Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN.

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Study of LFG bubble accumulation and discontinuous flow in the highly saturated region of landfill below the leachate level.

Landfills in developing countries are typically characterized by high waste water content and elevated leachate levels. Despite the ongoing biodegradation of waste in the highly saturated regions of these landfills, which leads to gas accumulation and bubble formation, the associated gas pressure that poses a risk to landfill stability is often overlooked. This paper introduces a landfill gas (LFG) bubble generation model and a two-fluid model that considers bubble buoyancy and porous medium resistance. The entire process can be divided into two stages based on the force balance and velocity of bubbles: Bubble Development Stage and the Two-Fluid Flow Stage. The models were validated using a one-dimensional analytical solution of hydraulic distribution that considers bubble generation, as well as an experiment involving air injection into a saturated medium. The mechanisms of LFG accumulation and ascent, leachate level rise, and discontinuous leachate-gas flow were then investigated in conjunction with continuous flow in the unsaturated region. The results indicate that the generation of LFG bubbles below the leachate level can cause a rise in the level height of more than 20%. During the Bubble Development Stage, there is a critical height for bubble ascent, above which the buoyancy exceeds the combined forces of gravity and resistance, resulting in less than 10% of bubbles continuously flowing into the unsaturated zone for recovery. The developed model effectively captures the accumulation and flow of LFG bubbles below the leachate level and could be further utilized to study leachate-gas pumping in the future.

Investigation of gaseous end products produced by thulium fiber laser lithotripsy of cystine, uric acid, and calcium oxalate monohydrate stones: A gas chromatographic and electron microscopic analysis.

Laser lithotripsy mechanisms can cause the chemical decomposition of stone components and the emergence of different end products. However, the potentially toxic end products formed during thulium fiber laser (TFL) lithotripsy of cystine stones have not been sufficiently investigated. The aim of our in vitro study is to analyze the chemical content of the gas products formed during the fragmentation of cystine stone with TFL. Human renal calculi consisting of 100% pure cystine, calcium oxalate monohydrate, or uric acid were fragmented separately with TFL in experimental setups and observed for gas release. After the lithotripsy, only the cystine stones showed gas formation. Gas chromatography-mass spectrometry was used to analyze the gas qualitatively, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and X-ray diffraction was used to examine the dried cystine stone fragments. Fragmentation of the cystine stones released free cystine, sulfur, hydrogen sulfide, and carbon disulfide gas. The SEM-EDX and X-ray diffraction analyses revealed that the free cystine in the dried fragments contained 43.1% oxygen, 28.7% sulfur, 16.1% nitrogen, and 12.1% carbon atoms according to atomic weight. The detection of potentially toxic gases after lithotripsy of cystine stones with TFL indicates a risk of in vivo production. Awareness needs to be increased among healthcare professionals to prevent potential inhalation and systemic toxicity for patients and operating room personnel during TFL lithotripsy of cystine stones.

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification.

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

Characterization of occupational inhalation exposures to particulate and gaseous straight and water-based metalworking fluids.

Exposure assessments to metalworking fluids (MWF) is difficult considering the complex nature of MWF. This study describes a comprehensive exposure assessment to straight and water-based MWFs among workers from 20 workshops. Metal and organic carbon (OC) content in new and used MWF were determined. Full-shift air samples of inhalable particulate and gaseous fraction were collected and analysed gravimetrically and for metals, OC, and aldehydes. Exposure determinants were ascertained through observations and interviews with workers. Determinants associated with personal inhalable particulate and gaseous fractions were systematically identified using mixed models. Similar inhalable particle exposure was observed for straight and water-based MWFs (64-386 µg/m3). The gaseous fraction was the most important contributor to the total mass fraction for both straight (322-2362 µg/m3) and water-based MWFs (101-699 µg/m3). The aerosolized particles exhibited low metal content irrespective of the MWF type; however, notable concentrations were observed in the sumps potentially reaching hazardous concentrations. Job activity clusters were important determinants for both exposure to particulate and gaseous fractions from straight MWF. Current machine enclosures remain an efficient determinant to reduce particulate MWF but were inefficient for the gaseous fraction. Properly managed water-based MWF meaning no recycling and no contamination from hydraulic fluids minimizes gaseous exposure. Workshop temperature also influenced the mass fractions. These findings suggest that exposures may be improved with control measures that reduce the gaseous fraction and proper management of MWF.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Response Enhancement in High-Temperature H2S-Treated Metal Oxide Gas Sensors.

