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The large scale character of the interannual variation of precipitation and the urban effect on local annual precipitation anomaly are investigated in this paper based on the 1960-2000 annual precipitation observations at 20 stations in the Beijing region. The results show that: the annual precipitation in the Beijing region possesses the large scale variation character with the linear trend of -1. 197/10 yr, which corresponds to a total reduction of 27.82 mm in annual precipitation in the 41 years; the local annual precipitation anomalies (percent of the normal 1960-2000) show a positive center near the urban area, i. e. urban precipitation island (UPI), whose intensity increases with the linear trend of 0.6621% /10 yr, opposite to the interannual trend of large scale precipitation over the Beijing region; changes in the UPI are also associated with the intensity of synoptic processes of precipitation, and when the synoptic processes are strong (wet years), the intensity of UPI strengthens, while the synoptic processes are weak (dry years), and the UPI disappears in the Beijing region. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control. [ABSTRACT FROM AUTHOR]

Currently, Haze is one of the greatest environmental problems with serious impacts on human health in China, especially in capital region (Beijing-Tianjin-Hebei region). To alleviate this problem, the Chinese government introduced a National Air Pollution Control Action Plan (NAPCAP) with air pollutants reduction targets by 2017. However, there is doubt whether these targets can be achieved once the plan is implemented. In this work, the effectiveness of NAPCAP is analyzed by developing models of the statistical relationship between PM2.5 concentrations and air pollutant emissions (SO2, NOx, smoke and dust), while taking into account wind and neighboring transfer impacts. The model can also identify ways of calculating the intended emission levels in the Beijing–Tianjin–Hebei area. The results indicate that haze concentration control targets will not be attained by following the NAPCAP, and that the amount of progress needed to meet the targets is unrealistic. A more appropriate approach to reducing air emissions is proposed, which addresses joint regional efforts. [ABSTRACT FROM AUTHOR]

Background: Heavy fine particulate matter (PM2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing. Methods: Daily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender. Results: A total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM2.5 concentration was 102.1±73.6 μg/m3. Every 10 μg/m3 increase in PM2.5 concentration at lag0 was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM2.5 concentration at lag0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO2, O3, CO and NO2. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM2.5 exposure. Conclusion: PM2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM2.5 pollution varied by gender and age. [ABSTRACT FROM AUTHOR]

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. [ABSTRACT FROM AUTHOR]

To estimate the deposition effect of PM2.5 (particle matter with aerodynamic diameter <2.5 µm) in forests in northern China, we used the gradient method to measure the deposition velocity of PM2.5 during the winter and spring above a deciduous forest in Olympic Forest Park and above a coniferous forest in Jiufeng National Forest Park. Six aerosol samplers were placed on two towers at each site at heights of 9, 12 and 15 m above the ground surface. The sample filters were exchanged every four hours at 6∶00 AM, 10∶00 AM, 2∶00 PM, 6∶00 PM, 10∶00 PM, and 2∶00 AM. The daytime and nighttime deposition velocities in Jiufeng Park and Olympic Park were compared in this study. The February deposition velocities in Jiufeng Park were 1.2±1.3 and 0.7±0.7 cm s−1 during the day and night, respectively. The May deposition velocities in Olympic Park were 0.9±0.8 and 0.4±0.5 cm s−1 during the day and night, respectively. The May deposition velocities in Jiufeng Park were 1.1±1.2 and 0.6±0.5 cm s−1 during the day and night, respectively. The deposition velocities above Jiufeng National Forest Park were higher than those above Olympic Forest Park. The measured values were smaller than the simulated values obtained by the Ruijgrok et al. (1997) and Wesely et al. (1985) models. However, the reproducibility of the Ruijgrok et al. (1997) model was better than that of the Wesely et al. (1985) model. The Hicks et al. (1977) model was used to analyze additional forest parameters to calculate the PM2.5 deposition, which could better reflect the role of the forest in PM2.5 deposition. [ABSTRACT FROM AUTHOR]

Wetlands, and especially their littoral zones, are considered to be CH4 emissions hotspots. The recent creation of reservoirs has caused a rapid increase in the area of the world’s littoral zones. To investigate the effects of water depth and water level fluctuation on CH4 fluxes, and how these are coupled with vegetation and nutrients, we used static closed chamber and gas chromatography techniques to measure CH4 fluxes in the littoral zone of a large reservoir near Beijing, China, from November 2011 to October 2012. We found that CH4 flux decreased significantly along a transect from open water to dry land, from 3.1 mg m−2 h−1 at the deep water site to approximately 1.3 mg m−2 h−1 at the shallow water site, and less than 0.01 mg m−2 h−1 in the non-flooded area. Water level influenced CH4 flux by affecting soil properties including soil redox potential, soil carbon and nitrogen, and bulk density. The largest emission of all was from the seasonally flooded site after a flooding event (up to 21.1 mg m−2 h−1), which may have been caused by vegetation decomposition. Submerged sites had greater emissions, while the driest site had lower emissions. Immediately after the monthly measurements had been made, we removed the aboveground vegetation to enable an assessment of the gas transportation per unit of biomass. Removal of biomass decreased emissions by up to 53%. These results indicated the dominant effect of water depth on CH4 flux through effects of soil conditions, plant species composition and distribution. This study suggests that temporally flooded wetlands, including littoral zones, contribute significantly to the global CH4 burden. However, the current challenge is to capture their spatial extent and temporal variation in the fluxes. [ABSTRACT FROM AUTHOR]