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He X.,Nanjing Normal University | He X.,Kaili University | He X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Lin Z.-S.,Nanjing Normal University | Lin Z.-S.,State Key Laboratory Cultivation Base of Geographical Environment Evolution
Huanjing Kexue/Environmental Science | Year: 2017

In this paper, the generalized additive model (GAM) was introduced to analyze the interactive effects of the influencing factors on the change of PM2.5 concentration during 2013-2015 in Nanjing city. The results showed as follows: PM2.5 and its influencing factors appeared to follow normal distribution. There were strong correlations among the influencing factors, especially among the temperature(TEM), pressure(PRS) and water vapor pressure(VAP). For the single influencing factor GAM models of PM2.5 concentration, all influencing factors passed the significance test. Moreover, the equation fitting degrees of SO2, CO, and NO2 were much higher. In the multiple influencing factors GAM models of PM2.5 concentration, the contribution of the SO2, CO, NO2, O3, precipitation (PRE), wind and relative humidity(RHU) to the change of PM2.5 concentration was 73.9% with significant impacts on the change of PM2.5 concentration. Based on the diagnostic analysis of the effect of multi factors on the change of PM2.5 concentration, there were linear relationship between PM2.5 and SO2, NO2 and wind, and non-linear relationship between PM2.5 and CO, O3, PRE and RHU. The GAM models, which considered the interaction of SO2 respectively with CO, PRE and RHU, the interaction of CO respectively with NO2, O3, PRE, Wind and RHU, and the interaction of NO2 respectively with Wind, PRE and RHU, all passed the significance test(P<0.01 or P<0.05). The interaction of SO2, CO and NO2 respectively with other factors such as meteorological factors had the most important influence on the change of PM2.5 concentration. At last, through the visualized three-dimensional map of the GAM models considering the interaction of the influencing factors on the PM2.5 concentration, the interactive effects of the influencing factors on PM2.5 concentration were quantitatively modeled. Our results demonstrated that GAM could be used to quantitatively analyze the interactive effect of the influencing factors on the change of PM2.5 concentration. Therefore, the research method is innovative and important for PM2.5 pollution and control. © 2017, Science Press. All right reserved.


Yang B.,Wuhan University | Dong Z.,Wuhan University | Liu Y.,Wuhan University | Liang F.,Wuhan University | And 3 more authors.
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2017

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects. © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)


Zhang J.,Nanjing Normal University | Zhang J.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Tian P.,Northeast Forestry University | Tang J.,CAS Chengdu Institute of Mountain Hazards and Environment | And 5 more authors.
Journal of Geophysical Research G: Biogeosciences | Year: 2016

It is important to clarify the quantity and composition of hydrologic N export from terrestrial ecosystem and its primary controlling factors, because it affected N availability, productivity, and C storage in natural ecosystems. The most previous investigations were focused on the effects of N deposition and human disturbance on the composition of hydrologic N export. However, few studies were aware of whether there were significant differences in the concentrations and composition of hydrologic N export from natural ecosystems in different climate zones and what is the primary controlling factor. In the present study, three natural forest ecosystems and one natural grassland ecosystem that were located in different climate zones and with different soil pH range were selected. The concentrations of total dissolved N, dissolved organic nitrogen (DON), NH4 +, NO3 − in soil solution and stream water, soil properties, and soil gross N transformation rates were measured to answer above questions. Our results showed that NO3 − concentrations and the composition pattern of hydrologic N export from natural ecosystems varied greatly in the different climate zones. The NO3 − concentrations in stream water varied largely, ranging from 0.1 mg N L−1 to 1.6 mg N L−1, while DON concentration in stream water, ranging from 0.1 to 0.9 mg N L−1, did not differ significantly, and the concentrations of NH4 + were uniformly low (average 0.1 mg N L−1) in all studied sites. There was a trade-off relationship between the proportions of NO3 − and DON to total dissolved N in stream water. In subtropical strongly acidic forests soil site, DON was the dominance in total dissolved N in stream water, while NO3 −-N became dominance in temperate acidic forests soil site, subtropical alkaline forests soil region, and the alpine meadow sites on the Tibetan Plateau. The proportions of NO3 − to total dissolved N in both soil solution and stream water significantly increased with the increasing of the gross autotrophic nitrification rates (p < 0.01). Our results indicated that the characteristics of soil N transformations were the most primary factor regulating the composition of hydrologic N losses from ecosystems. The nitrification was the central soil N transformation processes regulating N composition in soil solution and hydrologic N losses. These results provided important information on understanding easily the composition of hydrologic N export from terrestrial ecosystem. ©2016. American Geophysical Union. All Rights Reserved.


