Nowadays, as water pollution is increasing from agricultural sectors due to nitrogen and phosphorus, phycoremediation is used to remove nutrients with microalgae. In this paper, a mathematical model is developed to use the extended Monod model to analyze the growth of microalgae that have adsorbed nutrients by considering various flow velocities and levels of nitrogen as effects of biomass concentration. This model has been calculated with the numerical finite difference method using the Saulyev technique. Numerical simulations show results for various scenarios with flow velocities and levels of nitrogen that affect the growth of microalgae.

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 (CO2) 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 CO2 concentration can be effectively quantified as a proxy for indoor air quality within hospital environments. These calculated CO2 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.

Salinity intrusion problems pose hazards for a river as well as affecting human health and agriculture. The problem of saltwater intrusion in the Chao Phraya River in Bangkok, Thailand, and its vicinity is a complex issue with several contributing factors. There are three methods to measure or predict the salinity in a river. First, the sampling water method by monitoring stations has been used to collect the actual data. Second, a mathematical model is introduced to simulate the salinity in a river. Third, a forecasting method is used to predict the salinity in a river. If there is an early warning system to warn the Metropolitan Waterworks Authority (MWA) about the harmful levels of the saltwater intrusion in the Chao Phraya River, such as an early warning of 2-3 days, it can help them have time to prevent themselves. In this research, an early warning system to warn the MWA about the harmful levels of the saltwater intrusion in the Chao Phraya River is proposed. The Samlae Raw Water Pumping Station, located in Ban Krachaeng Subdistrict, Mueang District, Pathum Thani Province, is under the care of the MWA. It is the starting point for receiving raw water from the Chao Phraya River on the east side of the canal before entering the production process and being distributed to households in the Bangkok metropolitan area 100 percent. The saltwater intrusion in the Chao Phraya River data at Samlae Raw Water Pumping Station, for 4 months, from March 25, 2021, to July 31, 2021, was collected. A proposed early warning system for the harmful levels of saltwater intrusion was developed using the k-nearest neighbors machine learning algorithm. As the results show, the proposed technique gives an acceptable prediction for the earliest warning by 2-7 days.

In environmental research, challenges with water contamination assessment are generally prevalent. Through data collection, pollution levels in a system may be determined. This is quite challenging and involved; the measurements of what was measured vary from one point to another in every location. The governing equations for a uniform flow pollution dispersion model are used in water quality modeling. The advection-diffusion-reaction equation used in water quality model-ing for a uniform flow stream is a stable pollution dispersion model. This study presents a one-dimensional mathematical model for measuring stream water quality by collocation higher order Legendre polynomial functions. A water pol-lutant concentration can be approximated using the collocation method. A related water quality quantification method may also be employed with the suggested mathematical simulation to approximate the solution.

The problem of wastewater from shrimp farming affects the environment, both in terms of wastewater discharge and soil deterioration. Wastewater management is also quite expensive for the production costs of shrimp farmers. Therefore, the approach to using shrimp farming technology in closed-system farms is proposed, which reduces wastewater discharge into the environment and reduces the cost of wastewater treatment for farmers. This research presents a simple mathematical model for assessing water quality in such closed-system shrimp farms. The method for determining various parameters for determining the mathematical model is presented. The model solution is estimated by the Runge-Kutta method of the fourth order. This research simulates the situation to compare the different parameter values in each situation, which affect the level of water quality in closed-system shrimp farms at different times. The research found that the initial water quality, the rate of chemical reaction of pollutants, the rate of pollution formation, the rate of pollution decomposition, the rate of decrease in pollution concentration due to water circulation between the farm and the water treatment pond, and time all affect water quality. The results from the calculation can help closed-system shrimp farmers know the trend of pollution concentration changes in closed-system shrimp farms in order to find ways to develop techniques for improving water quality.

