[Health Risk Assessment of Heavy Metals in Soil and Wheat Grain in the Typical Sewage Irrigated Area of Shandong Province].

Sewage irrigation is a common alternative to make up for the shortage of agricultural irrigation in intensive agricultural areas. Abundant organic matter and nutrients in sewage can improve soil fertility and crop yield, but hazardous materials, such as heavy metals, will damage the soil environmental quality and threaten human health. To better understand the characteristics of heavy metal enrichment and potential health risk in a sewage irrigated soil-wheat system, a total of sixty-three pairs of topsoil and wheat grain samples were collected from the sewage irrigated area of Longkou City in Shandong Province. The contents of Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg were determined to analyze heavy metal contamination and calculate bio-accumulation factor (BAF), estimated daily absorption (EDA), as well as hazard quotient (HQ). The results showed that the average contents of the eight heavy metals were 61.647, 30.439, 29.769, 36.538, 63.716, 8.058, 0.328, and 0.028 mg·kg-1, respectively, which all exceeded the background values of corresponding heavy metals in the eastern Shandong Province. Especially, the average content of Cd was higher than the current standard value of soil environmental quality of agricultural land soil pollution risk control, indicating the apparent soil contamination. However, the correlations between the heavy metal contents in soil and wheat grains were not significant, suggesting that it is difficult to conclude the enrichment degree of heavy metals in wheat grains merely by the heavy metal contents in soil. The results of BAF showed that the high enrichment capacity of wheat grain was primarily obtained with Zn, Hg, Cd, and Cu. According to the national food safety limit standard, the over-limit ratios of Ni (100%) and Pb (96.8%) in wheat grains were the most serious. As a result, under the current consumption of local wheat flour, the EDAs of Ni and Pb were high, accounting for 28.278% and 1.955% of the acceptable daily intakes (ADI) for adults and 131.980% and 9.124% of the ADIs for children. The results of the health risk assessment exhibited that As and Pb were the main sources causing health risks, accounting for approximately 80% of the total risk. Although the sums of the HQ of the eight heavy metals for adults and children were below 10, the total HQ of children was 1.245 times higher than that of adults. The food safety of children should receive more attention. When considering spatial characteristics, the health risk in the southern study area was higher than that in the northern part of the study area. The prevention and control of heavy metal contamination in the southern area should be strengthened in the future.

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm.

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg-1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg-1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features.

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R2CV), a root mean square error for cross-validation (RMSECV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg-1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Revealing Impacts of Human Activities and Natural Factors on Dynamic Changes of Relationships among Ecosystem Services: A Case Study in the Huang-Huai-Hai Plain, China.

Understanding the dynamic changes of relationships between ecosystem services (ESs) and their dominant factors can effectively adjust human activities to adapt proactively to global climate change. In this study, the Huang-Huai-Hai Plain (HHHP) was selected to assess the dynamics of four key ESs (NPP, net primary productivity; WY, water yield; SC, soil conservation; FP, food production) from 2000 to 2020. The constraint lines of interactions among ESs were extracted based on a segmented quantile regression model. On this basis, the effects of both human activities and natural factors on the key features of the interactions between ESs were quantified with the help of automatic linear model. The results indicated that two types of constraint relationships, including exponential and humped-shaped, existed among the six pairs of ESs. In the past two decades, small changes in NPP thresholds would lead to large variations in other ESs thresholds. Precipitation and normalized difference vegetation index were the key factors to determine the constraint strength of ESs in the HHHP. The potential maximum value of WY in the HHHP could be increased by adjusting landscape shape to make it more complicated. This study helps to improve the potential of target ESs and provides a decision-making basis for promoting regional sustainable development.

Impacts of Human Activities and Climate Change on Water Storage Changes in Shandong Province, China.

