石油类污染物是造成雾霾等空气污染问题的重要原因。去噪处理的有效性是石油类污染物荧光光谱检测中的热点问题。提出一种基于经验模态分解-提升小波变换(EMD-LWT)相结合的低浓度石油类污染物荧光光谱去噪方法。经验模态分解法（EMD)可自适应地滤除微弱荧光信号中的噪声，但去噪过程中第一个本征模态函数（IMF)包含的频率范围过宽，影响了去噪准确性和有效性。引入提升小波变换（LWT）对IMF1实现更精细的分解，有效分离出IMF1的有用信息，改善信噪分离效果。将EMD-LWT联用方法和传统的EMD或LWT去噪法分别运用于煤油荧光光谱检测中，仿真结果表明，与只用EMD或LWT相比，EMD-LWT相结合的光谱去噪法得到的信噪比和均方根误差均显著提高，验证了该方法的有效性和可行性。

The petroleum pollutant is an important factor causing air pollution problems such as haze. The de-noising effectiveness is the focus in petroleum pollutant detection by fluorescence spectroscopy. A fluorescence spectrum de-noising method for low concentration petroleum pollutants combining the empirical model decomposition (EMD) and the lifting wavelet transform (LWT) is proposed. The EMD method can filter the noise in weak fluorescence signal adaptively, but the first intrinsic mode function (IMF) contains a too wide frequency range, and thus the de-noising accuracy and effectiveness is reduced. LWT is introduced to realize more precise decomposition of IMF1, extract more useful information from IMF1, and improve separation effect of signal and noise. The three de-noising methods, EMD-LWT, EMD and LWT, are applied to kerosene fluorescence spectrum detection, respectively. The simulation results show that the EMD-LWT method makes the signal-to-noise ratio，root mean square error significantly improved compared with only EMD or LWT used, verifying the effectiveness and feasibility of the proposed method.