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Drought prediction plays an important guiding role in drought risk management. The standardized precipitation index (SPI) is a popular meteorological drought indicator to measure the degree of drought. The SPI time series is non-stationary, whereas the conventional artificial neural network (ANN) model has limitations to predict non-stationary time series. To overcome this limitation, it is essential to investigate input data preprocessing to improve the ANN model. In this paper, a hybrid model coupled with singular spectrum analysis (SSA) and backpropagation ANN is proposed (SSA-BP-ANN). The advantage of this model is that the SSA of finite-length SPI sequences does not require the adoption of boundary extensions to suppress boundary effects, while the most predictable components of the SPI can be efficiently extracted and incorporated into the model. The proposed SSA-BP-ANN model is tested in case studies at three meteorological stations in Northern Shannxi Province, China. The results show that the SSA-BP-ANN model can produce more accurate predictions than the BP-ANN model. In addition, the performance improvement of SSA on the BP-ANN model is slightly better than wavelet decomposition and empirical mode decomposition. This new hybrid prediction model has great potential for promoting drought early warning in arid regions. [ABSTRACT FROM AUTHOR]

Although Shaanxi is one of the birthplaces of China's Buddhism and Taoism, and temples are extensively distributed across the region, there is not much research on the temple gardens. This is especially true in the Northern Shaanxi area where has harsh natural environmental conditions. Here, a number of mountain temples reflect the constructional wisdom of the ancient monks and conform to the Chinese classical garden. The space environment of Buddhist temples, Taoist temples and the celebrity temples that are both in good condition and located in the mountainous environment are taken as the object of this research. In addition to the analysis of examples, a systematic review of the distribution, site selection and space construction of mountain temples in Northern Shaanxi is performed, to provide references for the space environment construction of modern temples or gardens. [ABSTRACT FROM AUTHOR]

Background: The wide range and complex combinations of factors that cause birth defects impede the development of primary prevention strategies targeted at high-risk subpopulations. Methods: Latent class analysis (LCA) was conducted to identify mutually exclusive profiles of factors associated with birth defects among women between 15 and 49 years of age using data from a large, population-based, cross-sectional study conducted in Shaanxi Province, western China, between August and October, 2013. The odds ratios (ORs) and 95% confidence intervals (CIs) of associated factors and the latent profiles of indicators of birth defects and congenital heart defects were computed using a logistic regression model. Results: Five discrete subpopulations of participants were identified as follows: No folic acid supplementation in the periconceptional period (reference class, 21.37%); low maternal education level + unhealthy lifestyle (class 2, 39.75%); low maternal education level + unhealthy lifestyle + disease (class 3, 23.71%); unhealthy maternal lifestyle + advanced age (class 4, 4.71%); and multi-risk factor exposure (class 5, 10.45%). Compared with the reference subgroup, the other subgroups consistently had a significantly increased risk of birth defects (ORs and 95% CIs: class 2, 1.75 and 1.21–2.54; class 3, 3.13 and 2.17–4.52; class 4, 5.02 and 3.20–7.88; and class 5, 12.25 and 8.61–17.42, respectively). For congenital heart defects, the ORs and 95% CIs were all higher, and the magnitude of OR differences ranged from 1.59 to 16.15. Conclusions: A comprehensive intervention strategy targeting maternal exposure to multiple risk factors is expected to show the strongest results in preventing birth defects. [ABSTRACT FROM AUTHOR]

A continuous measurement of ambient volatile organic compounds (VOCs) was carried out from May to June 2018 at a background site, the Wudang Mountain (Mt. Wudang) in Central China. The characteristics, sources of VOCs and their contributions to ozone formation were investigated for the first time in this area. Based on the National Ambient Air Quality Standard, 29 O 3 non-attainment days were identified. The average mixing ratio of 82 total VOCs (∑VOCs) was 12.17 ± 3.66 ppb. Higher mixing ratios of individual species were found compared with those measured at global baseline sites. The results revealed that the dominant VOCs components were alkanes and OVOCs, accounting for 79.92%. Five sources, i.e. industrial processes, fuel evaporation, vehicular exhaust, solvent utilization and biogenic emission were identified by the positive matrix factorization (PMF) model. From potential source contribution function (PSCF) analysis, local area and the adjacent provinces especially Shaanxi province, had some extent influence on Mt. Wudang. The OH radical loss rate (L OH) and ozone formation potential (OFP) calculations revealed that biogenic emission and vehicular exhaust were both the major sources for ozone formation. Through the Observation-Based Model (OBM) simulation, the daytime average net ozone production rate was 4.1 ppb/h and the ozone production pathway was dominated by the reaction of HO 2 + NO. This study shows that local emission and long-range transport of air pollutants should be taken into account for developing the science-based air pollution control strategies at Mt. Wudang. Unlabelled Image • First study on the characteristics and sources of VOCs, and the contributions to ozone formation at Mt. Wudang. • The mixing ratios of ozone depleting substances at Mt. Wudang were much lower than the mean global baseline values. • Vehicular exhaust and biogenic emission were the dominant sources for the photochemical ozone formation at Mt. Wudang. • The daytime net photochemical ozone production rate was 4.1 ppb/h and the main production pathway was HO 2 + NO reaction. [ABSTRACT FROM AUTHOR]