Mapping of high-resolution daily particulate matter (PM) concentration at the city level through a machine learning-based downscaling approach.

PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is crucial for assessing air quality. However, Vietnam's state-of-the-art (SOTA) dataset with a 3 km resolution needs to be revised to depict spatial variation in smaller regions accurately.

Accurate PM urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

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), 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 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution.

Street-scale dispersion modelling framework of road-traffic derived air pollution in Hanoi, Vietnam.

Traffic is an important source of air pollution in Vietnamese cities. The spatio-temporal variation of air pollution derived from traffic is poorly understood. Application of dispersion modelling can help but is hindered by the local scarcity of suitable input data.

Outdoor air quality data for spatiotemporal analysis and air quality modelling in Ho Chi Minh City, Vietnam: A part of HealthyAir Project.

This article presents outdoor air pollution data acquired from the real-time Air Quality Monitoring Network (AQMN), which was established by the Healthyair project team in Ho Chi Minh City (HCMC), Vietnam. The AQMN is made up of six air pollution monitoring stations spread over the city (Traffic, Residential, and Industrial). Each station measures the same contaminants in the air, including PM, TSP, NO, SO, O, CO, and two meteorological factors, temperature and humidity.

Apply MIKE 11 model to study impacts of climate change on water resources and develop adaptation plan in the Mekong Delta, Vietnam: a case of Can Tho city.

Can Tho city in the Mekong Delta is in the top ten areas affected by climate change. Therefore, assessing climate change impacts, social and economic activities require proposed solutions to respond to climate change. This study aims to (i) apply the MIKE 11 model (Hydrodynamic module and Advection-Dispersion module) to simulate the impacts of climate change scenarios on water resources in Can Tho city; (ii) calculate water balance in Can Tho city; and (iii) suggest climate change adaptation plan for sustainable social-economic activities of the city.

Combination of data-driven models and interpolation technique to develop of PM10 map for Hanoi, Vietnam.

The degradation of air quality is the most concerned issue of our society due to its harmful impacts on human health, especially in cities with rapid urbanization and population growth like Hanoi, the capital of Vietnam. This study aims at developing a new approach that combines data-driven models and interpolation technique to develop the PM concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM concentration with the meteorological factors at the air quality monitoring stations in the study area were developed using the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) algorithms.

Study loading capacties of air pollutant emissions for developing countries: a case of Ho Chi Minh City, Vietnam.

Ho Chi Minh City (HCMC) is one of the cities in developing countries where many concentrations of air pollutants exceeded the Vietnam national technical regulation in ambient air quality including TSP, NO, Ozone and CO. These high pollutant concentrations have destroyed the human health of people in HCMC. Many zones in HCMC can't receive more air pollutants.