High Traffic Areas Research Articles

“Water First” School Water Promotion and Access Intervention: A Cost Analysis Study

BackgroundAdequate water intake is associated with improved cognitive and physical performance, prevention of dental caries, and overweight and obesity. However, access to free drinking water in schools remains inadequate. Water First, a school-based intervention promoting water consumption, was shown effective in preventing overweight, yet its costs have not been quantified. ObjectiveTo evaluate the costs of Water First from the school's perspective over one academic year. DesignSecondary analysis of a cluster-randomized clinical trial assessing the cost of Water First. Participants/SettingSix public schools in the San Francisco Bay Area in the 2018-2019 academic year, with an average of 578±69 students per school and 110±2 fourth-grade students in intervention schools, and 508±190 students per school and 101±5 fourth-grade students in control schools. InterventionSchools were randomized to receive water dispensers and stations in cafeterias and high-traffic areas and a schoolwide water promotion campaign (school-level intervention). Fourth-grade students and teachers received reusable water bottles, lessons, and materials for school and home (class-level intervention). Main outcome measuresActivities, resources, and the value needed to implement the school- and classroom-level intervention were systematically captured over one academic year, including labor, materials, and utilities costs. Statistical analysisThe intervention costs per school and per student were estimated by summing the component-specific costs. Results are presented for both school- and classroom-level interventions in 2023 USD. ResultsThe Water First intervention costs $20 per student for the school- and $131 per student for the classroom-level intervention over one academic year. Accounting for the useful life of the installed water stations and dispensers beyond one year, the annualized cost of the school-level intervention was $11 per student. ConclusionsThese cost estimates provide useful insights for schools and stakeholders to plan and implement effective school-based interventions that promote access to drinking water.

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

Abstract. Urban air quality is an important part of human well-being, and its detailed and precise modeling is important for efficient urban planning. In this study the potential sources of errors in large eddy simulation (LES) runs of the PALM model in stable conditions for a high-traffic residential area in Prague, Czech Republic, with a focus on street canyon ventilation, are investigated. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes in meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale turbulent kinetic energy (TKE), adjusting the profiles of parameters of the synthetic turbulence generator in PALM, and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modeling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease in concentration overestimation in the range 30 %–66 % while exhibiting a negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors which occurred during such extremely stable conditions.

Vaccines are coming closer to you: the experience of the Corporate Healthcare Company (ASL) Rome 4

Abstract Issue The post-Covid period is characterised by vaccine fatigue and declining coverage, especially among children. Access to vaccination for adults and the elderly, outside of the influenza vaccination campaign, has historically been very low due to limited awareness of opportunities and difficulties in accessing vaccination centres. Description of the problem ASL Roma 4 is characterised by a dispersed population across a large territory of 28 municipalities, many of which are small and predominantly populated by elderly people. In order to reach this population, ‘proximity clinics’ were opened in 6 small municipalities in 2023, where the vaccination service offers a wide range of vaccines every 1-2 months. Since June 2023, two mobile vaccination units have also been used to extend the reach of vaccination activities. Results In 2023, vaccination activities at nearby clinics reached 343 people (166 males and 177 females), with a total of 473 vaccines administered. More than 70% of those vaccinated were over 65 years of age, including 47% over 80 years of age. Between September and December 2023, vaccination vans were deployed in high-traffic areas such as markets, supermarkets, squares, or in locations with hard-to-reach populations such as migrant reception centres, volunteer centres, and senior centres. As a result, 1183 people (585 men and 557 women) with an average age of 49 years were reached and 1699 vaccines were administered. 62% were over 65, 37% over 80, 41 were homeless and 87 were migrants. 56% of the vaccines administered were influenza and covid, but all types were provided. Lessons Although this method of vaccine delivery is still being implemented, its value lies mainly in vaccinating people who would not have voluntarily sought vaccination services, demonstrating the need to bring prevention as close to the user as possible. Key messages • The post-Covid period is characterised by vaccine fatigue and declining coverage. • It is necessary to bring vaccination prevention as close to the user as possible.

