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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
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Sooknum, Jenjira
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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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Long-term insect surveillance and facility-level risk assessment to support integrated pest management in a fruit juice manufacturing facility
(2026-06-01)
Chantarapitak, Chaiamon
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Pumnuan, Jarongsak
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Chanpitak, Chaiwat
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Kramchote, Somsak
Long-term insect surveillance is a critical component of industrial pest management in food manufacturing facilities. This study assessed the effectiveness of an Integrated Pest Management (IPM) program implemented in a fruit juice factory in Pathum Thani, Thailand, from January 2023 to June 2025. Weekly monitoring using 28 insect light traps and sticky boards was conducted across the facility. Capture data were aggregated on a monthly basis. To reduce the confounding influence of seasonal variation, temporal trends were assessed using same-period half-year comparisons. Diptera, particularly fruit flies, was predominant (85.7%), followed by Hemiptera (including former Homoptera) (7.9%), Hymenoptera (1.7%), and Coleoptera (1.3%). Other insects occurred at very low frequencies, collectively accounting for < 1% of the total captures. Temporal analysis using same-period half-year comparisons showed reductions of −19.1%, −28.0%, and −12.6%, followed by stabilization in 2025, with a distinct peak in November 2023 suppressed by corrective measures such as fogging, light management, and door control. The response followed a stepwise IPM framework with intensified actions only when required. Spatial analysis showed higher captures in Building 2 and the staff canteen than in Building 1. Risk classification showed most traps remained within acceptable thresholds, while persistent Level 3 risks were limited to Traps 10, 18, and 19 in Building 2. These results indicate a progressive reduction in insect captures across successive monitoring intervals under the implemented IPM program, followed by stabilization at comparatively low levels. The study provides a long-term dataset and a practical framework for facility-level insect risk assessment, supporting evidence-based pest management in industrial settings.
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Monitoring of organochlorine pesticide residues in school milk and associated health risk assessment in Thailand
(2026-06-01)
Cha-aim, Chatartorn
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Manamoongmongkol, Kanjana
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Nisapai, Wiparat
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Phumjan, Lamphung
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Oomnog, Niroth
School milk products are recognized as an important and affordable food source for Thai people, particularly students. However, milk was classified as an agricultural product in which organochlorine pesticide (OCP) residues can be detected due to its fat content. This study investigated the levels of organochlorine pesticides in school milk products, including raw milk, pasteurized milk, and UHT milk, using the QuEChERS method. The fat content of these three types of milk was 3.50%, 3.03%, and 3.33%, respectively. A correlation coefficient (R2) of 0.9953–0.9998 confirmed the validity of the QuEChERS method. The limit of detection (LOD) for each pesticide ranged from 0.003 to 0.0615 mg/kg, and the limit of quantification (LOQ) ranged from 0.010 to 0.2050 mg/kg, with recovery rates of 80.95–110.00%. The analysis for 13 organochlorine pesticide residues, including Aldrin, Cis-Chlordane, Trans-Chlordane, Dieldrin, p,p'-DDD, p,p'-DDE, p,p'-DDT, Endrin, Heptachlor, Heptachlor epoxide, alpha-HCH, beta-HCH, and gamma-HCH using a GC–MS, revealed that no residues were detected above the LOQ (0.01 mg/kg) in any samples, indicating compliance with food safety standards. A scenario-based risk assessment using estimated daily intake (EDI) and hazard quotient (HQ) suggested that, although current exposure is negligible, potential risks may arise under worst-case conditions, particularly for aldrin, dieldrin, and heptachlor in high-consumption groups. Overall, school milk products in Thailand are considered safe; however, continuous monitoring and risk assessment remain essential to ensure long-term food safety.
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Integrated landslide risk assessment utilizing open source satellite and ground data in the Himalayas of Northern Pakistan
(2025-07-01)
Ahmad, Naseem
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Shafique, Muhammad
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Hussain, Mian Luqman
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Shah, Munawar
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Jamjareegulgarn, Punyawi
Landslides have devastating effect on communities, infrastructure, and the environment, making them one of the most recurring and harmful natural hazards globally. This study propose an integrated approach of freely available geospatial data and semi-quantitative techniques to evaluate landslide hazard, vulnerability, and risk in one of the most landslide-prone valleys i.e., Kaghan Valley, northern Pakistan. The Google Earth Pro, high-resolution DEM and satellite images are used to develop a landslide inventory, derived causative factors and assess the landslide hazard and risk assessment in Kaghan Valley. The landslide susceptibility map is then integrated with landslide-triggering factors to derive a landslide hazardindex map. A geospatial database of element-at-risk data of 66,282 building footprints, typological data, road network, population, and land cover are obtained through remote sensing and extensive field surveys. The quantitative analysis revealed that 8.43 km2 (0.66%) of the total area falls under the very high-risk category, while 271.19 km2 (21.11%) is classified as high risk, and 80.91 km2 (6.30%) as moderate risk, establishing a strong basis for risk assessment.

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