Composite Time Series Research Articles

Research on State Detection Method of Electrical Equipment Based on Wireless Sensor Network Signal Processing

False alarms and omissions of key measurement signals may even affect the reliability of decision-making in power IoT system, lead to safety accidents and bring huge economic losses. In order to improve the reliability of system operation and the information management level of electrical equipment, it is necessary to identify and extract suddenly changed signals from massive measurement signals collected by wireless sensor networks, and to detect the working state of electrical equipment by judging the source of signals. Therefore, this article studies the state detection method of electrical equipment based on wireless sensor network signal processing. In the second chapter, a cluster splitting and merging method is designed to solve the problem that the existing detection methods tend to ignore the imbalance of cluster size. In the third chapter, according to the data characteristics of measurement signals collected by wireless sensor networks, a similarity measurement criterion for composite time series of measurement signals is proposed, and the corresponding distance matrix is generated based on this criterion. In the fourth chapter, wavelet decomposition is used to decompose the initial measurement signals collected by wireless sensor networks, and then the signals are compressed twice based on compressed sensing. Then the abnormal signal data information is imported into support vector machine for training to realize the real-time detection of abnormal signals of electrical equipment state. Experimental results verify the effectiveness of the proposed algorithm.

Teleconnection between the Low Index Phase of Southern Oscillation and Precipitation Patterns over the Southeastern United States

The purpose of this analysis is to examine the spatiotemporal extent to which the extreme phase of Southern Oscillation affects precipitation patterns over the southeastern region of the United States. Composite and harmonic analyses using monthly precipitation data were conducted based on the 2-year composite resulting in harmonic dials, which represent the spatial coherence of the candidate regions. For the candidate regions identified in the harmonic dial maps, aggregate composite and index time series of precipitation were analyzed to determine the core regions which represent the temporal consistency in relation to the low index phase of Southern Oscillation (LISO) events. As a result, the precipitation patterns of the core regions, North Region (UR), Middle Region (MR), and South Region (LR), were influenced by the occurrence of the extreme phase of SO events, with SR having the greatest response. During the LISO cycle, the harmonic dial tends to face southeast in UR, northeast in MR, and east in LR. Furthermore, the spatial coherence rates of the three core regions range from 0.94 to 0.99, and the temporal consistency rates are 0.76 to 0.83. In addition, from the cross-correlation and annual cycle analyses, the seasonal and annual effect of LISO forcing on the precipitation variability over the three core regions can be recognizable. In conclusion, Southern Oscillation phenomena have a great influence on the precipitation patterns over the southeastern region of the United States.

Two decades of satellite surface chlorophyll a concentration (1998–2019) in the Spanish Mediterranean marine waters (Western Mediterranean Sea): Trends, phenology and eutrophication assessment

Time series of satellite surface chlorophyll of more than 20 years were built for the Spanish Mediterranean waters. Chlorophyll data from SeaWiFS, MODIS-Aqua and VIIRS-Suomi-NPP were generated based on the locally developed SMED algorithms. Nevertheless, it was necessary to harmonize the trends between the three different sensors before merging the data in the composite time series. Cluster analysis of the monthly chlorophyll averages allowed distinguishing four main areas with distinctive seasonal and phenological features in the study region. The western Alboran Sea showed high productivity during all the year. In the other areas, the average duration of the productive period was also extensive (about 180 days) starting at the beginning of November, but coastal waters in general showed a belated ending. These results suggest that nutrients are the main limiting factor of the primary production in the region. During the last 20 years, a general decreasing trend in the surface chlorophyll concentration was observed in the Spanish Mediterranean waters; nevertheless, a strong variability in the intensity of the productive annual period probably related with long scale climatic oscillations was observed. The surface chlorophyll concentration reduced in a decade from 10% in coastal waters to 20% in offshore waters. Some patches in the region showed positive trends, including offshore areas around the Balearic Islands in winter and the coastal areas of the Gulf of Valencia in summer, but in the southern coasts of the Gulf of Alicante the positive trends were persistent during all the year. The performed analysis can be used globally for eutrophication assessment as well as for the evaluation of changes on the phytoplankton phenology probably driven by the global change.

