Maintaining Air Quality Research Articles

The synergistic effect of La dopant/willow catkins template on SnO2 monotubes for efficient detection of triethylamine

The detection of volatile organic compounds (VOCs) such as triethylamine (TEA) is essential for maintaining air quality and human health. This study explores the synergistic effects of lanthanum (La) doping and the use of willow catkins as a template for the performance of SnO₂ gas sensors for TEA detection. The morphology and structure of the as-synthesized samples were investigated, and a series of experiments were conducted to explore the TEA sensing characterization of SnO2-based sensors. The results of gas sensing demonstrated that the response of La2-SnO₂ sensors is up to about 178 at 100 ppm TEA, showing a 45 % increase compared to pure SnO₂ with a detection limit as low as 5 ppb. The fabricated sensors demonstrated a rapid response time of 60 s and a recovery time of 98 s at 100 ppm TEA concentration. Moreover, the sensors showed stable and repeatable responses over multiple cycles, indicating excellent reproducibility and long-term stability. The enhanced TEA sensing performance can be attributed to the abundant oxygen vacancies and promising tubular structure, which not only offer more active sites to absorb oxygen species but also promote the adsorption-desorption process of gas molecules. This study highlights the potential of La-doped SnO₂ sensors fabricated using a sustainable willow catkins template as a promising candidate for practical TEA detection applications.

Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs

Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.

Ambient Air Quality Detection System Powered by Internet of Things-Based Sansevieria Plant (Case Study of Manduro Health Center)

Puskesmas Manduro is one of the public health service centers located at Jl. Raya Ngoro KM 14, Manduro MG Village, Ngoro District, Mojokerto Regency, East Java. As a basic health service center, it is important to monitor air quality and maintain air quality in accordance with the Environmental Quality Standard (SBMKL). This study aims to design an Internet of Things-based air quality detection system that utilizes Sansevieria plants as one of the natural solutions in absorbing air pollution levels. The method used is the research and development method which includes system design, tool making, website design, tool integration, and system testing. The sensors used are MQ-7 sensors for carbon monoxide gas, MQ-135 sensors for nitrogen dioxide gas, MQ-131 sensors for ozone gas, Dust GP2Y1010AU0F sensors for PM2.5, and DHT22 sensors for temperature and humidity. The results of testing the system for three days obtained the average temperature 30.64C, humidity 73.62%, CO 735.63 µg/m³. NO2 27.30 µg/m³, O3 32.13 30 µg/m³, and PM2.5 2.14 µg/m³. Based on the test results that have been carried out, it shows that the air quality monitoring system can provide data, graphs, and indicator levels for the Air Pollution Standard Index (ISPU). Air quality data obtained from sensors can be displayed on the LCD and also displayed in real-time through the website.

Comparative Analysis and Evaluation of Modeling Methods for Nuclear-Grade HEPA Filters

High-Efficiency Particulate Air (HEPA) filtration plays a crucial role in maintaining air quality in critical environments such as lean rooms, hospitals, and nuclear facilities. The point of this study is to look into how well nuclear-grade HEPA filters work and behave by looking at the main ways they catch particles using two modeling methods to figure out how well the filters work overall. This study encompasses particles with diameters ranging from 0.05 to 5.00 µm and a density of 1500 kg/m3. The current study systematically examined key parameters such as particle size, fiber diameter, and filtration velocity, which revealed their significant influence on the HEPA filter efficiency. Notably, the most penetrating particle size (MPPS) is identified within the expected range of 0.1–0.3 µm for both approaches. A critical threshold in fiber diameter is discovered when it exceeds 0.85 µm, resulting in a substantial shift in particle penetration and overall collection efficiency. This study also explored the impact of filtration velocity on filter performance, demonstrating increasing deviations as velocity rises, following a polynomial trend. The current study also rigorously validated the model predictions against experimental data from uranine particle filtration tests, confirming the model’s accuracy and applicability. These findings provide essential insights for optimizing the design and operation of nuclear HEPA filters, emphasizing the necessity of considering the particle size, fiber diameter, and filtration velocity. Both modeling approaches exhibit a negligible 0.04% deviation in the MPPS efficiency, which increases polynomially with the filtration velocity. Importantly, both approaches consistently identified the same MPPS regardless of the filtration velocity. Additionally, the model reinforces the substantial impact of fiber size on filter efficiency. A comprehensive comparison with the experimental data yielded closely aligned results with a maximum deviation of 1.14%. This validation strengthens the model’s ability to elucidate the underlying physical phenomena governing the influence of filtration velocity on efficiency, making it a valuable tool in nuclear HEPA filter research and development.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

