Suchergebnisse

Abstract. In regions with distinct seasons, soil salinity usually
varies greatly by season. Thus, the seasonal dynamics of soil salinization
must be monitored to prevent and control soil salinity hazards and to reduce
ecological risk. This article took the Kenli District in the Yellow River
delta (YRD) of China as the experimental area. Based on Landsat data from
spring and autumn, improved vegetation indices (IVIs) were created and then
applied to inversion modeling of the soil salinity content (SSC) by
employing stepwise multiple linear regression, back propagation neural
network and support vector machine methods. Finally, the optimal SSC model
in each season was extracted, and the spatial distributions and seasonal
dynamics of SSC within a year were analyzed. The results indicated that the
SSC varied by season in the YRD, and the support vector machine method
offered the best SSC inversion models for the precision of the calibration
set (R2>0.72, RMSE < 6.34 g kg−1) and the validation
set (R2>0.71, RMSE < 6.00 g kg−1 and RPD > 1.66). The best SSC inversion model for spring could be applied to the SSC
inversion in winter (R2 of 0.66), and the best model for autumn could be applied to the SSC inversion in summer (R2 of 0.65). The SSC exhibited a gradual increasing trend from the southwest to northeast in the Kenli District. The SSC also underwent the following seasonal dynamics: soil salinity accumulated in spring, decreased in summer, increased in autumn and reached its peak at the end of winter. This work provides data support for the control of soil salinity hazards and utilization of saline–alkali soil in the YRD.

AbstractAccumulating evidence suggested that the risk of preterm births (PTBs) following prenatal exposure to air pollution was inconclusive. The aim of this study is to investigate the relationship between air pollution exposure in the days before delivery and PTB and assess the threshold effect of short-term prenatal exposure to air pollution on PTB. This study collected data including meteorological factors, air pollutants, and information in Birth Certificate System from 9 districts during 2015–2020 in Chongqing, China. Generalized additive models (GAMs) with the distributed lag non-linear models were conducted to assess the acute impact of air pollutants on the daily counts of PTB, after controlling for potential confounding factors. We observed that PM2.5 was related to increased occurrence of PTB on lag 0–3 and lag 10–21 days, with the strongest on the first day (RR = 1.017, 95%CI: 1.000–1.034) and then decreasing. The thresholds of PM2.5 for lag 1–7 and 1–30 days were 100 μg/m3 and 50 μg/m3, respectively. The lag effect of PM10 on PTB was very similar to that of PM2.5. In addition, the lagged and cumulative exposure of SO2 and NO2 was also associated with the increased risk of PTB. The lag relative risk and cumulative relative risk of CO exposure were the strongest, with a maximum RR at lag 0 (RR = 1.044, 95%CI: 1.018, 1.069). Importantly, the exposure–response curve of CO showed that RR increased rapidly when the concentration exceeded 1000 μg/m3. This study indicated significant associations between air pollution and PTB. The relative risk decreases with day lag, while the cumulative effect increases. Thus, pregnant women should understand the risk of air pollution and try to avoid high concentration exposure.

Hongyan Chen,1,* Yang He,2,* Xiaoling Zeng,3,* Qing Chen,3 Niya Zhou,3 Huan Yang,3 Wenzheng Zhou,1 Liwen Zhang,3 Rong Yang,4 Qiao Huang,4 Hua Zhang5 1Quality Management Department, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China; 2Operating Room, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China; 3Key Lab of Medical Protection for Electromagnetic Radiation, Ministry of Education of China, Institute of Toxicology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, People's Republic of China; 4Obstetric Outpatient Department, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China; 5Administration Office, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China*These authors contributed equally to this workCorrespondence: Hua Zhang, Administration Office, Chongqing Health Center for Women and Children, No. 120, Longshan Road, Yubei District, Chongqing, People's Republic of China, Email cqfyzhanghua@163.com Qiao Huang, Obstetric Outpatient Department, Chongqing Health Center for Women and Children, No. 120, Longshan Road, Yubei District, Chongqing, People's Republic of China, Email 1013658406@qq.comPurpose: To investigate whether pregnant women's subjective sleep quality during the first trimester independently predicted blood glucose and gestational diabetes mellitus (GDM).Methods: A total of 4550 pregnant women in the first trimester were enrolled in Chongqing Health Center for Women and Children, China, from January to October 2020.The Pittsburgh Sleep Quality Index (PSQI) was used to measure subjective sleep quality. Depression symptoms and anxiety were measured with the Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7). Oral glucose tolerance tests (OGTT) and blood glucose area under the curve (AUC) were used for estimation of blood glucose and diagnosis of GDM during the second trimester. Linear, mixed model, and logistic regression were used to analyze the association between PSQI and blood glucose as well as GDM.Results: 946/4550 were diagnosed with GDM (20.8%). In the mixed model analysis, the blood glucose level of the highest-scoring group (PSQI score = 18) was 1.94 (95% CI: 0.45∼ 3.43, P = 0.011) mmol/L higher than that of the lowest-scoring group (PSQI score = 0). After adjusting for potential confounders, a one-point PSQI score increase was associated with a 0.014 (95% CI: 0.001∼ 0.027, P = 0.039) mmol/L increase in blood glucose level. Blood glucose AUC was also positively associated with PSQI scores (β = 0.034, 95% CI: 0.003∼ 0.064, P = 0.030). The results for the logistic regression model showed that PSQI was marginal positively correlated with GDM (OR = 1.146, 95% CI: 0.995∼ 1.321, P = 0.059) when age and BMI were not controlled for. When investigating the association between PSQI and the GDM-diagnosed time window, the 1-h diagnosed GDM had a borderline positive correlation with PSQI (OR = 1.182, 95% CI: 0.993∼ 1.405, P = 0.060).Conclusion: Sleep quality during the first trimester may be a risk factor for elevated blood glucose and GDM later in gestation.Keywords: gestational diabetes mellitus, PSQI, blood glucose AUC, sleep disturbance