Tran Cong-Thanh

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.

Clean air policies are effective only if air pollution control strategies translate to sustain public health improvements. Yet most policies continue to prioritize emission reduction without systematically evaluating corresponding health outcomes. This study investigates whether significant changes in Particulate Matter (PM _2.5 ) exposure trends across countries are associated with measurable changes in disease burden and evaluate policy implications. Using a structural breakpoint detection method, significant shifts in exposure trajectories were identified and an Integrated Exposure Response (IER) model was used to quantify population attributable fraction (PAF) of health burden, by which we isolated the effect of PM _2.5 exposure from population growth. Our analysis revealed that several European Countries achieved sustained health improvements with COPD-related PAF declining by about 30% were policy interventions focused on research-based controls coupled with long-term pollution health monitoring systems. In contrast, China’s larger PM _2.5 reductions (~11.3 µg/m ³ ) yielded relatively less health gains reflecting non-linear exposure-response dynamics where benefits taper at high baseline concentration. Meanwhile, countries such as Chile, South Africa and Brazil’s experience underscored the absence of sustained health evaluation and equity-oriented design. Together, these cross-country exposure, health and policy analysis highlights the need for integrating health assessment with air quality management and maintaining adaptive policy evaluation.

PM 2.5 pollution remains a pressing environmental concern in Bien Hoa city, a key industrial center within Vietnam’s Southern Key Economic Zone. This research utilized low-cost sensors to measure indoor and outdoor PM 2.5 levels in five urban households from April 2021 to January 2022, with calibration performed using the DustTrak™ II Aerosol Monitor 8530. During the COVID-19 lockdown, daily average PM 2.5 concentrations ranged from 22 to 40 μg/m³, meeting Vietnam’s air quality standards but exceeding the World Health Organization’s guidelines. Indoor PM 2.5 levels were significantly elevated in older homes due to factors such as smoking and incense burning, while outdoor levels were primarily impacted by traffic and other local sources, with peaks observed during rush hours. Seasonal analysis revealed reduced PM 2.5 concentrations during the rainy season, underscoring the mitigating effects of rainfall and natural ventilation on indoor air pollution. The significant linear correlation between indoor and outdoor PM 2.5 concentrations underscores the need for thorough air quality evaluations. This study is the first to assess indoor-outdoor PM 2.5 relationships in Bien Hoa City households using low-cost sensors over nearly a year, including the COVID-19 control period. The study demonstrates that low-cost sensors are an effective and practical option for air quality monitoring in resource-limited settings like Vietnam.

Air pollution, particularly fine particulate matter (PM2.5), remains a critical issue in urban areas within tropical monsoon regions, such as Ho Chi Minh City (HCMC). This study utilized PM2.5 data the monitoring station at the United States Consulate General HCMC and meteorological variables (rainfall, air temperature, relative humidity, and wind speed) collected by local meteorological agencies between 2016 and 2022. Bayesian linear regression models with variable selection were employed to examine the relationships between meteorological factors and PM2.5 concentrations, focusing on lag effects across various time scales, and comparing seasonal and non-seasonal models. The results revealed a strong inverse relationship between rainfall and PM2.5, with significant reductions observed in the first few hours after rain (lag0 and lag1), particularly within the immediate aftermath of rainfall, where the impact is most pronounced. Temperature and wind speed were identified as critical factors in reducing PM2.5 concentrations, especially during the dry season, where their effects were more substantial and longer-lasting. Seasonal models outperformed non-seasonal models, underscoring the importance of including seasonal variations for accurate PM2.5 predictions. The study also demonstrated the value of Bayesian variable selection in improving regression models by identifying key meteorological factors and their cumulative effects. These findings provide a scientific foundation for developing season-specific air quality management strategies in tropical monsoon regions.

Exposure to ambient fine particulate matter (PM2.5) is a substantial environmental health issue for urban inhabitants in Ho Chi Minh City (HCMC). The effects of short-term exposure to ambient PM2.5 on cardiorespiratory mortality and morbidity were determined in HCMC by a retrospective, hospital-based, unidirectional and symmetric bidirectional case-crossover study. Ambient PM2.5 concentrations were measured by validated low-cost particulate matter sensors in four urban districts from December 2019 to March 2021. Mortality, emergency visits, and hospital admissions for cardiorespiratory diseases were obtained from electronic hospital-based data of three hospitals of the study area. Control days in each stratum were defined as days before or after the case day, day of the event (e.g., death or illness), and each case had 4 control days. We applied conditional logistic regression and generalized linear mixed models to explore the associations between PM2.5 and cardiorespiratory mortality and morbidity. A total of 2,457 subjects with cardiorespiratory diseases were included in this study analysis. The daily PM2.5 concentrations of more than 80% of days during the study period exceeded the World Health Organization's standard of 15 µg m-3. The first event day (lag0) was associated a significant risk for the diseases (odds ratio, 1.06, 95% confidence interval, 1.01–1.10, for a 10 µg m-3 increase in daily PM2.5). The largest effect was observed with cardiovascular cases under 35 years of age. Males were observed to be more susceptible to PM2.5-related cardiovascular diseases than females, whereas females were more vulnerable to PM2.5-related respiratory diseases. We found robust evidence for the short-term exposure to PM2.5 with the increase in adverse cardiovascular and respiratory events in urban inhabitants of HCMC, Vietnam. The findings may appeal to Vietnamese policymakers to modify reasonable safe standards for ambient PM2.5 in urban areas.

