Shih-Chun Candice Lung

While commuting significantly contributes to particulate matter (PM2.5 and PM1) exposure, relying solely on ambient monitoring frequently underestimates real-world exposure. This study evaluated commuting/ambient (C/A) ratios for PM2.5 and PM1 across five commuting modes (walking, bicycling, scooters, sedans, and buses) using a consistent personal monitoring methodology in four Asian cities: New Taipei City (NWT), Bandung (BD), Ho Chi Minh City (HCMC), and Quezon City (QCPH). C/A ratios revealed substantial exposure variability driven by commuting mode, vehicle ventilation, and urban context. QCPH exhibited the highest mean commuting PM2.5 levels (39.0 ± 24.6 μg/m3), while HCMC recorded the highest mean C/A PM2.5 ratio (1.22 ± 1.72). Open-air modes and open-window vehicles in BD significantly increased personal exposure by 24% to 44% relative to ambient PM2.5. Conversely, bicycling in NWT, facilitated by dedicated lanes, reduced PM2.5 and PM1 exposure by 16% and 19%, respectively. Rush hours significantly intensified exposure, increasing BD's walking C/A PM2.5 ratio to 1.40 ± 0.83 from 1.13 ± 0.45 during non-rush periods. Enclosed vehicles with air conditioning provided the most effective protection; sedans with AC in HCMC reduced personal PM2.5 and PM1 exposure by 48% and 36%, respectively. These established C/A ratios provide a critical tool for epidemiological studies to translate ambient data into realistic personal exposure estimates and improve health risk assessments.

Background: Agricultural workers are a highly exposed yet understudied group in the context of climate change. Objective: This study filled the research gaps in assessing personal heat exposure, its association with immediate changes in heart rate variability (HRV), and intervention choices for agricultural workers. Methods: Twenty-five participants were recruited from a pear orchard community, a rice seedling field, and a vegetable greenhouse farm in 2020 summer. Seven-day exposure of wet-bulb globe temperature (WBGT) was assessed for each participant with personal sensors for temperature, relative humidity, and solar radiation, as well as fixed-location monitoring for wind speed. These data were used as inputs for thermodynamic-principle-based Liljegren equations to calculate WBGT. Additionally, participants wore small medically certified HRV biosensors for 96 h. Results: WBGT levels were 27.0 ± 1.7, 31.9 ± 3.7, and 32.6 ± 6.3 °C for the participants at work in the pear orchards, rice fields, and greenhouse farm, respectively. The highest percentages above 32.2 °C of the rice-field and greenhouse participants were 60.8% and 66.3%, respectively. Peak WBGT reached 48.2 °C. The ambient WBGT from a fixed-location monitor underestimated the mean exposure, maximum exposure, and percentages above the critical thresholds of 32.2 °C and 36 °C for outdoor unshaded participants. The evident interpersonal variations emphasized the importance/necessity of individual WBGT assessments. A 1 °C WBGT increase was statistically significantly associated with a 1.05–2.44% decrease in all HRV indicators, except low-frequency/high-frequency, and a 0.65% increase in HR. Wearing gloves, masks, and long garments may aggravate HRV responses, whereas short garments may be protective. Significance: This is the first study applying personal environmental and biological sensors assessing WBGT and WBGT-HRV relationships of outdoor workers. The findings highlight the need for heat-health adaptation strategies, including personalized protective measures and WBGT-based warnings. The innovative methodology can be widely used for other outdoor workers. Impact: This study addresses critical knowledge gaps in occupational heat exposure research by employing innovative exposure and epidemiological methodologies to assess personal WBGT exposure and its association with HRV in agricultural workers. The findings highlight substantial discrepancies between ambient and personal WBGT levels. By integrating advanced sensors and the OSHA-approved Liljegren equations to obtain WBGT, this study establishes a scalable framework for researchers and policymakers worldwide to accurately assess heat exposure, exposure–health relationships, and intervention strategies. These insights are particularly valuable for protecting agricultural workers as climate change intensifies occupational heat stress, thus fostering resilience in vulnerable populations and promoting overall health.

