GIS-Based Analysis of Volatile Organic Compounds in Bucheon, Korea, Using Mobile Laboratory and Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry Methods.

Recently, volatile organic compounds (VOCs) have been shown to act as precursors of secondary organic particles that react with ultraviolet rays in the atmosphere and contribute to photochemical smog, global warming, odor, and human health risks, highlighting the importance of VOC management. In this study, we measured VOC concentrations in various contexts including industrial and residential areas of Bucheon, Korea, through mobile laboratory and proton-transfer-reaction time-of-flight mass spectrometry methods to determine winter VOC concentrations and visualized the data based on spatial information. Regional characteristics, temperature/humidity, atmospheric conditions, wind speed, traffic volume, etc., during the measurement period of the study site were comprehensively reviewed. For this purpose, global information system (GIS)-based air quality data and various environmental variables were comprehensively reviewed to assess spatial and temporal concentrations in three dimensions rather than in tables and graphs. Among VOCs, the levels of toluene, methanol, and n + i-butene were relatively high, with average concentrations of 48.3 ± 67.2, 34.4 ± 102.7, and 32.6 ± 57.7 ppb, respectively, at the end of the working day. The highest concentrations occurred near the Ojeong Industrial Complex. Mobile pollution sources are also a major driver of VOCs, highlighting the necessity of comprehensively reviewing traffic variables such as road level, estimated traffic volume, and average speed when identifying hotspots of air pollution. GIS-based visualization analysis techniques will improve the efficiency of air quality management.

BT100, a three-in-one, multipurpose disinfecting, deodorizing, and air-cleaning solution with an effective, gradual, and continuous gaseous chlorine dioxide-releasing substance.

Aerosols carrying viruses that are released from the oral cavity of infected individuals are the primary, if not the only, means of transmission during viral respiratory disease epidemics. This makes crowded rooms and tiny, enclosed public areas like bathrooms prime environments for the transmission of diseases. Volatile organic compounds (VOCs) and formaldehyde are two contaminants that pose serious threats to human health and well-being in indoor environments. The varied disinfectant properties of chlorine dioxide (ClO2) make it a key player in treating a range of air quality issues. To balance effectiveness and safety, however, the careful application of chlorine dioxide is essential to achieving the best results in air quality while preserving human health and well-being. This study explores the many functions of chlorine dioxide, including the prevention of the spread of viruses, the elimination of harmful gases like ammonia and hydrogen sulfide, and its effects on formaldehyde and total volatile organic compounds (TVOCs) in indoor environments using BT100. The results indicate a reduction of 98.5%, 81.01%, 62.22%, 46.5%, and 63.84% in minimizing aerosolized viruses, ammonia, and hydrogen sulfide gas in addition to formaldehyde and total volatile organic compounds.

The composition of the bacterial communities collected from the PM10 samples inside the Seoul subway and railway station.

Health implications of indoor air quality (IAQ) have drawn more attention since the COVID epidemic. There are many different kinds of studies done on how IAQ affects people's well-being. There hasn't been much research that looks at the microbiological composition of the aerosol in subway transit systems. In this work, for the first time, we examined the aerosol bacterial abundance, diversity, and composition in the microbiome of the Seoul subway and train stations using DNA isolated from the PM10 samples from each station (three subway and two KTX stations). The average PM10 mass concentration collected on the respective platform was 41.862 µg/m3, with the highest average value of 45.95 µg/m3 and the lowest of 39.25 µg/m3. The bacterial microbiomes mainly constituted bacterial species of soil and environmental origin (e.g., Acinetobacter, Brevundimonas, Lysinibacillus, Clostridiodes) with fewer from human sources (Flaviflexus, Staphylococcus). This study highlights the relationship between microbiome diversity and PM10 mass concentration contributed by outdoor air and commuters in South Korea's subway and train stations. This study gives insights into the microbiome diversity, the source, and the susceptibility of public transports in disease spreading.

Characterization of bacterial species and antibiotic resistance observed in Seoul, South Korea's popular Gangnam-gu area.

