A soil pot culture experiment with four supplied P levels （i.e. P30, P50, P100, P200, representing supplemental P 30, 50, 100, 200 mg/kg, respectively） was conducted to investigate uptake and use ability to P and Zn...A soil pot culture experiment with four supplied P levels （i.e. P30, P50, P100, P200, representing supplemental P 30, 50, 100, 200 mg/kg, respectively） was conducted to investigate uptake and use ability to P and Zn in the rice genotypes with different P-efficiency, of which rice genotypes 508, 99011, 580, 99112 were Iow-P tolerant and 99056, 99012 were Iow-P sensitive. Low-P tolerant rice 580 and 99011 absorbed more P than the others, and rice genotype 580 had stronger uptake ability especially at Iow-P level such as P50 and P30. 508 could absorb considerable P, and had the lowest P percentage of shoot, indicating it had good performance in P-use efficiency. These three rice genotypes had larger biomass and less response to changed P level than rice genotype 99112, 99056 and 99012. Rice genotype 99112 showed Low-P tolerance mainly by sacrificing biomass to maintain high relative grain yield. The least amount of P absorbed by 99056 showed it had the lowest P uptake efficiency, and the highest P percentage in shoot of 99012 meant it had the lowest P use efficiency. So they two showed Iow-P sensitivity. Zn contents in shoot under P200, P100 and P50 were similar, but P30 increased Zn content in shoot significantly. The Zn contents in shoot of 99112, 99056 and 99012 were higher than those of 508, 99011 and 580, especially at tillering stage and booting stage. As for total Zn content in shoot, Low-P tolerant rice genotype 580 had the largest amount and followed by 99011 and 508, Iow-P tolerant rice genotype 99012 had the smallest amount at the three sampling stage and followed by 99056. Furthermore, P/Zn in shoot of 99012 was the highest, and that of 99056 was the smallest at the same P level.展开更多
The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method co...The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics. The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan, a metropolis of central China. 124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn, Cu, Zn, Pb, Cd, Cr, Ni and Co). PCA results revealed that three major factors were responsible for soil heavy metal pollution, which were initially identified as "steel production", "agronomic input" and "coal consumption". The APCS technique, combined with multiple linear regression analysis, was then applied for source apportionment. Steel production appeared to be the main source for Ni, Co, Cd, Zn and Mn, agronomic input for Cu, and coal consumption for Pb and Cr. Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations. The introduced method appears to be an effective tool in soil pollution source apportionment and identification, and might provide valuable reference information for pollution control and environmental management.展开更多
文摘A soil pot culture experiment with four supplied P levels （i.e. P30, P50, P100, P200, representing supplemental P 30, 50, 100, 200 mg/kg, respectively） was conducted to investigate uptake and use ability to P and Zn in the rice genotypes with different P-efficiency, of which rice genotypes 508, 99011, 580, 99112 were Iow-P tolerant and 99056, 99012 were Iow-P sensitive. Low-P tolerant rice 580 and 99011 absorbed more P than the others, and rice genotype 580 had stronger uptake ability especially at Iow-P level such as P50 and P30. 508 could absorb considerable P, and had the lowest P percentage of shoot, indicating it had good performance in P-use efficiency. These three rice genotypes had larger biomass and less response to changed P level than rice genotype 99112, 99056 and 99012. Rice genotype 99112 showed Low-P tolerance mainly by sacrificing biomass to maintain high relative grain yield. The least amount of P absorbed by 99056 showed it had the lowest P uptake efficiency, and the highest P percentage in shoot of 99012 meant it had the lowest P use efficiency. So they two showed Iow-P sensitivity. Zn contents in shoot under P200, P100 and P50 were similar, but P30 increased Zn content in shoot significantly. The Zn contents in shoot of 99112, 99056 and 99012 were higher than those of 508, 99011 and 580, especially at tillering stage and booting stage. As for total Zn content in shoot, Low-P tolerant rice genotype 580 had the largest amount and followed by 99011 and 508, Iow-P tolerant rice genotype 99012 had the smallest amount at the three sampling stage and followed by 99056. Furthermore, P/Zn in shoot of 99012 was the highest, and that of 99056 was the smallest at the same P level.
基金Supported by the National Natural Science Foundation of China (No. 40971269)
文摘The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions, and apply it to a case study. The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics. The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan, a metropolis of central China. 124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn, Cu, Zn, Pb, Cd, Cr, Ni and Co). PCA results revealed that three major factors were responsible for soil heavy metal pollution, which were initially identified as "steel production", "agronomic input" and "coal consumption". The APCS technique, combined with multiple linear regression analysis, was then applied for source apportionment. Steel production appeared to be the main source for Ni, Co, Cd, Zn and Mn, agronomic input for Cu, and coal consumption for Pb and Cr. Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations. The introduced method appears to be an effective tool in soil pollution source apportionment and identification, and might provide valuable reference information for pollution control and environmental management.