Cao M.,Nanjing Normal University | Cao M.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Tang G.,Nanjing Normal University | Tang G.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Wang Y.,Chuzhou University
International Journal of Geographical Information Science | Year: 2015

This paper presents an intelligent approach to discover transition rules for cellular automata (CA) by using cuckoo search (CS) algorithm. CS algorithm is a novel evolutionary search algorithm for solving optimization problems by simulating breeding behavior of parasitic cuckoos. Each cuckoo searches the best upper and lower thresholds for each attribute as a zone. When the zones of all attributes are connected by the operator ‘And’ and linked with a cell status value, one CS-based transition rule is formed by using the explicit expression of ‘if-then’. With two distinct advantages of efficient random walk of Lévy flights and balanced mixing, CS algorithm performs well in both local search and guaranteed global convergence. Furthermore, the CA model with transition rules derived by CS algorithm (CS-CA) has been applied to simulate the urban expansion of Nanjing City, China. The simulation produces encouraging results, in terms of numeric accuracy and spatial distribution, in agreement with the actual patterns. Preliminary results suggest that this CS approach is well suitable for discovering reliable transition rules. The model validation and comparison show that the CS-CA model gets a higher accuracy than NULL, BCO-CA, PSO-CA, and ACO-CA models. Simulation results demonstrate the feasibility and practicability of applying CS algorithm to discover transition rules of CA for simulating geographical systems. © 2015 Taylor & Francis.


Zhang C.,Beijing Normal University | Zhang C.,Chinese University of Hong Kong | Chen M.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Chen M.,Nanjing Normal University | And 4 more authors.
Ecological Modelling | Year: 2015

Geography investigates changes in physical structures and distributions of objects in spatiotemporal world, which are shaped by geographic process (geo-process). With extensive simulation models used to study geo-process, this paper examines the status of geo-process modeling (namely model-based simulation) for multidisciplinary geo-processes across scales in virtual geographic environments (VGEs). The conceptual framework of integrated modeling in VGEs is proposed with a review of specific issues, including model sharing and management, collaborative modeling and uncertainty analysis. The contribution of a model base in model reusability and modeling management, concerning input data, parameterization, and simulation output, is detailed. Finally, this paper concludes with a discussion of future research directions for holistic geo-process modeling. © 2015 Elsevier B.V.


Wu S.,Nanjing Normal University | Han R.,Nanjing Normal University | Yang H.,Jiangsu Provincial Key Laboratory of Materials Cycling and Pollution Control | Wang Q.,Nanjing Normal University | And 2 more authors.
Chemistry and Ecology | Year: 2016

In order to reveal the historical context of metal element accumulation under the economic boom during the last decades in eastern China, concentrations of nine metal elements, radionuclides (210Pb), Pb isotope ratios (207Pb/206Pb) and sedimentary characteristics were investigated in two sediment cores collected from the Sheyang River. The sediments have recorded the heavy metal deposition and thus allow establishing a connection between the temporal evolution of the heavy metal pollution and historical changes in industrial and urban discharges. Enrichment factors (EFs) were calculated to estimate the level of contamination in these sediments. A significant anthropogenic enrichment of Cu, Ni, Pb, Cr and Zn was highlighted, which were identified from anthropogenic discharges from cities and industrial sources, according to a cluster analysis. According to the annual variation in GDP growth rate, industrial growth rate, ratio of 207Pb/206Pb and EFs, it was obvious that sedimentary accumulation of metals has a close relationship with anthropogenic activities. In the pre-industrial period, natural inputs prevailed with lower EF and constant 207Pb/206Pb ratios around 0.82. However, during 1980–1995, the rapid industrial development caused a gradual increase in EFs and 207Pb/206Pb (>0.83). Our results disinterred the evolution of anthropogenic metal inputs in the last century into the Sheyang River. © 2016 Informa UK Limited, trading as Taylor & Francis Group


Zhang G.,University of Wisconsin - Madison | Huang Q.,University of Wisconsin - Madison | Zhu A.-X.,Nanjing Normal University | Zhu A.-X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | And 2 more authors.
International Journal of Geographical Information Science | Year: 2016

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources. © 2016 Informa UK Limited, trading as Taylor & Francis Group


Zhang Y.,Nanjing Normal University | Zhao W.,Nanjing Normal University | Zhao W.,Nanjing University | Zhang J.,Nanjing Normal University | And 2 more authors.
Journal of Soils and Sediments | Year: 2016