Metal oxide gas sensors (MOGS), crucial components in monitoring air quality and detecting hazardous gases, are well known for their poisoning effects when exposed to certain gas molecules, such as hydrogen sulfide. Surprisingly, our research reveals that high-temperature H2S treatment leads to an enhancement effect rather than response decay. This study investigates the time-decaying response enhancement, being attributed to the formation of metal sulfide and metal sulfate on the metal oxide's surface, enhancing the electronic sensitization. Such an enhancement effect is demonstrated for various gases, including CO, CH3CH2OH, CH4, HCHO, and NH3. Additionally, the impacts of H2S treatment on the response and recovery time are also observed. Surface compositional analysis are conducted with X-ray photoelectron spectroscopy. A proposed mechanism for the enhancement effect is elaborated, highlighting the role of electronic sensitization and the sulfide-sulfate component. This research offers valuable insights into the potential applications of metal oxide sensors in sulfide-presented harsh environments in gas sensing, encouraging future exploration of optimized sensor materials, operation temperature, and the development of hydrogen sulfide poisoning-resistant and higher sensitivity MOGS.

In-Sensor Organic Electrochemical Transistor for the Multimode Neuromorphic Olfactory System.

The olfactory system is one of the six basic sensory nervous systems. Developing artificial olfactory systems is challenging due to the complexity of chemical information decoding and memory. Conventional chemical sensors can convert chemical signals into electric signals to decode gas information but they lack memory functions. Additional storage and processing units would significantly increase the complexity and power consumption of the devices, especially for portable and wearable devices. Here, an olfactory-inspired in-sensor organic electrochemical transistor (OI-OECT) is proposed, with the integrated functions of chemical information decoding, tunable memory level, and selectivity of vapor sensing. The ion-gel electrolyte endows the OI-OECT with the function of tunable memory levels and a low operating voltage. Typical synaptic behaviors, including inhibitory postsynaptic current and paired-pulse facilitations, are successfully achieved. Importantly, the gas memory level can be effectively modulated by the gate voltages (0 and -1 V), which realized the transformation of volatile and nonvolatile memory. Furthermore, benefiting from the recognition of multiple gases and ability to detect cumulative damage caused by gases, the OI-OECT is demonstrated for early warning system targeting leakage detection of two gases (NH3 and H2S). This work achieves the integrated functions of chemical gas information decode, tunable gas memory level, and selectivity of gas in a single device, which provides a promising pathway for the development of future artificial olfactory systems.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing.

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

Application of MXene composites for target gas detection in food safety.

Food contamination has long plagued agriculture, posing significant health risks to consumers. The use of volatile gases for food safety detection has proven highly effective, with composite gas sensors that leverage the two-dimensional material MXene exhibiting notable advancements in detecting various target gases. This paper reviews the progress of MXene-based composite gas sensors in the detection of food safety-related gases. The review begins by examining MXene material synthesis methods and then presents an overview of techniques aimed at enhancing MXene-based sensor detection capabilities. Recently, advancements in MXene composite gas sensors tailored for food safety gases have been highlighted. Finally, challenges encountered in gas-sensing applications of MXene-based composites are outlined, alongside predictions for their future development, aiming to offer insights for the application and advancement of intelligent gas sensors for target gases in food safety.

Size dependent dual functionality of CeO2 quantum dots: A correlation among parameters for hydrogen gas sensor and pollutant remediation.

