Correlation Maps Research Articles

Longitudinal changes in electrophysiology and widefield calcium imaging following electrode implantation.

Intracortical microelectrode arrays often fail to deliver reliable signal quality over chronic recordings, and the effect of an implanted recording array on local neural circuits is not completely understood. 

Approach: In this work we examined the degree of correlation and the spatial dependence of that relationship between widefield calcium imaging and electrophysiology in awake mice from the 4 days to 44 days post-implantation. Both correlation maps and spike-triggered averaging are used to characterize the relationship. 

Main results: We find that calcium imaging and electrophysiological signal are highly correlated in all animals, however, spatial variability in the correlation is affected by inherent correlation in the calcium imaging signal. Some animals exhibit a high degree of apparent neuronal synchrony in the vicinity of the probe at 4 days, while more diversity of response is detected at later timepoints.

Significance: Degree of synchrony appears to be related to the acute injury response to the implanted electrode, with later timepoints displaying less synchrony. Spike-triggered averaging may be used to uncover the diverse cortical connections of spiking units. &#xD.

SEMPNet: enhancing few-shot remote sensing image semantic segmentation through the integration of the segment anything model

ABSTRACT Few-shot semantic segmentation has attracted increasing attention due to its potential for low dependence on annotated samples. While extensively explored in the computer vision community, these techniques are primarily designed for natural images, resulting in limited generalization to remote sensing images. In contrast to the mostly individual and distinct objects presented in natural images, remote sensing images often feature clustered and regular patterns of objects. To bridge this gap, we propose a novel approach for few-shot remote sensing image semantic segmentation, which takes into account the specific characteristics of remote sensing imagery. Our approach introduces a mask classification pipeline, which initially extracts all independent objects within an image and subsequently assigns specific categories to each object guided by semantic information derived from few support images. To accomplish this, a robust mask extractor is imperative. Fortunately, the impressive segment anything model (SAM) possesses the potential to fulfill this role. Leveraging its remarkable zero-shot segmentation capabilities, we present the SAM-enhanced mask parsing network (SEMPNet), a novel few-shot remote sensing image semantic segmentation model. The method generates a set of masks for each image using SAM, transforming the segmentation task into a mask classification problem. To precisely classify each mask, we calculate pixel-wise correlations between each mask and the support features through cross-image position attention. Finally, a mask parsing module is utilized to decode the correlation maps and generate the segmentation results. The experiments on two remote sensing datasets testify the superiority of our method. Our code will be available at https://github.com/TinyAway/SEMPNet.

An improved method to accelerate the acquisition efficiency of the T1-T2 spectrum based on the IR-BSSFP-CPMG pulse sequence

The correlation map of the longitudinal relaxation time (T1) and the transverse relaxation time (T2) offers valuable information of different proton contributions and is served as a significant data for identifying, quantifying and characterizing complex relaxation components in unconventional reservoirs. However, the acquisition speed and spectral resolution for existing pulse sequences cannot be guaranteed simultaneously. We designed an improved pulse sequence which integrates the inversion recovery-balanced steady-state free precession (IR-BSSFP) sequence and the Carr-Purcell-Meiboom-Gill (CPMG) sequence, allowing for the measurement of two-dimensional T1-T2 spectrums. The IR-BSSFP sequence adopts a low refocusing angle and reduced flip angle pulse transmission power, eliminating the need for complete polarization. Moreover, the parameters of the improved pulse sequence such as the value and the number of the flip angle, the repetition time are discussed by numerical simulations. The result showed that the new pulse sequence achieves high-precision measurement performance, and the T1-T2 spectrum can be achieved within a few seconds.

