Cities,December 2019
JingSong,ZhengChang,WeifengLi*,ZheFeng,JianshengWu,QiwenCao,JianzhengLiu
Abstract:City areas experiencing disproportionate vulnerability levels to urban flooding events have attracted attention. Resilience is widely accepted as a strategy for reducing the risks of vulnerability and maintaining sustainable development. This research conceptualized vulnerability to hazard and exposure, and resilience to adaptation to urban flooding and explores their associations from a spatial balance perspective. The hazard of urban flooding was evaluated by hydrographic models, whereas exposure and adaptation were examined by indexes. Shenzhen, a densely populated socialist Chinese city, was selected as the case city. Results revealed that districts in the marginalized areas of Shenzhen experience high vulnerability to urban flooding because of poor geographic factors, immature drainage systems of urban villages, and the influx of rural migrants with sensible populations driven by high housing prices in urban center. The situation is becoming increasingly serious because of strong spatial mismatch between vulnerability and resilience with urban overutilization and the rural underutilization of adaptation resource allocation. Social segregation on adaptation resource occurs for public service provision in a marketization situation instead of socialism. Therefore, exploring the mechanisms of the spatial imbalance between vulnerability and resilience in socialist city, such as Shenzhen is necessary for reducing such impacts in future research.
58.Air pollution and lung cancer incidence in China: Who are faced with a greater effect?
Environment International, November 2019
HuaguiGuo, ZhengChang, JianshengWu, WeifengLi*
Abstract:
Background
Whether socioeconomic indicators modify the relationship between air pollution exposure and health outcomes remains uncertain, especially in developing countries.
Objective
This work aims to examine modification effects of socioeconomic indicators on the association between PM2.5 and annual incidence rate of lung cancer for males in China.
Methods
We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Using multivariable linear regression models controlling for weather conditions and socioeconomic indicators, we examined modification effects in the stratified and combined datasets according to the tertile and binary divisions of socioeconomic indicators. We also extensively investigated whether the roles of socioeconomic modifications were sensitive to the further adjustment of demographic factors, health and behaviour covariates, household solid fuel consumption, the different operationalization of socioeconomic indicators and PM2.5 exposure with single and moving average lags.
Results
We found a stronger relationship between PM2.5 and incidence rate of male lung cancer in urban areas, in the lower economic or lower education counties (districts). If PM2.5 changes by 10 μg/m3, then the shift in incidence rate relative to its mean was significantly higher by 3.97% (95% CI: 2.18%, 4.96%, p = 0.000) in urban than in rural areas. With regard to economic status, if PM2.5 changes by 10 μg/m3, then the change in incidence rate relative to its mean was significantly lower by 0.99% (95% CI: −2.18%, 0.20%, p = 0.071) and 1.39% (95% CI: −2.78%, 0.00%, p = 0.037) in the middle and high economic groups than in the low economic group, respectively. The change in incidence rate relative to its mean was significantly lower by 1.98% (95% CI: −3.18%, −0.79%, p = 0.001) and 2.78% (95% CI: −4.17%, −1.39%, p = 0.000) in the middle and high education groups compared with the low education group, respectively, if PM2.5 changes by 10 μg/m3. We found no robust modification effects of employment rate and urbanisation growth rate.
Conclusion
Male residents in urban areas, in the lower economic or lower education counties are faced with a greater effect of PM2.5 on the incidence rate of lung cancer in China. The findings emphasize the need for public health intervention and urban planning initiatives targeting the urban–rural, educational or economic disparities in health associated with air pollution exposure. Future prediction on air pollution-induced health effects should consider such socioeconomic disparities, especially for the dominant urban–rural disparity in China.
Science of The Total Environment，15 December 2019
Jing Song, Rui Yang, Zheng Chang, Weifeng Li, Jiansheng Wu*
Abstract:Frequent and intensive urban flooding requires an extensive adoption of low-impact development (LID) to supplement traditional drainage infrastructures. Our study conceptualizes the resilient infrastructure framework with a particular reference to adaptation, an adjustment capacity in the social–ecological system to withstand various natural hazards and absorb negative impacts. We argue that adaption is an indicator for measuring LID effectiveness. A methodological framework is adopted using a time-dependent technique with a hydrodynamic inundation model to evaluate LID effectiveness. Results of a case study in Gongming, Shenzhen, China, show that LID projects can effectively reinforce adaptation capacity. However, spatial inequality and accumulation of different levels of adaptation are evident. This outcome is due to a relatively low absorption capacity because most areas will have a relatively high recovery capacity but retain a low absorption capacity with the construction of LID projects. A relatively mild increase in absorption capacity is due to the quality of man-made infrastructural development is conflicting across different areas of Gongming, for example some infrastructures are constructed by the government, whereas others by developers and villagers. In addition, the topographical factor makes some areas in Gongming lower-lying than others and is therefore increasingly vulnerable to urban flooding during rainstorms given the difficulty of discharging the surface runoff, thereby limiting the effectiveness of LID projects. Furthermore, the spatial inequality of adaptation improvement where LID projects cannot be evenly distributed within the research area leads to the unequal distribution of adaptation. These findings can confirm that the government can practically use adaptation as an indicator in evaluating LID effectiveness and identifying the problematic stages of drainage resilience in urban flooding risk mitigation.
