, R. J.; Steele, L. P.; Spencer, D. A.; Langenfelds, R. L.; Law, R. M.; Krummel, P. B.; Fraser, P. J.; Etheridge, D. M.; Derek, N.; Coram, S. A.; Cooper, L. N.; Allison, C. E.; Porter, L.; Baly, S. The CSIRO (Australia) measurement of greenhouse gases in the global atmosphere. In: Tindale, N. W.; Derek, N.; Fraser, P. J. editors, editor/s. Baseline Atmospheric Program Australia. Melbourne: Bureau of Meteorology and CSIRO Atmospheric Research ; 2003. 42-53. http://hdl.handle.net/102.100.100/191835?index=1 Frazer-Nash. (2022). Fugitive hydrogen emissions in a future hydrogen economy . 52. Gerbig, C., Körner, S., & Lin, J. C. (2008). Vertical mixing in atmospheric tracer transport models: error characterization and propagation. In Atmos. Chem. Phys (Vol. 8). www.atmos-chem-phys.net/8/591/2008/ Glueckauf, E., & Kitt, G. P. (1957). The hydrogen content of atmospheric air at ground level. Quarterly Journal of the Royal Meteorological Society , 83 (358), 522–528. https://doi.org/10.1002/qj.49708335808 Hauglustaine, D. (2022). Unpacking the climate impact of hydrogen. In Unpacking the climate impact of hydrogen . Hauglustaine, D., Paulot, F., Collins, W., Derwent, R., Sand, M., & Boucher, O. (2022). Climate benefit of a future hydrogen economy. Communications Earth and Environment , 3 (1). https://doi.org/10.1038/s43247-022-00626-z IPCC. (2001). CLIMATE CHANGE 2001: THE SCIENTIFIC BASIS . Jordan, A., & Steinberg, B. (2009). Is it time for a WMO Hydrogen calibration scale? http://www.wmo.int/pages/prog/arep/gaw/gaw-reports.html Jordan, A., & Steinberg, B. (2011). Calibration of atmospheric hydrogen measurements. Atmospheric Measurement Techniques , 4 (3), 509–521. https://doi.org/10.5194/amt-4-509-2011 Karion, A., Sweeney, C., Tans, P., & Newberger, T. (2010). AirCore: An innovative atmospheric sampling system. Journal of Atmospheric and Oceanic Technology , 27 (11), 1839–1853. https://doi.org/10.1175/2010JTECHA1448.1 Klausner, T., Mertens, M., Huntrieser, H., Galkowski, M., Kuhlmann, G., Baumann, R., Fiehn, A., Jöckel, P., Pühl, M., & Roiger, A. (2020). Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018. Elementa , 8 (1). https://doi.org/10.1525/ELEMENTA.411 Lipiäinen, S., Lipiäinen, K., Ahola, A., & Vakkilainen, E. (2023). Use of existing gas infrastructure in European hydrogen economy. International Journal of Hydrogen Energy , 48 (80), 31317–31329. https://doi.org/10.1016/j.ijhydene.2023.04.283 McKinsey. (2020). Europe’s path to decarbonization McKinsey . https://www.mckinsey.com/~/media/mckinsey/business%20functions/sustainability/our%20insights/how%20 the%20european%20union%20could%20achieve%20net%20zero%20emissions%20at%20net%20zero%20cost/net-zero-europe-vf.pdf Najjar, Y. S. (2019). Hydrogen Leakage Sensing and Control: (Review). Biomedical Journal of Scientific & Technical Research , 21 (5). https://doi.org/10.26717/bjstr.2019.21.003670 Nguyen, L. N. T., Meijer, H. A. J., Van Leeuwen, C., Kers, B. A. M., Scheeren, H. A., Jones, A. E., Brough, N., Barningham, T., Pickers, P. A., Manning, A. C., & Luijkx, I. T. (2022). Two decades of flask observations of atmospheric (O2/N2), CO2, and APO at stations Lutjewad (the Netherlands) and Mace Head (Ireland), and 3 years from Halley station (Antarctica). Earth System Science Data , 14 (2), 991–1014. https://doi.org/10.5194/essd-14-991-2022 Novelli, P. C., Crotwell, A. M., & Hall, B. D. (2009). Application of gas chromatography with a pulsed discharge helium ionization detector for measurements of molecular hydrogen in the atmosphere. Environmental Science and Technology , 43 (7), 2431–2436. https://doi.org/10.1021/es803180g NWP. (2021). Werkplan Nationaal Waterstof Programma 2022-2025 . Ocko, I. B., & Hamburg, S. P. (2022). Climate consequences of hydrogen emissions. Atmospheric Chemistry and Physics , 22 (14), 9349–9368. https://doi.org/10.5194/acp-22-9349-2022 O’Shea, S. J., Allen, G., Fleming, Z. L., Bauguitte, S. J. B., Percival, C. J., Gallagher, M. W., Lee, J., Helfter, C., & Nemitz, E. (2014). Area fluxes of carbon dioxide, methane, and carbon monoxide derived from airborne measurements around Greater London: A case study during summer 2012. Journal of Geophysical Research , 119 (8), 4940–4952. https://doi.org/10.1002/2013JD021269 Paula Pérez-Peña, M., Fisher, J. A., Millet, D. B., Yashiro, H., Langenfelds, R. L., Krummel, P. B., & Kable, S. H. (2022). Evaluating the contribution of the unexplored photochemistry of aldehydes on the tropospheric levels of molecular hydrogen (H 2 ). Atmospheric Chemistry and Physics . https://doi.org/10.5194/acp-2021-1052 Paulot, F., Pétron, G., Crotwell, A. M., & Bertagni, M. B. (2023). Reanalysis of NOAA H2 observations: implications for the H2 budget . https://doi.org/10.5194/egusphere-2023-1602 Pétron, G., Crotwell, A. M., Mund, J., Crotwell, M., Mefford, T., Thoning, K., Hall, B., Kitzis, D., Madronich, M., Moglia, E., Neff, D., Wolter, S., Jordan, A., Krummel, P., Langenfelds, R., & Patterson, J. (2024). Atmospheric H 2 observations from the NOAA Global Cooperative Air Sampling Network . https://doi.org/10.5194/amt-2024-4 Prinn, R. G., Weiss, R. F., Arduini, J., Arnold, T., Langley Dewitt, H., Fraser, P. J., Ganesan, A. L., Gasore, J., Harth, C. M., Hermansen, O., Kim, J., Krummel, P. B., Li, S., Loh, Z. M., Lunder, C. R., Maione, M., Manning, A. J., Miller, B. R., Mitrevski, B., … Zhou, L. (2018). History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE). Earth System Science Data , 10 (2), 985–1018. https://doi.org/10.5194/essd-10-985-2018 PWC. (2021). HyWay 27: hydrogen transmission using the existing natural gas grid? Final report for the Ministry of Economic Affairs and Climate Policy . www.pwc.nl Röckmann, T., Rhee, T. S., & Engel, A. (2015). Atmospheric Chemistry and Physics Heavy hydrogen in the stratosphere. In Atmos. Chem. Phys (Vol. 3). www.atmos-chem-phys.org/acp/3/2015/ Schmidt, & Seiler. (1970). New method for recording molecular hydrogen in atmospheric air. J Geophys Res , 75 (9), 1713–1716. https://doi.org/10.1029/jc075i009p01713 Simmonds, P. G., Derwent, R. G., O’Doherty, S., Ryall, D. B., Steele, L. P., Langenfelds, R. L., Salameh, P., Wang, H. J., Dimmer, C. H., & Hudson, L. E. (2000). Continuous high-frequency observations of hydrogen at the Mace Head baseline atmospheric monitoring station over the 1994-1998 period. Journal of Geophysical Research Atmospheres , 105 (D10), 12105–12121. https://doi.org/10.1029/2000JD900007 Sun, T., Shrestha, E., Hamburg, S. P., Kupers, R., & Ocko, I. B. (2024). Climate Impacts of Hydrogen and Methane Emissions Can Considerably Reduce the Climate Benefits across Key Hydrogen Use Cases and Time Scales. Environmental Science & Technology . https://doi.org/10.1021/acs.est.3c09030 Tong, X., van Heuven, S., Scheeren, B., Kers, B., Hutjes, R., & Chen, H. (2023). Aircraft-Based AirCore Sampling for Estimates of N2O and CH4 Emissions. Environmental Science and Technology , 57 (41), 15571–15579. https://doi.org/10.1021/acs.est.3c04932 Tong, X., Van Heuven, S., Scheeren, B., Kers, B., Hutjes, R., & Chen, H. (2023). Supporting Information (SI) for Research Article Aircraft-based AirCore sampling for estimates of N 2 O and CH 4 emissions . Tromp, T. K., Shia, R.-L., Allen, M., Eiler, J. M., & Yung, Y. L. (2003). Potential Environmental Impact of a Hydrogen Economy on the Stratosphere . www.sciencemag.org U.S. Department of Energy. (2022). U.S. National Clean Hydrogen Strategy and Roadmap . Warwick, N., Griffiths, P., Keeble, J., Archibald, A., & Pyle, J. (2022). Atmospheric implications of increased Hydrogen use . Warwick, N. J., Archibald, A. T., Griffiths, P. T., Keeble, J., O’Connor, F. M., Pyle, J. A., & Shine, K. P. (2023). Atmospheric composition and climate impacts of a future hydrogen economy. Atmospheric Chemistry and Physics , 23 (20), 13451–13467. https://doi.org/10.5194/acp-23-13451-2023 Warwick, N. J., Bekki, S., Nisbet, E. G., & Pyle, J. A. (2004). Impact of a hydrogen economy on the stratosphere and troposphere studied in a 2‐D model. Geophysical Research Letters , 31 (L05107). Wentworth, W. E., Cai, H., & Stearnsb, S. (1994). Pulsed discharge helium ionization detector Universal detector for inorganic and organic compounds at the low picogram level. Journal of Chromatography A , 688 , 135–152. Yver, C., Schmidt, M., Bousquet, P., & Ramonet, M. (2011). Measurements of molecular hydrogen and carbon monoxide on the Trainou tall tower. Tellus, Series B: Chemical and Physical Meteorology , 63 (1), 52–63. https://doi.org/10.1111/j.1600-0889.2010.00520.x Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation21062024.docx Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Aug, 2024 Reviews received at journal 08 Aug, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 26 Jun, 2024 Editor assigned by journal 26 Jun, 2024 Editor invited by journal 25 Jun, 2024 Submission checks completed at journal 25 Jun, 2024 First submitted to journal 21 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4618373","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321266731,"identity":"029303ae-d0f2-4d89-8c83-a413707560fc","order_by":0,"name":"Iris M. Westra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACNgST+QCY4iNWiwSQmYBuCH4A1MJjQJwWPgbuBGaeiro68/Yz3z7z/GLII6iFjYF3AzPPmcMSMmdyN8/m7WMoJk5LbtsBCQmG3M3MvD0MiW3EaflXJyHB/+YxKVoamCUkJHKYmXl+EKOFmXfD4T/HDkvOkHhmzDi3QYKwFvn23o0PZ9TU8UvwJz9mePPHJrGfkBYGZgaGA3AOY5sEQQ3o4A/JOkbBKBgFo2AEAADgDTIO79Bc2AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Groningen","correspondingAuthor":true,"prefix":"","firstName":"Iris","middleName":"M.","lastName":"Westra","suffix":""},{"id":321266732,"identity":"92c3c785-baf9-420b-ad56-3724091c0d5b","order_by":1,"name":"Hubertus A. Scheeren","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Hubertus","middleName":"A.","lastName":"Scheeren","suffix":""},{"id":321266733,"identity":"824da21c-78f6-4846-b079-0776ec50a25a","order_by":2,"name":"Firmin