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Niu Q, Wang G, Liu B, Zhang R, Lei J, Wang H, Liu M. Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China. Sci Rep 2023; 13:22542. [PMID: 38110563 PMCID: PMC10728089 DOI: 10.1038/s41598-023-49877-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023] Open
Abstract
Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.
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Affiliation(s)
- Quanfu Niu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China.
- Emergency Mapping Engineering Research Center of Gansu Province, Lanzhou, 730050, China.
- Academician Expert Workstation of Gansu Dayu Jiu Zhou Space Information Technology Co., Ltd, Lanzhou, 730050, China.
| | - Gang Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Bo Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Ruizhen Zhang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jiaojiao Lei
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Hao Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Mingzhi Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
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Feng H, Ning E, Yu L, Wang X, Vladimir Z. The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory. ENVIRONMENT INTERNATIONAL 2023; 181:108287. [PMID: 37926062 DOI: 10.1016/j.envint.2023.108287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023]
Abstract
A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.
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Affiliation(s)
- Haixia Feng
- School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China; Shandong Intelligent Transportation Key Laboratory (Preparatory), Jinan 250023, China
| | - Erwei Ning
- School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China
| | - Lei Yu
- School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China; Texas Southern University, Houston 77004, USA.
| | - Xingyu Wang
- School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China
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Zhang Z, Zhou J, Liu J, Liu X, Zhu Y, Li H, Cui Y. Spatiotemporal changes of aerosol optical depth and its response to urbanization: a case study of Jinan City, China, 2009-2018. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:101522-101534. [PMID: 37651015 DOI: 10.1007/s11356-023-29546-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/23/2023] [Indexed: 09/01/2023]
Abstract
With the insidiously growing impact of urban development on the environment, the issue of air quality has attracted extensive attention nationally and globally. It is of great significance to study the influence of urbanization on air quality for the rational development of cities. MODIS-MAIAC (Moderate Resolution Imaging Spectroradiometer-Multi-Angle Implementation of Atmospheric Correction) Aerosol optical depth (AOD) product, DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night-light were used to explore the spatiotemporal variation and correlation between AOD and urbanization development before and after the promulgation of environmental governance policies in Jinan City from 2009 to 2018. Results show that (1) the spatial distribution of AOD in Jinan had the characteristics of high in the north and low in the south, high in the west and low in the east, and low in some parts of the central region; there was a significant seasonal variation in time, with the highest AOD in summer and the lowest in winter. During 2009-2013, the annual average variation of AOD increased by 20.6%, while during 2014-2018, it decreased by 35.3%; (2) The distribution of night-light in Jinan City has progressively expanded, mirroring the city's ongoing development. The spatial distribution of aerosols in urban areas was relatively low compared to the surrounding areas of the city. (3) From 2009 to 2013, there existed a significant positive correlation between the spatial and temporal distribution of AOD and night-light. However, from 2014 to 2018, with the implementation of environmental governance policies, this relationship shifted to a significant negative correlation between the spatial and temporal distribution of AOD and night-light. Through an analysis of the correlation between urban development and aerosol depth in Jinan City over the past decade, it can be concluded that urban development does not inevitably result in elevated AOD levels. Notably, the Jinan government has achieved remarkable results in controlling the atmospheric environment, as evidenced by recent years' improvements.
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Affiliation(s)
- Zeyu Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
- School of Geography and Environment, Jiangxi Normal University, Nanchang, 330022, Jiangxi, China
| | - Jun Zhou
- Institute of Groundwater and Earth Sciences, Jinan University, Jinan, 250022, China
| | - Jingzhe Liu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Xiaoqian Liu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Yanwen Zhu
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China
| | - Huixuan Li
- Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Yurong Cui
- College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
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Canto MV, Guxens M, Ramis R. Exposure to Traffic Density during Pregnancy and Birth Weight in a National Cohort, 2000-2017. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8611. [PMID: 35886463 PMCID: PMC9318762 DOI: 10.3390/ijerph19148611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/07/2022] [Accepted: 07/12/2022] [Indexed: 02/01/2023]
Abstract
The variation on birth weight is associated with several outcomes early on in life and low birth weight (LBW) increases the risk of morbidity and mortality. Some environmental exposures during pregnancy, such as particulate matters and other traffic-related pollutants can have a significant effect on pregnant women and fetuses. The aim of this study is to estimate the effect of exposure to traffic density during pregnancy over birth weight in Spain, from 2000-2017. This was a retrospective, cross-sectional study using the information from Spain Birth Registry Statistics database. The traffic density was measured using the Annual average daily traffic. Multivariate linear regression models using birth weight and traffic density were performed, as well as a logistic regression model to estimated Odds ratios for LBW and GAM models to evaluate the non-linear effect. Our findings showed that increases in traffic density were associated with reduction of birth weight and increases of LBW risk. Moreover, exposure to high and very-high traffic-density during pregnancy were associated with reduction of birth weight and increase on LBW risk comparing with exposure to low number of cars trespassing the neighborhoods. The results of this study agree with previous literature and highlights the need of effective policies for reducing traffic density in residential neighborhoods of cities and towns.
