Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

Accurate PM2.5 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 (PM2.5), 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 PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 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 PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 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 PM2.5 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.

Special Issue: Hydrological Extremes in a Warming Climate: Nonstationarity, Uncertainties and Impacts

The intensification of global water cycle, associated with anthropogenic climate change, is affecting the characteristics of hydrologic extreme events throughout the world [...]

Implications of changes in temperature and precipitation on the discharge of Brahmaputra River in the urban watershed of Guwahati, India

The urban watershed of Guwahati situated on the bank of the Brahmaputra River is one of the fastest growing cities of India. During the last two decades, water security concerns due to climatic variabilities have become a pronounced issue in the urban watershed of Guwahati. Thus, the study aims to calculate the long-term temporal trends of temperature, precipitation, extreme climate indices, and river discharge to assess the variations and patterns of hydro-climatic variations in the urban watershed of Guwahati from 1991 to 2019. Furthermore, the current study also tries to correlate these extreme climatic indices to river discharge to determine the degree of hydro-climatic variations. The Mann-Kendall statistical techniques and Sen's estimator were used to calculate the statistical significance, stability, and averaged magnitude of trends in the hydro-meteorological data. The result shows that the wetness indices, R20 and RX5Day, reported a decline in Guwahati's urban watershed from 1991 to 2019, resulting in a reduction in intensity and duration of heavy rainfalls while the dry spell (CDD) has been more distinct in the study area with a rise in the average temperature by 0.023 °C/year. Similarly, the most significant statistical trend was found in the monsoonal discharge of the Brahmaputra with a negative trend of - 204.16 m³/s/year. The results also show that fluctuations in rainfall patterns have a direct impact on the discharge of the Brahmaputra. These phenomena can affect the quantity of river water resulting in a severe impact on water security in the study area.