Complex Areas Research Articles

Ratiometric Fluorescent Probe Promotes Trans-differentiation of Human Mesenchymal Stem Cells to Neurons.

Development of multifunctional theranostics is challenging and crucial for deciphering complex biological phenomena and subsequently treating critical disease. In particular, development of theranostics for traumatic brain injury (TBI) and understanding its repair mechanism are challenging and highly complex areas of research. Recently, there have been interesting pieces of research work demonstrated that a small molecule-based neuroregenerative approach using stem cells has potential for future therapeutic lead development for TBI. However, these works demonstrated the application of a mixture of multiple molecules as a "chemical cocktail", which may have serious toxic effects in the differentiated cells. Therefore, development of a single-molecule-based potential differentiating agent for human mesenchymal stem cells (hMSCs) into functional neurons is vital for the upcoming neuro-regenerative therapeutics. This lead could be further extraploted for the design of theranostics for TBI. In this study, we have developed a multifunctional single-molecule-based fluorescent probe, which can image the transdifferentiated neurons as well as promote the differentiation process. We demonstrated a promising class of fluorescent probes (CP-4) that can be employed to convert hMSCs into neurons in the presence of fibroblast growth factor (FGF). This fluorescent probe was used in cellular imaging as its fluorescence intensity remained unaltered for up to 7 days of trans-differentiation. We envision that this imaging probe can have an important application in the study of neuropathological and neurodegenerative studies.

Avoiding Dense Pedestrian Regions: A New Rapidly‐Exploring Random Tree (RRT ∗) Algorithm for Shortest Travel Time

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning.

Implementation of a Mindful Walking Intervention in Breast Cancer Patients After Their Primary Oncologic Treatment: Results of a Qualitative Study Within a Randomized Controlled Trial.

Breast cancer survivors often suffer from diagnosis- and therapy-related long-term side effects, such as cancer related fatigue, restricted stress resilience and quality of life. Walking as a physical activity and mindfulness practice have been shown to be helpful in studies. The aim of this study was to compare the individual experiences and subjectively perceived effects of walking in combination with mindfulness practice with moderate walking alone in breast cancer patients. This paper focuses on the qualitative results of a mixed-methods pilot study. Breast cancer patients who had finished their primary oncologic treatment at least 6 months ago were randomized to an 8-week group intervention program of either mindful walking or moderate walking. Within the qualitative study part, semi-structured focus group interviews (2 interviews per study arm) were conducted and analyzed using a qualitative content analysis approach. Audio recorded interviews were transcribed verbatim and pseudonymized. The subsequent data analysis was performed by using MAXQDA®. A total of 51 women (mean age 55.8 [SD 10.9] years) were included in the RCT, among these 20 (mean age 56.7 [SD 12.0] years) participated in the focus group interviews (n = 11 patients of the mindful walking group; n = 9 patients of the walking group). Breast cancer patients in both groups described different effects in the complex areas of self-efficacy, coping, body awareness and self-reflection. While mindful walking primarily promoted body awareness and inner strength by mindfulness in breast cancer patients, moderate walking promoted self-efficacy by a confidence of their body and an easily integrated and accepted way of physical activity. Study interventions and the study setting triggered processes and reflections on one's own health and situation. However, mindful walking and moderate walking seem to address different resources. This important knowledge may help oncologists and other therapists to assess what type of interventions can best meet the needs and requirements of individual patients. DKRS00011521; prospectively registered 21.12.2016; https://drks.de/search/de/trial/DRKS00011521.

