Background While benefits of greenness exposure to health have been reported, findings specific to lung function are inconsistent. The purpose of this study is to assess the correlations of greenness exposure with multiple lung function indicators based on chronic obstructive pulmonary disease (COPD) monitoring database from multiple cities of Anhui province in China. Methods We assessed the greenness using the annual average of normalized difference vegetation index (NDVI) with a distance of 1000-meter buffer around each local community or village. Three types of lung function indicators were considered, namely indicators of obstructive ventilatory dysfunction (FVC, FEV1, FEV1/FVC, and FEV1/FEV3); an indicator of large-airway dysfunction (PEF); indicators of small-airway dysfunction (FEF25%, FEF50%, FEF75%, MMEF, FEV3, FEV6, and FEV3/FVC). Linear mixed effects model was used to analyze associations of greenness exposure with lung function through adjusting age, sex, educational level, occupation, residence, smoking status, history of tuberculosis, family history of lung disease, indoor air pollution, occupational exposure, PM2.5, and body mass index. Results A total of 2768 participants were recruited for the investigations. An interquartile range (IQR) increase in NDVI was associated with better FVC (153.33mL, 95%CI: 44.07mL, 262.59mL), FEV1 (109.09mL, 95%CI: 30.31mL, 187.88mL), FEV3 (138.04mL, 95%CI: 39.43mL, 236.65mL), FEV6 (145.42mL, 95%CI: 42.36mL, 248.47mL). However, there were no significant associations with PEF, FEF25%, FEF50%, FEF75%, MMEF, FEV1/FVC, FEV1/FEV6, FEV3/FVC. The stratified analysis displayed that an IQR increase in NDVI was related with improved lung function in less than 60 years, females, urban populations, nonsmokers, areas with medium concentrations of PM2.5 and individuals with BMI of less than 28 kg/m2. Sensitivity analyses based on another greenness indice (enhanced vegetation index, EVI) and annual maximum of NDVI remained consistent with the main analysis. Conclusions Our findings supported that exposure to greenness was strongly related with improved lung function.

How to measure and quantitatively assess hydrological drought (HD) in the inland river basins of Northwest China is a difficult problem because of the complicated geographical environment and climatic processes. To address this problem, we conducted a comprehensive approach and selected the Aksu River Basin (ARB) as a typical inland river basin to quantitatively assess the hydrological drought based on the observed data and reanalysis data during the period of 1980–2010. We used two mutual complementing indicators, i.e., the standardized runoff index (SRI) and standardized terrestrial water storage index (SWSI), to quantitatively measure the spatio-temporal pattern of HD, where the SRI calculated from the observed runoff data indicate the time trend of HD of the whole basin, while SWSI extracted from the reanalysis data indicate the spatial pattern of HD. We also used the auto-regressive distribution lag model (ARDL) to show the autocorrelation of HD and its dependence on precipitation, potential evapotranspiration (PET), and soil moisture. The main conclusions are as follows: (a) the western and eastern regions of the ARB were prone to drought, whereas the frequency of drought in the middle of the ARB is relatively lower; (b) HD presents significant autocorrelation with two months’ lag, and soil moisture is correlated with SWSI with two months’ lag, whereas PET and precipitation are correlated with SWSI with 1 month’ lag; (c) the thresholds of HD for annual PET, annual precipitation, and annual average soil moisture are greater than 844.05 mm, less than 134.52 mm, and less than 411.07 kg/m2, respectively. A drought early warning system that is based on the thresholds was designed.

Complex temperature processes are the coupling results of natural and human processes, but few studies focused on the interactive effects between natural and human systems. Based on the dataset for temperature during the period of 1980–2012, we analyzed the complexity of temperature by using the Correlation Dimension (CD) method. Then, we used the Geogdetector method to examine the effects of factors and their interactions on the temperature process in the Yangtze River Delta (YRD). The main conclusions are as follows: (1) the temperature rose 1.53 °C; and, among the dense areas of population and urban, the temperature rose the fastest. (2) The temperature process was more complicated in the sparse areas of population and urban than in the dense areas of population and urban. (3) The complexity of temperature dynamics increased along with the increase of temporal scale. To describe the temperature dynamic, at least two independent variables were needed at a daily scale, but at least three independent variables were needed at seasonal and annual scales. (4) Each driving factor did not work alone, but interacted with each other and had an enhanced effect on temperature. In addition, the interaction between economic activity and urban density had the largest influence on temperature.

The Plum Rains process is a complex process, and its spatiotemporal variations and influencing factors on different time scales still need further study. Based on a dataset on the Plum Rains in the Yangtze River Delta, from 33 meteorological stations during the period of 1960 to 2012, we investigated the spatiotemporal variations of Plum Rains and their relation with the East Asian Summer Monsoon (EASM), the El Niño-Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) using an integrated approach that combines ensemble empirical mode decomposition (EEMD), empirical orthogonal function (EOF), and correlation analysis. The main conclusions were as follows: (1) the plum rainfall (i.e., the rainfall during the period of Plum Rains) showed a trend of increasing first and then decreasing, and it had a three-year and six-year cycle on the inter-annual scale and a 13-year and 33-year cycle on the inter-decadal scale. The effect of the onset and termination of Plum Rains and the daily intensity of plum rainfall on plum rainfall on the inter-annual scale was greater than the inter-decadal scale, (2) the EOF analysis of plum rainfall revealed a dominant basin-wide in-phase pattern (EOF1) and a north-south out-of-phase pattern (EOF2), and (3) ENSO and EASM were the main influencing factors in the three-year and six-year periods, respectively.

Compaction quality evaluation of rockfill materials is an essential link in the construction process of runway. However, the traditional on-site limited sampling detection is not only time-consuming and labor-intensive, but also destructive. To address this challenge, the application of non-destructive ground penetrating radar (GPR) in runway compaction quality detection under different compaction conditions is discussed in this paper, combining laboratory test and field investigation. It is found that the crest factor (CF) index based on Hilbert -Huang transform (HHT) analysis of GPR signal can better detect the compaction quality. Based on an runway case, through the HHT analysis of GPR signals collected in the field, it is verified that the CF index can be used to predict the relative compaction of rockfill material, and the average error rate is 4.03%. At the same time, the kriging interpolation method is used to estimate the compaction quality of any point, and the corresponding evaluation heat map of compaction quality is generated. This method can greatly shorten the detection time in the construction process and provide a certain reference for the determination of the insufficient compaction area in the construction process.