The Influence of GDP, Coal Exports, and Fossil Energy Consumption On Indonesian Air Pollution Reviewed From CO2 Emissions

Air is very vital for the survival of living things, therefore it is important to maintain air quality to avoid pollution. One of the causes of air pollution is high carbon dioxide emissions resulting from economic and industrial activities which are trapped in the atmosphere, causing a greenhouse gas effect. This research aims to determine the influence of GDP, coal exports and fossil energy consumption on air pollution in Indonesia in terms of CO 2 emissions . This research also aims to test whether the Environmental Kuznet Curve (EKC) hypothesis applies in Indonesia. The type of data used in this research is secondary data in the form of quantitative data obtained from BPS, World Bank, etc. This research was conducted in all provinces in Indonesia. The data analysis technique used in this research is the Error Correction Model ( ECM). The research results show that in the short and long term the variables GDP, coal exports and energy consumption simultaneously have a significant effect on the level of CO 2 emissions . GDP in the short term and long term does not partially have a significant effect on the level of CO 2 emissions . Meanwhile, GDP squared in the short term partially has no significant effect and in the long term it partially has a positive and significant effect. This shows that the EKC hypothesis does not apply in Indonesia. Coal exports in the short term and long term partially do not have a significant effect. Fossil energy consumption in the short term and long term partially has a positive and significant effect. Considering the importance of air for human life, it is necessary to pay attention to the factors that influence air pollution.

An ethically guided preclinical device for phenotyping H2 production in laboratory rodents.

Dihydrogen (H2) is produced endogenously by the intestinal microbiota through the fermentation of diet carbohydrates. Over the past few years, numerous studies have demonstrated the significant therapeutic potential of H2 in various pathophysiological contexts, making the characterization of its production in laboratory species of major preclinical importance. This study proposes an innovative solution to accurately monitor H2 production in free-moving rodents while respecting animal welfare standards. The developed device consisted of a wire rodent cage placed inside an airtight chamber in which the air quality was maintained, and the H2 concentration was continuously analyzed. After the airtightness and efficiency of the systems used to control and maintain air quality in the chamber were checked, tests were carried out on rats and mice with different metabolic phenotypes, over 12 min to 1-h experiments and repeatedly. H2 production rates (HPR) were obtained using an easy calculation algorithm based on a first-order moving average. HPR in hyperphagic Zucker rats was found to be twice as high as in control Wistar rats, respectively, 2.64 and 1.27 nmol.s-1 per animal. In addition, the ingestion of inulin, a dietary fiber, stimulated H2 production in mice. HPRs were 0.46 nmol.s-1 for animals under control diet and 1.99 nmol.s-1 for animals under inulin diet. The proposed device coupled with our algorithm enables fine analysis of the metabolic phenotype of laboratory rats or mice with regard to their endogenous H2 production.

