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LLM - Digital Humanities 16

ERROR: type should be string, got "https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. A versatile spaceborne architecture for immediate monitoring of the\n1 \nglobal methane pledge \n2 \n \n3 \nYuchen Wang1, Xvli Guo1, Yajie Huo1, Mengying Li2, Yuqing Pan1*, Shaocai Yu2*, Alexander \n4 \nBaklanov3, Daniel Rosenfeld4, John H. Seinfeld5, and Pengfei Li1* \n5 \n \n6 \n1College of Science and Technology, Hebei Agricultural University, Baoding, Hebei 071000, P.R. China \n7 \n2Research Center for Air Pollution and Health; Key Laboratory of Environmental Remediation and Ecological Health, \n8 \nMinistry of Education, College of Environment and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058,\n9 \nP.R. China \n10 \n3Science and Innovation Department, World Meteorological Organization (WMO), Geneva, Switzerland \n11 \n4Institute of Earth Science, The Hebrew University of Jerusalem, Jerusalem, Israel \n12 \n5Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA \n13 \n \n14 \n*Correspondence to: Pengfei Li (lpf_zju@163.com) \n15 \nShaocai Yu (shaocaiyu@zju.edu.cn) \n16 \nYuqing Pan (panyuqing@hebau.edu.cn) \n17 \n \n18 \n \n19 \n \n20 \n \n21 \n \n22 \n \n23 \n \n24 \n \n25 \n \n26 \n \n27 \n \n28 \n \n29 \nSubmitted to \n30 \nAtmospheric Chemistry and Physics \n \n31 A versatile spaceborne architecture for immediate monitoring of the \n1 \nglobal methane pledge \n2 Yuchen Wang1, Xvli Guo1, Yajie Huo1, Mengying Li2, Yuqing Pan1*, Shaocai Yu2*, Alexander \n4 \nBaklanov3, Daniel Rosenfeld4, John H. Seinfeld5, and Pengfei Li1* \n5 Atmospheric Chemistry and Physics Atmospheric Chemistry and Physics 1 1 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Abstract. 32 \nThe global methane pledge paves a fresh, critical way toward Carbon Neutrality. However, it remains largely invisible and \n33 \nhighly controversial due to the fact that planet-scale and plant-level methane retrievals have rarely been coordinated. This has \n34 \nnever been more essential within a narrow window to reach the Paris target. Here we present a versatile spaceborne architecture \n35 \nto address this issue. Using this framework, we patrol the world, like the United States, China, the Middle East, and North \n36 \nAfrica, and simultaneously uncover methane-abundant regions and plumes. These include new super-emitters, potential \n37 \nleakages, and unprecedented multiple plumes in a single source. More importantly, this framework is shown to challenge \n38 \nofficial emission reports that possibly mislead estimates from global, regional, to site scales, particularly by missing super-\n39 \nemitters. We reveal that this framework can enable ready-made satellites to initiate monitoring of the global methane pledge \n40 \nimmediately and is also versatile for upcoming stereoscopic measurements and artificial intelligence techniques. 41 1. Introduction \n42 That \n53 \nis, on the eve of the Paris target, those targets and emissions remain largely invisible worldwide and thus hinder effective \n54 ,\ng\n,\ng\np\np\ny\n(\n,\n)\nis, on the eve of the Paris target, those targets and emissions remain largely invisible worldwide and thus hinder effective \n54 is, on the eve of the Paris target, those targets and emissions remain largely invisible world\n4 mitigation. The main issue is the Paris framework relies on countries or corporate giants to re\n5 2015; Ganesan et al., 2019). Moreover, the reports are based on indirect statistics, such as O&G inventories, rather than direct \n56 \nmeasurements(Deng et al., 2022). This leads to a broad consensus that prominent discrepancies exist between the reports. For \n57 \nexample, field campaigns nearly double official claims of methane emissions in the United States by correcting leak \n58 \ndetection(A. et al., 2018). 59 measurements(Deng et al., 2022). This leads to a broad consensus that prominent discrepancies exist between the reports. For \n57 \nexample, field campaigns nearly double official claims of methane emissions in the United States by correcting leak \n58 measurements(Deng et al., 2022). This leads to a broad consensus that prominent discrepancies exist between the reports. For \n57 \nexample, field campaigns nearly double official claims of methane emissions in the United States by correcting leak \n58 \ndetection(A. et al., 2018). 59 To this end, widespread super-emitters present a unique opportunity worldwide (Duren et al., 2019; Pandey et al., 2019; T. et \n60 \nal., 2022; Zavala-Araiza et al., 2015, 2017). They are typically responsible for the underestimates of methane emissions \n61 \n(Alvarez et al., 2018; Duren et al., 2019; Itziar et al., 2021; T. et al., 2022; Thompson et al., 2016). Moreover, there is increasing \n62 \nevidence that methane emissions follow a heavy-tailed distribution (Duren et al., 2019; Frankenberg et al., 2016; T. et al., \n63 \n2022), for which relatively small number of sources (so-called super-emitters) can account for a disproportionately large share \n64 \nof total emissions. In contrast to area sources (e.g., cities), super-emitters are typically coal mines, wells, gathering stations, \n65 \nstorage tanks, pipelines, and flares, with even less than dozens of metres in diameter but high-concentrated methane plumes \n66 \n(Allen et al., 2013; Miller et al., 2019; Subramanian et al., 2015; Varon et al., 2019). Abstract. \n32 2 2 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 1. Introduction \n42 Global methane pledges finalized at the COP26 (the 26th United Nations Climate Change Conference of the Parties) have \n43 \nbeen never more ambitious (Schellnhuber et al., 2016; Schurer et al., 2018; United Nations, 2021). More than 100 countries \n44 \nhave promised 30% methane emission reductions by 2030. Also, energy giants (e.g., Shell and BP) have committed to clear \n45 \ntargets of methane mitigation. Such pledges have never been more essential within a narrow window (< ten years) to reach the \n46 \nParis target. The scientific context is that atmospheric methane is a powerful greenhouse gas second only to carbon dioxide \n47 \n(CO2), trapping ~ 80 times more heat than the same amount of CO2 (per molecule) over a 20-year time horizon (Etminan et \n48 \nal., 2016; Saunois et al., 2016, 2020). Worse still, it is thought to rise since 2007 (Mikaloff and Hinrich, 2019), surge since \n49 \n2014 (Nisbet et al., 2019), and set another record in 2021 (National Oceanic and Atmospheric Administration, 2022). 50 \nFortunately, methane is short-lived (∼ ten years) (K. et al., 2013) and can be reduced in half using existing technologies (Ocko \n51 \net al., 2021). 52 However, a classic dilemma emerges, dimming the hopes of scientists and policymakes (Masood and Tollefson, 2021). That \n53 \nis, on the eve of the Paris target, those targets and emissions remain largely invisible worldwide and thus hinder effective \n54 \nmitigation. The main issue is the Paris framework relies on countries or corporate giants to report emissions (A. et al., 2018, \n55 \n2015; Ganesan et al., 2019). Moreover, the reports are based on indirect statistics, such as O&G inventories, rather than direct \n56 \nmeasurements(Deng et al., 2022). This leads to a broad consensus that prominent discrepancies exist between the reports. For \n57 \nexample, field campaigns nearly double official claims of methane emissions in the United States by correcting leak \n58 \ndetection(A. et al., 2018). 59 However, a classic dilemma emerges, dimming the hopes of scientists and policymakes (Masood and Tollefson, 2021). 1. Introduction \n42 We thus anticipate that significant \n67 \nemission mitigation could be achieved by deploying well-designed systems to identify methane super-emitters. For instance, \n68 \nin support of the Paris agreement, the 17th World Meteorological Congress (2015) requested an Integrated Global Greenhouse \n69 \nGas Information System (IG3IS) that aimed to develop a measurement framework for methane emission reductions (Phil \n70 \nDeCola and WMO Secretariat, 2017). 71 To date, a large body of field measurements (e.g., in situ and aircraft surveys) between 2012 and 2020 has been designed for \n72 \nmethane super-emitters. Despite this, they are spatially confined (e.g., regionally) and temporally infrequent (e.g., a few weeks), \n73 \nincapable of exploring global methane super-emitters (A. et al., 2018; Conley et al., 2016; Duren et al., 2019; Marchese et al., \n74 To date, a large body of field measurements (e.g., in situ and aircraft surveys) between 2012 and 2020 has been designed for \n72 \nmethane super-emitters. Despite this, they are spatially confined (e.g., regionally) and temporally infrequent (e.g., a few weeks), \n73 \nincapable of exploring global methane super-emitters (A. et al., 2018; Conley et al., 2016; Duren et al., 2019; Marchese et al., \n74 3 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 2015; Nisbet et al., 2020; Smith et al., 2017; Thompson et al., 2016; Thorpe et al., 2016). Today, substantial advances have \n75 \nbeen made towards detecting and quantifying methane super-emitters from space (Cusworth et al., 2019; Hu et al., 2018; Itziar \n76 \net al., 2022; Jacob et al., 2016; Pandey et al., 2019; Thompson et al., 2016). Such advances, however, have rarely been expanded \n77 \nto measure the global methane pledge because large-scale swath and high-resolution sampling have not been coordinated. First, \n78 \nglobal methane monitoring has become possible. A flagship satellite mission is the TROPOspheric Monitoring Instrument \n79 \n(TROPOMI) onboard the Copernicus Sentinel-5 Precursor satellite (T. et al., 2022; Veefkind et al., 2012). It can offer daily \n80 \nglobal insights for methane column concentrations, with a large swath width of ~ 2600 km, a moderate resolution of 7.0 × 5.5 \n81 \nkm2 (since August 2019), and high signal-to-noise ratios. However, its relatively coarse spatial sampling still limits its \n82 \napplication to detect methane super-emitters (T. et al., 2022). 1. Introduction \n42 Second, next-generation satellite missions, pioneered by the \n83 \nGHGSat constellation (three satellites at the moment), emerge for mapping methane super-emitters (Cusworth et al., 2019), \n84 \nwith a narrow swath (e.g., ~ 12 km) but a ground-breaking high-resolution spatial sampling (e.g., 25 ~ 50 m)(Jervis et al., 2021; \n85 \nVaron et al., 2020). Complementary to the GHGSat constellation, satellite-based hyperspectral imager spectrometers, such as \n86 \nPRISMA, Gaofen-5, ZY1, Sentinel-2, and Worldview-3, have shown great potentials (Guanter et al., 2021; Itziar et al., 2021; \n87 \nSánchez-García et al., 2021; Varon et al., 2021). They can resolve methane enhancements and attribute them to specific \n88 \ninfrastructures via similar narrow swath and high-resolution sampling (e.g., 30 m). Note that regions those satellites usually \n89 \ngazed at are originally well-known home to methane super-emitters. Narrow swath coverage thus remains a crucial limitation \n90 \nfor global surveys of methane super-emitters. Collectively, existing satellite missions still lack both global vision and keen \n91 \ninsight and thus cannot sustain the global methane pledge. 92 Here we present a multi-tiered, space-based framework for global-scale and high-resolution methane retrievals. The key is that \n93 \nready-made satellite missions alone have the potential to initiate immediate monitoring of the global methane pledge. Using \n94 \nthis framework, we patrol the world, with an experimental focus on China, the United States, Iraq, Kuwait, and Algeria, and \n95 \nreveal both region-scale hotspots and plant-level super-emitters. We can even gaze at a single source to map multiple plumes \n96 \nand inspect possible methane leakages. These results can challenge national reports that possibly miss unexpected super-\n97 \nemitters or mislead emission magnitude. On the eve of the Paris target, at least while a global methane monitoring network is \n98 \nnot in place, this multi-tiered satellite constellation presented in this study has important implications for measuring global \n99 \nmethane pledges. Further information on methane retrievals and emission estimates, as well as uncertainty analysis, are shown \n100 \nin Materials and Methods. 101 2.1 Multi-tiered satellite constellation \n103 4\nThe multi-tiered satellite constellation was designed to reconcile global-scale and high-resolution methane monitoring. First, \n104 \nTROPOMI offered a unique potential for global methane monitoring, depending on its large-scale (i.e., 2600 km) swath, daily \n105 \nrevisit time, regional footprint (i.e., 5.5 × 7 km2 since August 2019), and sounding precision and accuracy (i.e., < 1 %) \n106 4 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. (Veefkind et al., 2012). Approximately, TROPOMI observed a full swath per second, which resulted in ∼ 216 spectra per \n107 \nsecond. This instrument comprised two spectrometer modules, the first involving near-infrared (NIR) spectral channels, and \n108 \nthe second dedicated to the shortwave-infrared (SWIR) spectral channel. The NIR and SWIR channels were equipped with \n109 \nspectral resolutions of 0.38 and 0.25 nm and spectral sampling ratios of 2.8 and 2.5, respectively. Since the NIR and SWIR \n110 \ndetectors are incorporated in different instrument modules, the NIR spectra will be co-registered with the SWIR spectra before \n111 \nperforming methane retrievals. The methane total column-averaged dry-air mole fraction (XCH4) is retrieved from near-\n112 \ninfrared (NIR) (757 ~ 774 nm) and shortwave-infrared (SWIR) (2305 ~ 2385 nm) spectral measurements for sunlight \n113 \nbackscattered by Earth's surface and atmosphere (Hu et al., 2018). In this study, only high-quality measurements, retrieved \n114 \nunder cloud-free and low aerosol load conditions, were used. These measurements were filtered, in addition, for solar zenith \n115 \nangle (< 70°), low viewing zenith angle (< 60°), and smooth topography ( the surface elevation of < 80 m within 5 km radius) \n116 \nas described in Hu et al. (28) (Hu et al., 2018). 117 Hyperspectral satellite missions severed as the second tier, responsible for mapping localized methane super-emitters \n118 \ndepending on their unprecedented resolution (i.e., 3m ~ 50m). Therein PRISMA, as an open-access representative, was \n119 \nspecifically suitable for this work. It can image the solar radiation reflected by the Earth’s surface and atmosphere via hundreds \n120 \nof spectral channels between the visible and SWIR spectrum (~ 400 ~ 2500 nm). Measurements in the SWIR spectrum from \n121 \n2000 to 2500 nm sampled absorption features from water vapor, carbon dioxide, and methane. Therein the 2100 nm and 2450 \n122 \nnm windows were especially sensitive to methane. 2.1 Multi-tiered satellite constellation \n103 Furthermore, the signal-to-noise ratio was reported to be about 100 in the \n123 \nSWIR for a relatively dark vegetation pixel and increased to above 200 for bright soil surfaces in oil and gas extraction sites. 124 \nMore importantly, it covered areas of 30×30 km2 with a 30 m spatial sampling. 