Aedes aegypti surveillance under the International Health Regulations - experience at Madeira Island

Abstract Problem Aedes aegypti was first identified on Madeira Island in 2005, persisting since then. This mosquito transmits various haemorrhagic diseases and caused a Dengue outbreak in 2012-2013. The island has two seaports and one airport that may serve as points of entry or dissemination of these diseases, potentially leading to a new outbreak or international disease spread. We aimed to assess Ae. aegypti presence at the island’s seaports and airport during 2023. Description Data was collected from 52 entomological panels produced by the Regional Health Department throughout 2023. This data is publicly available on the Institute of Health Administration’s website. Ovitraps capture eggs, larvae, and pupae of Ae. aegypti, and BGtraps capture adult mosquitos. Positivity refers to the proportion of times an ovitrap recorded eggs in the entomological cycle. Results In 2023, there were 204 active ovitraps at the island (17 at the airport, 9 at Funchal’s seaport 8 at Caniçal’s seaport) and 24 BGtraps (2 at the airport, 2 at Funchal’s port and 1 at Caniçal’s port). Weekly reports revealed a median of 2243 (0-14448) eggs and 14 (0-234) mosquitos in all traps. Positivity was higher in the second semester. On average, ovitraps’ positivity at the airport (avg 6.4%; 0-59%) and at Caniçal’s port (avg 2.8%; 0-47%) were lower than the positivity of the island (avg 14.8%). Funchal’s port had a higher positivity than the island (avg 15.9%; 0-67%). Per-ovitrap, the average weekly positivity rate rose by 15%, 10% and 5% at Caniçal’s port, airport and Funchal’s port, respectively, compared to 2012-2022. Lessons Ae. aegypti’s eggs prevalence increased in 2023. The high positivity of eggs suggests an increased likelihood of mosquitos nearby. The detection of immature forms triggers actions to prospect the surrounding area for identification and elimination of breeding grounds, as well as the placement of salt in water accumulation areas. This is particularly important at high-traffic areas such as seaports and airports. Key messages • The increase in Ae. aegypti egg positivity in 2023 on Madeira Island suggests a higher risk of disease transmission. • It is essential to prioritize vector control measures at seaports and the airport in Madeira Island.

Effectiveness of noise barriers in sensitive urban areas: a comprehensive study on noise reduction strategies and public health implications.

This study investigates the effectiveness of noise management strategies, focusing on installing barriers to reduce urban noise pollution, particularly around hospitals. The research in Sakarya, Turkey, employs comprehensive noise measurements and modeling techniques to evaluate barrier interventions. The study included 24-h noise averages and specific measures for daytime, evening, and nighttime noise levels, using SoundPlan software for modeling and mapping. Results reveal that noise levels frequently exceed international thresholds, especially in high-traffic areas. However, introducing noise barriers significantly reduces noise levels, particularly at specific measurement points, demonstrating their effectiveness. This underscores the crucial role of noise management strategies in sensitive urban areas and highlights the urgency for proactive interventions to manage noise pollution. The study identifies the need for future research, including long-term studies to assess noise barriers' durability and sustained effectiveness, and the integration of other noise reduction measures such as green infrastructure. Practical applications of this research include informing urban planners and policymakers about effective strategies for mitigating noise pollution and improving public health. In conclusion, this study provides valuable insights into urban noise management by empirically demonstrating the effectiveness of barrier interventions in reducing noise pollution. It underscores the importance of proactive measures to protect human health and preserve environmental balance in sensitive urban areas. The findings advocate for the broader implementation of noise barriers and other complementary strategies in urban planning to enhance the quality of urban life.