Spojeno i odvojeno pisanje reči u pravopisu srpskoga jezika: dijahronijski pristup

The main goal of the paper is to determine the differences related to one of the most complex areas of Serbian orthography – open and closed compound words – explored from the diachronic perspective, that is, our goal is to examine what formal and se- mantic criteria authors/editors of orthography handbooks followed when it comes to writing compounds, semi-compounds, and syntagms. The corpus consists of the following: Srpski pravopis [Serbian Orthography] (1914); Pravopis srpskohrvatskog književnog jezika [Or- thography of the Serbo-Croatian Literary Language] (1923); Pravopisno uputstvo za sve os- novne, srednje i stručne škole Kralјevine Srba, Hrvata i Slovenaca [Orthographic Handbook for all Primary, Secondary, and Vocational Schools of the Kingdom of Serbs, Croats, and Slovenes] (1929); Novo pravopisno uputstvo srpskog književnog jezika [New Orthographic Handbook of the Serbian Literary Language] (1942); Pravopis srpskohrvatskoga književnog jezika [Orthography of the Serbo-Croatian Literary Language] (1960); Pravopis srpskoga jezika [Orthography of the Serbian Language] (1993); and Pravopis srpskoga jezika [Or- thography of the Serbian Language] (2014). The selection of the corpus is based on the fact that over a century (1914–2014) the editors of orthography handbooks did not always con- sistently adhere to the traditional rules of writing Serbian open and close compound words, and that inevitable changes in the .language imposed new orthographic rules to be applied in this area. This paper explores the extent to which the differences are visible, as well as the segments of writing open and closed compound words that have changed the most

Coverage Path Planning for IoUAVs With Tiny Machine Learning in Complex Areas Based On Convex Decomposition

Coverage Path Planning for IoUAVs With Tiny Machine Learning in Complex Areas Based On Convex Decomposition

Landslide Risk Management in Areas Affected by Wildfires or Floods: A Comprehensive Framework Integrating GIS, Remote Sensing Techniques, and Regional Climate Models

Over the past few decades, numerous studies and technical surveys have documented a significant number of landslides activated in areas that had recently been affected by wildfires or floods, thereby suggesting a potential link between landslides and these phenomena. With the climate crisis exacerbating the intensity and frequency of wildfires and floods, understanding this link has nowadays become even more crucial and requires further exploration. This study proceeds along this path and establishes a comprehensive framework for rapidly evaluating the effect of a wildfire or a flood on the local landslide mechanism, as well as for effectively managing landslide hazards in the affected area. The proposed framework incorporates advanced Geographical Information System (GIS) tools, remote sensing techniques, and state of the art regional climate models, to assess landslide hazard and risk from wildfires and floods on the impacted area, as well as to offer vital tools for landslide management. Consequently, it provides a comprehensive and thorough assessment of the impact of these catastrophic phenomena on affected areas. Remote sensing and GIS techniques offer a cost-effective solution, as these methods, contrary to traditional in-situ methods, can be easily and rapidly applied even on large and complex areas. The integration of regional climate models also ensures the long-term viability of the proposed approach, as it takes under consideration the impacts of the climate crisis. As a result, the proposed framework contributes to scientists’ ongoing efforts in understanding the dynamic character of the landslides phenomenon that evolves and interacts with other natural disasters. Simultaneously, the results of the proposed methodology can effectively contribute to the local stakeholders’ efforts to promptly assess the relative impact and make informed decisions regarding the required mitigation measures. Wildfires that ravaged the Chania regional unit in western Crete, Greece, in 2021, are selected as a case study to highlight the applicability and effectiveness of the proposed framework.

The First Hundred Years of the Scientific Field of Anaesthesiology and Reanimation in the Republic of Turkey.

As a scientific field, anaesthesiology and reanimation, with their significant place in the medical structure, have been practised since the beginning of surgical procedures. Today anaesthesiology and reanimation speciality cover more complex techniques and areas than alleviating patients' pain during surgery. In the first hundred years since the proclamation of the Turkish Republic, the path covered in our scientific field is to pave the way for the next hundred years.

Institutional Ethnography as Critical Policy Analysis

The use of the term health equity (HE) has increased dramatically in the past decade in Canada and globally. However, there is limited evidence that explosive growth in the use of the term is reflected in HE policy outcomes. In this paper we argue that the methodology of institutional ethnography (IE) is useful in understanding how policy outcomes (including, notably, policy inaction) are constructed. Applying IE methodology in critical policy analysis (CPA), we analyze how the discursive utilization of HE neutralizes effective policy change. The point of our analytical approach is to explain how three complex areas of investigation (HE, IE, CPA) may be usefully integrated to enhance policy action to tackle the structural, root causes of health inequities. We demonstrate how an integration of IE in CPA provides a deeper understanding of how the political forces shaping policy outcomes manifest themselves in discursive relationships and textually mediated sites of power.

Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources

Abstract The Kalatag Ore Cluster Area, located in the Eastern Tianshan metallogenic belt of Xinjiang, stands out as a notable copper polymetallic mineralization zone, recognized for its diverse ore types and untapped potential. Despite the foundational nature of traditional exploration methods, they have not fully exploited this potential. Addressing this, our study leverages modern geospatial technologies, especially ArcGIS, combined with multi-source geoscience data to refine ore formation predictions in Kalatag. We identified key ore-controlling factors: the ore-bearing strata of Daliugou and Dananhu Groups, buffer zones around faults and intrusions, and geophysical anomalies. From these, a conceptual model was developed using the weight of evidence model. This model pinpointed four ‘A’ class and three ‘B’ class targets for mineral exploration, highlighting the central role of faults in ore control. Significantly, all known ore deposits were encompassed within these targets. Our approach not only paves the way for improved ore prediction in Kalatag but also offers a blueprint for other mineral-rich areas. Merging traditional geology with advanced technology, we elevate mineral exploration’s precision, emphasizing the synergy of an integrated method, especially in geologically complex areas. The effectiveness of our model provides insights for future exploration, particularly in mining areas’ deeper zones.

URBAN GREEN SPACE IDENTIFICATION BY FUSING SATELLITE IMAGES FROM GF-2 AND SENTINEL-2

Abstract. This paper combines class hierarchy construction and feature-preferred random forest classification method, and the classification results are the best. In the urban center area with complex features, this method can be used to extract small and complex features more accurately, and for urban green spaces, small auxiliary green spaces between houses can be accurately extracted. This method first constructs a class hierarchy of four sizes, and then extracts different features from simple to complex, from large to small, and classifies them by membership function for the direct selection feature rules of easily extracted features. For the subdivision of green space and the classification of features in central complex areas, feature optimization is carried out, and the optimal feature combination is selected and then extreme random tree (ERT) classification is performed. The classification accuracy is the best 89.5%, and the classification results are analyzed correctly, which correctly distinguishes the smaller land categories in the central area, reduces the misclassification of grassland and agricultural land, and the classification results are optimal.

What Is The Influence Of Compensation And Workload On Employee Performance

Previous research states that job satisfaction is one of the most complex areas that managers face today in terms of managing their employees (Komariyah, 2023). Some factors affecting employee satisfaction are workloads and compensation (Komariyah, 2022). Workload has a negative impact on job satisfaction, and compensation has a positive impact on work satisfaction and has a strong relationship. The research was conducted using descriptive and verification methods, and calculations were performed using the SPSS 20.0 program. Data collection was obtained through observations and questionnaires. Sampling is purposive random sampling. The respondents in this study were employees of a textile company in Subang City. The results of this study conclude that: 1) Compensation in the category is excellent. 2) The workload in the category is sufficient. 3) Job satisfaction in the good category has a significant impact on job satisfaction. Thus, it can be concluded that there is an influence of compensation and workload on job satisfaction.

Development of an augmented reality guidance system for head and neck cancer resection.

The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three-dimensional (3D) holographic reconstructions of medical data. However, performing AR surgeries on complex areas such as the head and neck region poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumour margins in a cadaveric specimen. The authors present an intraoperative solution that enables surgeons to upload and visualize holographic reconstructions of resected cadaver tissues. The solution involves using a 3D scanner to capture detailed scans of the resected tissue, which are subsequently uploaded into our software. The software converts the scans of resected tissues into specimen holograms that are viewable through a head-mounted AR display. By re-aligning these holograms with cadavers with gestures or voice commands, surgeons can navigate the head and neck tumour site. This workflow can run concurrently with frozen section analysis. On average, the authors achieve an uploading time of 2.98min, visualization time of 1.05min, and re-alignment time of 4.39min, compared to the 20 to 30min typical for frozen section analysis. The authors achieve a mean re-alignment error of 3.1mm. The authors' software provides a foundation for new research and product development for using AR to navigate complex 3D anatomy in surgery.

Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques

Land cover classification, generated from satellite imagery through semantic segmentation, has become fundamental for monitoring land use and land cover change (LULCC). The tropical Andes territory provides opportunities due to its significance in the provision of ecosystem services. However, the lack of reliable data for this region, coupled with challenges arising from its mountainous topography and diverse ecosystems, hinders the description of its coverage. Therefore, this research proposes the Tropical Andes Land Cover Dataset (TALANDCOVER). It is constructed from three sample strategies: aleatory, minimum 50%, and 70% of representation per class, which address imbalanced geographic data. Additionally, the U-Net deep learning model is applied for enhanced and tailored classification of land covers. Using high-resolution data from the NICFI program, our analysis focuses on the Department of Antioquia in Colombia. The TALANDCOVER dataset, presented in TIF format, comprises multiband R-G-B-NIR images paired with six labels (dense forest, grasslands, heterogeneous agricultural areas, bodies of water, built-up areas, and bare-degraded lands) with an estimated 0.76 F1 score compared to ground truth data by expert knowledge and surpassing the precision of existing global cover maps for the study area. To the best of our knowledge, this work is a pioneer in its release of open-source data for segmenting coverages with pixel-wise labeled NICFI imagery at a 4.77 m resolution. The experiments carried out with the application of the sample strategies and models show F1 score values of 0.70, 0.72, and 0.74 for aleatory, balanced 50%, and balanced 70%, respectively, over the expert segmented sample (ground truth), which suggests that the personalized application of our deep learning model, together with the TALANDCOVER dataset offers different possibilities that facilitate the training of deep architectures for the classification of large-scale covers in complex areas, such as the tropical Andes. This advance has significant potential for decision making, emphasizing sustainable land use and the conservation of natural resources.

Accumulation and exploration enlightenment of shallow normal-pressure shale gas in southeastern Sichuan Basin, SW China

Based on the drilling, logging, experimental and testing data of Well PD1, a shallow normal-pressure shale gas well in the Laochangping anticline in southeastern Sichuan Basin, the shallow shale gas reservoirs of the Ordovician Wufeng Formation to Silurian Longmaxi Formation (Wufeng–Longmaxi) were investigated in terms of geological characteristics, occurrence mechanism, and adsorption-desorption characteristics, to reveal the enrichment laws and high-yield mechanism of shallow normal-pressure shale gas in complex structure areas. First, the shallow shale gas reservoirs are similar to the medium–deep shale gas reservoirs in static indicators such as high-quality shale thickness, geochemistry, physical properties and mineral composition, but the former is geologically characterized by low formation pressure coefficient, low gas content, high proportion of adsorbed gas, low in-situ stress, and big difference between principal stresses. Second, shallow shales in the complex structure areas have the gas occurrence characteristics including low total gas content (1.1–4.8 m3/t), high adsorbed gas content (2.5–2.8 m3/t), low sensitive desorption pressure (1.7–2.5 MPa), and good self-sealing. Third, the adsorbed gas enrichment of shales is mainly controlled by organic matter abundance, formation temperature and formation pressure: the higher the organic matter abundance and formation pressure, the lower the formation temperature and the higher the adsorption capacity, which is more beneficial for the adsorbed gas occurrence. Fourth, the shallow normal-pressure shale gas corresponds to low sensitive desorption pressure. The adsorbed gas can be rapidly desorbed and recovered when the flowing pressure is reduced below the sensitive desorption pressure. Fifth, the exploration breakthrough of Well PD1 demonstrates that the shallow complex structure areas with adsorbed gas in dominance can form large-scale shale reservoirs, and confirms the good exploration potential of shallow normal-pressure shale gas in the margin of the Sichuan Basin.

