Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM_2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM_2.5, NOx, and SO₂. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO₂ were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m³) and World Health Organization (WHO) (40 µg/m³). The annual average concentrations of PM_2.5 were 23 µg/m³ and were also much higher than the WHO (10 µg/m³) standard by about 2.3 times. In terms of public health impacts, PM_2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO₂ and SO₂ was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM_2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM_2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM_2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM_2.5 concentrations, considering meteorological conditions, the rolling mean of PM_2.5 time series, and temporal features. PM_2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM_2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Benzene, toluene, ethylbenzene, and xylenes (BTEX) are carcinogenic pollutants. However, the average concentration in 1 h of some pollutants belonging to BTEX, such as benzene, in Ho Chi Minh City (HCMC) is higher than the national standard QCVN 06:2009/BTNMT by about ten times. This research is the first to calculate the emission of BTEX for developing countries on a city scale. This paper developed a method to calculate cold emission factors based on hot emission factors for BTEX. Five spreadsheets developed and calculated these cold emission factors for five vehicle categories. A comprehensive emission inventory (EI) for BTEX was conducted in HCMC to determine the cause of BTEX pollution. An innovative methodology with bottom-up and top-down combination was applied to conduct BTEX EI, in which the EMISENS model was utilized to generate the EI for road traffic sources, and the emission factors method was utilized for other emission sources. Among emission reasons, motorcycles contribute the highest to HCMC air pollution, responsible for 93%, 90%, 98.9%, and 91.5% of benzene, toluene, ethylbenzene, and xylene, respectively. Cars contributed 5%, 6%, 0.8%, and 6.5% of benzene, toluene, ethylbenzene, and xylene, respectively. For LDVs, the emission from benzene, toluene, ethylbenzene, and xylene accounted for 1%, 2%, 0.2%, and 1.9%. The major reasons for point sources were metal production, which had 1%, 2%, and 0.1% for benzene, toluene, ethylbenzene, and xylenes emissions. The area source had a minority emission of total BTEX in Ho Chi Minh City. Our findings can be used to invest in the most significant sources to reduce BTEX in HCMC. Our approach can be applied in similar urban areas in BTEX EI. This research also developed nine measures to reduce BTEX in HCMC for human health protection.

Health impact due to population exposure to PM2.5 shows that the densely populated suburbs surrounding the urban core have the largest impact in terms of mortality. Health impact due to air pollution is higher in January (dry season) with estimated 625 deaths and 124 cardiovascular diseases (cvd) hospitalization as compared with estimated 94 deaths and 18 cvd hospitalization in July (wet season). One of the research questions posed by the city authority is whether converting diesel buses to electric buses can yield environmental and health benefits. Our work shows that the scenario of replacing 50% of fossil fuel combustion buses with electric buses does not yield perceptible change in mortality health effect. This is due to emission from buses is small as compared to those from the whole transport sector and other sectors. This study emphasizes the need for integrated, targeted emission control strategies to address spatial and temporal variability in pollution. The findings offer valuable insights for policymakers to develop effective measures in urban planning for improving air quality and protecting the health of people in Hanoi.

Agriculture is an important economic sector of Vietnam, the most common is wet rice cultivation. Wet rice cultivation is known as the main contributor to national greenhouse gas emissions. To better understand greenhouse gas exchange in wet rice cultivations and to investigate the factors influencing carbon cycling and sequestration in these types of ecosystems, since 2019, the first eddy covariance station has been installed in a paddy field in Long An province, Mekong Delta, Vietnam. The station is equipped with state-of-the-art equipment for CO2 and CH4 gas exchange and meteorological ancillary measurements. Data from the station are processed following the ICOS recommendations (Integrated Carbon Observation System) for CO2. For CH4, data are separately processed and gap-filled using a random forest model from methane-gap fill-ml, a machine learning package, as there is no standard method for CH4 flux gap-filling yet. Finally, the CO2 equivalent (CO2eq) based on CO2 and CH4 fluxes was estimated. The study area implemented a new water management practice called alternate wetting and drying, which helps to save water and reduce methane emissions. This practice resulted in the minor release of 0.8 kg CH4 per hectare in 2020 and 0.67 kg CH4 per hectare in 2021. However, CO2eq from the rice fields was negative, indicating that the rice fields acted as a sink for CO2eq, with -5.54 kg CO2eq per hectare in 2020 and -7.03 kg CO2eq per hectare in 2021. On a provincial level, rice cultivation activities in Long An, with a total area of 498293 ha, resulted in a CO2eq uptake of 2760 and 3503 tons in 2020 and 2021, respectively. This result is in contrast to the initial hypothesis that rice fields are a source of greenhouse gases. However, N2O was not investigated in this study, which is also known as a strong greenhouse gas.