Maogui Hu

A new estimate of the China temperature anomaly series and uncertainty assessment in 1900–2006

While global warming during the last century has been well recognized, the magnitude of the climate warming in regions such as China over the past 100years still has some uncertainty due to limited observations during the early years. Several series of temperature anomalies for the 20(th) century in China have been independently developed by different groups. The uncertainty arises mainly from the sparse observations before 1950, where statistics are sensitive to the small and potentially biased sample. In this study, BSHADE-MSN (Biased Sentinel Hospitals Areal Disease Estimation and Means of Stratified Nonhomogeneous Surface), a combination of two novel distinct statistical methods that are applicable with different sample situations to a spatial heterogeneous surface, is applied to estimate annual mean temperature anomalies for China. This method takes into account prior knowledge of geographical spatial autocorrelation and nonhomogeneity of target domains, remedies the biased sample, and maximizes an objective function for the best linear unbiased estimation (BLUE) of the regional mean quantity. For the period 1900-1999, the overall trend estimated by BSHADE-MSN is 0.80 degrees C with a 95% confidential interval between 0.41 degrees C and 1.18 degrees C. This is significantly lower than that calculated by Climate Anomaly Method (CAM) and Block Kriging. The new temperature anomaly series for China exhibits slightly warmer conditions for the period before 1950 than existing studies. All the methods applied so far agree well with each other for the period after 1950, when there are sufficient stations across the country for the estimation of temperature anomaly series. Cross validation shows that the new regional mean temperature anomaly series has smaller estimation error variance and higher accuracy than those based on the other methods assessed in this study.

Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models

Background Over the past two decades, major epidemics of hand, foot, and mouth disease (HFMD) have occurred throughout most of the West-Pacific Region countries, causing thousands of deaths among children. However, few studies have examined potential determinants of the incidence of HFMD. Methods Reported HFMD cases from 2912 counties in China were obtained for May 2008. The monthly HFMD cumulative incidence was calculated for children aged 9 years and younger. Child population density (CPD) and six climate factors (average-temperature [AT], average-minimum-temperature [ATmin], average-maximum-temperature [ATmax], average-temperature-difference [ATdiff], average-relative-humidity [ARH], and monthly precipitation [MP]) were selected as potential explanatory variables for the study. Geographically weighted regression (GWR) models were used to explore the associations between the selected factors and HFMD incidence at county level. Results There were 176,111 HFMD cases reported in the studied counties. The adjusted monthly cumulative incidence by county ranged from 0.26 cases per 100,000 children to 2549.00 per 100,000 children. For local univariate GWR models, the percentage of counties with statistical significance (p<0.05) between HFMD incidence and each of the seven factors were: CPD 84.3%, ATmax 54.9%, AT 57.8%, ATmin 61.2%, ARH 54.4%, MP 50.3%, and ATdiff 51.6%. The R 2 for the seven factors’ univariate GWR models are CPD 0.56, ATmax 0.53, AT 0.52, MP 0.51, ATmin 0.52, ARH 0.51, and ATdiff 0.51, respectively. CPD, MP, AT, ARH and ATdiff were further included in the multivariate GWR model, with R 2 0.62, and all counties show statistically significant relationship. Conclusion Child population density and climate factors are potential determinants of the HFMD incidence in most areas in China. The strength and direction of association between these factors and the incidence of HFDM is spatially heterogeneous at the local geographic level, and child population density has a greater influence on the incidence of HFMD than the climate factors.

Estimation of citywide air pollution in Beijing.

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM2.5 observation station of the U.S Embassy and the 18 PM10 observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM2.5 monitoring station and at the 18 official PM10 stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

Modeling Spatial Means of Surfaces With Stratified Nonhomogeneity

In geosciences, one often needs to estimate the spatial means of surfaces representing physical attributes. Under certain conditions, this kind of estimation is often performed by a simple summation of a random sample or by some kind of a Kriging (spatial regression) technique. For example, the naive sample mean assumes that the sample is randomly distributed across space, which is a restrictive assumption with limited applicability in real-world situations (e.g., in the case of nonhomogeneous surfaces, the naive sample mean is a biased estimate of the actual surface mean). Kriging techniques can generate unbiased estimates for certain kinds of homogeneous surfaces but may be not appropriate in cases of stratified nonhomogeneity when the covariances exhibit considerable differences between different strata of the surface. In this paper, we extend the Kriging concept to study surfaces with stratified nonhomogeneity. The corresponding analytical formulas are derived, and empirical studies are performed that involve real-world and simulated data sets. Numerical comparative analysis showed that the proposed method performed well compared to other methods commonly used for the purpose of estimating surface means across space.

