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The relationship between meteorological factors and mumps based on boosted regression tree model

SCIENCE OF THE TOTAL ENVIRONMENT 2019年 695卷 133758-000页

作者： Zhang, Dandan Guo, Yuming Rutherford, Shannon Qi, Chang Wang, Xu Wang, Peizhu Zheng, Zhaolei Xu, Qing Li, Xiujun Shandong Univ Sch Publ Hlth Dept Biostat Jinan Shandong Peoples R China Monash Univ Sch Publ Hlth & Prevent Med Dept Epidemiol & Prevent Med Melbourne Vic Australia Griffith Univ Sch Med Brisbane Qld 4111 Australia Shandong Ctr Dis Control & Prevent Jinan Shandong Peoples R China

Background: Mumps remains a major global public health problem. Many studies have explored the relationship between meteorological factors and mumps, few have comprehensively explored such associations considering nonlinear relationship, delayed effects and collinearity in order to more accurately estimate them. This study aims to explore the relationship between meteorological factors and mumps in consideration of nonlinearity, delayed effects and collinearity. Methods: We collected daily reported mumps cases and meteorological data for Jining City, Shandong Province, China from 1 January 2007 to 31 December 2016. By building a boosted regression tree model (BRT) for each day from lag 0 days to the maximum lag time, the optimal lag time was selected and the relationship between meteorological factors and mumps was explored for this lag time. Results: From 2007 to 2016, a total of 15,064 cases of mumps were reported in Jining, with a sex ratio of 2.11:1. Cases were most prevalent in 5-9-year-olds (42.15%) followed by 10-14-year-olds (24.72%). The optimal lag time identified was 10 days and the three meteorological factors that contributed the most to the risk of mumps were daily mean temperature, daily mean relative humidity and daily mean sunshine duration. Their relative contribution rates were 24.4%, 19.9% and 18.3%, respectively. The mean temperature and sunshine duration relationships approximated a U-shaped effect on the risk ofmumps, with estimated thresholds of 5.5 degrees C and 9.5 h, respectively. The effect of relative humidity on mumps increased slightly and then decreased rapidly, with a threshold of 64%. Conclusions: Our study indicates that daily mean temperature, relative humidity and sunshine duration were three significant meteorological factors associated with the incidence of mumps in Jining, China. Understanding the shape of relationships and their thresholds are critical for establishing early warning systems which are important tools in the pr

关键词： boosted regression tree model Mumps Meteorological factors Nonlinear Collinearity

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Ocean as source or sink for legacy persistent organic pollutants

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JOURNAL OF HAZARDOUS MATERIALS 2025年 486卷 136987页

作者： Chen, Chuchu Yang, Fen Wu, Qiang Yu, Jun Xie, Jingqian Fang, Yin Lin, Tian Shanghai Ocean Univ Coll Oceanog & Ecol Sci Shanghai 201306 Peoples R China Chinese Acad Sci Key Lab Land Surface Pattern & Simulat Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China East China Normal Univ Inst Ecochongming State Key Lab Estuarine & Coastal Res Shanghai 200241 Peoples R China

With the phase-out of legacy persistent organic pollutants (POPs), the ocean's role is evolving, potentially acting as both a reservoir and a source. This study investigates the air-sea fluxes of the first banned POPs, such as organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs), using literature from Web of Science up to 2023. OCP and PCB concentrations in air and seawater show significant spatiotemporal variability. In air, alpha-HCH and p,p'-DDE dominate, with concentrations decreasing over time and varying with latitude. In seawater, alpha-HCH and p,p'-DDTs show higher levels in the Northern Hemisphere and significant temporal decreases. PCB concentrations differ notably between low- and high-chlorinated compounds, with distinct spatial patterns. HCHs exhibit distinct flux patterns, with volatilization in equatorial regions and deposition in higher latitudes. DDTs mainly show deposition trends, except in Southeast Asia where recent increases in volatilization are observed. PCBs generally demonstrate deposition, with regional and seasonal variations. Time significantly impacts fluxes due to changes in human activity and regulations. High-chlorine PCBs are more affected by distance from the sea surface, while temperature increases POPs volatilization.