A modified deep learning method for Alzheimer’s disease detection based on the facial submicroscopic features in mice

Alzheimer’s disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerged as a pressing public health issue globally. Early detection of the disease is important, because increasing evidence has illustrated that early diagnosis holds the key to effective treatment of AD. In this work, we developed and refined a multi-layer cyclic Residual convolutional neural network model, specifically tailored to identify AD-related submicroscopic characteristics in the facial images of mice. Our experiments involved classifying the mice into two distinct groups: a normal control group and an AD group. Compared with the other deep learning models, the proposed model achieved a better detection performance in the dataset of the mouse experiment. The accuracy, sensitivity, specificity and precision for AD identification with our proposed model were as high as 99.78%, 100%, 99.65% and 99.44%, respectively. Moreover, the heat maps of AD correlation in the facial images of the mice were acquired with the class activation mapping algorithm. It was proven that the facial images contained AD-related submicroscopic features. Consequently, through our mouse experiments, we validated the feasibility and accuracy of utilizing a facial image-based deep learning model for AD identification. Therefore, the present study suggests the potential of using facial images for AD detection in humans through deep learning-based methods.

Image Stitching of Low-Resolution Retinography Using Fundus Blur Filter and Homography Convolutional Neural Network

Great advances in stitching high-quality retinal images have been made in recent years. On the other hand, very few studies have been carried out on low-resolution retinal imaging. This work investigates the challenges of low-resolution retinal images obtained by the D-EYE smartphone-based fundus camera. The proposed method uses homography estimation to register and stitch low-quality retinal images into a cohesive mosaic. First, a Siamese neural network extracts features from a pair of images, after which the correlation of their feature maps is computed. This correlation map is fed through four independent CNNs to estimate the homography parameters, each specializing in different corner coordinates. Our model was trained on a synthetic dataset generated from the Microsoft Common Objects in Context (MSCOCO) dataset; this work added an important data augmentation phase to improve the quality of the model. Then, the same is evaluated on the FIRE retina and D-EYE datasets for performance measurement using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The obtained results are promising: the average PSNR was 26.14 dB, with an SSIM of 0.96 on the D-EYE dataset. Compared to the method that uses a single neural network for homography calculations, our approach improves the PSNR by 7.96 dB and achieves a 7.86% higher SSIM score.

Optimizing Wrist Splint Fitting Parameters Through Artificial Intelligence Analysis.

Introduction The current method for determining the appropriate wrist splint size in the clinical setting relies on measuring wrist circumference, but this approach often fails to ensure optimal fit. This study evaluates additional hand features using 3-dimensional (3D) scanned data and Artificial Intelligence (AI) to improve the fit of pre-fabricated wrist splints. We hypothesize that wrist and forearm widths can provide a more accurate fitting than wrist circumference alone. Materials and methods We recruited 54 healthy volunteers to be scanned. Each volunteer was fitted with a standard wrist brace (Short Arm Brace, Ossur, Iceland), and 3D data from their hands were collected using an infrared-based 3D scanner (Einscan Pro, Shining3D, China). The 3D scanned data were then analyzed to identify and measure 14 distinct hand features. To explore the relationship between these hand features and the optimal splint size, we generated a categorical correlation map. This map identified hand features that were most strongly correlated with splint size categories (small, medium, large). Subsequently, we developed a classification algorithm to predict the appropriate splint size based on the correlated hand features. We utilized three different machine learning models for this purpose: Extreme Gradient Boosting (XGB) Classifier, RandomForestClassifier, and Support Vector Classifier (SVC). Each of these classifiers was trained and evaluated to determine their accuracy and effectiveness in predicting the correct splint size. Results Wrist width showed the highest classification accuracy (91%) for both the XGB Classifier and RandomForestClassifier. The measurements including hand wrist width, mid-forearm width, and hand crease line width also performed well with the XGB Classifier, achieving an accuracy of 90%. The SVCshowed consistent performance across various feature sets, with the highest accuracy of 81% for the measurements. Overall, these findings suggest that wrist width is the most predictive feature for splint size classification, with additional features providing minimal enhancement. Conclusions Artificial intelligence, combined with 3D scanning, can accurately predict wrist splint size from a single image acquisition, enabling contactless, personalized fitting.This approach can improve patient outcomes by enhancing the fit of prefabricated splints.

Multiple-image authentication method based on phase-only holograms and a logistic map.

