79. Climatic modification effects on the association between PM1 and lung cancer incidence in China
BMC Public Health, May 2021
Huagui Guo, Xin Li, Weifeng Li, Jiansheng Wu, Siying Wang, Jing Wei
Abstract
Context
Nationwide studies that examine climatic modification effects on the association between air pollution and health outcome are limited in developing countries. Moreover, few studies focus on PM1 pollution despite its greater health effect.
Objectives
This study aims to determine the modification effects of climatic factors on the associations between PM1 and the incidence rates of lung cancer for males and females in China.
Methods
We conducted a nationwide analysis in 345 Chinese counties (districts) from 2014 to 2015. Mean air temperature and relative humidity over the study period were used as the proxies of climatic conditions. In terms of the multivariable linear regression model, we examined climatic modification effects in the stratified and combined datasets according to the three-category and binary divisions of climatic factors. Moreover, we performed three sensitivity analyses to test the robustness of climatic modification effects.
Results
We found a stronger association between PM1 and the incidence rate of male lung cancer in counties with high levels of air temperature or relative humidity. If there is a 10 μg/m3 shift in PM1, then the change in male incidence rate relative to its mean was higher by 4.39% (95% CI: 2.19, 6.58%) and 8.37% (95% CI: 5.18, 11.56%) in the middle and high temperature groups than in the low temperature group, respectively. The findings of climatic modification effects were robust in the three sensitivity analyses. No significant modification effect was discovered for female incidence rate.
Conclusions
Male residents in high temperature or humidity counties suffer from a larger effect of PM1 on the incidence rate of lung cancer in China. Future research on air pollution-related health impact assessment should consider the differential air pollution effects across different climatic conditions.
Land Use Policy, July 2021
Zhe Feng, Xueru Jin, Tianqian Chen, Jiansheng Wu
Abstract: Understanding ecosystem service trade-offs and synergies is the foundation to achieve the efficient management of the ecosystem and improve human well-being. However, the current research involving the driving mechanism of ecosystem service relationship formation is still limited. In this paper, a semi-quantitative model named Bayesian belief networks is introduced to simulate ecological processes, which links the potential influencing factors with the ecosystem service supply. The purpose of this paper is to help understand the ecosystem service relationship and provide management decision-making reference. Taking the Beijing–Tianjin–Hebei region as a study area, four ecosystem services (habitat quality, carbon storage, water yield, and soil retention services) were quantified and mapped in 2015. Based on a created Bayesian belief network simulating the ecosystem service supply, the sensitivity analysis was used to identify the key factors affecting the ecosystem service supply. Besides, the relationship between ecosystem services was identified and the driving mechanism was analyzed through the Bayesian probabilistic inference. The main conclusions demonstrate the following. (1) The spatial heterogeneity of the ecosystem service supply in the Beijing–Tianjin–Hebei region is relatively strong. (2) The key factors affecting ecosystem services are the land use type, vegetation coverage, precipitation, slope, evapotranspiration, and population density. (3) Habitat quality, carbon storage, and soil retention services synergize one another, and there are trade-offs between water yield service and habitat quality, carbon storage, and soil retention services, respectively. (4) Among the land use type, vegetation coverage, slope, and population density, the land use type has the most important impact on ecosystem service trade-offs. As a practice of combining Bayesian belief networks and ecosystem services, this study can contribute to a research method of ecosystem service relationships and references for the management decision-making on maximizing the overall benefits of ecosystem services.
Journal of Cleaner Production, May 2021
Yuhang Luo, Jiansheng Wu, Xiaoyu Wang, JianPeng
Abstract: Developed areas are gradually expanding with the acceleration of urbanization. The degree of fragmentation of urban ecological land is increasing, and the risk of reducing the connectivity between important ecological patches is rising. In this context, the municipal governments in China have delineated ‘ecological control lines’ (ECLs) to protect landscape connectivity—however, its effectiveness has not been evaluated. In the study reported here, circuit theory was used to simulate flow among ecological sources, stepping stones carrying ecological flow were identified, and the effectiveness of the ECL, in terms of maintaining landscape connectivity, was evaluated. In this study, we classified the absence of stepping-stone protection based on stepping-stone area, distribution, and potential loss and identified key stepping-stone categories in urgent need of ECL protection, which will be useful for delineating the ecological red line in the future. The results showed that there were 1488 ecological stepping stones in Shenzhen, with an average area of 0.124 km2 and a total area of 185.2 km2. Under the current ECL protection, five stepping-stone groups were characterized as hotspots lacking protection, and this absence threatened the connectivity between some ecological sources. Through stepping stone classification and analysis, we were also able to show that there were 34 key stepping stones that were not protected effectively by the ECL, and they should be incorporate in the future. Using stepping stone and circuit theory models, our study has provided a reference for policy making that addresses connectivity under landscape fragmentation.