The over-exploitation of water resources causes water resource depletion, which threatens water security, human life, and social and economic development. Only by clarifying the spatial pattern, changing trends, and influencing factors of water storage can we promote the rational development of water resources and relieve the pressure on water resources. However, there is still a lack of research on these aspects. In this study, the water-scarce area in Shandong Province, China, was selected to quantify the spatial and temporal changes in the terrestrial water storage (TWS) and groundwater storage (GWS) over the past 30 years. Nighttime light data were used to characterize the urbanization level (UL) and explore the effects of human activities (i.e., UL) and climate change (temperature and precipitation) on the TWS and GWS. The results show that 1) from 1990 to 2018, the overall TWS exhibited a significant decreasing trend (- 0.084 cm yr-1). The change trend of the GWS was consistent with that of the TWS (- 0.516 m3 yr-1). Spatially, there was significant spatial heterogeneity in the trend of the TWS and GWS. At the grid and prefectural scales, the TWS mainly exhibited a downward trend in the central and western regions, and an upward trend in the eastern region of Shandong Province. For the GWS, all cities exhibited a decreasing trend at the prefectural scale, whereas 92% of the regions exhibited a decreasing trend with less spatial heterogeneity at the grid scale. 2) Precipitation was the mean factor controlling the total amount of TWS and GWS in Shandong Province. Precipitation and temperature positively affected water storage, and the UL negatively affected it. At the prefectural scale, except for a few cities which were greatly influenced by the UL, the dominant factor of the TWS and GWS was precipitation in the other cities. At the grid scale, for the TWS, precipitation was the predominant factor in 51.82% of the entire region, followed by the UL (44.14%) and temperature (4.04%). For the GWS, precipitation was the predominant factor in 55.73% of the area, and the other 44.27% of the area was mainly influenced by the UL. Overall, precipitation and the UL were the key factors affecting the TWS and GWS. The results of this study provide a theoretical and decision-making basis for the optimal allocation and sustainable use of regional water resources.

Multiscale analysis of factors affecting food security in China, 1980-2017.

Food security is an important issue affecting people's lives and social stability. Clarifying levels of food security and the factors affecting it (social, economic, agricultural, climatic) can help improve regional food security. The spatiotemporal patterns and driving factors of food security vary at different scales. There is, however, a lack of research that considers the various factors affecting food security at multiple scales. This study, therefore, analyzed dynamic spatiotemporal changes in food security at small (city), medium (province), and large (country) scales; identified hot and cold areas of food security; and revealed the main factors affecting food security at different scales. A food security index (FSI) was built based on the coupling of grain yield, population, and GDP, and spatial analysis was used to evaluate dynamic spatiotemporal changes in China's food security from 1980 to 2017. Further, the relationship between food security and its driving factors was quantitatively analyzed using stepwise regression. The results showed greater heterogeneity in food security at the smaller scale than at the larger scale. The key factors affecting food security varied substantially at different scales: the added value of tertiary industry dominated the prefecture level, and gross agricultural output value was the main factor at the provincial and national levels. Multiple-scale research can reveal the status and primary factors of food security and provide a decision-making basis for improving regional food security.

Pollution status and ecological risk of heavy metals in the soils of five land-use types in a typical sewage irrigation area, eastern China.

Sewage irrigation has been widespread in the water shortage area of eastern China and inevitably tends to result in heavy metal accumulation in soils. A total of 148 surface soil samples from five land-use types were collected in Longkou, a typical sewage irrigation area of China, and As, Cd, Cu, Pb, and Zn concentrations were determined. The Nemerow index method and improved fuzzy comprehensive evaluation method were used to examine the pollution status of heavy metals. The potential ecological risk was evaluated by the Hakanson model by adjusting the assessment threshold, and its spatial distribution was interpolated using geostatistical techniques. As, Cd, Cu, Pb, and Zn accumulated in different amounts in the five land-use types. Urban industrial land and mining land were moderately polluted, irrigated land was slightly polluted, orchards were minimally polluted, and bare land was at a safe level of pollution. Cd exhibited high percentages of strong and severe levels of potential ecological risks. For Cd, irrigated land, orchard, and bare land mainly presented moderate risks, whereas urban industrial land and mining land mainly presented high risks. The comprehensive ecological risk of the five heavy metals was at a severe level for all tested land-use classes except for bare land.

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text.

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.

Source apportionment of nitrogen and phosphorus from non-point source pollution in Nansi Lake Basin, China.

Nitrogen (N) and phosphorus (P) from non-point source (NPS) pollution in Nansi Lake Basin greatly influenced the water quality of Nansi Lake, which is the determinant factor for the success of East Route of South-North Water Transfer Project in China. This research improved Johnes export coefficient model (ECM) by developing a method to determine the export coefficients of different land use types based on the hydrological and water quality data. Taking NPS total nitrogen (TN) and total phosphorus (TP) as the study objects, this study estimated the contributions of different pollution sources and analyzed their spatial distributions based on the improved ECM. The results underlined that the method for obtaining output coefficients of land use types using hydrology and water quality data is feasible and accurate, and is suitable for the study of NPS pollution at large-scale basins. The average output structure of NPS TN from land use, rural breeding and rural life is 33.6, 25.9, and 40.5%, and the NPS TP is 31.6, 43.7, and 24.7%, respectively. Especially, dry land was the main land use source for both NPS TN and TP pollution, with the contributed proportions of 81.3 and 81.8% respectively. The counties of Zaozhuang, Tengzhou, Caoxian, Yuncheng, and Shanxian had higher contribution rates and the counties of Dingtao, Juancheng, and Caoxian had the higher load intensities for both NPS TN and TP pollution. The results of this study allowed for an improvement in the understanding of the pollution source contribution and enabled researchers and planners to focus on the most important sources and regions of NPS pollution.

[Ecological Risk Assessment of Soil Heavy Metals for Different Types of Land Use and Evaluation of Human Health].

Surface soils were collected from five types of land use in the northern plain of Longkou City and the contents of five heavy metals (Cu, Pb, Zn, Cd, and As) were determined. Based on results from preliminary studies on heavy metal pollution of soil, the potential ecological risks caused by heavy metals in the soil and risks to human health were evaluated using the Hakanson potential ecological risk assessment model after adjusting the evaluation threshold and the health assessment model after modifying parameters. The results show that the contents of five heavy metals in the study area exceed the background value, the potential ecological risk of Cd is high and complex, in irrigated land, orchard land, and bare land it is mostly the second-class risk, and urban industrial land and mining land are dominated by severe risk. The element As is equivalent to the first two levels of each land class; the minor risk areas of the other three types of heavy metals are larger than 70%. The comprehensive ecological risk of the five elements is higher than the three-level risk in the field, except for the bare ground. The area of four-level risk of urban industrial land and mining land is relatively large. The five types of soil heavy metals in this area present noncarcinogenic and carcinogenic risks to human health. The element Pb and heavy metal As, both entering the body by oral intake, pose a noncarcinogenic and carcinogenic risk to adults and children, respectively.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Concentration estimation of heavy metal in soils from typical sewage irrigation area of Shandong Province, China using reflectance spectroscopy.

Since sewage irrigation can markedly disturb the status of heavy metals in soils, a convenient and accurate technique for heavy metal concentration estimation is of utmost importance in the cropland using wastewater for irrigation. This study therefore assessed the feasibility of visible and near infrared reflectance (VINR) spectroscopy for predicting heavy metal contents including Cr, Cu, Ni, Pb, Zn, As, Cd, and Hg in the north plain of Longkou city, Shandong Province, China. A total of 70 topsoil samples were taken for in situ spectra measurement and chemical analysis. Stepwise multiple linear regression (SMLR) and principal component regression (PCR) algorithms were applied to establish the associations between heavy metals and reflectance spectral data pretreated by different transformation methods. Based on the criteria that minimal root mean square error (RMSE), maximal coefficient of determination (R 2) for calibration, and greater ratio of standard error of performance to standard deviation (RPD) is related to the optimal model, SMLR model using first deviation data (RD1) provided the best prediction for the contents of Ni, Pb, As, Cd, and Hg, calibration using SNV data for Cr and continuum removal spectra for Zn, while PCR equation employed RD1 values was fit for prediction of the contents of Cu. The determination coefficients of all the reasonable models were beyond 0.6, and RPD indicated a fair or good result. In general, first deviation preprocessing tool outperformed other methods in this study, while raw spectra reflectance performed unsatisfactory in all models. Overall, VINR reflectance spectroscopy technique could be applicable to the rapid concentration assessment of heavy metals in soils of the study area.