关键词： Legacy persistent organic pollutants Source-sink transformation Sea-air fluxes boosted regression tree model

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Quantifying the contributions of urban spatial morphology on the river cold island effect: Taking Changsha, China, as an example

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SUSTAINABLE CITIES AND SOCIETY 2025年 122卷

作者： Wang, Huiwen Chen, Cunyou Cent South Univ Forestry & Technol Coll Landscape Architecture Changsha 410004 Peoples R China Hunan Prov Nat Reserve Landscape Resources Big Dat Changsha 410004 Peoples R China Cent South Univ Forestry & Technol Inst Urban & Rural Landscape Ecol Changsha 410004 Peoples R China

Urban rivers serve a vital role in mitigating urban heat islands, a phenomenon that is becoming increasingly apparent against the backdrop of rapid urban expansion and global warming. We combined with the cold island amplitude and the maximum cold island distance, eleven indicators of three types of indexes were selected, and the cold island effect of eight rivers in Changsha urban area was analyzed by boosted regression tree model. Results show that (1) As the width of the river increases, the cold island effect increases. The cold island amplitude of the river with a width of 20.15-1717.35 m ranges from 2.23 to 8.93 degrees C, and maximum cold island distance ranges from 187.5 to 16,666.67 m. (2) The order of contribution is land cover index > building space index > landscape pattern index, among which the contribution of normalized building index (NDBI), normalized vegetation index (NDVI) and Mean architectural height (MAH) is the largest. (3) According to the marginal effect curve, Mean of patch area (AREA_MN) and Shannon diversity (SHDI) at 0.36-0.4 and 0.6-1.5, were weakly positively and negatively correlated with surface temperature. NDBI has a significant positive correlation with the surface temperature, and NDVI has a negative correlation with the surface temperature. MAH and tolerance for staggering buildings (TSB) have a cooling effect on the river after >45 m and 34.2 m, respectively. The research results provide valuable insights into urban space management and planning regulation.

关键词： Urban rivers Land surface temperature Cold island effect boosted regression tree model Marginal effect Megacities

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Evaluating the seasonal impact of urbanisation on surface air temperature in Beijing over three decades: 1990s, 2010s, and 2030s

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ENVIRONMENTAL RESEARCH COMMUNICATIONS 2025年第4期7卷 045021-045021页

作者： Vitanova, Lidia Lazarova Kusaka, Hiroyuki Doan, Quang-Van Chen, Fei Petrova-Antonova, Dessislava Subasinghe, Shyamantha Sofia Univ St Kliment Ohridski GATE Inst Sofia Bulgaria Univ Tsukuba Ctr Computat Sci Tsukuba Japan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Peradeniya Dept Geog Peradeniya Sri Lanka

Beijing's rapid urbanisation has driven economic growth and technological advancement while intensifying infrastructure challenges, air quality issues, and temperature rise. Utilising advanced machine learning (ML) and numerical physical modelling techniques, this study quantitively assesses the impact of the future (2030s) urbanisation on surface air temperatures in Beijing, a fast-growing Chinese city, by comparing these effects with those induced by past urbanisation (1990s and 2010s). The boosted regression tree model is used to predict changes in land use. In contrast, the Weather Research and Forecasting (WRF) model with 2-km horizontal resolution is applied to simulate the regional climate of January, July and September from 2008 to 2012. The control simulation results (2010s) are validated against observations, showing that the WRF model reasonably reproduces diurnal surface air temperature variations. Seasonal urbanisation impacts on surface air temperatures reveal a similar to 1.0 degrees C increase from the 1990s to 2010s and similar to 2.0 degrees C from the 1990s to 2030s in the city's central area. Additionally, the rapid expansion of the low-urbanised regions in Beijing leads to temperature increases of 1.1 degrees C (1990s to 2030s) and 0.8 degrees C (2010s to 2030s) at 0500 LST in July, as urbanised land grows from 384 km2 (1990s) to 1764 km2 (2030s).

关键词： urbanisation surface air temperature ML boosted regression tree model WRF model

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Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous

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CATENA 2024年 237卷

作者： Xiao, Xiong Guan, Qingyu Zhang, Zepeng Liu, Hanqi Du, Qinqin Yuan, Tingwei Lanzhou Univ Coll Earth & Environm Sci Gansu Key Lab Environm Pollut Predict & Control Lanzhou 730000 Peoples R China

Understanding vegetation evolution trends and their driving mechanisms are essential to uncover changes in ecosystem structure and function. Under the interaction of natural and human factors in cold-arid mountainous region, the main controlling factors and degree of nonlinear influence are not yet clear. This study utilized trend analysis, along with modeling techniques such as boosted regression trees (BRT) and structural equation model (SEM), to identify and quantify the contributions and nonlinear response thresholds of natural and human factors to normalized difference vegetation index (NDVI) in the Qilian Mountains from 2000 to 2020. The results showed that the implementation of ecological projects made the overall vegetation into greening trend (91.06%), while irrational human activities led to degradation in the central-eastern regions. The BRT showed that climatic factors dominated for overall vegetation changes, greening and significant greening. Thresholds exist for different drivers to influence both greening and degradation of vegetation. Regarding degradation/significant vegetation degradation, the importance of climate factors decreased, and topography ranked first, followed by population density and LUCC. The expansion of building and unused land will inhibit vegetation greening. SEM revealed that climate factors continued to maintain positive effect with the warm-humid change. The destruction of forestlands and grasslands led to significant degradation of vegetation in the central-eastern regions with the largest total negative effect. Topographic factors coupled with multi-factors inhibited vegetation growth in the central-eastern regions. This study provided a new framework for accurately assessing the dynamic evolution of vegetation, and better reveal the multi-factor driving mechanism vegetation changes in cold-arid mountainous regions. Future research should consider combining more accurate AI algorithms with field surveys to quantify the coupl

