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

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.

SUBMITTER: Nguyen DA

PROVIDER: S-EPMC7648064 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Nguyen Dung Anh DA Duong Son Hong SH Tran Phuong Anh PA Cao Hai Hoang HH Ho Bang Quoc BQ

Scientific reports 20201106 1

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<sub>10</sub> concentration maps from meteorological factors for the central area of Hanoi. Data-driven models that relate the PM<sub>10</sub> concentration ...[more]

PMID: 33159104

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Project description:BACKGROUND:Many studies have shown associations between rising temperatures, El Niño events and dengue incidence, but the effect of sustained periods of extreme high temperatures (i.e., heatwaves) on dengue outbreaks has not yet been investigated. This study aimed to compare the short-term temperature-dengue associations during different dengue outbreak periods, estimate the dengue cases attributable to temperature, and ascertain if there was an association between heatwaves and dengue outbreaks in Hanoi, Vietnam. METHODOLOGY/PRINCIPAL FINDINGS:Dengue outbreaks were assigned to one of three categories (small, medium and large) based on the 50th, 75th, and 90th percentiles of distribution of weekly dengue cases during 2008-2016. Using a generalised linear regression model with a negative binomial link that controlled for temporal trends, temperature variation, rainfall and population size over time, we examined and compared associations between weekly average temperature and weekly dengue incidence for different outbreak categories. The same model using weeks with or without heatwaves as binary variables was applied to examine the potential effects of extreme heatwaves, defined as seven or more days with temperatures above the 95th percentile of daily temperature distribution during the study period. This study included 55,801 dengue cases, with an average of 119 (range: 0 to 1454) cases per week. The exposure-response relationship between temperature and dengue risk was non-linear and differed with dengue category. After considering the delayed effects of temperature (one week lag), we estimated that 4.6%, 11.6%, and 21.9% of incident cases during small, medium, and large outbreaks were attributable to temperature. We found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks (around 14 weeks later) than small and medium outbreaks (4 to 9 weeks later). Compared with non-heatwave years, dengue outbreaks (i.e., small, moderate and large outbreaks combined) in heatwave years had higher weekly number of dengue cases (p<0.05). Findings were robust under different sensitivity analyses. CONCLUSIONS:The short-term association between temperature and dengue risk varied by the level of outbreaks and temperature seems more likely affect large outbreaks. Moreover, heatwaves may delay the timing and increase the magnitude of dengue outbreaks.

Dataset Information

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

Publications

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

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