Area Of Beijing City Research Articles

Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5) and 10 μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist of the main cause for many diseases. Based on the existence of statutory limits from the World Health Organization (WHO) for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict air pollutant concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural network (ANN) models. In the frame of a specific work, a significant number of ANNs are developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. The results showed that ANNs present a remarkable prognostic ability in order to forecast air pollution levels in an urban environment.

Identification and classification of urban employment centers based on big data: A case study of Beijing.

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

SPATIAL AND TEMPORAL VARIABILITY OF ECOSYSTEM SUPPORT SERVICES AND DRIVERS IN METROPOLITAN AREAS BASED ON THE INVEST MODEL

Abstract. With the rapid economic growth since the turn of the 21st century, the fast-degrading environment has emerged as a significant factor limiting the continued development of megacities such as Beijing and Shanghai. It has also placed unprecedented pressure on ecosystem services. Therefore, the present study uses the morphological spatial pattern analysis model and the index of connectivity (IIC) and possible connectivity (PC) indices to analyse changes in the spatial pattern of Beijing and combines the carbon sequestration module and habitat quality module of the InVEST model to evaluate the supporting functions of Beijing’s ecosystems from 2000 to 2020, to investigate the characteristics of spatial and temporal changes and the drivers of these changes, and to provide a scientific basis for urban ecosystem management. The results show that between 2000 and 2020, the overall area of green space in Beijing decreased, and the IIC and the index of PC decreased, indicating that the overall spatial connectivity of green space decreased and fragmentation increased. During this period, the carbon stock within the Beijing city area decreased by a total of about 2.94 Tg, and the habitat quality index decreased from 0.71 to 0.67, indicating a trend of degradation. This paper suggests that the expansion of urban and rural land use is a major factor in the decline of ecosystem support services, while the spatial pattern of the landscape also has an impact on habitat quality, which is generally higher in areas with better landscape connectivity.

Influence of Thermal and Dynamical Conditions over Beijing City Area on Strength of Down-to-Hill Thunderstorms

Influence of Thermal and Dynamical Conditions over Beijing City Area on Strength of Down-to-Hill Thunderstorms

Fluoroquinolones in the Wenyu River catchment, China: Occurrence simulation and risk assessment.

Concern is increasing regarding the environmental impact of the high usage rate and intensive release of antibiotics used for human and animal therapy in major urban areas of China. In the present study, regional environmental distribution simulations and risk assessments for 3 commonly used fluoroquinolones in the Wenyu River catchment were conducted using a typical catchment model widely used in Europe. The fluoroquinolone antibiotics investigated (ofloxacin, norfloxacin, and ciprofloxacin) are consumed at high levels for personal health care in China. These antibiotics were simulated in the aquatic environment of the Wenyu River catchment across the Beijing City area for annual average concentrations, with regional predicted environmental concentrations (PECs) of approximately 711 ng/L, 55.3 ng/L, and 22.2 ng/L and local PECs up to 1.8 µg/L, 116 ng/L, and 43 ng/L, respectively. Apart from hydrological conditions, the concentrations of fluoroquinolones were associated closely with the sewage treatment plants (STPs) and their serving population, as well as hospital distributions. The presence of these fluoroquinolones in the catchment area of the present study showed significant characteristics of the occurrence of pharmaceuticals in the aquatic environment in an urban river, with typical "down-the-drain" chemicals. Significantly high concentrations of specific antibiotics indicated non-negligible risks caused by the intensive use in the local aquatic environment in a metropolitan area, particularly ofloxacin in upstream Shahe Reservoir, middle stream and downstream Qing River, and Liangma River to the Ba River segment. Specific treatment measures for these pharmaceuticals and personal care products in STPs are required for such metropolitan areas.