T. Stroo","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Firmin","middleName":"T.","lastName":"Stroo","suffix":""},{"id":321266734,"identity":"06f2a686-c1f0-4142-822c-91b3a7b39202","order_by":3,"name":"Steven M.A.C. van Heuven","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"M.A.C. van","lastName":"Heuven","suffix":""},{"id":321266735,"identity":"ecd3cc77-f82d-462c-b314-d020b3e1035e","order_by":4,"name":"Bert A.M. Kers","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Bert","middleName":"A.M.","lastName":"Kers","suffix":""},{"id":321266736,"identity":"921d7b70-b983-4aba-a20e-d2755510a786","order_by":5,"name":"Wouter Peters","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Wouter","middleName":"","lastName":"Peters","suffix":""},{"id":321266737,"identity":"a1df6194-ad62-4699-baeb-4f8e91723bbd","order_by":6,"name":"Harro A.J. Meijer","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Harro","middleName":"A.J.","lastName":"Meijer","suffix":""}],"badges":[],"createdAt":"2024-06-21 16:30:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4618373/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4618373/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-76373-2","type":"published","date":"2024-10-15T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60472931,"identity":"d73919ad-a8ad-4ebb-93c3-1fc69390f029","added_by":"auto","created_at":"2024-07-17 07:15:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38665,"visible":true,"origin":"","legend":"\u003cp\u003eFor sampling from a passenger car, the active AirCore is positioned at the backseat, as indicated by the black box, with the inlet outside of the window. The active AirCore is set to the sampling \u0026amp; calibration mode, making the sampled air enter via the inlet. P being the pressure controller. MFC stands for mass flow controller and O for orifice at the end of the AirCore. The six-port valve is set as indicated in the figure.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/2abd80a2e21881f2621c156e.jpg"},{"id":60472513,"identity":"abbebcfd-e37d-4c51-9ed3-09b537df49a3","added_by":"auto","created_at":"2024-07-17 07:07:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24401,"visible":true,"origin":"","legend":"\u003cp\u003eFor the mobile platform, UAV, the active AirCore is positioned at the bottom of the UAV, as indicated by the black box. As for the passenger car, O is the orifice positioned at the end of the AirCore.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/be16c294ed676e221235d2b1.jpg"},{"id":60473877,"identity":"a10645aa-d24b-4d09-b5ef-12e1e86af2e8","added_by":"auto","created_at":"2024-07-17 07:23:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99186,"visible":true,"origin":"","legend":"\u003cp\u003eThe orange stars in the right-hand-side figure indicate (from left to right) the meteorological stations Lauwersoog, Lutjewad and Nieuw-Beerta. The orange rectangles represent Chemistry park Delfzijl (map by using Rstudio, Leaflet).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/e6b67a62960261ea77015330.jpg"},{"id":60472506,"identity":"79382b62-6490-4f05-a6a9-55c11ce93f9f","added_by":"auto","created_at":"2024-07-17 07:07:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115984,"visible":true,"origin":"","legend":"\u003cp\u003eWith an NW wind, the passenger car drove along the green trajectory while sampling, and at t\u003csub\u003eend\u003c/sub\u003e the UAV was flown, with the purple triangles indicating the H\u003csub\u003e2\u003c/sub\u003e-related industry.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/5645327e07fb99e62b2e0543.jpg"},{"id":60472011,"identity":"4e0ad8cb-1f1c-4cba-9e1f-6e145e8b4205","added_by":"auto","created_at":"2024-07-17 06:59:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111177,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction obtained from NW wind direction conditions, indicated by the blue arrow with the purple triangles indicating molecular hydrogen-related industry. (a) The colour of the columns indicates the sampling day, and the height of the column indicates the relative atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction to the background. (b) Data points represent the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions distributed over the park. The size of the purple triangle roughly indicates the size of the H\u003csub\u003e2\u003c/sub\u003e activity relative to the total at the chemistry park.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/4ed341c819efcab97b1b6f01.jpg"},{"id":60472006,"identity":"5bbb883f-f7f6-4b01-a4ab-16923b864e2d","added_by":"auto","created_at":"2024-07-17 06:59:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42004,"visible":true,"origin":"","legend":"\u003cp\u003eThe median atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction for each sampling day categorised by upwind (blue; no park contamination) and downwind (orange). On the x-axis, the date is shown with on the y-axis the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction (ppb). The error bars denote the measurement variability.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/f57ae9213fbb7687b8eeaac4.jpg"},{"id":60472508,"identity":"945fe70b-ff41-4660-ba5a-79e9ba6a1d80","added_by":"auto","created_at":"2024-07-17 07:07:15","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":63795,"visible":true,"origin":"","legend":"\u003cp\u003eThe median atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction for each sampling day is categorised by upwind (blue; no park contamination) and downwind (orange). (a) On the x-axis the date is shown with on the y-axis the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction (ppb). The error bar denotes the measurement variability. (b) three vertical profiles, with the height in metres on the y-axis and the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction (ppb) on the x-axis.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/a68c063180bd8a29b3bc9179.jpg"},{"id":60472933,"identity":"23fe7409-2233-46b8-b360-6ce41c4a5be7","added_by":"auto","created_at":"2024-07-17 07:15:15","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":54880,"visible":true,"origin":"","legend":"\u003cp\u003eThe summary of the results obtained from the flask data is categorised based on location into H\u003csub\u003e2\u003c/sub\u003e puff emissions (yellow), chemical processes (blue) and microbial processes (orange). The x-axis displays the H\u003csub\u003e2\u003c/sub\u003e mole fraction (ppb) with the (a) CO (ppb), (b) CH\u003csub\u003e4\u003c/sub\u003e (ppb) and (c) CO\u003csub\u003e2\u003c/sub\u003e (ppm) on the y-axis.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/435459eedbd238c79e515ab9.jpg"},{"id":60472511,"identity":"af387225-a708-4a88-9cd6-24839dbaabcb","added_by":"auto","created_at":"2024-07-17 07:07:15","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":59966,"visible":true,"origin":"","legend":"\u003cp\u003eEmission rate estimates and calculated loss rate for H\u003csub\u003e2\u003c/sub\u003e mole fraction from the inverse Gaussian and Mass Balance approach. Boxplots show the distribution of solutions for each day and data type (car or UAV). Boxes cover the first (Q1) to the third (Q3) quartile of solutions, with the median (Q2) solution in between. The whiskers extend to the rest of the data (except outliers). We define outliers as data points more than 1.5 times the interquartile range (IQR) below Q1 or above Q3 and are not shown to improve readability.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/02c823938695750e67a4231b.jpg"},{"id":67148940,"identity":"4a971d32-aa11-4525-bbcb-444d711b9315","added_by":"auto","created_at":"2024-10-21 16:10:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1347682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/9fbd8428-69b6-433b-ae82-737614631597.pdf"},{"id":60472014,"identity":"0e289336-17e2-4e3e-8b69-220c092e1aa9","added_by":"auto","created_at":"2024-07-17 06:59:15","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":11762392,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation21062024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4618373/v1/074c36c4bb9b651584539695.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"First detection of industrial hydrogen emissions using high-precision mobile measurements in ambient air","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTo achieve a zero or low-carbon energy economy, an energy carrier capable of zero emissions of air pollutants and greenhouse gases is needed. Molecular hydrogen (H\u003csub\u003e2\u003c/sub\u003e) emerges as a promising contender for this role in this energy transition \u003csup\u003e13,31\u003c/sup\u003e. Initiatives such as the U.S. National Clean Hydrogen Strategy and Roadmap, Germany\u0026rsquo;s \u0026lsquo;Energiewende\u0026rsquo;, and the hydrogen roadmap of the Netherlands (\u0026lsquo;Nationaal Waterstof Programma\u0026rsquo;) alongside numerous other programs, underscore countries' ambitions towards a hydrogen value chain \u003csup\u003e9,20,24,39\u003c/sup\u003e. However, due to hydrogen\u0026rsquo;s pivotal role in the energy transition, the expected increasing release of anthropogenic H\u003csub\u003e2\u003c/sub\u003e emissions into the atmosphere can result in enhanced global warming from indirect effects.\u003c/p\u003e \u003cp\u003eIncreased levels of atmospheric H\u003csub\u003e2\u003c/sub\u003e can result in the lengthening of the lifetime of CH\u003csub\u003e4\u003c/sub\u003e and ozone, and higher levels of stratospheric water vapour \u003csup\u003e4,12,13,27,32,38,42,44\u003c/sup\u003e. Adding up to a global warming potential of 12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 over 100 years and a perturbation lifetime of 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 years in the atmosphere, H\u003csub\u003e2\u003c/sub\u003e surpasses carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) in terms of greenhouse gas potency \u003csup\u003e12,14,25,40,41\u003c/sup\u003e. The current estimates of the loss rate potential (including venting, purging and uncontrolled leakage) of anthropogenic H\u003csub\u003e2\u003c/sub\u003e emissions, solely based on models, range from 1\u0026ndash;10% of the total production \u003csup\u003e13,35\u003c/sup\u003e. So far, however, these estimates have not been validated at all by actual measurements, due to the lack of appropriate measurement techniques.