76. Understanding ecological groups under landscape fragmentation based on network theory
Landscape and Urban Planning, June 2021
Yuhang Luo, Jiansheng Wu, Xiaoyu Wang, Yuhao Zhao, Zhe Feng
Abstract: In the context of landscape fragmentation, a variety of ecological conservation theories have been proposed to maintain habitat connectivity. These theories focus on the connectivity of the ecological corridors in ecological space but ignore the community structure of ecological space. However, community structure is ubiquitous in ecological space. Our study predicted habitat quality of ecological patches by using an artificial neural network and species occurrence points. Then, the ecological space network (ESN) was proposed and constructed by taking large ecological patches and fragmented patches as habitat nodes. Based on island biogeography theory (TTIB) and network community detection, the ecological groups in the ESN were proposed and identified. They are considered to be collections of ecological land with close species connections. After that, the hierarchy of the ecological group was analyzed, and the existing policy was evaluated. The results show that there were 39 ecological groups in the ESN. The dendrogram of the hierarchy shows that all ecological groups can form five bigger ecological groups. The extent of the bigger groups proves that the ecological groups in the eastern part are closely connected. By analyzing the protection of ecological control lines on the linkages among ecological groups, we found that the existing policy could protect most of the ecological groups separately, but could not maintain connectivity among ecological groups, leading to habitat isolation. We believe that the concept of “ecological group” makes up for the lack of “habitat isolation” in the existing theory. The idea can understand the hierarchical structure of ecological space, which is conducive to the maintenance of ecological integrity.
Landscape Ecology, March 2021
Yuhang Luo, Jiansheng Wu
Abstract
Context
Redundancy is a typical characteristic of complex networks. In ecological networks, redundancy also exists in the connectivity between key nodes. Understanding the redundant structure of an ecological network and identifying the backbone structure is very important in maintaining the stability of the ecological network system.
Objectives
Using Shenzhen City as an example, we aimed to construct an ecological network and identify its arterial corridors. A protection plan for arterial corridors is proposed through network analysis.
Methods
Using the concept of edge betweenness and a minimum spanning tree, we identifified the arterial corridors in the ecological network. Four corridor destruction scenarios were considered: random destruction, urban sprawl, ecological control line, and arterial corridor scenarios. The ecological network stability under the four scenarios is explored based on a network robustness analysis.
Results
The ecological network covers 32 important habitats and 49 ecological corridors. Among them, 31 arterial corridors connect 32 important habitats, forming the backbone structure of the ecological network. Both the control line policy and protection plan of the arterial corridor play a role in maintaining the stability of the ecological network. The stability of the ecological network under the arterial corridor protection scheme was 6% higher than that under the control line one.
Conclusions
This study provides an effective method for identifying arterial corridors in an ecological network. Protecting the arterial corridors and the redundant corridors based on edge betweenness can maintain the stability of the ecological network to the greatest extent. Our study has reference signifificance for the hierarchical protection of ecological corridors.
Environmental Pollution, September 2021
Jiansheng Wu, YuanWang, Jingtian Liang, FeiYao
Abstract: Particulate matter with an aerodynamic equivalent dimeter less than 2.5 mm (PM2.5) and ozone (O3) are major air pollutants, with coupled and complex relationships. The control of both PM2.5 and O3 pollution requires the identification of their common influencing factors, which has rarely been attempted. In this study, land use regression (LUR) models based on the least absolute shrinkage and selection operator were developed to estimate PM2.5 and O3 concentrations in China's Pearl River Delta region during 2019. The common factors in the tradeoffs between the two air pollutants and their synergistic effects were analyzed. The model inputs included spatial coordinates, remote sensing observations, meteorological conditions, population density, road density, land cover, and landscape metrics. The LUR models performed well, capturing 54e89% and 42e83% of the variations in annual and seasonal PM2.5 and O3 concentrations, respectively, as shown by the 10-fold cross validation. The overlap of variables between the PM2.5 and O3 models indicated that longitude, aerosol optical depth, O3 column number density, tropospheric NO2 column number density, relative humidity, sunshine duration, population density, the percentage cover of forest, grass, impervious surfaces, and bare land, and perimeter-area fractal dimension had opposing effects on PM2.5 and O3. The tropospheric formaldehyde column number density, wind speed, road density, and area-weighted mean fractal dimension index had complementary effects on PM2.5 and O3 concentrations. This study has improved our understanding of the tradeoff and synergistic factors involved in PM2.5 and O3 pollution, and the results can be used to develop joint control policies for both pollutants.