125 We collected dozens of daily measurements from the multi-tiered satellite constellation. These measurements experimentally \n126 \nmapped regional methane hotspots and localized methane super-emitters across the United States, China, the Middle East (Iraq \n127 \nand Kuwait), and North Africa (Algeria). The acquisitions were mostly taken between April 2020 and January 2022. 128 2.2 Multi-tiered methane retrievals \n129 146 In the second tier of our framework, we applied the matched-filter algorithm to calculate per-pixel methane enhancements with \n147 \nrespect to background levels based on the SWIR sample spectrum (i.e., the 2100 - 2450 nm window) onboard the PRISMA \n148 \n(Foote et al., 2020; Guanter et al., 2021; Itziar et al., 2021). In theory, the retrieval method can depend on physically-based or \n149 \ndata-driven algorithms. The former aims to explicitly resolve the radiative transfer between the surface, the atmosphere, and \n150 \nthe hyperspectral spectrometers. A key representative is the family of differential optical absorption spectroscopy (DOAS) \n151 \nmethods (Cusworth et al., 2019, 2020, 2021b, 2021a). The latter seeks a methane absorption spectrum across a hyperspectral \n152 \nimage using statistical methods. It is commonly based on the matched-filter and the singular vector decomposition concepts. 153 \nThese methods are both widely applied and evaluated, especially onboard satellite (e.g., PRISMA, GF-5, and ZY-1) and \n154 \nairborne (e.g., AVIRIS and AVIRIS-NG) platforms (Cusworth et al., 2020; Foote et al., 2020; Guanter et al., 2021; Itziar et \n155 \nal., 2021; Thompson et al., 2016; Thorpe et al., 2016). 156 In this study, the data-driven retrieval based on the matched-filter concept was used. The main reason was that it could \n157 \nimplicitly account for potential radiometric and spectral errors in satellite-based imaging spectroscopy. For instance, vertical \n158 \nstriping was prevalent in hyperspectral measurements due to detector inhomogeneity, thus substantially degrading methane \n159 \nretrievals. The matched-filter algorithm focused on the per-pixel columns and thus tackled this issue in principle. Besides, the \n160 \nphysically-based method had to consider background concentrations that were difficult to determine around the super-emitters. 161 \nIn contrast, the data-driven method was independent of background levels and can directly seek methane enhancements. Finally, \n162 \nthe data-driven method generally had a substantially superior computational efficiency compared to the physically-based \n163 \nmethod. 164 In this study, the data-driven retrieval based on the matched-filter concept was used. The main reason was that it could \n157 \nimplicitly account for potential radiometric and spectral errors in satellite-based imaging spectroscopy. For instance, vertical \n158 \nstriping was prevalent in hyperspectral measurements due to detector inhomogeneity, thus substantially degrading methane \n159 \nretrievals. The matched-filter algorithm focused on the per-pixel columns and thus tackled this issue in principle. Besides, the \n160 \nphysically-based method had to consider background concentrations that were difficult to determine around the super-emitters. 2.2 Multi-tiered methane retrievals \n129 In the first tier of our framework, we employed the operational methane products via TROPOMI onboard the Sentinel 5 satellite. 130 \nThe target product was the column-averaged dry-air volume mixing ratio of methane (XCH4), which will be retrieved \n131 \nsimultaneously with scattering properties of the atmosphere. The operational retrieval algorithm is based on RemoTeC (Butz \n132 \net al., 2009; Hasekamp and Butz, 2008), which was originally developed for CO2 and methane retrievals from GOSAT \n133 \nobservations (Butz et al., 2011). It attempted to fit spectra observed by the TROPOMI-based NIR and SWIR channels. Its \n134 \nsensitivities to atmospheric scattering properties, atmospheric input data, and instrument calibration errors had been \n135 \nextensively evaluated(Sha et al., 2021; Verhoelst et al., 2021). As a result, the operational products were proved to be critically \n136 \nstable, with a convergence rate of 99%, and high significance by comparisons with both satellite-based (e.g., GOSAT) and \n137 \nground-based (e.g., TCCON) measurements. The required accuracy and precision of < 1 % for the XCH4 product were met \n138 \nfor clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. Moreover, the forward model \n139 5 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. error was less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water did not influence the \n140 \nretrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure \n141 \nand temperature remain below 0.3% and 2 K, respectively. Of all instrument calibration errors, the retrieval results were the \n142 \nmost sensitive to an error in the instrument spectral response function of the shortwave infrared channel. 143 To achieve long-term (i.e., one year) methane retrievals, we oversampled the TROPOMI data at 5 × 5 km2 resolution following \n144 \nSun et al. (2018) (Sun et al., 2018) where the full spatial footprint of the observation was taken into account by attributing the \n145 \nobserved value to grid cells weighted by the spatial overlap of the observation with those grid cells. 2.2 Multi-tiered methane retrievals \n129 2) \n180 This solution made ΔXCH4 normalized by the albedo term, which was similar to the per-pixel normalization in previous \n181 \nhyperspectral analysis (Kraut et al., 2005). 182 The premise to launch the matched-filter algorithm was the accurate knowledge of the response of the instrument spectra to \n183 \nthe methane absorption nature. To this end, the objective was to gain the best fit between the simulated and reference spectra. 184 \nAn initial step was thus conducted to update the spectral calibration for the channels within the 2100 - 2400 nm window, in \n185 \nwhich the channel wavelength centre and width were updated for each across-track position in each scene. Other details are \n186 \nillustrated in previous attempts (Foote et al., 2020; Guanter et al., 2021; Itziar et al., 2022). 187 2.2 Multi-tiered methane retrievals \n129 161 \nIn contrast, the data-driven method was independent of background levels and can directly seek methane enhancements. Finally, \n162 \nthe data-driven method generally had a substantially superior computational efficiency compared to the physically-based \n163 \nmethod. 164 The matched-filter retrieval used here was similar to the one used by Thompson et al. (2016) (Thompson et al., 2016) for the \n165 \nHyperion imaging spectrometer onboard the EO-1 satellite. The calculation processes of methane enhancements (ΔXCH4, \n166 \nppb) were as follows. 167 The matched-filter retrieval used here was similar to the one used by Thompson et al. (2016) (Thompson et al., 2016) for the \n165 \nHyperion imaging spectrometer onboard the EO-1 satellite. The calculation processes of methane enhancements (ΔXCH4, \n166 \nppb) were as follows. 167 𝚫𝐗𝐂𝐇𝟒(𝐱⃗ ) =\n(𝐱⃗ −𝛍⃗⃗ )𝐓𝚺−𝟏𝐭 \n 𝐓\n𝟏 (Eq. 1). 168 𝚫𝐗𝐂𝐇𝟒(𝐱⃗ ) =\n(𝐱⃗ −𝛍⃗⃗ )𝐓𝚺−𝟏𝐭 \n𝐭 𝐓𝚺−𝟏𝐭 (Eq. 1). 68 The 𝒙⃗⃗ denoted the spectrum under analysis. The 𝝁⃗⃗ and 𝚺 represented the mean background radiance and corresponding \n169 \ncovariance, respectively. They were calculated based on per-column spectrums in order to consider different responses of \n170 \nacross-track detectors to radiance. The 𝒕 was the target spectrum that reflected the background radiance enhanced by the \n171 \nmethane plume. It was generated by the elementwise multiplication of 𝝁⃗⃗ and 𝒌⃗⃗ , This implicit parameter 𝒌⃗⃗ represented a unit \n172 6 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. methane absorption spectrum derived from a look-up table simulated by the MODTRAN radiative transfer model. Similarly, \n173 \nthe spectral convolution was also performed on a per-column basis. 174 In theory, methane enhancements detected in spectrometers generally exhibit sparsity, especially over low albedo surfaces. 175 \nWe thus accounted for such non-specificity effects to improve the basic version of the matched-filter model. A major measure \n176 \nto compensate for the albedo effect was to scale the target spectrum 𝒕 by the pixel-specific albedo factor due to the fact that \n177 \nthe Beer–Lambert absorption law depended on the initial radiance in the absence of the absorber. Here the pixel-specific scalar \n178 \n𝒇 was calculated based on the spectral average 𝝁⃗⃗ and the analysis spectrum 𝒙⃗⃗ as follows: \n179 \n𝒇=\n𝒙⃗⃗ 𝑻𝝁\n𝝁𝑇𝝁. (Eq. 2.3 Multi-tiered attribution of methane hotspots and plumes \n188 In the first tier of our framework, we filtered the TROPOMI-based methane retrievals to identify region-scale hotspots. In each \n189 \nscene, we focused on the grids with anomalous values that were noticeably higher than the average. In this study, we used a \n190 \nBoolean mask to define the pixels that were affected by the methane emissions (Pandey et al., 2019). Although automatic \n191 \nthreshold and algorithm might result in more consistent and flexible identifications, no satisfactory set of criteria was found \n192 \nthat could be applied for this study. This was mainly because, in localized regions, the methane budgets responded to the \n193 \nchanges in not only the super-emitters but also the background. Assisted by artificial intelligence techniques in the future \n194 \n(Ouerghi et al., 2021; Paoletti et al., 2018; Yang et al., 2018; Yu et al., 2017; Zhang et al., 2018), our framework can derive a \n195 \nglobal, operational, and open-access methane monitoring network. As expected, multiple hotspots of interest result, and here \n196 \nwe focused on those in the United States, China, Iraq, Kuwait, and Algeria. 197 In the second tier of our framework, we applied visual inspection to detect plumes using the PRISMA-based methane retrievals \n198 \n(Itziar et al., 2021; Martin et al., 2018; T. et al., 2022; Varon et al., 2020). To date, it was still challenging to distinguish \n199 \nmethane plumes in hyperspectral images using full physically-based algorithms. The main cause was potential methane \n200 \nretrieval artifacts from hyperspectral satellites that were spatially correlated to surface features. Specifically, we manually \n201 \nsearched for methane enhancement pixels with gas-plume-like shapes, i.e., high methane enhancements progressively \n202 \ndecreased downwind. The resulting pixels were subsequently compared to the spectral radiance data at the 2300 nm absorption \n203 \nfeature sensitive to low surface albedos. In this way, the fake positives due to specific surface features were prevented. On this \n204 7 7 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. basis, the candidate pixels were overlaid over simultaneous (i.e., hourly) wind fields and high-resolution imageries in \n205 \nindividual scenes. They would be considered to be true plumes if they roughly aligned with simultaneous wind direction and \n206 \noriginated from explicit infrastructures. Here the high-resolution satellite imageries were taken from the Google Map. 2.3 Multi-tiered attribution of methane hotspots and plumes \n188 The \n207 \nhourly wind field data came from the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather \n208 \nForecasts (ECMWF) (Hersbach et al., 2020; Hoffmann et al., 2019). Finally, we manually drew polygons to mask such \n209 \nresulting plumes out. As preparation for plume emission quantification, we removed the background using the threshold of the \n210 \nmedian values of the scenes. 211 These satellite imageries allowed us to categorize methane plumes within narrow spatial scales between 50 to 500 m2, such as \n212 \nO&G extraction platforms, storage tanks, and compressor stations. They even enabled the attribution of plumes to specific \n213 \nemission ports in individual sources due to their very high resolution. Furthermore, we could name them based on points of \n214 \ninterest in the Google Map. On this basis, such sources could be visually retrospected via long-term, high-resolution (i.e., 10 \n215 \nm) satellite images from the Sentinel-2 mission (Ehret et al., 2021; Varon et al., 2021). Their key details, like ages and statuses \n216 \n(e.g., active or inactive), were thus collected reliably. Note that, regarding such information, national reports were typically \n217 \ncredible but inaccessible, particularly in global missions. In addition, it should be highlighted that, on top of considerably high \n218 \nbudgets, like megacities, there must be super-emitters undetectable in our way. Other causes are discussed in uncertainty \n219 \nanalysis in Supplement Information. 220 3 Results and discussions \n262 3 Results and discussions \n262 2.5 Uncertainty Analysis \n248 The objective of this work was to promote a multi-tiered satellite constellation that can monitoring global methane pledges. 249 \nTo better understand the performance of our framework, we conducted comprehensive uncertain analysis. Note that the \n250 \nprotocol of the uncertain analysis on our framework originated from previous studies (Itziar et al., 2021; Varon et al., 2020). 251 \nSpecifically, we required to account for the uncertainties in the TROPOMI-based and PRISMA-based methane retrievals and \n252 \nsubsequent emission estimates. Therein the operational TROPOMI-based methane retrieval products had been evaluated \n253 \nstrictly and proved to be reliable globally (except in low- and high-albedo and snow-covered areas) (Lorente et al., 2021; Sha \n254 \net al., 2021). In this work, we thus focused on three main sources of uncertainties, specifically including (1) uncertainties in \n255 \nthe PRISMA-based methane retrievals; (2) uncertainties in the TROPOMI-based methane emission estimates; and (3) \n256 \nuncertainties in PRISMA-based methane emission estimates. During the analysis for the latter two uncertain sources, we would \n257 \nfurther investigate the potential wind impacts on the methane emission estimates. Note that it remained challenging to directly \n258 \nquantify the uncertainties in the wind fields across our cases due to the lack of measurements. We would thus assess the \n259 \nvariations in the methane emission estimates driven by distinct wind data. From such analysis, we could confirm the reliable \n260 \nperformance of our framework. Details can be found in Supplementary Information. 261 2.4 Multi-tiered quantification of methane emissions \n221 This processes were conducted in \n243 \nprevious studies(Cusworth et al., 2019, 2021b; Itziar et al., 2021). As shown in previous findings, the major error source came \n244 \nfrom the 𝑼𝟏𝟎 term. Its random distributions typically corresponded to the 50% random error. On this basis, this error was \n245 \nintegrated quadratically with the standard error of the 𝐈𝐌𝐄, the result of which can be treated as the final random error of 𝑸. 246 \nThe intrinsic errors of the IME model were quantified in the following uncertain analysis. 