Abstract
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 PM10 concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM10 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. Models’ performance comparison showed that ANN models yielded better goodness-of-fit indices than MLR models at all stations in the study area with average coefficient of correlation (r) and Nash–Sutcliffe Efficiency Index (NSE) of 0.51 and 0.34 for the former, and 0.7 and 0.49 for the latter. These indices indicates that the ANN-based data-driven models outperformed the MLR-based models. Thus, the ANN-based models and the Inverse Distance Weighting (IDW) interpolation technique were then combined for mapping the monthly PM10 concentration with a spatial resolution of 1 km from global meteorological data. With this combination, the PM10 concentration maps account for both local PM10 concentration and impacts of spatio-temporal variations of meteorological factors on the PM10 concentration. This study provides a promising method to predict the PM concentration with a high spatio-temporal resolution from meteorological data.
Highlights
Air pollution is one of the most concerned issues of our society due to its impacts on human health and the economy[1,2]
In order to reduce this number of features, the correlation coefficients between each feature with the PM10 concentration and between features were estimated
It is interesting that of these meteorological factors, only the mean daily pressure is positively correlated with the PM10 concentration, while the other factors have negative correlation coefficients
Summary
Air pollution is one of the most concerned issues of our society due to its impacts on human health and the economy[1,2]. A similar outcome was confirmed by Katsouyanni et al.[8] in a study of over 29 cities Does it have negative effects on human health, PM air pollution causes huge economic loss. Wong et al.[25] provided an excellent review on these techniques and divided them into four groups, namely spatial averaging, nearest neighbor, inverse distance weighting and kriging These interpolation techniques have been successfully employed to construct P M10 maps in many studies. The main drawback of these studies was that they only used the P M10 concentration measured at the air quality stations for interpolation without considering the impacts of the spatio-temporal variations of the meteorological factors on P M10 variation. There has been increasing attention and demand from both the local community and the government of Hanoi for a study on air quality and its controlling factors with P M10–2.5 concentration prediction being the top priority
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