\u003c/p\u003e \u003cp\u003eCurrently, H\u003csub\u003e2\u003c/sub\u003e detectors utilised in industry are used for safety purposes only. Since the flammability range of H\u003csub\u003e2\u003c/sub\u003e is at 4% volume, handheld detectors with a detection limit starting at 30 \u0026micro;mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e or ppm up to 10% volume are used. However, since the atmospheric background concentration (mole fraction) of molecular hydrogen is ~\u0026thinsp;0.5 ppm, anthropogenic H\u003csub\u003e2\u003c/sub\u003e from leakages with no flammability risk but a potential impact on the climate remains undetected. Precise atmospheric H\u003csub\u003e2\u003c/sub\u003e measurements within the scientific world started in 1957 with the introduction of the principle of liquefaction of air \u003csup\u003e11\u003c/sup\u003e, followed in the 1970s \u003csup\u003e33\u003c/sup\u003e with a gas chromatographic (GC) method, designed to analyse molecular hydrogen in atmospheric air based on the reduction of mercuric oxide. In 1994, Wentworth et al. \u003csup\u003e43\u003c/sup\u003e designed the pulsed discharge helium ionisation detector (PDHID), for use in a widespread range of applications outside atmospheric science. In 2009, Novelli et al. \u003csup\u003e23\u003c/sup\u003e adopted this method on a GC-system to measure molecular hydrogen in the atmosphere. The GC-PDHID technique showed a stable performance with a linear response over the 0-2000 nmol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e or ppb range (AGAGE \u003csup\u003e30\u003c/sup\u003e, CSIRO \u003csup\u003e8\u003c/sup\u003e, NOAA \u003csup\u003e28,29\u003c/sup\u003e). The combination of this lab-based-measurement system, and active AirCore sampling on mobile platforms, is the novel sampling technique designed and tested in this study. The active AirCore is a long thin tube that can preserve the profile of the trace gas of interest during sampling, storage and analysis with minimum diffusive mixing \u003csup\u003e1,17\u003c/sup\u003e. The active AirCore was first designed and used for applications focused on CH\u003csub\u003e4\u003c/sub\u003e from the energy (e.g. coal mines) and the agricultural sector (e.g. farms). In our study, the application of the active AirCore sampling technique is broadened to also include the sampling and analysis of atmospheric H\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eWhile the energy transition unrolls, further insights into the hydrogen value chain (production, transport, storage, end-use applications) and the potential risks of H\u003csub\u003e2\u003c/sub\u003e leakage are of great importance \u003csup\u003e9,19,21,42\u003c/sup\u003e. Historically, studies like EUROHYDROS \u003csup\u003e44\u003c/sup\u003e, Harvard Forest (1996\u0026ndash;1998 \u003csup\u003e3\u003c/sup\u003e, Mace head 1994\u0026ndash;1998 \u003csup\u003e34\u003c/sup\u003e, focused on the natural hydrogen budget through short-term campaigns. Long-established international networks (AGE-AGAGE, NOAA, more recently ICOS \u003csup\u003e29,30\u003c/sup\u003e) have been measuring atmospheric H\u003csub\u003e2\u003c/sub\u003e in an accurate and systematic way, but their stations are mostly remote. Until now field campaigns specifically focused on regional and local anthropogenic H\u003csub\u003e2\u003c/sub\u003e emission sources originating from the hydrogen value chain have been absent. In Sun et al. (2024) \u003csup\u003e35\u003c/sup\u003e it is rightly pointed out that: \u003cem\u003e\u0026ldquo;It is important to note that the rates of hydrogen emissions are currently unknown across the value chain. Empirical measurements are needed to improve our understanding of where emissions are coming from and in what quantities.\u0026rdquo;.\u003c/em\u003e Consequently, to bridge the gap between model predictions and reality, our study offers innovative and versatile sampling techniques combined with a state-of-the-art high-precision hydrogen analysis system to provide empirical data from atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions originating from industrial activities.\u003c/p\u003e \u003cp\u003eOur study is the first -to our knowledge- that provides such empirical measurements from atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions originating from industrial activities. The proof of concept for this study entails detailed measurements of atmospheric H\u003csub\u003e2\u003c/sub\u003e using the active AirCore sampling technique at an industrial site in the province of Groningen (the Netherlands). We first outline our analysis and sampling techniques, after which we discuss the measurement site and necessary a priori information. We then present our observations from two mobile platforms (car and unmanned aerial vehicle (UAV)) before quantifying the emissions from the downwind sources. We use both a mass balance approach and an inverse Gaussian Plume model with multiple source configurations, and we discuss their respective uncertainties. We finish the paper with conclusions and a future outlook for our novel methodology.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sampling methods\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Active AirCore\u003c/h2\u003e \u003cp\u003eThe AirCore is an atmospheric sampling system that consists of a thin-wall stainless-steel (S.S.) tubing in the shape of a coil with a passivated inner surface, invented and patented by Pieter Tans \u003csup\u003e17\u003c/sup\u003e. The original design is used to obtain a vertical atmospheric profile by filling itself using the air pressure gradient in the atmosphere. Our \u0026ldquo;active\u0026rdquo; AirCore (length 245\u0026ndash;285 m, \u003csup\u003e3\u003c/sup\u003e/\u003csub\u003e16\u003c/sub\u003e\" OD) collects air samples via the use of a micro-pump (KNF NMP015 KPDC-B 6V) and a mass flow controller (Bronkhorst IQFlow-200C-AAD-11-V-S)\u003csup\u003e2, 36\u003c/sup\u003e. Using this technique, the AirCore is filled, through a chemical dryer using magnesium perchlorate located at the inlet of the system, with a pressurised and dried profile of the trace gas of interest along a given measurement trajectory \u003csup\u003e2,36,37\u003c/sup\u003e. For our experiments, a similar AirCore as designed and described by Tong et al. (2023) was used, and for more details the reader is referred to this paper. The active AirCore was used on two mobile platforms: driven with a passenger car and flown with a UAV. For the passenger car, we used an active AirCore with a sample volume of ~\u0026thinsp;4.1 L. This AirCore is filled to an end-pressure of up to 1.6 bar over the course of about 2 hours of sampling, resulting in up to 38 useful discrete H\u003csub\u003e2\u003c/sub\u003e samples for the GC-PDHID (described in detail in section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e). The sampling flow rate was constant and was set to either 45 or 60 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, depending on the desired duration of the sampling. For the active AirCore applied on the UAV the sample volume was 3.7 L, and it was filled with a flow of 200 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at atmospheric pressure, allowing for up to 21 useful discrete H\u003csub\u003e2\u003c/sub\u003e samples over the course of about 15 minutes of flying time. Prior to each field campaign, both AirCores were filled with synthetic air (UN1956; 20.5 Vol. % O\u003csub\u003e2\u003c/sub\u003e, rest N\u003csub\u003e2\u003c/sub\u003e), to clean the coil and identify a clear starting point of zero mole fraction H\u003csub\u003e2\u003c/sub\u003e during the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Flasks samples\u003c/h2\u003e \u003cp\u003eAs a complementary method to validate the AirCore H\u003csub\u003e2\u003c/sub\u003e measurements, vacuumized and dried 2.4 L glass flasks (with two Louwers Hapert Viton sealed valves) were filled at atmospheric pressure in pairs along the measurement trajectory to collect discrete dry air samples, by applying a magnesium perchlorate dryer tube on the flask inlet. All mole fraction analyses of the glass flasks were conducted by both the GC-PDHID for H\u003csub\u003e2\u003c/sub\u003e and a cavity ringdown spectrometer (CRDS) system (Picarro Inc. CA, model G2401) for mole fractions of CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and CO, the latter to get additional information on the potential emission sources co-located with H\u003csub\u003e2\u003c/sub\u003e (described in more detail in the Supplementary information section 1 and \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Methods \u0026amp; Materials and Results \u0026amp; Discussion) \u003csup\u003e5,22\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis methods\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 GC-PDHID system\u003c/h2\u003e \u003cp\u003eFor the detection of molecular H\u003csub\u003e2\u003c/sub\u003e in the atmosphere we use an Agilent 8890 Gas Chromatograph (GC) equipped with a pulsed discharge helium ionisation detector (PDHID, Agilent) designed after Novelli et al. (2009). In addition, our GC-system was fitted with a separate analysis line for N\u003csub\u003e2\u003c/sub\u003eO and SF\u003csub\u003e6\u003c/sub\u003e equipped with a micro electron capture detector (\u0026micro;ECD, Agilent) of which more details are given in the Supplementary information. For the purpose of this paper, we will focus here on the PDHID analysis used for the detection of H\u003csub\u003e2\u003c/sub\u003e. First, sample air is flushed over