Spatial-temporal characteristics of urban air pollution in 337 Chinese cities and their influencing factors.

Urban air pollution, especially in the form of haze events, has become a serious threat to socio-economic development and public health in most developing countries. It is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of our study is to develop consistent methodologies for constructing an index system and for assessing the influencing factors of the urban air pollution occurrence based on the Driver-Pressure-State-Impact-Response (DPSIR) framework by incorporating spatial analysis, geographical detector, and geographically weighted regression models. The 27 influencing factors were selected for assessing their influences on the urban air pollution occurrence in 337 Chinese cities. The results indicate that the spatial pattern of the urban air pollution in China was mostly consistent with the Chinese population-based Hu Line. Urban air pollution frequently occurred in North China, Central China, Northeast China, and East China, and displayed strong seasonality. The influencing factors of urban air pollution were complex and diverse, varying from season to season. Influencing factor analysis also shows that the explanatory power between any two influencing factors was greater than that of a single influencing factor of the urban air pollution. Furthermore, most influencing factors had both positive and negative effects and local effects on urban air pollution. Finally, we put forward five suggestions on reducing urban air pollution occurrence, which can provide the basis and reference for the government to make policies on urban air pollution control in China.

Spatial characteristics and risk factor identification for land use spatial conflicts in a rapid urbanization region in China.

Land use conflict is a complex problem driven by a myriad of risk factors as a result of rapid socioeconomic development and urbanization. Analyzing the spatial characteristics of land use conflict and identifying its risk factors using statistical models will help us to better understand the causes and effects of the land use conflicts for sustainable management of the limited land resources under the pressure of rapid urbanization. In this study, regression models including multiple linear regression (MLR), spatial autoregressive (SAR), and geographically weighted regression (GWR) models were employed to identify risk factors for the land use spatial conflicts in the Urban Agglomeration around Hangzhou Bay (UAHB) of China in the past 25 years. Our results showed that the overall extent and the higher-level land use spatial conflicts were actually on the decline, and their spatial autocorrelation has been weakening in the UAHB. The key risk factors that mainly caused the land use spatial conflicts in the UHAB appeared to be different at the global and local scales. This knowledge should help urban managers and policymakers to be better informed when developing pertinent land use policies at the regional and local levels. This study also underlined the importance of considering spatial autocorrelation and scale effects when identifying the risk factors for land use spatial conflicts. The lessons learned from this particular context can be extended to other areas under rapid urbanization to assess and better manage their land resources for sustainable use. Graphical abstract.

Conflict or coordination? Assessing land use multi-functionalization using production-living-ecology analysis.

Land use multi-functionalization (LUMF) promotes efficient and sustainable land use, reduces land pressures from limited land resources, and elevates urbanization quality in the midst of the increasingly tense relationship between humans and nature. In this study, we propose a new conceptual index system using system science, entropy weight method, triangle model, and coupling coordination degree model for LUMF assessment as well as an analysis of the relationship among land use sub-functions. This framework was applied to six cities in the urban agglomeration around Hangzhou Bay (UAHB) in eastern China's Zhejiang Province using twenty-two indicators in terms of production-living-ecology analysis during 2004-2013. The UAHB LUMF level increased over the past ten years, being affected by the designated functions and the "planning effect" for the six cities in the UAHB. The relationships among land use sub-functions in the six cities displayed strong variabilities at the spatial and temporal scales. The overall patterns of the relative importance of these sub-functions also differed from each other. Our research also shows that urban development in the UAHB had focused more on economic growth than on ecological protection and the regional development in the UAHB's six cities was unbalanced. Therefore, we suggest urban and land use management need to embrace more integrated planning and design in order to maintain efficient and sustainable land use.

Identifying and managing risk factors for salt-affected soils: a case study in a semi-arid region in China.

Soil salinization and desalinization are complex processes caused by natural conditions and human-induced risk factors. Conventional salinity risk identification and management methods have limitations in spatial data analysis and often provide an inadequate description of the problem. The objectives of this study were to identify controllable risk factors, to provide response measures, and to design management strategies for salt-affected soils. We proposed to integrate spatial autoregressive (SAR) model, multi-attribute decision making (MADM), and analytic hierarchy process (AHP) for these purposes. Our proposed method was demonstrated through a case study of managing soil salinization in a semi-arid region in China. The results clearly indicated that the SAR model is superior to the OLS model in terms of risk factor identification. These factors include groundwater salinity, paddy area, corn area, aquaculture (i.e., ponds and lakes) area, distance to drainage ditches and irrigation channels, organic fertilizer input, and cropping index, among which the factors related to human land use activities are dominant risk factors that drive the soil salinization processes. We also showed that ecological irrigation and sustainable land use are acceptable strategies for soil salinity management.

Assessing secondary soil salinization risk based on the PSR sustainability framework.

Risk assessment of secondary soil salinization, which is caused in part by the way people manage the land, is an essential challenge to agricultural sustainability. The objective of our study was to develop a soil salinity risk assessment methodology by selecting a consistent set of risk factors based on the conceptual Pressure-State-Response (PSR) sustainability framework and incorporating the grey relational analysis and the Analytic Hierarchy Process methods. The proposed salinity risk assessment methodology was demonstrated through a case study of developing composite risk index maps for the Yinchuan Plain, a major irrigation agriculture district in northwest China. Fourteen risk factors were selected in terms of the three PSR criteria: pressure, state, and response. The results showed that the salinity risk in the Yinchuan Plain was strongly influenced by the subsoil and groundwater salinity, land use, distance to irrigation canals, and depth to groundwater. To maintain agricultural sustainability in the Yinchuan Plain, a suite of remedial and preventative actions were proposed to manage soil salinity risk in the regions that are affected by salinity at different levels and by different salinization processes. The weight sensitivity analysis results also showed that the overall salinity risk of the Yinchuan Plain would increase or decrease as the weights for pressure or response risk factors increased, signifying the importance of human activities on secondary soil salinization. Ideally, the proposed methodology will help us develop more consistent management tools for risk assessment and management and for control of secondary soil salinization.

Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis.

Land salinization and desalinization are complex processes affected by both biophysical and human-induced driving factors. Conventional approaches of land salinization assessment and simulation are either too time consuming or focus only on biophysical factors. The cellular automaton (CA)-Markov model, when coupled with spatial pattern analysis, is well suited for regional assessments and simulations of salt-affected landscapes since both biophysical and socioeconomic data can be efficiently incorporated into a geographic information system framework. Our hypothesis set forth that the CA-Markov model can serve as an alternative tool for regional assessment and simulation of land salinization or desalinization. Our results suggest that the CA-Markov model, when incorporating biophysical and human-induced factors, performs better than the model which did not account for these factors when simulating the salt-affected landscape of the Yinchuan Plain (China) in 2009. In general, the CA-Markov model is best suited for short-term simulations and the performance of the CA-Markov model is largely determined by the availability of high-quality, high-resolution socioeconomic data. The coupling of the CA-Markov model with spatial pattern analysis provides an improved understanding of spatial and temporal variations of salt-affected landscape changes and an option to test different soil management scenarios for salinity management.