Ventilation Quality Assessment of Dual Courtyard Configuration

Maintaining air quality within building spaces is a challenge under the increased pollutants resources. The optimization of the courtyard building layout toward natural ventilation has emerged as a critical factor in shaping air quality conditions. This study delves into the assessment of outdoor air quality infiltration within a dual courtyard typology characterized by varying proportions and orientations for both the connecting link and openings. To comprehensively analyse air quality within these integrated courtyards, a combination of on-site particle dispersion measurements and flow pattern simulations were employed. The outcomes of this investigation highlight the pronounced impact of atmospheric conditions, particularly wind direction, on the performance of courtyard configurations. The results revealed that dual courtyard provide an approach to address the geometric conflict between thermal comfort and air quality optimization. Even in circumstances with limited flow velocity, the direct connection between courtyards deepens the upward recirculating flow into the courtyard cavity at the activity level. The optimal air quality is attained by planning each courtyard’s upper-level intake and outflow openings, as it provides an evacuation flow out of court cavity and decline the PNC than outside surrounding conditions.

Studi Perbandingan Kualitas Udara di Kota Indonesia Terdampak Infeksi Saluran Pernapasan Akut

Air quality affects the activities carried out by humans. Healthy air quality will support human activities. On the other hand, unhealthy air quality will disrupt human activities by causing respiratory tract diseases. The aim of the research carried out was a comparative study of the air quality of Indonesian cities based on cases of acute respiratory infections. The research method is descriptive analysis of the air quality index (AQI) and air particles (PM) in the city of Palembang, the city of Cileungsir, the city of Serang, the city of Jambi, the city of South Tangerang, the city of Bandung, the city of Jakarta, the city of Pekanbaru, the city of Denpasar and the city of Semarang on effective work, weekends and morning, afternoon, evening and night time periods. The results of the research show that the air quality is quite good in the cities of Denpasar and the city of Semarang and the city with high pollution is the city of Palembang. This research provides information and awareness in maintaining air quality so that it can maintain and strive for healthy air quality so that it supports human activities and does not cause respiratory infections

Rain-Based Train Washing: A Sustainable Approach to Reduce PM Concentrations in Underground Environments

Fine particle concentrations measured in many underground rail systems around the world consistently exceed those observed at ground level, potentially posing significant implications for human health. While numerous authors have observed these high particle concentrations and analyzed both their atomic compositions and health impacts, few have investigated devices and technologies capable of reducing these high levels in underground environments. In light of these considerations and recognizing the multifaceted challenges associated with maintaining air quality in underground metro systems, the aim of this paper was to evaluate the usefulness and effectiveness of utilizing rainwater for washing trains to abate particulate matter (PM) concentrations in underground rail systems. To achieve this aim, an ad hoc case study was considered: the Naples Metro Line 1 (Italy), which is characterized by 4.5 km in the ground level and 13.5 km underground. A measurement campaign was carried out during storms of strong intensity through PM measuring instruments placed on station platforms along the metro line. Precisely, the trains were washed by the rain in the initial ground level section, and then continued wet within the underground one. The results of this measurement campaign were compared with those of a comparable survey carried out during average clear weather conditions, and the results showed that the train washing produces a significant PM10 concentration reduction of up to about 60% in the underground environment. If confirmed in other experimental settings, these results could lay the groundwork for the introduction of structured washing system devices (e.g., periodically washing trains and/or tunnels) for the reduction of PM concentration in underground metro systems. The present study sought to contribute valuable insights towards sustainable and environmentally conscious approaches to addressing air quality concerns, particularly by harnessing the natural resource of rainwater during specific meteorological events.