Purpose: Agricultural practises impact soil properties and N transformation rate, and have a greater effect on N2O production pathways in agricultural soils compared with natural woodland soils. However, whether agricultural land use affects N2O production pathways in acidic soils in subtropical regions remains unknown. Materials and methods: In this study, we collected natural woodland soil (WD) and three types of agricultural soils, namely upland agricultural (UA), tea plantation (TP) and bamboo plantation (BP) soils. We performed paired 15N-tracing experiment to investigate the effects of land use types on N2O production pathways in acidic soils in subtropical regions in China. Results and discussion: The results revealed that heterotrophic nitrification is the dominant pathway of N2O production in WD, accounting for 44.6 % of N2O emissions, whereas heterotrophic nitrification contributed less than 2.7 % in all three agricultural soils, due to a lower organic C content and soil C/N ratio. In contrast, denitrification dominated N2O production in agricultural soils, accounting for 54.5, 72.8 and 77.1 % in UA, TP and BP, respectively. Nitrate (NO3 −) predominantly affected the contribution from denitrification in soils under different land use types. Autotrophic nitrification increased after the conversion of woodland to agricultural lands, peaking at 42.8 % in UA compared with only 21.5 % in WD, and was positively correlated with soil pH. Our data suggest that pH plays a great role in controlling N2O emissions through autotrophic nitrification following conversion of woodland to agricultural lands. Conclusions: Our results demonstrate the variability in N2O production pathways in soils of different land use types. Soil pH, the quantity and quality of organic C and NO3 − content primarily determined N2O emissions. These results will likely assist modelling and mitigation of N2O emissions from different land use types in subtropical acidic soils in China and elsewhere. © 2016 Springer-Verlag Berlin Heidelberg


Zhang C.,Nanjing Normal University | Zhang C.,State Key Laboratory Cultivation Base of Geographical Environment Evolution | Huang Z.,Nanjing Normal University | Huang Z.,State Key Laboratory Cultivation Base of Geographical Environment Evolution
Applied Artificial Intelligence | Year: 2015

Prediction of tourist decision-making processes, including tourist motive, attitude, behavior, and so on, is of great importance for the development of tourism marketing strategies. Recently, applying machine learning techniques to predict tourist decision-making processes has drawn much attention. However, many machine learning techniques applied in the tourist decision-making prediction task fail to address two practical yet important problems. One is the failure of constructing models that can generate accurate yet comprehensible predictions at the same time, and the other is the failure to accommodate the characteristics of data collected from tourists that is usually small yet noise prone. In this article, we address the two entangled problems using the twice-learning framework to predict tourist motive from tourist external and internal features data collected through on-site survey. The results indicate that, based on the two-phase learning process, we can predict tourist motive accurately as well as extract meaningful insights, which are useful for targeted marketing strategies development from the real-world data. © 2015 © 2015 Taylor & Francis Group, LLC.


Zhang L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Zhang L.,University of Chinese Academy of Sciences | Nan Z.,Nanjing Normal University | Xu Y.,Nanjing Normal University | And 2 more authors.
PLoS ONE | Year: 2016

Land use change and climate variability are two key factors impacting watershed hydrology, which is strongly related to the availability of water resources and the sustainability of local ecosystems. This study assessed separate and combined hydrological impacts of land use change and climate variability in the headwater region of a typical arid inland river basin, known as the Heihe River Basin, northwest China, in the recent past (1995-2014) and near future (2015-2024), by combining two land use models (i.e., Markov chain model and Dyna-CLUE) with a hydrological model (i.e., SWAT). The potential impacts in the near future were explored using projected land use patterns and hypothetical climate scenarios established on the basis of analyzing long-term climatic observations. Land use changes in the recent past are dominated by the expansion of grassland and a decrease in farmland; meanwhile the climate develops with a wetting and warming trend. Land use changes in this period induce slight reductions in surface runoff, groundwater discharge and streamflow whereas climate changes produce pronounced increases in them. The joint hydrological impacts are similar to those solely induced by climate changes. Spatially, both the effects of land use change and climate variability vary with the sub-basin. The influences of land use changes are more identifiable in some sub-basins, compared with the basin-wide impacts. In the near future, climate changes tend to affect the hydrological regimes much more prominently than land use changes, leading to significant increases in all hydrological components. Nevertheless, the role of land use change should not be overlooked, especially if the climate becomes drier in the future, as in this case it may magnify the hydrological responses. © 2016 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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