[Source Analysis, Spatial Distribution and Pollution Assessment of Heavy Metals in Sewage Irrigation Area Farmland Soils of Longkou City].

Farmland soils in sewage irrigation area at Longkou City were collected, soil pH together with the heavy metal content were tested. Taking 70 soil points as the study object, this paper investigated the source of heavy metals in this area based on the correlation analysis and PCA of multivariate statistical analysis theory. We studied the spatial variation and distribution characteristics about heavy metals using both the theory of geostatistics and GIS spatial interpolation method. At last, the heavy metal pollution was evaluated in the way of Nemerow Index and improved fuzzy evaluation method. It turned out that, 9 kinds of heavy metal elements in the soil of research area had a certain degree of enrichment, among them the average of Cd was 3.06 times as high as the background value, and its enrichment was most severe. The result of Nemerow Index showed that, the values of comprehensive pollution index of Cu, Cd and Pb respectively were 7.06, 6.10 and 5.54, and they all belonged to high levels of pollution. According to the results of correlation analysis and principal component analysis, Cu, Zn together with Pb, Cd were mainly affected by human factors, sewage irrigation was their common pollution factor, the pollution sources for the first two heavy metals included excessive use of chemical fertilizers and pesticides in agricultural production and the accumulation of long time, whereas pollution from northern coal mining and coal gangue piled up as well as plating, machinery manufacturing and other industrial pollution were the pollution sources of the latter two elements. Other elements (Co, Cr, Mn, Ni and As) were mainly influenced by natural factors such as parent material. Comprehensive evaluation results showed that, among the 70 points, 13 points had moderate pollution,23 points belonged to light pollution, 28 points were at alert level, 6 points were in the safe range. From the perspective of spatial distribution,high value areas of heavy metal contents were mainly concentrated in towns of Zhuyouguan and Xufu. This showed that, sewage irrigation caused a certain degree of heavy metal pollution to local soil.

[Pollution Assessment and Spatial Distribution Characteristics of Heavy Metals in Soils of Coal Mining Area in Longkou City].

The present paper takes the coal mining area of Longkou City as the research area. Thirty-six topsoil (0-20 cm) samples were collected and the contents of 5 kinds of heavy metals were determined, including Cd, As, Ni, Ph, Cr. Geo-statistics analysis was used to analyze the spatial distribution of heavy metals. Principal component analysis (PCA) was used to explore the pollution sources of heavy metals and the degree of heavy metals pollution was evaluated by weighted average comprehensive pollution evaluation method. The results showed that enrichment phenomenon was significant for the 5 kinds of heavy metals. Taking secondary standard of National Environment Quality Standard for Soil as the background value, their exceed standard rates were 72.22%, 100%, 100%, 91.67%, 100%, respectively. Average contents of heavy metals in the soil samples were all over the national standard level two and were 1.53, 11.86, 2.40, 1.31, 4.09 times of the background value. In addition, the average contents were much higher than the background value of the topsoil in the eastern part of Shandong Province and were 9.85, 39.98, 8.85, 4.29, 12.71 times of the background value. According to the semivariogram model, we obtained the nugget-effects of 5 kinds of heavy metals and their values were in the order of As (0.644) > Cd (0.627) > Cr (0.538) > Ni (0.411) > Pb (0.294), all belonging to moderate spatial correlation. On the whole, the central part of the Sangyuan Coal Mine and its surrounding areas were the most seriously polluted, while the pollution of heavy metals in the east and west of the study area was relatively light. Principal component analysis suggested that the enrichment of Cd, As, Ni, Cr was due to irrigation of wastewater, the discharge of industry and enterprise, and the industrial activity. Automobile exhaust and coal combustion were the main pollution sources of Pb. The single-factor assessment of heavy metals pollution showed that the degree of different heavy metals pollution was in the order of As > Cr > Ni > Cd > Pb. Simultaneously, comprehensive pollution evaluation showed that the degree of heavy metals pollution in the study area was very serious, with comprehensive pollution index ranging from 2.17 to 4.66, among which, the numbers of moderate and heavy pollution samples were 10 and 26, respectively. Areas with heavy pollution were mainly distributed in the Sangyuan Coal Mine, Beizao Coal Mine, Liuhai Coal Mine; and the areas with moderate pollution covered Wali Coal Mine, Liangjia Coal Mine, and other regions. The results of this paper will provide data reference and theoretical support for the study of ecological risk assessment in the study area.