AIR QUALITY AND PUBLIC HEALTH: A REVIEW OF URBAN POLLUTION SOURCES AND MITIGATION MEASURES

Urbanization has led to unprecedented challenges in maintaining air quality, significantly impacting public health. This review delves into the diverse sources of urban pollution and explores effective mitigation measures to safeguard public well-being. The rising concentration of pollutants in urban areas, primarily attributed to industrial activities, vehicular emissions, and domestic energy consumption, poses a severe threat to the respiratory and cardiovascular health of urban populations. The review begins by examining the key sources of urban pollution, highlighting the complex interplay between anthropogenic activities and their cumulative impact on air quality. Industrial emissions, characterized by the release of particulate matter, volatile organic compounds, and hazardous chemicals, contribute significantly to urban air pollution. Likewise, the surge in vehicular traffic, fueled by rapid urbanization, introduces pollutants such as nitrogen oxides and carbon monoxide, further exacerbating the problem. To address these challenges, the review explores a spectrum of mitigation measures. Implementation of stringent emission standards for industries, adoption of cleaner technologies, and promotion of renewable energy sources emerge as critical strategies. Additionally, urban planning initiatives focusing on efficient public transportation systems, pedestrian-friendly infrastructure, and green spaces are examined as effective means to reduce vehicular emissions and enhance air quality. The review also underscores the importance of public awareness campaigns and community engagement in fostering a collective commitment to air quality improvement. Innovative technological solutions, such as air quality monitoring networks and real-time data analytics, are discussed as indispensable tools for informed decision-making and prompt intervention. This review synthesizes current knowledge on urban pollution sources and mitigation measures, emphasizing the urgent need for a multidimensional approach to protect public health. By comprehensively addressing the intricate web of pollution contributors and implementing proactive measures, urban environments can be transformed into healthier and sustainable spaces for all residents.
 Keywords: Air Quality, Public Health, Urban Pollution, Mitigation, Review.

Grey water footprint of crop in Riau Province

The escalating severity of the water problem poses a potential threat to the prospects of sustainable development in the future. The grey water footprint is an indicator of the need for fresh water to mix and dilute pollutants and maintain air quality according to water quality standards. The evaluation of the grey water footprint (GWF) serves as a valuable measure in the mitigation and management of water contamination. The main objective of this study is to determine the grey water footprint associated with crop production along the Kampar Watershed and develop strategies to mitigate pollution levels. The grey water footprint is calculated using a water footprint assessment method. The finding show that the grey water footprint of rice farming (17.01 m3/ton) is larger than the maize (9.51 m3/ton), this indicate that necessary to improve water management on rice and maize agriculture. The water footprint performance scores of rice and corn plants are both in the poor category with scores of 11.93 and 45 respectively. To improve grey water performance and reduce air pollution, it can be done by using fertilizer according to plant needs, replacing inorganic fertilizer with organic fertilizer, implementing practices conventional tillage and maintain soil moisture.

Pathways to green urbanism: evaluating Jeddah’s environmental sustainability progress and prospects

Abstract This research provides a comprehensive analysis of environmental sustainability in Jeddah, focusing on the unique challenges and opportunities it faces as a rapidly urbanizing city in an arid region. The study evaluates Jeddah’s performance across multiple environmental areas, including energy and carbon dioxide emissions, land and building use, transportation, waste management, water use, sanitation and air quality. A detailed SWOT analysis identifies the city’s strengths, weaknesses, opportunities and threats in achieving sustainable urban development. The results reveal high per capita carbon dioxide emissions, energy consumption and waste generation, along with challenges in managing water resources and maintaining air quality. The study highlights Jeddah’s efforts in renewable energy policies, urban mass transportation and waste management strategies. The conclusion provides a set of comprehensive recommendations for policy improvement, focusing on the need for sustainable urban planning, energy conservation, improved waste and water management and public engagement. This research contributes significantly to the understanding of urban sustainability in arid environments, providing a valuable framework for policymakers and urban planners.

Chiral Reticular Chemistry: A Tailored Approach Crafting Highly Porous and Hydrolytically Robust Metal-Organic Frameworks for Intelligent Humidity Control.

