Seasonal effects of fish, seaweed and abalone cultures on dissolved organic matter and carbon sequestration potential in Sansha Bay, China.

Coastal bays serve as undeniable dissolved organic matter (DOM) reactors and the role of prevalent mariculture in DOM cycling deserves investigation. This study, based on four seasonal field samplings and a laboratory incubation experiment, examined the source and seasonal dynamics of DOM and fluorescent dissolved organic matter (FDOM) in the seawater of fish (Larimichthys crocea, LC), seaweed (Gracilaria lemaneiformis, GL) and abalone (Haliotis sp., HA) culturing zones in Sansha Bay, China. Using three-dimensional fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC), three fluorescent components were identified, i.e. protein-like C1, protein-like C2, and humic-like C3. Our results showed that mariculture activities dominated the DOM pool by seasonal generating abundant DOM with lower aromaticity and humification degrees. Accounting for 40-95 % of total fluorescent components, C1 (Ex/Em = 300/340 nm) was regarded the same as D1 (Ex/Em = 300/335 nm) identified in a 180-day degradation experiments of G. lemaneiformis detritus, indicating that the cultured seaweed modulated DOM through the seasonal production of C1. In addition, the incubation experiment revealed that 0.7 % of the total carbon content of seaweed detritus could be preserved as recalcitrant dissolved organic carbon (RDOC). However, fish culture appeared to contribute to liable DOC and protein-like C2, exerting a substantial impact on DOM during winter but making a negligible contribution to carbon sequestration, while abalone culture might promote the potential export and sequestration of seaweed-derived carbon to the ocean. Our results highlight the influences of mariculture activities, especially seaweed culture, in shaping DOM pool in coastal bays. These findings can provide reference for future studies on the carbon accounting of mariculture.

Accurate carbon storage estimation for the salt marsh ecosystem based on Bayesian maximum entropy approach - A case study for the Spartina alterniflora ecosystem.

The blue carbon ecosystem, including the salt marsh ecosystem, possesses a significant carbon sequestration potential. Therefore, accurately quantifying the carbon storage within such ecosystems is crucial for the adequate accounting of carbon sequestration. The present work chose a Spartina alterniflora ecosystem in the Xiaogan Island (China) as the study area (approximately 11 ha), and employed the Bayesian maximum entropy (BME) approach to assimilate both hard organic carbon (OC) data and soft OC data measured from 2 cm and 10 cm stratified samples. A 3-dimensional model was developed for space-time OC estimation purposes based on the sediment chronology results. The 10-fold BME cross validation results demonstrated a high estimation accuracy, with the R2, RMSE and MAE values equal to 0.8564, 0.1026 % and 0.0748 %, respectively. A noteworthy outcome was the BME-generated carbon storage density maps with 1 m spatial resolution. These maps revealed that the carbon storage density at the top 30 cm sediment depth in the stable zone (with elder stand age of S. alterniflora) was higher than that in the rapid expansion zone, i.e., 71.79 t/ha vs. 69.82 t/ha, respectively. Additionally, the study found that the averaged carbon burial rate and the total carbon storage at the top 30 cm sediment depth across the study area were 266 g C/m2/yr and 781.50 t, respectively. Lastly, the proposed BME-based framework of carbon storage estimation was found to be versatile and applicable to other blue carbon ecosystems. This approach can foster the development of a standardized carbon sink metrological methodology for diverse blue carbon ecosystems.

Carbon sequestration potential of transplanted mangroves and exotic saltmarsh plants in the sediments of subtropical wetlands.

