Elevated ground-level ozone (O₃), which is an important aspect of air quality related to public health, has been causing increasing concern. This study investigated the spatiotemporal distribution of ground-level O₃ concentrations in China using a dataset from the Chinese national air quality monitoring network during 2013–2015. This research analyzed the diurnal, monthly and yearly variation of O₃ concentrations in both sparsely and densely populated regions. In particular, 6 major Chinese cities were selected to allow a discussion of variations in O₃ levels in detail, Beijing, Chengdu, Guangzhou, Lanzhou, Shanghai, and Urumchi, located on both sides of the Heihe-Tengchong line. Data showed that the nationwide 3-year MDA8 of ground-level O₃ was 80.26 μg/m³. Ground-level O₃ concentrations exhibited monthly variability peaking in summer and reaching the lowest levels in winter. The diurnal cycle reached a minimum in morning and peaked in the afternoon. Yearly average O₃ MDA8 concentrations in Beijing, Chengdu, Lanzhou, and Shanghai in 2015 increased 12%, 25%, 34%, 22%, respectively, when compared with those in 2013. Compared with World Health Organization O₃ guidelines, Beijing, Chengdu, Guangzhou, and Shanghai suffered O₃ pollution in excess of the 8-hour O₃ standard for more than 30% of the days in 2013 to 2015.

Internet of things (IoT) in urban transportation systems have been ubiquitously embedded into a variety of devices and transport entities. The IoT-enabled smart transportation system (STS) has thus gained growing tractions amongst scholars and practitioners. However, several IoT challenges in relation to cyber-physical security are exposed due to the heterogeneity, complexity and decentralization of the IoT network. There also exist geospatial security concerns with respect to the embeddings of 5G networks into public infrastructures that are interconnected with the transport system via IoT. To address these concerns, this paper aims to apply geospatial modelling approach to propose a Smart Transportation Security Systems (STSS). It is modelled and simulated by undertaking an experimental study in the city of Beijing, China. The simulation outcome of the proposed architecture is expected to offer a strategic guide for strategic security management of urban smart transportation.

Eighteen years of sun/sky photometer measurements at seven worldwide AErosol RObotic NETwork (AERONET) sites in typical biomass burning regions were used in this research. The AERONET measurements were analyzed with the help of Moderate-resolution Imaging Spectroradiometer (MODIS) fire products and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The variation in the physicochemical and optical properties of biomass burning aerosols (BBAs), as well as their shortwave radiative forcing, was revealed for different vegetation types in different aging periods. The result indicated that, with aerosol aging, the BBA characteristics have a non-negligible evolution trend with obvious clustering features for different burning vegetation types. During the aging process, the volume fraction of black carbon (BC) declined (with a maximum drop of 38%) accompanied by particle size growth (with a maximum increment of 0.017 μm). Driven by the change in physicochemical properties, the Single Scattering Albedo (SSA) and the asymmetry factor increased as the aerosol aged (with maximum increments of 0.026 and 0.018 for the SSA and asymmetry factor respectively). The grass and shrub type had a higher volume fraction of BC (2.5 times higher than that in the forest and peat type) and a smaller fine mode volume median radius (with a difference of 0.037 μm from that of the forest and peat type). Such a phenomenon results in a lower SSA (with a difference of 0.103) and asymmetry factor (with a difference of 0.035) in the grass and shrub type when compared to the forest and peat type. Negative (-74 to -30 W/m2) clear-sky top of atmosphere (TOA) shortwave radiative forcing, strengthened during the aging process, was generally found for BBA. The BBA in the forest and peat region usually had stronger negative radiative forcing efficiency.

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO₂, and CO all decreased year by year during 2015–2017. However, the annual mean NO₂ concentrations were almost unchanged, while the annual mean O₃ concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO₂ played an important role in the formation of PM2.5 and O₃. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.