Uneven sediment transport is a major cause of coastal erosion. Using groin structures is one method to help slow the outflow of sediment from the shoreline. Studying coastal behavior and forecasting future shoreline changes are crucial for managing and assessing the viability of remediation strategies. This research presents simulations of shoreline evolution with a single groin structure using two different methods, such as mathematical modeling and an alternative machine learning. A mathematical model is a representation of a real-world shoreline evolution phenomenon using partial differential equations. A machine learning algorithm is designed to learn patterns and relationships directly from real data. In this research, an alternative machine learning algorithm is designed to learn patterns and relationships directly from mathematical simulation data and let the machine make a decision in a situation that it has never learned before. For mathematical modeling, we introduced a one-dimensional model to predict the shoreline evolution. The initial and the boundary conditions with related parameter settings are introduced. The Saulyev finite difference method is used to obtain the approximated solution. An alternative machine learning algorithm for unexpected shoreline evolution prediction is also proposed. For alternative machine learning simulations, we identified six suitable features for the training dataset and developed an alternative K-nearest neighbor algorithm. It provides a way of predicting the evolution of the shoreline with a single groin structure. Additionally, an exact solution in an ideal scenario is used to test the precision of the simulation as well. The results show that the Saulyev technique outperforms an alternative K-nearest neighbor algorithm due to the lower root mean square error value. Both results of them are closed together. According to the research, mathematical modeling outperforms the KNN regression technique in terms of computational effectiveness during time periods of 0.5, 1, 5, 10, 15, and 20 years. Based on the modeling configuration and parameter simplicity, the KNN algorithm is still a good option for non-expert users.

Bangkok frequently experiences concerning levels of air pollution, particularly during its dry season. This pollution is primarily driven by emissions from vehicles, agricultural burning in nearby regions, industrial activities, and construction dust, all exacerbated by unfavorable weather conditions that trap pollutants. Samphanthawong District, especially Yaowarat, consistently grapples with severe PM2.5 pollution during this period. Our research emphasizes the critical role of meteorological factors, such as wind speed and natural ventilation, in dispersing urban pollutants. While the simulation model proved stable and its computational results were not significantly affected by the chosen time step, it highlighted a major issue: PM2.5 concentrations in Samphanthawong decrease very slowly due to light winds and inadequate airflow. Therefore, to effectively manage air pollution in this area, additional measures are needed to improve ventilation or directly reduce PM2.5 sources, as natural factors alone cannot achieve rapid pollutant reduction.

Air pollution, particularly particulate matter smaller than 2.5 microns (PM2.5), has grown to be a serious issue that has an impact on people's health, especially the respiratory system. There are several studies that have found that the level of PM2.5 in the Bangkok region is high, as is how it affects individuals with respiratory illnesses. In this research, a numerical simulation of PM2.5 concentration was performed using a zero-dimensional model of PM2.5 measurement due to the daily vehicle density in Bangkok. It is evident that the wind speed and daily vehicle density have an impact on the simulated PM2.5 concentration in Bangkok. The daily density of vehicles greatly influences PM2.5 emissions. Wind speed was measured in this experiment. The hourly vehicle density in Bangkok, which was represented by calculating functions for wind speed and PM2.5 emission rate, is what produces the computed PM2.5 emission rate. The simulation includes three 24-hour scenarios: low vehicle density with medium wind speed, high vehicle density with low wind speed, and medium vehicle density with high wind speed. All of the models indicated that the PM2.5 level would drop as wind speed increased and vehicle density decreased. The daily vehicle density and wind speed are two factors that affect the PM2.5 level. Focusing, especially on wind speed, will not always lead to PM2.5 reductions. However, daily vehicle density also has a significant role in PM2.5 management. Wind speed and vehicle density influence PM2.5 concentrations, with three scenarios demonstrating that higher wind speed and lower vehicle density reduce PM2.5 levels. While wind speed helps to reduce PM2.5 levels, vehicle density also has a substantial impact on emissions. Managing PM2.5 requires addressing both daily vehicle density and wind speed, as focusing on only wind speed may not always result in reductions.