The coronavirus disease 2019 (COVID-19) measures during 2020−2021 may lead to a downward trend of fine particulate matter (PM2.5) concentrations in Ho Chi Minh City (HCMC), which is one of the most air-polluted cities in Vietnam. This study aimed to compare PM2.5 variations during the COVID-19 period (2020−2021) with a short-term trend of PM2.5 (2016−2019) in HCMC in conjunction with meteorological conditions. Five fixed-site locations were chosen to monitor PM2.5 concentrations by using low-cost particulate matter sensors (Purple Air II-SD) in five urban districts of HCMC from December 2019 to March 2021. This study also collected hourly PM2.5 concentrations from the United States Consulate General HCMC and meteorological variables (i.e., wind speed, wind direction, air temperature, and relative humidity) from the weather station at Tan Son Nhat International Airport, 2016–2021. PM2.5 concentrations of the COVID-19 period in HCMC were notably less than those of the 2016−2019 period by 18%. However, about half of the days during this period still had daily PM2.5 concentrations exceeding the World Health Organization's standard of 15 µg m-3. Besides the impacts of COVID-19 restrictions on the PM2.5 decrease, noticeable co-effects of meteorological variables on the decline of PM2.5 in HCMC were also observed in the rainy season and with the west-south-west monsoon wind direction. Wind speed and air temperature substantially contributed to PM2.5 reductions in Bayesian multiple linear regression models. In conclusion, the large decrease of PM2.5 concentrations during the unprecedented period of COVID-19 pandemic gave us a better chance to more fully comprehend the PM2.5 pollution status in HCMC for future reference.

The in-traffic microenvironment can enhance personal exposure to fine particulate matter (PM). With this study, we aimed to calibrate a DustTrak instrument (DustTrak 8533 DRX Aerosol Monitor, TSI Incorporated, Shoreview, MN, USA) and low-cost sensors (AS-LUNG-P sensors) and then assess inhalation exposure to PM_2.5 and PM₁ for different commuters in central areas of Ho Chi Minh City (HCM). The DustTrak instrument and low-cost sensors were calibrated using a gravimetric method under side-by-side conditions. Relationships between the DustTrak signals and PM concentrations measured by the gravimetric method were identified using simple linear regression models for PM_2.5 (R² = 0.998, p-value < 0.05) and PM₁ (R² = 0.989, p-value < 0.05). Meanwhile, PM concentrations determined by the AS-LUNG-P sensors and the gravimetric method were correlated using two-segmented linear regressions. To obtain the corresponding two-segment regression equations, the response of the AS-LUNG-P sensors was compared with the corrected DustTrak data. The coefficient of variation (CV) evaluated for all sensors was smaller than 10%, indicating that the data were applicable for particle assessment. For inhalation exposure assessment, the results showed that commuters using open transport modes, such as bikes, motorbikes, and walking, were exposed to more PM than those using closed transport modes (e.g., cars). Specifically, the bicyclists had the highest inhaled doses of PM among the open transport groups. PM exposure levels in the morning were higher than in the afternoon. Additionally, exposure levels to PM concentrations rapidly increased when passing through intersections of major roads and moderately decreased when using surgical facemasks.

Wetlands account for about 41% of the total global ecosystem services value; therefore, they preserve a role as the most precious ecosystems on Earth. However, 35% of global wetland areas have been lost since 1970 as many wetlands have been exploited unsustainably. In this context, a hydrogeomorphic delineation approach has been developed to divide a wetland landscape into units which have homogeneous hydrological regime, geomorphology, and soil, named as hydrogeomorphic units (HGMUs). These units are fundamental units of a wetland for functional and resilience assessment of a wetland in different uses; then, to determine optimal management strategies for maintaining its complete ecosystem functions. This study used Tram Chim National Park for applying the hydrogeomorphic delineation approach for breaking a landscape of the park down into HGMUs. The findings showed that the park may be divided as three classes, including depression, riverine, and flat. Each class continued to be subdivided as subclasses. The results showed that there were six subclasses, i.e. six HGMUs, which identified in the park. Based on the results, the study described characteristics of HGMUs and created maps of HGMUs distribution of the park. These results will be a basis for next steps of applying functional assessment procedures for analyzing potential of the HGMUs to perform ecosystem functions in our upcoming study for preservation and management purposes in Tram Chim National Park.