Acoustic and vibrations measurments in an autonomous shuttle bus

In response to the ADEME's call for Autonomous Road Vehicle Experimentation projects, a consortium was created to propose the SAM project about Safety and Acceptability of Autonomous Driving and Mobility. It was composed of industrial and research teams, associated with local partners. SAM is a national project involving large-scale trials of autonomous driving and mobility on open roads. The assessments covered several topics, including environmental impacts, focusing on air pollution, noise and the life cycle of components. Cerema proposed a methodology to characterize acoustic and vibration levels for autonomous driving in the specific site near Paris. The tests consisted in instrumenting the interiors of an automated shuttle and a conventional car to determine the user's feelings. The vehicles were driven in a quiet area and in a high traffic area. The results displayed by Cerema are based on statistical studies on 20 vehicle passages for each experiment. The indicators were characterized for the entire route and by homogeneous sections. In fluid navigation situation, the measurements showed that the acoustic and vibration impacts are minimal. However, the tests have highlighted the effects of the lack of anticipation of an automated shuttle during specific events requiring rapid braking.

A data-driven approach to optimize a sanitary survey for communities in Maputo, Mozambique

ABSTRACT Current approaches to align sanitary surveys to environmental fecal waste indicators have demonstrated limited success, leaving stakeholders focused on fecal waste management with limited and costly tools. We analyzed data from the Maputo Sanitation Project using exploratory factor analysis (EFA) and structural equation modeling (SEM) to enhance the survey's correlation with Escherichia coli concentration data. EFA grouped related survey questions and sampling locations into distinct latent factors. SEM was then used to assess the relationship between the survey latent factors and the mean E. coli concentrations from grouped locations. The results suggested four survey question subgroups: latrine structure, latrine cleanliness, compound/household waste management, and community waste management. In addition, three sampling location subgroups were identified: high-traffic areas (HTAs), food activity locations, and bodily cleaning areas. The largest significant effect size identified suggested that for every 1-unit increase in community waste management, there was a decrease of 1.94 in log10 E. coli per gram of soil in HTAs (p = 0.03), a substantial improvement from the initial 0.50 decrease reported for an expert-weighted metric of all survey questions. These results underscore the importance of community-level waste management and demonstrate the use of a data-driven approach in enhancing environmental health assessments and planning interventions.

Evaluating the impact of unmanned aerial vehicles (UAVs) on air quality management in smart cities: A comprehensive analysis of transportation-related pollution

Urban environments face significant air pollution challenges affecting health and sustainability. Using unmanned aerial vehicles for air quality monitoring offers a promising solution. This research aims to improve these vehicles' ability to identify pollution sources and develop strategies, with a focus on transportation-related pollution. Detailed air pollution tests revealed that pollutant concentrations surged during morning rush hours, especially in high-traffic coastal areas, while areas away from traffic had much lower levels. Evening tests showed that pollutants from daytime traffic had dispersed throughout the urban area. The consistent northward shift in pollutant concentrations underscored the link between traffic patterns and pollutant distribution. Quantitative analyses and the resulting air pollutant maps showed average increases in pollutant concentrations between morning and evening hours in high-traffic areas: 12.1% for NO2, 5.3% for CO, 9.8% for PM10, and 11.4% for PM2.5. In contrast, the increases in pollutant concentrations in less trafficked urban regions were 45.9%, 48.2%, 32.7%, and 29.5%, respectively. This demonstrates how air pollution originating from areas with heavy traffic impacts other regions through environmental and geographical factors. The findings underscore the need for comprehensive air quality management strategies at the city level, targeting emissions in high-traffic areas and adapting to temporal fluctuations in pollutant levels.