247 2.4 Multi-tiered quantification of methane emissions \n221 In the first tier of our framework, the effective wind speed (𝑼𝐞𝐟𝐟) was defined as the 10-m wind speed 𝑼𝟏𝟎 obtained from the \n236 \nERA5 reanalysis dataset. According to the detected hotspot, the value at the nearest hour and location were used. 237 \nIn the second tier of our framework, we applied an ensemble of large eddy simulations (LES) to establish an empirical, linear \n238 \nrelationship between 𝑼𝐞𝐟𝐟 and the measured 10-m wind speed 𝑼𝟏𝟎 as follows (Fig. S9) \n239 In the first tier of our framework, the effective wind speed (𝑼𝐞𝐟𝐟) was defined as the 10-m wind speed 𝑼𝟏𝟎 obtained from the \n236 \nERA5 reanalysis dataset. According to the detected hotspot, the value at the nearest hour and location were used. 237 \nIn the second tier of our framework, we applied an ensemble of large eddy simulations (LES) to establish an empirical, linear \n238 \nrelationship between 𝑼𝐞𝐟𝐟 and the measured 10-m wind speed 𝑼𝟏𝟎 as follows (Fig. S9) \n239 \n𝑼𝐞𝐟𝐟= 𝟎. 𝟖𝟔𝟎𝟐𝑰𝒏(𝑼𝟏𝟎) + 𝟏. 𝟏𝟓𝟏𝟑. (Eq. 5) \n240 \nThe configurations of these simulations, such as spatial resolution and precision, were comparable to our PRISMA data. Other \n241 \ndetails in this methodology were described in Varon et al. (2018) (Varon et al., 2018). 242 \nWe estimated the uncertainties of 𝑸 by propagating the random errors in 𝑼𝟏𝟎 and 𝐈𝐌𝐄. This processes were conducted in \n243 \nprevious studies(Cusworth et al., 2019, 2021b; Itziar et al., 2021). As shown in previous findings, the major error source came \n244 \nfrom the 𝑼𝟏𝟎 term. Its random distributions typically corresponded to the 50% random error. On this basis, this error was \n245 \nintegrated quadratically with the standard error of the 𝐈𝐌𝐄, the result of which can be treated as the final random error of 𝑸. 246 \nThe intrinsic errors of the IME model were quantified in the following uncertain analysis. 247 The configurations of these simulations, such as spatial resolution and precision, were comparable to our PRISMA data. Other \n241 \ndetails in this methodology were described in Varon et al. (2018) (Varon et al., 2018). 242 The configurations of these simulations, such as spatial resolution and precision, were comparable to our PRISMA data. Other \n241 \ndetails in this methodology were described in Varon et al. (2018) (Varon et al., 2018). 242 We estimated the uncertainties of 𝑸 by propagating the random errors in 𝑼𝟏𝟎 and 𝐈𝐌𝐄. 2.4 Multi-tiered quantification of methane emissions \n221 In our framework, we calculated the total excess mass of methane in kilograms in the detected hotspots (in the first tier) and \n222 \nplumes (in the second tier) using the so-called integrated mass enhancement (IME) model(Frankenberg et al., 2016; Varon et \n223 \nal., 2018). To make conservative estimates, we defined the background levels as the 10% of the average methane \n224 \nconcentrations in the TROPOMI-based and PRISMA-based scenes (Figs. 1b ~ 1g)(Frankenberg et al., 2016; Varon et al., \n225 \n2018). On this basis, we eliminated the interferences from the background concentrations and calculated IMEs as the methane \n226 \nmasses of the masked hotspots and plumes. 227 Overall, this method linked the emission rate (𝑸) with the measured IME via the residence time of methane (𝑰𝑴𝑬/𝑸). This \n228 \nresidence time relied on an effective wind speed (𝑼𝐞𝐟𝐟) and a characteristic plume size (𝑳) as follows: \n229 \n𝑸=\n𝑼𝐞𝐟𝐟⋅𝐈𝐌𝐄\n𝑳\n. (Eq. 3) \n230 Overall, this method linked the emission rate (𝑸) with the measured IME via the residence time of methane (𝑰𝑴𝑬/𝑸). This \n228 \nresidence time relied on an effective wind speed (𝑼𝐞𝐟𝐟) and a characteristic plume size (𝑳) as follows: \n229 \n𝑸=\n𝑼𝐞𝐟𝐟⋅𝐈𝐌𝐄\n𝑳\n. (Eq. 3) \n230 Specifically, the 𝑰𝑴𝑬 and 𝑳 can be inferred from the observations of the hotspots or plumes. During this process, we carefully \n231 \napplied a Boolean plume mask that separated the pixels (𝒊) with notable signals (∆𝜴𝒊) from background pixels and thus defined \n232 \nthe total areas (𝜮𝒊=𝟏\n𝑵𝑨𝒊) of the hotspots or plumes. The 𝑳 was defined as the square root of the total plume areas. Hence, the \n233 \n𝑰𝑴𝑬 was calculated as follows: \n234 8 8 In the first tier of our framework, the effective wind speed (𝑼𝐞𝐟𝐟) was defined as the 10-m wind speed 𝑼𝟏𝟎 obtained from the \n236 \nERA5 reanalysis dataset. According to the detected hotspot, the value at the nearest hour and location were used. 237 \nIn the second tier of our framework, we applied an ensemble of large eddy simulations (LES) to establish an empirical, linear \n238 \nrelationship between 𝑼𝐞𝐟𝐟 and the measured 10-m wind speed 𝑼𝟏𝟎 as follows (Fig. S9) \n239 \n𝑼\n𝟎𝟖𝟔𝟎𝟐𝑰(𝑼\n)\n𝟏𝟏𝟓𝟏𝟑(E\n5)\n240\nhttps://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 3.1 Multi-tiered imaging of global methane hotspots and super-emitters \n263 Figure 1 presents representative sets of methane hotspots and associated super-emitters across the United States, China, the \n264 \nMiddle East (Iraq and Kuwait), and North Africa (Algeria) via our multi-tiered satellite constellation. Each group first clarified \n265 9 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. a methane-abundant region and further focused in on explicit super-emitters. Among them, five methane-abundant regions \n266 \nwere captured in Wattenberg (the United States), Yangquan (China), Rumaila (Iraq), Burgan (Kuwait), and Hassi Messaoud \n267 \n(Algeria) (Fig. 1a and Table S1). These accounted for 4805 ~ 46138 kg/h methane emissions based on our daily first-tiered \n268 \n(i.e., TROPOMI-based) monitoring. From the perspective of a state-of-the-art global methane emission inventory (i.e., \n269 \nEDGARv6.0), such high values ranked among the top 1% regarding emission intensities per unit area (km2) (Fig. S1)(Crippa \n270 \net al., 2020). The Rumaila field, for example, was known as the largest oil field in Iraq (in terms of both reserves and yields). 271 \nIn this work, it was found with a significant methane emission intensity exceeding 45000 kg/h (Fig. 1b). In addition to such \n272 \nwell-known oil fields (Figs. 1c ~ 1f), methane hotspots emerged in developing coal mines, like Yangquan, with comparable \n273 \nemission levels (> 30000 kg/h) (Fig. 1g). 274 We attributed these methane enhancements to specific methane plumes via the second-tiered (i.e., PRISMA-based) monitoring \n275 \n(Figs. 1b1 ~ 1g2). There are substantial variations in the methane plumes’ amounts, types, and magnitude, even in a single \n276 \nmethane-abundant region. For instance, in the Burgan field, the second-tiered monitoring detected up to eight methane plumes \n277 \nin a handful of grids in the first-tiered monitoring (Figs. 1c1 ~ 1c4 and 1d1 ~ 1d4). Such intensive distributions were also \n278 \nfound in previous region-oriented surveys in the Permian basin and California (Duren et al., 2019; Itziar et al., 2021). Together \n279 \nwith high-definition images (Fig. S2), we found that such plumes originated from various sources, such as flares, factories, \n280 \nand wells. A breakthrough was the capture of two distinctive plumes in an individual methane source with extremely high \n281 \nemissions (> 10000 kg/h), unprecedented in previous satellite-based exploration and only observable in aircraft surveys (Fig. 282 \n1b1). Such precise distinction benefited from the high resolution of the second-tiered monitoring, despite being limited by the \n283 \nrelatively higher detection threshold (~ 300 kg/h) (Guanter et al., 2021). 3.1 Multi-tiered imaging of global methane hotspots and super-emitters \n263 Besides, factories and wells can also emit such evident \n284 \nplumes (Fig. 1c1 and Figs. 1e1 and 1e2). By comparison, other plumes were typically more diffuse but with comparable \n285 \nemission magnitude (~ 1000 ~ 7000 kg/h). 286 Note that the above results represent only snapshots at the overpass moments of the satellites (i.e., TROPOMI and PRISMA) \n287 \n(Figure 1). Specifically, for a given set (including both a methane-abundant region and associated super-emitters), the overpass \n288 \ntiming of TROPOMI can be nearly concordant with that of PRISMA. The temporal gaps could be frequently controlled within \n289 \nten days (e.g., Figs. 1b and 1d), even two days (Figs. 1e, 1f, and 1g). For instance, within only two days (August 18th and 19th, \n290 \n2021, November 15th and 17th, 2021), our multi-tiered satellite constellation went through the Hassi Messaoud field and the \n291 \nYangquan coal mine and provided in-depth views of methane budgets, including methane-abundant regions and their drivers \n292 \n(Figs. 1e and 1g). Even, in just one day (July 7th, 2021), our multi-tiered satellite constellation not only uncovered methane \n293 \nenhancements in the Wattenberg field (Fig. 1f) but also tracked them back to explicit methane super-emitters (Figs. 1f1 and \n294 \n1f2). As expected, if we extended the monitoring window of our framework to years, more methane super-emitters were \n295 \nsubsequently captured (Figs. S3 and S4). Moreover, our framework via multi-tiered satellite constellation paves an in-time \n296 \nway for routine monitoring of global methane hotspots and associated super-emitters. 297 Note that the above results represent only snapshots at the overpass moments of the satellites (i.e., TROPOMI and PRISMA) \n287 \n(Figure 1). Specifically, for a given set (including both a methane-abundant region and associated super-emitters), the overpass \n288 \ntiming of TROPOMI can be nearly concordant with that of PRISMA. The temporal gaps could be frequently controlled within \n289 \nten days (e.g., Figs. 1b and 1d), even two days (Figs. 1e, 1f, and 1g). For instance, within only two days (August 18th and 19th, \n290 \n2021, November 15th and 17th, 2021), our multi-tiered satellite constellation went through the Hassi Messaoud field and the \n291 \nYangquan coal mine and provided in-depth views of methane budgets, including methane-abundant regions and their drivers \n292 \n(Figs. 1e and 1g). Even, in just one day (July 7th, 2021), our multi-tiered satellite constellation not only uncovered methane \n293 \nenhancements in the Wattenberg field (Fig. 3.2 Multi-tiered verification of global methane super-emitters \n298 Four unexpected cases occurred in Burgan (Iraq), Hassi Messaoud (Algeria), and Yangquan (China), potentially explainable \n299 \nif we took mutual verification of the first- and second-tiered monitoring into consideration. First, an anomalous methane plume \n300 \nwas detected in the Burgan field (Fig. 1c4) of high emission magnitude (> 1500 kg/h), notably exceeding typical O&G facilities, \n301 \nfrom an elusive source (i.e., no clear source could be attributed) (Fig. S2). The long-term measurements of our multi-tiered \n302 \nsatellite constellation intermittently, rather than accidentally, observed this abnormal plume (Figs. S4). Furthermore, uncertain \n303 \nanalysis (see Materials and Methods) helped confirm this real plume. In particular, the methane plumes were clearly \n304 \nuncorrelated with the surface brightness from space (Fig. S5). Consequently, the most likely hypothesis for this super-emitter \n305 \nwas methane leakage from gigantic O&G pipelines as shown in the Google Map (Fig. S2). 306 Second, we observed suspect trails of methane plumes above the storage tanks in the Burgan field (Fig. 1d4). Conceivably, the \n307 \ntechnical noise driven by albedo effects bore the brunt, although it was believed to be corrected reliably (See Materials and \n308 \nMethods). To this end, we applied a multi-spectral retrieval algorithm to eliminate this effect to a large extent. The detailed \n309 \nillustrations are shown in Materials and Methods (Fig. S6). Consequently, we provided evidence that un-negligible methane \n310 \nemissions (> 3500 kg/h) may very well be the unique explanation, likely related to fugitive methane leaks from the storage \n311 \ntanks. This was only seen in previous aircraft-based surveys(Frankenberg et al., 2016). Therefore, our multi-tiered outcomes \n312 \nindicate even more widespread methane leaks than expected. Note that the multi-spectral retrieval algorithm cannot completely \n313 \nremove the albedo effects on our framework. As such, our framework could lead to efficient on-site re-inspection on worldwide \n314 \nand innumerable O&G fields. 315 Third, our framework detected a new methane super-emitter in the Hassi Messaoud field on December 7, 2021 (Fig. 1e4). By \n316 \nrevisiting historical satellite images in the second-tiered monitoring (Fig. S7), we could confirm that this super-emitter arose \n317 \nbetween October 18th and November 12, 2021. These results indicate that monitoring of global methane super-emitters can \n318 \nattain monthly resolution via current satellite constellation alone. Conceivably, more satellite observations would further close \n319 \nthe time window. 3.1 Multi-tiered imaging of global methane hotspots and super-emitters \n263 1f) but also tracked them back to explicit methane super-emitters (Figs. 1f1 and \n294 \n1f2). As expected, if we extended the monitoring window of our framework to years, more methane super-emitters were \n295 \nsubsequently captured (Figs. S3 and S4). Moreover, our framework via multi-tiered satellite constellation paves an in-time \n296 \nway for routine monitoring of global methane hotspots and associated super-emitters. 297 10 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 3.2 Multi-tiered verification of global methane super-emitters \n298 Even though some regions of interest \n339 \nin this study were far less famous than the California field, their emission intensities were much higher. Specifically, these \n340 \nplumes detected by the second-tiered monitoring had emission intensities (1142 ~ 11698 kg/h) that exceeded the median value \n341 \nin the California field. Satellite-based surveys conducted repeatedly for the Permian basin (one of the top O&G bases \n342 \nworldwide) from 2019 to 2020 (Fig. 3) as compared to the surveys in the California field, they achieved a much higher number \n343 \nof strong methane super-emitters, the median emission rates (1850 kg/h) much closer to ours (2888 kg/h). Collectively, this \n344 \ndirect comparison indicates the outstanding strength of our results that, though derived from a small dataset, could be analogous \n345 \nto abundant outcomes from field campaigns. More importantly, this highlights the urgent need for global monitoring of \n346 \n‘nameless’ O&G facilities that possibly emit methane as much as the California field and Permian basin. 347 3.2 Multi-tiered verification of global methane super-emitters \n298 Fourth, a distinct methane plume appeared in a coal mine in a mountainous area (Yangquan, China), \n320 \nexceeding all of the detected O&G super-emitters regarding the emission rate (> 7000 kg/h) (Fig. 1g1). 