Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models （GAMs） and Bootstrap

Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.

Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model

In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.

基于曲线拟合误差估计的阻抗优化识别方法

在雷达系统、舰船系统、数据中心等大型复杂系统中,供配电系统变流器端口阻抗信息的完整、快速、准确获取,是研判系统可靠性的重要基础。现有研究大都使用分段线性拟合值方法进行阻抗识别,不仅需要大量阻抗测量才能获得完整的阻抗特性,并且可能因过度测量而对系统的正常运行造成干扰。针对这些问题,从阻抗特性曲线的非线性拟合入手,提出一种“非线性曲线拟合、全局误差估计、优选测量点频率”的阻抗识别方法,实现基于曲线拟合误差估计的测量点优化选择,以较少阻抗测量代价获取宽频带阻抗特性。与现有的方法相比,所提方法更有助于测量点的合理选择、阻抗谐振峰的准确识别,从而更高效地实现宽频带阻抗的识别,为复杂供配电系统运行状况的研判提供可靠依据。算例仿真证实了所提方法的有效性。

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

佳芦河流域设计年径流和设计洪水的广义可加模型

受气象要素变化和人类活动的影响,径流序列的一致性常遭到破坏,需要直接针对非一致性水文序列进行研究。本文以黄土高原区佳芦河流域的年径流量和年最大洪峰流量为研究对象,基于广义可加模型(GAMLSS),优选气温、降水和库坝控制下的流域有效产流面积作为协变量进行模拟分析,并采用平均设计寿命水平法(ADLL)与等效可靠性法(ER)估算非平稳序列的设计年径流及洪峰流量。结果表明:两个水文序列均具有非一致性,所构建的多协变量年径流模型的低分位数曲线和高分位数曲线的拟合效果较好,洪峰流量模型整体模拟效果均较好;GAMLSS模型及相应的ADLL和ER方法在特定重现期或概率区间能提供更高精度的洪峰流量及年径流量设计值,且较好地体现了水文序列变化趋势对设计值的影响。

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.

Modeling deformation resistance for hot rolling based on generalized additive model

A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.

长江口浮游植物群落特征及影响因素分析

为了解长江口浮游植物的群落组成、结构特征、时空分布及关键影响驱动因子,基于2018—2020年8月(夏季)、11月(秋季)长江口浮游植物调查数据,运用群落多样性分析指标及广义加性模型(generalized additive models, GAM)探究了长江口浮游植物群落特征及与各影响因子间的关系。结果表明:本次调查共采集浮游植物8门99属185种,其中,硅藻门(Bacillariophyta)、蓝藻门(Cyanophyta)和绿藻门(Chlorophyta)是主要的浮游植物类群,优势种主要包括中肋骨条藻(Skeletonema costatum)、颗粒直链藻(Aulacoseira granulata)、具槽直链藻(Melosira sulcata)、小环藻(Cyclotella sp.)和衣藻(Chlamydomonas sp.)等,其中中肋骨条藻长期占绝对优势;在时间上,夏季浮游植物丰度和种类数显著高于秋季(P<0.05),但秋季浮游植物群落多样性指数和丰富度指数更高,分布更均匀;在空间上,浮游植物平均丰度整体上呈现东滩>南支>北支的分布格局;GAM分析显示,在夏季,水温(Tem)、酸碱度(pH)和氮磷比(TN/TP)是显著影响长江口浮游植物丰度分布的环境因子(P<0.05),其中TN/TP的贡献率最高(71.86%),在秋季,盐度(Sal)、溶解氧(DO)和化学需氧量(CODMn)是显著影响长江口浮游植物丰度分布的环境因子(P<0.05),其中DO的贡献率最大(48.48%)。研究表明,长江口浮游植物群落的组成、结构、时空分布及影响因素存在季节差异,本研究结果可为掌握长江口浮游植物资源动态提供参考依据。

