Time filter

Source Type

Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Christakos G.,Zhejiang University | Christakos G.,San Diego State University
Environmental Science and Technology | Year: 2015

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. © 2015 American Chemical Society. Source

Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Christakos G.,Zhejiang University
Environmental Monitoring and Assessment | Year: 2015

Mapping the space–time distribution of heavy metals in soils plays a key role in contaminated site classification under conditions of in situ uncertainty, whereas uncertainty assessment is based on the quantification of the specific uncertainties in terms of exceedance probabilities. Geostatistical space-time kriging (STK) is increasingly used to estimate pollutant concentrations in soils. Sequential indicator simulation (SIS) technique is popular in uncertainty assessment of heavy metal contamination of soils. However, these techniques cannot handle multi-temporal data. In this work, spatiotemporal sequential indicator simulation (STSIS) based on an additive space–time semivariogram model (STSIS_A) and on a non-separable space–time semivariogram model (STSIS_NS) was used to assimilate multi-temporal data in the mapping and uncertainty assessment of heavy metal distributions in contaminated soils. Cu concentrations in soils sampled during the period 2010–2014 in the Qingshan district (Wuhan City, Hubei Province, China) were used as the experimental data set. Based on a number of STSIS realizations, we assessed different kinds of mapping uncertainty, including single-location uncertainty during 1 year and during multiple years, multi-location uncertainty during 1 year, and during multiple years. The comparison of the STSIS technique vs. SIS and STK techniques showed that STSIS performs better than both STK and SIS. © 2015, Springer International Publishing Switzerland. Source

Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,San Diego State University | Wu J.,Zhejiang University | Christakos G.,Zhejiang University
Ecological Indicators | Year: 2015

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.). © 2015 Elsevier Ltd. Source

Zhang C.,Huazhong Agricultural University | Zhang C.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,Huazhong Agricultural University | Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River
Communications in Computer and Information Science | Year: 2013

In consideration of the correlation of soil properties and the surrounding environment, this paper proposed a method to compute histogram soft data based on soil-landscape model with soil attribute values of the sample sites and environmental factors data. The soft data was used in Bayesian Maximum Entropy (BME) to predict the spatial distribution of soil organic matter in Shayang County, Hubei Province, center China. The method of prediction was compared with the ordinary Kriging (OK) by mean error (ME) and mean squared error (MSE). Results showed that the BME predictions were more accurate and successfully estimates the degree of fluctuation in the observations. In this situation the method proposed by this paper to get soft data is applicative and the BME is an effective approach to improve the spatial distribution of soil properties prediction accuracy. © Springer-Verlag Berlin Heidelberg 2013. Source

Yang Y.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Yang Y.,Huazhong Agricultural University | Zhang C.T.,Key Laboratory of Arable Land Conservation Middle and Lower Reaches of Yangtse River | Zhang C.T.,Huazhong Agricultural University | And 3 more authors.
Journal of Environmental Informatics | Year: 2015

The concern of this work is the systematic synthesis of site-specific samples and auxiliary information (including continuous and categorical variables) aiming at improving spatial prediction of natural attributes (soil properties, contaminant processes etc.). Bayesian Maximum Entropy (BME) is the theoretical support of the proposed synthesis. The significance of the synthesis is that it can increase the prediction accuracy of natural attributes in a physically meaningful and technically efficient manner. The spatial prediction approach is applied in a real world case study that combines soil organic matter (SOM) content samples with auxiliary information (terrain indices, soil types, and soil texture) to generate predictive maps. Prediction was affected by soil type and soil texture (prediction accuracy increased when categorical variables were included). In the same case study, the BME-based approach was compared with mainstream spatial statistics techniques, like Regression Kriging (RK) with auxiliary information, and hard data-driven Ordinary Kriging (OK). The numerical results demonstrated the superiority of the BME-based approach over the Kriging-based techniques, whereas it was found that some key BME parameters (counts of soft data, predicted variables categories, and continuous auxiliary variable categories) can have different effect on SOM prediction accuracy. The success of BME-based prediction relied heavily on finding adequate auxiliary information about the study situation. © 2015 ISEIS All rights reserved. Source

Discover hidden collaborations