Quality Assessment Strategies Research Articles

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Water Quality Assessment and Management Strategies for Nishan Reservoir, Sihe River, and Yihe River Based on Scientific Evaluation

Water Quality Assessment and Management Strategies for Nishan Reservoir, Sihe River, and Yihe River Based on Scientific Evaluation

Methodology for evaluating the quality of a digital channel of a satellite communication system based on an integral indicator and a fuzzy logical conclusion

The problem of evaluating the quality of digital channels of satellite communication systems (DCSCS) is considered. It is shown that two quality assessment strategies are used in practice: the first is an assessment of quality from the position of consumers of services and specialists in the provision of communication services in the market; the second is an assessment of quality from the position of specialists in technical operation. It is concluded that the first strategy does not allow to identify the degradation of DCSCS quality. It is suggested that it is advisable to use the second strategy. A reasonable choice of particular quality indicators was made: error probability, jitter and phase drift, slippage, signal transmission delay, reliability, relative transmission rate, energy reserve, error vector modulus. A quality assessment criterion is proposed that forms three possible outcomes: satisfactory quality, marginal quality, and unsatisfactory quality. A single integral indicator is proposed, formed on the basis of particular quality indicators and fuzzy logical inference. The structure of a fuzzy expert quality assessment system is considered. An illustrative example is given.

Comparative evaluation of spatiotemporal variations of surface water quality using water quality indices and GIS

AbstractThere is a need for a comprehensive comparative analysis of spatiotemporal variations in surface water quality, particularly in regions facing multiple pollution sources. While previous research has explored the use of individual water quality indices (WQIs), there is limited understanding of how different WQIs perform in assessing water quality dynamics in complex environmental settings. The objective of this study is to evaluate the effectiveness of three WQIs (Canadian Council of Ministers of the Environment (CCME), National Sanitation Foundation (NSF) and System for Evaluation of the Quality of rivers (SEQ-Eau) and a national water quality regulation in assessing water quality dynamics. The pilot study area is the Acısu Creek in Antalya City of Turkey, where agricultural practices and discharge of treated wastewater effluents impair the water quality. A year-long intensive monitoring study was conducted includig on-site measurements, analysis of numerous physicochemical and bacteriological parameters. The CCME and SEQ-Eau indices classified water quality as excellent/good at the upstream, gradually deteriorating to very poor downstream, showing a strong correlation. However, the NSF index displayed less accuracy in evaluating water quality for certain monitoring stations/sessions due to eclipsing and rigidity problems. The regulatory approach, which categorized water quality as either moderate or good for different sampling sessions/stations, was also found less accurate. The novelty of this study lies in its holistic approach to identify methodological considerations that influence the performance of WQIs. Incorporating statistical analysis, artificial intelligence or multi-criteria decision-making methods into WQIs is recommended for enhanced surface water quality assessment and management strategies.

Enhancing Radiological Diagnosis: A Comprehensive Review of Image Quality Assessment and Optimization Strategies.

Image quality plays a pivotal role in the accurate diagnosis and effective management of diseases in radiology. This review explores the principles, methodologies, and strategies for assessing and optimizing image quality across various imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine. We discuss key factors influencing image quality, such as spatial resolution, noise, contrast, and artifacts, and highlight techniques for quality assurance, image optimization, and dose reduction in clinical practice.

Air Quality Assessment and Pollution Mitigation Strategies in Beijing: A Comprehensive Study on PM2.5 Trends, Influencing Factors, and Case Studies

Air Quality Assessment and Pollution Mitigation Strategies in Beijing: A Comprehensive Study on PM2.5 Trends, Influencing Factors, and Case Studies

QAGait: Revisit Gait Recognition from a Quality Perspective

Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

Quantifying the combined impacts of anthropogenic CO2 emissions and watershed alteration on estuary acidification at biologically-relevant time scales: a case study from Tillamook Bay, OR, USA.

The impacts of ocean acidification (OA) on coastal water quality have been subject to intensive research in the past decade, but how emissions-driven OA combines with human modifications of coastal river inputs to affect estuarine acidification dynamics is less well understood. This study presents a methodology for quantifying the synergistic water quality impacts of OA and riverine acidification on biologically-relevant timescales through a case study from a small, temperate estuary influenced by coastal upwelling and watershed development. We characterized the dynamics and drivers of carbonate chemistry in Tillamook Bay, OR (USA), along with its coastal ocean and riverine end-members, through a series of synoptic samplings and continuous water quality monitoring from July 2017 to July 2018. Synoptic river sampling showed acidification and increased content in areas with higher proportions of watershed anthropogenic land use. We propagated the impacts of 1). the observed riverine acidification, and 2). modeled OA changes to incoming coastal ocean waters across the full estuarine salinity spectrum and quantified changes in estuarine carbonate chemistry at a 15-minute temporal resolution. The largest magnitude of acidification (-1.4 units) was found in oligo- and mesohaline portions of the estuary due to the poor buffering characteristics of these waters, and was primarily driven by acidified riverine inputs. Despite this, emissions-driven OA is responsible for over 94% of anthropogenic carbon loading to Tillamook Bay and the dominant driver of acidification across most of the estuary due to its large tidal prism and relatively small river discharges. This dominance of ocean-sourced anthropogenic carbon challenges the efficacy of local management actions to ameliorate estuarine acidification impacts. Despite the relatively large acidification effects experienced in Tillamook Bay (-0.16 to -0.23 units) as compared with typical open ocean change (approximately -0.1 units), observations of estuarine would meet existing state standards for . Our analytical framework addresses pressing needs for water quality assessment and coastal resilience strategies to differentiate the impacts of anthropogenic acidification from natural variability in dynamic estuarine systems.