73.Who are more exposed to PM2.5 pollution: A mobile phone data approach
Environment International，October 2020
Huagui Guo, Weifeng Li, Fei Yao, Jiansheng Wu, Xingang Zhou, Yang Yue, Anthony G.O.Yeh
Abstract
Background
Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales.
Objectives
This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales.
Methods
The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects.
Results
We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons).
Conclusions
There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.
Landscape and Urban Planning,October 2020
Jian Peng, Qianyuan Liu, Zihan Xu,Danna Lyu, Yueyue Du,Ruilin Qiao, Jiansheng Wu
Abstract:Climatic warming and urbanization have exacerbated urban heat island (UHI) effect globally. Waterbodies have significant cooling effect while the current UHI mitigation researches mostly focus on green spaces. Although the cooling effect of waterbodies has been highlighted and measured in previous studies, the impact of local socioeconomic development surrounding the waterbodies remains unclear. The scarcity of land resource in the city has also posed urgent need to explore cooling efficiency of waterbody patch size. Highly urbanized and densely scattered with waterbody, the Pearl River Delta (PRD) urban agglomeration has suffered severe UHI effect. Taking four PRD cities as the case study area, this study analyzed the impact of local socioeconomic development on the daytime cooling intensity of waterbodies, and identified the dominant impact factor as well as the threshold value of efficiency (TVoE) in diversely developed areas. The results showed that the cooling intensity of waterbodies had obvious spatial heterogeneity with an average of 1.1 °C and a maximum of 5.54 °C, which was dominated by patch size and strongly affected by local socioeconomic development. The mean cooling intensity increased along with the increasing of local socioeconomic development, and the TVoEs of waterbody patch size were 0.49 ha, 0.55 ha and 0.70 ha in such three levels of local socioeconomic development as low, medium and high, respectively. The results can provide quantitative guidance for blue landscape planning in regard to UHI effect mitigation.
71.Will polycentric cities cause more CO2 emissions? A case study of 232 Chinese cities
Journal of Environmental Sciences,October 2020
Wei Sha,Ying Chen,Jiansheng Wu,Zhenyu Wang
Abstract:From 2000 to 2010 China experienced rapid economic development and urbanization. Many cities in economically developed areas have developed from a single-center status to polycentricity. In this study, we used exploratory spatial data analysis (ESDA) to identify the population centers, which identified 232 cities in China as having urban centers. COMP was used to represent urban agglomeration, and POLYD (representing how far is the city's sub-centers to the main center), POLYC (representing the number of a city's centers), and POLYP (representing the population distributed between the main center and the sub-centers) were used to indicate urban polycentricity. Night light data were used to determine the CO2 emissions from various cities in China. A mixed model was used to study the impact of urban aggregation and polycentric data on the CO2 emission efficiency in 2000 and 2010. The study found that cities with higher compactness were distributed in coastal areas, and the cities with higher multicentricity were distributed in the Yangtze River Delta and Shandong Province. The more compact the city was, the less conducive it was to improving CO2 emission efficiency. Polycentric development of the city was conducive to improving the CO2 emission efficiency, but the number of urban centers had no significant relationship with the CO2 emission efficiency. Our research showed that the compactness and multicentricity of the city had an impact on the CO2 emission efficiency and provided some planning suggestions for the low carbon development of the city.
Remote Sensing of Environment,September 2020
JianPeng,Ruilin Qiao,Yanxu Liu,Thomas Blaschke,Shuangchen Li,Jiansheng Wu, Zihan Xu,Qianyuan Liu
Abstract:Land surface temperature (LST), as an effective indicator measuring urban thermal environment, is significantly influenced by a range of human and natural factors at different scales. However, the scale-dependence of LST influencing factors has not been fully explored, due to relatively discrete scales or single factor used in previous studies. It is a great challenge to explore the approach to prioritizing research scales in view of the influencing factors of LST. Taking the urban group of Xi'an City and Xianyang City in Western China as a case study area, this study proposed a wavelet coherence approach to identifying the prioritizing LST influencing factors and research scales. Based on the sample transects, the results showed that around 1 km could be used as the prioritizing research scale for simultaneously exploring multiple LST influencing factors. And the normalized difference build-up index was the dominant influencing factor with the strongest multi-scale stability. The coherence relationships with LST of the area percentage of blue land, and the mean patch area of blue land represented high spatial heterogeneity, with multi-scale stability in the area of widespread water body. The normalized difference vegetation index should also be highlighted due to the multi-scale stability and stable medium coherence with LST. This study proposed a wavelet coherence approach to exploring spatial heterogeneity and scale-dependence of the relationship between LST and multiple influencing factors.
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