Control of humidity within confined spaces is critical for maintaining air quality and human well-being, with implications for environments ranging from international space stations and pharmacies to granaries and cultural relic preservation sites. However, existing techniques rely on energy-intensive electrically driven equipment or complex temperature and humidity control (THC) systems, resulting in imprecision and inconvenience. The development of innovative techniques and materials capable of simultaneously meeting the stringent requirements of practical applications holds the key to creating intelligent and energy-efficient humidity control devices. In this study, we introduce chiral reticular chemistry as a tailored synthetic approach, targeting a highly porous hea topological framework characterized by intrinsic interpenetrating pore architecture. This groundbreaking design successfully circumvents the traditional compromise between the pore volume and hydrolytic stability. Our metal-organic framework (MOF) exhibits an extraordinary working capacity, setting a new record at 1.35 g g-1 within the relative humidity (RH) range of 40-60%, without exhibiting hysteresis. Consequently, it emerges as a state-of-the-art candidate for intelligent humidity regulation within confined spaces. Utilizing single-crystal X-ray measurements and molecular simulations, we unequivocally elucidate the mechanism of water clustering and pore filling, underscoring the pivotal role of the linker functionality in governing the water seeding process. Our findings represent a significant advancement in the field, paving the way for the development of highly efficient humidity control technologies and offering promising solutions for diverse real-world scenarios.

Characteristics of fine particulate matter in Linyi during the 24th Olympic winter Games: An insight into the components, sources, transport pathways, and source distribution

To maintain air quality and mitigate pollution during the 24th Olympic Winter Games, strict control measures were implemented in Beijing and surrounding areas, including Linyi. Fine particulate matter (PM2.5) sampling was conducted in the urban area of Linyi between November 10th, 2021, and February 20th, 2022. This sampling period was divided into a no-control period (NCP) and a control period (CP). The PM2.5 concentration decreased by 25.0% during the CP, whereas the atmospheric oxidation increased. This led to the formation of secondary components of PM2.5. Although the conversion rate of the precursors into secondary components was increased during the CP, the nitrate (NO3−) and ammonium (NH4+) concentrations were reduced due to emission reduction. Elemental carbon (EC) and primary organic carbon concentrations were also decreased, whereas the organic carbon (OC)/EC ratio and proportion of secondary organic carbon in PM2.5 increased. Total detected inorganic element concentrations decreased by 23.6% during the CP. The results of source apportionment revealed that secondary sources were the leading PM2.5 contributors during both periods. Vehicle pollution prevention and control should be strengthened as no restrictions on private-car travel during the CP. Controlling combustion and industrial sources had a significant impact on air quality during the CP. Biomass burning and industrial emissions declined from 20.7% to 16.1% (NCP) to 2.0% and 9.1% (CP), respectively. Backward trajectories revealed that air masses largely originated from local regions during both periods, with the highest PM2.5 concentrations found in air masses from the north (NCP) and east (CP) of Linyi. Potential source regions were also identified. The location of these regions showed that local and regional collaborative control of PM2.5 is urgently needed, particularly in northern and southern Linyi (NCP) and eastern Linyi (CP).

Challenges and Way Forward to Maintain Air Quality Standard in Urban Areas

This thesis explores the intricate relationship between urban air pollution, economic growth, population dynamics, and energy consumption. Addressing impacts on climate change, biodiversity, agriculture, and human health, it emphasizes compromised urban air quality due to pollution sources such as power generation, vehicle traffic, and construction. Key pollutants like particulate matter, carbon dioxide, sulfur dioxide, and nitrogen dioxide pose significant health risks. The study identifies road transportation as a primary contributor, underlining alarming statistics from WHO on global air quality, particularly impacting low-level socio-economic regions. Legislations and policies dating back over a century form the foundation for global air pollution control efforts. The research highlights innovative solutions like urban green spaces, smart traffic management, and renewable energy investments. It stresses the importance of public transportation, electric vehicles, clean construction practices, and initiatives to reduce industrial emissions. In response to challenges, the thesis proposes a comprehensive mitigation plan covering strategy such as promoting public transport, energy conservation, recycling, and afforestation. It outlines a way forward, emphasizing integrated urban planning, public awareness campaigns, government policies, and international collaboration. In conclusion, the thesis calls for collective responsibility to address urban air pollution's adverse effects on public health and the environment. The proposed roadmap aims to create sustainable, resilient, and healthier urban environments through a holistic and collaborative approach.