Groundwater pollution monitoring is critical for preserving drinking water quality. If a landfill is to be established, the potential impact on groundwater quality must be assessed, which can be done with a mathematical model. This work proposes a long-term assessment of groundwater quality using a heterogeneous soil model. Two numerical models are presented by using one-dimensional advection-diffusion equation. The standard forward-time and Centered-space finite differences method is employed to estimate the concentration of Contaminants in the groundwater within the nearby region. The concentration is also estimated with the fourth-order Runge-Kutta method. A comparison is conducted between an FTCS and the approximate solutions. Both numerical methods produce an accurate approximate solution. However, the fourth-order Runge-Kutta approach achieves a higher accuracy than the conventional method.

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 decisionmonitoring 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.

The development of a more effective model and the prediction of trends in shorelines were the two goals of this study. For simulations of shoreline evolution utilizing the straight twin groin structure, we used two mathematical models. A one-dimensional evolution model makes up the initial model. The first model is transformed into a nondimensional evolution model in the second model. We propose a method for transforming one-dimensional models into nondimensional models, that involves creating initial and boundary conditions for each model. The forward time centered space (FTCS) technique and the Saulyev finite difference technique were applied to approximately represent shoreline evolution each year. Their simulation results demonstrate that when the engineering structure was built on the nearby shorelines, shoreline evolution accelerated annually. As the Saulyev finite difference technique is not restricted by the stability conditions, it produces better simulations.

Beach erosion is a process that results in changes to the materials of a shoreline, with erosion being the removal of material from the shoreline more than its addition. Beach erosion on the shorelines causes loss of landforms and a reduction in size, leading to the need for the development of various structures to mitigate beach erosion. Groin is one of the commonly utilized structures for coastal erosion prevention, and groins of various shapes and forms have been developed to minimize beach erosion to the greatest extent possible. We have focused on assessing the impacts of I -head and T -head groin structures on shoreline evolution, approximated through a shoreline evolution model. Various techniques for setting initial conditions and boundary conditions have been discussed. Additionally, we have explored the structural impacts of these two groin types. We considered the average wave crest impact angle obtained from a wave crest impact model on both the left and right sides of the shoreline, differing over a span of four wavelengths. To estimate shoreline evolution for each year, we employed traditional forward time -centered space techniques and the unconditionally stable Saulyev finite differential techniques. The results of shoreline evolution calculations for both groin structures were found to be consistent across the four cases of the wave crest impact model.

The problem of particulate matter with a diameter of less than 2.5–10 microns, such as PM2.5–PM10 in Bangkok, affects the health of people because there are small particles that can penetrate deep into the alveoli. If there is an early warning system to warn people about the harmful levels of PM2.5 in Bangkok, such as an early warning of 2-3 days, it can help the people have time to prevent themselves. In this research, an early warning system to warn people about the harmful levels of PM2.5 in Bangkok is proposed. The air quality data of the Bang Khun Tian station, Bangkok, for 2 months, from December 1, 2020, to January 31, 2021, were selected because the area is an air-quality-worrying area. A proposed early warning system for the harmful levels of PM2.5 around Bang Khun Tian, Bangkok, was developed using the k-nearest neighbors machine learning algorithm. As the results show, the proposed technique gives an agreeable prediction for the earliest warning by 4 days.

In Chiang Mai, Thailand, the air pollution issue caused by atmospheric particulate matter with a diameter of less than 2.5 μm, or PM2.5, has been identified as an ongoing crisis. PM2.5 not only has a direct impact on people's health and way of life, but it also has a negative impact on the national economy. Residents in such PM2.5-polluted locations are particularly susceptible to respiratory diseases, skin diseases, inflammatory eye diseases, and cardiovascular problems. As a result, this study is going to analyze PM2.5 data using the k-nearest neighbors machine learning algorithm as a guideline to warn people, particularly in Changpuek, Chiang Mai, Thailand, to handle the PM2.5 characterization problem.