Remote Sensing, October 2019
Wu, Jiansheng, Liang, Jingtian, Zhou Liguo,Yao Fei,Peng, Jian
Abstract:Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., >= 1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015-2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM2.5 concentrations were 0.234-0.329 and 0.340-0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1-5 km, AOD at finer spatial scales generally had higher correlations with PM2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM2.5 measurements in regional air pollution studies.
55.Using the modified i-Tree Eco model to quantify air pollution removal by urban vegetation
Science of The Total Environment, 20 October 2019
Jiansheng Wu, Yi Wang, Sijing Qiu, Jian Peng*
Abstract:Fine particulate matter (PM2.5) can pose health problems for humans following urbanization. Because urban vegetation has a large surface area to filter PM2.5 out of the air, it can be an effective long-term way to mitigate air pollution. Various studies have quantified PM2.5 removal by vegetation in cities, but the spatial variability of removal within cities and future scenarios have not been well documented. To ensure more reasonable and effective urban tree planting regimes, we used the spatiotemporal i-Tree Eco model combined with the vertical distribution of vegetation in a case study in Shenzhen City, China. The results indicated that the PM2.5 removal by urban vegetation in 2015 was 1000.1 tons, and the average removal rate by vegetation was 1.6 g m−2 year−1. A maximum hourly local air quality improvement of up to 3% could be achieved, with an average of 1%, which differed significantly with elevation. In terms of vegetation type, evergreen shrubs, evergreen broadleaved forests, and evergreen needle-leaved forests had the highest removal efficiency within <100, 100–300, and >300 m, respectively. For five future planting scenarios, by increasing vegetation cover by 5% in different elevation zones (<100, 100–300, and >300 m), an annual amount of 1220.6–1308 tones could be achieved. Specifically, it was estimated that an increase in evergreen shrubs cover in the developed area (<100 m) would have the best removal potential.
54.Applying ant colony algorithm to identify ecological security patterns in megacities
Environmental Modelling & Software，20 March 2019
Jian Peng∗, Shiquan Zhao, Jianquan Dong, Yanxu Liu, Jeroen Meersmans, Huilei Li,Jiansheng Wu
Abstract:Ecological security patterns composed of ecological sources and corridors provide an effective approach to conserving natural ecosystems. Although the direction of ecological corridors has been identified in previous studies, the precise range remains unknown. To address this crucial gap, ant colony algorithm and kernel density estimation were applied to identify the range and restoration points of ecological corridors, which is important for natural conservation and ecological restoration. In this case study of Beijing City, ecological sources were identified based on habitat importance and landscape connectivity. The results showed that, in total 3119.65 km2 of ecological land had been extracted as ecological sources, which were mainly located in the northern, northwestern and northeastern mountainous areas. The identified key ecological corridor covered an area of 198.86 km2, with 567.30 km2 for potential ecological corridors, both connecting the ecological sources. 34 key points were also identified with priority in restoring ecological corridors.
ISPRS Journal of Photogrammetry and Remote Sensing，May 2019
Fei Yao, Jiansheng Wu, Weifeng Li, Jian Peng
Abstract: While the aerosol optical depth (AOD) product from the Visible Infrared Imaging Suite (VIIRS) instrument has proven effective for estimating regional ground-level particle concentrations with aerodynamic diameters less than 2.5 μm (PM2.5), its performance at larger spatial scales remains unclear. Despite the wide application of statistical models in building ground-level PM2.5 satellite remote sensing retrieval models, a limited number of studies have considered the spatiotemporal heterogeneities for model structures. Taking China as the study area, we used the VIIRS AOD, together with multi-source auxiliary variables, to develop a spatially structured adaptive two-stage model to estimate ground-level PM2.5 concentrations at a 6-km spatial resolution. To this end, we first defined and calculated a dual distance from the ground-level PM2.5 monitoring data. We then applied the unweighted pair-group method with arithmetic means on dual distances and obtained 13 spatial clusters. Subsequently, we combined the time fixed effects regression (TEFR) model and geographically weighted regression (GWR) model to develop the spatially structured adaptive two-stage model. For each spatial cluster, we examined all possible combinations of auxiliary variables and determined the best model structure according to multiple statistical test results. Finally, we obtained the PM2.5 estimates through regression mapping. At least seven model-fitting data records per day made a good threshold that could best overcome the model overfitting induced by the second-stage GWR model at the minimum price of losing samples. The overall model fitting and ten-fold cross validation (CV) R2 were 0.82 and 0.60, respectively, under that threshold. Model performances among different spatial clusters differed to a certain extent. High-CV R2 values always exceeded 0.6 while low-CV R2 values less than 0.5 also existed. Both the size of the model-fitting data records and the extent of urban-industrial characteristics of spatial clusters accounted for these differences. The PM2.5 estimates agreed well with the PM2.5 observations with correlation coefficients all exceeding 0.5 at the monthly, seasonal, and annual scales. East of Hu’s line and north of the Yangtze River were characterized by high PM2.5 concentrations. This study contributes to the understanding of how well VIIRS AOD can retrieve ground-level PM2.5 concentrations at the national scale and strategies for building ground-level PM2.5 satellite remote sensing retrieval models.
