Lirong Yin

Measuring County Resilience After the 2008 Wenchuan Earthquake

The catastrophic earthquake that struck Sichuan Province, China, in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern China. In recent years, great attention has been paid to the resilience of the affected area. This study applied the resilience inference measurement (RIM) model to quantify and validate the community resilience of 105 counties in the impacted area. The RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure, damage, and recovery conditions. The model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county’s resilience. Analysis results show that counties located at the epicenter had the lowest resilience, but counties immediately adjacent to the epicenter had the highest resilience capacities. Counties that were farther away from the epicenter returned to normal resiliency quickly. Socioeconomic variables—including sex ratio, per capita GDP, percent of ethnic minority, and medical facilities—were identified as the most influential characteristics influencing resilience. This study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.

Impact of human activities on haze in Beijing based on grey relational analysis

Based on the grey relational analysis of haze components and the factors associated with socio-economic development in Beijing, this paper found that social development activities, such as energy consumption per GDP unit, urban road construction and urban greening, have great impact on haze, including both promotion and inhibition impacts. It should be noticed that human activities which gained much attention before, such as ownership of motor vehicles and residential energy consumption, do not have a strong effect on haze according to our study, which is contrary to previous studies and government reports.

Predicting seismicity trend in southwest of China based on wavelet analysis

The earthquake shows certain characteristics of periodicity or quasi-periodicity, which has been constantly proved by facts and these characteristics have been widely used in analyzing the trend of the seismic activities. On the basis of wavelet analysis, the seismic data in southwest from 1900 to 2013 was analyzed and the features on different time scales were studied. Moreover, an analysis of the future seismic trend was conducted by using wavelet coefficients on all scales and the main period of seismic activities.

Spatiotemporal heterogeneity of urban air pollution in China based on spatial analysis

This study aims to explore the spatial heterogeneity and temporal dynamics of urban air pollution. The PM10 data from 2004 to 2013 was chosen as the index of air pollutant. Thirty-one major cities including Beijing and its adjacent cities were selected as the study area. Using Global Spatial Autocorrelation, evaluation of whether the air pollutant’s pattern is clustered in Chinese mainland area during the study period is conducted. Moreover, the Local Moran’s I Index was employed to detect the spatial cluster and outlier distribution of air pollutant. The result shows that air pollution in the study area has made increasing influence on annual average concentration of PM10 in the surrounding area and increasing heterogeneity of PM10. By observing the Local Moran’s I Index distribution of PM10 from 2004 to 2013, it is found that several cities around Beijing in north China, which have been suffering from serious air pollution, are spatially aggregated and have influence on their surrounding air quality. In contrast, Haikou and other coastal cities suffer less air pollution and have some purification effects on air environment of surrounding cities. The aggregated distribution pattern is comparatively stable from 2004 to 2013, which led to the speculation that there is interaction on air pollution between the large-scaled aggregated regions and individual cities. This paper is significant to inspire the awareness of air pollution by local government and the public.

Influence of Social-economic Activities on Air Pollutants in Beijing, China

Abstract With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socio-economic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.

Wavelet analysis of the temporal-spatial distribution in the Eurasia seismic belt

Due to the growing frequency of earthquakes, safeties of human lives and properties are facing serious threats. However, the research in the field of spatial-temporal distribution of earthquake is quite a few. In this paper, we use wavelet model to analyze the spatial-temporal distribution of earthquakes. Because the spatial-temporal distribution of earthquake activity is closely related to the distribution of the earthquake fault zone, we analyze large-scale earthquake clusters by selecting the Eurasia seismic belt and the surrounding region as the research area. From the perspective of the time domain, the results show that the seismic energy of the earthquake fault zone presences compact support or similar compact support distribution, suggesting that the seismic zone exists a relatively quiet period and active stage. This indicate that the seismic zone is periodical. The period of strong earthquakes above normal and less than normal is different by time changes. The cycles of earthquakes are different due to different regions and different geological and geographical environment.