High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (2003–2021) with multi-stage data reconstruction and ensemble machine learning methods

In this contribution, we applied a multi-stage machine learning (ML) framework to map daily values of nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) at a 1 km2 resolution over Great Britain for the period 2003–2021. The process combined ground monitoring observations, satellite-derived products, climate reanalyses and chemical transport model datasets, and traffic and land-use data. Each feature was harmonized to 1 km resolution and extracted at monitoring sites. Models used single and ensemble-based algorithms featuring random forests (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), as well as lasso and ridge regression. The various stages focused on augmenting PM2.5 using co-occurring PM10 values, gap-filling aerosol optical depth and columnar NO2 data obtained from satellite instruments, and finally the training of an ensemble model and the prediction of daily values across the whole geographical domain (2003–2021). Results show a good ensemble model performance, calculated through a ten-fold monitor-based cross-validation procedure, with an average R2 of 0.690 (range 0.611–0.792) for NO2, 0.704 (0.609–0.786) for PM10, and 0.802 (0.746–0.888) for PM2.5. Reconstructed pollution levels decreased markedly within the study period, with a stronger reduction in the latter eight years. The pollutants exhibited different spatial patterns, while NO2 rose in close proximity to high-traffic areas, PM demonstrated variation at a larger scale. The resulting 1 km2 spatially resolved daily datasets allow for linkage with health data across Great Britain over nearly two decades, thus contributing to extensive, extended, and detailed research on the long-and short-term health effects of air pollution.

Ten years of oil pollution detection in the Eastern Mediterranean shipping lanes opposite the Egyptian coast using remote sensing techniques

The Eastern Mediterranean region, a vital conduit for global maritime trade, faces significant environmental challenges due to marine pollution, particularly from oil spills. This is the first study covering the long period of comprehensive monitoring of oil pollution using the full mission of Sentinel-1 Synthetic Aperture Radar (SAR) data in the Mediterranean Sea, so this research aims to detect and analyze comprehensively the occurrence of oil spills in the Eastern Mediterranean over a decade (2014–2023). This study focuses on identifying geographical distribution patterns, proximity to shorelines, frequency across maritime zones, and potential sources of these spills, especially around major ports and maritime routes. This study utilizes SAR data from the Sentinel-1 satellite. The methodology included automated detection algorithms within the Sentinel application platform (SNAP) and integration with GIS mapping to study oil spill patterns and characteristics. Over 1000 Sentinel-1 scenes were investigated in the northern Mediterranean waters off the coast of Egypt, to detect and analyze 355 oil spill events with a total impacted area of more than 6000 km2. The analysis of temporal spill distribution reveals significant fluctuations from year to year. Within the entire timeline of the study, 2017 had the largest spatial areas covering one thousand square kilometers. In contrast, the single largest spill recorded during the study period occurred in 2020, covering 198.73 square kilometers. The results identified a non-uniform distribution of oil spills and primarily exhibiting elongated patterns aligned with the navigation routes. The distinct increase of oil spill incidents was within the Exclusive Economic Zone (EEZ), obviously drifted to the coastline and around major ports. The study emphasizes the critical role of remote sensing technologies in addressing environmental challenges caused by the maritime transport sector, advocating for enhanced monitoring and regulatory enforcement to protect marine ecosystems and support sustainable naval activities. The findings highlight the urgent need for targeted continuous monitoring and rapid response strategies in high-traffic maritime areas, particularly around the EEZ and major ports.

Polycyclic aromatic hydrocarbons (PAHs) in blood serum of adults living in high and low-traffic volume areas in Malaysia: A comparative cross-sectional study