Coastal blue carbon ecosystems offer promising benefits for both climate change mitigation and adaptation. While there have been widespread efforts to transplant mangroves from the tropics to the subtropics and to introduce exotic saltmarsh plants like Spartina alterniflora in China, few studies have thoroughly quantified the chronological records of carbon sequestration with different organic carbon (OC) sources. To understand how variations in OC sources can affect the carbon sequestration potential of coastal wetland environment over time, we conducted a study on typical islands with two scenarios: S. alterniflora invasion and mangrove transplantation. Our study determined chronological records of carbon sequestration and storage from five sediment profiles and traced changes in the OC sources using carbon stable isotope (δ13C) and C:N ratios in response to these scenarios. The S. alterniflora invasion resulted in an 84 ± 19 % increase in the OC burial rate compared to unvegetated mudflats, while mangrove transplantation resulted in a 167 ± 74 % increase in the OC burial rate compared to unvegetated mudflats. S. alterniflora and mangroves showed greater carbon sequestration potential in areas with high supplies of suspended particulate matter, while mangroves needed to grow to a certain scale to display obvious carbon sequestration benefits. In the mangrove saltmarsh ecotone, mature mangrove habitats exhibited resistance to the S. alterniflora invasion, while mangrove transplantation in the environment invaded by S. alterniflora had a significant effect on OC contribution. Besides, plant-derived OC can be exported to the surrounding environment due to the rapid turnover of sediments. The blue carbon chronosequence-based estimation of OC sources and burial rates provides a useful reference for establishing carbon accounting policies.

Microplastics distribution in different habitats of Ximen Island and the trapping effect of blue carbon habitats on microplastics.

Sediments are considered to be important sinks of microplastics, but the enrichment process of microplastics by blue carbon ecosystems is poorly studied. This study analyzed the spatial distribution and temporal changes, assessed the polymer types and morphological characteristics of microplastics in sediments of five ecosystems, i.e. forests, paddy fields, mangroves, saltmarshes and bare beaches on Ximen Island, Yueqing Bay, China. The trapping effect of blue carbon (mangrove and saltmarsh) sediments on microplastic was further explored. Temporal trends in microplastic abundance showed a significant increase over the last 20 years, with the enrichment of microplastics in mangrove and saltmarsh sediments being 1.7 times as high as that in bare beach, exhibiting blue carbon vegetations have strong enrichment effect on microplastics. The dominant color, shape, size, and polymer type of microplastics in sediments were transparent, fibers and fragments, <1 mm, and polyethylene, respectively. Significant differences in the abundance and characteristics of microplastics between intertidal sediments and terrestrial soils reveal that runoff input is the main source of microplastics. This study provided the evidence of blue carbon habitats as traps of microplastics.

Underestimated PAH accumulation potential of blue carbon vegetation: Evidence from sedimentary records of saltmarsh and mangrove in Yueqing Bay, China.

Sediments of blue carbon vegetation are becoming a sink of natural and anthropogenic pollutants, such as polycyclic aromatic hydrocarbons (PAHs). However, the extent to which PAHs are accumulated and varied in blue carbon sediments, and the impact of blue carbon vegetation on the accumulation and retention capacity of PAHs, have been poorly explored. This study examines the sedimentary records of PAHs in profiles from mangrove plantation, saltmarsh, and mudflat in Ximen Island and Maoyan Island of Yueqing Bay, China. The existence of blue carbon vegetation provides a sheltered environment for the accelerated burial of sediment and OC. Decadal PAH sedimentation records show staged changes characterized by the emission of PAHs and colonization of blue carbon vegetation, reflecting the accelerated burial of PAHs in sediments by blue carbon vegetation colonization. In addition, the colonization of blue carbon vegetation contributes to the shift of PAH compositions in sediments. This study provides new insights into the underestimated PAH accumulation potential and retention capacity of blue carbon vegetation and the corresponding underlying sediments, supporting the environmental benefits of blue carbon vegetation.

Distribution, accumulation and health risk assessment of trace elements in Sargassum fusiforme.

This study compared the ability of Sargassum fusiforme to accumulate As, Cd, Cr, Cu, Ni, Pb and Zn in its five tissues (main branch, lateral branch, leaf, receptacles and pneumathode). The concentrations of these trace elements in seawater, surface sediments and different tissues of S. fusiforme were analyzed in different areas in Dongtong County (Wenzhou City, China). The presence of receptacle at all sites indicated that S. fusiforme had entered the mature stage. However, the proportion of each tissue in S. fusiforme in different sites was varied, indicating subtle differences in growth. S. fusiforme has a great capacity to accumulate trace elements, showing relatively high levels of As (28.2-64.2 mg kg-1) and Zn (19.9-80.8 mg kg-1). The elements are mainly stored in leaf, receptacles and pneumathode. Compared to element concentrations in the surrounding environment, the seaweed exhibited stronger bioconcentration capacity for As and Cd than for other elements. According to our health risk assessment results, the hazard index and carcinogenic risk were below the limit, suggesting that the S. fusiforme ingestion would not pose any health risk and the potential risk of intake branches was even lower than that of other tissues.

