This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mix...This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mixture Analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear Spectral Mixture Analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.展开更多
There is an increasing interest in understanding ambient bioaerosols due to their roles both in health and in climate. Here, we deployed an Ultraviolet Aerodynamic Particle Sizer to monitor viable （fluorescent） bioa...There is an increasing interest in understanding ambient bioaerosols due to their roles both in health and in climate. Here, we deployed an Ultraviolet Aerodynamic Particle Sizer to monitor viable （fluorescent） bioaerosol concentration levels at city centers （highly polluted） and their corresponding suburbs （near pristine） （total 40 locations） in 11 provinces featuring different climate zones in China between July 16 and 28, 2013. The concentration levels of viable bioaerosol particles （BioPM） of 〉0.5 μm were measured, and corresponding percentages of BioPM% （biological fraction of total PM） and BioPM2.5% （biological fraction of PM2.5） in particulate matter （PM） and BioPM, respectively, were determined. For some key cities, indoor viable bioaerosol levels were also obtained. In addition, bacterial structures of the air samples collected across these monitoring locations were studied using pyrosequencing. BioPM concentration levels ranged from 2.1 ×10^4 to 2.4 × 10^5/m3 for city centers [BioPM% = 6.4 % （4-6.3 %）] and 0.5 × 10^4 to 4.7 × 10^5/m3 for suburbs [BioPM% = 10 % （4-8.7 %）]. Distinctive bioaerosol size distribution patterns were observed for different climate zones, e.g., some had fluorescence peaks at 3 μm, while the majority had peaks at 1 μm. Ambient bacterial aerosol community structures were also found different for different geophysical locations. Results suggest that there was a poor overall relationship between PM and BioPM across 40 monitoring locations （R2= 0.081, two-tailed P value = 0.07435）. Generally, city centers had higher PM concentrations than suburbs, but not BioPM and BioPM%. Indoor bioaerosol levels were found at least tenfold higher than those corresponding outdoors. Bacillus was observed to dominate the bacterial aerosol community in the air sample.展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 40701177)
文摘This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mixture Analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear Spectral Mixture Analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.
基金supported by the National Natural Science Foundation of China(21277007,21477003,and 41121004)the Ministry of Science and Technology of China(2015DFG92040,2015CB553401)Ministry of Education(20130001110044)
文摘There is an increasing interest in understanding ambient bioaerosols due to their roles both in health and in climate. Here, we deployed an Ultraviolet Aerodynamic Particle Sizer to monitor viable （fluorescent） bioaerosol concentration levels at city centers （highly polluted） and their corresponding suburbs （near pristine） （total 40 locations） in 11 provinces featuring different climate zones in China between July 16 and 28, 2013. The concentration levels of viable bioaerosol particles （BioPM） of 〉0.5 μm were measured, and corresponding percentages of BioPM% （biological fraction of total PM） and BioPM2.5% （biological fraction of PM2.5） in particulate matter （PM） and BioPM, respectively, were determined. For some key cities, indoor viable bioaerosol levels were also obtained. In addition, bacterial structures of the air samples collected across these monitoring locations were studied using pyrosequencing. BioPM concentration levels ranged from 2.1 ×10^4 to 2.4 × 10^5/m3 for city centers [BioPM% = 6.4 % （4-6.3 %）] and 0.5 × 10^4 to 4.7 × 10^5/m3 for suburbs [BioPM% = 10 % （4-8.7 %）]. Distinctive bioaerosol size distribution patterns were observed for different climate zones, e.g., some had fluorescence peaks at 3 μm, while the majority had peaks at 1 μm. Ambient bacterial aerosol community structures were also found different for different geophysical locations. Results suggest that there was a poor overall relationship between PM and BioPM across 40 monitoring locations （R2= 0.081, two-tailed P value = 0.07435）. Generally, city centers had higher PM concentrations than suburbs, but not BioPM and BioPM%. Indoor bioaerosol levels were found at least tenfold higher than those corresponding outdoors. Bacillus was observed to dominate the bacterial aerosol community in the air sample.