An interesting security method for a multiple-image authentication scheme is proposed based on computer-generated holograms and a logistic map. First, each original image is encoded as the complex-valued hologram under the point light source model. The resulting hologram is then converted to a phase-only hologram using the Floyd-Steinberg dithering algorithm. Second, each phase-only hologram is randomly sampled with the aid of a binary mask. Through the catenation of all selected pixels, a phase-only pixel sequence is formed. Finally, a non-periodic and non-converging sequence generated with the logistic map is used to scramble this sequence. After only preserving the phase data of the scrambled sequence, the real-valued ciphertext carrying the information of all original images is obtained. In the process of authentication, although no valid information can be discerned from noisy reconstructed images at a small sampling rate, the verification of original images can be efficiently accomplished using the nonlinear correlation maps. Besides binary masks, the parameters of the logistic map are served as secret keys. Due to their high sensitivity, the security of the proposed method is greatly enhanced. The proposed authentication mechanism has been demonstrated to be effective and robust through experiments. To our knowledge, it is the first time to implement multiple-image authentication using phase-only holograms, which can provide a new perspective for optical information security.

Numerical investigation of pre-chamber holes diameter geometry on combustion parameters in a hydrogen-powered Turbulent Jet Ignition engine

The search for substitutes for conventional fuels leads to the use of hydrocarbon-free or synthetic fuels. One of them is hydrogen. The use of hydrogen in combination with a two-stage charge creation system leads to the combustion of lean (λ > 1) or very lean (λ > 3) charges. The simulation tests carried out were aimed at determining the best configuration of the chamber connecting the prechamber with the main combustion chamber. Three variants of the diameter of the holes connecting the chambers were selected (d = 0.5; 1.0 and 1.5 mm) in combination with different fuel doses fed to the prechamber. A passive chamber (qo_PC = 0 mg) and an active chamber (qo_PC = 0.4 and 1.2 mg) were used, while simultaneously sending a dose of fuel to the main chamber (qo_MC = 6 mg). The research was conducted using AVL Fire 2022.1 software using the moving mesh of the LDV engine. As a result of simulation work, the most favorable conditions for carrying out the process were determined, considering the thermodynamic effects of the process and the accepted values of nitrogen oxide concentration. The resulting correlation maps of the sizes of the chamber openings and the fuel doses fed to the prechamber may determine the initial selection of possibilities for controlling hydrogen combustion in the TJI system.

Prioritizing Performance Indicators for the Circular Economy Transition in Healthcare Supply Chains

AbstractThe emergence of the circular economy (CE) paradigm is a key driver in steering industrial sectors towards sustainability. The formulation of a robust circularity strategy depends on a comprehensive assessment, which is closely related to the overarching goal of managing the transition to circularity. The initial phase of transition within the healthcare sector reinforces the need to seamlessly integrate CE principles. It is therefore imperative to define and carefully monitor indicators that will help assess and improve sustainability, operational efficiency, supply chain resilience and stakeholder satisfaction, including both consumers and patients. The primary objective of this research is to prioritize relevant performance indicators that will be critical in measuring the effectiveness of the transition to a circular economy within the healthcare supply chain. To achieve this objective, the study employs the Analytic Hierarchy Process (AHP), a well-established methodology known for its effectiveness in solving complex multi-criteria challenges. The AHP is used to prioritize performance indicators that facilitate the management of the circular economy transition in the healthcare supply chain. The focus is on improving the quality of care, optimizing delivery efficiency, and emphasizing environmental sustainability within the healthcare paradigm. The methodological framework includes the identification of key main criteria covering environmental, social, economic, and logistical dimensions. These criteria are further refined with the inclusion of 14 sub-criteria, resulting in a list of 16 key performance indicators (KPIs). Additionally, the study proposes an alternative business model advocating for the implementation of the Circular Supply Chain Operation Reference Model (circular SCOR). This model offers a perspective for rapid change in supply chain processes towards circular transition within the healthcare system. The final stage of the research involves verifying the results through sensitivity analysis and correlation mapping of the indicator results with the Circular SCOR model. This process helps organizations refine their performance measurement protocols, enhancing their capacity for effective evaluation and strategic decision-making within the circular economy framework. This comprehensive approach enables healthcare organizations to proactively contribute to a more sustainable and environmentally conscious future while maintaining a focus on quality care for patient well-being.