a 2 ml sample loop for H\u003csub\u003e2\u003c/sub\u003e (and 5 ml for N\u003csub\u003e2\u003c/sub\u003eO/SF\u003csub\u003e6\u003c/sub\u003e) at a rate of 52\u0026thinsp;\u0026plusmn;\u0026thinsp;2 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 1.37 minutes. As such, the average sample size used for one measurement is 137\u0026thinsp;\u0026plusmn;\u0026thinsp;5 ml which includes the overshoot of the pressure controller and the dead volume of the tubing and valves (using \u0026sup1;\u0026frasl;\u003csub\u003e16\u003c/sub\u003e\u0026rdquo; OD and ⅛\u0026rdquo; OD S.S. Swagelok) between the sample carrier (flask or AirCore) and the loops (2 ml \u0026amp; 5 ml). Secondly, for 30 s the sample loop is equilibrated from 1.5 bar filling pressure to ambient pressure (the exhaust of the loop is equipped with a 1 m x \u0026sup1;\u0026frasl;\u003csub\u003e16\u003c/sub\u003e\u0026rdquo; OD coil to prevent back-diffusion of lab air). Then, the sample is injected onto the first packed pre-column (Agilent S.S. packed column, 4.5 m x ⅛\u0026rdquo; OD x 2 mm, Hayesep-DB, 100\u0026ndash;120 mesh) where H\u003csub\u003e2\u003c/sub\u003e is separated from the air matrix. Right after elution of the H\u003csub\u003e2\u003c/sub\u003e onto the second analytical column (Agilent S.S. packed column, 4.5 m x ⅛\u0026rdquo; OD x 2 mm, Hayesep-DB, 80\u0026ndash;100 mesh), the pre-column is set into backflush mode to prevent oxygen and other contaminants from reaching the analytical column and detector. At 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 minutes the H\u003csub\u003e2\u003c/sub\u003e peak reaches the detector, and it shows a 19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1 seconds wide chromatogram. The total measurement time for one sample is 7 minutes. Potential drift is corrected by measuring a reference tank every 3 samples. Our GC-PDHID measures H\u003csub\u003e2\u003c/sub\u003e with a precision\u0026thinsp;\u0026lt;\u0026thinsp;2 ppb and it is calibrated against a suite of in-house made dry whole-air working standards which themselves are calibrated against 3 primary standards linked to the international NOAA-H2-X1996 hydrogen scale (maintained by the Max Planck Institute for Biogeochemistry (MPI-BGC) Jena, Germany) \u003csup\u003e15,16\u003c/sup\u003e. The absolute accuracy of the primary standards is \u0026lt;\u0026thinsp;1 ppb. For more details about our GC-setup and measurement procedure, we refer to the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAirCore analysis on the GC-PDHID\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring analysis, the inlet of the active AirCore is connected to a push gas, while the outlet is connected to the sample inlet of the GC. As a push gas, the same H\u003csub\u003e2\u003c/sub\u003e-free synthetic air as for the prefilling (at 1 bar over-pressure) was used, clearly marking the start and end of the atmospheric sampling sequence.\u003c/p\u003e \u003cp\u003eThe GC-system uses an electronic pressure controller to regulate the flow. Typically, the push gas is set to the same pressure as the AirCore sample being around 1.6 bar absolute to minimise smearing of the sample. However, in our system, a slight overpressure of 0.4 hPa is required to push the sample through our sample loop. Potential drift is corrected for by utilising a bracketing method, wherein up to 3 samples are bracketed by a known low and high standard for calibration. It should be noted that measurement uncertainty cannot be minimised by repetition or duplication, since by the very nature of the AirCore sampling technique, it is considered a series of unique samples.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAirCore sample storage time, sample resolution and positioning\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe accuracy and precision of the active AirCore samples are dependent on the storage time following the completion of the field campaign. An AirCore sample is typically measured in the lab directly after a field experiment, to keep the storage time, and thus the smearing effects by molecular diffusion, as short as possible. Extensive laboratory storage tests were done to evaluate the profile loss, i.e. the storability of molecular hydrogen in an active AirCore and determine the necessary maximum time for which an accurate retrieval can be guaranteed. Further details regarding these experiments are available in the Supplementary information (section 1: Methods \u0026amp; Materials). Across all sampling days, the median storage time, calculated from the ending of the sampling time, was for the car AirCore 0.75 h while for the UAV it was 1.41 h, before we started our analysis.\u003c/p\u003e \u003cp\u003eGiven the continuous air sampling in the AirCore but discrete analyses on the GC, a relation needs to be established between the H\u003csub\u003e2\u003c/sub\u003e mole fractions and the path driven or flown. The spatial distribution is primarily influenced by sample size, sampling flow and mobility. With a constant sampling flow, each discrete sample linearly corresponds to a specific time duration and range of GPS coordinates. Smaller sample sizes increase the resolution of the trace gas profile and decrease the spatial distribution. Considering the spatial distribution, the samples are categorised into stationary and mobile, depending on whether the mobile platform (passenger car or UAV) was stationary or in motion. The analysis of the stationary samples is straightforward, the GPS-coordinates directly pinpoint the representative location. The mobile samples require additional interpretation and assumptions. As the mobile platform (passenger car or UAV) moves during sampling, each discrete sample corresponds to a range of GPS-coordinates. Given the filling mode of the sample loop (see 2.2.1), the actual sampled air of the discrete sample corresponds to the final segment of the loop flush. The assigned location is deducted to be at 83 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e 10% of the discrete sample\u0026rsquo;s transect, based on the loss during equilibration and the flushing time, a more extensive explanation is given in the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFlasks analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe flasks filled in the field were measured on the GC-PDHID for H\u003csub\u003e2\u003c/sub\u003e and on the CRDS system for CO\u003csub\u003e2\u003c/sub\u003e, CO and CH\u003csub\u003e4\u003c/sub\u003e with well-established methods in our lab \u003csup\u003e22\u003c/sup\u003e, more details in the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Measurement platforms\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.1 Passenger car\u003c/h2\u003e \u003cp\u003eFor sampling from a passenger car, the sampler is installed on the backseat next to the operator. The inlet of the AirCore is positioned outside of the car's rear window (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with the filter facing backwards and downwards to protect the inlet from potential rainfall and impact from insects. The measurement starts when the AirCore is set to sampling mode and the pump flow is set to either 45 or 60 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A detailed description of the car AirCore is provided in the Supplementary information (section 1: Methods \u0026amp; Materials). The ambient air is dried at the inlet of the AirCore with magnesium perchlorate. The active AirCore in the passenger car is equipped with a GPS tracker and a logger for timestamp, pump pressure, coil pressure, volume sampled, and volume collected in the coil. After the field campaign is completed, typically 2 hours duration from start sampling, the AirCore is transported immediately back to the laboratory for analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.2 Unmanned Aerial Vehicle (UAV)\u003c/h2\u003e \u003cp\u003eThe UAV or drone has a carrier capacity of 8 kg, sufficient for our AirCore system with a payload capacity of 6 kg. Considering the sample volume and flow rate, the maximum flight duration is 15 minutes. Although the UAV can ascend vertically up to 500 m, flight restrictions limited the vertical profile to an altitude of maximally 140 m. The UAV AirCore system has a similar sampling method as the passenger car, only simplified, with a manual pump switch and without parameter registration. GPS tracking, flight speed, altitude, and other technical parameters are recorded by the UAV. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a simple schematic of the aerial AirCore setup, for more details we refer to the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement site\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Site description\u003c/h2\u003e \u003cp\u003eLocated on the northeast side of Groningen (the Netherlands), the chemistry park Delfzijl (53.3105 N, 6.9752 E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) offers an ideal site for the field campaigns outlined for this study. With the Wadden Sea located north of the park and rural areas extending at least 18 km southward (SLD: Straight-line distance), minimum external influences on the atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions are ensured. The chemistry park is a confined piece of land surrounded by a rural environment with large-scale and fully operational chemical processes in which molecular hydrogen is either produced, transported, stored or consumed within the park. With predominantly north-westerly winds, the emissions from the park itself are easily distinguishable downwind of the park, while easterly winds allow for observing background conditions from the surrounding agricultural area. The closest potential polluter outside of the park, is the seaport Eemshaven, 17 km SLD away to the North-West, hosting coal-and gas-fired power plants but so-far no known H\u003csub\u003e2\u003c/sub\u003e emitting processes. Its emissions are potentially only visible (co-emitted with CO\u003csub\u003e2\u003c/sub\u003e and CO) with strong north-western winds. In the WSW at 28 km SLD a potential H\u003csub\u003e2\u003c/sub\u003e emitter is the urban area of the city of Groningen, with e.g. a H\u003csub\u003e2\u003c/sub\u003e fuelling station and H\u003csub\u003e2\u003c/sub\u003e buses. The optimal accessibility to the chemistry park, without the need for permits or registration for all mobile platforms, facilitates the field campaigns. Furthermore, the cooperation with the industry stakeholders strengthens the eventual emission estimates and loss rate predictions, because of the specifications given on the processes and production rates (through personal communication).