Dataset Information

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China.

ABSTRACT: Air quality issue such as particulate matter pollution (PM2.5 and PM10) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO2 and CO were key factors affecting PM2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

SUBMITTER: Yang H

PROVIDER: S-EPMC7242410 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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The unidirectional causality influence of factors on PM2.5 in Shenyang city of China.

Yang Hongmei H Peng Qin Q Zhou Jun J Song Guojun G Gong Xinqi X

Scientific reports 20200521 1

Air quality issue such as particulate matter pollution (PM2.5 and PM10) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of ...[more]

PMID: 32439904

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Project description:BackgroundHemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China.MethodsThe annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables.ResultsA distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51).ConclusionThe temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.

Project description:BackgroundBirth defects, as a kind of diseases that seriously affect human life, have always attracted much attention. In the past, perinatal data have been studied for birth defects. This study analyzed the surveillance data of birth defects during the perinatal period and the whole of pregnancy, as well as the independent influencing factors, to help to minimize their risk of birth defects.MethodsA total of 23,649 fetuses delivered in the hospital from January 2017 to December 2020, were enrolled in this study. There were 485 cases of birth defects, including live births and stillbirths by detailed inclusion and exclusion criteria. Maternal and neonatal clinical data were collated to analyze the influencing factors associated with birth defects. Pregnancy complications and comorbidities were diagnosed according to the criteria of the Chinese Medical Association. Univariate and multivariate logistic regression models were used to investigate the association between independent variables and birth defect events.ResultsThe incidence of birth defects during the whole of pregnancy was 175.46/10,000, while the incidence of perinatal birth defects was 96.22/10,000. The birth defect group had significantly higher maternal age, gravidity, parity, rate of preterm birth, cesarean section (CS) rate, scarred uterus, stillborn, and male newborns compared to the control group. Multivariate logistic regression model analysis showed that preterm birth [odds ratio (OR): 1.69, 95% confidence interval (CI): 1.01 to 2.86], CS (OR: 1.46, 95% CI: 1.08 to 1.98), scarred uterus (OR: 1.70, 95% CI: 1.01 to 2.85), and low birth weight (OR >4 compared to the other two classes) were significantly associated with birth defects during the whole of pregnancy (all P<0.05). The independent influencing factors associated with perinatal birth defects included CS (OR: 1.43, 95% CI: 1.05 to 1.93), gestational hypertension (OR: 1.70, 95%: 1.04 to 2.78), and low birth weight (OR >3.70 compared to the other two classes).ConclusionsThe discovery and monitoring of known influencing factors associated with birth defects, such as, preterm birth, gestational hypertension, low birth weight, should be enhanced. For the controllable influencing factors, obstetrics providers should work with patients to minimize their risk of birth defects.

Dataset Information

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China.

Publications

The unidirectional causality influence of factors on PM<sub>2.5</sub> in Shenyang city of China.

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