中西太平洋海山特征对延绳钓渔业和围网渔业黄鳍金枪鱼CPUE的影响

海山是海底重要的生物栖息地类型之一,是研究海洋生物多样性的热点区域。黄鳍金枪鱼(Thunnus albacares)广泛分布于中西太平洋,具有极高的生态和经济价值,然而,鲜有关于海山及其相关特征对黄鳍金枪鱼资源丰度和分布影响的研究。基于2010—2021年中西太平洋渔业委员会(Western and Central Pacific Fisheries Commission,WCPFC)汇总的延绳钓和围网渔业数据结合海山特征数据,采用广义加性模型(Generalized additive model,GAM)分析两种不同捕捞方式的黄鳍金枪鱼单位捕捞努力量渔获量(Catch per unit effort,CPUE)与海山相关特征之间的关系。结果表明,中西太平洋两种渔业方式的黄鳍金枪鱼渔获量主要来源于海山区域,海山特征对两种渔业黄鳍金枪鱼的CPUE均产生了极显著性影响(P<0.001)。在延绳钓渔业中,较高的CPUE出现在山顶深度、粗糙度、底面积和海山密度较小、坡度较缓的区域;而在围网渔业中,较高的CPUE则出现在粗糙度较小、山顶深度较大、底面积较大、较陡峭且密集的海山区域。研究探讨了中西太平洋海山特征对黄鳍金枪鱼不同群体的影响机制,为今后进一步探索黄鳍金枪鱼种群分布和资源丰度变化与海洋环境的关系提供了参考与新思路。

应用地基激光雷达三维点云数据构建长白落叶松树干削度方程

使用地基激光雷达(TLS)三维点云数据提取的落叶松干形数据,构建树干削度方程,为落叶松干形精准预测提供依据。以吉林省一面山林场和杨木林林场落叶松人工林为研究对象,获取71株落叶松点云信息,并提取树干干形数据。选择简单、可变指数、三角函数和分段函数等9个基础削度方程进行比较,利用分位数回归和广义加性模型方法构建削度方程。结果表明:在9个基础削度方程中,Bi(2000)削度方程的拟合效果最好,多重共线性指标条件数也小于100;Bi(2000)基础削度方程构建的分位数回归模型,在9个分位点(τ=0.1、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9)处均能收敛,其中在τ=0.5的分位点处的拟合效果最好,略优于非线性回归的拟合结果。在以相对直径为因变量,以相对高的平方根、胸径的平方和树高为自变量的广义加性削度方程中,6种光滑样条函数(三次回归样条函数(CR)、B-样条函数(BS)、薄板回归样条函数(TP)、P-样条函数(PS)、Duchon样条函数(DS)和高斯过程平滑样条函数(GP))的拟合效果相差不大,但广义加性削度方程使用(DS+CR)光滑样条函数比一种光滑样条函数的拟合效果好(相对误差4.407、均方根误差1.158、确定系数0.966),广义加性削度方程的各检验统计量均优于基础削度方程和分位数回归削度方程,且在树干相对高度10%~80%,广义加性削度方程也表现最优(相对误差4.534、均方根误差1.191、确定系数0.964)。因此,(DS+CR)组合光滑样条函数的广义加性削度方程预测精度最高,可用于该区域的落叶松干形预测。

不同城市绿地类型碳源/汇的城乡梯度格局--以杭州市为例

探究城市化对绿地空间碳源/汇的空间分布格局的影响,对评估城市生态系统的碳足迹和制定相应的碳收支管理措施具有重要意义。以净生态系统生产力(NEP)做为碳源/汇的反映指标,基于净初级生产力和土壤呼吸估算杭州市主城区绿地碳源/汇的空间分布格局,关注城乡梯度对不同绿地类型碳源/汇水平的作用。基于净初级生产力和土壤呼吸数据综合获得绿地空间NEP,通过土地利用数据和Fragstats软件进行景观格局分析,采用多元线性回归模型和逐步回归模型筛选影响NEP的景观、植被和气象因子,最后利用广义加性模型探讨NEP与各因子之间的关系。此外,分别比较了相同统计过程在不同城乡梯度和不同绿地类型之间的模型差异。结果表明:杭州市绿地空间NEP分布及其影响因子存在显著的城乡梯度与绿地类型差异。2019—2022年杭州市主城区绿地空间,整体表现为碳源,年均NEP为-0.277 kg C m^(-2) a^(-1);其中表现为碳汇的绿地主要分布在杭州市主城区的西部,而碳源绿地主要分布在中部和东部。整体绿地空间的NEP大小与绿地斑块面积、乔木盖度和灌木盖度呈正相关,与灌木物种丰富度和气温呈负相关。NEP随城区、城郊结合部、郊区的城乡梯度逐步增强;城区NEP与乔木盖度呈正相关,与景观多样性和气温呈负相关;城郊结合部NEP与乔木物种丰富度和灌木盖度呈正相关,与绿地斑块密度和气温呈负相关;郊区NEP则与聚集度指数、乔木盖度和灌木盖度呈正相关。公园、农田、自然植被的NEP依次增大并受到不同因素的调控。公园NEP与聚集度指数、乔木盖度和灌木盖度呈正相关,与景观分割指数、灌木物种丰富度和气温呈负相关;农田NEP与聚集度指数和灌木盖度呈正相关,与气温呈负相关;而自然植被NEP则与乔木盖度呈正相关,与景观多样性指数和气温呈负相关。研究进一步揭示了城市化对绿地空间碳源/汇的影响,为城乡碳收支的差异化管理提供了一定的理论和数据支持。