Holocene relative water level and storminess variation recorded in the coastal peat bogs of the Vistula Lagoon, southern Baltic Sea

The history of regional sea-level changes is critically important for building global environmental reconstruction models and identifying major driving forces. The coastal peatlands located on the terraces of the Vistula Lagoon (Gulf of Gdańsk, southern Baltic Sea) are one of the most valuable, and not yet fully explored, archives of the regional history of sea-level and storminess changes during the middle and late Holocene. The comprehensive analysis of peat and gyttja allowed establishing radiocarbon calibrated time series of stable carbon isotope composition (δ13C), pollen and diatom data. The results indicate that the sea-level increased from ∼20 to 8 m below present sea level between 9000 and 7000 yr b2k at an average rate of ∼6 mm/year. Around 5000 yr b2k, the sea-level was ∼2.5 m lower than at present, which indicates a decrease in the rate of sea-level rise to ∼2.75 mm/year between 7000 and 5000 yr b2k. During the last 5000 years, the sea-level had risen at ∼0.5 mm/year to the present position. Changes in brackish diatoms, Pediastrum sp. concentrations, and maxima of δ13C suggest ten periods of increased rates of sea-level rise and storminess between 7000 and 1500 yr b2k. Increased sea-levels and stormy surges on the peat surface occurred mainly during cold periods or the periods of transitions. These observations are in good agreement with climate fluctuations in the Northern Hemisphere related to the variations in total regional solar irradiation.

Investigating the response of hydrological processes to El Niño events using a 100-year dataset from the western Pacific Ocean

Study regionWestern Tropical Pacific Ocean (Philippines) Study focusEl Niño Southern Oscillation (ENSO) modulates rainfall amount variability and, by extension, river discharge for the Philippines on seasonal to interannual temporal scales. The El Niño phase of ENSO considerably decreases rainfall amounts on a seasonal scale with varying degrees of heterogeneity across the Philippines. The hydrological response of El Niño on an interannual scale is relatively immature. To investigate the hydrological response, a composite time series of 29 rainfall and 61 river discharge stations spanning 1901–2020 and 1908–2017 C.E., respectively, and covering the four major climate types in the Philippines were critically assessed. New hydrological insights for the regionOur statistical analyses results of a 100-year dataset demonstrate a decreasing trend compared to pre-El Niño conditions for both river discharge and rainfall. The median response suggests the decreasing trend can last up to 7 years regardless of climate type. Rainfall amount returns to pre-El Niño conditions faster than river discharge. The sign (increasing or decreasing) of the hydrological response is either decreasing, if at conception of an El Niño phase, or increasing, if at the termination of an El Niño phase. Our results have implications for water resource management and water resiliency of island nations that strongly rely on the timely delivery of rainfall amount. Further, our results highlight the legacy effects of El Niño events that likely initiate long-term droughts for island nations in western tropical Pacific Ocean.

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

Data Fusion of Total Solar Irradiance Composite Time Series Using 41 Years of Satellite Measurements

AbstractSince the late 1970s, successive satellite missions have been monitoring the sun's activity and recording the total solar irradiance (TSI). Some of these measurements have lasted for more than a decade. In order to obtain a seamless record whose duration exceeds that of the individual instruments, the time series have to be merged. Climate models can be better validated using such long TSI time series which can also help to provide stronger constraints on past climate reconstructions (e.g., back to the Maunder minimum). We propose a 3‐step method based on data fusion, including a stochastic noise model to take into account short and long‐term correlations. Compared with previous products scaled at the nominal TSI value of ∼1361 W/m2, the difference is below 0.2 W/m2 in terms of solar minima. Next, we model the frequency spectrum of this 41‐year TSI composite time series with a Generalized Gauss‐Markov model to help describe an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum‐likelihood estimator. Our results show that the amplitude of such trend is ∼−0.004 ± 0.004 W/(m2yr) for the period 1980–2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these composite time series is mostly an artifact due to the colored noise.