АНАЛИЗ ЦЕНТРОБЕЖНЫХ ПЫЛЕУЛОВИТЕЛЕЙ ДЛЯ ПРИМЕНЕНИЯ В ПРОИЗВОДСТВЕ СТРОИТЕЛЬНЫХ МАТЕРИАЛОВ

NNowadays, maintaining air quality, especially during industrial activities, is a pressing issue. Instability in the composition of industrial waste can lead to serious consequences for human health and the environment. This topic is studied in various fields of science and engineering, for example, environmental, physical and mechanical, etc. The air condition around industrial facilities can be improved in several ways, including by improving the air purification system. Air quality control must meet sanitary and hygienic requirements and ensure high economic feasibility. Return air cyclones are considered the most popular means of air purification. These designs have been known for a very long time, but there is a constant need to further improve their design. Due to the extreme complexity of the processes occurring in complex multicomponent swirling flows, it has not yet been possible to develop a generally accepted and widely used model of the process of separation of dust particles in the cylindrical part of the cyclone. The proposed mathematical model is capable of taking into account the turbulent mode of movement of the dust-air flow to represent the deposition of particles in the cylindrical part of the cyclone, a rotating flow.

Machine learning-based fault diagnosis for three-phase induction motors in ventilation systems

ABSTRACTInduction motors are crucial in industrial drive systems and nuclear and radiological facilities due to their durability and ease of maintenance. Even though the systems are reliable, they have several faults. One of these faults is the instability of the facility’s electrical network. This may lead to catastrophic consequences such as instability in HVAC and ventilation systems, which affects operational safety. Air instability in the HVAC system is a critical concern in the radio pharmaceutical industry. Controlled environments are vital for safety and regulatory compliance. Maintaining air quality and stability is crucial to protect radioactive materials and ensure personnel safety. A healthy and faulty motor condition dataset proposed and used in the study was generated based on a simulated actual supply of electric data. A fault diagnosis is necessary to increase reliability and minimise risks. This paper proposes a simple, reliable, and economical machine-learning-based fault classifier for induction motors. The method analyzes stator currents, motor speed, torque, and three-phase voltage supply to classify some faults resulting from high amplitude and zeros of voltage. A case study of an air handling unit with a 2 hp, 4-pole, 50 Hz three-phase induction motor was modelled and tested with the proposed dataset using various classifiers, including decision trees, logistic regression, discriminant analysis, Naive Bayes, Ensemble, and K-Nearest Neighbor (KNN).

Monitoring of public buildings via energy-efficient Z-Wave wireless sensors

Simultaneously managing energy consumption and ensuring a health environment in public shared buildings presents significant challenges. This study explores the use of advanced monitoring techniques and specific Z-Wave sensors to optimize building management systems. The focus is on maintaining air quality, minimizing energy usage, and achieving significant energy savings. Various sensor types are employed, and data collection and processing are performed through a central gateway. The methodology is tested in university buildings, demonstrating the potential for energy savings by dynamically interacting with HVAC systems based on occupancy levels. Effective monitoring and control strategies can contribute to improved energy efficiency and occupant well-being. In specific cases, most notably when the occupancy of rooms is low, energy consumption can be reduced by up to 70%.

Implementation of the K-Nearest Neighbor Algorithm to Predict Air Pollution

Air pollution is a serious issue that impacts air quality and human health. In this study, the K-Nearest Neighbor (KNN) algorithm is applied using Rapidminer software to predict air pollution levels. The research aims to predict air pollution levels based on various air quality parameters such as particulates, PM10, PM2.5, CO, NO2, SO2, and O3. By implementing the K-Nearest Neighbor algorithm in Rapidminer, the predicted values for air pollution data resulted in an accuracy of 93.94%. This study concludes that employing the K-Nearest Neighbor algorithm using Rapidminer software can be an effective method for predicting air pollution levels. With a strong accuracy rate of 93.94%, this can have a positive impact on both human health and the environment. The predictive model developed can aid decision-making and enhance awareness among the public regarding the importance of maintaining air quality management.