Advancing Discharge Ratings: A Novel Approach Based on Observed and Derivable GIS Factors in Alluvial Systems

Depth–discharge rating is required at gauged and ungauged locations for hydrologic modeling of alluvial systems to evaluate streamflow and manage regional water resources. Spanning low to high-flow conditions, manual field measurements are used to develop discharge ratings at gauged locations, producing continuous flow data from automated water depth measurements. The discharge rating is dependent on channel geometry, stream slope, vegetation, roughness coefficient, sediment load, and bank stability. To construct discharge ratings for many locations within larger model domains (hundreds to thousands of km2), intensive GIS and manual (spreadsheet) data manipulation are often required. In this analysis, available USGS gauging stations and readily available GIS coverages were used to learn and implement a novel method to characterize the depth–discharge relationships for hydrologic modeling of larger or complex areas using commonly available data and normalization techniques. The improved procedure simply uses drainage area, channel slope, and channel width, readily derivable GIS data, to develop discharge ratings for gauged and ungauged sections. The discharge rating curves for 70 USGS streamflow gauges were reproduced using the procedure. Then, the produced and observed discharge rating curves were compared to evaluate the accuracy of the method. In the analysis of streamflow depth predictions, the average Root Mean Squared Error was recorded at approximately 0.38 m (≈1.24 ft), with an interquartile range between 0.21 m and 0.49 m. The Mean Error remained centered around 0 m, with interquartile values ranging from −0.24 m to 0.24 m.

To the question of the need to improve the financial planning mechanism when making investment decisions

The article focuses on the key problems of improving the financial planning mechanism when making investment decisions by organizations Based on the proposed methodology of financial planning as a systematized set of basic principles and methods of financial planning, applied indicators, impact measures necessary for the implementation of the financial plan, its monitoring, etc., the structure of this mechanism aimed at making investment decisions is substantiated, as well as the functions and tasks of the financial planning process of investment activity are defined. The authors substantiate that financial planning of investment activity is one of the most complex areas of financial planning, which is largely due to the complexity of making investment decisions, during which the state of the macroeconomic environment, objective limitations of investment activity, alternative investment opportunities, the state of the internal environment of the organization, its subjective investment preferences and other external and internal factors should be taken into account. To solve these problems, it is proposed to use such tools of the financial planning mechanism as budgeting of investment activities.

Enhanced anxiety-like behavior induced by chronic neuropathic pain and related parvalbumin-positive neurons in male rats

Anxiety commonly co-occurs with and exacerbates pain, but the interaction between pain progression and anxiety, and its underlying mechanisms remain unclear. Inhibitory interneurons play a crucial role in maintaining normal central nervous system function and are suggested to be involved in pain-induced anxiety. This study aimed to elucidate the time-dependent effects of neuropathic pain on the developmental anxiety-like behaviors and related inhibitory interneurons; parvalbumin (PV)- and cholecystokinin (CCK)-positive neurons in corticolimbic regions. Using an 8-week-old male Wistar rat model with partial sciatic nerve ligation (pSNL), anxiety-like behaviors were biweekly assessed post-surgery through open field (OF) and elevated plus maze (EPM) tests. From 4 weeks post-surgery, pSNL rats exhibited reduced OF center time, rearing, and initial activity, along with diminished EPM open-arm activities (time spent, head dips, movement, and rearing), which correlated with the paw withdrawal threshold. These effects were absent at 2 weeks post-surgery. At 8 weeks post-surgery, specific behaviors (decreased total rearing and increased inactive time in EPM) were observed in the pSNL group. Immunohistochemistry revealed changes in PV- and CCK-positive neurons in specific corticolimbic subregions of pSNL rats at 8 weeks post-surgery. Notably, PV-positive neuron densities in the basolateral amygdaloid complex (BLC) and hippocampal cornu ammonis areas 1 and 2 correlated with anxiety-like behavioral parameters. PV-positive neurons in the BLC of pSNL rats were predominantly changed in large-cell subtypes and were less activated. These findings indicate that anxiety-like behaviors emerge in the late phase of neuropathic pain and relate to PV-positive neurons in corticolimbic regions of pSNL rats.