The metal oxide-based nanostructures of variable size and shape are found effective in optimizing the gas sensing ability and pollutant degradation. The size induced lattice strain and large band gap in 3nm CeO2 quantum dots evolved the ability towards hydrogen gas sensing and dye degradation compared to nanopebbles and nanoparticles of sizes 15 ± 3, and 30 ± 12 nm. The smaller CeO2 quantum dots than Debye length was found underlying reason for nearly four times sensor response and selectivity towards reducing hydrogen gases than the oxidizing gases at 1-10 ppm level. The lattice strain calculated by Rietveld refinement and W-H analysis was found in-line with the size of CeO2 nanostructures. The enhancement in lattice strain and optical band gap (2.66, 2.78, and 2.89 eV) with decrease in size are found critical for determining the overall efficiency of CeO2 nanostructures for photocatalytic activity, attributed to the strong quantum confinement effect. The higher catalytic activity of 98 % was achieved CeO2 quantum dots in comparison to the 95 % and 94 % obtained for CeO2 nanopebbles and nanoparticles. The impact of change in degradation efficacy and gas sensing ability of different CeO2 nanomaterials is discussed in detail. This work offers a novel and simplistic method to produce CeO2 quantum dots as an efficient sensor for selective detection of H2 gas and photocatalyst. The correlation between size, Debye length, band gap, and lattice strain gives an insight for understanding the underlying detection mechanism for selective detection of reducing gas molecules and efficient pollutant remediation.

Synergistic Etching and Hydrogen Bonding-Induced Self-Assembly of MXene/MOF Hybrid Aerogel for Flexible Room-Temperature Gas Sensing.

Limited by insufficient active sites and restricted mechanical strength, designing reliable and wearable gas sensors with high activity and ductility remains a challenge for detecting hazardous gases. In this work, a thermally induced and solvent-assisted oxyanion etching strategy was implemented for selective pore opening in a rigid microporous Cu-based metal-organic framework (referred to as CuM). A conductive CuM/MXene aerogel was then self-assembled through cooperative hydrogen bonding interactions between the carbonyl oxygen atom in PVP grafted on the surface of defect-rich Cu-BTC and the surface functional hydroxyl group on MXene. A flexible NO2 sensing performance using the CuM/MXene aerogel hybridized sodium alginate hydrogel is finally achieved, demonstrating extraordinary sensitivity (S = 52.47 toward 50 ppm of NO2), good selectivity, and rapid response/recovery time (0.9/4.5 s) at room temperature. Compared with commercial sensors, the relative error is less than 7.7%, thereby exhibiting significant potential for application in monitoring toxic and harmful gases. This work not only provides insights for guiding rational synthesis of ideal structure models from MOF composites but also inspires the development of high-performance flexible gas sensors for potential multiscenario applications.

Ambipolar Heterojunction Sensors: Another Way to Detect Polluting Gases.

Gas sensors based on ambipolar materials offer significant advantages in reducing the size of the analytical system and enhancing its efficiency. Here, bilayer heterojunction devices are constructed using different octafluorinated phthalocyanine complexes, with Zn and Co as metal centers, combined with a lutetium bisphthalocyanine complex (LuPc2). Stable p-type behavior is observed for the ZnF8Pc/LuPc2 device under both electron-donating (NH3) and -oxidizing (NO2 and O3) gaseous species, while the CoF8Pc/LuPc2 device exhibits n-type behavior under reducing gases and p-type behavior under oxidizing gases. The nature of majority of the charge carriers of Co-based devices varies depending on the nature of target gases, displaying an ambipolar behavior. Both heterojunction devices demonstrate stable and observable response toward all three toxic gases in the sub-ppm range. Remarkably, the Co-based device is highly sensitive toward ammonia with a limit of detection (LOD) of 200 ppb, whereas the Zn-based device demonstrates exceptional sensitivity toward oxidizing gases, with excellent LOD values of 4.9 and 0.75 ppb toward NO2 and O3, respectively, which makes it one of the most effective organic heterojunction sensors reported so far for oxidizing gases.

Visible Light-Activated Room Temperature NO2 Gas Sensing Based on the In2O3@ZnO Heterostructure with a Hollow Microtube Structure.