Ambient polycyclic aromatic hydrocarbons (PAHs) originate predominantly from fuel combustion of motor vehicles and have the potential to affect human health. However, there is insufficient knowledge regarding serum PAHs health risks among the Malaysian population. This study aims to compare PAH concentrations, distributions, correlations, and health risks in 202 blood serum samples drawn from residents living in high-traffic volume areas (Kuala Lumpur) and low-traffic volume areas (Hulu Langat) in Malaysia. Solid phase extraction and gas chromatography-mass spectrometry (GC-MS) were employed to extract and analyze blood serum samples. Questionnaires were distributed to obtain sociodemographic and contributing factors of serum PAHs. The mean total PAHs concentration in serum of the Kuala Lumpur group was 54.44 ng g−1 lipids, double the Hulu Langat group's concentration (25.7 ng g−1 lipids). Indeno(1,2,3-cd)pyrene (IcP) and acenaphthene (ACP) feature the most and least abundant compounds in both study groups. The mean concentrations of IcP and ACP in the Kuala Lumpur and Hulu Langat groups were 26.8 vs 12.68 and 0.27 vs 0.14 ng g−1 lipids, respectively. High-molecular-weight PAHs (HMW-PAHs) composed 85% of serum total PAHs in both groups. Significant correlations were found (i) between the individual serum PAH congeners (p < 0.01) and (ii) between serum PAHs and total lipids (p < 0.01). According to the questionnaire data, high traffic volume and outdoor hobbies were the only contributory factors that confirmed significant relationships with serum PAHs (p < 0.001). Health risk assessment was computed using benzo(a)pyrene (BaP) equivalent (BaPeq) and demonstrated that the Kuala Lumpur group has twofold greater carcinogenic risk than the Hulu Langat group (16.11 vs 7.76 ng g−1 lipids). Our study reveals that traffic volumes notably impact serum PAH levels and general health among the Malaysian population.

Application of Belief Theories for Railway Track Defect Detection

Faced with increasing traffic, railway infrastructures are encountering growing demands, particularly in high-traffic areas. In this context, rail and sleepers emerge as the components most susceptible to failure. To assist infrastructure managers (IM) in optimizing network maintenance, we have explored a novel method for detecting critical defects on the track. The objective is to develop a process for real-time analysis of railway infrastructure that is both frugal and efficient and can be installed on board commercial trains. This new infrastructure monitoring system integrates deep learning networks with a data fusion model based on belief theory. By modeling the decision-making process of a human operator, this processing chain has achieved detection rates exceeding 90% for the five primary defects: defective fasteners, broken fishplates and rails, surface defects, and missing nuts.

A BIBLIOMETRIC ANALYSIS OF COPPER AND ANTIMICROBIAL COPPER COATINGS

Along with technological development, scientific literature has grown exponentially. Although the data resources are open access, filtering a large amount of information remains a significant problem. This paper aims to search and analyze the specialized literature on copper coatings and antimicrobial copper coatings. The aim was to extract the data from the Web of Science, which is the largest database for scientific literature. We used the VOSviewer software for analyzing the raw data that we collected from the Web of Science Core Collection. Within this domain, visualizing networks of key terms from published works holds significant importance. Thus, the objective of this research is to showcase advancements and research focuses within antimicrobial copper coatings, particularly those employed in high-traffic areas such as public transportation, airports, educational institutions, and notably in hospitals worldwide. This entailed two sets of keyword analyses: one focused on the overarching term describing the copper coatings field, namely "copper coatings," and the other aimed at fulfilling the primary study goal, using the keywords "antimicrobial copper" in the search. When searching for "copper coatings" OR "copper-coat*" in all WOS-indexed databases at the time of the inquiry, 9,302 results were found, according to the Web of Science database. When searching for "antimicrobial copper" OR "antimicrob* copper*" in the Web of Science Core Collection, 71 results were found. Through processing these findings with VOSviewer software, distinct clusters of keywords were generated, with their significance filtered based on various criteria as outlined in each map's description. This study presents future avenues for research based on the findings.

High Traffic Roads and Adverse Birth Outcomes: Comparing Births Upwind and Downwind of the Same Road.

Traffic related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult since high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007-2016 (n=3,570,272 births) and computed hourly wind estimates for residential addresses within 500 m of high-traffic roads (i.e., annual average daily traffic greater than 25,000) (10.9% of births). We matched pregnant individuals predominantly upwind to pregnant neighbors downwind of the same road segment (n=37,631 pairs). Living downwind was associated with an 11.6 gram (95% CI: -18.01, -5.21) decrease in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50 m was associated with a -36.3 gram (95% CI: -67.74, -4.93) decrease in term birth weight and living 51-100m downwind was associated with an odds ratio of 3.68 (95% CI: 1.71, 7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads.

