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 PM... 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 PM... 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 PM... exceeded 75, 115, 150, and 250 ...g m... varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM... pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM... exceeded 150 ...g m... during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM... pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM... concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold. (ProQuest: ... denotes formulae/symbols omitted.)
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IJS, KILJ, NUK, PNG, UL, UM
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 PM... 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 PM... 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 PM... exceeded 75, 115, 150, and 250 ...g m... varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM... pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM... exceeded 150 ...g m... during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM... pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM... concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold. (ProQuest: ... denotes formulae/symbols omitted.)
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IJS, KILJ, NUK, PNG, UL, UM
The validity of certain critical reasoning steps carried out during or on the sidelines of the environmental science, public health survey, medical experiment, population risk assessment, or disease ...space–time mapping under conditions of in situ uncertainty and space–time heterogeneity, is often not given sufficient attention and may even be out of the investigator's line of thought. For example, the technical complexity of an environmental exposure experiment may overshadow the logical assumptions made when moving from one phase of the experiment to the next, or the study of population risk assessment may focus on analytical and computational matters, whereas methodological and cultural factors are neglected.
Sea surface temperature (SST) is an important oceanography attribute that has been used to study ocean climatic conditions, ocean dynamics and air-sea interactions. In the present work, the Bayesian ...maximum entropy (BME) method was used to interpolate a FY-3 C /VIRR satellite dataset in the region with coordinates 120°-150°W and 45°-60°S and during January 2020. A novel approach of constructing valuable soft data was developed by combining BME interpolation with highly correlated SST day and night data differences. The BME interpolation accuracy was assessed by cross-validation, and the results showed that the average RMSE (root mean squared error) was 0.700 and the average bias was 0.441°C. Furthermore, using the Argo data as a basis of comparison, the coverage and accuracy of the BME interpolation of the FY-3 C/VIRR satellite SST dataset were compared numerically with those of the Ordinary Kriging (OK) interpolation of the FY-3 C/VIRR satellite SST dataset and the Optimum Interpolation Sea Surface Temperature (OISST) of the AVHRR satellite SST dataset. It was found that BME had the best SST interpolation performance among the three methods with the lowest average bias and the largest correlation coefficient. Although OISST had a full product coverage rate overall (due to its use of more perfect treatment means), BME's coverage rate (97.5%) considerably improved that of the FY-3 C/VIRR SST data. Also, both the BME and OK products maintained a 12hrs temporal resolution and a 0.05 decimal degrees longitude/latitude spatial resolution, which is an improvement over OISST data with a 24hrs time resolution and a 0.25 decimal degrees longitude/latitude spatial resolution. Another advantage of BME is that because of its broad theoretical support its performance in practice can be improved further as more knowledge sources become available (which can only be incorporated by BME).
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
The effects of Kandelia obovata introduction and Spartina alterniflora invasion on the microorganisms are very important for coastal ecology and restoration. MiSeq sequencing of the 16S rRNA gene and ...Tax4Fun predictive functional profiles were used to investigate the diversity estimators, community structures and potential metabolic functions of benthic bacteria in surface sediments of coastal wetlands invaded by S. alterniflora and artificially planted with K. obovata at stand ages of 2-, 8-, 11-, 16- and 60-years on Ximen Island, Yueqing Bay, China. The results showed that the sediment bacterial richness was significantly decreased in 60-year K. obovata stands compared with younger K. obovata stands and S. alterniflora stands. The age of K. obovata and specie of exotic plants formed distinct bacterial communities and functional structures in sediments, respectively. With increasing plantation of K. obovata, the bacterial communities shifted from the class Anaerolineae, the genus Sulfurovum, and bacteria involved in sulfate reduction as abundant taxa to higher proportions of bacteria involved in degradation of plant polysaccharide and nitrate reduction. The shift in bacterial community structures was mainly driven by changes in sediment total organic matter, total nitrogen, total phosphorus, ammonium concentrations, pH and temperature. The community functions also changed from nitrogen fixation to more nitrate reduction and denitrification processes. Compared with 60-year K. obovata, S. alterniflora was occupied by higher proportions of the phylum Bacteroidetes, the orders Rhodobacterales, Flavobacteriales and Desulfuromonadales, and total nitrogen, total phosphorus concentrations and sediment temperature were major environmental factors affecting the variation. Among the major sulfur cycling processes examined, higher potential of dissimilatory sulfate reduction was observed in S. alterniflora. Our results indicated that K. obovata introduction has a greater effect on the bacterial community diversity and structure than S. alterniflora invasion. This study could improve the understanding of microbial processes and potential functions of K. obovata introduction and S. alterniflora invasion and provide reference in the restoration and management of the coastal wetlands.