An AHP-based regional COVID-19 vulnerability model and its application in China.

Since the COVID-19 outbreak, four cities-Wuhan, Beijing, Urumqi and Dalian-have experienced the process from outbreak to stabilization. According to the China Statistical Yearbook and China Center for Disease Control records, regional, pathological, medical and response attributes were selected as regional vulnerability factors of infectious diseases. Then the Analytic Hierarchy Process (AHP) method was used to build a regional vulnerability index model for the infectious disease. The influence of the COVID-19 outbreak at a certain place was assessed computationally in terms of the number of days of epidemic duration and cumulative number of infections, and then fitted to the city data. The resulting correlation coefficient was 0.999952. The range of the regional vulnerability index for COVID-19 virus was from 0.0513 to 0.9379. The vulnerability indexes of Wuhan, Urumqi, Beijing and Dalian were 0.8733, 0.1951, 0.1566 and 0.1119, respectively. The lack of understanding of the virus became the biggest breakthrough point for the rapid spread of the virus in Wuhan. Due to inadequate prevention and control measures, the city of Urumqi was unable to trace the source of infection and close contacts, resulting in a relatively large impact. Beijing has both high population density and migration rate, which imply that the disease outbreak in this city had a great impact. Dalian has perfect prevention and good regional attributes. In addition, the regional vulnerability index model was used to analyze other Chinese cities. Accordingly, the regional vulnerability index and the prevention and control suggestions for them were discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s40808-021-01244-y.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data.

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R2 can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m3) during the entire study period. The area with SSCC >5 mg/m3 in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m3 decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

The decade long achievements of China's marine ecological civilization construction (2006-2016).

China needs to balance between current population pressures and a vulnerable marine environment, creating a national, political outline or management strategy dubbed an ecological civilization construction. The nation's effort to protect and maintain a sustainable ocean and address the relevant economic, resource and environmental issues relies on Marine Ecological Civilization (MEC) construction. The quantification of MEC progress is essential to track the management performance and guide the subsequent development and implementation. This study evaluates the performance of China's MEC from 2006 to 2016 based on a comprehensive index system. Our findings are as follows: During 2006-2016, the overall MEC performance score increased from 0.3426 to 0.4850 nationwide. Large space-time variations exist among the eleven coastal regions. The Shandong and Guangdong regions showed relatively good performances, whereas the Jiangsu, Guangxi and Shanghai regions had low scores. A decade long change in MEC scores showed that Hebei achieved the largest increase ratio. Marine management was improved by implementing various conservation strategies by China's government. Marine education and human talent introduction deserve more attention in less developed areas such as Hainan and Guangxi, and poor marine environmental quality was an urgent issue of the Yangtze river estuary economic zone. More accessible marine monitoring dataset are needed to track future space-time progress dynamics towards MEC construction. Our results provide a decade long retrospect of China's MEC achievements, and the quantified evaluation for each coastal region can provide valuable insight to policy-makers.

Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran.

The COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1st-level Response to Major Public Health Emergency in each region, peaking after 1-2 days, reaching their inflection points after 10-19 days, and dropping to zero after 11-18 days since the 1st-level response was launched. By March 29th, the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25th, in Italy as in Guangdong on February 25th, and in Iran as in Xinjiang on March 8th. The results showed that by June 15th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide.

Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy.

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R2 value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m3). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m3, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m3. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.

Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Improved heavy metal mapping and pollution source apportionment in Shanghai City soils using auxiliary information.