Monitoring of Atmospheric Particles in Beijing and Dunhuang Using a Raman Lidar with Enhanced Dynamics

In high urbanized areas, the atmospheric particulate matter concentration (the so called PM) can exceeds the healthy limit values. Human activities, such as the burning of fossil fuels, industrial activities, and ground transport contribute to measured PM exceedance. Natural contribution is originated mainly from dust storms and volcanic eruptions. The Gobi desert, for instance, is the major source of mineral dust in China, that is one of the most interesting regions for aerosol study being surrounded by the main sources of anthropogenic and natural aerosol. On the base of the above considerations, the need of a new advanced scanning lidar system raised in order to better understand the formation, emission and diffusion of particulate from natural and anthropic sources and to evaluate their relative contribution, to characterize the chemical and physical properties of atmospheric aerosols, their spatial and temporal distribution and the main transport mechanisms. To this aim, a new, versatile and portable Raman scanning lidar system has been designed and developed in the frame of the AMPLE (Aerosol Multi-wavelength Polarization Lidar Experiment) project, the first action of the recently founded China-Italy Laser Remote Sensing Joint Research Center between the National Consortium of Italian Universities for the Physical Science of the Matter (CNISM) and the Beijing Research Institute for Telemetry (BRIT). The AMPLE lidar device has been installed at the Beijing Research Institute for Telemetry in the Beijing city area, which is strongly affected from anthropogenic pollution and sand dust from Gobi desert in order to carry out 4-D (space and time) imaging of the atmospheric aerosol distributions, their optical properties, and microphysical characterization. A first demonstrative measurement campaign has been performed on May 2013 in Beijing, while on August 2013 AMPLE has been carried in Dunhuang close to the Gobi desert and far away from the urban area, in order to study sand dust directly at source. Results of those measurements are described in this paper.

Implementation of High Dynamic Raman Lidar System for 3D Map of Particulate Optical Properties and Their Time Evolution

Fast scanning lidar system can provide aerosol volume distribution and time evolution in atmosphere. Usually fast scanning lidar has a relative simple configuration. Multiwavelength depolarization and Raman measurements gave us more information about the particulate shape, type and dimension. The most Raman lidars use high power laser source in order to get enough Raman scattering signal. One of a challenge for Raman lidar system is to have enough dynamic range to satisfy the measurements both for pure molecule Rayleigh scattering and high dene aerosol, such as dust storm, volcano emitted aerosol and high polluted urban aerosol. The estimation of microphysical properties requires independent measurements of both backscatter and extinction coefficient at several wavelengths (multiwavelength Raman lidar). Additional information can be retrieved from simultaneous measurements of the depolarization signal and water vapor mixing ratio, since those measurements are particularly useful to correlate aerosol optical properties with their shape and hygroscopicity. A multi-wavelength depolarization and Raman lidar system has been designed to perform volume scanning of the atmosphere and to retrieve high quality 3D map of particulate optical properties and their time evolution. This system is equipped with a doubled and tripled Nd:YAG diode-pumped laser that is specifically designed for this device, with a repetition rate of 1KHz and average optical power of 0.6W at 355nm, 1.5W at 532nm and 1W at 1064nm. The relative high repetition rate laser source can increase the detectable signal dynamic range. The receiving system is based on a 25cm modified Cassegrain telescope. The spectral selection of the backscatter elastic and Raman signals is made through a system of dichroic beam splitters and narrow band (0.5 nm) interferential filters. Fast single photon counting photomultipliers are used to collect the selected radiation. Each detected signal is acquired by multichannel scalers with a raw spatial resolution varying from 30cm to 30m. Moreover, polarization purity of laser line allows to perform polarization measurements at both 355 and 532nm. This device is installed in the Beijing city area, which is strongly affected from anthropogenic pollution and sand dust from Gobi desert.

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map, vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains, especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃between Beijing city and suburb areas, comparatively is 8- 10℃between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative. The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481). The results demonstrate that the vegetation greenness is an important factor for reducing the UHI, and large-scale construction of greenbelts can considerably reduce the UHI effect.