关键词： Vegetation changes Driving mechanisms boosted regression tree model Structural equation model Cold -arid mountainous region

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Exploring the relationship between seasonal variations of land surface temperature and urban morphological factors in complex urban areas

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第21期30卷 59861-59876页

作者： Liu, Jiachen Zhang, Shumin Yin, Le Li, Bin Zhang, Baolei Shandong Normal Univ Coll Geog & Environm Jinan 250358 Peoples R China Shandong Univ Finance & Econ Inst Reg Econ Jinan 250014 Peoples R China

The urban heat island effect is an increasingly serious problem in urban areas. Previous studies suggest that spatial variation in the urban land surface temperature (LST) is determined by interactions among urban morphological factors, but few studies have explored the main factors that affect the LST in different seasons in complex urban areas, especially at a fine scale. By considering the central Chinese city of Jinan as an example, we selected 19 parameters related to the architectural morphological factors, ecological basis factors, and humanistic factors and explored their effects on the LST in different seasons. A correlation model was used to identify the key factors and to analyze the main impact thresholds in different seasons. In the four seasons, the 19 factors all had significant correlations with LST. In particular, architectural morphological factors comprising the average building height and high building ratio had significant negative correlations with the LST in the four seasons. The architectural morphological factors comprising the floor area ratio, spatial concentration degree, building volume density, and urban surface pattern index comprising the mean nearest neighbor distance to green land, as well as humanistic factors comprising the point of interest density, nighttime light intensity, and human activity intensity of land surface had significant positive correlations with LST in the summer and autumn. Ecological basis factors made the main contributions to the LST in the spring, summer, and winter, whereas humanistic factors contributed the most in the autumn. The contributions of architectural morphological factors were relatively low in the four seasons. The dominant factors differed in each season but their thresholds had similar characteristics. The results obtained in this study deepen our understanding of the relationships between urban morphology and the urban heat island effect, and provide practical suggestions for improving the u

关键词： Architectural morphological factors Ecological basis factors Humanistic factors Seasonal effects boosted regression tree model Jinan city

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Landscape Ecological Risk Assessment and Impact Factor Analysis of the Qinghai-Tibetan Plateau

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REMOTE SENSING 2022年第19期14卷

作者： Wang, Sishi Tan, Xin Fan, Fenglei Tianjin Univ Sch Environm Sci & Engn Tianjin 300350 Peoples R China Tibet Univ Sch Sci Lhasa 850000 Peoples R China South China Normal Univ Sch Geog Guangzhou 510631 Peoples R China Tibet Univ Joint Lab Plateau Surface Remote Sensing Lhasa 850000 Peoples R China

The Qinghai-Tibetan Plateau (QTP) is one of the most vulnerable ecosystems worldwide. Over the last few decades, the QTP has been subjected to increasing external pressures, such as climate change, human activity, and natural hazards. Therefore, ecological risk assessment is vital for the environmental protection and sustainable development of the QTP. A landscape ecological risk (LER) assessment based on landscape disturbance and vulnerability was performed to explore the spatiotemporal characteristics associated with LER in the QTP from 1990 to 2020. Furthermore, the impact of LER was quantitatively evaluated with a boosted regression tree model. Results showed that more than 70% of the locations in the QTP exhibited below-medium LER. The LER for the QTP demonstrated downward trends from 1990 to 2020. The LER presented downward trends during the periods from 1990 to 2001 and from 2012 to 2020 and no significant trend during the period from 2002 to 2011. Additionally, high-LER areas were concentrated in the northwestern QTP, whereas low-LER areas were mainly in the southeastern QTP. The LER displayed clustering characteristics across the QTP. Changes in climate, topographic distribution, and human activity influenced the ecological stability of the study area. Precipitation and temperature had the strongest effects on the LER, followed by elevation and grazing intensity. Lower precipitation and temperatures were associated with higher LER. Our results provide precise and specific support for the environmental protection and ecological management of the QTP and other ecologically fragile areas.

