PDF(Pubmed)
Abstract：
The most widely used isoprene emission algorithm, G93 formula, estimates instantaneous leaf-level isoprene emission using the basal emission factor and light and temperature dependency parameters. The G93 parameters have been suggested to show variation depending on past weather conditions, but no study has closely examined the relationship between past meteorological data and the algorithm parameters. Here, to examine the influence of the past weather on these parameters, we monitored weather conditions, G93 parameters, isoprene synthase transcripts and protein levels, and MEP pathway metabolites in the tropical tree Ficus septica for 12 days and analyzed their relationship with cumulative temperature and light intensity. Plants were illuminated with varying (ascending and descending) light regimes, and our previously developed Ping-Pong optimization method was used to parameterize G93. The cumulative temperature of the past 5 and 7 days positively correlated with CT2 and α, respectively, while the cumulative light intensity of the past 10 days showed the highest negative correlation with α. Concentrations of MEP pathway metabolites and IspS gene expression increased with increasing cumulative temperature. At best, the cumulative temperature of the past 2 days positively correlated with the MEP pathway metabolites and IspS gene expression, while these factors showed a biphasic positive and negative correlation with cumulative light intensity. Optimized G93 captured well the temperature and light dependency of isoprene emission at the beginning of the experiment; however, its performance significantly decreased for the latter stages of the experimental duration, especially for the descending phase. This was successfully improved through separate optimization of the ascending and descending phases, emphasizing the importance of the optimization of formula parameters and model improvement. These results have important implications for the improvement of isoprene emission algorithms, particularly under the predicted increase in future global temperatures.
摘要：
最广泛使用的异戊二烯排放算法，G93公式，使用基础排放因子以及光和温度依赖性参数估算瞬时叶级异戊二烯排放。G93参数已被建议根据过去的天气情况显示变化，但是没有研究仔细研究过去的气象数据和算法参数之间的关系。这里,为了检查过去天气对这些参数的影响，我们监测了天气状况,G93参数,异戊二烯合酶转录物和蛋白质水平，和MEP途径代谢产物在热带树下12天，并分析了它们与累积温度和光照强度的关系。植物被不同的（上升和下降）光状态照亮，我们以前开发的乒乓优化方法用于参数化G93。过去5天和7天的累积温度与CT2和α呈正相关，分别,而过去10天的累积光照强度与α呈最高负相关。MEP途径代谢物和IspS基因表达的浓度随着累积温度的升高而增加。充其量，过去2天的累积温度与MEP途径代谢产物和IspS基因表达呈正相关,而这些因素与累积光照强度呈双相正相关和负相关。优化的G93在实验开始时很好地捕获了异戊二烯排放的温度和光依赖性；然而，在实验持续时间的后期，其性能显着下降，尤其是下降阶段。通过对上升和下降阶段的单独优化，成功地改善了这一点，强调了配方参数优化和模型改进的重要性。这些结果对异戊二烯排放算法的改进具有重要意义。特别是在未来全球气温上升的预测下。
