Mills G, Ball G, Hayes F, Fuhrer J, Skärby L, Gimeno B, De Temmerman L, Heagle A
Institute of Terrestrial Ecology-Bangor Research Unit, Deiniol Road, Bangor LL57 2UP, UK.
Environ Pollut. 2000 Sep;109(3):533-42. doi: 10.1016/s0269-7491(00)00057-9.
Results are presented from the UN/ECE ICP Vegetation (International Cooperative Programme on effects of air pollution on natural vegetation and crops) experiments in which ozone(O(3))-resistant (NC-R) and -sensitive (NC-S) clones of white clover (Trifolium repens cv. Regal) were exposed to ambient O(3) episodes at 14 sites in eight European countries in 1996, 1997 and 1998. The plants were grown according to a standard protocol, and the forage was harvested every 28 days for 4-5 months per year by excision 7 cm above the soil surface. Biomass ratio (NC-S/NC-R) was related to the climatic and pollutant conditions at each site using multiple linear regression (MLR) and artificial neural networks (ANNs). Twenty-one input parameters [e.g. AOT40, 7-h mean O(3) concentration, daylight vapour pressure deficit (VPD), daily maximum temperature] were considered individually and in combination with the aim of developing a model with high r(2) and simple structure that could be used to predict biomass change in white clover. MLR models were generally more complex, and performed less well for unseen data than non-linear ANN models. The ANN model with the best performance had five inputs with an r(2) value of 0.84 for the training data, and 0.71 for previously unseen data. Two inputs to the model described the O(3) conditions (AOT40 and 24-h mean for O(3)), two described temperature (daylight mean and 24-h mean temperature), and the fifth input appeared to be differentiating between semi-urban and rural sites (NO concentration at 17:00). Neither VPD nor harvest interval was an important component of the model. The model predicted that a 5% reduction in biomass ratio was associated with AOT40s in the range 0.9-1.7 ppm x h (microl l(-1) h) accumulated over 28 days, with plants being most sensitive in conditions of low NO(x), medium-range temperature, and high 24-h mean O(3) concentration.
本文展示了联合国欧洲经济委员会国际空气污染对自然植被和农作物影响合作计划（UN/ECE ICP Vegetation）的实验结果。在该实验中，1996年、1997年和1998年，将耐臭氧（NC-R）和对臭氧敏感（NC-S）的白三叶草（Trifolium repens cv. Regal）克隆植株置于欧洲8个国家14个地点的环境臭氧暴露环境中。植株按照标准方案种植，每年4 - 5个月，每隔28天在土壤表面上方7厘米处收割草料。使用多元线性回归（MLR）和人工神经网络（ANNs）将生物量比率（NC-S/NC-R）与每个地点的气候和污染物条件相关联。单独或组合考虑了21个输入参数[例如，AOT40、7小时平均臭氧浓度、日光蒸气压亏缺（VPD）、日最高温度]，目的是开发一个具有高r²且结构简单的模型，可用于预测白三叶草生物量变化。MLR模型通常更复杂，对于未见过的数据，其表现不如非线性ANN模型。性能最佳的ANN模型有5个输入，训练数据的r²值为0.84，之前未见过的数据的r²值为0.71。该模型的两个输入描述臭氧条件（AOT40和臭氧24小时平均浓度），两个描述温度（日光平均温度和24小时平均温度），第五个输入似乎用于区分半城市和农村地点（17:00时的NO浓度）。VPD和收割间隔都不是该模型的重要组成部分。该模型预测，生物量比率降低5%与28天内累积的AOT40在0.9 - 1.7 ppm·h（微升⁻¹·小时）范围内相关，在低NOₓ、中等温度范围和高24小时平均臭氧浓度条件下，植株最为敏感。