The persistent challenge of poor recovery characteristics of NO2 sensors operated at room temperature remains significant. However, the development of In2O3-based gas sensing materials provides a promising approach to accelerate response and recovery for sub-ppm of NO2 detection at room temperature. Herein, we propose a simple two-step method to synthesize a one-dimensional (1D) In2O3@ZnO heterostructure material with hollow microtubes, by coupling metal-organic frameworks (MOFs) (MIL-68 (In)) and zinc ions. Meanwhile, the In2O3@ZnO composite-based gas sensor exhibits superior sensitivity performance to NO2 under visible light activation. The response value to 5 ppm of NO2 at room temperature is as high as 1800, which is 35 times higher than that of the pure In2O3-based sensor. Additionally, the gas sensor based on the In2O3@ZnO heterostructure demonstrates a significantly reduced response/recovery time of 30 s/67 s compared to the sensor based on pure In2O3 (74 s/235 s). The outstanding gas sensing properties of the In2O3@ZnO heterostructure-based sensors can be attributed to the enhanced photogenerated charge separation efficiency resulting from the heterostructure effect, and the improved receptor function toward NO2, which can increase the reactive sites and gas adsorption capacity. In summary, this work proposes a low-cost and efficient method to synthesize a 1D heterostructure material with microtube structures, which can serve as a fundamental technique for developing high-performance room-temperature gas sensors.

Facile Fabrication of Lilac-Like Multiple Self-Supporting WO3 Nanoneedle Arrays with Cubic/Hexagonal Phase Junctions for Highly Sensitive Ethylene Glycol Gas Sensors.

Metal oxides with nanoarray structures have been demonstrated to be prospective materials for the design of gas sensors with high sensitivity. In this work, the WO3 nanoneedle array structures were synthesized by a one-step hydrothermal method and subsequent calcination. It was demonstrated that the calcination of the sample at 400 °C facilitated the construction of lilac-like multiple self-supporting WO3 arrays, with appropriate c/h-WO3 heterophase junction and highly oriented nanoneedles. Sensors with this structure exhibited the highest sensitivity (2305) to 100 ppm ethylene glycol at 160 °C and outstanding selectivity. The enhanced ethylene glycol gas sensing can be attributed to the abundant transport channels and active sites provided by this unique structure. In addition, the more oxygen adsorption caused by the heterophase junction and the aggregation of reaction medium induced by tip effect are both in favor of the improvement on the gas sensing performance.

Operando Investigation of WS2 Gas Sensors: Simultaneous Ambient Pressure X-ray Photoelectron Spectroscopy and Electrical Characterization in Unveiling Sensing Mechanisms during Toxic Gas Exposure.

Ambient pressure X-ray photoelectron spectroscopy (APXPS) is combined with simultaneous electrical measurements and supported by density functional theory calculations to investigate the sensing mechanism of tungsten disulfide (WS2)-based gas sensors in an operando dynamic experiment. This approach allows for the direct correlation between changes in the surface potential and the resistivity of the WS2 sensing active layer under realistic operating conditions. Focusing on the toxic gases NO2 and NH3, we concurrently demonstrate the distinct chemical interactions between oxidizing or reducing agents and the WS2 active layer and their effect on the sensor response. The experimental setup mimics standard electrical measurements on chemiresistors, exposing the sample to dry air and introducing the target gas analyte at different concentrations. This methodology applied to NH3 concentrations of 100, 230, and 760 and 14 ppm of NO2 establishes a benchmark for future APXPS studies on sensing devices, providing fast acquisition times and a 1:1 correlation between electrical response and spectroscopy data in operando conditions. Our findings contribute to a deeper understanding of the sensing mechanism in 2D transition metal dichalcogenides, paving the way for optimizing chemiresistor sensors for various industrial applications and wireless platforms with low energy consumption.

Real time detection and identification of fish quality using low-power multimodal artificial olfaction system.