Every day, a large number of patients visit the facility, creating a serious infectious transmission problem that might infect patients with respiratory infectious illnesses in outpatient rooms, putting their health at risk. TB, COVID-19, MERS, and SARS are all significant infectious diseases that are transmitted by the air or aerosol via coughing, spitting, sneezing, speaking, or wounds. COVID-19, tuberculosis, MERS, and SARS are all hazards, and the probability of a serious disease increasing the number of people admitted to the hospital. We should also be informed about how patients in the outpatient room are managed. When the number of patients in each room changes over time, it is challenging to measure and manage carbon dioxide in a hospital with a ventilation system. We should also be informed about the management of patients with these conditions. This research investigates the mathematical modeling of carbon dioxide concentration measurement and the risk assessment of airborne infection in an outpatient room with a ventilation system, while the number of patients in each room changes over time. As a result, efficient air quality monitoring, such as carbon dioxide (CO2) concentrations, is required to monitor and decrease the possibility of contaminated air. It is indeed difficult to measure and manage carbon dioxide in a hospital with a ventilation system when the number of patients in each room changes over time. This research provided a risk model of airborne transmission and vaccination effectiveness in an outpatient room with a ventilation system. When the number of people and the rate of ventilation change, the model modifies the carbon dioxide concentration. To approximate the model solution, the fourth-order Runge-Kutta technique is used. In the presented simulations, there are several scenarios for improving air quality. The proposed approach balances the number of people allowed to stay in the room with the capacity of the air ventilation system in the air quality management process. As can be seen, the risk of infection is dependent on the number of people present, the rate of ventilation, and the efficacy of each type of vaccination. If there is a public vaccination database system, this research may be used to help control the risk of airborne infection to the desired level.

The airborne infection is spread through the air, especially in indoor spaces. Indoor spaces present a significant risk of infection, although this may be reduced by employing all methodologies to prevent infection via aerosols. TB, COVID-19, MERS, and SARS are all hazardous communicable diseases that spread from person to person through air or aerosol in a variety of ways, including coughing, spitting, sneezing, speaking, or through wounds. COVID-19, TB, MERS, and SARS are all risks, and the elevated risk of a lethal infection leads more patients to become infected in indoor spaces. We should also be notified about the recognition and prevention of these diseases. As a result, proper air quality control, such as carbon dioxide (CO2) concentrations, is needed to monitor and reduce the potential for infected air. It is difficult to assess and monitor carbon dioxide in a room with a ventilation system where the number of people in each room changes frequently. In this research, the numerical model of carbon dioxide concentration measurement in a space with an opened ventilation system is proposed. The model is used to calculate the concentration of carbon dioxide at any time when the number of persons and the rate of ventilation vary. The standard fourth-order Runge-Kutta method is employed to approximate the model solution. There are many scenarios for improving air quality in the suggested simulations. The proposed model for the air quality control system achieves a balance between the number of persons permitted to remain in the room and the air ventilation system's efficiency.

Air pollution is the release of pollutants into the atmosphere that are harmful to human health and the ecosystem as a whole.Initially, urban air pollution was considered to be a regional problem caused largely by domestic heating and industrial emissions, both of which are now well under control.The building's canyon structure and the geometry of the streets in urban areas are street canyons.Side Street connects the two sides of the street, which are made up of portions of buildings.Street canyons, which are urban streets bordered on both sides by structures, have shown high levels of pollution.Pedestrians, cyclists, vehicles, and residents will most likely be surrounded by pollution concentrations higher than current air quality limits on these walkways.The research is focused on detecting air pollution in a street canyon.There will be an introduction to a transient two-dimensional advection-diffusion equation.A two-dimensional vertically averaged air pollution measurement model is utilized to characterize the air pollution concentration along a street canyon.The model delivers the pollutant concentration in the air each and every time.The model's air pollutant concentration is approximated using a finite difference technique.An approximation approach to open and closed boundary conditions is proposed.Wind direction effects are also modelled.The suggested numerical approaches performed well in producing a high level of agreement.Simple explicit schemes have the benefit of being simple to compute.These techniques may be used to simulate air pollution measurements in a variety of street canyons.