含竞争指标的广义可加混合效应树高-胸径模型

广义可加混合效应模型(GAMM)兼具参数模型与非参数模型的优点,同时适于处理多层次分组数据。通过运用广义可加混合效应模型模拟胸径及树高之间关系,加入竞争因子作为辅助变量,并与传统非线性混合效应模型进行比较,能够为建立树高曲线及提高模型精度提供新方法。根据吉林省汪清林业局金沟岭林场2块100 m×100 m次生混交林样地中的实测单木数据,按照7∶3比例随机划分建模与验证数据。随机效应设定为林木分级,辅助变量选择大于对象木胸高断面积之和(B_(AL))或简单竞争指数(Hegyi指数,H_(EG)),根据随机效应的设定位置共构建15个广义可加混合效应模型,对照模型以Logistic及Richard方程为基础模型,共构建6个非线性混合效应树高-胸径模型。结果表明:所有广义可加混合效应模型均能较好地描述自变量与树高之间的关系,决定系数(R^(2))为0.8897~0.8998,相对均方根误差(R_(RMSE))为17.87%~18.74%,平均绝对误差(M_(AE))为1.7881~1.8745 m,赤池信息量(A_(IC))为4120.42~4162.23,均优于相同自变量下的非线性混合模型,R^(2)平均提高0.005,相对均方根误差、平均绝对误差、赤池信息量分别平均降低0.46%、0.0587 m、41.49。对于验证数据的预测可以看出,模型5具有最小的预测相对均方根误差,为20.28%,同时具有最小的预测平均绝对误差,为2.1038 m。但部分广义可加混合效应模型的预测表现略差于非线性混合模型。综合考虑参数与非参数估计显著性、模型估计精度及预测能力,所有模型中的最优模型为模型5,即以B_(AL)为辅助变量,考虑唯一全局平滑函数并以具有相同扭曲程度的分组水平平滑函数为基础添加随机效应。竞争因子选择B AL作为辅助变量能够提升树高模型的精度,而选择Hegyi指数为辅助变量的促进效果不明显。研究建立的广义可加混合效应树高胸径模型相较于传统非线性混合效应模型具有更高的估计精度及预测效果,B AL适宜作为树高模型的辅助变量来反映林木竞争状况的影响。

环境卫生与医院感染的时间序列研究:基于广义相加模型(GAM)

目的定量分析环境卫生对医院感染发生的影响。方法收集某三甲医院2018年1月—2022年12月医院感染与环境卫生学监测资料,采用时间序列的广义相加模型分析环境检出菌落形成单位(CFU)对医院感染发生的影响。结果单污染模型显示,医院感染与工作人员手细菌菌落数之间存在显著正相关性(β1=0.009,P=0.012),工作人员手月度平均菌落形成单位(MCFU/Dish)每升高1个四分位数间距(IQR),医院感染发生率增加13.28%(95%CI:2.82%~24.81%);亚组分析与滞后效应分析显示,工作人员手月度MCFU/Dish(卫生手消毒后)升高1个IQR,当月(lag0)医院感染超额风险(ER)为16.26%(95%CI:15.45%~17.09%)。多污染模型中,物体表面污染与医院感染的相关性同样具有统计学意义。结论医院环境卫生与医院感染之间存在显著相关性。