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Affiliation(s)
| | - Mònica Guxens
- Barcelona Institute for Global Health (ISGlobal), 08003 Barcelona, Spain;
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Department of Medicine and Live Sciences, Universitat Pompeu Fabra, 08002 Barcelona, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, 3015 GE Rotterdam, The Netherlands
| | - Rebeca Ramis
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Chronic Diseases Department, National Centre for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain
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Use of Geographically Weighted Regression (GWR) to Reveal Spatially Varying Relationships between Cd Accumulation and Soil Properties at Field Scale. LAND 2022. [DOI: 10.3390/land11050635] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The spatial variation of correlation between Cd accumulation and its impact factors plays an important role in precise management of Cd contaminated farmland. Samples of topsoils (n = 247) were collected from suburban farmland located at the junction of the Yellow River Basin and the Huaihe River Basin in China using a 200 m × 200 m grid system. The total and available contents of Cd (T-Cd and A-Cd) in topsoils were analyzed by ICP-MS, and their spatial distribution was analyzed using kriging interpolation with the GIS technique. Geographically weighted regression (GWR) models were applied to explore the spatial variation and their influencing mechanisms of relationships between major environmental factors (pH, organic matter, available phosphorus (A-P)) and Cd accumulation. Spatial distribution showed that T-Cd, A-Cd and their influencing factors had obvious spatial variability, and high value areas primarily cluster near industrial agglomeration areas and irrigation canals. GWR analysis revealed that relationships between T-Cd, A-Cd and their environmental factors presented obvious spatial heterogeneity. Notably, there was a significant negative correlation between soil pH and T-Cd, A-Cd, but with the increase of pH in soil the correlation decreased. A novel finding of a positive correlation between OM and T-Cd, A-Cd was observed, but significant positive correlation only occurred in the high anthropogenic input area due to the complex effects of organic matter on Cd activity. The influence intensity of pH and OM on T-Cd and A-Cd increases under the strong influence of anthropogenic sources. Additionally, T-Cd and A-Cd were totally positively related to soil A-P, but mostly not significantly, which was attributed to the complexity of the available phosphorus source and the differences in Cd contents in chemical fertilizer. Furthermore, clay content might be an important factor affecting the correlation between Cd and soil properties, considering that the correlation between Cd and pH, SOM, A-P was significantly lower in areas with lower clay particles. This study suggested that GWR was an effective tool to reveal spatially varying relationships at field scale, which provided a new idea to further explore the related influencing factors on spatial distribution of contaminants and to realize precise management of a farmland environment.
