Investigating the Co-movement Nexus Between Air Quality, Temperature, and COVID-19 in California: Implications for Public Health

This research aims to look at the link between environmental pollutants and the coronavirus disease (COVID-19) outbreak in California. To illustrate the COVID-19 outbreak, weather, and environmental pollution, we used daily confirmed cases of COVID-19 patients, average daily temperature, and air quality Index, respectively. To evaluate the data from March 1 to May 24, 2020, we used continuous wavelet transform and then applied partial wavelet coherence (PWC), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Empirical estimates disclose a significant association between these series at different time-frequency spaces. The COVID-19 outbreak in California and average daily temperature show a negative (out phase) coherence. Similarly, the air quality index and COVID-19 also show a negative association circle during the second week of the observed period. Our findings will serve as policy implications for state and health officials and regulators to combat the COVID-19 outbreak.

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Co-variance nexus between COVID-19 mortality, humidity, and air quality index in Wuhan, China: New insights from partial and multiple wavelet coherence

Air Quality Atmosphere & Health ◽

10.1007/s11869-020-00847-1 ◽

2020 ◽

Vol 13 (6) ◽

pp. 673-682 ◽

Cited By ~ 12

Author(s):

Zeeshan Fareed ◽

Najaf Iqbal ◽

Farrukh Shahzad ◽

Syed Ghulam Meran Shah ◽

Bushra Zulfiqar ◽

...

Keyword(s):

Air Quality ◽

Quality Index ◽

Air Quality Index ◽

Wavelet Coherence ◽

Multiple Wavelet Coherence

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Prediction of Air Quality Index Based on Wavelet Transform Combination Model

2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ◽

10.1109/ihmsc.2019.00044 ◽

2019 ◽

Author(s):

Yiwendi Zhu ◽

Xiaofeng Zhou

Keyword(s):

Wavelet Transform ◽

Air Quality ◽

Quality Index ◽

Air Quality Index ◽

Combination Model

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A Novel BP Neural Network with Wavelet Transform Inputs for Air Quality Index Prediction

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/735/1/012059 ◽

2020 ◽

Vol 735 ◽

pp. 012059

Author(s):

Zheng Fang ◽

Lei Zhang ◽

Yakun Huang

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Air Quality ◽

Bp Neural Network ◽

Quality Index ◽

Air Quality Index

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Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world

Environmental Sciences Europe ◽

10.1186/s12302-021-00575-y ◽

2021 ◽

Vol 33 (1) ◽

Author(s):

Mohammad Sarmadi ◽

Sajjad Rahimi ◽

Mina Rezaei ◽

Daryoush Sanaei ◽

Mostafa Dianatinasab

Keyword(s):

Air Quality ◽

Relative Humidity ◽

Quality Index ◽

Linear Regression Analysis ◽

Environmental Pollutants ◽

Air Quality Index ◽

Rank Test ◽

Bivariate Correlation ◽

The World ◽

Before And After

Abstract Background Coronavirus disease 2019 (COVID-19) pandemic provided an opportunity for the environment to reduce ambient pollution despite the economic, social and health disruption to the world. The purpose of this study was to investigate the changes in the air quality indexes (AQI) in industrial, densely populated and capital cities in different countries of the world before and after 2020. In this ecological study, we used AQI obtained from the free available databases such as the World Air Quality Index (WAQI). Bivariate correlation analysis was used to explore the correlations between meteorological and AQI variables. Mean differences (standard deviation: SD) of AQI parameters of different years were tested using paired-sample t-test or Wilcoxon signed-rank test as appropriate. Multivariable linear regression analysis was conducted to recognize meteorological variables affecting the AQI parameters. Results AQI-PM2.5, AQI-PM10 and AQI-NO2 changes were significantly higher before and after 2020, simultaneously with COVID-19 restrictions in different cities of the world. The overall changes of AQI-PM2.5, AQI-PM10 and AQI-NO2 in 2020 were – 7.36%, – 17.52% and – 20.54% compared to 2019. On the other hand, these results became reversed in 2021 (+ 4.25%, + 9.08% and + 7.48%). In general, the temperature and relative humidity were inversely correlated with AQI-PM2.5, AQI-PM10 and AQI-NO2. Also, after adjusting for other meteorological factors, the relative humidity was inversely associated with AQI-PM2.5, AQI-PM10 and AQI-NO2 (β = − 1.55, β = − 0.88 and β = − 0.10, P < 0.01, respectively). Conclusions The results indicated that air quality generally improved for all pollutants except carbon monoxide and ozone in 2020; however, changes in 2021 have been reversed, which may be due to the reduction of some countries’ restrictions. Although this quality improvement was temporary, it is an important result for planning to control environmental pollutants.

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