The Influence of Environment Factors on Chronic Non-Communicable Diseases in a Heavy Industry City—A Case of Xigu District of Lanzhou City

Taking Xigu District of Lanzhou City as an example, this paper systematically analyzes the spatio-temporal distribution characteristics of patients with chronic non-communicable diseases (NCD) and compares the differences between heating period and non-heating period. Furthermore, the impact paths of natural environmental factors and built-up environmental factors on NCD are probed with the help of the geographic detector. The results are as follows: In time, the incidence of NCD in Xigu district fluctuated from 2012 to 2019. In space, there was an overall declining trend in high incidence rate from the central area to the surrounding areas, among which Xigucheng street was the high-risk area. The incidence of NCD in heating period was higher than that of in non-heating period, and the number of H-H cluster areas was witnessed an obviously increasing growth in Sijiqing Street. There are significant differences in the explanatory power of different factors (if any) for NCD. The explanatory power of each index in Xigu District is as follows: Facility > SO2 > NO2 > PM2.5 > food > Beverage Service > Green Facilities > Traffic Regulations > medical facilities. The interaction between plant facilities and SO2 has the strongest effect on NCD. Except for the negative correlation between greening and medical facilities and the incidence of NCD, all the influencing factors were positively correlated with NCD.

Magnetism and Grain-Size Distribution of Particles Deposited on the Surface of Urban Trees in Lanzhou City, Northwestern China

Studies on the variation in the particulate matter (PM) content, Saturation Isothermal Remanent Magnetization (SIRM), and particle grain-size distribution at a high spatial resolution are helpful in evaluating the important role of urban forests in PM removal. In this study, the trees located in dense urban forests (T0) retained more PM than trees located in open spaces (T1–T4); the SIRM and PM weight of T0 were 1.54–2.53 and 1.04–1.47 times more than those of T1–T4, respectively. In addition, the SIRM and PM weight decreased with increasing distance to the road, suggesting that distance from pollution sources plays a key role in reducing the air concentration of PM. The different grain-size components were determined from frequency curve plots using a laser particle-size analyzer. A unimodal spectrum with a major peak of approximately 20 μm and a minor peak between 0.1 and 1 μm was observed, indicating that a large proportion of fine air PM was retained by the needles of the study trees. Additionally, more <2.5 μm size fraction particles were observed at the sampling site near the traffic source but, compared to a tree in a row of trees, the percentage of the >10 μm size fraction for the tree in the dense urban forest was higher, indicating that the particles deposited on the needle surface originating from traffic sources were finer than those from natural atmospheric dust. The exploration of the variation in the PM weight, SIRM, and grain size of the particles deposited on the needle surface facilitates monitoring the removal of PM by urban forests under different environmental conditions (e.g., in closed dense urban forests and in open roadside spaces), different distances to roads, and different sampling heights above the ground.

Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China

Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%.

Interaction of interregional O3 pollution using complex network analysis

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O3 in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O3 pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O3 network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O3 area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O3 between each area revealed that the impact of O3 on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O3 governance.

Public’s Intention and Influencing Factors of Dockless Bike-Sharing in Central Urban Areas: A Case Study of Lanzhou City, China

Taking the main district in Lanzhou city of China as an example, the questionnaires were designed and distributed, and then the effects of five factors, i.e., behavioral attitude, subjective norm, perceived behavioral control, perceived ease of use and perceived usefulness, on the behavioral intention of dockless bike-sharing (DBS) use were empirically analyzed based on the integrated model of technology acceptance model (TAM) and the theory of planned behavior (TPB) as well as the structural equation model. Results show that the five factors all impose significantly positive effects on the public’s behavioral intention of DBS use but differ in influencing degrees. Behavioral attitude, subjective norm and perceived behavioral control can all directly affect the public’s behavioral intention of DBS use, with direct influence coefficients of 0.691, 0.257 and 0.198, while perceived ease of use and perceived usefulness impose indirectly effects on behavioral intention, with indirect influence coefficients of 0.372 and 0.396. Overall, behavioral attitude imposes the most significant effect, followed by perceived ease of use, perceived usefulness and subjective norm, and finally perceived behavioral control. This indicates that the public’s behavioral intention of DBS use depends heavily on their behavioral attitude towards the shared bikes. In view of the limited open space of the main district in Lanzhou, the explosive growth of shared bikes, oversaturated arrangements, disordered competition, unclear and unscientific divisions of parking regions, and hindrance of traffic, this study proposes a lot of policy suggestions from the research results. A series of supporting service systems related to DBS should be formulated. The shared bikes with different characteristics should be launched for different age groups, gender groups and work groups. The corresponding feedback platform for realtime acquisition, organization, analysis and solution of data information, as well as the adequate platform feedback mechanism, should be established.

A Stable Isotope Approach for Estimating the Contribution of Recycled Moisture to Precipitation in Lanzhou City, China

The proportional contribution of recycled moisture to local precipitation is a geographically dependent parameter that cannot be ignored in water budgets. Stable hydrogen and oxygen isotopes are sensitive to environmental changes and can be applied to investigate the modern water cycle. In this study, a three-component mixing model is used to calculate the contribution of different water vapors (advection, evaporation and transpiration) to summer precipitation in Lanzhou city, Northwest China. The results show that for all sampling sites in Lanzhou, the contribution of advection vapor to precipitation is the largest, followed by the plant transpiration vapor, and the contribution of surface evaporation water vapor is usually the least, with the average values of 87.96%, 9.1% and 2.9%, respectively. The spatial differences of plant transpiration vapor are generally larger than those of advection vapor and surface evaporation vapor, and the high values appear in Yongdeng, Daheng and Gaolan.