52.Landslides-oriented urban disaster resilience assessment—A case study in ShenZhen, China
Science of the Total Environment，15 April 2019
Xiwen Zhang , Jing Song , Jian Peng, JianshengWu⁎
Abstract: With the increasing expansion of cities associated with rapid urbanization, the ecological environment is being severely damaged, exposing cities to frequent extreme weather events. Urban ecological ecosystems are under great threat. Research on urban disaster resilience is conducive to a better understanding of disaster prevention and mitigation capacity, and provides valuable references for resilient city construction. In this study, a typical city under rapid urbanization in China – Shenzhen – was chosen as the research area, including the city's 57 sub-districts. Urban disaster resilience to rainfall-induced landslides was conceptually framed into the dimensions of physical resilience and social resilience. Support vector machine (SVM) was applied to evaluate the physical resilience and a Delphi-analytic hierarchy process (Delphi-AHP) model was used to assess social resilience on a sub-district scale in 2016. The results show that the physical resilience and social resilience of Shenzhen demonstrate obvious spatial concentration trends. Areas with low physical resilience were located in sub-districts of Dapeng New District with intense rainfall and complex topography, as well as those in Guangming New District with lateritic red earth derived from arenaceous shale. Areas with low social resilience were mainly located in eastern Shenzhen, including sub-districts in Longgang District and Dapeng New District, with undeveloped economy, inadequate infrastructures and many vulnerable people. All sub-districts in the three districts of Pingshan New District, Dapeng New District and Guangming New District need attention because of their low comprehensive resilience. Comparison of the physical resilience and social resilience indicated that the performance of physical resilience was significantly better than that of social resilience; only 26% of the sub-districts of Shenzhen had a higher level of social resilience than of physical resilience. Therefore, the government should strengthen urban management of social services and physical infrastructure provision to improve social resilience to cope with urban disasters.
Ecological Indicators，21 December 2018
Tian Hu, Jiansheng Wu∗, Weifeng Li
Abstract: Recent studies on ecosystem services have highlighted the relationship between multiple services. It is widely accepted that the increase in one ecosystem service may alter the provision of another. However, the individual difference and time effect are often difficult to integrate when the relationship is explored. In this study, we took soil erosion control (SEC) and water yield (WY) as examples to analyze relationships between multiple ecosystem services. Firstly, the biophysical values were evaluated using seven series of land use data from 2000 to 2012.Secondly, the spatial relationship was explored using local autocorrelation and the barycenter model on the basis of correlation analysis on regional and watershed scale. Finally, the pooled regression models, fixed effects models, two-way fixed effects models, and random effects models were introduced to explore the relationship of two ecological indicators for considering time effect and individual differences. We concluded that the SEC and WY presented a positive linear correlation on a watershed scale across time, and showed co-occurrence patterns from a spatial perspective. The SEC was positively affected by WY and the year the data were collected in. There were opportunities to enhance co-benefits between SEC and WY to achieve win-win outcomes.
Journal of Cleaner Production，11 March 2019
Qiwen Cao, Xiwen Zhang, Dongmei Lei, Liying Guo, Xiaohui Sun, Fan'en Kong,Jiansheng Wu
Abstract: Landscape ecological risk assessment is an effective tool to support 1 sustainable ecosystem management in regions with rapid urbanization. However, the characterization method of landscape ecological risk probability needs urgent improvement. This study put forwards a comprehensive index system for risk probability characterization using the factors of terrain, artificial threats, ecological resilience, and landscape vulnerability. In addition, the ordered weighted averaging (OWA) algorithm was introduced to realize a multi-scenario simulation to facilitate different decision-making preferences. The results showed that (1) the overall landscape ecological risk probability in Shenzhen, China, was higher in the west than in the east, and the dominated probability levels were low and moderate. (2) Three decision-making scenarios were simulated: basic risk control, moderate risk control, and strict risk control. About 307.88 km2 of unstable risk probability areas were identified, of which the relationship between development and protection should be scrutinized more in the "eastward strategy" for the future. (3) As for the methodology, this index system contained multiple dimensions including ecological processes, external threats, and landscape feature patterns. It is advantageous in that it provides an ecological risk measure for the future, the characterization of spatial heterogeneity, stable and reliable results, and the definite implications of the risks.
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