Single gas quantification and mixed gas identification have been the major challenges in the field of gas detection. To address the shortcomings of chemo-resistive gas sensors, sensor arrays have been the subject of recent research. In this work, the research focused on both optimization of gas-sensing materials and further analysis of pattern recognition algorithms. Four bimetallic oxide-based gas sensors capable of operating at room temperature were first developed by introducing different modulating techniques on the sensing layer, including constructing surface oxygen defects, polymerizing conducting polymers, modifying Nano-metal, and compositing flexible substrates. The signals derived from the gas sensor array were then processed to eliminate noise and reduce dimension with the feature engineering. The gases of were qualitatively identified by support vector machine (SVM) model with an accuracy of 98.86 %. Meanwhile, a combined model of convolutional neural network and long short-term memory network (CNN-LSTM) was established to remove the interference samples and quantitatively estimate the concentration of the target gases. The combined model based on deep learning, which avoids the overfitting with local optimal solutions, effectively boosts the performance of concentration recognition with the lowest root mean square error (RMSE) of 2.3. Finally, a low-power artificial olfactory system was established by merging the multi-sensor data and applied for real-time and accurate judgment of the food freshness.

Image Sensing of Gaseous Acetone Using Secondary Alcohol Dehydrogenase-Immobilized Mesh for Exhaled Air.

Human-borne acetone is a potent marker of lipid metabolism. Here, an enzyme immobilization method for secondary alcohol dehydrogenase (S-ADH), which is suitable for highly sensitive and selective biosensing of acetone, was developed, and then its applicability was demonstrated for spatiotemporal imaging of concentration distribution. After various investigations, S-ADH-immobilized meshes could be prepared with less than 5% variation by cross-linking S-ADH with glutaraldehyde on a cotton mesh at 40 °C for 15 min. Furthermore, high activity was obtained by adjusting the concentration of the coenzyme nicotinamide adenine dinucleotide (NADH) solution added to the S-ADH-immobilized mesh to 500 µM and the solvent to a potassium phosphate buffer solution at pH 6.5. The gas imaging system using the S-ADH-immobilized mesh was able to image the decrease in NADH fluorescence (ex 340 nm, fl 490 nm) caused by the catalytic reaction of S-ADH and the acetone distribution in the concentration range of 0.1-10 ppm-v, including the breath concentration of healthy people at rest. The exhaled breath of two healthy subjects at 6 h of fasting was quantified as 377 and 673 ppb-v, which were consistent with the values quantified by gas chromatography-mass spectrometry.

Assessing the potential for gas supersaturation downstream of hydropower plants in Norway, Austria and Germany.

Hydroelectric power facilities can generate episodic total dissolved gas supersaturation (TDGS), which is harmful to aquatic life. We developed a decision tree-based risk assessment to identify the potential for TDGS at hydropower plants and conducted validation measurements at selected facilities. Applying the risk model to Norway's hydropower plants (n = 1696) identified 473 (28 %) high-risk plants characterized by secondary intakes and Francis or Kaplan turbines, which are prone to generating TDGS when air is entrained. More than half of them discharge directly to rivers (283, 17 % of total). Measurements at 11 high-risk plants showed that 8 of them exhibited biologically relevant TDGS (120 % to 229 %). In Austria and Germany, the analysis of hydropower plants was limited due to significant data constraints. Out of 153 hydropower plants in Austria, 80 % were categorized at moderate risk for TDGS. Two Austrian plants were monitored, revealing instances of TDGS in both (up to 125 %). In Germany, out of 403 hydropower plants, 265 (66 %) fell into the moderate risk, with none in the high-risk category. At a dam in the Rhine River, TDGS up to 118 % were observed. Given the uncertainty due to limited data access and the prevalence of run-of-river plants in Austria and Germany, there remains an unclarified risk of TDGS generation in these countries, especially at spillways of dams and below aerated turbines. The results indicate a previously overlooked potential for the generation of biologically harmful TDGS at hydropower installations. It is recommended to systematically screen for TDGS at hydropower installations through risk assessment, monitoring, and, where needed, the implementation of mitigation measures. This is increasingly critical considering the expanding global initiatives in hydropower and efforts to maintain the ecological status of freshwater ecosystems.