Categorizing E-cigarette-related tweets using BERT topic modeling

BackgroundSocial media platforms are critical channels for promoting e-cigarettes, particularly among youth, making analysis of their vast and diverse content essential for public health interventions. Prevalence rates of e-cigarette use are high and evidence suggests that social media are popular forums that promote e-cigarette use through direct and indirect marketing techniques. The volume and diverse nature of e-cigarette-related information on social media is challenging and may obfuscate public health prevention messaging. Traditional hand-coding methods are labor-intensive and limit scalability. In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. The present study focused on ascertaining the extent to which themes and topics in tweets related to e-cigarettes can be successfully rendered into useful homogenous units using machine learning. A better understanding of current depictions and discussions around e-cigarette products and use on social media can inform public health counter messaging and policy interventions. MethodsWe used topic modeling (BERTopic) to iteratively derive vape-related tweet clusters and calculate the importance of particular words to these groupings. We conducted a qualitative content analysis to study clustered tweets. We also sought to determine the geographic locations of e-cigarette conversations using automated geoparsing methods, which translate toponyms in textual data into geographic identifiers, to attempt to infer the location of tweets. ResultsWe were able to successfully identify >100,000 tweets in broad thematic categories in English and Spanish. Our correlation and inter-topic map analysis of the machine-derived topics, which examines the relationships between topics, indicated that most of the topics were unique (correlation value < 0.5) and did not overlap with each other. We identified six topics: Flavors and Disposable Vapes, Cannabis, Vape Shops and Refillable Vapes, Vape Culture, Anti-vaping and Quitting, and Spanish Tweets and Vaping Nicotine. Further analysis of these topics using qualitative methods identified themes within each topic. For example, Category 6 (Spanish Tweets and Vaping Nicotine) included four topics focused on the health risks of vaping, personal motivations for vaping, and the regulation of vaping products. Using geoparsing, which automatically detects location information, we found that the United States had the highest number of tweets related to vaping. Discussion/conclusionResults underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

Algal EPS modifies the toxicity potential of the mixture of polystyrene nanoplastics (PSNPs) and triphenyl phosphate in freshwater microalgae Chlorella sp.

Triphenyl phosphate (TPP) and polystyrene nanoplastics (PSNPs) are prevalent freshwater contaminants obtained mainly from food packaging, textiles and electronics. Algal extracellular polymeric substances (EPS), a part of natural organic matter, may influence these pollutants' behaviour and toxicity. The presence of EPS can enhance the aggregation of TPP-PSNP mixtures, and reduce the bioavailability, and thus the toxicity potential. Understanding the mutual interactions between TPP, PSNPs, and EPS in the aquatic environment is a prerequisite for the environmental risk assessment of these chemicals. The study examines the toxicity effects of various surface-modified PSNPs (1 mg/L of plain, animated, and carboxylated) and TPP (0.05, 0.5, and 5 mg/L) in pristine and combined forms on freshwater microalgae, Chlorella sp., as a model organism. The physical-chemical interactions of algal EPS (10 mg/L) with PSNPs and TPP and their mixtures were studied. The toxicity potential of the PSNPs was estimated by quantifying growth inhibition, oxidative stress, antioxidant activity, and photosynthesis in the cells. TPP toxicity increased in the presence of the PSNPs, however the addition of algal EPS reduced the combined toxic effects. EPS plays a protective role by reducing oxidative stress and enhancing photosynthetic efficiency in the algal cells. The Pearson modeling analysis indicated a positive correlation between growth inhibition, and reactive oxygen species, malondialdehyde production. The cluster heatmap and correlation mapping revealed a strong correlation among the oxidative stress, growth inhibition, and photosynthetic parameters. The study clearly highlights the potential of EPS in mitigating the risk of mixed emerging pollutants in the aquatic environment.

Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events

Several studies indicate that Indonesia’s position on the equator, where winds from the northern and southern hemispheres converge, leads to year-round rainfall in Indonesia. Previous studies have also shown the presence of monsoon winds over Indonesia, which causes rain in Indonesia to follow the Asian and Australian monsoon wind patterns. Recently, variations in rainfall patterns and intensity in Indonesia have been highly volatile and difficult to predict. This study uses gridded data analysis of ocean-atmospheric rainfall for 69 years (1948–2016) to detect monsoonal, equatorial, and local signals and relate them to ENSO and IOD events. Rainfall analysis reveals that the first mode accounts for 38.57%, the second 13.92%, and the third 8.93% of the total variance. These results show that these variants are very variable, both in space and time. Monsoonal patterns were detected in the southern region of Indonesia, equatorial patterns in northern Sumatra and western Kalimantan, while local patterns were detected in Maluku and the Maluku Islands. Furthermore, the monsoonal and local rainfall patterns are strongly correlated (&gt; 0.85) with local SST, but the equatorial rainfall pattern is weakly correlated (&lt; 0.5) with local SST. Note that the equatorial pattern is associated with the Inter Tropical Convergence Zone (ITCZ) phase. The varied vector correlation map illustrates the asymmetric fluctuations in sea surface temperature and the simultaneous existence of winds coming from the west from the Indian Ocean and winds coming from the east from the Pacific Ocean during the period of heavy precipitation.

Retraction Note: Learning and prediction of cyber attack based on correlation mapping in classical optical networks

Retraction Note: Learning and prediction of cyber attack based on correlation mapping in classical optical networks

Femtosecond Terahertz Pulse Propagation Measurements and Simulations of Dewetting Kinetics in Real Time.

We report a terahertz time-domain study of dewetting kinetics on two time scales: femtoseconds and in real time (on the order of minutes). We recorded full electric field terahertz time domain signals with femtosecond time resolution during dewetting of water in cellulose. The femtosecond time-domain signals were analyzed with respect to the amplitude and signal emission times and how these two quantities changed over the course of dewetting kinetics. The femtosecond time-domain signals were modeled by a combination of finite-difference time-domain electromagnetic simulations of linear terahertz pulse propagation and an effective medium description of the dielectric permittivity. A logistic regression mechanism was incorporated into the electrodynamics simulations to account for the observed kinetics. In addition, analysis of the area-normalized, real-time time-dependent terahertz spectra was used to identify broad regions in the terahertz spectral range that correlated with the kinetic process. Real-time two-dimensional terahertz correlation maps were used to identify the pure rotational spectrum of water vapor, thus characterizing the evaporation part of the dewetting kinetics problem. We conclude with a kinetic model that accounts for our observations through a microscopic mechanism involving the interconversion among bulk water, water clusters, and individual water molecules in the gas phase. Overall, the approach presented here illustrates an application of femtosecond time-resolved experiments in the terahertz spectral range to the study of kinetic processes.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Investigating critical metals Ge and Ga in complex sulphide mineral assemblages using LIBS mapping

Elemental imaging using Laser-Induced Breakdown Spectroscopy (LIBS) mapping is increasingly applied to a wide range of materials. In geomaterials, phase identification and characterization (mineralogy) are crucial for solving a variety of problems in earth sciences and mining industry.Here we applied LIBS mapping to ore samples from the world-class Kipushi deposit, Democratic Republic of Congo, because these ores are known for their great mineral complexity and economic value, especially for their high potential in critical metals such as Ge and Ga.The mineralogy of the samples was reconstructed by combining two approaches: 1) element colocalization in LIBS maps that were selected based on the knowledge of occurring mineral species at the site and 2) correlation maps obtained by applying the Interesting Features Finder (IFF) algorithm. Reconstructed minerals include chalcopyrite, bornite, chalcocite, sphalerite, galena, pyrite, tennantite-(Zn), renierite, tungstenite, betekhtinite, carrollite, gallite, and a series of non-sulphide gangue minerals (quartz, carbonate and clay minerals). SEM-EDS was used to confirm the LIBS-derived mineralogy, especially for sub-pixel inclusions.Among other results, Ge and Ga are hosted as major elements in renierite and gallite, respectively, and also as trace-elements in chalcopyrite, bornite and tungstenite (for Ge), and chalcopyrite, bornite, sphalerite and renierite (for Ga). Particular attention was paid to iron interfering with gallium at 417.20 nm, for which simple background-corrected peak intensities were compared to a more advanced chemometric method (MCR-ALS). All the results were obtained with minimal time and efforts and, while only qualitative in nature, they already provide essential information about ore texture, mineralogical composition and trace-element distribution across the ore.