SANITIZATION SYSTEM FOR HOSPITALS

The paper introduces a robot equipped with UV light and spray sanitizer for cleaning surfaces and objects. It's designed to combat the spread of pathogens by sanitizing various environments, with a focus on hospitals, especially ICU and operation theatres. Its dual sanitization mechanisms, UV light and spray sanitizer dispenser provides optimal efficiency. This combination ensures thorough cleaning and disinfection of diverse surfaces, enhancing public health and safety. The robot's versatility makes it suitable for use in hospitals, transportation systems, educational institutions, and other high-traffic areas where sanitation is crucial. By integrating UV sanitization technology with a spray sanitizer mechanism, the robot offers comprehensive coverage against infectious diseases, providing an effective solution for maintaining cleanliness and hygiene in various settings. Overall, the robot's autonomous operation and remote monitoring capabilities make it a valuable tool for promoting public health and mitigating the spread of harmful pathogens. Key Words: sanitization, robot, UV light, spray sanitizer, pathogen control.

How does local context matter? Assessing the heterogeneous impact of electric vehicle incentive policies in China

This study reassesses the effectiveness of electric vehicle (EV) incentive policies in China and explores potential disparities in policy impact across different local contexts. Initially, employing panel data analysis, it evaluates policy effectiveness using data from 80 Chinese pilot cities spanning 2010 to 2022. Subsequently, cities are stratified based on varying income levels, traffic conditions, population density, and administrative hierarchy to facilitate an in-depth examination of the heterogeneous impacts of EV policies. Additionally, the Difference-in-Differences (DID) estimation method is employed to scrutinize the dynamic effects of these policies over time, enhancing the credibility of the panel model results. The findings reveal that purchase subsidies, parking benefits, driving privileges, and charging infrastructure have significant positive impacts on EV market share. Specifically, financial incentives are more effective in low-income, low-traffic, low-density cities, as well as in provincial capital cities while driving privileges are more suitable for high-income, high-traffic, and high-density areas. This study also finds that usage-phase policies increasingly drive EV adoption over time, offsetting the negative impact of subsidy reductions. The results of this study hold substantial implications for policymakers, providing insights to design context-based EV policies tailored to specific local conditions.

Speed Breaker

The growing demand for electrical energy amidst dwindling fossil fuel resources necessitates innovative approaches for sustainable power generation. In response, this study introduces a novel method harnessing the untapped energy from human foot traffic. Leveraging piezoelectric transducers embedded within specially designed tiles, the kinetic energy generated by footstep vibrations is converted into electrical energy. These tiles, strategically placed in high-traffic areas such as crowded pavements or exercise zones, serve as unobtrusive energy harvesters. By utilizing series-parallel connections of piezoelectric transducers, the system maximizes energy extraction efficiency. This approach stands out for its simplicity and cost-effectiveness compared to other methods like electromagnetic generators, offering a promising solution for decentralized power generation. Through the integration of sensors such as rack and pinion, generators, rectifiers, batteries, voltage regulators, and filters, the footstep power generator system ensures smooth energy conversion and storage, enabling the provision of electricity to rural areas and promoting non-conventional energy sources. The system's adaptability to various terrains and its ability to operate independently of climate conditions further underscore its potential as a sustainable energy solution. Keywords: piezoelectric transducers, footstep energy harvesting, sustainable power generation, decentralized energy, renewable energy, kinetic energy conversion, energy harvesting tiles, series-parallel connection, sensor integration, rural electrification.