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Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models. FORESTS 2022. [DOI: 10.3390/f13020346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background and Objectives: It is important to understand the temporal and spatial distributions of standing wood carbon storage in forests to maintain ecological balance and forest dynamics. Such information can provide technical and data support for promoting ecological construction, formulating different afforestation policies, and implementing forest management strategies. Long-term series of Landsat 5 (Thematic Mapper, TM) and Landsat 8 (Operational Land Imager, OLI) remote sensing images and digital elevation models (DEM), as well as multiphase survey data, provide new opportunities for research on the temporal and spatial distributions of standing wood carbon storage in forests. Methods: The extracted remote sensing factors, terrain factors, and forest stand factors were analyzed with stepwise regression in relation to standing wood carbon storage to identify significant influential factors, build a global ordinary least squares (OLS) model and a linear mixed model (LMM), and construct a local geographically weighted regression (GWR), multiscale geographically weighted regression model (MGWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR). Model evaluation indicators were used to calculate residual Moran’s I values, and the optimal model was selected to explore the spatiotemporal dynamics of standing wood carbon storage in the Liangshui Nature Reserve. Results: Remote sensing factors, topographic factors (Slope), and stand factors (Age and DBH) were significantly correlated with standing wood carbon storage, and the constructed global models exhibited fitting effects inferior to those of the established local models. LMM is also used as a global model to add random effects on the basis of OLS, and R2 is increased to 0.52 compared with OLS. The local models based on geographically weighted regression, namely, GWR, MGWR, TWR, and GTWR, all have good performance. Compared with OLS, the R2 is increased to 0.572, 0.589, 0.643, and 0.734, and the fitting effect of GTWR is the best. GTWR can overcome spatial autocorrelation and temporal autocorrelation problems, with a higher R2 (0.734) and a more ideal model residual than other models. This study develops a model for carbon storage (CS) considering various influential factors in the Liangshui area and provides a possible solution for the estimation of long-term carbon storage distribution.
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Yang Z, Wang C, Nie Y, Sun Y, Tian M, Ma Y, Zhang Y, Yuan Y, Zhang L. Investigation on spatial variability and influencing factors of drinking water iodine in Xinjiang, China. PLoS One 2021; 16:e0261015. [PMID: 34919574 PMCID: PMC8682909 DOI: 10.1371/journal.pone.0261015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/22/2021] [Indexed: 11/18/2022] Open
Abstract
Background and objectives Xinjiang is one of the areas in China with extremely severe iodine deficiency. The health of Xinjiang residents has been endangered for a long time. In order to provide reasonable suggestions for scientific iodine supplementation and improve the health and living standards of the people in Xinjiang, it is necessary to understand the spatial distribution of iodine content in drinking water and explore the influencing factors of spatial heterogeneity of water iodine content distribution. Methods The data of iodine in drinking water arrived from the annual water iodine survey in Xinjiang in 2017. The distribution of iodine content in drinking water in Xinjiang is described from three perspectives: sampling points, districts/counties, and townships/streets. ArcGIS was used for spatial auto-correlation analysis, mapping the distribution of iodine content in drinking water and visualizing the distribution of Geographically Weighted Regression (GWR) model parameter. Kriging method is used to predict the iodine content in water at non-sampling points. GWR software was used to build GWR model in order to find the factors affecting the distribution of iodine content in drinking water. Results There are 3293 sampling points in Xinjiang. The iodine content of drinking water ranges from 0 to 128 μg/L, the median is 4.15 μg/L. The iodine content in 78.6% of total sampling points are less than 10 μg/L, and only that in the 3.4% are more than 40 μg/L. Among 1054 towns’ water samples in Xinjiang, 88.9% of the samples’ water iodine content is less than 10 μg/L. Among the 94 studied areas, the median iodine content in drinking water in 87 areas was less than 10 μg/L, those values in 7 areas were between 10–40 μg/L, and the distribution of water iodine content in Xinjiang shows clustered. The GWR model established had found that the effects of soil type and precipitation on the distribution of iodine content in drinking water were statistically significant. Conclusions The iodine content of drinking water in Xinjiang is generally low, but there are also some areas which their drinking water has high iodine content. Soil type and precipitation are the factors affecting the distribution of drinking water iodine content, and are statistically significant (P<0.05).
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Affiliation(s)
- Zhen Yang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University, Urumqi, China
| | - Chenchen Wang
- Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Yanwu Nie
- College of Public Health, Xinjiang Medical University, Urumqi, China
| | - Yahong Sun
- College of Public Health, Xinjiang Medical University, Urumqi, China
| | - Maozai Tian
- Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Yuhua Ma
- Department of Pathology, Karamay Central Hospital of Xinjiang Karamay, Karamay, Xinjiang Uygur Autonomous Region, China
| | - Yuxia Zhang
- Department of Clinical Nutrition, Urumqi Maternal and Child Health Institute, Urumqi, China
| | - Yimu Yuan
- Department of General Practice Medicine, Xinjiang Corps Hospital, Urumqi, China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
- * E-mail:
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