Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

Wang, Lili; Xiong, Qiulin; Wu, Gaofeng; Gautam, Atul; Jiang, Jianfang; Liu, Shuang; Zhao, Wenji; Guan, Hao

doi:10.3390/ijerph16214276

Cited by 38 publications

(13 citation statements)

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“…The “high-high” clusters were in south, southwest, and some northwest areas, while the “low-low” clusters were in northeast, central, and north areas in 2018–2019. This distribution pattern was also similar with some findings in other researches [ 47 , 48 , 49 ]. However, in the results of 2019–2020, the “high-high” clusters were mainly located in central, east, and south areas.…”

Section: Resultssupporting

confidence: 92%

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Fan

Zhan

Yang

et al. 2020

IJERPH

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Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

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Section: Resultssupporting

confidence: 92%

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Fan

Zhan

Yang

et al. 2020

IJERPH

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“… 21 Specifically, in some high-polluted areas such as Beijing-Tianjin-Hebei region of 2013, the annual average concentrations of PM 2.5 had reached 98.9 μg/m 3 , and daily average concentrations had exceeded 300 μg/m 3 . 22 23 Furthermore, the magnitude of the effects for each 10 μg/m 3 increment in PM 2.5 was also similar compared with other studies. 5 7 24–26 For example, one study found that each 10 μg/m 3 in PM 2.5 was associated with increases of 0.45 and 0.07 mm Hg in SBP and DBP, respectively.…”

Section: Discussionsupporting

confidence: 80%

Association of long-term exposure to PM_2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study

Song

Gao

et al. 2021

BMJ Open

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ObjectiveEvidence of the effects of long-term fine particulate matter (PM2.5) exposure on hypertension and blood pressure is limited for populations exposed to high levels of PM2.5. We aim to assess associations of long-term exposure to PM2.5 with hypertension prevalence and blood pressure, and further explore the subpopulation differences and effect modification by participant characteristics in these associations in China.MethodsWe analysed cross-sectional data from 883 827 participants aged 35–75 years in the China Patient-Centred Evaluative Assessment of Cardiac Events Million Persons Project. Data from the monitoring station were used to estimate the 1-year average concentration of PM2.5. The associations of PM2.5 exposure with hypertension prevalence and blood pressure were investigated by generalised linear models, with PM2.5 included as either linear or spline functions.ResultsThe 1-year PM2.5 exposure of the study population ranged from 8.8 to 93.8 µg/m3 (mean 49.2 µg/m3). The adjusted OR of hypertension prevalence related to a 10 μg/m3 increase in 1-year PM2.5 exposure was 1.04 (95% CI, 1.02 to 1.05). Each 10 μg/m3 increment in PM2.5 exposure was associated with increases of 0.19 mm Hg (95% CI, 0.10 to 0.28) and 0.13 mm Hg (95% CI, 0.08 to 0.18) in systolic blood pressure and diastolic blood pressure, respectively. The concentration–response curves for hypertension prevalence and systolic blood pressure showed steeper slopes at higher PM2.5 levels; while the curve for diastolic blood pressure was U-shaped. The elderly, men, non-current smokers and obese participants were more susceptible to the exposure of PM2.5.ConclusionsLong-term exposure to PM2.5 is associated with higher blood pressure and increased risk of hypertension prevalence. The effects of PM2.5 on hypertension prevalence become more pronounced at higher PM2.5 levels. These findings emphasise the need to reduce air pollution, especially in areas with severe air pollution.

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“… 43 and Wang et al. 44 who observed low levels of variability of annual

from monitoring sites. Given the reasonably good performance of our

prediction model, the annual level predicted and monitored

are highly consistent, which leads to generated errors being small.…”

Section: Discussionmentioning

confidence: 96%

The Impact of Exposure Measurement Error on the Estimated Concentration–Response Relationship between Long-Term Exposure to PM2.5 and Mortality

Wei

Qiu

Yazdi

et al. 2022

Environ Health Perspect

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Background: Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. Objectives: We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration–response relationships between annual exposure to fine particulate matter [PM in aerodynamic diameter ( )] and all-cause mortality. Methods: In this simulation study, we use daily predictions at spatial resolution to estimate annual exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration–response relationship between exposure and mortality (linear, quadratic, or soft-threshold—i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration–response relationship between exposure and mortality, adjusting for the confounders. Results: We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration–response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. Discussion: Our findings suggest that the causal determination for long-term exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389

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Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

Cited by 38 publications

References 50 publications

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Association of long-term exposure to PM_2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study

The Impact of Exposure Measurement Error on the Estimated Concentration–Response Relationship between Long-Term Exposure to PM2.5 and Mortality

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Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

Cited by 38 publications

References 50 publications

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

Association of long-term exposure to PM2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study

The Impact of Exposure Measurement Error on the Estimated Concentration–Response Relationship between Long-Term Exposure to PM2.5 and Mortality

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Association of long-term exposure to PM_2.5 with hypertension prevalence and blood pressure in China: a cross-sectional study