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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]

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m 3 and 0.191 mg/m 3 , respectively, and the root mean square error (RMSE) values were 0.158 mg/m 3 and 0.321 mg/m 3 , respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m 3) and the RMSE value (1.082 mg/m 3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion. [ABSTRACT FROM AUTHOR]

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 R 2 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. • BME and SVR (ML) were combined to retrieve Chl-a in optically complex waters. • SVR shows great ability to retrieve Chl-a using satellite remote sensing data. • Introducing BME can significantly reduce the uncertainty of Chl-a estimation. • The distribution pattern of Chl-a at GSL varies a lot during the summer season. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]