Disclosure of a Concealed Michelangelo-Inspired Depiction in a 16th-Century Painting.

Some paintings may have hidden depictions beneath the visible surface, which can provide valuable insights into the artist's creative process and the genesis of the artwork. Studies have shown that these covered paintings can be revealed through image-based techniques and integrated data processing. This study analyzes an oil painting by Beceri from the mid-16th century depicting the Holy Family, owned by the Uffizi Galleries. During the analysis of the materials, we discovered evidence of pictorial layers beneath the visible scene. To uncover the hidden figuration, we applied a multimodal approach that included microprofilometry, reflectance imaging spectroscopy, macro X-ray fluorescence, and optical coherence tomography. We analyzed the brushstrokes of the hidden painting, visualized the underdrawing, located the painted areas beneath the outermost painting, and quantified the thicknesses of the pictorial layers. The pigments used for the underpainting were identified through cross-analysis of X-ray fluorescence and spectral correlation maps. The underlying pictorial subject, Leda and the Swan, appears to be inspired by a long-lost and replicated work by Michelangelo. This information places Beceri and his production in a more defined context.

Impact of sea surface temperature in the Arabian Sea on the variability of Summer Monsoon Rainfall over Pakistan Region

The seas and oceans are essential in preserving energy in the Earth, Ocean, and atmosphere system. Assessing past trends and expected sea surface temperature (SST) shifts is critical for future climate scenarios within this framework. Previous studies have emphasized the importance of SST in the emergence and intensification of heavy rainfall events in the South Asian basin and the development of heat waves in the coastal regions of South Asia. This study has focused to investigate the relationship between SST of the Arabian Sea and mean rainfall in South Asia using Singular Value Decomposition Analysis (SVDA) for the July, August, and September (JAS) season, as most of the region's rainfall is in this season. The first mode accounts for 69 % of the covariance between Arabian Sea SST and regional JAS rainfall, with three dominant SVDA modes explaining 96 % of the total squared covariance. The degree to which the connected fields are correlated has been investigated using Root Mean Squared Covariance. By heterogeneous correlation maps, it is concluded how significant is the impact of one field (SST) on the other (rainfall), which validates our hypothesis that the SST of the Arabian Sea affects variation in precipitation. The empirical results from the SVDA are consistent with those of the composite diagrams. Dry and wet years have been defined and analyzed to examine the impact of SST on regional JAS rainfall further. Vertically integrated moisture transport reveals a significant difference in moisture transport over the Arabian Sea between wet and dry years during the JAS season. Water vapor transport is much stronger during wet years compared to dry years.

A description of the physical ageing and annealing processes in poly (N-vinyl carbazole) using differential scanning calorimetry and two-dimensional correlation mapping analysis

A description of the physical ageing and annealing processes in poly (N-vinyl carbazole) using differential scanning calorimetry and two-dimensional correlation mapping analysis

Environmental conditions affecting air pollution dispersion over Richards Bay, South Africa

Air pollution dispersion over Richards Bay, South Africa is studied using satellite, reanalysis and in-situ measurements. Near-surface concentrations of trace gases and particulates reach high concentrations under dry stable ‘berg’ winds. Temporal records of CO, NO2, O3, PM2.5, SO2 (2000-2023) exhibit no trend and short-term dose exceedances of individual pollutants are rare and confined to winter mornings. Regional point-to-field correlation maps of a Richards Bay winter air pollution index found that anomalous warming of the west Indian Ocean accelerates the jet stream over Madagascar and pulls smoke- and dust-laden north-westerly winds across the South African plateau, implicating long-range transport. At daily and hourly time scales dewpoint temperature and boundary layer height best indicate changes in local air pollution dispersion that impact respiratory health. A case study of 15-17 June 2021 revealed a 10ºC thermal inversion caused by negative sensible heat fluxes of -40 W/m2 through the night, leading to SO2 concentrations above 100 ppb in the morning along the main access road (28.8ºS, 32ºE). Poor dispersion seldom happens in the summer when turbulent mixing and rainfall cleanse the air.