Extending the National Burned Area Composite Time Series of Wildfires in Canada

Wildfires are a major natural disturbance in Canada that are postulated to increase under a warming climate. To derive accurate trends in burned area and to quantify the effects of fire frequency, duration, and extent, a sufficiently long time series of reliable burned area maps is required. With that in mind, we extended Canada’s National Burned Area Composite (NBAC) dataset from its previous start year (2004) back to 1986. NBAC consists of annual maps in polygon format where the area burned in each fire event is represented by the best available delineation among various mapping methods and data sources of varying quality. Ordered from more to less reliability, in the new 35-year time series (1986 to 2020), 10% of the total burned area was derived from airborne and high-resolution (<5 m) satellite imagery, 81% from change detection methods using 30 m Landsat satellite imagery, and the remaining 9% was largely from aerial surveys. Total (Canada-wide) annual burned area estimates ranged from 215,797 ha in 2020 to 6.7 million ha (Mha) in 1989. We computed 95% confidence intervals for the estimate of each year from 1986 to 2020 based on the accuracy and relative contribution in that year of the different data sources, for both the new NBAC time series and the polygon version of the Canadian National Fire Database (CNFDB), a commonly used source of spatially explicit data on burned area in Canada. NBAC confidence intervals were on average ±9.7% of the annual figure, about one-third the width of the confidence intervals derived for CNFDB. The NBAC time series also included nearly 5000 fire events (totalling 4 Mha, with the largest event being 120,661 ha in size) that are missing in the CNFDB. In a regional analysis for the Northwest Territories, retroactive fire mapping from Landsat imagery reduced historical estimates by 3 Mha (16%), which would result in a 1.6 Mha increase in the reported undisturbed critical habitat for threatened woodland caribou. The NBAC dataset is freely downloadable from the Canadian Wildland Fire Information System.

Measurement report: Atmospheric new particle formation in a coastal agricultural site explained with binPMF analysis of nitrate CI-APi-TOF spectra

Abstract. The occurrence of new particle formation (NPF) events detected in a coastal agricultural site, at Qvidja, in Southwestern Finland, was investigated using the data measured with a nitrate ion-based chemical-ionization atmospheric-pressure-interface time-of-flight (CI-APi-TOF) mass spectrometer. The binned positive matrix factorization method (binPMF) was applied to the measured spectra. It resulted in eight factors describing the time series of ambient gas and cluster composition at Qvidja during spring 2019. The most interesting factors related to the observed NPF events were the two factors with the highest mass-to-charge ratios, numbered 7 and 8, both having profiles with patterns of highly oxygenated organic molecules with one nitrogen atom. It was observed that factor 7 had elevated intensities during the NPF events. A variable with an even better connection to the observed NPF events is fF7, which denotes the fraction of the total spectra within the studied mass-to-charge ratio range between 169 and 450 Th being in a form of factor 7. Values of fF7 higher than 0.50±0.05 were observed during the NPF events, of which durations also correlated with the duration of fF7 exceeding this critical value. It was also observed that factor 8 acts like a precursor for factor 7 with solar radiation and that the formation of factor 8 is associated with ozone levels.

Time after time: detecting annual patterns in stream bacterial biofilm communities.

SummaryTo quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36‐months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non‐cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

A Rationale for Past/Prediction Span Proportion in Markov Chain-Based Predictive Modeling of Energy-Related Compositional Time Series Data

A Rationale for Past/Prediction Span Proportion in Markov Chain-Based Predictive Modeling of Energy-Related Compositional Time Series Data

Observations of the Development and Vertical Structure of the Lake-Breeze Circulation during the 2017 Lake Michigan Ozone Study

Abstract Ground-based thermodynamic and kinematic profilers were placed adjacent to the western shore of Lake Michigan at two sites as part of the 2017 Lake Michigan Ozone Study. The southern site near Zion, Illinois, hosted a microwave radiometer (MWR) and a sodar wind profiler, while the northern site in Sheboygan, Wisconsin, featured an Atmospheric Emitted Radiance Interferometer (AERI), a Doppler lidar, and a High Spectral Resolution Lidar (HSRL). Each site experienced several lake-breeze events during the experiment. Composite time series and time–height cross sections were constructed relative to the lake-breeze arrival time so that commonalities across events could be explored. The composited surface observations indicate that the wind direction of the lake breeze was consistently southeasterly at both sites regardless of its direction before the arrival of the lake-breeze front. Surface relative humidity increased with the arriving lake breeze, though this was due to cooler air temperatures as absolute moisture content stayed the same or decreased. The profiler observations show that the lake breeze penetrated deeper when the local environment was unstable and preexisting flow was weak. The cold air associated with the lake breeze remained confined to the lowest 200 m of the troposphere even if the wind shift was observed at higher altitudes. The evolution of the lake breeze corresponded well to observed changes in baroclinicity and calculated changes in circulation. Collocated observations of aerosols showed increases in number and mass concentrations after the passage of the lake-breeze front.

