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Human activities interacting with coastal waters lead to large amounts of nutrient loading and severe water pollution in China's near Seas. In this context, a comprehensive quantitative characterization of the spatiotemporal variation of nutrient pollutant concentrations is a key component of any reliable seawater quality assessment and integrated coastal management plan. The present work combines the Bayesian maximum entropy method with stochastic site indicators to estimate monthly nitrate and phosphate concentrations in China's near seas during 2015, explore their spatiotemporal variation, and provide an explicit quantitative assessment of seawater quality in conditions of in situ uncertainty. This makes it the first study of space‐time nutrient pollutant characterization at a national‐scale in a coastal seawater environment. The results showed that nitrate and phosphate distributions exhibit the same spatial trends along China's near seas, whereas high nutrient pollution levels are found in the Yangtze River, Liaohe River, and Pearl River estuaries. Local differences of temporal trends exist between nitrate and phosphate distributions, which suggest that distinct remediation strategies are needed to properly satisfy the required seawater quality standards. The average nitrate and phosphate concentrations across space‐time were found to be equal to 0.271 and 0.015 mg/L, respectively. The nitrate and phosphate concentrations exceeding the fourth grade seawater quality standard during 2015 were about 11% and 2.6%, respectively. The study of both the seasonal changes in human activities along the coastal cities and the temporal marine hydrodynamics can offer a better understanding of the seawater quality and the biogeochemical process of nutrient transport and distribution. Key Points: Bayesian maximum entropy maps showed that nitrate and phosphate concentrations had similar spatial distributionsNitrate and phosphate distributions exhibited different seasonal patterns that may have been affected by coastal hydrodynamics and human activitiesStochastic site indicators showed that nitrate pollution was much more serious than phosphate pollution [ABSTRACT FROM AUTHOR]

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 (PM 2.5 ) 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 PM 2.5 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 PM 2.5 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 PM 2.5 pollution (including 3 days of serious pollution). The PM 2.5 pollution exhibited a decreasing spatial trend from the western to the eastern part of southern Jiangsu. A similar temporal PM 2.5 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. [ABSTRACT FROM AUTHOR]

This work presents a random field model of disease attribute (incidence, mortality etc.) that transfers the study of the attribute distribution from the original spatiotemporal domain onto a lower-dimensionality traveling domain that moves along the direction of disease velocity. The partial differential equations connecting the disease attribute covariances in the original and the traveling domain are derived with coefficients that are functions of the disease velocity. These equations offer epidemiologic insight concerning the strength of the space-time dependence between the disease attribute values in the two domains. The traveling disease model has certain theoretical and computational advantages in the study and prediction of space-time disease attribute distributions in conditions of uncertainty. Estimates of the disease attribute are derived in the traveling domain and then used to generate maps of space-time disease attribute distribution in the original domain. The theoretical model is illustrated and additional insight is gained by means of a numerical mortality simulation study, which shows that the proposed model is at least as accurate but computationally more efficient than mainstream mapping techniques of higher dimensionality. These findings concerning the very good predictability of the proposed model also strongly support its adequacy to represent the space-time mortality distribution. [ABSTRACT FROM AUTHOR]

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China. [ABSTRACT FROM AUTHOR]

Background: Typhoid and paratyphoid fever are endemic in Hongta District and their prevalence, at 113 per 100,000 individuals, remains the highest in China. However, the exact sources of the disease and its main epidemiological characteristics have not yet been clearly identified. Methods and Findings: Numbers of typhoid and paratyphoid cases per day during the period 2006 to 2010 were obtained from the Chinese Center of Disease Control (CDC). A number of suspected disease determinants (or their proxies), were considered for use in spatiotemporal analysis: these included locations of discharge canals and food markets, as well as socio-economic and environmental factors. Results showed that disease prevalence was spatially clustered with clusters decreasing with increasing distance from markets and discharge canals. More than half of the spatial variance could be explained by a combination of economic conditions and availability of health facilities. Temporal prevalence fluctuations were positively associated with the monthly precipitation series. Polluted hospital and residential wastewater was being discharged into rainwater canals. Salmonella bacteria were found in canal water, on farmland and on vegetables sold in markets. Conclusion: Disease transmission in Hongta district is driven principally by two spatiotemporally coupled cycles: one involving seasonal variations and the other the distribution of polluted farmland (where vegetables are grown and sold in markets). Disease transmission was exacerbated by the fact that rainwater canals were being used for disposal of polluted waste from hospitals and residential areas. Social factors and their interactions also played a significant role in disease transmission. [ABSTRACT FROM AUTHOR]

