Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

Yu, Zhichen; Zhong, Shaobo; Wang, Chaolin; Yang, Yalin; Yao, Guannan; Huang, Quanyi

doi:10.3390/ijgi6020052

Cited by 11 publications

(2 citation statements)

References 30 publications

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“…Unlike other cities where PM 2.5 is high only in the winter, in Beijing, air pollution is high in both the autumn and winter [36]. According to the long-term observations performed by the Chinese Academy of Sciences [37], the average annual value of PM 2.5 in Beijing was 92.7 μg/m 3 during 2006–2017, reaching 110.7 μg/m 3 in 2006, the historical maximum.…”

Section: Methodsmentioning

confidence: 99%

Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

Zhong

Zhu

2019

IJERPH

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There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM10, PM2.5, SO2, O3, NO2, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O3 and NO2 have the highest health impact, followed by PM10 and PM2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RRTotal than that between AQI and RRTotal endorsed our efforts.

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Section: Methodsmentioning

confidence: 99%

Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

Zhong

Zhu

2019

IJERPH

Self Cite

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“…e model that more often describes the reality geological statistics [24,25] is the Gaussian model based on the following equation:…”

Section: N)mentioning

confidence: 99%

Application of Kriging Algorithm Based on ACFPSO in Geomagnetic Data Interpolation

Zhou

Zhang

Wang

et al. 2019

Mathematical Problems in Engineering

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High-precision geomagnetic field model is the key to magnetic anomaly detection and localization technology. The model is usually constructed through Kriging interpolation. Aiming at the problem of insufficient fitting of variogram in the existing Kriging interpolation methods, this paper proposes a particle swarm optimization algorithm with an adaptive compression factor (ACFPSO). The algorithm utilizes the degree of particle aggregation and the number of iterations to dynamically change the compression factor so as to achieve an effective balance between global optimization and local exploration. The cross-validation results show that the ACFPSO algorithm has the same convergence speed as the conventional particle swarm optimization algorithm, but the convergence accuracy is higher. Compared with the commonly used high-efficiency interpolation methods, such as the plain Kriging, the inverse distance weighting, and the radial basis function, the ACFPSO-optimized Kriging method achieves better performance (the mean absolute error is around 0.3 nT).

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Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

Kalantari,

Gholami,

Malakooti

et al. 2024

Environ Sci Pollut Res

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Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

Cited by 11 publications

References 30 publications

Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

Application of Kriging Algorithm Based on ACFPSO in Geomagnetic Data Interpolation

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

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