Soil heavy metal pollution can be a serious threat to human health and the environment. The accurate mapping of the spatial distribution of soil heavy metal pollutant concentrations enables the detection of high pollution areas and facilitates pollution source apportionment and control. To make full use of auxiliary soil properties information and show that they can improve mapping, a synthesis of the Bayesian Maximum Entropy (BME) theory and the Geographically Weighted Regression (GWR) model is proposed and implemented in the study of the Shanghai City soils (China). The results showed that, compared to traditional techniques, the proposed BME-GWR synthesis has certain important advantages: (a) it integrates heavy metal measurements and auxiliary information on a sound theoretical basis, and (b) it performs better in terms of both prediction accuracy and implementation flexibility (including the assimilation of multiple data sources). Based on the heavy metal concentration maps generated by BME-GWR, we found that the As, Cr and Pb concentration levels are high in the eastern part of Shanghai, whereas high Cd concentration levels were observed in the northwestern part of the city. Organic carbon and pH were significantly correlated with most of the heavy metals in Shanghai soils. We concluded that Cd pollution is mainly the result of agricultural activities, and that the Cr pollution is attributed to natural sources, whereas Pb and As have compound pollution sources. Future studies should investigate the implementation of BME-GWR in the case of space-time heavy metal mapping and its ability to integrate human activity information and soil category variables.

Heavy metal contamination assessment of surface sediments of the East Zhejiang coastal area during 2012-2015.

This work is the first systematic quantitative analysis of the heavy metal situation along the Zhejiang coastal region focusing on the integrative assessment of the concentrations of seven heavy metals (Cu, Cd, Hg, Zn, Pb, Cr, and As) in surface sediments during the 2012-2015 period. Different heavy metal contamination indices were used for surface sediment quality assessment purposes. The numerical results revealed a noticeable spatial fluctuation of the degree of contamination throughout the region during the four years considered. Higher contamination levels and ecological risks were detected in the southern part of the Zhejiang coastal region. It was found that the Cu, Cd and Hg were the predominant contaminants along the Zhejiang coast with mean regional concentrations varying between 29.1 and 34.2, 0.12-0.17, and 0.044-0.052 mg/kg, respectively. The Cr and Pb exhibited lower contamination levels than the other metals during each one of the years 2012-2015. Stochastic site indicators of heavy metal contamination were used to assess regional uncertainties and obtain useful physical interpretations of the state of contamination of the Zhejiang coast. These indicators can be expressed explicitly in terms of probabilities of heavy metal contamination (either at a global scale or spatially distributed over the coastal region), and therefore they can be considered as risk indicators. It was found that the fraction of the coastal region where excess contamination occurred could never exceed the ratio of the mean heavy metal contamination over the selected threshold. In half of the coast study region, the degree of heavy metal contamination was higher than the median spatial contamination values during the month of August of the years 2012-2015. The spatial means of excess contamination and excess differential contamination increased as the relative area of over-contamination increased. Within the substantially contaminated sub-region of the Zhejiang coast, stronger contamination correlations were observed between locations separated by shorter distances. These correlations were higher when smaller thresholds were considered. As regards the spatial connectivity of the corresponding contamination risks, it was found that 44%, 31%, 39% and 63% of the location pairs in the Zhejiang coast simultaneously experienced moderate risks during the years 2012, 2013, 2014 and 2015, respectively. The ratio of the probability of excess contamination at both locations separated by distances < 20 km over the probability of excess contamination at either one of these two locations was high even for large thresholds, indicating that locations with high contamination are concentrated rather than being dispersed along the Zhejiang coast. Lastly, another interesting finding is that the characterization of the Zhejiang coastal region as over-contaminated is very sensitive to the DC threshold considered, that is, a small increase in the threshold selected can reduce significantly the probability that region is characterized as over-contaminated.

Spatiotemporal variation of the association between climate dynamics and HFRS outbreaks in Eastern China during 2005-2016 and its geographic determinants.

BACKGROUND: 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. METHODS AND FINDINGS: 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). CONCLUSIONS: 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.

Space-time PM2.5 mapping in the severe haze region of Jing-Jin-Ji (China) using a synthetic approach.

Long- and short-term exposure to PM2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM2.5 exposure during the period January 10-31, 2013. Technically, the processing of large space-time PM2.5 datasets and the mapping of the space-time distribution of PM2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM2.5 case study covering the severe haze Jing-Jin-Ji region during October 1-31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

The association between heavy metal soil pollution and stomach cancer: a case study in Hangzhou City, China.