Sporadic Low Salinity Signals in the Oceanic Mixed Layer Observed by the Kuroshio Extension Observatory Buoy

The hourly measurements of air-sea interaction parameters made at the Kuroshio Extension Observatory (KEO) buoy since 2004 have produced an extensive dataset that can be used to understand the dynamics of the oceanic mixed-layer under extreme weather conditions. Our analysis of the KEO-buoy measurements reveals the presence of sporadic low-salinity signals (SLSSs) with duration of 1–2 days in the mixed-layer when, for example, tropical cyclones (TCs) approach. Here SLSS events are identified within the buoy data by using a flag which is set to unity when the value of normalized salinity falls below −1 (minus one). Application of this definition to the hourly time series of salinity measured at 10 m depth by the KEO buoy has allowed us to identify 244 SLSS events over the 11-year analysis period. Both winter (January-March) and spring (April-June) seasons show a peak in the composite time series of wind velocity subdiurnal variation (WVDV) near the start time of SLSS events. SLSS events can be terminated by the entrainment of saltier subsurface water parcels to the mixed-layer in response to storms. The discovery of the WVDV peaks following SLSS commencement is the reason the present study has adopted a time scale of 1-day wind velocity analysis. This temporal resolution has enabled us to identify a TC-related delay in the development of the WVDV peaks, which may represent a fundamental characteristic of the influence of TCs on these events.

Monitoring the Spatio-Temporal Dynamics of Shale Oil/Gas Development with Landsat Time Series: Case Studies in the USA

Shale oil/gas extraction has expanded rapidly in the last two decades due to the rising energy prices and the advancement of technologies. Its development can have huge impacts on and, at the same time, is also deeply affected by energy markets, especially in an era with high economic uncertainty. Understanding and monitoring shale oil/gas development over large regions are critical for both energy policies and environmental protection. However, there are currently no applicable methods to track the spatio-temporal dynamics of shale oil/gas development. To fill this gap, we propose a new NDVI Trajectroy Matching algorithm to track shale oil/gas development using the annual Landsat NDVI composite time series from 2000 to 2020. The results reveal that our algorithm can accurately extract the location and time of shale oil/gas exploitation in Eagle Ford and Three Forks, with an accuracy of 83.80% and 81.40%, respectively. In the Eagle Ford area, accuracy for all disturbance year detection was greater than 66.67%, with the best in 2011 and 2019 at 90.00%. The lowest accuracy in the Three Forks area was 63.33% in 2002, while the highest accuracy was 93.33% in 2019. In conclusion, the algorithm can effectively track shale oil/gas development with considerable accuracy and simplicity. We believe that the algorithm has enormous potential for other applications, such as built-up regions, forests, farmlands, and water body expansion and contraction involving vegetation damage.

Four years of daily stable water isotope data in stream water and precipitation from three Swiss catchments

Time series of the natural isotopic composition (2H, 18O) of precipitation and streamwater can provide important insights into ecohydrological phenomena at the catchment scale. However, multi-year, high-frequency isotope datasets are generally scarce, limiting our ability to study highly dynamic short-term ecohydrological processes. Here we present four years of daily isotope measurements in streamwater and precipitation at the Alp catchment (area 47 km2) in Central Switzerland and two of its tributaries (0.7 km2 and 1.6 km2). This data set reveals short-term responses of streamflow isotopes to precipitation events, which otherwise remain obscured when isotopes are sampled weekly or monthly. The observations span the period June 2015 through May 2019, during which several hydrometeorologic extreme events occurred, including a very dry summer in 2018 and below-average snow accumulation in winter 2016/2017. In addition, we provide daily time series of key hydrometeorological variables that, in combination with the isotope data, can be useful for assessing the robustness of ecohydrological models.

