Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Hou, Kun Mean; Xu, Xia

doi:10.3390/atmos13010024

Cited by 18 publications

(8 citation statements)

References 44 publications

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“…As the result, there was a strong negative correlation between RH and SO 2 , as well as wind speed and NO 2 . These findings were consistent with previous research [14,15]. Thus, we exclude these confounders to avoid potential multicollinearity.…”

Section: Discussionsupporting

confidence: 92%

The lag-effects of meteorological factors and air pollutants on child respiratory diseases in Fuzhou, China

Miao

et al. 2022

J Glob Health

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BackgroundThe effects of meteorological factors and air pollutants on respiratory diseases (RDs) were various in different populations according to the demographic characteristics, and children were considered a vulnerable population. Previous studies were mainly based in cities with serious air pollution. This study aimed to qualify the lag effects of meteorological factors and air pollution on respiratory diseases among children under 18 years old in Fuzhou.Methods Meteorological data, air pollutants concentrations and hospital admission data of Fujian Maternity and Child Health Hospital between 2015 and 2019 were collected. A Distributed Lag Nonlinear Model (DLNM) was used to evaluate the nonlinear and lagged effect of meteorological factors and air pollutants on daily RDs number. A subgroup analysis was also conducted to evaluate the effect on different sex groups and age groups.Results A total number of 796 125 RDs visits was included during the study period. For meteorological factors, lower mean temperature and relative humidity were significantly associated with daily RDs number (peak relative risk (RR) = 1.032 (95% confidence interval (CI) = 1.011-1.053) and 1.021 (95% CI = 1.013-1.029)), while lower wind speed showed a significant association at low range (peak RR = 0.995 (95% CI = 0.992-0.999)). Temperature warming was a significant protective factor for RDs (peak RR = 0.989 (95% CI = 0.986-0.993)). For air pollutants, SO 2 , NO 2 , PM 10 and PM 2.5 were all significantly associated with RDs (peak RR = 1.028 (95% CI = 1.022-1.035), 1.024 (95% CI = 1.013-1.034), 1.036 (95% CI = 1.025-1.047), 1.028 (95% CI = 1.019-1.037)), and the relationship had no threshold. The estimated RR and peak lag day did not change extremely between subgroups. ConclusionsThe findings provide statistical evidence for the prevention of child RDs. In addition, our findings suggested that even at low concentrations, air pollutants still have negative effects on the respiratory system.Due to the high incidence rate, respiratory diseases (RDs) have become serious threats to people' s health, especially children. According to the statistics, over four million people die due to RDs each year [1]. As the susceptible population, nearly three million children die of pneumonia and low respiratory tract infec- VIEWPOINTS RESEARCH THEME 7:HEALTH TRANSITIONS IN CHINA

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Section: Discussionsupporting

confidence: 92%

The lag-effects of meteorological factors and air pollutants on child respiratory diseases in Fuzhou, China

Miao

et al. 2022

J Glob Health

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“…Three megacities of China were considered with their temporal and seasonal variation. Hou and Xu [14] integrated the generalized additive model with marginal effects for the analysis. In conclusion, the authors discovered that temperature, relative humidity, and visibility have a substantial relationship with PM 2.5 concentration, whereas O 3 has a strong relationship with temperature and relative humidity.…”

Section: Related Studymentioning

confidence: 99%

Meteorological Factors Correlation with Air Pollutants: A Case Study in Delhi

Malhotra¹,

Aulakh²

2023

IJESD

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Air pollution is increasing at an alarming rate, and meteorological elements have a significant influence on the movement of major air pollutants. This article conducted a detailed investigation of air pollutants (SO2, NO2, NO, CO, O3, PM2.5, PM10) and their interaction with meteorological factors in Delhi, India, from 2019 to 2021. We used correlation and partial correlation methods to examine the association. To better understand the relationship between air contaminants and meteorological characteristics, we controlled single and multiple parameters. We discovered that changing the variable causes a significant shift in the relationship between seasonal and regional attributes. We also explored the correlation between the Air Quality Index (AQI) and all of the air contaminants and climatic variables available for the study. Our research will help in the development of a better decision-making system based on the kind of meteorological parameters and air pollutants.

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“…Air pollution might have a linkage to meteorological conditions or vice versa. Some research has been conducted to analyze the linkage of air pollutants and meteorological conditions 8 – 13 . A study in Taiwan reveals that temperature was associated with the incidence of CO poisoning 14 .…”

Section: Introductionmentioning

confidence: 99%

An integrated analysis of air pollution and meteorological conditions in Jakarta

Handhayani

2023

Sci Rep

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Air pollution and climate change are general problems for society. This paper proposes an integrated analysis of the Air Quality Index (AQI) and meteorological conditions in Jakarta. The column-based data integration model is applied to create integrated data of the Air Quality Index and meteorological conditions. The integrated data is then used to generate a causal graph using the PC algorithm. The causal graph reveals that there exist causal relationships between pollutants and meteorological conditions, e.g, humidity, rainfall, wind speed, and duration of sunshine affect particulate matter 10 (PM$$_{10}$$ 10 ); wind speed affects sulfur dioxide (SO$$_2$$ 2 ); temperature affects ozone (O$$_3$$ 3 ). The historical data records that the average wind speed is decreased and the number of unhealthy days has risen. Ozone and particulate matter are two pollutants that mainly influence poor air quality in Jakarta. The integrated data is also used to train Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting. Experimental results show that LSTM using integrated data produces smaller errors for forecasting AQI and meteorological conditions.

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Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Cited by 18 publications

References 44 publications

The lag-effects of meteorological factors and air pollutants on child respiratory diseases in Fuzhou, China

The lag-effects of meteorological factors and air pollutants on child respiratory diseases in Fuzhou, China

Meteorological Factors Correlation with Air Pollutants: A Case Study in Delhi

An integrated analysis of air pollution and meteorological conditions in Jakarta

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