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998-1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country. [ABSTRACT FROM AUTHOR]

With rapid economic development, industrialization and urbanization, the ambient air PM 2.5 has become a major pollutant linked to respiratory, heart and lung diseases. In China, PM 2.5 pollution constitutes an extreme environmental and social problem of widespread public concern. In this work we estimate ground-level PM 2.5 from satellite-derived aerosol optical depth (AOD), topography data, meteorological data, and pollutant emission using an integrative technique. In particular, Geographically Weighted Regression (GWR) analysis was combined with Bayesian Maximum Entropy (BME) theory to assess the spatiotemporal characteristics of PM 2.5 exposure in a large region of China and generate informative PM 2.5 space-time predictions (estimates). It was found that, due to its integrative character, the combined BME-GWR method offers certain improvements in the space-time prediction of PM 2.5 concentrations over China compared to previous techniques. The combined BME-GWR technique generated realistic maps of space-time PM 2.5 distribution, and its performance was superior to that of seven previous studies of satellite-derived PM 2.5 concentrations in China in terms of prediction accuracy. The purely spatial GWR model can only be used at a fixed time, whereas the integrative BME-GWR approach accounts for cross space-time dependencies and can predict PM 2.5 concentrations in the composite space-time domain. The 10-fold results of BME-GWR modeling (R 2 = 0.883, RMSE = 11.39 μ g / m 3 ) demonstrated a high level of space-time PM 2.5 prediction (estimation) accuracy over China, revealing a definite trend of severe PM 2.5 levels from the northern coast toward inland China (Nov 2015–Feb 2016). Future work should focus on the addition of higher resolution AOD data, developing better satellite-based prediction models, and related air pollutants for space-time PM 2.5 prediction purposes. [ABSTRACT FROM AUTHOR]

Soil heavy metal concentrations exhibit significant space-time trends due to their accumulation along the time axis and the varying distances from the pollution sources. Thus, concentration trends cannot be ignored when performing spatiotemporal soil heavy metal predictions in an area. In this work, datasets were used of soil cadmium (Cd) concentrations in the Qingshan district (Wuhan City, Hubei Province, China) sampled during the period 2010–2014. Spatiotemporal Kriging with four Trend models (STKT) and non-separable space-time correlation was implemented to assimilate multi-temporal data in the mapping of Cd distribution within the contaminated soil area. Soil Cd trends were represented by four different space-time polynomial functions, and a non-separable power function-exponential variogram model of Cd distribution was assumed. Plots of the predicted space-time Cd distributions revealed a marked tendency of the Cd concentrations over time to spread from the southwest part to the entire study area (higher soil Cd concentrations are found in the southwest part of the Qingshan area, whereas the temporal Cd trend is characterized by a constant increase from 2010 to 2014). Thus, the maps indicate that the entire study area is contaminated by Cd, a situation that seems to be stable over time. STKT can reduce prediction errors in practically and statistically significant ways. A numerical comparison of the STKT technique vs. the mainstream Spatiotemporal Ordinary Kriging (STOK) technique showed that STKT can perform better than STOK when the trend model's goodness of fit to the Cd data was satisfactory (producing minimal data fit error statistics), implying that adequate trend modeling is a key issue for space-time prediction accuracy purposes. In particular, quantitative results obtained at the Qingshan region showed that, by incorporating local Cd values and distance-based dependence structures the STKT techniques produced the best prediction error statistics, resulting in considerable prediction error reductions (the level of which depend on the trend model specification; e.g., in the case of STKT with trend model 3 the improvement comparing to STOK was almost 30%). Future studies of Cd contamination in the region (sampling design optimization) can benefit from the results of the geostatistical analysis of the present paper (variogram and trend modeling, etc.). [ABSTRACT FROM AUTHOR]