\u003c/p\u003e \u003cp\u003eConsidering the sampling equipment, the proximity of Delfzijl to our laboratory ensured the profile accuracy by maintaining a minimum storage time. Even though the primary processes in the chemistry park in Delfzijl are not entirely representative of the complete hydrogen value chain, it is crucial to identify potential H\u003csub\u003e2\u003c/sub\u003e leakages or emissions from real facilities, before a widespread implementation is completed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Industrial hydrogen production and emission estimates\u003c/h2\u003e \u003cp\u003eFor our purpose, the chemistry park Delfzijl houses four relevant industries, based on a priori information about production and emission processes, to ensure confidentiality the industries in question will be referred to by an arbitrary numbering. At industry N1 (Industry N1 consists of two processes), NaOH, Cl and H\u003csub\u003e2\u003c/sub\u003e are produced from brine via the process of electrolysis (1a). The surplus H\u003csub\u003e2\u003c/sub\u003e is transported over the park to a nearby power plant for combustion (1b) and to industry F3 for direct usage at a hydrogen fuelling station. Any remaining H\u003csub\u003e2\u003c/sub\u003e is directly vented. At industry E2 hydrogen peroxide is produced, for which in-house H\u003csub\u003e2\u003c/sub\u003e is produced from steam-methane reformation.\u003c/p\u003e \u003cp\u003eFor industries N1 and E2, hydrogen emissions from purging and venting on sampling days are known (personal communication). Since no emission data for F3 are available, a loss rate estimation of 0.5% (~\u0026thinsp;0.24% high-pressure storage, ~\u0026thinsp;0.25% compressor leakage) is used from the known storage present \u003csup\u003e9\u003c/sup\u003e. Industry R4 encompasses all remaining factories/companies for which no a priori information is available regarding H\u003csub\u003e2\u003c/sub\u003e production, consumption, purging or venting. The daily production is calculated using the average annual production rate for industry N1 and industry E2. Combined with the H\u003csub\u003e2\u003c/sub\u003e consumption of industry F3, the estimated production and emission rates per day are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where only for day 1 the H\u003csub\u003e2\u003c/sub\u003e emissions differ from the rest of the measurement days. A detailed description of the daily production and emission estimates per category is provided in the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProduction and emission estimates, as provided via personal communication.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate (dd-mm-yy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduction per day (10\u003csup\u003e7\u003c/sup\u003e g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmission per day (10\u003csup\u003e3\u003c/sup\u003e g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04-08-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (*10\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06-09-23 to 20-12-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Emission estimate methods and uncertainties\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Mass Balance approach\u003c/h2\u003e \u003cp\u003eWe apply a Mass Balance approach to estimate the amount of H\u003csub\u003e2\u003c/sub\u003e emitted in g s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e by the Delfzijl chemistry park industries, by using the enhancement of atmospheric H\u003csub\u003e2\u003c/sub\u003e in an assumedly homogenous distributed 2D plane downwind of the park \u003csup\u003e1,7,18,26,36\u003c/sup\u003e. We use the following simple Mass Balance equation to derive the H\u003csub\u003e2\u003c/sub\u003e flux (Q in g s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) across the transect within the downwind plane of the chemistry park:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Q = C\\cdot \\frac{u\\cdot cos\\left(\\theta \\right)\\cdot \\varDelta A\\cdot {M}_{H2}\\cdot P}{R\\cdot T}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the Mass Balance Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), C is the enhancement of the H\u003csub\u003e2\u003c/sub\u003e mole fraction [mol H\u003csub\u003e2\u003c/sub\u003e/mol air] over background values. The average wind speed is denoted by ū [m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e], and the area of a vertical grid box perpendicular to the wind direction is given by ΔA [m\u003csup\u003e2\u003c/sup\u003e]. To account for uncertainty in the mean wind direction, a deviation to the wind angle (assumed perpendicular to the grid box) is represented by θ [degrees]. Finally, the molar mass of H\u003csub\u003e2\u003c/sub\u003e is given by M\u003csub\u003eH2\u003c/sub\u003e, P is the air pressure [Pa], R is the universal gas constant [m\u003csup\u003e3\u003c/sup\u003e\u0026sdot;Pa\u0026sdot;K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026sdot;mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e] and T is the mean atmospheric temperature [K]. We assume a standard atmospheric air pressure of 101325 Pa and an air temperature of 288.15 K. We run the Mass Balance in a Monte Carlo approach (N\u0026thinsp;=\u0026thinsp;500 simulations) to account for parameter uncertainties. The determination and uncertainties for the parameters used in the Mass Balance approach are discussed in more detail in the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Inverse Gaussian dispersion model approach\u003c/h2\u003e \u003cp\u003eAs a control and substantiation to the Mass Balance approach, we derive emission rates using the inverse Gaussian approach. For this, we used a standard point source Gaussian dispersion model (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in combination with the three-dimensional mole fraction data from the active AirCore measurements.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$C(x,y,z) = \\frac{Q}{2\\pi {\\sigma }_{y}{\\left(x\\right)\\sigma }_{z}\\left(x\\right)ū}exp(-\\frac{(y-{y}_{s}{)}^{2}}{2{{\\sigma }_{y}}^{2}\\left(x\\right)})\\left[exp\\right(\\frac{(z-{z}_{s}{)}^{2}}{2{{\\sigma }_{z}}^{2}\\left(x\\right)})+exp(\\frac{(z+{z}_{s}{)}^{2}}{2{{\\sigma }_{z}}^{2}\\left(x\\right)}\\left)\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the Gaussian dispersion model, C(x, y, z) are the enhanced H\u003csub\u003e2\u003c/sub\u003e mole fractions [mol H\u003csub\u003e2\u003c/sub\u003e mol air\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e] inside the plume at specific coordinates (x, y, z in metres) downwind from a source in the Delfzijl chemistry park. Q is the emission rate given in [g s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e] and ū is the wind speed along the plume direction in [m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]. The stability parameters \u0026#120590;\u003csub\u003e\u0026#119910;\u003c/sub\u003e and \u0026#120590;\u003csub\u003e\u0026#119911;\u003c/sub\u003e with units [m] (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) describe horizontal and vertical mixing, they depend on atmospheric stability and can be calculated using the Pasquill Gifford parameters found in the Supplementary information (section 1: Methods \u0026amp; Materials). The last exponential term in the equation represents the reflection of plumes from the surface \u003csup\u003e6\u003c/sup\u003e.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\sigma }_{y} = \\frac{r\\cdot x}{(1+\\frac{x}{a}{)}^{P}} {\\sigma }_{z} = \\frac{s\\cdot x}{(1+\\frac{x}{a}{)}^{q}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe identify 5 point sources for H\u003csub\u003e2\u003c/sub\u003e in the Delfzijl chemistry park that can be linked to activities involving significant production, storage or usage of H\u003csub\u003e2\u003c/sub\u003e (see section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e2.3.2\u003c/span\u003e). Point source locations (surface coordinates and emission height) are chosen based on process type and personal communication. Similar to the Mass Balance approach, a Monte Carlo approach is run 500 times to ensure not only local minima are found and parameter ranges are significantly explored. For more detailed explanations of the determination of uncertainties and parameters, see the Supplementary information (Section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Field experiments\u003c/h2\u003e \u003cp\u003eA total of 7 sampling transects on 7 separate days have been made with the active AirCore system aboard the passenger car. For 3 out of 7 transects driven on 4th Augustus, 11th September and 6th December, in the afternoon between 14h:00m and 16h:00m local time (UTC\u0026thinsp;+\u0026thinsp;1), the wind direction spanned West to North (270\u0026ndash;350 degrees), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The UAV flights included four transects that lasted between 10h:30m to 13h:13m minutes carried out on 5th, 12th, 17th October and the 6th December of 2023 in the afternoon between 14h:00m and 16h:00m local time, of which two were flown successfully downwind and one upwind of the chemistry park. The height of the vertical profiles spans from 0 m (ground level) to ~\u0026thinsp;140 m in altitude. Both the passenger car and UAV flight details are summarised in the Supplementary information (section 1: Methods \u0026amp; Materials).