ABSTRACTSustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined. 相似文献
Trimethylolpropane (TMP) wastewater is one of the most toxic petrochemical wastewater. Toxicants with high concentrations in TMP wastewater often inhibit the activity of microorganisms associated with biological treatment processes. The hydrolysis acidification process (HAP) is widely used to pretreat petrochemical wastewater. However, the effects of HAP on the reduction of wastewater toxicity and the relevant underlying mechanisms have rarely been reported. In this study, an HAP reactor was operated for 240 days, fed with actual TMP wastewater diluted by tap water in varying ratios. The toxicity of TMP wastewater was assessed with the inhibition ratio of oxygen uptake rate. When the organic loading rates were lower than 7.5 kg COD/m3/d, the toxicity of TMP wastewater was completely eliminated. When the actual TMP wastewater was directly fed into the reactor, the toxicity of TMP wastewater decreased from 100% to 34.9%. According to the results of gas chromatographymass spectrometry analysis, four main toxicants contained in TMP wastewater, namely, formaldehyde, 2-ethylacrolein, TMP and 2-ethylhexanol, were all significantly removed, with removal efficiencies of 93.42%, 95.42%, 72.85% and 98.94%, respectively. Compared with the removal efficiency of CODCr, the reduction rate of toxicity is markedly higher by HAP. In addition, the change of microbial community in the HAP reactor, along the operation period, was studied. The results revealed that, compared with the seed sludge, Firmicutes became the dominant phylum (abundance increased from 0.51% to 57.08%), followed by Proteobacteria and Bacteroidetes (abundance increased from 59.75% to 25.99% and from 4.70% to 8.39%, respectively).
