Nowadays, most of the research on air pollution and its adverse effects on public health in China has focused on megacities and heavily-polluted regions. Fewer studies have focused on cities that are slightly polluted. Shenzhen used to have a favorable air environment, but its air quality has deteriorated gradually as a result of development in recent years. So far, no systematic investigations have been conducted on the adverse effects of air pollution on public health in Shenzhen. This research has applied a time series analysis model to study the possible association between different types of air pollution and respiratory hospital admission in Shenzhen in 2013. Respiratory hospital admission was divided into two categories for comparison analysis among various population groups: acute upper respiratory infection and acute lower respiratory infection. The results showed that short-term exposure to ambient air pollution was significantly associated with acute respiratory infection hospital admission in Shenzhen in 2013. Children under 14 years old were the main susceptible population of acute respiratory infection due to air pollution. PM₁₀, PM_2.5 and NO₂ were the primary air pollutants threatening respiratory health in Shenzhen. Though air pollution level is generally relatively low in Shenzhen, it will benefit public health to control the pollution of particulate matter as well as other gaseous pollutants.

Air pollution caused by atmospheric particulate and gaseous pollutants has drawn broad public concern globally. In this paper, the spatial-temporal distributions of major air pollutants in Shenzhen from March 2013 to February 2014 are discussed. In this study, ground-site monitoring data from 19 monitoring sites was used and spatial interpolation and spatial autocorrelation methods were applied to analyze both spatial and temporal characteristics of air pollutants in Shenzhen City. During the study period, the daily average concentrations of Particulate Matter (PM₁₀ and PM_2.5) ranged from 16–189 μg/m³ and 10–136 μg/m³, respectively, with 13 and 44 over-limit days, indicating that particulate matter was the primary air pollutant in Shenzhen. The highest PM occupation in the polluted air was observed in winter, indicating that fine particulate pollution was most serious in winter. Meanwhile, seasonal agglomeration patterns for six kinds of air pollutants showed that Guangming, Baoan, Nanshan, and the northern part of Longgang were the most polluted areas and PMs were their primary air pollutants. In addition, wind scale and rainfall played an important role in dissipating air pollutant in Shenzhen. The wind direction impacted the air pollution level in Shenzhen in multiple ways: the highest concentrations for all air pollutants all occurred on days with a northeast wind; the second highest ones appeared on the days with no wind. The concentrations on days with north-related winds are higher on average than those of days with south-related winds.

Precipitation is a major driven factor in water cycle and hydrological process. Since satellite sensors have been the main sources for acquiring globally continuous precipitation data, inter-comparison between satellite precipitation products (SPPs) from different sensors becomes increasingly significant, especially at daily or sub-daily scale and in regions suffering from frequent heavy rainfall and floods. This paper assessed the performance of four daily SPPs, including data from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards group Infrared Precipitation with Stations (CHIRPS), Climate prediction center MORPHmorphing technique (CMORPH) and Advanced SCATterometer (ASCAT) based Soil Moisture to RAINfall algorithm (SM2RAIN-ASCAT), using the ground gauge measurements from 2010 to 2014 over the Pearl River Basin (PRB), China. Accuracy of precipitation estimates and the capability in detecting rainy/non-rainy days and different precipitation categories were evaluated at both basin and station scale. The findings show that: 1) Performance of the SPPs varies temporally and spatially, and better performance can be observed in wet season and south-eastern part of the PRB, when or where precipitation is abundant. 2) All SPPs have poor performance in estimating extreme precipitation in the PRB; 3) Among the four SPPs, TRMM 3B42 exhibits the best performance in the PRB, followed by CMORPH, while CHIRPS performs the worst and is inapt for precipitation estimates in the PRB; SM2RAIN-ASCAT has quite high estimate errors, but it shows advantages against other products in detecting heavy precipitation events. Findings in this study are compared with recent studies conducted in other regions, and some limitations are discussed. This study provides significant reference for understanding the performance of daily SPPs in the PRB as well as areas with similar climate and surface condition.

Chronic obstructive pulmonary disease (COPD) is a major and increasingly prevalent respiratory health problem worldwide and the fine particulate matter (PM_2.5) is now becoming a rising health threat to it. This study aims to conduct a comparison analysis of health effect on acute exacerbation of COPD (AECOPD) associated with PM_2.5 exposure in two typical cities (Beijing and Shenzhen) with different levels of PM_2.5 pollution. Both correlational relationship and causal connection between PM_2.5 exposure and AECOPD are investigated by adopting a time series analysis based on the generalized additive model (GAM) and convergent cross mapping (CCM). The results from GAM indicate that a 10 μg/m³ increase in PM_2.5 concentration is associated with 2.43% (95% CI, 0.50–4.39%) increase in AECOPD on Lag0-2 in Beijing, compared with 6.65% (95% CI, 2.60–10.87%) on Lag0-14 in Shenzhen. The causality detection with CCM reveals similar significant causative impact of PM_2.5 exposure on AECOPD in both two study areas. Findings from two methods agree that PM_2.5 has non-negligible health effect on AECOPD in both two study areas, implying that air pollution can cause adverse consequences at much lower levels than common cognition. Our study highlights the adverse health effect of PM_2.5 on people with COPD after exposure to different levels of PM_2.5 and emphasizes that adverse effect in area with relative low pollution level cannot be overlooked. Governments in both high-pollution and low-pollution cities should attach importance to the adverse effects of PM_2.5 on humans and take corresponding measures to control and reduce the related losses.

Bone age assessment has become a common clinical practice in the diagnosis of endocrine disorders and bone maturity. An end-to-end bone age assessment method based on deep convolutional neural networks is proposed, which uses deep learning to segment the hand region and predict key points for correcting hand posture. By optimizing the dice loss function in DenseUNet for segmentation, the model is easier to converge and has better segmentation quality. Squeeze-and-excitation (SE) module and Position/Channel Attention (PA/CA) module can help network to focus on useful feature channels and position. By merging InceptionV3 with SE-module and PA/CA module, the assesssment model can extract the features of hand bone more efficiently. The experimental results show that the proposed network is superior to traditional and other automated bone age assessment methods both in accuracy and efficiency on the data of the 2017 children's bone age challenge organized by the Radiological Society of North America. Finally, the visualization of the region activation for bone age assessment is implemented, which is helpful to understand the connection between the image appearance and the bone age.