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results \u0026 discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Active AirCore passenger car results\u003c/h2\u003e \u003cp\u003eWe measured strong enhancements of H\u003csub\u003e2\u003c/sub\u003e mole fractions with values up to 1346\u0026thinsp;\u0026plusmn;\u0026thinsp;2 ppb downwind of the chemistry park, relative to 530\u0026thinsp;\u0026plusmn;\u0026thinsp;11 ppb upwind. We summarise this data in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e with a 3D visualisation of individual samples\u0026rsquo; enhancement over the background, displayed along the north-west angle of incidence, corresponding to the wind direction. Out of 53 downwind samples analysed, 35 (66%) had\u0026thinsp;\u0026gt;\u0026thinsp;100 ppb enhancements, a number that is much larger than the typical seasonal changes, trends, and enhancements found at long-term monitoring sites that are situated away from local sources \u003csup\u003e29\u003c/sup\u003e. With no atmospheric chemical pathway to produce this hydrogen in-situ, this unequivocally points towards substantial emissions of H\u003csub\u003e2\u003c/sub\u003e in the Delfzijl park.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese emissions are not incidental but systematic (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), as we find them across all sampling days and also under different wind directions and atmospheric conditions. Compared to the samples collected upwind of the park, the downwind data shows consistent enhanced atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions, averaging 30 up to 280 ppb each day. Furthermore, due to an inhomogeneous release of anthropogenic H\u003csub\u003e2\u003c/sub\u003e emissions from the chemistry park, there is high variability (\u0026plusmn;\u0026thinsp;92 ppb) in the enhancement of individual samples collected on the same day. In contrast, the upwind sampled data remains consistently close to the atmospheric background mole fraction of H\u003csub\u003e2\u003c/sub\u003e (530\u0026thinsp;\u0026plusmn;\u0026thinsp;10 ppb). Moreover, the very low variability of the upwind data emphasises the stability of the atmospheric background mole fraction as well as that of our sampling and analysis system, as expected when no nearby sources have influenced the samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Active AirCore UAV results\u003c/h2\u003e \u003cp\u003eUAV-based sampling confirms the existence of substantial H\u003csub\u003e2\u003c/sub\u003e sources in the Delfzijl industrial park, although downwind measured mole fraction enhancements at the UAV flight level were lower (40\u0026ndash;100 ppb of H\u003csub\u003e2\u003c/sub\u003e) than we measured at the surface. However, the small variability (\u0026plusmn;\u0026thinsp;5 ppb) within daily flights relative to those in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e makes these signals very robust indicators of enhanced H\u003csub\u003e2\u003c/sub\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarises this by comparing two downwind-sampling days with one where we sampled upwind vertical profiles. The vertical profiles show that H\u003csub\u003e2\u003c/sub\u003e for all measurement days was relatively well mixed over the vertical plane, and the average enhancements over the background exceed the vertical H\u003csub\u003e2\u003c/sub\u003e gradients and variability. Local wind-shear effects near the surface could possibly have impacted data points below 20 metres in both downwind profiles; such near surface effects would also contribute to the higher variability found for our car samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Flasks sampling results\u003c/h2\u003e \u003cp\u003eOur flask-derived data confirms the variability in enhanced H\u003csub\u003e2\u003c/sub\u003e mole fractions found at the chemistry park in Delfzijl. The H\u003csub\u003e2\u003c/sub\u003e mole fraction measured from the car and in the flasks for similar GPS-coordinates show for upwind locations a clear background signal while over the span of the park enhanced H\u003csub\u003e2\u003c/sub\u003e mole fractions up to 950 ppb were found, see the Supplementary information for more details (section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Results \u0026amp; Discussion).\u003c/p\u003e \u003cp\u003eNext to this, our flask-derived data gives insight into co-located GHG emissions associated with atmospheric H\u003csub\u003e2\u003c/sub\u003e for three categories at the park: chemical processes from industry, microbial processes from biomass, and puff emissions from a hydrogen fuelling station. From Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it is evident that general chemical processes from industry exhibit no clear co-location with CO and CH\u003csub\u003e4\u003c/sub\u003e. Furthermore, the most enhanced atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions, with no co-located GHGs, originate from the hydrogen fuelling station situated at the start of the park. Only at the rear of the park, near the biomass waste incinerator, a clear co-location between atmospheric H\u003csub\u003e2\u003c/sub\u003e and microbial processes was found based on observations of CO and CH\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Emission estimates\u003c/h2\u003e \u003cp\u003eWe calculate loss rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) between 0.2 and 4.6% for H\u003csub\u003e2\u003c/sub\u003e-related activities at the chemistry park Delfzijl, relative to estimated daily production. On a day-to-day basis we find variations in emission rates originating from measured mole fractions and/or meteorological conditions, with median values ranging from 0.9\u0026ndash;5.0 g s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Mass Balance approach) to 0.1\u0026ndash;2.8 g s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (inverse Gaussian). This range partly shows the significant impact of the environmental conditions on the model\u0026rsquo;s outcome, but especially the variability in the observed H\u003csub\u003e2\u003c/sub\u003e mole fraction enhancements originating from the park contributes strongly to the quoted range. The variation in the enhanced H\u003csub\u003e2\u003c/sub\u003e mole fraction suggests consistent but not uniform emissions over time, leading to diverse emission estimates. Furthermore, the relative homogeneity of the flown UAV profiles substantiates the continuous wide spread of emissions. On the days when both a car and UAV enhancements were sampled and measured, the emission estimates are highly similar. This similarity in the determined leak rate by the UAV flight and the car data enhances the robustness of the outcome.\u003c/p\u003e \u003cp\u003eWe find that, on average, the inverse Mass Balance approach produces median emission rates 2.6 times as high as those by the inverse Gaussian approach for all days. We expect this is due to the presence of high mole fraction samples near sources in the passenger car dataset, which in the Mass Balance approach causes an overestimation of emissions due to an unrealistically large grid box area given the proximity to the source and the resulting short mixing time. Contrarily, the Inverse Gaussian approach can explain these high mole fraction samples using relatively low emission rates given the proximity to the source.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions \u0026 Outlook","content":"\u003cp\u003eWe developed a measurement method for low-level in-situ (semi)-continuous (industrial) H\u003csub\u003e2\u003c/sub\u003e emissions using an active AirCore sampler and a GC-PDHID analysis system. During a number of field experiments significantly enhanced atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fractions were detected at the industrial chemistry park in Delfzijl (Groningen province, the Netherlands), ranging from downwind mole fractions of 580 ppb up to 1500 ppb. The consistency in the enhanced atmospheric H\u003csub\u003e2\u003c/sub\u003e mole fraction during every experiment indicated a continuity in H\u003csub\u003e2\u003c/sub\u003e emissions from the chemistry park. In addition to the AirCore samples, we analysed grab samples from flasks collected during the field experiments. These flasks were intercompared on H\u003csub\u003e2\u003c/sub\u003e with the AirCore data (at similar GPS-coordinates), and besides H\u003csub\u003e2\u003c/sub\u003e, analysed on mole fractions of CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, and CO to gather additional information on co-located processes. From the flask data, the enhanced H\u003csub\u003e2\u003c/sub\u003e mole fractions found at the chemistry park could be divided into three sections: microbial production, chemical production and H\u003csub\u003e2\u003c/sub\u003e purge emissions. The flask results indicated that most of the enhanced H\u003csub\u003e2\u003c/sub\u003e mole fractions were substantiating the AirCore data and did not correlate with enhanced mole fractions of CO\u003csub\u003e2,\u003c/sub\u003e CO and CH\u003csub\u003e4\u003c/sub\u003e except for CH\u003csub\u003e4\u003c/sub\u003e and CO emissions in the proximity of a biomass waste incinerator at the park. The UAV-based AirCore downwind vertical profiles were relatively well mixed showing some plume variability in H\u003csub\u003e2\u003c/sub\u003e mole fraction along the 140 m altitude. The background upwind profile showed a nearly constant continuous background signal throughout the profile. Our first empirically determined emission estimates showed percentages (0.2\u0026ndash;5%) well within the range of model predictions. Our results represent a stepping stone in the development of an easy-to-use and highly accurate sampling technique to detect and quantify in situ H\u003csub\u003e2\u003c/sub\u003e emissions from leakage, purging and storage, pivotal for the development of the energy transition.