Quality Assessment and Enhancement Strategies for Learning Environments in Top Classes of Kindergarten—A Reflection Based on the Perspective of School Readiness

Quality Assessment and Enhancement Strategies for Learning Environments in Top Classes of Kindergarten—A Reflection Based on the Perspective of School Readiness

Water Quality Monitoring in Agriculture: Applications, Challenges and Future Prospectus with IoT and Machine Learning

Water is one of the key elements involved in maintaining the quality of life. It is essential in the field of agriculture for food production and livestock farming. Water quality is continuously declining as a consequence of the growing trend of urbanization and industrialization, which can harm the environment and human health. In recent years, several researchers in the field of agriculture have shown advancements in techniques with the implementation of the Internet of Things (IoT) Artificial Intelligence (AI) machine learning (ML) and smart technologies. These advancements aim at improving water usage and enhancing the quality and quantity of different crops, with the ability to lower analysis costs, time and facilitate the achievement of management results. This paper outlines an overview of recent studies on water control and management using the IoT and ML models in the field of agriculture. Thus, new technologies for analyzing water quality metrics like pH, temperature, color, turbidity, Total Dissolved Solids (TDS), salinity, and nitrogen are described. It also describes few exposed contests used to draw related study suggestions in the future, such as the use of advanced intelligent tools and strategies for water quality assessment and management in the agriculture sector.

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment.

Strategies for quality assessment: the human factor

Strategies for quality assessment: the human factor

Tree-rings analysis to reconstruct atmospheric mercury contamination at a historical mining site.

Mercury (Hg) is a global environmental concern due to its toxicity (especially high in methylated form) and the long-range distribution of its gaseous elemental form (GEM). Hg-contaminated areas, such as abandoned mining sites, pose intrinsic difficulties for their management and heavy monitoring costs. In these environments, plant-based solutions may play a key role in the ecosystem quality assessment and support remediation strategies, combining reliability and cost-effectiveness. In this study, we adopted a biomonitoring approach by using tree rings of four different species collected in the proximity of the mining-metallurgical area of Abbadia San Salvatore, central Italy, a major former Hg mining district whose reclamation is currently in progress. Our dendrochemical analysis was aimed at identifying the historical changes of local atmospheric Hg contamination and at singling out, for the first time in the study area, other potentially toxic elements (PTEs) associated with the past mining activity. Collected cores dated back to early as 1940 and provided the temporal patterns of atmospheric Hg emission vs the produced liquid quantities, so reconstructing the historical impact of the mining site on nearby terrestrial ecosystems and resident human population. Current GEM contamination was found about twenty times lower than that of the fully operational mine periods. From a first survey on other PTEs, thallium (Tl) and lead (Pb) appeared to be potentially associated with the mining activity, thus suggesting new working assumptions for further dendrochemical analyses and for the inclusion of Pb in human biomonitoring surveys of the Mt. Amiata area, actually not present in the control list. The results prompt a more thorough assessment by tracking for a longer time span a critical site that is an ideal open-field lab to study the ecophysiology of different tree species in relation to environmental behavior of PTEs for better-assessing wildlife and human exposures.

Canna-inspired superhydrophobic and icephobic regenerated cellulose based hierarchical intelligent colorimetric sensing film for food real-time visual monitoring

Unsafe food cause serious threat to public health and the economy, exploiting effective food quality assessment strategies has attracted concerns worldwide. Herein, inspired by Canna micro-nano hierarchical structure, a generic, facile, and highly processable roll-to-roll and solvent evaporation-induced self-assembly fabrication method is introduced to fabricate regenerated cellulose based superhydrophobic and icephobic hierarchical colorimetric sensing film for real-time visual monitoring of food freshness. The synergistic effect from macromolecular chains attraction and nanoscaled aggregation renders the colorimetric sensing film with excellent mechanical strength of 105.07 MPa and superhigh shear modulus of 17.28 GPa. Upon applied tension, the vigorous intermolecular interactions make the film extraordinary structure stability. The optimized colorimetric sensing film exhibits microscaled pores for sensing performance and three-dimensional nanoflower morphologies for superhydrophobic (water contact angle of 155.1 °and roll-off angle of 7.8°) and icephobic that delay the freezing time of 40 μL water droplets (45 s to 265 s) and reduce the bonding strength of ice (599.72 kPa to 45.53 kPa). What counts is the first time bestowed with icephobicity to food freshness colorimetric sensing film, which mitigating the interference of the covered ice layer against the colorimetric, and providing a more reasonable monitoring in food freshness. These results benefit for stimulating new design principles of colorimetric sensing films, and also provide promising applications in extremely cold environments.

Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products.

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low-cost strategies for quality assessment. Here, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Single-Cell Identification, Viability and Vitality tests, and Source-tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O-probed single-cell Raman spectrum (SCRS) enables species-level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C-D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source-tracking, single-cell Raman-activated Cell Sorting and Sequencingcan proceed, producing indexed, precisely one-cell-based genome assemblies that can reach ~99.40% genome-wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20-fold faster, >10-timecheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

Innovative in vivo and in vitro bioassays for the establishment of toxicity thresholds of pollutants in sediment quality assessment using polychaetes and their immune cells

Sediment toxicity testing has become a crucial component for assessing the risks posed by contaminated sediments and for the development of sediment quality assessment strategies. Commonly used organisms for bioassays with estuarine sediments include amphipods, Arenicola marina polychaetes and echinoids. Among the latter, the Sea Urchin Embryo test (SET) is the most widely used. However, one relevant limitation of this bioassay is the unavailability of gametes all year-round, particularly outside the natural spawning seasons. Consequently, the establishment of an appropriate and complementary model organism for a continuous assessment of sediment quality is recommended. A reliable assessment of the hazards resulting from pollutants in sediments or pore water, can be achieved with ecologically relevant species of sediment such as the polychaete Hediste diversicolor, which is widespread in estuaries and has the capacity to accumulate pollutants. The aim of this work was to develop reliable in vivo and in vitro bioassays with H. diversicolor and its coelomocytes (immune cells) to determine the toxicity thresholds of different contaminants bounded to sediments or resuspended into water. Polychaetes were exposed to sublethal concentrations of CuCl2 (in vivo) and a non-invasive method for collection of polychaetes coelomocytes was applied for the in vitro bioassay, exposing cells to a series of CuCl2 and AgNPs concentrations. Same reference toxicants were used to expose Paracentrotus lividus following the SET (ICES Nº 51; Beiras et al., 2012) and obtained toxicity thresholds were compared between the two species. In vivo exposure of polychaetes to high concentrations of Cu produced weight loss and histopathological alterations. After in vitro approaches, a significant decrease in coelomocytes viability was recorded for both toxicants, in a monotonic dose-response curve, at very short-exposure times (2 h). The toxicity thresholds obtained with polychaetes were in line with the ones obtained with the SET, concluding that their sensitivity is similar. In conclusion, in vivo and in vitro bioassays developed with H. diversicolor are accurate toxicity screenings of pollutants that could be bounded to sediments or dissolved in the pore water, and may complement the SET outside the spawning period of the echinoderms. The bioassays herein developed could be applied not only to establish the toxicity thresholds of individual compounds or mixtures, but also to assess the toxicity of field collected sediments.

Finding optimal strategies for river quality assessment using machine learning and deep learning models

Finding optimal strategies for river quality assessment using machine learning and deep learning models

Improved water pollution index for determining spatiotemporal water quality dynamics: Case study in the Erdao Songhua River Basin, China

The Erdao Songhua River provides drinking water for 16.82 million people; therefore, ensuring that the water quality adequate is of particular concern. In this study, an improved water pollution index (IWPI) and multiple statistical methods were employed to assess the overall water pollution situation and investigate spatiotemporal variations of seven physicochemical parameters such as the permanganate index (PI), dissolved oxygen (DO), chemical oxygen demand (CODcr), five-day biochemical oxygen demand (BOD5), total nitrogen (TN), total phosphorus (TP) and ammonia nitrogen (NH3-N), which were collected monthly at 20 sites within the mainstream and major tributaries of the Erdao Songhua River Basin (ESRB) from 2015 to 2020. Stepwise regression analysis was conducted to build a minimum improved water pollution index (IWPImin) model consisted of four key elements (PI, DO, CODcr, and BOD5) proposed from seven parameters. The results demonstrated water quality within the ESRB was considered to be “Good” with the mean IWPI values <40. However, the water quality deteriorated from the upstream to the downstream within the basin, manifested as the mean value of IWPI of the downstream is 1.7 times of upstream. Seasonally, an improving trend of water quality was observed during the monitoring period and the mean IWPI value decreased by 23%. Furthermore, Seasonal variation in the IWPI value was evident, and the best water quality was found in winter (lowest IWPI value of 14.1) and the worst in summer (highest IWPI value of 21.7). The proposed IWPImin model uses the selected four crucial parameters and the weights of those parameters has exhibited excellent performance in the water quality assessment, with the highest coefficient of determination (R2) and lowest Root Mean Square Error (RMSE) values of 0.996 and 0.51, respectively, which can be used to optimize water quality assessment strategies at a lower cost. For future management, the water quality of middle and downstream should be carefully inspected, and strictly control the effects of point source and non-point source pollution in the ESRB.

Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives

Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy.