Direct Radiative Effect of Aerosols on Net Ecosystem Carbon Exchange in the Pearl River Delta Region

The environmental impact of aerosols is currently a hot issue that has received worldwide attention. Lacking simultaneous observations of aerosols and carbon flux, the understanding of the aerosol radiative effect of urban agglomeration on the net ecosystem carbon exchange(NEE) is restricted. In 2009-2010, an observation of the aerosol optical property and CO_(2) flux was carried out at the Dongguan Meteorological Bureau Station(DMBS) using a sun photometer and eddy covariance systems. The different components of photosynthetically active radiation(PAR),including global PAR(GPAR), direct PAR(DPAR), and scattered PAR(FPAR), were calculated using the Santa Barbara DISORT Atmospheric Radiative Transfer(SBDART) model. The effects of PAR on the NEE between land-atmosphere systems were investigated. The results demonstrated that during the study period the aerosol optical depth(AOD)reduced the DPAR by 519.28±232.89 μmol photons · m^(-2)s^(-1), but increased the FPAR by 324.93±169.85μmol photons ·m^(-2)s^(-1),ultimately leading to 194.34±92.62 μmol photons · m^(-2)s^(-1);decrease in the GPAR. All the PARs(including GPAR,DPAR, and FPAR) resulted in increases in the NEE(improved carbon absorption), but the FPAR has the strongest effect with the light use efficiency(LUE) being 1.12 times the values for the DPAR. The absorption of DPAR by the vegetation exhibited photo-inhibition in the radiation intensity > 600 photons · m^(-2)s^(-1);in contrast, the absorptions of FPAR did not exhibit apparent photo-inhibition. Compared with the FPAR caused by aerosols, the DPAR was not the primary factor affecting the NEE. On the contrary, the increase in AOD significantly increased the FPAR, enhancing the LUE of vegetation ecosystems and finally promoting the photosynthetic CO_(2) absorption.

Gap Filling of Net Ecosystem CO₂Exchange (NEE) above Rain-Fed Maize Using Artificial Neural Networks (ANNs)

The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).