A multi-year dataset of measurements of CO₂ concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO₂ concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO₂ concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r²=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO₂ concentrations. Sensitivity analysis of CO₂ concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO₂ concentrations reduction). For all these reasons, large-scale policies would be needed.

已分析了佛罗伦萨（意大利）市中心的CO ₂浓度，涡旋协方差通量和气象参数的多年数据集，以评估人为排放和气象在控制城市CO ₂浓度中的作用。后者与气温，风速，太阳辐射和显热通量呈负相关，与相对湿度和排放呈正相关。已经开发了线性和人工神经网络（ANN）模型，并已对3-h CO ₂浓度的短期模型进行了验证。ANN模型的效果更好，平均偏差为0.58 ppm，均方根误差在30 ppm以内，r ²= 0.49。通过自组织地图进行数据聚类，可以弄清排放量和气象参数在影响CO ₂浓度方面的作用。CO ₂浓度的敏感性分析表明，气象参数尤其是风速起着主要作用。这些结果突出表明：（i）应将当地城市规模的减排行动与实际和预期的气象条件更好地联系在一起；（ii）仅这些行动的作用有限（例如，减排20％将导致3％的CO ₂浓度减少）。由于所有这些原因，将需要大规模的政策。