Leachate from a landfill has the potential to pollute groundwater. Mathematical models are often used to describe groundwater circulation, which can help planners choose suitable places for a landfill. The construction of landfills may have an influence on the groundwater supplies of adjacent settlements. As a result, impact assessments must be completed prior to the start of the project. This study used three mathematical models to simulate groundwater contamination concentrations with variable flow velocities. The first model is a groundwater flow model that estimates the hydraulic head of groundwater flow. The second model is used to calculate the groundwater flow velocity. The third model is a dispersion model, in which the governing factor is the two-dimensional dispersion equation, which yields the groundwater pollution concentration. In the simulation phase, we use the finite difference method for all models. This investigation considers the effects of industrial water usage, such as pumping water up to the surface, on groundwater flow. This research focuses on the effects of pumping water to adjacent settlements on groundwater flow and the quality of the water produced. Construction of a landfill should always be done in an area with a low hydraulic head. Pumping wells near landfills may also assist to reduce pollution in household groundwater. As a result, the calculated water quality in the faraway area was improved while groundwater volume and flow velocity were preserved.

In many countries, shrimp is one of the most valuable export commodities. Shrimp farming raises a number of issues, including shrimp waste contamination, shrimp feed residues, and biochemical reactions in the shrimp pond. In this research, mathematical models were utilized to analyze the water quality in shrimp ponds and wastewater treatment ponds for circulation systems, with BOD serving as a significant indication of water quality. In the circulation system, two separate ponds were investigated: the shrimp pond and the wastewater treatment pond. The shrimp pond was tested for pollutant levels generated by shrimp excretion, shrimp feed residues, and biochemical reactions. A Chaipattana low-speed surface aerator was used to treat the shrimp pond pollutants, and some of the waste was drained to the next pond. The pollutant levels in the treatment pond were investigated. This pond is polluted by sewage from the shrimp pond as well as biological reactions. Lower-efficiency aerators treat the contaminants in the treatment pond, and part of the waste is transferred to the next pond. The advection equation is being used to describe the pollutant concentration in two ponds, and Runge-Kutta order 4 is also being used to determine the approximated solution to the problem. The results of the mathematical model are presented in graphs and tables comparing the pollutant concentrations in many cases. The last section shows an example of wastewater treatment by aerator in a shrimp pond. It was found to reduce the number of days needed for wastewater treatment. The water quality could generate shrimp in this condition, but the water quality could not grow shrimp if the aerator was not turned on the first day of shrimp farming and then turned on the next day. On the first day of shrimp farming, the aerator should not be turned off since the pollutant concentration would be high, making wastewater treatment difficult the next day. In addition, the research showed a maximum five-day reduction in wastewater treatment time (last days of the month). When wastewater is treated every other day, every three days, or every five days, the pollutant concentration must be lower than the minimum necessary for shrimp farming. It can also be used to reduce the cost of water treatment by saving energy.

A vast number of patients visit the facility every day, causing a major air pollution issue that may pose a risk of exposure of respiratory infectious diseases in outpatient rooms and harm human health. TB, COVID-19, MERS, and SARS are dangerous communicable diseases that transmit from person to person through the air or aerosol in a variety of forms, such as coughing, spitting, sneezing, speaking, or through wounds. COVID-19, TB, MERS and SARS are risks and the chances of success toward lethal infection make more patients ill in the hospital. We should also be notified of the care and control of these diseases. As a result, effective air quality monitoring is needed to monitor and reduce the potential for infected air, such as carbon dioxide (CO2) concentrations. Measuring and controlling carbon dioxide in a hospital with a ventilation system where the number of patients in each room varies in time is challenging. In this research, the numerical model of carbon dioxide concentration measurement in a space with an opened ventilation system is proposed. The model sets the concentration of carbon dioxide at any point when the number of people and the rate of ventilation varies. The classical fourth-order Runge-Kutta method is employed to approximate the model solution. There are many cases of scenarios for improving air quality in the proposed simulations. In the air quality management process, the proposed model provides a balance between the number of persons allowed to stay in the room and the capacity of the air ventilation system.