Public transportation facilities, especially road crossings, which raise the pathogenic potential of urban environments, are the most conducive places for the transfer of germs between people and the environment. It is necessary to study the variety of the microbiome and describe its unique characteristics to comprehend these relationships. In this investigation, we used 16 S rRNA gene sample sequencing to examine the biological constituents and inhalable, thoracic, and alveolar particles in aerosol samples collected from busy areas in the Gangnam-gu district of the Seoul metropolitan area using a mobile vehicle. We also conducted a comparison analysis of these findings with the previously published data and tested for antibiotic resistance to determine the distribution of bacteria related to the human microbiome and the environment. Actinobacteria, Cyanobacteria, Bacteriodetes, Proteobacteria, and Firmicutes were the top five phyla in the bacterial 16 S rRNA libraries, accounting for >90 % of all readings across all examined locations. The most prevalent classes among the 12 found bacterial classes were Bacilli (45.812 %), Gammaproteobacteria (25.238 %), Tissierellia (13.078 %), Clostridia (5.697 %), and Alphaproteobacteria (5.142 %). The data acquired offer useful information on the variety of bacterial communities and their resistance to antibiotic drugs on the streets of Gangnam-gu, one of the most significant social centers in the Seoul metropolitan area. This work emphasizes the relevance of biological particles and particulate matter in the air, and it suggests more research is needed to perform biological characterization of the ambient particulate matter.

Removal of Particulate Matter by a Non-Powered Brush Filter Using Electrostatic Forces.

In urban areas, a major source of harmful particulate matter is generated by vehicles. In particular, bus stops, where people often stay for public transportation, generate high concentrations of particulate matter compared to the general atmosphere. In this study, a non-powered type brush filter that generates electrostatic force without using a separate power source was developed to manage the concentration of particulate matter exposed at bus stops, and the removal performance of particulate matter was evaluated. The dust collection performance of the non-motorized brush filter varied by material, with particle removal efficiencies of 82.1 ± 3.4, 76.1 ± 4.7, and 73.7 ± 4.5% for horse hair, nylon, and stainless steel, respectively. In conditions without the fan running to see the effect of airflow, the particle removal efficiency was relatively low at 58.2 ± 8.4, 53.6 ± 9.2, and 58.0 ± 7.3%. Then, to check the dust collection performance according to the density, the number of brushes was increased to densify the density, and the horse hair, nylon, and stainless steel brush filters showed a maximum dust collection performance of 89.6 ± 2.2, 88.3 ± 3.2, and 82.1 ± 3.8%, respectively. To determine the replacement cycle of the non-powered brush filter, the particulate removal performance was initially 88.0 ± 3.2% when five horse hair brushes were used. Over time, particulate matter tended to gradually decrease, but after a period of time, particulate matter tended to increase again. The purpose of this study is to evaluate the particulate matter removal performance using a brush filter that generates electrostatic force without a separate power source. This study's brush filter is expected to solve the maintenance problems caused by the purchase and frequent replacement of expensive HEPA filters that occur with existing abatement devices, and the ozone problems caused by abatement devices that use high voltages.

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.

Evaluation of Health Economic Loss Due to Particulate Matter Pollution in the Seoul Subway, South Korea.

Evaluating an illness's economic impact is critical for developing and executing appropriate policies. South Korea has mandatory national health insurance in the form of NHIS that provides propitious conditions for assessing the national financial burden of illnesses. The purpose of our study is to provide a comprehensive assessment of the economic impact of PM2.5 exposure in the subway and a comparative analysis of cause-specific mortality outcomes based on the prevalent health-risk assessment of the health effect endpoints (chronic obstructive pulmonary disease (COPD), asthma, and ischemic heart disease (IHD)). We used the National Health Insurance database to calculate the healthcare services provided to health-effect endpoints, with at least one primary diagnosis in 2019. Direct costs associated with health aid or medicine, treatment, and indirect costs (calculated based on the productivity loss in health effect endpoint patients, transportation, and caregivers, including morbidity and mortality costs) were both considered. The total cost for the exposed population for these endpoints was estimated to be USD 437 million per year. Medical costs were the largest component (22.08%), followed by loss of productivity and premature death (15.93%) and other costs such as transport and caregiver costs (11.46%). The total incurred costs (per 1000 persons) were accounted to be USD 0.1771 million, USD 0.42 million, and USD 0.8678 million for COPD, Asthma, and IHD, respectively. Given that the economic burden will rise as the prevalence of these diseases rises, it is vital to adopt effective preventative and management methods strategies aimed at the appropriate population.

Fine Particulate Matter (PM2.5) Sources and Its Individual Contribution Estimation Using a Positive Matrix Factorization Model.