关键词： Qinghai-Tibetan Plateau boosted regression tree model landscape ecological risk assessment landscape ecological risk index

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Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

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REMOTE SENSING 2023年第20期15卷 4946-4946页

作者： Su, Jiajia Liu, Zhihua Wang, Wenjuan Jiao, Kewei Yu, Yue Li, Kaili Lue, Qiushuang Fletcher, Tamara L. Chinese Acad Sci Inst Appl Ecol Shenyang 110016 Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100091 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130028 Peoples R China

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (boosted regression tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

关键词： forest fire boosted regression tree model Random Forest model Support Vector Machine model

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Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm

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ECOLOGICAL INDICATORS 2023年第1期151卷

作者： Xiao, Xiong Wang, Qingzheng Guan, Qingyu Zhang, Zepeng Yan, Yong Mi, Jimin Yang, Enqi Lanzhou Univ Coll Earth & Environm Sci Gansu Key Lab Environm Pollut Predict & Control Lanzhou 730000 Peoples R China

The main influencing factors and their nonlinear effects on the changes of vegetation in China's mountainous areas under the interaction of different factors are not yet clear, and comprehending the evolutionary trends and driving mechanisms of vegetation is crucial to reveal the changes in ecosystem structure and function. In this study, trend analysis (M-K, T-S and EEMD) combined with machine learning algorithm, namely boosted regression tree model (BRT), were used to quantify the trends of nonlinear responses and thresholds for bioclimatic variables, topography, soil properties and anthropogenic factors for vegetation changes in Longnan. The results showed that the trend analysis clearly confirm the increasing trend of vegetation at multiple spatio-temporal scales. The BRT indicated that total precipitation (bio12, 15.22%), land use (LUCC, 12.68%), elevation (DEM, 11.20%), and population density (Pd, 9.20%) were the more important factors of dominant vegetation greening. Bioclimatic variables were found to revealed the effects of climate with vegetation more clearly. In addition, the BRT revealed that the selected factors have the different nonlinear response relationships to vegetation greening trend and specific thresholds. Among them, increasing of cropland, grassland and forestland can promote vegetation greening. However, GDP, Pd, DEM, bio12, mean diurnal range and temperature sea-sonality (bio2, bio4) exceed the threshold can significantly inhibit vegetation growth. The BRT combined with trend analysis revealed the nonlinear response relationships and thresholds of the drivers behind the vegetation change patterns, which have obvious effects in exploring the driving mechanisms of vegetation changes in mountainous areas. This study provided an important reference for better revealing the interaction mechanisms between vegetation changes and drivers in the semi-humid zone in East Asia even globally.

关键词： Bioclimatic variables boosted regression tree model Nonlinear trend Spatio-temporal variation of vegetation

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Quantifying the effects of 2D/3D urban landscape patterns on land surface temperature: A perspective from cities of different sizes

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BUILDING AND ENVIRONMENT 2023年第1期233卷

作者： Xu, Hongchao Li, Chunlin Hu, Yuanman Li, Shuai Kong, Ruixue Zhang, Zhibin Chinese Acad Sci Inst Appl Ecol CAS Key Lab Forest Ecol & Management Shenyang 110016 Peoples R China Shandong Normal Univ Coll Geog & Environm Jinan 250300 Peoples R China Shenyang Jianzhu Univ Sch Municipal & Environm Engn Shenyang 110000 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

With the continuous development of urbanization, two-dimensional (2D) landscape pattern is insufficient to explain the complex urban thermal phenomenon, while three-dimensional (3D) urban composition directly af-fects the process of surface energy exchange and becomes an important influencing factor in regard to land surface temperature (LST). There have been some studies on the relationship between 2D/3D urban patterns and LST, however, there are relatively few studies on the effects of 2D/3D urban landscape on LST in cities of different sizes. In this research, the relationship of 2D/3D urban patterns and LST in three cities (Beijing, Shi-jiazhuang and Cangzhou) of different sizes were analyzed by using the boosted regression tree model. The results were as follows: 1) With the increase in city size, LST gradually increased in summer and decreased in winter. 2) The larger the size of the city was, the greater the number of factors that significantly affected LST (p < 0.05). 3) In spring, summer and autumn, volume of tree (TV), mean tree height (MTH) and the largest patch index of impervious surface (LPI_IS) had a high relative influence, and the average total relative influences of these three metrics were 80.08%, 78.99% and 45.14%, respectively. 4)There was a significant negative correlation between TV and LST, a positive correlation between LPI_IS and LST and a combination of positive and negative corre-lations between MTH and LST. The study of the effects of 2D and 3D urban morphology on LST could help urban planners and managers create more scientific urban planning and development management measures to miti-gate urban thermal environment problems.

关键词： Landscape metrics Land surface temperature 2D 3D urban morphology boosted regression tree model

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