A small-scale silica gel column chromatography method for separating carbazole compounds from highly mature crude oil

The concentration of carbazoles in highly mature crude oil is quite low, making it challenging to separate carbazole compounds for the gas chromatography-mass spectrometry (GC–MS) detection. This study presents a small-scale column chromatography method for separating carbazoles from highly mature crude oil using silica gel as a solid phase adsorbent and a Pasteur pipette as a separation device. The carbazole-rich crude oil from the Pearl River Mouth Basin was selected to explore the impact of reagent polarity and injection mode on the separation of carbazoles. The oil sample was eluted with solvents mixed with different volume proportions of n-hexane and dichloromethane and each eluted fraction was collected for GC–MS testing. The results indicated that increasing the reagent polarity caused the aromatic hydrocarbons and carbazole compounds in crude oil to be eluted sequentially. Most aromatic compounds in the crude oil could be selectively eluted using a reagent polarity ratio of 9:1 (Vn-hexane: Vdichloromethane), with no carbazole compounds. A significant amount of carbazole compounds were eluted in the polar segments of 8:2–6:4, with the eluted carbazoles concentration accounting for more than 98 % of the total concentration. Moreover, the concentration and recovery of carbazoles eluted by direct injection mode were about 10 % higher than those after adsorption by silica gel. The standard deviation of the parameter ratio for the separated carbazole compounds in the three groups of repeatable parallel experiments was less than 0.2 %. Our method is superior to traditional two-step method and C18 column method in separation efficiency and damage to human body. This method can be applied to both highly mature crude oil and other kinds of oils including biodegradable oil. It could be a versatile method for the carbazoles separation and provide technical support in unveiling the geochemical implications of these compounds in complex areas.

Sustainable Energy Production in Smart Cities

Finding a method to provide the installed Internet of Things (IoT) nodes with energy that is both ubiquitous and long-lasting is crucial for ensuring continuous smart city optimization. These and other problems have impeded new research into energy harvesting. After the COVID-19 pandemic and the lockdown that all but ended daily activity in many countries, the ability of human remote connections to enforce social distancing became crucial. Since they lay the groundwork for surviving a lockdown, Internet of Things (IoT) devices are once again widely recognised as crucial elements of smart cities. The recommended solution of energy collection would enable IoT hubs to search for self-sustaining energy from ecologically large sources. The bulk of urban energy sources that could be used were examined in this work, according to descriptions made by researchers in the literature. Given the abundance of free resources in the city covered in this research, we have also suggested that energy sources can be application-specific. This implies that energy needs for various IoT devices or wireless sensor networks (WSNs) for smart city automation should be searched for near those needs. One of the important smart, ecological and energy-harvesting subjects that has evolved as a result of the advancement of intelligent urban computing is intelligent cities and societies. Collecting and exchanging Internet of Things (IoT) gadgets and smart applications that improve people’s quality of life is the main goal of a sustainable smart city. Energy harvesting management, a key element of sustainable urban computing, is hampered by the exponential rise of Internet of Things (IoT) sensors, smart apps, and complicated populations. These challenges include the requirement to lower the associated elements of energy consumption, power conservation, and waste management for the environment. However, the idea of energy-harvesting management for sustainable urban computing is currently expanding at an exponential rate and requires attention due to regulatory and economic constraints. This study investigates a variety of green energy-collecting techniques in relation to edge-based intelligent urban computing’s smart applications for sustainable and smart cities. The four categories of energy-harvesting strategies currently in use are smart grids, smart environmental systems, smart transportation systems, and smart cities. In terms of developed algorithms, evaluation criteria, and evaluation environments, this review’s objective is to discuss the technical features of energy-harvesting management systems for environmentally friendly urban computing. For sustainable smart cities, which specifically contribute to increasing the energy consumption of smart applications and human life in complex and metropolitan areas, it is crucial from a technical perspective to examine existing barriers and unexplored research trajectories in energy harvesting and waste management.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.