\u003c/p\u003e \u003cp\u003eWith the data obtained from this study, we hope to inform industry and policy makers to not oversee the environmental impact of current small H\u003csub\u003e2\u003c/sub\u003e emissions, considering the plans for significant upscaling of sectors along the hydrogen value chain. For future work, data gathered with our sampling and analysis method has the potential to substantiate model emission estimates across the hydrogen value chain. Further work will focus on increasing the number of transects for the further expansion of data sets, not only for the chemical industry but also focused on other parts of the hydrogen value chain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the technical support of Marc O. Bleeker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;W.P. and H.A.J.M. initiated the study. I.M.W. and H.A.S. contributed to the study concept and design. Material preparation and all experiments were conducted by I.M.W., H.A.S. and S.M.A.C.. Data analysis was done by I.M.W. and H.A.S. The system setup was supported by B.A.M.K.. The model development was done by F.T.S. and W.P.. The draft of the manuscript was written by I.M.W. with support of H.A.S. and F.T.S.. All authors read, commented, and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and analysis software used in this manuscript is accessible from an open access data archive under ‘https://github.com/IrisMWestra/Atmospheric-hydrogen-Delfzijl-.git’. For more information correspondence should be addressed to I.M.W.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project has received financial support from the Nationaal Programma Groningen and the European Union via the subsidy ‘Waterstof Werkt: Train and Learn Hub ’ and by the Gas \u0026amp; Hydrogen Partnerships Shell Nederland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to I.M.W..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndersen, T. (2021). \u003cem\u003eQuantifying Local to Regional Emissions of Methane Using UAV-based Atmospheric Concentration Measurements\u003c/em\u003e (Vols. 978-94-6416-887\u0026ndash;7).\u003c/li\u003e\n\u003cli\u003eAndersen, T., Scheeren, B., Peters, W., \u0026amp; Chen, H. (2018). A UAV-based active AirCore system for measurements of greenhouse gases. \u003cem\u003eAtmospheric Measurement Techniques\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(5), 2683\u0026ndash;2699. https://doi.org/10.5194/amt-11-2683-2018\u003c/li\u003e\n\u003cli\u003eBarnes, D. H., Wofsy, S. C., Fehlau, B. P., Gottleib, E. W., Elkins, J. W., Dutton, G. S., \u0026amp; Novelli, P. C. (2003). Hydrogen in the atmosphere: Observations above a forest canopy in a polluted environment. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(6). https://doi.org/10.1029/2001jd001199\u003c/li\u003e\n\u003cli\u003eBertagni, M. B., Pacala, S. W., Paulot, F., \u0026amp; Porporato, A. (2022). Risk of the hydrogen economy for atmospheric methane. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 7706. https://doi.org/10.1038/s41467-022-35419-7\u003c/li\u003e\n\u003cli\u003eCrosson, E. R. (2008). A cavity ring-down analyzer for measuring atmospheric levels of methane, carbon dioxide, and water vapor. \u003cem\u003eApplied Physics B: Lasers and Optics\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(3 SPECIAL ISSUE), 403\u0026ndash;408. https://doi.org/10.1007/s00340-008-3135-y\u003c/li\u003e\n\u003cli\u003eCsanady, G. T. (1973). \u003cem\u003eTurbulent diffusion in the environment\u003c/em\u003e (Vol. 3). Boston, D. Reidel Pub. Co.\u003c/li\u003e\n\u003cli\u003eFiehn, A., Kostinek, J., Eckl, M., Klausner, T., Galkowski, M., Chen, J., Gerbig, C., R\u0026ouml;ckmann, T., Maazallahi, H., Schmidt, M., Korbeń, P., Ne\u0026ccedil;ki, J., Jagoda, P., Wildmann, N., Mallaun, C., Bun, R., Nickl, A. L., J\u0026ouml;ckel, P., Fix, A., \u0026amp; Roiger, A. (2020). Estimating CH4, CO2and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach. \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(21), 12675\u0026ndash;12695. https://doi.org/10.5194/acp-20-12675-2020\u003c/li\u003e\n\u003cli\u003eFrancey, R. J.; Steele, L. P.; Spencer, D. A.; Langenfelds, R. L.; Law, R. M.; Krummel, P. B.; Fraser, P. J.; Etheridge, D. M.; Derek, N.; Coram, S. A.; Cooper, L. N.; Allison, C. E.; Porter, L.; Baly, S. The CSIRO (Australia) measurement of greenhouse gases in the global atmosphere. In: Tindale, N. W.; Derek, N.; Fraser, P. J. editors, editor/s. Baseline Atmospheric Program Australia. Melbourne: \u003cem\u003eBureau of Meteorology and CSIRO Atmospheric Research\u003c/em\u003e; 2003. 42-53. http://hdl.handle.net/102.100.100/191835?index=1 \u003c/li\u003e\n\u003cli\u003eFrazer-Nash. (2022). \u003cem\u003eFugitive hydrogen emissions in a future hydrogen economy\u003c/em\u003e. 52.\u003c/li\u003e\n\u003cli\u003eGerbig, C., K\u0026ouml;rner, S., \u0026amp; Lin, J. C. (2008). Vertical mixing in atmospheric tracer transport models: error characterization and propagation. In \u003cem\u003eAtmos. Chem. Phys\u003c/em\u003e (Vol. 8). www.atmos-chem-phys.net/8/591/2008/\u003c/li\u003e\n\u003cli\u003eGlueckauf, E., \u0026amp; Kitt, G. P. (1957). The hydrogen content of atmospheric air at ground level. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(358), 522\u0026ndash;528. https://doi.org/10.1002/qj.49708335808\u003c/li\u003e\n\u003cli\u003eHauglustaine, D. (2022). Unpacking the climate impact of hydrogen. In \u003cem\u003eUnpacking the climate impact of hydrogen\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHauglustaine, D., Paulot, F., Collins, W., Derwent, R., Sand, M., \u0026amp; Boucher, O. (2022). Climate benefit of a future hydrogen economy. \u003cem\u003eCommunications Earth and Environment\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1). https://doi.org/10.1038/s43247-022-00626-z\u003c/li\u003e\n\u003cli\u003eIPCC. (2001). \u003cem\u003eCLIMATE CHANGE 2001: THE SCIENTIFIC BASIS\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eJordan, A., \u0026amp; Steinberg, B. (2009). \u003cem\u003eIs it time for a WMO Hydrogen calibration scale?\u003c/em\u003e http://www.wmo.int/pages/prog/arep/gaw/gaw-reports.html\u003c/li\u003e\n\u003cli\u003eJordan, A., \u0026amp; Steinberg, B. (2011). Calibration of atmospheric hydrogen measurements. \u003cem\u003eAtmospheric Measurement Techniques\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 509\u0026ndash;521. https://doi.org/10.5194/amt-4-509-2011\u003c/li\u003e\n\u003cli\u003eKarion, A., Sweeney, C., Tans, P., \u0026amp; Newberger, T. (2010). AirCore: An innovative atmospheric sampling system. \u003cem\u003eJournal of Atmospheric and Oceanic Technology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(11), 1839\u0026ndash;1853. https://doi.org/10.1175/2010JTECHA1448.1\u003c/li\u003e\n\u003cli\u003eKlausner, T., Mertens, M., Huntrieser, H., Galkowski, M., Kuhlmann, G., Baumann, R., Fiehn, A., J\u0026ouml;ckel, P., P\u0026uuml;hl, M., \u0026amp; Roiger, A. (2020). Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO2 and CH4 in situ observations in summer 2018. \u003cem\u003eElementa\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1). https://doi.org/10.1525/ELEMENTA.411\u003c/li\u003e\n\u003cli\u003eLipi\u0026auml;inen, S., Lipi\u0026auml;inen, K., Ahola, A., \u0026amp; Vakkilainen, E. (2023). Use of existing gas infrastructure in European hydrogen economy. \u003cem\u003eInternational Journal of Hydrogen Energy\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(80), 31317\u0026ndash;31329. https://doi.org/10.1016/j.ijhydene.2023.04.283\u003c/li\u003e\n\u003cli\u003eMcKinsey. (2020). \u003cem\u003eEurope\u0026rsquo;s path to decarbonization McKinsey\u003c/em\u003e. https://www.mckinsey.com/~/media/mckinsey/business%20functions/sustainability/our%20insights/how%20\u003cbr/\u003ethe%20european%20union%20could%20achieve%20net%20zero%20emissions%20at%20net%20zero%20cost/net-zero-europe-vf.pdf\u003c/li\u003e\n\u003cli\u003eNajjar, Y. S. (2019). Hydrogen Leakage Sensing and Control: (Review). \u003cem\u003eBiomedical Journal of Scientific \u0026amp; Technical Research\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5). https://doi.org/10.26717/bjstr.2019.21.003670\u003c/li\u003e\n\u003cli\u003eNguyen, L. N. T., Meijer, H. A. J., Van Leeuwen, C., Kers, B. A. M., Scheeren, H. A., Jones, A. E., Brough, N., Barningham, T., Pickers, P. A., Manning, A. C., \u0026amp; Luijkx, I. T. (2022). Two decades of flask observations of atmospheric (O2/N2), CO2, and APO at stations Lutjewad (the Netherlands) and Mace Head (Ireland), and 3 years from Halley station (Antarctica). \u003cem\u003eEarth System Science Data\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), 991\u0026ndash;1014. https://doi.org/10.5194/essd-14-991-2022\u003c/li\u003e\n\u003cli\u003eNovelli, P. C., Crotwell, A. M., \u0026amp; Hall, B. D. (2009). Application of gas chromatography with a pulsed discharge helium ionization detector for measurements of molecular hydrogen in the atmosphere. \u003cem\u003eEnvironmental Science and Technology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(7), 2431\u0026ndash;2436. https://doi.org/10.1021/es803180g\u003c/li\u003e\n\u003cli\u003eNWP. (2021). \u003cem\u003eWerkplan Nationaal Waterstof Programma 2022-2025\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOcko, I. B., \u0026amp; Hamburg, S. P. (2022). Climate consequences of hydrogen emissions. \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(14), 9349\u0026ndash;9368. https://doi.org/10.5194/acp-22-9349-2022\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Shea, S. J., Allen, G., Fleming, Z. L., Bauguitte, S. J. B., Percival, C. J., Gallagher, M. W., Lee, J., Helfter, C., \u0026amp; Nemitz, E. (2014). Area fluxes of carbon dioxide, methane, and carbon monoxide derived from airborne measurements around