The effective management and regulation of fine particulate matter (PM2.5) is essential in the Republic of Korea, where PM2.5 concentrations are very high. To do this, however, it is necessary to identify sources of PM2.5 pollution and determine the contribution of each source using an acceptance model that includes variability in the chemical composition and physicochemical properties of PM2.5, which change according to its spatiotemporal characteristics. In this study, PM2.5 was measured using PMS-104 instruments at two monitoring stations in Bucheon City, Gyeonggi Province, from 22 April to 3 July 2020; the PM2.5 chemical composition was also analyzed. Sources of PM2.5 pollution were then identified and the quantitative contribution of each source to the pollutant mix was estimated using a positive matrix factorization (PMF) model. From the PMF analysis, secondary aerosols, coal-fired boilers, metal-processing facilities, motor vehicle exhaust, oil combustion residues, and soil-derived pollutants had average contribution rates of 5.73 µg/m3, 3.11 µg/m3, 2.14 µg/m3, 1.94 µg/m3, 1.87 µg/m3, and 1.47 µg/m3, respectively. The coefficient of determination (R2) was 0.87, indicating the reliability of the PMF model. Conditional probability function plots showed that most of the air pollutants came from areas where PM2.5-emitting facilities are concentrated and highways are present. Pollution sources with high contribution rates should be actively regulated and their management prioritized. Additionally, because automobiles are the leading source of artificially-derived PM2.5, their effective control and management is necessary.

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms.

Particulate matter (PM) of sizes less than 10 µm (PM10) and 2.5 µm (PM2.5) found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future PM10 and PM2.5 on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed PM10 and PM2.5 forecasting framework is demonstrated using comparisons with the different existing deep learning models.

Study on the Ventilation Method to Maintain the PM10 Concentration in a Subway Cabin below 35 µg/m3.

The city of Seoul will limit the maximum particulate matter (PM10) concentration to ≤35 µg/m3 (from 2024). Herein, a numerical parametric study was conducted on the PM removal efficiency of the heating, ventilation, and air conditioning (HVAC) filters installed in the ceiling of subway cabins. The PM10 concentration distribution was explored according to the flow rate and flow rate ratio of the air introduced into the cabin. Under the current ventilation conditions of the subway train HVAC system, the PM10 concentration was highest in the cabin central area where exhaust outlets are located and decreased toward both ends of the cabin. The indoor airflow was improved and the PM10 concentration was reduced by increasing the flow rate of the supplied air at both ends of the cabin while decreasing it in the central area. It was found that the strengthened PM10 concentration criterion of Seoul can be met by increasing the ventilation flow rate to 700 CMH (currently, 500 CMH) and the filter efficiency to 85% (currently, 70%) while maintaining the current flow rate ratio. These results are expected to be used as important reference data for reducing the PM concentration in subway cabins and thereby improving indoor air quality.

Effects of Brush-Type Ionizer Materials on Virus Inactivation.

Many studies have found that bioaerosols are harmful to humans. In particular, infectious viruses, such as the virus that causes COVID-19, are increasing. Therefore, the research on methods for reducing bioaerosols is becoming progressively more important. The purpose of this study was to improve the existing electrostatic precipitator, which generates high concentrations of ozone, by reducing bioaerosols effectively without significant ozone production. A brush-type ionizer was studied as a replacement for the existing electrostatic precipitator. The study, which was conducted at the laboratory scale, determined the amounts of ions generated with different ionizer materials (carbon, copper, and stainless steel) and voltages (-1, -2, and -3 kV), as well as it compared the virus inactivation efficiency under the various conditions. As a result, about two million ions were produced when a voltage of -3 kV was applied to all of the materials, and 99.9 ± 0.2% and 98.8 ± 0.6% virus inactivation efficiencies were confirmed in the cases of carbon and copper, respectively. In addition, an assessment of the effect of flow velocity confirmed that the inactivation efficiency decreased as the flow velocity increased. However, the results for the flow velocities of 0.2 and 0.4 m/s had similar trends. Therefore, this system can be used with flow velocities up to 0.4 m/s.

Reducing PM10 and PM2.5 Concentrations in a Subway Station by Changing the Diffuser Arrangement.

According to the stringent regulations on particulate matter (PM) concentrations in Seoul, Korea, the PM10 and PM2.5 concentrations in subway stations must be maintained below 50 and 30 µg/m3, respectively, by 2024. Therefore, the PM concentrations in a subway station were analyzed considering air-conditioning diffuser arrangement and filtration efficiency, with the total ventilation flow rate of the station maintained constant. Dynamic analysis was performed under a worst-case scenario, wherein outdoor air was introduced through ground entrances and high-concentration dust (PM10, PM2.5) was introduced from stationary train cabins into the platforms through open platform screen doors (PSDs). Although the average PM concentrations were predicted to satisfy the reinforced criteria of Seoul under the existing operating conditions, the recommended limits were exceeded in certain local areas. To address this, the PM concentrations were predicted by changing the diffuser arrangement in the waiting room and maintaining the total ventilation flow rate constant. When the diffusers were placed near the waiting room walls, the PM10 and PM2.5 concentrations were reduced by approximately 10.5 and 5%, respectively, compared to the previous diffuser arrangement. Thus, the required PM concentration criteria were satisfied in nearly all areas of the target station, except for certain areas close to PSDs. The study findings can form the basis for improving the air quality of other subway stations.

Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview.

Several epidemiologic and toxicological studies have commonly viewed ambient fine particulate matter (PM2.5), defined as particles having an aerodynamic diameter of less than 2.5 µm, as a significant potential danger to human health. PM2.5 is mostly absorbed through the respiratory system, where it can infiltrate the lung alveoli and reach the bloodstream. In the respiratory system, reactive oxygen or nitrogen species (ROS, RNS) and oxidative stress stimulate the generation of mediators of pulmonary inflammation and begin or promote numerous illnesses. According to the most recent data, fine particulate matter, or PM2.5, is responsible for nearly 4 million deaths globally from cardiopulmonary illnesses such as heart disease, respiratory infections, chronic lung disease, cancers, preterm births, and other illnesses. There has been increased worry in recent years about the negative impacts of this worldwide danger. The causal associations between PM2.5 and human health, the toxic effects and potential mechanisms of PM2.5, and molecular pathways have been described in this review.

Human Health Impacts of Residential Radon Exposure: Updated Systematic Review and Meta-Analysis of Case-Control Studies.

This study investigated the impact of residential radon exposure on human cancers (i.e., lung cancer and childhood leukemia) through a systematic review and meta-analysis of case−control studies. A total of 9724 articles obtained from electronic databases were assessed; however, only 55 case−control studies were eligible after manually screening and eliminating unnecessary studies. The causal associations were addressed by determining the meta-analysis's estimated size effects (i.e., ORs/RRs) of the meta-analysis. Residential radon was revealed to significantly increase the incidence of lung cancer and childhood leukemia with pooled ORs of 1.38 [1.19; 1.60] (I2 = 90%; p < 0.00001) and 1.43 [1.19; 1.72] (I2 = 0% and p = 0.51), respectively. In addition, subgroup analyses were performed to reduce the heterogeneity of the initial meta-analyses. The results provided strong evidence that inhaling radon in the indoor environments is closely associated with the development of lung cancer and childhood leukemia in patients living in Europe and areas with high radon levels (≥100 Bq/m3).

Quantification of the Ecological Value of Railroad Development Areas Using Logistic Regression Analysis.

According to the national railway network construction plan, Investment in railways has increased due to the need for environmentally friendly transportation, and the rail network is expanding throughout South Korea. Railway projects should be evaluated using strategic environmental impact assessments. In the "Guidelines for the Construction of Environment-friendly Railways", seven priority headings that must be considered for railway projects are described. This guide notes that qualitative evaluation must be conducted during the survey process to reasonably predict impacts on the environment. However, quantitative evaluation with specific indicator values may also be necessary. In this study, independence analysis and logistic regression analysis were used to quantitatively evaluate railway environmental and ecological indicators. The results were used to develop a regression model reflecting seven indicators; biodiversity class, ecosystem type, vegetation conservation class, tree age class, ecological naturalness, presence of river ecosystems, and fragmented patch size. The fitness regression model showed 90.3% classification accuracy and the receiver operating curve (ROC) model fit was 88.6%. An environmental quality assessment map was prepared by classifying areas of environmental quality according to five grades. This is the first model for environmental and ecological evaluation of railway projects. Evaluation using the map showed that the railroad passes through areas with lower protection values compared to the results obtained using the national environmental evaluation map. Kappa analysis showed a low level of agreement between the two maps (kappa coefficient = 0.212). The results of this study can be applied to railway development project sites and may help to identify the best sites for the development of an environmentally friendly railway system.

A New Method of Removing Fine Particulates Using an Electrostatic Force.

Many studies have found that the concentration of fine particulates in the atmosphere has increased. In particular, when using the bus, the situation in which people are exposed to relatively high concentrations of fine particulates is increasing. The purpose of this study is to reduce exposure to these potentially harmful particulates by introducing open shelters at outdoor bus stops. In order to use it as an outdoor fine particulates reduction device, a brush filter using electrostatic force (EF) was used on an experimental scale and the generation of electrostatic force, according to the material, was examined. As electrostatic force was generated, the fine particulates collection performance was about 90% efficiency. In addition, it was confirmed that the efficiency of each particle size was improved by 57% through structural improvement. Finally, through experimentation, it was confirmed that the brush module can be used for about 70 days.

