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Three-dimensional numerical modeling for assessing airborne infection risk in hospital waiting rooms with various ventilation approaches
(2026-06-01)
Suebyat, Kewalee
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Pochai, Nopparat
;
Sooknum, Jenjira
;
Oyjinda, Pravitra
Airborne infectious diseases, such as COVID-19, TB, MERS, and SARS, constitute a profound threat to public health and quality of life. These pathogens are transmitted primarily via atmospheric particles, especially within clinical environments, where they often circulate. Effective ventilation controls to mitigate pathogens and air pollution are thus essential for reducing hospital-based transmission of airborne infections. The purpose of this research is to assess the risk of airborne infectious diseases within a hospital in Thailand using a mathematical model. Specifically, the finite difference technique is employed to estimate carbon dioxide (CO<inf>2</inf>) concentration as a proxy for indoor air quality to indicate and assess the risk of airborne infectious diseases. The hospital layout is categorized into waiting areas and circulation areas with disparate occupant densities. Three simulation scenarios are conducted, accounting for variations in ventilation rates and architectural structure of hospitals. The results of this research demonstrate that CO<inf>2</inf> concentration can be effectively quantified as a proxy for indoor air quality within hospital environments. These calculated CO<inf>2</inf> levels are subsequently used to model the risk of airborne infection at a hospital, providing a robust framework for assessing this risk. Crucially, by integrating ventilation dynamics that reflect the physical constraints and structure of the hospital, this research enables precise evaluation of infection risks. The findings indicate that ventilation control can reduce the incidence of airborne infection, with significant practical utility in real-world clinical settings.
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A Mathematical Model of Hazardous Smoke Emission Control Considering Primary and Secondary Pollution Concentrations
(2024-01-01)
Oyjinda, Pravitra
;
Pochai, Nopparat
The major cause of air pollution concerns is industrial development, which has an influence on human health, human lifestyle, and the environment around an industrial zone. Air quality management assists in the control and improvement of air pollution in order to lower the quantity of numerous air contaminants. The purpose of this investigation is to examine various air pollution emission control and quality control mechanisms. Several atmospheric diffusion equations are used to solve numerous air pollution concentration indices that can represent how air pollutants disperse in the atmosphere. Primary and secondary pollutant concentrations are approximated by using the finite difference technique. Monitoring points are installed for checking the air pollutant concentration levels of sulfur dioxide (SO2), sulfur trioxide (SO3), and sulfuric acid (H2SO4). Suitable emission control scenarios are proposed. The approximate solutions of air pollution control simulations at each monitoring point are compared. The air quality standard is also used to compare the results of the experiments. There are suitable emission control scenarios presented. At each monitoring location, the approximate solutions of air pollution control models are compared. The proposed strategy selects a good decision-monitoring point. According to the research, an observation area should be near an industrial area. The chosen monitoring location provides the most effective overall air quality for emission control techniques around industry and residential areas. As a result, the location of collecting for each monitoring station influences the air quality of the air pollution control schedule.
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A mathematical model for the risk analysis of airborne infectious disease in an outpatient room with personal classification factor
(2020-12-01)
Suebyat, Kewalee
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Oyjinda, Pravitra
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Konglok, Sureerat A.
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Pochai, Nopparat
Every day, a large number of people will use a hospital, creating a main air quality problem which may mean the risk of airborne infectious disease contamination in outpatient rooms, and affects human health. TB, COVID-19, MERS, and SARS are a hazardous communicable disease which are spread from person to person through the air or the aerosol in different ways, such as through coughing, spitting, sneezing, speaking, or through wounds. US scientists in the laboratory have shown that the virus can live in an aerosol and remain infectious for at least 3 hours. A new human coronavirus now known as the serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly known as HCoV-19) emerged in late 2019 in Wuhan, China, and is now triggering a pandemic. COVID-19, TB, MERS and SARS-threats and opportunities progress against deadly infection make more people sick in the hospital. Therefore, we should be aware of the care and control of these diseases. Consequently, good air quality management is required to control and reduce possible infected air, such as carbon dioxide (CO2) concentration. In this research, a mathematical model for the risk analysis of airborne infectious disease in an outpatient room is proposed. Not only considering one type of person but also in this research, people are considered according to personal classifications. There are 4 types-patient, relative, worker, and outsider, staying in an outpatient room, which is in accordance with the real world. Air quality control manipulations are simulated using the inlet and outlet ventilation rates adjustment under the condition of a number of surrounding people with a personal classified factor. The fourth-order Runge-Kutta (RK4) is used to approximate the model solution. The proposed numerical model can be used to describe the dynamical dispersion of airborne infectious disease in an outpatient room. The results of the model are satisfactory, and it will be able to control airborne disease in more complicated structures.
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Numerical Simulation of an Air Pollution Model on Industrial Areas by Considering the Influence of Multiple Point Sources
(2019-01-01)
Oyjinda, Pravitra
;
Pochai, Nopparat
A numerical simulation on a two-dimensional atmospheric diffusion equation of an air pollution measurement model is proposed. The considered area is separated into two parts that are an industrial zone and an urban zone. In this research, the air pollution measurement by releasing the pollutant from multiple point sources above an industrial zone to the other area is simulated. The governing partial differential equation of air pollutant concentration is approximated by using a finite difference technique. The approximate solutions of the air pollutant concentration on both areas are compared. The air pollutant concentration levels influenced by multiple point sources are also analyzed.
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Numerical Simulation to Air Pollution Emission Control near an Industrial Zone
(2017-01-01)
Oyjinda, Pravitra
;
Pochai, Nopparat
A rapid industrial development causes several environment pollution problems. One of the main problems is air pollution, which affects human health and the environment. The consideration of an air pollutant has to focus on a polluted source. An industrial factory is an important reason that releases the air pollutant into the atmosphere. Thus a mathematical model, an atmospheric diffusion model, is used to estimate air quality that can be used to describe the sulfur dioxide dispersion. In this research, numerical simulations to air pollution measurement near industrial zone are proposed. The air pollution control strategies are simulated to achieve desired pollutant concentration levels. The monitoring points are installed to detect the air pollution concentration data. The numerical experiment of air pollution consisted of different situations such as normal and controlled emissions. The air pollutant concentration is approximated by using an explicit finite difference technique. The solutions of calculated air pollutant concentration in each controlled and uncontrolled point source at the monitoring points are compared. The air pollutant concentration levels for each monitoring point are controlled to be at or below the national air quality standard near industrial zone index.

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