Developing PM₂.₅ mitigation solutions based on the analysis of the relationships between PM₂.₅ concentrations and precursor factors: a case study of Hanoi, Vietnam

Abstract

Air pollution, particularly from aerosol like PM₂.₅, is a serious global issue, especially for densely populated cities such as Hanoi, the capital of Vietnam. Monitoring results indicate that days with PM_2.5 concentrations ranging from 50.5 to 150.4 µg/m3, corresponding to poor and very poor air quality levels, account for 30% of the total monitored days in a year. Several decisions to reduce PM_2.5 pollution are less effective because they do not consider the distribution of emission sources of the precursors that create this pollutant. It is not uncommon for PM_2.5 pollution in a particular area, such as the center of a megacity, to result from pollution transport from other areas rather than local emissions. Therefore, solutions to reduce PM_2.5 pollution must be considered on a regional scale with consideration of the emission sources location. To achieve this goal, a new approach has been developed based on the combination of modeling and big data technology, clarifying the relationship between the spatial–temporal distribution of PM_2.5 pollution and the emission sources of its precursors. To comprehensively evaluate, meteorological factors are also considered. This approach is based on analyzing the relationship between three datasets: concentration, emissions, and meteorology, hourly on a 3 km × 3 km grid. The study results show that the four main precursors contributing to PM_2.5 pollution are CO, OC, BC, and NO_x, with respective proportions of 39.6%, 31%, 16%, and 7.6%. The analysis also indicates the contribution rates of the four main sectors: industry (ind), transportation (tro), residential (res), and agricultural waste burning (awb). Mitigation solutions focus on transitioning from old technology to green technology and limiting or eliminating environmentally polluting activities.

Graphical Abstract