scholarly journals Analysis of Spatio-Temporal Heterogeneity and Socioeconomic Driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1324
Author(s):  
Ju Wang ◽  
Ran Li ◽  
Kexin Xue ◽  
Chunsheng Fang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Shandong Province ◽  
Temporal Heterogeneity ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Spatio Temporal ◽  
Surrounding Areas ◽  
Pm2.5 Concentration

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

scholarly journals Visualization of Correlation for Air Pollution Time Series using TAQMN Data

2019 ◽  
Vol 8 (3) ◽  
pp. 7922-7927
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Pearson Correlation ◽  
Fine Particulate Matter ◽  
Correlation Method ◽  
Well Being ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

scholarly journals How Do Economic Growth, Urbanization, and Industrialization Affect Fine Particulate Matter Concentrations? An Assessment in Liaoning Province, China

2020 ◽  
Vol 17 (15) ◽  
pp. 5441
Author(s):  
Tuo Shi ◽  
Yuanman Hu ◽  
Miao Liu ◽  
Chunlin Li ◽  
Chuyi Zhang ◽  
...  
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Rapid Development ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Liaoning Province ◽  
Fine Particulate ◽  
Pm2.5 Concentration

With China’s rapid development, urban air pollution problems occur frequently. As one of the principal components of haze, fine particulate matter (PM2.5) has potential negative health effects, causing widespread concern. However, the causal interactions and dynamic relationships between socioeconomic factors and ambient air pollution are still unclear, especially in specific regions. As an important industrial base in Northeast China, Liaoning Province is a representative mode of social and economic development. Panel data including PM2.5 concentration and three socio-economic indicators of Liaoning Province from 2000 to 2015 were built. The data were first-difference stationary and the variables were cointegrated. The Granger causality test was used as the main method to test the causality. In the results, in terms of the causal interactions, economic activities, industrialization and urbanization processes all showed positive long-term impacts on changes of PM2.5 concentration. Economic growth and industrialization also significantly affected the variations in PM2.5 concentration in the short term. In terms of the contributions, industrialization contributed the most to the variations of PM2.5 concentration in the sixteen years, followed by economic growth. Though Liaoning Province, an industry-oriented region, has shown characteristics of economic and industrial transformation, policy makers still need to explore more targeted policies to address the regional air pollution issue.

scholarly journals Association between Airborne Fine Particulate Matter and Residents’ Cardiovascular Diseases, Ischemic Heart Disease and Cerebral Vascular Disease Mortality in Areas with Lighter Air Pollution in China

2018 ◽  
Vol 15 (9) ◽  
pp. 1918 ◽  
Author(s):  
Junfang Cai ◽  
Shuyuan Yu ◽  
Yingxin Pei ◽  
Chaoqiong Peng ◽  
Yuxue Liao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Heart Disease ◽  
Particulate Matter ◽  
Ischemic Heart Disease ◽  
Fine Particulate Matter ◽  
Average Concentration ◽  
Cerebral Vascular Disease ◽  
Fine Particulate ◽  
Pm2.5 Concentration ◽  
Cvd Mortality

Background: China began to carry out fine particulate matter (PM2.5) monitoring in 2013 and the amount of related research is low, especially in areas with lighter air pollution. This study aims to explore the association between PM2.5 and cardiovascular disease (CVD), ischemic heart disease (IHD) and cerebral vascular disease (EVD) mortality in areas with lighter air pollution. Methods: Data on resident mortality, air pollution and meteorology in Shenzhen during 2013–2015 were collected and analyzed using semi-parametric generalized additive models (GAM) with Poisson distribution of time series analysis. Results: Six pollutants were measured at seven air quality monitoring sites, including PM2.5, PM10, SO2, NO2, CO and O3. The PM2.5 daily average concentration was 35.0 ± 21.9 μg/m3; the daily average concentration range was from 7.1 μg/m3 to 137.1 μg/m3. PM2.5 concentration had significant effects on CVD, IHD and EVD mortality. While PM2.5 concentration of lag5 and lag02 rose by 10 μg/m3, the excess risk (ER) of CVD mortality were 1.50% (95% CI: 0.51–2.50%) and 2.09% (95% CI: 0.79–3.41%), respectively. While PM2.5 concentration of lag2 and lag02 rose by 10 μg/m3, the ER of IHD mortality were 2.87% (95% CI: 0.71–5.07%) and 3.86% (95% CI: 1.17–6.63%), respectively. While PM2.5 concentration of lag4 and lag04 rose by 10 μg/m3, the ER of EVD mortality were 2.09% (95% CI: 2.28–3.92%) and 3.08% (95% CI: 0.68–5.53%), respectively. Conclusions: PM2.5 increased CVD mortality. The government needs to strengthen the governance of air pollution in areas with a slight pollution.

