No abstract

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM2.5 observation station of the U.S Embassy and the 18 PM10 observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM2.5 monitoring station and at the 18 official PM10 stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

No abstract

No abstract

The textural and spatial information extracted from very high resolution (VHR) remote sensing imagery provides complementary information for applications in which the spectral information is not sufficient for identification of spectrally similar landscape features. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. Specifically, eight GLCM texture features (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) were first calculated from IKONOS NIR band (Band 4) to determine an optimal window size (13 × 13) and an optimal direction (45°). Then, the optimal window size and direction were applied to the three other IKONOS MS bands (blue, green, and red) for calculating the eight GLCM textures. Next, an optimal distance value (5) and an optimal neighborhood rule (Queen's case) were determined for calculating the four Gi features from the four IKONOS MS bands. Finally, different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 93.3% created with the all 32 GLCM features calculated from the four IKONOS OPEN ACCESS Remote Sens. 2015, 7 9021 MS bands with a window size of 13 × 13 and direction of 45°; (4) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen's neighborhood rule; and (5) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The most important feature ranked by RF classifier was GLCM texture mean calculated from Band 4, followed by Gi feature calculated from Band 4. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) the IKONOS NIR band was more powerful than visible bands in quantifying varying degrees of forest crown dieback.

No abstract

No abstract

A comprehensive analysis of heavy metal pollution was conducted in the representative limnetic ecosystems of eastern China, which are subject to rapid economic development and population growth. The results demonstrated that the average contents with standard deviations of Cd, Cr, Cu, Ni, Pb and Zn in the surface sediments were 0.925 ± 0.936, 142 ± 46.8, 54.7 ± 29.1, 60.5 ± 21.6, 61.9 ± 36.0 and 192 ± 120 mg/kg dry wt., respectively, and that higher values were mainly observed in the southern portion of the study area, especially in the basins of Southeast Coastal Rivers (SCRB) and the Zhu River (ZRB). The six heavy metals in the surface sediments all had anthropogenic origins. In addition, the limnetic ecosystems, especially in the southern portion of the study area were found to be polluted by heavy metals, especially Cd. Overall, two hotspots of heavy metal pollution in the limnetic ecosystems of eastern China were found, one that consisted of the heavy pollution regions, SCRB and ZRB, and another composed of Cd pollution. These results indicate that heavy metal contamination, especially Cd, should be taken into account during development of management strategies to protect the aquatic environment in the limnetic ecosystems of eastern China, especially in the two aforementioned basins.