This study developed a Geospatial Artificial Intelligence (Geo-AI)–based framework to estimate and visualize the three-dimensional (3-D) distribution of ultrafine particles (PM₀.₁) and associated population exposure across Taichung City, Taiwan. An unmanned aerial vehicle (UAV) platform equipped with a P-Trak Ultrafine Particle Counter was deployed to collect high-resolution 3-D PM₀.₁ concentration data across varying altitudes and land-use types. These 3-D PM₀.₁ data were integrated with multi-source geospatial datasets, including 3-D building models, meteorological variables, and emission inventories. The SHapley Additive exPlanations (SHAP) method was then employed to identify key predictors for machine-learning modeling. The optimized model was applied to map the continuous 3-D pollution field and used to estimate and visualize population exposure for each floor level. The resulting Geo-AI model achieved strong predictive performance, with R² values of 0.95 for training and above 0.85 for validation, demonstrating high robustness and predictive capability. Visualizations reveal a nonlinear vertical structure of PM₀.₁ in 3-D space, characterized by near-ground peaks in industrial and traffic zones alongside persistent localized hotspots at mid-to-high elevations. Population exposure assessments highlighted that, despite lower concentrations at higher elevations, the total exposure burden remains significant in mid-to-high-rise residential buildings due to higher population density. This research presents an advanced framework for assessing 3-D air pollution exposure risks in dense urban environments, demonstrating the potential of Digital Twin technologies in supporting air quality management and public health decision-making.

Heat warning systems represent a critical health adaptation strategy under global boiling. This study evaluates the appropriateness of the existing criteria of heat warning categories, which were established based on climatology. The objectives were (1) to evaluate the association of the daily maximum temperature (Tmax) and multiple health outcomes (including emergency visits, hospitalization cases, and mortality of heat-related illness (HRI), cardiovascular, respiratory, diabetes, and renal diseases) considering both the lag and cumulative effects, and (2) to identify vulnerable groups (considering their demographic, occupation, geographic, and health status) and areas of heat-health impacts. Weather and health records in Taiwan from May to October from 2000 to 2019 were analyzed using generalized additive models. The results show that HRI was the most sensitive health outcome, and the relative risk (RR) was 1.81 (confidence interval (CI): 1.51 – 2.18) and 2.99 (CI: 1.99 – 4.49) for emergency visits and hospitalizations, respectively, when Tmax was ≥ 34 °C. The corresponding RRs were 2.00 (CI: 1.67 – 2.39) and 2.39 (CI: 1.44 – 3.95) when Tmax was ≥ 32 and ≥ 31 °C for three consecutive days, respectively. The morbidity risks of cardiovascular, respiratory, diabetes, and renal diseases all increased at different temperature thresholds. Significant associations between Tmax and health outcomes occurred at thresholds lower than the current warning thresholds, indicating the need for revision. Both lag and cumulative effects need to be considered in heat-health warning systems. People with hypertension, hyperglycemia, or hyperlipidemia were found to be more vulnerable, as they had higher RRs for pneumonia, COPD, and stroke than the general population. Among different occupations, farmers were found to be most vulnerable. This study demonstrates a methodology considering both lag and cumulative effects that can be applied in other countries to assist in the establishment of evidence-based heat-health warning systems.

More exposure assessment and epidemiological studies are needed for PM ₁ , a health-relevant but understudied pollutant, to better understand and mitigate its health risks. This study conducted a 3.5-year PM ₁ monitoring campaign in a mountainous community, where air pollutants are more easily trapped, using research-grade low-cost sensors, namely, AS-LUNG-O sets. Nine solar-powered AS-LUNG-O sets were installed at street level, with an additional unit positioned 10 m above the ground to serve as an ambient reference. Street-level sensors were placed 3–5 m from potential sources such as traffic, temples, and vendors. During the 3.5-year period, the overall mean PM ₁ concentration across the nine street-level locations was 20.9 ± 11.0 μg/m ³ , compared to 17.0 ± 8.4 μg/m ³ at the elevated site. PM ₁ levels were typically higher in winter and on religious days across all locations. Spatial analysis revealed that PM ₁ hot spots varied by season, day of the week (weekday vs. weekend), and event type (religious vs. typical days). These hot spots were commonly located near traffic sources, temples, and markets. Notably, most of the maximum PM ₁ values across the ten locations occurred on weekdays and typical days, highlighting the unpredictable nature of PM ₁ levels influenced by community events. Source contributions to PM ₁ were estimated using a generalized additive mixed model; the PM ₁ contribution from religious events, traffic, and vendors reached statistical significance. The identified hot spots and source contributions can inform targeted control strategies addressing local pollution sources. This study offers a more representative assessment of PM ₁ exposure for local residents than data from ambient monitoring stations or short-term monitoring campaigns. The methodology presented can also be applied in other countries to support community-based PM ₁ studies. Graphical abstract