A spatial sampling optimization package using MSN theory

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area.

Spatial and temporal characteristics of particulate matter in Beijing, China using the Empirical Mode Decomposition method

Air pollution has become a serious problem in Beijing, China. Daily PM10 mass concentration measurements were collected at 27 stations in Beijing over a 5-year period from January 1, 2008 to October 31, 2012. We used a new clustering method (kernel K-means) and a new period and trend decomposition method (Empirical Mode Decomposition, EMD) to explore the spatial and temporal characteristics of the PM10 mass concentration in the City. The temporal period and trend of each cluster center were decomposed using the EMD method, which is an adaptive data analysis method that requires no prior information. The daily PM10 mass concentrations varied greatly from 5 μg/m(3) to more than 600 μg/m(3). All of the stations were partitioned into three clusters by the kernel K-means method, and which represent the low-, middle- and high-pollution stations, respectively. The first cluster contained nine stations, mainly located in the north suburban area. The second cluster, whose degree of pollution was much more serious than the first cluster, contained 13 stations distributed in urban and peri-urban areas. The pollution level in the southern part of Beijing was much more serious than in the northern part of the City. The third cluster contained five stations located outside the second-cluster stations. The total decreased amplitudes of the three clusters during the whole period were 19 μg/m(3), 10 μg/m(3) and 4 μg/m(3), respectively. Although the global trend of the PM10 mass concentration decreased in general, it was not the same for each season and station. The trends in summer and winter declined, while in spring, it has been increasing in recent years. Five types of trends can be found for stations, including monotonic decreasing, rise fall, fall rise fall, fall rise and rise. The rising trend of the regional background air pollution monitoring station, Miyun-reservoir, indicates an increase in the Citys background PM10 mass concentration.

Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique

Hand, foot and mouth disease (HFMD) is a common infectious disease, causing thousands of deaths among children in China over the past two decades. Environmental risk factors such as meteorological factors, population factors and economic factors may affect the incidence of HFMD. In the current paper, we used a novel model—geographical detector technique to analyze the effect of these factors on the incidence of HFMD in China. We collected HFMD cases from 2,309 counties during May 2008 in China. The monthly cumulative incidence of HFMD was calculated for children aged 0–9 years. Potential risk factors included meteorological factors, economic factors, and population density factors. Four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) were used to analyze the effects of some potential risk factors on the incidence of HFMD in China. We found that tertiary industry and children exert more influence than first industry and middle school students on the incidence of HFMD. The interactive effect of any two risk factors increases the hazard for HFMD transmission.

Spatial estimation of antibiotic residues in surface soils in a typical intensive vegetable cultivation area in China

Antibiotic residues in surface soils can lead to serious health risks and ecological hazards. Spatial mean concentration of antibiotic residues in the soil is the most important indicator of a regions environmental risk to antibiotic residues. Considerable estimation error would lead to an inefficient strategy of pollution control that happens when sample size is small and the estimation model does not match the spatial features of the object to be surveyed. On the basis of the available datasets, it was found that the distribution of antibiotics residue in soil follows a spatial stratification pattern. Accordingly, we used a new spatial estimation method called Mean of Surface with Non-homogeneity (MSN) to estimate antibiotic concentrations in surface soil of the Shandong Province, an important vegetable growing region in China. The standard error of the mean estimates obtained by MSN was significantly smaller (by about 1.02-6.82 μg/kg) than the estimation errors produced by three mainstream methods, simple arithmetic estimation (2.9-11.8 μg/kg), stratified estimation (2.5-10.6 μg/kg) and ordinary kriging estimation (2.2-8.2 μg/kg).

Area Disease Estimation Based on Sentinel Hospital Records

Background Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased. Methods and Findings A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospitals records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators. Conclusions The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Krigings inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimators limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.

Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE*

Some climate datasets are incomplete at certain places and times. A novel technique called the point estimationmodelofBiasedSentinelHospitals-basedAreaDiseaseEstimation(P-BSHADE)isintroducedto interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.