Heat exposure, physiological strain, and wet bulb globe temperature prediction among outdoor agricultural workers in Pak Chong, Thailand

This study assessed heat exposure, physiological responses, and the risk of chronic kidney disease among outdoor agricultural workers in Pak Chong, Thailand, and developed a predictive model for Wet Bulb Globe Temperature (WBGT). A cross-sectional study was conducted among 170 agricultural workers. Field WBGT was measured at 33 locations in April and July and corrected for Clothing Adjustment Factor to determine effective WBGT. Core temperature, skin temperature, heart rate, blood pressure, and body weight were monitored before, during, and after work shifts. Urine dipstick and microalbumin tests were performed to evaluate dehydration and kidney disorder. Workloads were classified according to American Conference of Governmental Industrial Hygienists (ACGIH) metabolic rate categories. Meteorological data were used to train machine learning models—including linear regression, decision tree, random forest, and extreme gradient boosting—to predict WBGT values. Effective WBGT at fruit gardens and field crops sites frequently exceeded ACGIH limits for moderate-to-heavy work. Workers’ systolic/diastolic blood pressure and heart rate increased during work (transient cardiovascular strain) while body temperature remained stable. Post-shift, 34.7% showed moderate dehydration; microalbumin screening was positive in 31/170 (18.2%) suggesting possible early renal involvement but not chronic kidney disease diagnosis. Among the predictive models, extreme gradient boosting achieved the best performance (root mean square error = 0.798°C, mean absolute error = 0.576°C, R² = 0.81). The most influential predictors were air temperature, sunshine duration, and site-specific factors. Agricultural workers in Pak Chong are frequently exposed to heat stress that exceeds ACGIH standards, predisposing them to dehydration and kidney impairment. The findings support evidence-based preventive strategies such as regulated work–rest cycles, hydration management, and medical surveillance. Moreover, the validated WBGT prediction tool offers a practical approach for real-time monitoring and proactive climate adaptation to protect outdoor workers in heat-prone environments.

Authors

Institutions

Publication Details

Journal
Global Health Journal
Published
2025-12-01
DOI
https://doi.org/10.1016/j.glohj.2025.11.004
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Heat exposure, physiological strain, and wet bulb globe temperature prediction among outdoor agricultural workers in Pak Chong, Thailand

Tosaphol Ratniyomchai, Thanatchai Kulworawanichpong, Kiattisak Batsungnoen, Pirutchada Musigapong et al.
Global Health Journal
Climate Change and Health Impacts
article

Heat exposure, physiological strain, and wet bulb globe temperature prediction among outdoor agricultural workers in Pak Chong, Thailand

Tosaphol Ratniyomchai, Thanatchai Kulworawanichpong, Kiattisak Batsungnoen, Pirutchada Musigapong, Aduldatch Sailabaht
article en

Abstract

This study assessed heat exposure, physiological responses, and the risk of chronic kidney disease among outdoor agricultural workers in Pak Chong, Thailand, and developed a predictive model for Wet Bulb Globe Temperature (WBGT). A cross-sectional study was conducted among 170 agricultural workers. Field WBGT was measured at 33 locations in April and July and corrected for Clothing Adjustment Factor to determine effective WBGT. Core temperature, skin temperature, heart rate, blood pressure, and body weight were monitored before, during, and after work shifts. Urine dipstick and microalbumin tests were performed to evaluate dehydration and kidney disorder. Workloads were classified according to American Conference of Governmental Industrial Hygienists (ACGIH) metabolic rate categories. Meteorological data were used to train machine learning models—including linear regression, decision tree, random forest, and extreme gradient boosting—to predict WBGT values. Effective WBGT at fruit gardens and field crops sites frequently exceeded ACGIH limits for moderate-to-heavy work. Workers’ systolic/diastolic blood pressure and heart rate increased during work (transient cardiovascular strain) while body temperature remained stable. Post-shift, 34.7% showed moderate dehydration; microalbumin screening was positive in 31/170 (18.2%) suggesting possible early renal involvement but not chronic kidney disease diagnosis. Among the predictive models, extreme gradient boosting achieved the best performance (root mean square error = 0.798°C, mean absolute error = 0.576°C, R² = 0.81). The most influential predictors were air temperature, sunshine duration, and site-specific factors. Agricultural workers in Pak Chong are frequently exposed to heat stress that exceeds ACGIH standards, predisposing them to dehydration and kidney impairment. The findings support evidence-based preventive strategies such as regulated work–rest cycles, hydration management, and medical surveillance. Moreover, the validated WBGT prediction tool offers a practical approach for real-time monitoring and proactive climate adaptation to protect outdoor workers in heat-prone environments.

Global Health JournalVol. 9(4)
Ubon Ratchathani University (TH), Suranaree University of Technology (TH)
Openalex Percentile: Top 53%
Climate Change and Health Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.