Long Term Observation of Fractional Vegetation Cover in Qingyang of Gansu Province and Its Response to Climate Change

Vegetation is seen as a sensitive indicator of global change because of its crucial role in connecting the atmosphere, soil, and water. Fractional vegetation cover (FVC), in turn, is an important indicator of vegetation status. Qingyang is a typically ecologically sensitive region, with a range of changes in vegetation in the last decade as a result of climatic and non-climatic factors. However, the exact impact of climate change and human activities remains unclear. Satellite observations can help to clarify that impact, allowing us to assess trends in vegetation change in the last two decades (2000–2019). In this study, daily and composite time series vegetation variations were derived from moderate resolution imaging spectroradiometer (MODIS) data and the impact of climate and human activity factors was examined for different administrative districts. By deploying multiple regression models, the research revealed that human activity has contributed 46% to the FVC variation, while the remaining 54% was led by climate factors. In areas where FVC was increasing, human activity contributed 55.89% while climate factors contributed 44.11%. In areas where FVC was decreasing, human activity and climate factors contributed 24.58% and 75.42%, respectively. The study also looks at the impacts of El Nino/IOD events in FVC dynamics in the study site. The FVC inversion result from MODIS proved capable of capturing long-term and seasonal vegetation patterns and thus provide a valuable archive for decadal-scale vegetation dynamics in the study area. Moreover, the improvement in FVC was a dual effect of climatic and human activities, while the latter owns a higher contribution especially for the implementation of ecological construction projects.

Coral perspective on temperature seasonality and interannual variability in the northern South China Sea during the Roman Warm Period

Few reconstructions of sea-surface temperature (SST) have focused on seasonal and interannual variability, two major components of the global climate system, due to the limited temporal resolution of proxies. This study presents a combined 228-year-long, monthly resolved strontium to calcium ratios (Sr/Ca) record covering the period of 2070–1740 a BP (years before 1950 CE (Common Era)) extracted from three U-series-based sub-fossil Porites corals located on the Xisha Islands, northern South China Sea (SCS). The composite time series allowed for accurate assessment of the natural range of SST variations during snapshots of the Roman Warm Period (RWP). Reconstructed SST revealed that the RWP was characterized by cooler conditions compared with the 20th century, consistent with climatic variations in the western Pacific and East Asia. The amplitude of SST seasonality was within the modern range, except for a lower value at 1980–1928 a BP. Interannual variability associated with El Niño–Southern Oscillation (ENSO) activity was enhanced by 39% relative to 1980–2014 CE. The results of the sliding window demonstrate that ENSO variability persistently strengthened during 2070–2010 a BP, followed by an overall fluctuating attenuation during 1980–1928 a BP. Then, the trends of rising first before descending twice during 1852–1800 a BP appeared. Furthermore, ENSO activity played a leading role in steering short-term changes in SST seasonality in the northern SCS, manifested as stronger ENSO activity with more frequent El Niño events and decreased SST seasonality. Considering that the frequency of extreme ENSO events may strengthen in the future under global warming, the climate in the northern SCS might become more variable and complex.

Machine Learning Approach to Simulation of Continuous Seeded Crystallization of Gibbsite

Continuous seeded crystallization is characterized by oscillations of particle size distribution (PSD) and liquor productivity. To describe these oscillations using analytical methods is a complicated task due to non-linearity and slow response of the process. This paper uses a statistical approach to the preparation of initial data, determination of the significant factors and arrangement of the said factors by their impact on the dynamics of crystal population development. Various methods of machine learning were analyzed to develop a model capable of forecasting the time series of particle size distribution and composition of the final solution. The paper proposes to use deep learning methods for predicting the distribution of crystals by grades and liquor productivity. Such approach has never been used for these purposes before. The study shows that models based on long short-term memory (LSTM) cells provide for better accuracy with less trainable parameters as compared with other multilayer neural networks. Training of the models and the assessment of their quality are performed using the historical data collected in the hydrate crystallization area at the operating alumina refinery

Disaggregated PROBA-V data allows monitoring individual crop phenology at a higher observation frequency than Sentinel-2