A UAV-Based Air Quality Evaluation Method for Determining Fugitive Emissions from a Quarry during the Railroad Life Cycle.

Gravel is used in railway infrastructure to reduce environmental impacts and noise, but gravel on tracks must be replaced continuously because it deforms due to wear and weathering. It is therefore necessary to review the entire railroad life cycle. In this study, an unmanned aerial vehicle (UAV) was used to measure resuspended dust over a wide area. The dust was generated from transport movements in relation to the operation of a quarry, which represents the first stage of the railway life cycle. The dust was measured at Gangwon-do quarry using a Sniffer4D module, which can provide measurements at 1 s intervals through a light scattering method and has high reliability (R2 = 0.95 for PM2.5, R2 = 0.88 for PM10). The hourly generation of fugitive dust was calculated as 2937.5 g/h for PM2.5 and 4293.2 g/h for PM10. The social cost of dust generation was calculated as KRW 36.59 billion. The amount of dust generated per hour at the quarry was ~12 times greater than that generated by the operation of a regulator as a maintenance vehicle, with the largest amount of fugitive dust generated by the washing-type vehicle. This is the first study to measure the amount of fugitive dust generated in real time at 1 s intervals by monitoring the first stage of the railroad life cycle over a wide area using a Sniffer4D module attached to a UAV. This method can be replicated for use in various studies.

Study on Characteristics of Particulate Matter Resuspension in School Classroom through Experiments Using a Simulation Chamber: Influence of Humidity.

Resuspension of particulate matter (PM) in classrooms, which increases the risk of negative impact on student health from exposure to PM, is influenced by humidity level in the indoor environment. The goal of this study is to investigate the properties of PM resuspension in accordance with relative humidity through classroom test chamber experiments. In actual classrooms, it is challenging to control factors influencing resuspension. Therefore, the classroom chamber that reflects the environment of elementary school classroom (e.g., structure, floor material) is used in this study. The humidity of the classroom chamber is adjusted to 35%, 55%, 75%, and 85% by placing it inside a real-size environmental chamber, which allows artificial control of climatic conditions. At the respective humidity conditions, PM resuspension concentration and resuspension factor caused by occupant walking across the classroom chamber are analyzed. The results show that both of the resuspension concentration and resuspension factor reveal a linear negative correlation to humidity increase. Furthermore, coefficient of determination (R2) indicating goodness-of-fit of the linear regression model between the resuspension concentration and humidity is 0.88 for PM10 and 0.93 for PM2.5. It implies that accuracy of the regression model for estimating PM10 and PM2.5 resuspension concentrations is 88% and 93%, respectively.

Influence of Chitosan-Based Carbon Dots on Astaxanthin Production of Green Alga Tetraselmis sp.

CDs are carbon fluorescent nanomaterials that have gained significant attention in recent years owing to their unique properties. In this work, we utilized a simple solution to produce CDs with func-tionalized amino groups via a one-step carbonization from a chitosan precursor. This simultaneous approach does not use special reagent for either the formation step or the amino-functionalization step of CDs. The as-prepared amino-functionalized CDs that possesses expected characteristics, such as nano-size distribution, monodispersible, high blue light emission, high absolute quantum yield of 5.52%, and functionalized amino groups on the surface. Furthermore, this work demonstrated the low cytotoxicity and high biocompatibility of the CDs, through the improvements in the astaxanthin production of alga Tetraselmis sp. (more than doubled (up to 0.044 mg/L), relative to the control). Thus, as-prepared CDs have promising properties not only for applications in bioimaging, drug delivery or sensors, but also as promoter in algal biorefinery.

Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities.

Particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM2.5 concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health. However, the performance of existing forecasting approaches relies on a considerable amount of historical sensor data, which is usually not available in practical situations due to hostile monitoring environments or newly installed equipment. Transfer learning (TL) provides a solution to the scant data problem, as it leverages the knowledge learned from well-measured subway stations to facilitate predictions on others. This paper presents a TL-based residual neural network framework for sequential forecast of health risk levels traced by subway platform PM2.5 levels. Experiments are conducted to investigate the potential of the proposed methodology under different data availability scenarios. The TL-framework outperforms the RNN structures with a determination coefficient (R2) improvement of 42.84%, and in comparison, to stand-alone models the prediction errors (RMSE) are reduced up to 40%. Additionally, the forecasted data by TL-framework under limited data scenario allowed the ventilation system to maintain IAQ at healthy levels, and reduced PM2.5 concentrations by 29.21% as compared to stand-alone network.