Breast cancer is one of the most commonly diagnosed cancers worldwide. The primary aim of this work is the study of breast cancer disparity among Chinese women in urban vs. rural regions and its ...associations with socioeconomic factors. Data on breast cancer incidence were obtained from the Chinese cancer registry annual report (2005-2009). The ten socioeconomic factors considered in this study were obtained from the national population 2000 census and the Chinese city/county statistical yearbooks. Student's T test was used to assess disparities of female breast cancer and socioeconomic factors in urban vs. rural regions. Pearson correlation and ordinary least squares (OLS) models were employed to analyze the relationships between socioeconomic factors and cancer incidence. It was found that the breast cancer incidence was significantly higher in urban than in rural regions. Moreover, in urban regions, breast cancer incidence remained relatively stable, whereas in rural regions it displayed an annual percentage change (APC) of 8.55. Among the various socioeconomic factors considered, breast cancer incidence exhibited higher positive correlations with population density, percentage of non-agriculture population, and second industry output. On the other hand, the incidence was negatively correlated with the percentage of population employed in primary industry. Overall, it was observed that higher socioeconomic status would lead to a higher breast cancer incidence in China. When studying breast cancer etiology, special attention should be paid to environmental pollutants, especially endocrine disruptors produced during industrial activities. Lastly, the present work's findings strongly recommend giving high priority to the development of a systematic nationwide breast cancer screening program for women in China; with sufficient participation, mammography screening can considerably reduce mortality among women.
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Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 ...concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques.
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•BME combined with PMF are proposed for spatial quantitatively source apportionment.•1/5 of soils show different pollution degrees and Cd, Cu, As are the main pollutants.•Cd and As are the main ...elements causing ecological and health risks, respectively.•Industry like electroplating is the main source for As in northwest and northeast.•Agricultural activities are the main source for Cd in northwestern and north-central.
Soil pollution by toxic metals has become an important environmental problem over the last several decades. Because of environmental factor variation, specific spatial patterns of pollution and sources exist. However, commonly used methods rarely take natural spatial heterogeneity into account. Positive matrix factorization and Bayesian maximum entropy models combined with specific environmental factors were proposed for quantitative source apportionment to account for spatial heterogeneity. The proposed method was implemented in a region located in southeastern China using dense samples (3627 total samples containing Cd, Hg, As, Pb, Cr, Cu, Zn and Ni data). The results showed that more than one-fifth of soils in the northwest, north-central and southeast of the study region exhibited different degrees of integrated pollution. Cd, Cu and As were the main pollutants, with proportions that exceeded the national standards of 26%, 10% and 7%, respectively. In addition, Cd was the primary element responsible for ecological risk, and As was the greatest hazard to human health. Five main pollution sources were extracted: 72.11% of the toxic metal pollution could be ascribed to anthropogenic sources, and natural sources explained the remaining 27.89%. Traffic emissions (24.31%) consistent with the major road distribution were the main source of Pb and Zn, and atmospheric deposition during the coal combustion (18.04%) distributed across the study area, except for the southeastern mountain areas, was the main source of Hg. Agricultural activities (16.81%) distributed mainly in the north-central regions contributed the most to Cd and Cu, and industrial activities (12.95%) clustered in the northwestern areas contributed the most to As. In addition, natural sources were closely linked to Ni and Cr in the southeastern mountain areas.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•WCA-BME can be used to determine quantitatively the SSCC-SST association.•Strong synchronous SSCC and SST variations occurred at 1 and 5-year periods.•Strong association detected in mid-latitude ...regions of Pacific and Atlantic Ocean.
Sea surface temperature (SST) can influence the phytoplankton biomass, measured as sea surface chlorophyll concentration (SSCC), by affecting the physical and chemical properties of the seawater, living environment, and the consumption of zooplankton in a complex way. Yet, the quantitative assessment of the spatiotemporal variation of the inherent synchronous association between SSCC and SST at large spatial and temporal scales is still lacking. Accordingly, in the present study a synthetic approach was proposed that combines wavelet coherency analysis (WCA) with Bayesian maximum entropy (BME) modeling and hotspot analysis in order to evaluate the association between SSCC and SST globally during the period July 2002-February 2019. The WCA-based statistical results showed that SSCC has strong association with SST; particularly strong synchronous variations between SSCC and SST were found at the 1-year and the 5-year periods. During the 1-year period, cluster characteristics were explored in the BME-generated space–time maps of the association strength as well as in the corresponding hotspot maps. Geographically, high association strengths between SSCC and SST were detected in the mid-latitude regions of the Pacific Ocean, in the south and north of the tropical regions of the Atlantic Ocean, and in the southern part of the Indian Ocean. Temporally, most of the sub-regions exhibited a stable level of association strength during the entire study period (only a few sub-regions exhibited fluctuations or a slightly decreasing association strength trend). In conclusion, by assimilating the available knowledge bases at each grid point the proposed synthetic approach assessed quantitatively the strength of the periodic association between SSCC and SST globally; and the approach could be employed to map the space–time variation of the association strength between related natural attributes in the space–time-frequency domain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Hemorrhagic fever with renal syndrome (HFRS) is a rodent-associated zoonosis caused by hantavirus. The HFRS was initially detected in northeast China in 1931, and since 1955 it has been detected in ...many regions of the country. Global climate dynamics influences HFRS spread in a complex nonlinear way. The quantitative assessment of the spatiotemporal variation of the "HFRS infections-global climate dynamics" association at a large geographical scale and during a long time period is still lacking.
This work is the first study of a recently completed dataset of monthly HFRS cases in Eastern China during the period 2005-2016. A methodological synthesis that involves a time-frequency technique, a composite space-time model, hotspot analysis, and machine learning is implemented in the study of (a) the association between HFRS incidence spread and climate dynamics and (b) the geographic factors impacting this association over Eastern China during the period 2005-2016. The results showed that by assimilating core and city-specific knowledge bases the synthesis was able to depict quantitatively the space-time variation of periodic climate-HFRS associations at a large geographic scale and to assess numerically the strength of this association in the area and period of interest. It was found that the HFRS infections in Eastern China has a strong association with global climate dynamics, in particular, the 12, 18 and 36 mos periods were detected as the three main synchronous periods of climate dynamics and HFRS distribution. For the 36 mos period (which is the period with the strongest association), the space-time correlation pattern of the association strength indicated strong temporal but rather weak spatial dependencies. The generated space-time maps of association strength and association hotspots provided a clear picture of the geographic variation of the association strength that often-exhibited cluster characteristics (e.g., the south part of the study area displays a strong climate-HFRS association with non-point effects, whereas the middle-north part displays a weak climate-HFRS association). Another finding of this work is the upward climate-HFRS coherency trend for the past few years (2013-2015) indicating that the climate impacts on HFRS were becoming increasingly sensitive with time. Lastly, another finding of this work is that geographic factors affect the climate-HFRS association in an interrelated manner through local climate or by means of HFRS infections. In particular, location (latitude, distance to coastline and longitude), grassland and woodland are the geographic factors exerting the most noticeable effects on the climate-HFRS association (e.g., low latitude has a strong effect, whereas distance to coastline has a wave-like effect).
The proposed synthetic quantitative approach revealed important aspects of the spatiotemporal variation of the climate-HFRS association in Eastern China during a long time period, and identified the geographic factors having a major impact on this association. Both findings could improve public health policy in an HFRS-torn country like China. Furthermore, the synthetic approach developed in this work can be used to map the space-time variation of different climate-disease associations in other parts of China and the World.