321 Figure 2a illustrates that, in our multi-tiered satellite constellation, the extent to which the explicit plumes in the second tier \n322 \nexplained the regional budget detected by the first tier. Overall, the plumes in the former were mostly responsible for large \n323 \nshares (> 8.2%) of regional budgets in the latter. In the Rumaila, Burgan, and Wattenberg fields, the detected methane plumes \n324 \nplayed a more critical role, with contributions up to 53.8 ~ 65.9%. Note that such contribution estimates might occasionally \n325 \nexceed 100% mainly owing to the inconsistent overpass moments between the first- and second-tier monitoring. By \n326 \ncomparison, the relatively low but still significant contributions in the Hassi Messaoud field (8.2%) and Yangquan coal mine \n327 \n(35.7%) were partly due to the technical limitation of our framework in detecting methane plumes on top of high background \n328 \nlevels. Collectively, the heavy-tail law of methane plume distributions, early reported for regional O&G fields (like the Permian \n329 \nbasin and California) (Duren et al., 2019; Itziar et al., 2021), possibly applied worldwide. 330 11 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. To further explore such a hypothesis, we extended the temporal sample window of our multi-tiered framework. As expected, \n331 \nthe first tier pictured less vivid methane enhancements mainly due to wind averages (Martin et al., 2018; McLinden et al., \n332 \n2016; Sun et al., 2018) (Fig. S3), while the second tier could capture more methane plumes (Fig. S4). This would lead to more \n333 \nvolatile contributions of the plumes to regional budgets, which, however, remained at high levels. This reinforces the above \n334 \nhypothesis for the widespread occurrence of methane super-emitters. 335 hypothesis for the widespread occurrence of methane super-emitters. 335 \nA regional survey in a California field was considered as the best reference, owing to its utilization of systematic airborne \n336 \nmeasurements to detect and quantify methane super-emitters (Duren et al., 2019). They survey reported 1181 methane plumes, \n337 \nmore than 500 times larger than previous aerial studies (Englander et al., 2018), with a median emission intensity of 170 kg/h. 338 \nThese results were thus used to directly evaluate the outcomes in the second tier (Fig. 3). 3.3 Multi-tiered challenges of national emission inventories \n348 These multi-tiered results challenge traditional methane emission inventories (Fig. 4). Here the conventional emission data \n349 \nwas obtained from a state-of-the-art bottom-up emission inventory (i.e., EDGARv6.0) for the year 2018. Consequently, for \n350 \nthe methane hotspots, this inventory was mostly consistent with the present results (-49.9 ~ 91.8%), with a fine average bias \n351 \n(63.2%). The Hassi Messaoud field in Algeria was a unique exception, where the O&G sector was in rapid development, with \n352 \na relatively larger bias (489.2%). By comparison, this inventory significantly undervalued the methane super-emitters (up to \n353 \norders of magnitude). This indicates that traditional emission inventories might have acceptable performance for traditional \n354 \nmethane-abundant regions while incapable of tracking methane super-emitters. 355 \nFirst, outdated spatial proxies might explain the large divergence between our plant-based estimates and the EDGARv6.0 (Fig. 356 \n1b1 and Fig. S8). Moreover, the EDGARv6.0 was designed for the year 2018, missing the newly established O&G plants with \n357 \nhigh methane emissions. Second, in principle, conventional inventories directly missed high emissions caused by abnormal \n358 \noperations (e.g., equipment failures) (Fig. 1c4 and Fig. S8) such as the O&G blowout shown in on-site surveys (Pandey et al., \n359 \n2019). A compromise was downwind measurements, yet insufficiently reliable as shown in previous findings(A. et al., 2018). 360 \nIn addition, the relatively low bias in the Rumaila and Hassi Messaoud fields might be explained by other causes (Figs. 1b2 \n361 \nand 1e3) such as outdated emission factors. Empirically, a plant-level inventory, once optimized by direct measurements, can \n362 These multi-tiered results challenge traditional methane emission inventories (Fig. 4). Here the conventional emission data \n349 \nwas obtained from a state-of-the-art bottom-up emission inventory (i.e., EDGARv6.0) for the year 2018. Consequently, for \n350 \nthe methane hotspots, this inventory was mostly consistent with the present results (-49.9 ~ 91.8%), with a fine average bias \n351 \n(63.2%). The Hassi Messaoud field in Algeria was a unique exception, where the O&G sector was in rapid development, with \n352 \na relatively larger bias (489.2%). By comparison, this inventory significantly undervalued the methane super-emitters (up to \n353 \norders of magnitude). This indicates that traditional emission inventories might have acceptable performance for traditional \n354 \nmethane-abundant regions while incapable of tracking methane super-emitters. 355 First, outdated spatial proxies might explain the large divergence between our plant-based estimates and the EDGARv6.0 (Fig. 356 \n1b1 and Fig. S8). 3.3 Multi-tiered challenges of national emission inventories \n348 Moreover, the EDGARv6.0 was designed for the year 2018, missing the newly established O&G plants with \n357 \nhigh methane emissions. Second, in principle, conventional inventories directly missed high emissions caused by abnormal \n358 \noperations (e.g., equipment failures) (Fig. 1c4 and Fig. S8) such as the O&G blowout shown in on-site surveys (Pandey et al., \n359 \n2019). A compromise was downwind measurements, yet insufficiently reliable as shown in previous findings(A. et al., 2018). 360 \nIn addition, the relatively low bias in the Rumaila and Hassi Messaoud fields might be explained by other causes (Figs. 1b2 \n361 \nand 1e3) such as outdated emission factors. Empirically, a plant-level inventory, once optimized by direct measurements, can \n362 First, outdated spatial proxies might explain the large divergence between our plant-based estimates and the EDGARv6.0 (Fig. 356 \n1b1 and Fig. S8). Moreover, the EDGARv6.0 was designed for the year 2018, missing the newly established O&G plants with \n357 \nhigh methane emissions. Second, in principle, conventional inventories directly missed high emissions caused by abnormal \n358 \noperations (e.g., equipment failures) (Fig. 1c4 and Fig. S8) such as the O&G blowout shown in on-site surveys (Pandey et al., \n359 \n2019). A compromise was downwind measurements, yet insufficiently reliable as shown in previous findings(A. et al., 2018). 360 \nIn addition, the relatively low bias in the Rumaila and Hassi Messaoud fields might be explained by other causes (Figs. 1b2 \n361 \nand 1e3) such as outdated emission factors. Empirically, a plant-level inventory, once optimized by direct measurements, can \n362 12 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. raise total methane emissions by ~ 60%, although source categories vary substantially (A. et al., 2018). Besides, temporal \n363 \nvariability might also explain top-down and bottom-up differences in methane emission estimates. For instance, the peak \n364 \nemission rate could exceed 40% higher than the average, which might occur in the middle afternoon due to specific processes, \n365 \nlike episodic venting from manual liquid unloading (Vaughn et al., 2018). This aligned with the sampling time of the satellites, \n366 \nthus biasing bottom-up inventories. Collectively, it is necessary to carefully consider all factors affecting methane emissions, \n367 \nincluding emission factor updating and spatiotemporal variations, in order to develop effective strategies for mitigating \n368 \nmethane emissions. 369 3.4 Implications for global methane monitoring \n370 We present a multi-tiered, space-based framework that can harmonize planet-scale and plant-level methane retrievals (Fig. 5). 371 \nUsing this framework, we patrol the world, with synergistic, proactive detections on the methane-abundant regions and \n372 \nmethane super-emitters across the United States, China, the Middle East (Iraq and Kuwait), and North Africa (Algeria). We \n373 \neven lock new methane super-emitters, track potential methane leakages from storage tanks, and distinguish multiple methane \n374 \nplumes in a single source. Such achievements are mostly unprecedented in satellite surveys and only observed in aircraft \n375 \ncampaigns. On this basis, our results challenge national reports that possibly miss unexpected super-emitters or mislead \n376 \nemission magnitude, partly due to surges of oil and gas (O&G) facilities and widespread abnormal operations. 377 \nOur data prove that depending on ready-made satellite missions alone can initiate immediate, proactive monitoring of global \n378 \nmethane pledges, in contrast to existing surveys that have to focus on a priori methane-abundant regions. As such, as the \n379 \nwindow for achieving the Paris target is rapidly closing, we will not need to sit back and wait for upcoming space missions, \n380 \nlike MethaneSAT and SBG in the United States, EnMAP in Germany, a new version of GF-5 in China, and, later, the European \n381 \nSpace Agency’s CHIME from 2025 to 2030 (Cusworth et al., 2019). In addition, while scientific communities persistently \n382 \ndebates the drivers of the recent methane surge (G. et al., 2014; Nisbet et al., 2019; Turner et al., 2019), the consequences of \n383 \nour outcomes are clear, not only holding clues but also facilitating mitigation. 384 We present a multi-tiered, space-based framework that can harmonize planet-scale and plant-level methane retrievals (Fig. 5). 371 \nUsing this framework, we patrol the world, with synergistic, proactive detections on the methane-abundant regions and \n372 \nmethane super-emitters across the United States, China, the Middle East (Iraq and Kuwait), and North Africa (Algeria). We \n373 \neven lock new methane super-emitters, track potential methane leakages from storage tanks, and distinguish multiple methane \n374 \nplumes in a single source. Such achievements are mostly unprecedented in satellite surveys and only observed in aircraft \n375 \ncampaigns. On this basis, our results challenge national reports that possibly miss unexpected super-emitters or mislead \n376 \nemission magnitude, partly due to surges of oil and gas (O&G) facilities and widespread abnormal operations. 3.4 Implications for global methane monitoring \n370 377 Our data prove that depending on ready-made satellite missions alone can initiate immediate, proactive monitoring of global \n378 \nmethane pledges, in contrast to existing surveys that have to focus on a priori methane-abundant regions. As such, as the \n379 \nwindow for achieving the Paris target is rapidly closing, we will not need to sit back and wait for upcoming space missions, \n380 \nlike MethaneSAT and SBG in the United States, EnMAP in Germany, a new version of GF-5 in China, and, later, the European \n381 \nSpace Agency’s CHIME from 2025 to 2030 (Cusworth et al., 2019). In addition, while scientific communities persistently \n382 \ndebates the drivers of the recent methane surge (G. et al., 2014; Nisbet et al., 2019; Turner et al., 2019), the consequences of \n383 \nour outcomes are clear, not only holding clues but also facilitating mitigation. 384 It should be noted that the multi-tiered framework is sustainable (Fig. 5). On the one hand, it can harmonize multiple satellites. 385 \nThe potential representatives include upcoming official missions (e.g., the GF-5) (Itziar et al., 2021), current private \n386 \nconstellations (e.g., the GHGSat series) (Jervis et al., 2021; Varon et al., 2020), and explorable multispectral products (e.g., \n387 \nthe Worldview-3 and Sentinel-2) (Sánchez-García et al., 2021). On the other hand, the framework is not confined to satellites \n388 \nand can be expanded by integrating in situ (e.g., Global Atmosphere Watch Programme) (World Meteorological Organization, \n389 \n2022), aircraft, and unmanned aerial vehicles (UAVs) (Cusworth et al., 2020; Gålfalk et al., 2021; Tuzson et al., 2020). 390 \nParticularly, on the basis of our framework, rapid advances in artificial intelligence (AI) techniques are projected to completely \n391 \nreplace manpower to seek faint signals of methane enhancements in Earth’s surface, and to significantly optimize data-driven \n392 \nalgorithms of methane emission estimates (Reichstein et al., 2019; Yuan et al., 2020). In principle, subsequent mitigation of \n393 13 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. such super-emitters via routine maintenances, leak detections, or emergent repairs can provide effective, efficient, and \n394 \neconomical solutions toward the Paris target (Mayfield et al., 2017). 395 such super-emitters via routine maintenances, leak detections, or emergent repairs can provide effective, efficient, and \n394 \neconomical solutions toward the Paris target (Mayfield et al., 2017). 405 \nFig. 1. Methane hotspots and associated super-emitters across the United States, China, Iraq, Kuwait, and Algeria via \n406 \nthe multi-tiered daily satellite constellation. (a) Methane-abundant regions and associated super-emitters are captured by \n407 \nthe TROPOMI and PRISMA, respectively. Their locations are marked by black rectangles and dots. Their names are obtained \n408 \nfrom the Google Map, usually being the names of the nearest O&G fields and coal mines. (b ~ g) Each group clarifies a \n409 \nmethane-abundant region and explicit super-emitters (b1 ~ b4, c1 ~ c4, d1 ~ d4, e1 ~ e4, f1 ~ f2, and g1 ~ g2). For each super-\n410 \nemitter (five-pointed stars), the overpass moments of the multi-tiered satellite constellation and the consequent emission \n411 \nestimate are presented. Its base map is obtained from the ©Google Map. The second color bar for the PRISMA is suitable \n412 for the super-emitters in China, while the first is for other countries. 3.4 Implications for global methane monitoring \n370 395 \nThese outcomes have important ramifications for low- and middle-income countries. World powers, like the United States and \n396 \nEuropean Union, lead new national methane pledges. They are separately on the way to creating vast operational infrastructures \n397 \nto monitor ambitious climate goals. Still, huge holes remain in coverage and authority, at least by the middle of this decade. 398 \nThis situation is especially worse for low- and middle-income countries, where the tight budget dims the hopes for filling up \n399 \nthose holes by 2030, while methane emissions are likely to rise as countries develop. In this context, the present framework \n400 \ncan at once serve as the cost-effective piece of the global methane monitoring network and thus support fair climate \n401 \nnegotiations between countries. 402 \n \n \n403 14 14 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 15 404 405 \nFig. 1. Methane hotspots and associated super-emitters across the United States, China, Iraq, Kuwait, and Algeria via \n406 \nthe multi-tiered daily satellite constellation. (a) Methane-abundant regions and associated super-emitters are captured by \n407 \nthe TROPOMI and PRISMA, respectively. Their locations are marked by black rectangles and dots. Their names are obtained \n408 \nfrom the Google Map, usually being the names of the nearest O&G fields and coal mines. (b ~ g) Each group clarifies a \n409 \nmethane-abundant region and explicit super-emitters (b1 ~ b4, c1 ~ c4, d1 ~ d4, e1 ~ e4, f1 ~ f2, and g1 ~ g2). For each super-\n410 \nemitter (five-pointed stars), the overpass moments of the multi-tiered satellite constellation and the consequent emission \n411 \nestimate are presented. Its base map is obtained from the ©Google Map. The second color bar for the PRISMA is suitable \n412 for the super-emitters in China, while the first is for other countries. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 405 \nFig. 1. Methane hotspots and associated super-emitters across the United States, China, Iraq, Kuwait, and Algeria via \n406 \nthe multi-tiered daily satellite constellation. (a) Methane-abundant regions and associated super-emitters are captured by \n407 \nthe TROPOMI and PRISMA, respectively. Their locations are marked by black rectangles and dots. 3.4 Implications for global methane monitoring \n370 Their names are obtained \n408 \nfrom the Google Map, usually being the names of the nearest O&G fields and coal mines. (b ~ g) Each group clarifies a \n409 \nmethane-abundant region and explicit super-emitters (b1 ~ b4, c1 ~ c4, d1 ~ d4, e1 ~ e4, f1 ~ f2, and g1 ~ g2). For each super-\n410 \nemitter (five-pointed stars), the overpass moments of the multi-tiered satellite constellation and the consequent emission \n411 \nestimate are presented. Its base map is obtained from the ©Google Map. The second color bar for the PRISMA is suitable \n412 for the super-emitters in China, while the first is for other countries. 405 \nFig. 1. Methane hotspots and associated super-emitters across the United States, China, Iraq, Kuwait, and Algeria via \n406 \nthe multi-tiered daily satellite constellation. (a) Methane-abundant regions and associated super-emitters are captured by \n407 \nthe TROPOMI and PRISMA, respectively. Their locations are marked by black rectangles and dots. Their names are obtained \n408 \nfrom the Google Map, usually being the names of the nearest O&G fields and coal mines. (b ~ g) Each group clarifies a \n409 \nmethane-abundant region and explicit super-emitters (b1 ~ b4, c1 ~ c4, d1 ~ d4, e1 ~ e4, f1 ~ f2, and g1 ~ g2). For each super-\n410 \nemitter (five-pointed stars), the overpass moments of the multi-tiered satellite constellation and the consequent emission \n411 \nestimate are presented. Its base map is obtained from the ©Google Map. The second color bar for the PRISMA is suitable \n412 for the super-emitters in China, while the first is for other countries. 16 413 \nFig. 2. High contributions of methane super-emitters to corresponding regional methane budgets. 414 \nhttps://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 413 \nFig. 2. High contributions of methane super-emitters to corresponding regional methane budgets. 414 \n \n415 \nFig. 3. Comparison of emission estimates of methane plumes between surveys. The surveys for the California f\n416 \nPermian basin are selected as the references. They report 1181 and 39 methane plumes, while our second-tiered survey a\n417 \n29 plumes. Violin plots show statistical distributions of methane plume emission rates for these surveys. For each sur\n418 \ngrey dots refer to the emission rates of the individual plumes and the red dot represents the median value. 419 \nhttps://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 413 \nFig. 2. 3.4 Implications for global methane monitoring \n370 High contributions of methane super-emitters to corresponding regional methane budgets. 414 413 413 \nFig. 2. High contributions of methane super-emitters to corresponding regional methane budgets. 414 Fig. 2. High contributions of methane super-emitters to corresponding regional methane budgets. 414 Fig. 2. High contributions of methane super-emitters to corresponding regional methane budgets. 414 \n \n415 \nFig. 3. Comparison of emission estimates of methane plumes between surveys. The surveys for the California field and \n416 \nPermian basin are selected as the references. They report 1181 and 39 methane plumes, while our second-tiered survey attempts \n417 \n29 plumes. Violin plots show statistical distributions of methane plume emission rates for these surveys. For each survey, the \n418 \ngrey dots refer to the emission rates of the individual plumes and the red dot represents the median value. 419 15 mparison of emission estimates of methane plumes between surveys. The surveys for the Calif g\np\np\ny\ny\nPermian basin are selected as the references. They report 1181 and 39 methane plumes, while our second-tiered survey attempts \n417 \n29 plumes. Violin plots show statistical distributions of methane plume emission rates for these surveys. For each survey, the \n418 \ngrey dots refer to the emission rates of the individual plumes and the red dot represents the median value. 419 17 420 \nFig. 4. Multi-tiered emission estimates versus bottom-up emission inventories. We first interpolate the bottom-up emission \n421 \ninventories into the resolution consistent with our multi-tiered results. On this basis, the bottom-up emission rates in the grids \n422 \nthat the detected hotspots and plumes cover are summed up to compare with the results. The detected hotspots (yellow dots) \n423 \nand plumes (blue dots) correspond to those as shown in Fig. 1. The grey dashed line represents the ratio of the bottom-up \n424 \nemissions to the top-down ones of 1:1. 425 420 \nFig. 4. Multi-tiered emission estimates versus bottom-up emission inventories. We\n421 Multi-tiered emission estimates versus bottom-up emission inventories. We first interpolate the bo Fig. 4. Multi-tiered emission estimates versus bottom-up emission inventories. We first interpolate the bottom-up emission \n421 \ninventories into the resolution consistent with our multi-tiered results. On this basis, the bottom-up emission rates in the grids \n422 \nthat the detected hotspots and plumes cover are summed up to compare with the results. The detected hotspots (yellow dots) \n423 \nand plumes (blue dots) correspond to those as shown in Fig. 1. 3.4 Implications for global methane monitoring \n370 The grey dashed line represents the ratio of the bottom-up \n424 \nemissions to the top-down ones of 1:1. 425 18 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. 26 Fig. 5. Multi-tiered satellite framework for immediate global methane monitoring. This framework harmonizes global-\n427 \nscale and high-resolution methane retrievals, with a dual focus on mapping region-scale and plant-level drivers. In this work, \n428 \nthe framework reconciles the spacious swath of TROPOMI (i.e., ~ 2600 km) with the high resolution of PRISMA (i.e., 30 × \n429 \n30 m2), in contrast to conventional satellite-based surveys that were of either insufficient samplings or narrow views. Looking \n430 \nforward, developments of Earth’s monitoring platforms (e.g., satellites, aircrafts, and unmanned drones) and artificial \n431 \nintelligence will continue to strengthen the performance of methane plume retrievals and emission estimates. On eve of the \n432 \nParis target, at least while a super methane satellite with spacious swath, high resolution, and agile analysis is not in place, our \n433 \nmulti-tiered satellite constellation has important implications for measuring global methane pledges. The appearances of the \n434 \nTROPOMI, MethaneSAT, PRISMA, and EnMAP are obtained from http://www.tropomi.eu/, https://www.methanesat.org, \n435 \nhttps://www.asi.it/en/earth-science/prisma/, and https://www.enmap.org/, respectively. The methane maps from the \n436 \nTROPOMI and PRISMA refer to the results in Figs. 1e and 1b1. The grey marks indicate upcoming platforms (i.e., \n437 \nMethaneSAT and EnMAP) and techniques (e.g., AI techniques that can optimize the identification and quantification of \n438 \nmethane super-emitters). 439 \n440 Fig. 5. Multi-tiered satellite framework for immediate global methane monitoring. This framework harmonizes global-\n427 \nscale and high-resolution methane retrievals, with a dual focus on mapping region-scale and plant-level drivers. In this work, \n428 \nthe framework reconciles the spacious swath of TROPOMI (i.e., ~ 2600 km) with the high resolution of PRISMA (i.e., 30 × \n429 \n30 m2), in contrast to conventional satellite-based surveys that were of either insufficient samplings or narrow views. Looking \n430 \nforward, developments of Earth’s monitoring platforms (e.g., satellites, aircrafts, and unmanned drones) and artificial \n431 \nintelligence will continue to strengthen the performance of methane plume retrievals and emission estimates. On eve of the \n432 \nParis target, at least while a super methane satellite with spacious swath, high resolution, and agile analysis is not in place, our \n433 \nmulti-tiered satellite constellation has important implications for measuring global methane pledges. Competing interests. \n455 The authors declare no competing interests. 456 Supplementary information. \n450 Supplementary information accompanies this paper. 451 Author contributions. 452 Data availability. \n441 Data availability. 441 \nThe operational TROPOMI product is available at https://scihub.copernicus.eu/, https://www.temis.nl/emissions/data.php. The \n442 \nPRISMA data are publicly available to registered users at https://prisma.asi.it/. The WRF-CHEM model code is available at \n443 \nhttps://ruc.noaa.gov/wrf/wrf-chem/. All Sentinel-2 satellite data are publicly available through the Copernicus Open Access \n444 \nHub (https://scihub.copernicus.eu/). The HITRAN line spectra is publicly available through the HITRANonline database \n445 \n(https://hitran.org/). The ERA5 data come from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The \n446 \nEDGARv6.0 dataset comes from https://edgar.jrc.ec.europa.eu/gallery?release=v60ghg&substance=CH4&sector=TOTALS. 447 \nCode availability\n448 The codes are available upon request to corresponding author. 449 Supplementary information. 450 3.4 Implications for global methane monitoring \n370 The appearances of the \n434 \nTROPOMI, MethaneSAT, PRISMA, and EnMAP are obtained from http://www.tropomi.eu/, https://www.methanesat.org, \n435 \nhttps://www.asi.it/en/earth-science/prisma/, and https://www.enmap.org/, respectively. The methane maps from the \n436 \nTROPOMI and PRISMA refer to the results in Figs. 1e and 1b1. The grey marks indicate upcoming platforms (i.e., \n437 \nMethaneSAT and EnMAP) and techniques (e.g., AI techniques that can optimize the identification and quantification of \n438 \nmethane super-emitters). 439 \n440 g. 5. Multi-tiered satellite framework for immediate global methane monitoring. This framew Fig. 5. Multi-tiered satellite framework for immediate global methane monitor\n427 Fig. 5. Multi-tiered satellite framework for immediate global methane monitoring. This framework harmonizes global-\n427 19 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Data availability. 441 \nThe operational TROPOMI product is available at https://scihub.copernicus.eu/, https://www.temis.nl/emissions/data.php. The \n442 \nPRISMA data are publicly available to registered users at https://prisma.asi.it/. The WRF-CHEM model code is available at \n443 \nhttps://ruc.noaa.gov/wrf/wrf-chem/. All Sentinel-2 satellite data are publicly available through the Copernicus Open Access \n444 \nHub (https://scihub.copernicus.eu/). The HITRAN line spectra is publicly available through the HITRANonline database \n445 \n(https://hitran.org/). The ERA5 data come from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The \n446 \nEDGARv6.0 dataset comes from https://edgar.jrc.ec.europa.eu/gallery?release=v60ghg&substance=CH4&sector=TOTALS. 447 \nCode availability. 448 \nThe codes are available upon request to corresponding author. 449 \nSupplementary information. 450 Data availability. 441 \nThe operational TROPOMI product is available at https://scihub.copernicus.eu/, https://www.temis.nl/emissions/data.php. The \n442 \nPRISMA data are publicly available to registered users at https://prisma.asi.it/. The WRF-CHEM model code is available at \n443 \nhttps://ruc.noaa.gov/wrf/wrf-chem/. All Sentinel-2 satellite data are publicly available through the Copernicus Open Access \n444 \nHub (https://scihub.copernicus.eu/). The HITRAN line spectra is publicly available through the HITRANonline database \n445 \n(https://hitran.org/). The ERA5 data come from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The \n446 \nEDGARv6.0 dataset comes from https://edgar.jrc.ec.europa.eu/gallery?release=v60ghg&substance=CH4&sector=TOTALS. 447 \nCode availability. 448 Author contributions. \n452 P. L. designed this study and wrote the manuscript. P. L. and Y. W. developed the retrieval algorithm. P. L., Y. W., X. G., Y. 453 \nH., and Y. P. performed the data analysis. S. Y., A.B., D. R., and J. H. S. contributed to the manuscript. 454 P. L. designed this study and wrote the manuscript. P. L. and Y. W. developed the retrieval algorithm. P. L., Y. W., X. G., Y. 453 \nH., and Y. P. performed the data analysis. S. Y., A.B., D. R., and J. H. S. contributed to the manuscript. 454 Competing interests. 455 Acknowledgements. \n457 We thank ESA and the S-5P/TROPOMI team for the great work on initiating and realizing TROPOMI data. We also thank \n458 \nthe Italian Space Agency for the great work on the PRISMA data. This study is supported by National Natural Science \n459 \nFoundation of China (No. 22006030, 22076172, 21577126 and 41561144004), Science and Technology Program of Hebei \n460 \nProvince (22343702D), Hebei Youth Top Fund (BJ2020032), Research Foundation of Education Bureau of Hebei \n461 \n(QN2019184), Basic Scientific Research Foundation of Hebei (KY2021024), Initiation Fund of Hebei Agricultural University \n462 \n(412201904 and YJ201833), the Department of Science and Technology of China (No. 2016YFC0202702, 2018YFC0213506 \n463 \nand 2018YFC0213503), and National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107). 464 We thank ESA and the S-5P/TROPOMI team for the great work on initiating and realizing TROPOMI data. We also thank \n458 \nthe Italian Space Agency for the great work on the PRISMA data. This study is supported by National Natural Science \n459 \nFoundation of China (No. 22006030, 22076172, 21577126 and 41561144004), Science and Technology Program of Hebei \n460 \nProvince (22343702D), Hebei Youth Top Fund (BJ2020032), Research Foundation of Education Bureau of Hebei \n461 \n(QN2019184), Basic Scientific Research Foundation of Hebei (KY2021024), Initiation Fund of Hebei Agricultural University \n462 \n(412201904 and YJ201833), the Department of Science and Technology of China (No. 2016YFC0202702, 2018YFC0213506 \n463 \nand 2018YFC0213503), and National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107). 464 We thank ESA and the S-5P/TROPOMI team for the great work on initiating and realizing TROPOMI data. We also thank \n458 \nthe Italian Space Agency for the great work on the PRISMA data. This study is supported by National Natural Science \n459 \nFoundation of China (No. 22006030, 22076172, 21577126 and 41561144004), Science and Technology Program of Hebei \n460 \nProvince (22343702D), Hebei Youth Top Fund (BJ2020032), Research Foundation of Education Bureau of Hebei \n461 \n(QN2019184), Basic Scientific Research Foundation of Hebei (KY2021024), Initiation Fund of Hebei Agricultural University \n462 \n(412201904 and YJ201833), the Department of Science and Technology of China (No. 2016YFC0202702, 2018YFC0213506 \n463 \nand 2018YFC0213503), and National Research Program for Key Issues in Air Pollution Control in China (No. DQGG0107). 464 20 https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. References \n465 A., A. R., Daniel, Z.-A., R., L. D., T., A. D., R., B. Z., R., B. A., J., D. K., C., H. S., J., J. D., Anna, K., A., K. E., K., L. B., \n466 \nThomas, L., D., M. J., J., M. A., Mark, O., W., P. 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Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. https://doi.org/10.5194/acp-2022-518\nPreprint. Discussion started: 21 July 2022\nc⃝Author(s) 2022. CC BY 4.0 License. Zavala-Araiza, D., Alvarez, R. A., Lyon, D. R., Allen, D. T., Marchese, A. J., Zimmerle, D. J. and Hamburg, S. P.: Super-\n728 \nemitters in natural gas infrastructure are caused by abnormal process conditions, Nat. Commun., 8(1), 14012, \n729 \ndoi:10.1038/ncomms14012, 2017. 730 \nZhang, M., Li, W. and Du, Q.: Diverse Region-Based CNN for Hyperspectral Image Classification, IEEE Trans. Image \n731 \nProcess., 27(6), 2623–2634, doi:10.1109/TIP.2018.2809606, 2018. 732 \n \n733 \n \n734 Zhang, M., Li, W. and Du, Q.: Diverse Region-Based CNN for Hyperspectral Image Classification, IEEE Trans. Image \n731 \nProcess., 27(6), 2623–2634, doi:10.1109/TIP.2018.2809606, 2018. 732 29"