Satellite-based monitoring of crop phenology is commonly built on the analysis of Vegetation Index (VI) time series by extracting phenological metrics. The relatively fine detection of the various timings in crops growth during their development cycle depends on the density and regularity of valid observations. Medium spatial resolution (MSR) daily observations provide consistent cloud-free n-day composite time series, suitable for phenological applications, but do not offer an adequate spatial resolution. MSR pixels are generally mixed pixels or “mixels”, composed of several land cover classes, which complicate crop-specific monitoring from space. To address the MSR mixel problem, we implemented a spatial disaggregation (SD) approach that estimates a crop-specific VI based on the crop fraction in a mixel provided by a land use map. First, SD was applied on synthetic MSR data (i.e. Sentinel-2 data aggregated at 300 m) in order to test the method in an ideal case. After validation, the method was applied to PROBA-V data, using 300 m and 10-day composites over a large area around Paris, for four main crops (i.e. winter cereals, spring barley, oilseed rape and maize) in 2016–2017. The evaluation of SD was done by comparing disaggregated data with reference data (i.e. Sentinel-2 10 m). Indeed, two main results were observed, i) SD was able to reconstruct the crop-specific VI time series of all crops and ii) PROBA-V data increased the number of crop-specific VI valid observations at certain stages of the crop's growth period compared to Sentinel-2 data, this with a consistent and regular revisit throughout the growth cycle. In conclusion, SD can be used to improve the exploitation of MSR data in seasonal crop monitoring, especially during the transition periods when the VI of crops are likely to change quickly. This paves the way for monitoring crop phenology over fragmented landscapes, from sensors such as MODIS or SPOT-VEGETATION, even for years before Sentinel-2 launch.

Methane (CH<sub>4</sub>) sources in Krakow, Poland: insights from isotope analysis

Abstract. Methane (CH4) emissions from human activities are a threat to the resilience of our current climate system. The stable isotopic composition of methane (δ13C and δ2H) allows us to distinguish between the different CH4 origins. A significant part of the European CH4 emissions, 3.6 % in 2018, comes from coal extraction in Poland, the Upper Silesian Coal Basin (USCB) being the main hotspot. Measurements of CH4 mole fraction (χ(CH4)), δ13C, and δ2H in CH4 in ambient air were performed continuously during 6 months in 2018 and 2019 at Krakow, Poland, in the east of the USCB. In addition, air samples were collected during parallel mobile campaigns, from multiple CH4 sources in the footprint area of the continuous measurements. The resulting isotopic signatures from sampled plumes allowed us to distinguish between natural gas leaks, coal mine fugitive emissions, landfill and sewage, and ruminants. The use of δ2H in CH4 is crucial to distinguish the fossil fuel emissions in the case of Krakow because their relatively depleted δ13C values overlap with the ones of microbial sources. The observed χ(CH4) time series showed regular daily night-time accumulations, sometimes combined with irregular pollution events during the day. The isotopic signatures of each peak were obtained using the Keeling plot method and generally fall in the range of thermogenic CH4 formation – with δ13C between −59.3 ‰ and −37.4 ‰ Vienna Pee Dee Belemnite (V-PDB) and δ2H between −291 ‰ and −137 ‰ Vienna Standard Mean Ocean Water (V-SMOW). They compare well with the signatures measured for gas leaks in Krakow and USCB mines. The CHIMERE transport model was used to compute the CH4 and isotopic composition time series in Krakow, based on two emission inventories. The magnitude of the pollution events is generally underestimated in the model, which suggests that emission rates in the inventories are too low. The simulated isotopic source signatures, obtained with Keeling plots on each simulated peak, indicate that a higher contribution from fuel combustion sources in the EDGAR v5.0 inventory would lead to a better agreement than when using CAMS-REG-GHG v4.2 (Copernicus Atmosphere Monitoring Service REGional inventory for Air Pollutants and GreenHouse Gases). The isotopic mismatches between model and observations are mainly caused by uncertainties in the assigned isotopic signatures for each source category and the way they are classified in the inventory. These uncertainties are larger for emissions close to the study site, which are more heterogenous than the ones advected from the USCB coal mines. Our isotope approach proves to be very sensitive in this region, thus helping to evaluate emission estimates.