Dataset Information

Understanding the Rising Phase of the PM_2.5 Concentration Evolution in Large China Cities.

ABSTRACT: Long-term air quality observations are seldom analyzed from a dynamic view. This study analyzed fine particulate matter (PM_2.5) pollution processes using long-term PM_2.5 observations in three Chinese cities. Pollution processes were defined as linearly growing PM_2.5 concentrations following the criteria of coefficient of determination R²?>?0.8 and duration time T???18?hrs. The linear slopes quantitatively measured pollution levels by PM_2.5 concentrations rising rates (PMRR, ?g/(m³·hr)). The 741, 210 and 193 pollution processes were filtered out, respectively, in Beijing (BJ), Shanghai (SH), and Guangzhou (GZ). Then the relationships between PMRR and wind speed, wind direction, 24-hr backward points, gaseous pollutants (CO, NO₂ and SO₂) concentrations, and regional PM_2.5 levels were studied. Inverse relationships existed between PMRR and wind speed. The wind directions and 24-hr backward points converged in specific directions indicating long-range transport. Gaseous pollutants concentrations increased at variable rates in the three cities with growing PMRR values. PM_2.5 levels at the upwind regions of BJ and SH increased at high PMRRs. Regional transport dominated the PM_2.5 pollution processes of SH. In BJ, both local contributions and regional transport increased during high-PMRR pollution processes. In GZ, PM_2.5 pollution processes were mainly caused by local emissions.

SUBMITTER: Lv B

PROVIDER: S-EPMC5404509 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Understanding the Rising Phase of the PM2.5 Concentration Evolution in Large China Cities.

Lv Baolei B Cai Jun J Xu Bing B Bai Yuqi Y

Scientific reports 20170425

Long-term air quality observations are seldom analyzed from a dynamic view. This study analyzed fine particulate matter (PM2.5) pollution processes using long-term PM2.5 observations in three Chinese cities. Pollution processes were defined as linearly growing PM2.5 concentrations following the criteria of coefficient of determination R2 > 0.8 and duration time T ≥ 18 hrs. The linear slopes quantitatively measured pollution levels by PM2.5 c ...[more]

PMID: 28440282

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Project description:This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Dataset Information

Understanding the Rising Phase of the PM_2.5 Concentration Evolution in Large China Cities.

Publications

Understanding the Rising Phase of the PM<sub>2.5</sub> Concentration Evolution in Large China Cities.

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