A GIS-based approach to determining optimal location for decentralized inner city smart filters: Toward net zero cities

Climate change has already begun to take visible effect globally in recent years. Given the climate change paradox and urbanization trends, cities’ success would not only depend on smartness and sustainability, but also resilience to all forthcoming economic, environmental, or behavioral changes. Numerous technologies have surfaced and proved effective in CO2 removal from the local environment. However, the optimal placement of these smart filters is a complex task and require logical and strategic decision-making. Determining the optimal location is one of the key factors for establishing a network of smart air filters. This study used a GIS-based suitability analysis for identifying optimal locations for smart filters based on pollution hotspots (population and spatial proximity to industry, commercial centers, roads, high-traffic areas, and intersections). The spatial analysis involves the determination and preparation of input layers, ranking layers, assigning weights to each criterion, and generation of a suitability map. The sites with a higher suitability score (7 or above) are optimum sites for air filters. The sites are spatially distributed over different regions. The findings revealed that GIS-based suitability analysis can be an effective technique for placing smart filters within an urban environment. These findings can help decision-makers to prioritize the location considering environmental constraints. The proposed solution aims to pave the way for fostering resilient, smart, and sustainable cities through a community sensing platform targeting hotspots within spatial variations.

Influence of Sustainable Grout Material on the Moisture Damage of Semi-flexible Pavement

Semi-flexible pavement (SFP), also known as grouted pavement, is a type of pavement structure consisting of a porous asphalt skeleton with air voids between 25 and 35% injected with cementitious grout materials. The special skeleton of SFP provided enhanced durability and resilience, making it a promising option for the construction of road surfaces in high-traffic areas and severe conditions. The main aim of the current research is to investigate the rutting behavior and moisture resilience of SFP-containing sustainable grout using ceramic waste powder (CWP). This research introduced the use of CWP as a replacement for conventional grout (cement) in SFP for the first time. CWP partially replaced cement at ratios of 15, 20, 30, 40, and 50% of the cement weight. Indirect Tensile Strength (ITS) and Hamburg Wheel Tracking Tests (HWTT) were used to evaluate the performance of SFP to reduce the detrimental effect of moisture. The grout modified with CWP shows excellent results in the ITS and Tensile Strength Ratio (TSR), and all modified SFP mixtures give higher values in these tests compared with Control Mix (CM). In the HWTT, the minimum rut depth of the modified SFP was 2.8 mm and 3.10 mm. Compared to CM, rutting decreased by 73%–76% for CWP mixes with 20% and 30% replacement. This indicates the high fluidity of CWP, which enabled it to penetrate all voids in the porous pavement (PA) and form a dense microstructure due to its excellent pozzolanic interaction, making it a strong structure capable of bearing rutting.

Smart Footstep Energy Harvesting System

This research paper introduces a novel approach to footstep energy harvesting by integrating piezoelectric sensors into flooring surfaces. The project aims to generate sustainable electrical power from footsteps, contributing to the development of energy-efficient systems. The proposed system incorporates a unique feature that enables users to easily identify and address sensor failures or damages. The core components of the system include piezoelectric sensors strategically placed in high-traffic areas, an Arduino microcontroller for signal processing and energy management, and a rechargeable battery for energy storage. A rectifier circuit is employed to convert AC signals generated by the sensors to DC, while a voltage regulator ensures a stable output voltage. The energy harvested is stored in the battery, which is connected to a USB charging module capable of charging electronic devices. To enhance the system's usability and maintenance, LEDs are integrated with each piezoelectric sensor. These LEDs act as indicators for sensor status, allowing users to visually identify any damaged or failed sensors. The Arduino code is designed to continuously monitor sensor values and activate the corresponding LED when a sensor falls below a predefined threshold, signaling a potential issue. This unique feature enhances user awareness, simplifies troubleshooting, and aids in the efficient maintenance of the energy harvesting infrastructure. The research focuses on the practical implementation of the system, exploring its effectiveness in real-world scenarios. Various aspects, such as energy generation efficiency, adaptability to different environments, and the ease of sensor failure identification, are evaluated through extensive experimentation. The paper discusses the system's potential applications, including sustainable power generation for small electronic devices and its integration into smart infrastructure. The proposed Smart Footstep Energy Harvesting System with Sensor Failure Identification addresses a crucial aspect of sustainability and smart infrastructure management. The findings from this research contribute to the growing field of energy harvesting technologies and pave the way for future developments in efficient, user-friendly, and maintainable footstep energy harvesting systems.