BackgroundLung cancer is the most common cancer in China. Previous studies have indicated that lung cancer incidence exhibits remarkable spatial heterogeneity, and lung cancer is related to outdoor air pollution. However, the non-linear spatial association between outdoor air pollution and lung cancer incidence in China remains unclear.MethodsIn this study, the relationships between the lung cancer incidence of males and females from 207 counties in China in 2013 with annual concentrations of PM2.5, PM10, SO2, NO2, CO and O3 were analysed. GeoDetector q statistic was used for examining the non-linear spatial association between outdoor air pollution and incidence of lung cancer.ResultsAn apparent spatial and population gender heterogeneity was found in the spatial association between outdoor air pollution and lung cancer incidence. Among the six selected pollutants, SO2 has the greatest influence on lung cancer (q = 0.154 in females) in north China. In the south, each selected pollutant has a significant impact on males or females, and the mean q value in the south is 0.181, which is bigger than that in the north (q = 0.154). In addition, the pollutants have evident non-linear interaction effects on lung cancer. In north China, the interaction between SO2 and PM2.5 is the dominant interaction, with q values of 0.207 in males and 0.334 in females. In the south, the dominant interactive factors are between SO2 and O3 in males and between SO2 and CO in females, with q values of 0.45, 0.232 respectively. Smoking is a substantial contributor to lung cancer among men, either in South or North China, with q value of 0.143 and 0.129 respectively, and the interaction between smoking and air pollutants increases this risk.ConclusionsThis study implies that the influence of SO2 and PM2.5 on lung cancer should be focused on in north China, and in the south, the impact of O3 and CO as well as their interaction with SO2 need to be paid more attention. Smoking, particularly in men, remains a significant risk factor for lung cancer in both North and South China.

In recent years, remote sensing images have played an important role in environmental monitoring, weather forecasting, and agricultural planning. However, remote sensing images often contain a large number of cloud layers. These clouds can cover a large amount of surface information. Therefore, an increasing number of cloud recognition methods have been proposed to reduce the impact of cloud cover. There are many difficulties in traditional cloud recognition methods. For example, the threshold method based on spectral features improves the accuracy of cloud detection, but it often leads to omission or misjudgment in cloud detection and depends on prior knowledge. To improve the accuracy and efficiency of cloud recognition, we use deep learning to address cloud recognition problems in remote sensing imagery. We propose a series of methods from the acquisition and production of training datasets to neural network training and cloud recognition applications. This paper describes a realization of cloud recognition of remote sensing imagery based on SegNet architecture. We have proposed two architectures named P_SegNet and NP_SegNet, which are modified from SegNet. Some parallel structures were also employed into the SegNet architecture to improve the accuracy of cloud recognition. Due to these changes, this paper also discusses the impact of the symmetry network structure on the final accuracy. Our proposed method was compared with the well-known fully convolutional neural network (FCNN) approach. The results have demonstrated the feasibility and practicality of using deep learning approach for cloud recognition in remote sensing images.INDEX TERMS Deep learning, remote sensing imagery, cloud detection, neural network.

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Scientific planning, digital management of construction and intelligent service are needed for a smart city. This paper focusses on city management based on the smart information system and digital technologies in China. The establishment of the smart intercommunication system of landscape, water conservancy, environmental protection and other industries is basic for a smart city. This paper introduced a framework of digital technologies for the construction of a smart city, which made full use of the city information modelling based on the geographic information system, building information modelling, internet of things, and integrated digital system and service platform such as satellite remote sensing, global navigation satellite system, mobile applications, cloud computing, visualisation technology etc. The study of the case implemented show that the framework is applicable to the smart city with digital technologies that includes the data model and system, integrating the urban basic geographic data, and data of infrastructure and other public facilities related to traffic, urban construction, ‘One Map’ of land and resources etc. The governance is more effective through data acquisition, data transmission, data analysis and processing based on the data platform and system.

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.