•Bacteria involved in degradation of plant polysaccharide increased with KO ages.•Bacteria involved in sulfate reduction were more abundant in younger-aged KO & SA.•Bacteria variation is associated with soil TOM, TN, TP, NH4+-N, pH & temperature.•Effect of SA invasion on bacterial community was outweighed by KO introduction.•KO affected more on N cycle, SA affected S cycle by stimulating sulfate reduction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Regression is often conducted assuming independent model errors. The detection of atypical values in regression (leverage and influential points) assumes independent errors. However, such ...independence could be unrealistic in geostatistics. In this article, we propose a methodology based on least squares and geostatistics to identify such values in spatial regression. Our procedure uses the hat matrix to detect leverage points. A modified Cook distance is employed to confirm whether these points are influential. The methodology is evaluated with stationary and non-stationary geostatistical data. We apply this methodology to real georeferenced data related to depth, dissolved oxygen, and temperature. First, an autoregressive model is fitted to depth data. Second, a regression between oxygen and temperature is estimated. In both models, spatial correlation is assumed to determine the parameters, leverage, and influential observations. Our methodology can be used in regression with geographical information to avoid misinterpreted results. Not considering this information may under- or over-estimate geographical indicators, such as the mean depth, which can affect the circulation of water masses or dissolved oxygen variability. Our results reveal that including spatial dependence to identify high leverage points is relevant and must be considered in any geostatistical analysis.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
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•Mariculture activities dominated the dynamics of dissolved organic matter (DOM).•Mariculture resulted in lower degrees of DOM humification and aromaticity.•Seaweed culture modulated DOM by the seasonal production of protein-like C1.•Fish culture contributed to seasonal variations in liable DOC and protein-like C2.•Abalone culture might promote the export of recalcitrant carbon from seaweed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fine particle matter (PM2.5) has been receiving increasing attention by the government due to its considerable adverse health effects, especially in the north part of China. Even though a number of ...techniques of estimating PM2.5 exposure have been developed, what is still lacking is a systematic comparison of commonly used techniques based on classical statistics, artificial intelligence, and geostatistics. To address this need, the land use regression (LUR), the artificial neural networks (ANN), and the Bayesian maximum entropy (BME) techniques were all used to map the space-time PM2.5 concentration distribution in the highly polluted Jing-Jin-Ji region (Huabei plain of North China) during the period June 2015-May 2016. The tenfold cross-validation analysis and the entropic information theory were used to evaluate numerically the performance of the three techniques at monthly, seasonal, and annual time scales. Our results showed that the performance of each mapping technique was affected by the temporal scale and the degree of spatial heterogeneity. All three techniques were suitable for low temporal resolution (annual) datasets with low spatial variability. BME also showed a noticeable ability to analyze higher temporal resolution (monthly) datasets exhibiting high spatial heterogeneity. BME involved a single dependent variable (PM2.5) and generated complete (full-coverage) space-time PM2.5 maps, whereas LUR and ANN produced incomplete maps because of lacking independent variables (such as satellite data). Due to its self-learning feature, ANN showed better modeling performance than LUR and produced more informative maps. Overall, the ANN and BME techniques perform better than the LUR technique.
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.).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