ERROR: type should be string, got "https://doi.org/10.1007/s10639-022-11108-2\nEducation and Information Technologies (2022) 27:11759–11779 https://doi.org/10.1007/s10639-022-11108-2\nEducation and Information Technologies (2022) 27:11759–11779 Keywords  Mobile phone app · Educational technology · Digital literacy · Mobile \ndevice · Secondary school Smartphone applications for physical activity promotion \nfrom physical education Francisco Javier Gil‑Espinosa1   · Adriana Nielsen‑Rodríguez2   · \nRamón Romance2   · Rafael Burgueño3 Received: 24 December 2021 / Accepted: 11 May 2022 / \n© The Author(s) 2022\nPublished online: 19 May 2022 Received: 24 December 2021 / Accepted: 11 May 2022 / \n© The Author(s) 2022\nPublished online: 19 May 2022 *\t Francisco Javier Gil‑Espinosa \n\t\njaviergil@uma.es Extended author information available on the last page of the article *\t Francisco Javier Gil‑Espinosa \n\t\njaviergil@uma.es\nExtended author information available on the last page of the article 1  Introduction The digital technology (e.g., digital blackboard, tablets, smartphone apps) has been \nincorporated as key elements into every sphere of our society such as health, work \nand education (Organisation for Economic Cooperation and Development, 2019). The use of digital technology to support the teaching and learning process has \ngrown sharply in recent years (Sargent & Casey, 2020), and its implementation in \neducation system has been accentuated by the COVID-19 pandemic. Indeed, Cal-\nderón-Garrido et al. (2022) review concluded that the scientific production seems \nto bet on the use of smartphones in class, as it is beneficial for educational objec-\ntives. In physical education (PE), while most research has focused on reporting use-\nfulness and advantages derived from the use of digital technology in PE (Cushion \nand Townsend, 2019; Sargent & Casey, 2020), little attention has been paid to the \nquestion about whether there are smartphone apps that could be linked with PE \ncurriculum to promote leisure-time physical activity (PA) in adolescents from the \ncontext of the secondary school PE. There is thus a need to help PE teachers take \na curricular approach to the selection and implementation of smartphone applica-\ntions (apps) that contribute to addressing the curriculum. This would allow us to \nrecommend an improved use of smartphone apps for PE lessons, which would lead \nto improve the quality of the teaching and learning process with more meaningful \nlearning experiences for students, and to accomplish one of the main educational \ngoals, such as leisure-time PA promotion (SHAPE America– Society of Health and \nPhysical Educators, 2014). This research sought to carry out a systematic search \nfor smartphone apps focused on PA that are available in the Google Play. Next, the \ninterplay between every included smartphone app and the secondary PE curriculum \nwas further analysed. Abstract Smartphone applications (apps) are thought to be an adequate instructional strategy \nnot only to improve the quality of the teaching in physical education (PE), but also \nto effectively promote leisure-time physical activity (PA) of adolescent students in \nthis context. Although the use of smartphone apps has been generalized in PE, lit-\ntle is known about the curricular approach of smartphone apps to be implemented \nby teacher to teach specific curricular contents in PE lessons. Therefore, the aim \nof this research was threefold: a) to conduct a systematic search for smartphone \napps focused on PA and sport; b) to assess the features, content and quality of every \nincluded smartphone app; and c) to analyze the relationships between every selected \napp and the secondary PE curriculum. Systematic searches were completed on \nGoogle Play Store from January 2021 to March 2021. Apps were included when \nthey met: main goal focused on PA and sport; permitted use by underage; they are \nfree; user scores of at least 4. The app selection process was carried out by sev-\neral reviewers and concordance measures were estimated. Additionally, an app \nquality assessment was independently conducted by three reviewers. A total of 18 \napps focused on PA were included. Particularly, eight apps were suitable for fitness, \nhealth and quality of life curricular content; two for sports content; four for body \nexpression content; and four apps for outdoor PA content. The mean quality score \nwas 4.00. Apps could be helpful for teachers to implement the secondary PE cur-\nriculum and effectively promote PA among adolescent students. Keywords  Mobile phone app · Educational technology · Digital literacy · Mobile \ndevice · Secondary school 0123456789)\n1 3 012341 456789)\n3 Education and Information Technologies (2022) 27:11759–11779 11760 1.1  \u0007Secondary physical education curriculum In common with another national PE curricula (e.g., SHAPE America– Society of \nHealth and Physical Educators, 2014; Irish Proffessional Service for Teachers, 2022; \nBritish Council for the Curriculum, Examinations and Assessment, 2022), one the \nof the main curricular goals for PE, in Spain, is to develop students as physically \nliterate people capable of applying the knowledge, skills and attitudes needed for \nlifelong health-related PA (Orden 15 of January, 2021; Royal Decree 1105/, 2014). In order to structure the knowledge, skills and attitudes to be learnt by students \nwhen completing secondary education, the content blocks of Spanish curriculum for \nsecondary PE could be organized according four: a) fitness, health and life quality, \nwhich includes contents related to the development of basic physical capacities, the \npromotion of healthy lifestyles (e.g., regular PA, nutrition and adequate rest breaks) \nand avoidance of potential health-damaging behaviours (e.g., sedentarism, tobacco, \nalcohol and drugs), as well as the identification of health benefits derived from a \ngood level of fitness and regular leisure-time PA; b) games and sports, which com-\nprises contents referring to the understanding of games and sports as sociocultural 1 3 3 Education and Information Technologies (2022) 27:11759–11779 11761 phenomena, the knowledge of Spanish traditional games, as well as the development \nof technical and tactical skills, the knowledge and respect for the rules of individual \nand team sports; c) body expression, which brings together contents concerning cre-\native and artistic communication and emotional regulation using body (e.g., dance, \nchoreography, and dramatization) d) outdoor PA, which tackles contents regard-\ning the interaction between students and nature through PA (e.g., hiking, orientat-\ning, and outdoor challenges), and the understanding and appreciation of the natural \nenvironment. The Spanish curriculum for secondary PE states two 60-minute lessons per week, \ndespite the substantial body of evidence indicating PE helped students meet daily \nPA recommendations (Kerr et al., 2018; Kwon et al., 2020; Lee & Gao, 2020). Thus, \nthere is a need for teachers to develop and implement instructional strategies aiming \nto promote students’ levels of leisure-time PA from PE lessons. For this goal, the \nuse of digital technology, including smartphone apps, is recommended not only to \ndevelop key digital competence, but also to improve the comprehensive education of \nstudents in general, and promote leisure-time PA from the context of the secondary \nschool PE in particular (Orden 15 of January, 2021; Royal Decree 1105/, 2014). 1.3  \u0007Literature review on apps in physical education The digital technology could allow a greater variety of instructional strategies in PE \nwhile helping to develop in students attitudes, knowledge and behaviours for a more \nphysically active life. Therefore, teachers need to experiment with apps related to \nthe PE teaching-learning process (Yu et al., 2018). Accordingly, previous research \nabout the use of apps, conducted in the school context of PE, has evidenced advan-\ntages in motivation, knowledge of results, assessment, and the improvement of stu-\ndent autonomy, among others (Kerner & Goodyear, 2017; Klenk et al., 2017; Phil-\nlips et al., 2014; Vega-Ramírez et al., 2020). Schwartz and Baca (2016) point out \nthat many PA apps are based on behavioural theory and use elements of gamifica-\ntion for success, with personal goals and specific feedback. Lee (2018) recommends \nusing apps to facilitate students’ group activities, as well as the knowledge of results. Similarly, a growing body of research has pointed out that the implementation of \nsmartphone apps was an efficient instructional strategy to increase in-classroom PA, \nas well as to promote leisure-time PA among adolescent students in PE (Böhm et al., \n2019; Brickwood et al., 2019; Gil-Espinosa et al., 2020; Lau et al., 2011). Likewise, Mokmin and Jamiat (2021) designed a mobile application taking into \nconsideration the motor learning theory (Muratori et al., 2014) and Mayer’s cogni-\ntive theory of multimedia learning (Mayer, 2011), obtaining satisfactory results in \nmotivation and PA performance after its implementation with students. In turn, the \nresults of Papastergiou et al. (2021) research with primary school students conclude \nthat the use of apps improved their interest, enjoyment, and motivation, while teach-\ners had more time to provide individualised feedback. Similarly, research carried out \nby Yu (2020) in secondary education found that, through the use of mobile appli-\ncations, students improved their tactics and performance of badminton skills while \nallowing for more active learning and improved knowledge of the results, conclud-\ning that PE teachers can integrate the use of mobile applications in their teaching \nwork.i In any case, the educational benefits of using mobile applications in PE will be \ndeterminate by the design of the learning activities (Greve et al., 2022), which is \nwhy their link with the curriculum is crucial. PE teachers are recommended to align \nthe selection of apps with the PE learning goals (Lee & Gao, 2020). Krause et al. 1.2  \u0007Smartphone apps in physical education The use of digital technology in PE is considered as a relevant instructional recourse \ngiven its potential in supporting the teaching and learning process in a manner that \nfits the nature of PE (Casey et al., 2017). For instance, digital technology such as \ndigital blackboard, tablets, video cameras, electronic devices, and smartphone apps \nprovide students with opportunities to reinforce their learning, and to foster their \nautonomy (Hyeonho & Taemin, 2021). Recently, inquiring app-integrated PE in \nwhich several apps are utilized on the smartphone in PE has been on the rise to \nease the teaching learning process (Krause & Sanchez, 2014; Zhu & Dragon, 2016). PE experts have suggested smartphone apps perform different roles in improving \nthe quality of PE: a) they can serve as communicational tools such as scoreboard, \nwhiteboard or display platforms; b) they can serve as classroom management tools \nby being useful for timers, music displayers, and microphones; c) they can be used \nas tools for information delivery, feedback, lesson plans, assessment; and d) they \nshould be personalised based on every student’s need and skills (Goodyear et al., \n2019; Penney et al., 2012; Pyle & Esslinger, 2014; Sinelnikov, 2012). It is, thus, thought to develop students as physical literate people via smartphone \napps may represent an effective and innovative strategy to promote leisure-time PA \nfrom the context of the secondary school PE. This idea relied on the fact that most \nsecondary students have a generalised use of digital technology in their everyday \nlife through the utilisation of social networks, websites, blogs, and smartphones, and \nuser accounts for several apps (Böhm et al., 2019; Direito et al., 2015; Goodyear & \nArmour, 2021; Vega-Ramírez et al., 2020). 1 3 Education and Information Technologies (2022) 27:11759–11779 11762 1.3  \u0007Literature review on apps in physical education (2020) recommend increasing research on the use of technology in PE, as well as \nimproving the training of PE teachers in the integrated use of technology in the \nsubject. 1.4  \u0007The current research and research question Smartphone apps represent a potential means both to develop students as physical \nliterate, and to promote leisure-time PA from the context of the secondary school \nPE. In accordance with previous research, the number of smartphone apps, particu-\nlarly those related to PA and sports, has been on the Google Play (Arigo et al., 2020). This great quantity of smartphone apps represents a good opportunity to provide \nstudents with improved meaningful learning experiences, yet it also makes it quite 1 3 Education and Information Technologies (2022) 27:11759–11779 11763 difficult for teachers to select and implement the most suitable app in accordance \nwith curricular content to be taught in lessons. Although previous research has well \ndocumented the benefits from the use of smartphone apps on learning-related out-\ncomes in students such as increased levels of in-classroom and leisure-time PA, the \nquestion about the curricular approach to smartphone apps to be used by teachers to \nteach specific curricular contents in the classroom remains still to be examined. To \nthe best of our knowledge, no studies were found to analyse smartphone apps to pro-\nmote PA in the school context and its potential relationships to the curriculum and \npossible use by PE teachers. At the same time, Koekoek and van Hilvoorde (2018) \nthought over the need to understand the way teachers select digital technology, with-\nout losing educational goals. Further, this question becomes even more important \ndue to the great use of digital technology in general, and particularly smartphone \napps in PE to adapt the implementation of curriculum to the new instructional mod-\nels derived from the COVID-19 pandemic (López-Fernández et al., 2021). This research attempts to provide a response to the questions about which smart-\nphone apps on PA and sports are linked directly to secondary curriculum for PE, \nand how PE teachers might implement them to teach curricular content with the \npurpose of promoting leisure-time PA from the school context of PE. Therefore, the \naim of this research was threefold. The first objective was to carry out a systematic \nsearch for smartphone apps focused on PA and sport that are available in the Google \nPlay. The second objective included a quality assessment for every smartphone app \nincluded in this study. 