[Spatial Characteristics and Environmental Risk of Heavy Metals in Typical Gold Mining Area of Shandong Province].

In order to reveal the influence of anthropogenic factors on soil environment quality, a total of seventy-seven samples in topsoils were collected from Jiaojia gold mining area in Shandong province and were determined for Cu, Pb, Zn, Cr contents. Spatial structure, spatial distributions of concentrations and risk probability of heavy metals were analyzed using spatial statistic analysis. The average concentrations of Cu, Pb, Zn and Cr were 19.41 mg·kg-1, 27.32 mg·kg-1, 49.81 mg·kg-1 and 39.27 mg·kg-1, respectively. Pb, Zn and Cr were distributed normally and Cu was distributed normally after logarithm transformation. Semivariance analysis demostrated that Pb could be fitted to exponential model, and Cu, Zn and Cr were fit for spherical model. Nugget coefficents of Cu and Pb were between 0.25 and 0.75, which illustrated middle spatial autocorrelation; Zn and Cr showed the structural variation with nugget values below 0.25. Cu and Pb in the topsoils were distributed dispersedly due to effects of some human factors, whereas contents of Zn and Cr indicated relatively regular distributions and were mainly affected by natural factors. Spatial distributions of the 4 heavy metals were approximately consisitent and the high value areas appeared in the gold mines band. The result of hot spot analysis and indicator kriging interpolation revealed that the relatively high risk areas were located in Jincheng town, the boundary zone of Xinzhuang town and Canzhuang town, while the safe zone was situated in south part of the study area. Pb had higher probability exceeding the threshold and the middle or high environmental risk areas of Pb were distributed widely, which should be paid more attentions.

A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

BACKGROUND: Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). OBJECTIVE: Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. METHODS: 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2, SO2, PM10, O3, CO) during January 2009-December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. RESULTS: At residence, concentrations of SO2, PM10, NO2, CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2, PM10, CO, O3, NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2, O3, CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2, O3, CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 µg/m(3) increase of PM10 at workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. CONCLUSIONS: Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 µg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research.

A split-path schema-based RFID data storage model in supply chain management.

In modern supply chain management systems, Radio Frequency IDentification (RFID) technology has become an indispensable sensor technology and massive RFID data sets are expected to become commonplace. More and more space and time are needed to store and process such huge amounts of RFID data, and there is an increasing realization that the existing approaches cannot satisfy the requirements of RFID data management. In this paper, we present a split-path schema-based RFID data storage model. With a data separation mechanism, the massive RFID data produced in supply chain management systems can be stored and processed more efficiently. Then a tree structure-based path splitting approach is proposed to intelligently and automatically split the movement paths of products . Furthermore, based on the proposed new storage model, we design the relational schema to store the path information and time information of tags, and some typical query templates and SQL statements are defined. Finally, we conduct various experiments to measure the effect and performance of our model and demonstrate that it performs significantly better than the baseline approach in both the data expression and path-oriented RFID data query performance.

Behavior-based cleaning for unreliable RFID data sets.

Radio Frequency IDentification (RFID) technology promises to revolutionize the way we track items and assets, but in RFID systems, missreading is a common phenomenon and it poses an enormous challenge to RFID data management, so accurate data cleaning becomes an essential task for the successful deployment of systems. In this paper, we present the design and development of a RFID data cleaning system, the first declarative, behavior-based unreliable RFID data smoothing system. We take advantage of kinematic characteristics of tags to assist in RFID data cleaning. In order to establish the conversion relationship between RFID data and kinematic parameters of the tags, we propose a movement behavior detection model. Moreover, a Reverse Order Filling Mechanism is proposed to ensure a more complete access to get the movement behavior characteristics of tag. Finally, we validate our solution with a common RFID application and demonstrate the advantages of our approach through extensive simulations.