scholarly journals The Combined Effects of Fine Particulate Matter and Temperature on Preterm Birth in Seoul, 2010–2016

2021 ◽  
Vol 18 (4) ◽  
pp. 1463
Author(s):  
Youngrin Kwag ◽  
Min-ho Kim ◽  
Shinhee Ye ◽  
Jongmin Oh ◽  
Gyeyoon Yim ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Preterm Birth ◽  
Pregnant Women ◽  
Low Temperatures ◽  
Fine Particulate Matter ◽  
Temperature Data ◽  
Fine Particulate ◽  
The Mean ◽  
Pm2.5 Exposure ◽  
Pm2.5 Concentration

Background: Preterm birth contributes to the morbidity and mortality of newborns and infants. Recent studies have shown that maternal exposure to particulate matter and extreme temperatures results in immune dysfunction, which can induce preterm birth. This study aimed to evaluate the association between fine particulate matter (PM2.5) exposure, temperature, and preterm birth in Seoul, Republic of Korea. Methods: We used 2010–2016 birth data from Seoul, obtained from the Korea National Statistical Office Microdata. PM2.5 concentration data from Seoul were generated through the Community Multiscale Air Quality (CMAQ) model. Seoul temperature data were collected from the Korea Meteorological Administration (KMA). The exposure period of PM2.5 and temperature were divided into the first (TR1), second (TR2), and third (TR3) trimesters of pregnancy. The mean PM2.5 concentration was used in units of ×10 µg/m3 and the mean temperature was divided into four categories based on quartiles. Logistic regression analyses were performed to evaluate the association between PM2.5 exposure and preterm birth, as well as the combined effects of PM2.5 exposure and temperature on preterm birth. Result: In a model that includes three trimesters of PM2.5 and temperature data as exposures, which assumes an interaction between PM2.5 and temperature in each trimester, the risk of preterm birth was positively associated with TR1 PM2.5 exposure among pregnant women exposed to relatively low mean temperatures (<3.4 °C) during TR1 (OR 1.134, 95% CI 1.061–1.213, p < 0.001). Conclusions: When we assumed the interaction between PM2.5 exposure and temperature exposure, PM2.5 exposure during TR1 increased the risk of preterm birth among pregnant women exposed to low temperatures during TR1. Pregnant women should be aware of the risk associated with combined exposure to particulate matter and low temperatures during TR1 to prevent preterm birth.

scholarly journals Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure

2021 ◽  
Author(s):  
Cavin K. Ward‐Caviness, ◽  
Mahdieh Danesh Yazdi, ◽  
Joshua Moyer, ◽  
Anne M. Weaver, ◽  
Wayne E. Cascio, ◽  
...  
Keyword(s):  
Heart Failure ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Total Hospital ◽  
Fine Particulate ◽  
Patients With Heart Failure ◽  
Hospital Visits ◽  
Pollution Exposure

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

scholarly journals Effect of modes of transportation on commuters' exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in Chennai, India

2019 ◽  
Vol 25 (6) ◽  
pp. 898-907 ◽  
Author(s):  
M. Gokul Raj ◽  
S. Karthikeyan
Keyword(s):  
Particulate Matter ◽  
Nitrogen Dioxide ◽  
Urban Community ◽  
Fine Particulate Matter ◽  
Exposure Concentration ◽  
Ambient Particulate Matter ◽  
Fine Particulate ◽  
The Mean ◽  
Maximum Exposure ◽  
Pm2.5 Concentration

Daily commuting increases level of contaminants inhaled by urban community and it is influenced by mode and time of commuting. In this study, the commuters’ exposure to ambient particulate matter (PM2.5) and nitrogen dioxide (NO2) was assessed during three modes of travel in six different road stretches of Chennai. The mean distance of road stretches was 25 km and the exposure to pollutants was assessed during peak hours and off-peak hours. The average travel duration was in the range of 39 to 91 min in motorbike, 83 to 140 min in car and 110 to 161 min in bus. Though there was variation on exposure to concentration in modes of transportation, the maximum exposure concentration of PM2.5 was observed as 709 μg/m<sup>3</sup> in bus and the minimum exposure concentration was 29 μg/m<sup>3</sup> in closed car. Similarly, the maximum exposure concentration of NO2 was observed to be 312 μg/m<sup>3</sup> in bus and the minimum exposure concentration was 21 μg/m<sup>3</sup> in car. The concentration of elements in PM2.5 was in the order of Si > Na > Ca > Al ≥ K > S ≥ Cd, with Si and Cd concentration as 60% and < 1% of the PM2.5 concentration.

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