1.4  \u0007The current research and research question The third objective consisted of the analysis of the relation-\nship between every smartphone app and the PE curriculum, as well as the provision \nof possible strategies for the implementation of each app. 2.1  \u0007Search strategy Systematic searches were individually completed by two researchers (R1 and R2) \nin Google Play Store between 12th January 2021 and 31st March 2021. The identi-\nfication of apps was performed using the following search terms: “physical educa-\ntion”, “physical activity”, “health”, “fitness”, “sport”, “body expression”, “corporal \nlanguage”, “dance”, “outdoor physical activity”. Search terms were entered into the \nGoogle Play Store in isolation or in different combinations based on Boolean logic \n(i.e., AND, NOT, OR). 2.3  \u0007Quality assessment In line with previous app reviews, an app quality assessment was completed through \nthe Mobile App Quality Ratings (MARS) (Stoyanov et  al., 2015). It includes 23 \nitems divided into 5 dimensions: engagement (5 items, e.g., “interest”), functionality \n(4 items, e.g. “ease of use”), aesthetics (3 items, e.g. “Visual appeal: How well does \nthe app look?”), information quality (7 items, e.g. “accuracy of app description”), \nand subjective quality (4 items, e.g. “Would you recommend this app?”) (Stoyanov \net al., 2015). Every item is answered on a 5-point Likert scale ranging from 1 (inad-\nequate) to 5 (excellent). To gather quality evidence for each app, a global average \nscore from the mean of each MARS dimension was estimated (Stoyanov et  al., \n2015). This assessment was independently carried out by three reviewers (R1, R3 \nand R6). Likewise, disagreements were solved by a consensus meeting between the \nreviewers and the main authors of this study. 2.2  \u0007Inclusion criteria and selection process The apps selection process was developed in two phases. In the first phase and \nafter removing duplicates, apps were retrained and registered in a database \nwhen they met the following criteria: a) they were written in Spanish or English \n(description and application), and b) they had the main goal focused on PA pro-\nmotion. Apps could be used in isolation or in combination with an external device 3 3 Education and Information Technologies (2022) 27:11759–11779 11764 (e.g., PA tracker) or a back-office system, for instance, to communicate with a PE \nor PA professional. This first phase was carried out by two reviewers (R3 and R4) \nwho individually evaluated the names and descriptions of the apps against these \ntwo criteria using a checklist (response: yes, or not). To solve disagreements, a \nconsensus meeting was held between both reviewers and the main author of this \nresearch. The apps identified in this first phase were, then, included in the sec-\nond selection phase. In this phase, a second set of inclusion criteria was estab-\nlished to identify the apps that would be included in this study. They would be \nincluded when: a) their use is allowed to underage; b) they are free (Bearne et al., \n2020; Simões et al., 2018); c) user score of at least 4 (scale range 1 to 5) follow-\ning previous app reviews (Bearne et al., 2020; Simões et al., 2018); d) a number \nof user scores equal to 100 or higher (Bearne et al., 2020; Simões et al., 2018). Two reviewers (R5 and R6) individually evaluated the names and descriptions of \nthe apps against the established inclusion criteria using a checklist (answer: yes, \nor not). A consensus meeting was held to resolve disagreements between both \nreviewers with the help of the main author of this research. (e.g., PA tracker) or a back-office system, for instance, to communicate with a PE \nor PA professional. This first phase was carried out by two reviewers (R3 and R4) \nwho individually evaluated the names and descriptions of the apps against these \ntwo criteria using a checklist (response: yes, or not). To solve disagreements, a \nconsensus meeting was held between both reviewers and the main author of this \nresearch. The apps identified in this first phase were, then, included in the sec-\nond selection phase. 2.2  \u0007Inclusion criteria and selection process In this phase, a second set of inclusion criteria was estab-\nlished to identify the apps that would be included in this study. They would be \nincluded when: a) their use is allowed to underage; b) they are free (Bearne et al., \n2020; Simões et al., 2018); c) user score of at least 4 (scale range 1 to 5) follow-\ning previous app reviews (Bearne et al., 2020; Simões et al., 2018); d) a number \nof user scores equal to 100 or higher (Bearne et al., 2020; Simões et al., 2018). Two reviewers (R5 and R6) individually evaluated the names and descriptions of \nthe apps against the established inclusion criteria using a checklist (answer: yes, \nor not). A consensus meeting was held to resolve disagreements between both \nreviewers with the help of the main author of this research. 3.1  \u0007App selection process Figure 1 shows a flow diagram for the app selection process. A total of 4650 apps \nin Google Play Store were initially identified. After removing duplicates (κ = 1.00) \nand out-of-scope apps (κ = 0.95), 432 app titles and descriptions were screened for \neligibility based on a set of inclusion criteria (κ ranging from 0.85 to 1.00). Once \nthis app selection process was completed, 18 apps were definitively included in this \nstudy. 2.4  \u0007Relationships between apps included and PE curriculum Once apps were included in this research, the authors individually downloaded the \ndifferent apps and proceeded to individually analyse them by taking into considera-\ntion the four content blocks (i.e., fitness, health and life quality; games and sports; \nbody expression; and outdoor physical activities), established after the analysis of \nthe Spanish secondary PE curriculum (Orden 15 of January, 2021; Royal Decree \n1105/, 2014), and its respective implementation in the classroom. Subsequently, \neach author proposed a series of possible practical applications for every app. Lastly, \na meeting was held to agree both points and to resolve disagreements among the \nauthors. 3 1 Education and Information Technologies (2022) 27:11759–11779 11765 2.5  \u0007Data analysis For categorical data from a checklist, the degree of agreement among reviewers was \ncomputed using the kappa (κ) index. Consistent with Landis and Koch (1977), there \nis an insignificant agreement with values below 0.20, small with values between \n0.21 and 0.40, moderate with values between 0.41 and 0.60, acceptable with values \nbetween 0.61 and 0.80, and excellent with values between 0.81 and 1.00. For con-\ntinuous data from MARS scores, the degree of agreement between reviewers was \nestimated by the intraclass correlation coefficient (ICC, model: bidirectional mixed \neffects, absolute agreement). According to Koo and Li (2016), there is a poor agree-\nment when values are less than 0.50, moderate when values are between 0.50 and \n0.75, good when values are between 0.75 and 0.90, and excellent when values are \ngreater than 0.90. 3.2  \u0007App quality The total MARS mean score was 4.00 (SD = 0.50) out of 5 (ICC = 0.90, \n95%CI = 0.80–1.00) for the totally of apps included in this research. The dimen-\nsion with the highest score was subjective quality (M = 4.30, SD = 0.50; \nICC = 0.89, 95%CI = 0.80–0.97), followed by functionally (M = 4.25, SD = 0.25; \nICC = 0.85 95%CI = 0.76–0.94), aesthetics (M = 4.20, SD = 0.20; ICC = 0.90, \n95%CI = 0.79–0.95), information quality (M = 3.75, SD = 0.70; ICC = 0.89, \n95%CI = 0.80–0.97), \nand \nengagement \n(M = 3.70, \nSD = 0.60; \nICC = 0.81, \n95%CI = 0.76–0.88). 3.3  \u0007Links with the secondary PE curriculum The 18 apps that met the inclusion criteria were organized according to the four \ncontent blocks present in the Spanish secondary PE curriculum: 1) fitness, health \nand life quality, 2) games and sports, 3) body expression, and 4) outdoor PA (Orden \n15 of January, 2021; Royal Decree 1105/, 2014). A description of each of them is 3 3 Education and Information Technologies (2022) 27:11759–11779 11766 Fig. 1   Flow diagram displaying the app selection process Fig. 1   Flow diagram displaying the app selection process shown, as well as proposals for possible applications to PE, which should be under-\nstood as ideas that teachers must adapt to variables such as the characteristics of \ntheir students and schools, among others. 3.3.1  \u0007Apps for fitness, health and life quality Table 1 yields eight apps to be used in PE for the curricular contents related to fit-\nness, health and life quality. These apps would allow students and/or teachers to \ncreate individual and collective physical challenges, to encourage them to prepare \nexercise for the group-class or to work with other subjects in an interdisciplinarity \nmanner. 3.3.4  \u0007Apps for outdoor physical activities Table 4 shows the four apps that can be used in order to address outdoor physi-\ncal activities from a curricular perspective in PE. They would allow students to \norganise and conduct sports events in natural and/or urban environments, in addi-\ntion to facilitating work with other subjects in an interdisciplinary way. 3.3.2  \u0007Apps for games and sport Table 2 shows the two apps selected for games and sports contents in PE. Both apps \nwould enable students to organise competitions and to create teams for sports activi-\nties. In addition, the two apps would facilitate interdisciplinary work with other \nsubjects. 1 3 Education and Information Technologies (2022) 27:11759–11779 11767 Table 1   Apps linked to the fitness, health and life quality curriculum content block\n(*) Based on (Cabrera Ramos et al., 2019; Cummiskey, 2011; Gil-Espinosa et al., 2020; Mokmin & Jamiat, 2021; Papastergiou et al., 2021; Vega-Ramírez et al., 2020)\nFitness, health and life quality\nApp\nDescription\nPossible practical applications in ­PE(*)\nAdidas Runtastic\nDevelops activity tracking apps and services such as train-\ning logs, data analysis, comparisons to other users, and \nother functions to help users improve their overall fitness\nCreation of individual or collective challenges. Interdisciplinarity with subjects such as Geography and history and biol-\nogy, among others. Encourage the autonomy of the students by facilitating them to prepare \nthe exercises and physical activity and then share it with the rest of the \ngroup-class. Gowod\nAllows to test and improve the mobility and flexibility\nMapmyrun\nKnow your distance, pace, calorie burn, elevation, and more\nRealfooding\nFood database, food scan, food log, user community\nSmartwod generator\nWorkouts to choose based on the equipment available\nStrava\nInternet service for tracking human exercise which incorpo-\nrates social network features. It is mostly used for cycling \nand running using GPS data\nStretching & Flexibility at home\nAllows you to know popular stretching exercises\nSworkit\nAllows to customize and play personalized video workouts Education and Information Technologies (2022) 27:11759–11779 11768 Table 2   Apps linked to the games and sports curriculum content block 1 3 1 3 3 Education and Information Technologies (2022) 27:11759–11779 11769 3.3.3  \u0007Apps for body expression Table 3 displays four apps to address the curricular contents of body-expression \nin PE. These apps emphasise the possibility of creating choreographies, rhythms \nand characterisations autonomously. Furthermore, they would allow students to \nwork on creative projects. 4  \u0007Discussion The objective of this research was threefold. The first of them was to carry out a \nsystematic search for smartphone apps focused on PA and sport that are available \nin the Google Play. The second objective included a quality assessment for every \nsmartphone app included in this study. The third objective consisted of the analy-\nsis of the relationship between every smartphone app and the PE curriculum, as \nwell as the provision of possible strategies for the implementation of each app. The main results revealed a total of 18 smartphone apps included: eight apps \nwere suitable for fitness, health and quality of life curricular content; two for \ngames and sports content; four for body expression content; and four apps for \noutdoor PA content. The mean quality score was 4.00. This research contributes \nto the scientific literature a list of apps that, linked to curriculum blocks of PE \ncontent, could help to improve their educational use by PE teachers, consistent \nwith Almusawi et al. (2021), who concluded that PE teachers need technology to \nsave class time, making it more convenient for PE. A second contribution of the \nresearch is the design of possible practical applications in PE of the apps, linked \nto each of the curricular contents, based on the opinion of PE teachers and the \nreview of the scientific literature. This study expands on literature by shedding \nthe need to link the use of apps to the school curriculum and its contents. When we refer to the educational context, teachers must be very demanding \nwith the criteria for selecting the apps to be used. Of the 4650 apps initially iden-\ntified, only 18 were selected for possible application in the educational context. This suggests a proliferation and offer of apps that, although they might seem \nuseful in the educational process, for various reasons are not recommended. Of \nthe 18 selected apps, eight have been linked to the “fitness, health and life qual-\nity” content block, two to “games and sport”, four to “body expression” and four \nto “outdoor physical activities”. This could be motivated by the creation of these \napps for society in general and not specifically for the educational context and, 1 3 3 Education and Information Technologies (2022) 27:11759–11779 11770 Table 3   Apps linked to the body expression curriculum content block\n*) Based on (Bodsworth & Goodyear, 2017; Gorman, et al. 4  \u0007Discussion 2019; Martínez, 2019; Phelps et al., 2021)\nBody expression\nApp\nDescription\nPossible practical applications in ­PE(*)\nAcrosport eps\nPre-made body figures and involve students in a creation process\nLearning application based on creative projects, choreographic compositions, \nrhythm, characterizations, among others. ust dance\nSongs and choreographies without the need for a game console\nTikTok\nIs a video-sharing social networking service\nYoucam makeup\nTry makeup trends with live camera. Edit selfies with retouching Table 3   Apps linked to the body expression curriculum content block\n*) Based on (Bodsworth & Goodyear, 2017; Gorman, et al. 2019; Martínez, 2019; Phelps et al., 2021)\nBody expression\nApp\nDescription\nPossible practical applications in ­PE(*)\nAcrosport eps\nPre-made body figures and involve students in a creation process\nLearning application based on creative projects, choreographic compositions, \nrhythm, characterizations, among others. ust dance\nSongs and choreographies without the need for a game console\nTikTok\nIs a video-sharing social networking service\nYoucam makeup\nTry makeup trends with live camera. Edit selfies with retouching Table 3   Apps linked to the body expression curriculum content block\n(*) Based on (Bodsworth & Goodyear, 2017; Gorman, et al. 2019; Martínez, 2019; Phelps et al., 2021)\nBody expression\nApp\nDescription\nPossible practical applications in ­PE(*)\nAcrosport eps\nPre-made body figures and involve students in a creation process\nLearning application based on creative projects, choreographic compositions, \nrhythm, characterizations, among others. Just dance\nSongs and choreographies without the need for a game console\nTikTok\nIs a video-sharing social networking service\nYoucam makeup\nTry makeup trends with live camera. Edit selfies with retouching 1 1 3 3 Education and Information Technologies (2022) 27:11759–11779 11771 Table 4   Apps linked to the outdoor PA curriculum content block\n(*)B\nd\n(G ll\nL\nt l 2017 J\nt l 2019 Mi h l ki\nt l 2020 R i\nt l 2020)\nOutdoor physical activity\nApp\nDescription\nPossible practical applications in ­PE(*)\nGeocaching\nIt is based on hiding and finding “treasures” (objects left by \nusers) with the help of GPS\nCarrying out tours on the natural or urban environment. Interdisciplinarity with other subjects such as geography and history, biology, mathematics, \nphysics and chemistry. Calculation of scales and mathematics. 