Stomach cancer (SC) is a severe health burden, with nearly half of the world's cases found in China. Noticeably, the emissions of heavy metals into the environment have increased alongside rapid urbanization and industrialization in China. However, as regards carcinogenic associations, the relationship between heavy metals and SC is yet unclear. Based on 9378 newly diagnosed SC cases in Hangzhou City from 2009 to 2012, this work is concerned with the quantitative characterization of the spatial distribution pattern of SC incidence and its geographical association with soil heavy metals by means of a novel geographical model. The results show that (a) Cd is one of the severe soil pollutants in Hangzhou; (b) higher SC incidence clusters are in central Hangzhou, whereas lower clusters are found in the northeast and southwest with a male to female incidence ratio about 2.2:1; (c) although when considered separately, the heavy metals in this work do not have a considerable impact on the distribution of SC incidence in Hangzhou City, nevertheless, the joint effects of multiple heavy metals have significant impacts on SC risk. The present work calls for a rigorous quantitative assessment of the integrated heavy metal soil pollution and its effects on SC incidence.

Spatiotemporal characterization and mapping of PM2.5 concentrations in southern Jiangsu Province, China.

As a result of rapid industrialization and urbanization, China is experiencing severe air pollution problems. Understanding the spatiotemporal variation and trends of air pollution is a key element of an improved understanding of the underlying physical mechanisms and the implementation of the most effective risk assessment and environmental policy in the region. The motivation behind the present work is that the study region of southern Jiangsu province of China is one of the most populated and developed regions in China. The daily concentrations of particulate matter with particle diameter smaller than 2.5 µm (PM2.5) in southern Jiangsu province obtained during the year 2014 were used to derive the variogram model that provided a quantitative characterization of the spatiotemporal (ST) variation of PM2.5 concentrations in the study region. A spatiotemporal ordinary kriging (STOK) technique was subsequently employed to generate informative maps of the ST pollutant distribution in southern Jiangsu province. The results generated by STOK showed that during 2014 about 29.3% of the area was PM2.5 polluted (at various severity levels, according to the criteria established by the Chinese government), and that the number of days characterized as polluted varied from 59 to 164 at different parts of the study region. Nanjing, the capital of Jiangsu province, was the place with the highest PM2.5 pollution (including 3 days of serious pollution). The PM2.5 pollution exhibited a decreasing spatial trend from the western to the eastern part of southern Jiangsu. A similar temporal PM2.5 pattern was found from the western to the eastern part of southern Jiangsu, which was characterized by 4 peaks and 3 troughs linked to different meteorological conditions and human factors.

Contribution of industrial density and socioeconomic status to the spatial distribution of thyroid cancer risk in Hangzhou, China.

BACKGROUND: The thyroid cancer (TC) incidence in China has increased dramatically during the last three decades. Typical in this respect is the case of Hangzhou city (China), where 7147 new TC cases were diagnosed during the period 2008-2012. Hence, the assessment of the TC incidence risk increase due to environmental exposure is an important public health matter. METHODS: Correlation analysis, Analysis of Variance (ANOVA) and Poisson regression were first used to evaluate the statistical association between TC and key risk factors (industrial density and socioeconomic status). Then, the Bayesian maximum entropy (BME) theory and the integrative disease predictability (IDP) criterion were combined to quantitatively assess both the overall and the spatially distributed strength of the "exposure-disease" association. RESULTS: Overall, higher socioeconomic status was positively correlated with higher TC risk (Pearson correlation coefficient=0.687, P<0.01). Compared to people of low socioeconomic status, people of median and high socioeconomic status showed higher TC risk: the Relative Risk (RR) and associated 95% confidence interval (CI) were found to be, respectively, RR=2.29 with 95% CI=1.99 to 2.63, and RR=3.67 with 95% CI=3.22 to 4.19. The "industrial density-TC incidence" correlation, however, was non-significant. Spatially, the "socioeconomic status-TC" association measured by the corresponding IDP coefficient was significant throughout the study area: the mean IDP value was -0.12 and the spatial IDP values were consistently negative at the township level. It was found that stronger associations were distributed among residents mainly on a stripe of land from northeast to southwest (consisting mainly of sub-district areas). The "industrial density-TC" association measured by its IDP coefficient was spatially non-consistent. CONCLUSIONS: Socioeconomic status is an important indicator of TC risk factor in Hangzhou (China) whose effect varies across space. Hence, socioeconomic status shows the highest TC risk effect in sub-district areas.

Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, 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.