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Coastal saltmarshes are key ecosystems with important ecological functions. Yet, they have experienced a widespread decline. Due to their importance, the conservation and restoration of saltmarshes ...are globally shared objectives, including China. Despite multiple local studies, nationwide information about saltmarshes in China is scarce. Thus, we used remote sensing to delineate the spatial distribution and areal extent of saltmarshes along coastal China and resolve their species composition. By interpreting 10 m spatial resolution Sentinel-2 images on Google Earth Engine, assisted with field survey and literature search, a total of 118 010 ha of saltmarshes were delineated in coastal China in 2019. Seven typical saltmarsh species were identified, with Phragmites australis , Spartina alterniflora, and Scirpus mariquater as dominant species, accounting for 95.5% of total saltmarsh extent, while Suaeda salsa , Tamarix chinensis , Cyperus malaccensis, and Sesuvium portulacastrum were present in limited abundance. The P. australis and exotic species S. alterniflora grow along almost all coastal provinces, but P. australis dominates in the north while S. alterniflora dominates in the middle part of coastal China. Suaeda salsa occurs mainly in the north and has suffered large losses. Tamarix chinensis is abundant in Shandong province, S. mariquater in the Yangtze River delta, C. malaccensis in Guangdong and Guangxi provinces, and S. portulacastrum in Taiwan. The exotic species S. alterniflora expanded extensively along the coast and its expansion rate continues to increase. The results provided conform a much-needed baseline for future monitoring efforts and the assessment of progress in the conservation and restoration projects toward recovering saltmarshes in China.
Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that ...improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.
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Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by hantavirus (belongs to Hantaviridae family). A large amount of HFRS cases occur in China, especially in the Heilongjiang Province, ...raising great concerns regarding public health. The distribution of these cases across space-time often exhibits highly heterogeneous characteristics. Hence, it is widely recognized that the improved mapping of heterogeneous HFRS distributions and the quantitative assessment of the space-time disease transition patterns can advance considerably the detection, prevention and control of epidemic outbreaks.
A synthesis of space-time mapping and probabilistic logic is proposed to study the distribution of monthly HFRS population-standardized incidences in Heilongjiang province during the period 2005-2013. We introduce a class-dependent Bayesian maximum entropy (cd-BME) mapping method dividing the original dataset into discrete incidence classes that overcome data heterogeneity and skewness effects and can produce space-time HFRS incidence estimates together with their estimation accuracy. A ten-fold cross validation analysis is conducted to evaluate the performance of the proposed cd-BME implementation compared to the standard class-independent BME implementation. Incidence maps generated by cd-BME are used to study the spatiotemporal HFRS spread patterns. Further, the spatiotemporal dependence of HFRS incidences are measured in terms of probability logic indicators that link class-dependent HFRS incidences at different space-time points. These indicators convey useful complementary information regarding intraclass and interclass relationships, such as the change in HFRS transition probabilities between different incidence classes with increasing geographical distance and time separation.
Each HFRS class exhibited a distinct space-time variation structure in terms of its varying covariance parameters (shape, sill and correlation ranges). Given the heterogeneous features of the HFRS dataset, the cd-BME implementation demonstrated an improved ability to capture these features compared to the standard implementation (e.g., mean absolute error: 0.19 vs. 0.43 cases/105 capita) demonstrating a point outbreak character at high incidence levels and a non-point spread character at low levels. Intraclass HFRS variations were found to be considerably different than interclass HFRS variations. Certain incidence classes occurred frequently near one class but were rarely found adjacent to other classes. Different classes may share common boundaries or they may be surrounded completely by another class. The HFRS class 0-68.5% was the most dominant in the Heilongjiang province (covering more than 2/3 of the total area). The probabilities that certain incidence classes occur next to other classes were used to estimate the transitions between HFRS classes. Moreover, such probabilities described the dependency pattern of the space-time arrangement of HFRS patches occupied by the incidence classes. The HFRS transition probabilities also suggested the presence of both positive and negative relations among the main classes. The HFRS indicator plots offer complementary visualizations of the varying probabilities of transition between incidence classes, and so they describe the dependency pattern of the space-time arrangement of the HFRS patches occupied by the different classes.
The cd-BME method combined with probabilistic logic indicators offer an accurate and informative quantitative representation of the heterogeneous HFRS incidences in the space-time domain, and the results thus obtained can be interpreted readily. The same methodological combination could also be used in the spatiotemporal modeling and prediction of other epidemics under similar circumstances.
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China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution ...distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space–time variability of PM2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space–time PM2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM2.5 exceeded 75, 115, 150, and 250 μg m–3 varied: 43–253, 13–128, 4–66, and 0–15 days, respectively. The PM2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM2.5 exceeded 150 μg m–3 during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM2.5 pollution intensity and space–time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.
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