4  \u0007Discussion QR scanner\nDecode QR codes directly\nMaps\nNavigate with GPS in real time\nMunzee\nGame based on geolocation, that is, through locations via GPS\nnologies (2022) 27:11759–11779 Table 4   Apps linked to the outdoor PA curriculum content block\n*) Based on (Gallego-Lema et al., 2017; Jansson et al., 2019; Michalakis et al., 2020; Ruiz et al. 2020)\nOutdoor physical activity\nApp\nDescription\nPossible practical applications in ­PE(*)\nGeocaching\nIt is based on hiding and finding “treasures” (objects left by \nusers) with the help of GPS\nCarrying out tours on the natural or urban environment. Interdisciplinarity with other subjects such as geography and history, biology, mathematics, \nphysics and chemistry. Calculation of scales and mathematics. QR scanner\nDecode QR codes directly\nMaps\nNavigate with GPS in real time\nMunzee\nGame based on geolocation, that is, through locations via GPS Table 4   Apps linked to the outdoor PA curriculum content block\n(*) Based on (Gallego-Lema et al., 2017; Jansson et al., 2019; Michalakis et al., 2020; Ruiz et al. 2020)\nOutdoor physical activity\nApp\nDescription\nPossible practical applications in ­PE(*)\nGeocaching\nIt is based on hiding and finding “treasures” (objects left by \nusers) with the help of GPS\nCarrying out tours on the natural or urban environment. Interdisciplinarity with other subjects such as geography and history, biology, mathematics, \nphysics and chemistry. Calculation of scales and mathematics. QR scanner\nDecode QR codes directly\nMaps\nNavigate with GPS in real time\nMunzee\nGame based on geolocation, that is, through locations via GPS 1 3 Education and Information Technologies (2022) 27:11759–11779 11772 therefore, the most of them are related to physical condition, health and quality \nof life. Therefore, it would be advisable to involve and coordinate educational adminis-\ntrations in order to design apps for educational purposes, both in collaboration with \nteachers and with companies specializing in new technologies. In this way, com-\npliance with the inclusion criteria that are determined adequate would be guaran-\nteed and the work of the teaching staff would be facilitated. In this vein, Almusawi \net al. (2021) suggests a focus on readiness encourages collaboration between sectors \nand industries to bring out the best innovative solutions for PE, as well as the col-\nlaborations of different Ministries in the app design. Just as the administration, for \nexample, coordinates, controls and finances the textbooks to be used, one should \nstart thinking about a similar implication with new technologies. 4  \u0007Discussion The quality of apps \ndesigned for children and adolescents (evaluation of commitment and quality of \ninformation) correlates with the number of techniques identified to change health \nbehaviors (Ng et  al., 2019). Another interesting variable to be considered in the \ndesign of the apps is that the interest in the use of mobile devices can have a novel \neffect that later disappears (Ridgers et al., 2018). However, initial motivation could \nserve to establish individual awareness of PA levels. PE teachers must assess the \napps, prior to use with students, so that they comply with the provisions of Organic \nLaw 3/, 2018, on the Protection of Personal Data and guarantee of digital rights, \nwhile also adjusting to the proposed objectives and the students involved. In minors, \nrequest authorization from legal guardians for use by students. Teachers are ultimately responsible for making the decision regarding the selec-\ntion and use of the apps, recommending a group or collective involvement of the \nstudents, whenever possible, in order to avoid isolation. A possible strategy could \nbe to provide challenging activities such as defiant, seeking self-efficacy, motiva-\ntion and that stimulate social support (Martins et al., 2016), paying special attention \nto promoting the adolescent’s perception of competence and positive experiences \n(Martins et al., 2018). In fact, presenting collective challenges has proven to be an \neffective methodological strategy (Gil-Espinosa et al., 2020). Thus, physical activi-\nties that promote social relationships or include team activities are more likely to be \nsuccessful (Johansson & Ruud, 2016). Furthermore, combining school interventions \nwith family or community participation appears to be an effective strategy (Parody \net al., 2019). It is important to consider variables such as whether or not they are \nfree, the minimum age for registration and use, the need for authorization from legal \nguardians, the authorization of the educational center for the use of personal mobile \ndevices or the existence of advertising in the apps, among others. In any case, it is essential that teachers frame the use of apps for educational pur-\nposes such as increasing the students’ motivation towards PA, the implementation \nof more students-centered models or the work of digital competence, as some inves-\ntigations have already concluded (Gil-Espinosa et al., 2020; Zhao et al., 2016). In \nthis vein, Lau et al. (2011), in their review, provide evidence supporting the pos-\nitive effects of the use of digital technology in PA interventions for children and \nadolescents. 4  \u0007Discussion 3 1 3 11773 Education and Information Technologies (2022) 27:11759–11779 Undoubtedly, the integration of the use of new technologies by students should \nguide us to work towards a responsible, formative, safe and educational use of them. In the case of PE, it is essential to guide students towards apps based on adequate \nscientific and technical criteria. Therefore, teachers must seek to empower students \nin the digital information age and, for this, they must have sufficient training to inte-\ngrate the use of digital technology in learning (Goodyear & Armour, 2018; Pereira \net al., 2019). This is how the Spanish curriculum establishes it in secondary educa-\ntion (Royal Decree 1105/, 2014). Therefore, taking into consideration that PE time during school hours is insuf-\nficient, the increase of PA levels of students during non-school hours should be pro-\nmoted (Gil-Espinosa et al., 2020). In this context, the use of apps is presented as a \nresource to promote strategies that increase adolescents’ PA and health (Böhm et al., \n2019; Cummiskey, 2011; Dute et al., 2016; Simões et al., 2018). Likewise, Lee and \nGao (2020) concluded that teachers are recommended to align the use of apps with \nthe PE learning goals. Indeed, Cheng and Chen (2018) argued that the combination \nof traditional and a mobile APP support learning system is an effective approach \nthat would help students to improve their health-related fitness achievements. i\nThe strengths of this study include the systematic search for apps from Google Play, \nthe use of the MARS instrument to assess quality and its relationships with the PE cur-\nriculum in secondary education. Another strength is the participation of PE teachers of \nSecondary Education and University. Further, app ratings were performed by review-\ners and other ones participated in different phases. One highlight of this research is \nthat digital technology can be integrated with the PE curriculum to improve the teach-\ning-learning process and promote PA among students. Future research should test the \noverall effectiveness of apps designed to promote PA among adolescents considering \nits link with the official curriculum. Finally, research examining the accuracy of app \ncontent and developer expertise should also be of high priority. The limitations of this study include the exclusion of apps with low interrater \nreliability for the scoring of app quality through the MARS scale. This may be due \nto the subjective nature of some sections of the scale. 4  \u0007Discussion The temporal relevancy of the \nresults from this study may also be considered a limitation. Another limitation was \nthe short period used for the individual assessment of each app. Likewise, it would \nbe convenient to carry out similar research in the App store. Conflict of interest  The author declares no conflict of interest. Conflict of interest  The author declares no conflict of interest. Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, \nwhich permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as \nyou give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-\nmons licence, and indicate if changes were made. The images or other third party material in this article \nare included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the \nmaterial. If material is not included in the article’s Creative Commons licence and your intended use is \nnot permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission \ndirectly from the copyright holder. To view a copy of this licence, visit http://​creat​iveco​mmons.​org/​licen​\nses/​by/4.​0/. 5  \u0007Conclusions The results suggest that popular apps for measuring and, potentially, promoting PA \nare of moderate quality if we look at its use in the school context. The app content \nquality, particularly the use of international guidelines on linking PA to the official \ncurriculum could be improved. Furthermore, based on the findings from this assess-\nment, we suggest that education administrators and teachers should be involved \nin the development of apps targeting student’s PA behaviors; that apps should be \ndeveloped considering the target group (aged, motivation…) and the respective 1 3 Education and Information Technologies (2022) 27:11759–11779 11774 PA recommendations established by the WHO (2020); and consider the official \ncurriculum.fl More effort is needed to incorporate components that are more likely to influ-\nence PA behavior change, which is ultimately what they were developed for, while \nmaintaining good functionality. Educational administration and developers should \ncollaborate to provide self-monitoring and social comparison in students to enhance \na comprehensive, competency-based education. In summary, this study seeks to \nadvance research on the use of apps in PE, focusing on its link with the curriculum \ncontents. Funding  Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidad de Málaga / CBUA. Rafael Burgueño is, specifically, sup-\nported by a “Margarita Salas” postdoctoral fellowship (grant number: RR_A_2021_02) from the Spanish \nMinistry of Universities. Adriana Nielsen-Rodríguez is supported by “Formación del Profesorado Univer-\nsitario” grants from the Ministry of Education, Culture and Sport (Spain), grant number FPU17/01554. Data availability  The dataset generated for this study is available on request to the corresponding author. Declarations Competing interests  The authors report there are no competing interests to declare. References Almusawi, H. A., Durugbo, C. M., & Bugawa, A. M. (2021). Innovation in physical education: Teach-\ners’ perspectives on readiness for wearable technology integration. 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Acta Gymnica, 46(2), 59–67. doi.​org/​10.​2196/​mheal​th.​9199 https://​doi.​org/​10.​\n5507/​ag.​2016.​010 Publisher’s note  Springer Nature remains neutral with regard to jurisdictional claims in published \nmaps and institutional affiliations. 1 3 11779 Education and Information Technologies (2022) 27:11759–11779 Authors and Affiliations\nFrancisco Javier Gil‑Espinosa1   · Adriana Nielsen‑Rodríguez2   · \nRamón Romance2   · Rafael Burgueño3 \n\t\nAdriana Nielsen‑Rodríguez \n\t\nadriananielsen@uma.es\n\t\nRamón Romance \n\t\narromance@uma.es\n\t\nRafael Burgueño \n\t\nrmburgueno@ual.es\n1\t\nAndalucía Tech, Faculty of Educational Sciences, IBIMA, Researching in Sport Sciences (RSS) \nResearch Group, Universidad de Málaga, Campus de Teatinos s/n, 29010 Málaga, Spain\n2\t\nAndalucía Tech, Faculty of Educational Sciences, Department of Didactics of Languages, \nArts and Sports, Human Kinetics and Body Composition Laboratory, Universidad de Málaga, \nCampus de Teatinos s/n, 29010 Málaga, Spain\n3\t\nDepartment of Education, University of Almeria, Almeria, Spain Francisco Javier Gil‑Espinosa1   · Adriana Nielsen‑Rodríguez2   ·\nRamón Romance2   · Rafael Burgueño3 Adriana Nielsen‑Rodríguez \nadriananielsen@uma.es Ramón Romance \narromance@uma.es 1\t\nAndalucía Tech, Faculty of Educational Sciences, IBIMA, Researching in Sport Sciences (RSS) \nResearch Group, Universidad de Málaga, Campus de Teatinos s/n, 29010 Málaga, Spain 1\t\nAndalucía Tech, Faculty of Educational Sciences, IBIMA, Researching in Sport Sciences (RSS) \nResearch Group, Universidad de Málaga, Campus de Teatinos s/n, 29010 Málaga, Spain 2\t\nAndalucía Tech, Faculty of Educational Sciences, Department of Didactics of Languages, \nArts and Sports, Human Kinetics and Body Composition Laboratory, Universidad de Málaga, \nCampus de Teatinos s/n, 29010 Málaga, Spain 3\t\nDepartment of Education, University of Almeria, Almeria, Spain 1 3"