Greater London: A case study during summer 2012. \u003cem\u003eJournal of Geophysical Research\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(8), 4940\u0026ndash;4952. https://doi.org/10.1002/2013JD021269\u003c/li\u003e\n\u003cli\u003ePaula P\u0026eacute;rez-Pe\u0026ntilde;a, M., Fisher, J. A., Millet, D. B., Yashiro, H., Langenfelds, R. L., Krummel, P. B., \u0026amp; Kable, S. H. (2022). Evaluating the contribution of the unexplored photochemistry of aldehydes on the tropospheric levels of molecular hydrogen (H 2 ). \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e. https://doi.org/10.5194/acp-2021-1052\u003c/li\u003e\n\u003cli\u003ePaulot, F., P\u0026eacute;tron, G., Crotwell, A. M., \u0026amp; Bertagni, M. B. (2023). \u003cem\u003eReanalysis of NOAA H2 observations: implications for the H2 budget\u003c/em\u003e. https://doi.org/10.5194/egusphere-2023-1602\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;tron, G., Crotwell, A. M., Mund, J., Crotwell, M., Mefford, T., Thoning, K., Hall, B., Kitzis, D., Madronich, M., Moglia, E., Neff, D., Wolter, S., Jordan, A., Krummel, P., Langenfelds, R., \u0026amp; Patterson, J. (2024). \u003cem\u003eAtmospheric H 2 observations from the NOAA Global Cooperative Air Sampling Network\u003c/em\u003e. https://doi.org/10.5194/amt-2024-4\u003c/li\u003e\n\u003cli\u003ePrinn, R. G., Weiss, R. F., Arduini, J., Arnold, T., Langley Dewitt, H., Fraser, P. J., Ganesan, A. L., Gasore, J., Harth, C. M., Hermansen, O., Kim, J., Krummel, P. B., Li, S., Loh, Z. M., Lunder, C. R., Maione, M., Manning, A. J., Miller, B. R., Mitrevski, B., \u0026hellip; Zhou, L. (2018). History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE). \u003cem\u003eEarth System Science Data\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 985\u0026ndash;1018. https://doi.org/10.5194/essd-10-985-2018\u003c/li\u003e\n\u003cli\u003ePWC. (2021). \u003cem\u003eHyWay 27: hydrogen transmission using the existing natural gas grid? Final report for the Ministry of Economic Affairs and Climate Policy\u003c/em\u003e. www.pwc.nl\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;ckmann, T., Rhee, T. S., \u0026amp; Engel, A. (2015). Atmospheric Chemistry and Physics Heavy hydrogen in the stratosphere. In \u003cem\u003eAtmos. Chem. Phys\u003c/em\u003e (Vol. 3). www.atmos-chem-phys.org/acp/3/2015/\u003c/li\u003e\n\u003cli\u003eSchmidt, \u0026amp; Seiler. (1970). New method for recording molecular hydrogen in atmospheric air. \u003cem\u003eJ Geophys Res\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(9), 1713\u0026ndash;1716. https://doi.org/10.1029/jc075i009p01713\u003c/li\u003e\n\u003cli\u003eSimmonds, P. G., Derwent, R. G., O\u0026rsquo;Doherty, S., Ryall, D. B., Steele, L. P., Langenfelds, R. L., Salameh, P., Wang, H. J., Dimmer, C. H., \u0026amp; Hudson, L. E. (2000). Continuous high-frequency observations of hydrogen at the Mace Head baseline atmospheric monitoring station over the 1994-1998 period. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(D10), 12105\u0026ndash;12121. https://doi.org/10.1029/2000JD900007\u003c/li\u003e\n\u003cli\u003eSun, T., Shrestha, E., Hamburg, S. P., Kupers, R., \u0026amp; Ocko, I. B. (2024). Climate Impacts of Hydrogen and Methane Emissions Can Considerably Reduce the Climate Benefits across Key Hydrogen Use Cases and Time Scales. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e. https://doi.org/10.1021/acs.est.3c09030\u003c/li\u003e\n\u003cli\u003eTong, X., van Heuven, S., Scheeren, B., Kers, B., Hutjes, R., \u0026amp; Chen, H. (2023). Aircraft-Based AirCore Sampling for Estimates of N2O and CH4 Emissions. \u003cem\u003eEnvironmental Science and Technology\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(41), 15571\u0026ndash;15579. https://doi.org/10.1021/acs.est.3c04932\u003c/li\u003e\n\u003cli\u003eTong, X., Van Heuven, S., Scheeren, B., Kers, B., Hutjes, R., \u0026amp; Chen, H. (2023). \u003cem\u003eSupporting Information (SI) for Research Article Aircraft-based AirCore sampling for estimates of N 2 O and CH 4 emissions\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eTromp, T. K., Shia, R.-L., Allen, M., Eiler, J. M., \u0026amp; Yung, Y. L. (2003). \u003cem\u003ePotential Environmental Impact of a Hydrogen Economy on the Stratosphere\u003c/em\u003e. www.sciencemag.org\u003c/li\u003e\n\u003cli\u003eU.S. Department of Energy. (2022). \u003cem\u003eU.S. National Clean Hydrogen Strategy and Roadmap\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWarwick, N., Griffiths, P., Keeble, J., Archibald, A., \u0026amp; Pyle, J. (2022). \u003cem\u003eAtmospheric implications of increased Hydrogen use\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWarwick, N. J., Archibald, A. T., Griffiths, P. T., Keeble, J., O\u0026rsquo;Connor, F. M., Pyle, J. A., \u0026amp; Shine, K. P. (2023). Atmospheric composition and climate impacts of a future hydrogen economy. \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(20), 13451\u0026ndash;13467. https://doi.org/10.5194/acp-23-13451-2023\u003c/li\u003e\n\u003cli\u003eWarwick, N. J., Bekki, S., Nisbet, E. G., \u0026amp; Pyle, J. A. (2004). Impact of a hydrogen economy on the stratosphere and troposphere studied in a 2‐D model. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(L05107).\u003c/li\u003e\n\u003cli\u003eWentworth, W. E., Cai, H., \u0026amp; Stearnsb, S. (1994). Pulsed discharge helium ionization detector Universal detector for inorganic and organic compounds at the low picogram level. \u003cem\u003eJournal of Chromatography A\u003c/em\u003e, \u003cem\u003e688\u003c/em\u003e, 135\u0026ndash;152.\u003c/li\u003e\n\u003cli\u003eYver, C., Schmidt, M., Bousquet, P., \u0026amp; Ramonet, M. (2011). Measurements of molecular hydrogen and carbon monoxide on the Trainou tall tower. \u003cem\u003eTellus, Series B: Chemical and Physical Meteorology\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(1), 52\u0026ndash;63. https://doi.org/10.1111/j.1600-0889.2010.00520.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4618373/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4618373/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProjections towards 2050 of the global hydrogen (H\u003csub\u003e2\u003c/sub\u003e) demand indicate an eight-fold increase in present-day hydrogen consumption. Leakage during production, transport, and consumption therefore presents a large potential for increases in the atmospheric hydrogen burden. Although not a greenhouse gas itself, hydrogen has indirect climate effects: through oxidation with the OH radical in the atmosphere the lifetime of methane increases, tropospheric ozone is produced, and the concentration of stratospheric water vapour increases. The Global Warming Potential of H\u003csub\u003e2\u003c/sub\u003e is estimated to be 12.8 times that of CO\u003csub\u003e2\u003c/sub\u003e. Available technologies to detect hydrogen emissions have been limited to risk assessments of industrial facilities, while smaller climate-relevant emissions remain undetected. The latter requires measurement capacity at the parts-per-billion level (ppb). We developed and tested a simple and effective method to detect small hydrogen emissions from industrial installations combining active AirCore sampling with ppb-precision analysis by gas chromatography. We applied our methodology at a chemistry park in the Groningen province, the Netherlands, where several hydrogen production and storage facilities are concentrated. From a car and an unmanned aerial vehicle, we detected for the first time small but consistent industrial emissions from leakage and purging across the hydrogen value chain, which include electrolysers, a hydrogen fuelling station, and chemical production plants. Our emission estimates indicate current loss rates between 1-5% of the estimated production and storage in these facilities. This is sufficiently large to urgently flag the need for monitoring and verification of H\u003csub\u003e2\u003c/sub\u003e emissions for the purpose of understanding our climate change trajectory in the 21\u003csup\u003est\u003c/sup\u003e century.\u003c/p\u003e","manuscriptTitle":"First detection of industrial hydrogen emissions using high-precision mobile measurements in ambient air","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 06:59:10","doi":"10.21203/rs.3.rs-4618373/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-21T14:59:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T22:59:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T00:08:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37355866870938538784876263823513021944","date":"2024-07-29T21:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115313783970057240196428027543954084646","date":"2024-07-29T19:10:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-26T07:10:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-26T04:05:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-26T03:15:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-25T08:39:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-21T16:05:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1eccc9f3-f966-49fb-981b-ab2a66cf995b","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33964751,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":33964752,"name":"Physical sciences/Energy science and technology/Renewable energy/Hydrogen energy"}],"tags":[],"updatedAt":"2024-10-21T16:00:52+00:00","versionOfRecord":{"articleIdentity":"rs-4618373","link":"https://doi.org/10.1038/s41598-024-76373-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-15 15:57:20","publishedOnDateReadable":"October 15th, 2024"},"versionCreatedAt":"2024-07-17 06:59:10","video":"","vorDoi":"10.1038/s41598-024-76373-2","vorDoiUrl":"https://doi.org/10.1038/s41598-024-76373-2","workflowStages":[]},"version":"v1","identity":"rs-4618373","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4618373","identity":"rs-4618373","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}