基于豌豆叶色素乙醇溶液在蓝紫色的发光二极管(LED)照射下产生红色荧光,该红色荧光能被Hg2+熄灭的现象,建立一种简便、快速、定性鉴定溶液中Hg2+的新方法,实验证明其他阳离子基本上不产生干扰。该方法可直接从阳离子混合溶液中定性鉴定H...基于豌豆叶色素乙醇溶液在蓝紫色的发光二极管(LED)照射下产生红色荧光,该红色荧光能被Hg2+熄灭的现象,建立一种简便、快速、定性鉴定溶液中Hg2+的新方法,实验证明其他阳离子基本上不产生干扰。该方法可直接从阳离子混合溶液中定性鉴定Hg2+,其检测Hg2+的最低浓度为5×10-4m o l/L,检出限为5μg。展开更多
Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time pe...Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-s展开更多
Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed ...Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.展开更多
文摘基于豌豆叶色素乙醇溶液在蓝紫色的发光二极管(LED)照射下产生红色荧光,该红色荧光能被Hg2+熄灭的现象,建立一种简便、快速、定性鉴定溶液中Hg2+的新方法,实验证明其他阳离子基本上不产生干扰。该方法可直接从阳离子混合溶液中定性鉴定Hg2+,其检测Hg2+的最低浓度为5×10-4m o l/L,检出限为5μg。
文摘Remote sensing techniques are proven methods for quantifying chlorophyll-a levels by inference algal concentrations in reservoirs. One traditional method is to use Landsat imagery and field data from a limited time period to develop a model for a reservoir which relates reflectance in various bands to measured algal (or chlorophyll-a) concentrations and use that model and associated imagery to determine spatial algal concentrations in the reservoir. In this work, we extend these techniques to use historical Landsat data over long time periods to develop seasonal models that will more accurately describe the conditions throughout the growing season. Previous work at Deer Creek included the development of a chlorophyll-a model using data from the months of August to September. This model did not account for seasonal variation and algal succession, which affects the relationship between measured reflectance and algal concentration. Early summer algal blooms are dominated by diatoms (yellow-brown), while the algae vary from chlorophyta (green) in the mid-summer to cyanobacteria (blue-green) in late summer months. This study presents and explores the development and use of seasonal algorithms based on reflective characteristics of various algal communities to create a more accurate model for the reservoir. This study uses water quality data collected over a 20-year period during non-ice conditions along with associated Landsat data. As the field measurements were not taken to support remote sensing measurements, this study evaluates the use of historical data to support remote sensing analysis. It is assumed that reservoir conditions do not change rapidly, the field data can be used to develop correlations with satellite imagery taken within a day of the field measurements, and the seasonal algal communities have different reflective properties (or colors). We present statistical analysis that shows the seasonal algorithms better fit the data than the non-seasonal model and the traditional model calibrated with late-s
文摘Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.
