﻿ 多波段激光雷达颗粒物质量浓度探测方法

*通讯联系人 e-mail: tingyao.he@xaut.edu.cn

Remote Sensing of Particle Mass Concentration Using Multi-Wavelength Lidar
RAO Zhi-min, HE Ting-yao*, HUA Deng-xin, CHEN Ruo-xi
School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Abstract

A novel method for particulate matter mass concentration measurement has been proposed based on a multi-wavelength lidar covering the ultraviolet to the near infrared spectra. The proposed method combined extinction coefficients at working wavelengths with quantity of mass extinction efficiency (MEE), which is defined as the ratio of extinction coefficient and mass concentration of particulate matter in unit volume, makes the mass concentration of particulate matter retrievable. To determine the values of the MEE, a mathematical model was developed based on the extinction efficiency data of certain wavelength reported with the Mie theory and the particle size distribution data derived from a multi-wavelength lidar. Retrieved results of mass concentration from two experimental cases with clear and fog/haze weather conditions, which s in line with the monitoring results at the ground level reported by the Environmental Agency. The proposed method invert the recent problem on aerosol sources monitoring and open new lidar capabilities on atmospheric research.

Keyword: Aerosol; Multi-wavelength lidar; Mass extinction efficiency; Particulate matter mass concentration

1 理论分析
1.1 多波段激光雷达系统

 Figure Option 图1 多波段激光雷达结构示意图Fig.1 Schematic of the multi-wavelength lidar system

1.2 颗粒物质量浓度反演算法流程

 Figure Option 图2 颗粒物质量浓度反演算法流程图Fig.2 Calculation steps for inversion of particulate matter mass concentration

1.3 消光效率的计算

$Qi=σiπr2(1)$

$Qext=2α2∑n=1∞(2n+1)Re(an+bn)(2)$

 Figure Option 图3 消光效率随粒子半径变化规律Fig.3 Changes of extinction efficiency with the particle radius

1.4 气溶胶粒子谱分布反演

$gp(λ)=∫rminrmax34rQext(r, λ, m)dvdrdr(3)$

$Kp(r, λ, m)=34rQext(r, λ, m)(4)$

$gp(λ)=∫rminrmaxKp(r, λ, m)dvdrdr(5)$

$dvdr=∑nwnBn(r)+ε(r)(6)$

$dvdr=∑nwnBn(r)(7)$

$gp(λ)=∑nApn(m)wn(8)$

$Apn(m)=∫rminrmaxKp(r, m)Bn(r)dr(9)$

$g=Aw(10)$

$w=A-1g(11)$

$wn=(ATA+γH)-1ATg(12)$

1.5 颗粒物质量浓度计算

$MEE=π∫rminrmaxr2Qext(r, λ, m)n(r)dr43πρ∫rminrmaxr3n(r)dr(13)$

$C(z)=σ(z)MEE g·m-3(14)$

2 实验验证

2.1 晴朗天气

2014年7月2日西安地区天气晴朗, 根据西安市环境保护局公布的数据, 该日空气质量二级(良), 地表观测的PM10和PM2.5颗粒物质量浓度日平均值分别为81和37 μ g· m-3。 采用传统的Klett和Fernald法[19, 20]反演多波段激光雷达回波信号, 得到355, 532和1 064 nm三个不同波段的气溶胶消光系数和后向散射系数随高度变化曲线如图4所示。 从图中可以看出, 消光系数值较小, 随着高度的增加而减小, 且随波长的增大而减小。

 Figure Option 图4 2014年7月2日观测的西安上空气溶胶消光(a)与后向散射(b)系数随高度变化廓线Fig.4 Vertical profiles of aerosol extinction coefficient (a) and backscatter coefficient (b) performed at Xi’ an on 2 July 2014

 Figure Option 图5 1.0, 1.5以及3.0 km三个不同高度处的气溶胶粒子体积谱分布Fig.5 Particle size distributions at different heights of 1.0, 1.5 and 3.0 km respectively

 Figure Option 图6 2014年7月2日晴朗天气条件下的质量消光效率和质量浓度廓线Fig.6 Vertical profiles of mass extinction efficiency (a) and mass concentration (b) on 2 July 2014

2.2 雾霾天气

2015年1月26日西安地区为雾霾天气, 根据西安市环境保护局公布的数据, 该日空气质量为五级(重度污染), 地表观测的PM10和PM2.5颗粒物质量浓度日平均值分别为331和244 μ g· m-3。 反演得到三波段的气溶胶消光系数和后向散射系数随高度变化曲线如图7所示。 从图中可以看出, 由于雾霾天气颗粒物的浓度较大, 气溶胶的光学参量值都非常大, 信号存在较大衰减, 探测高度明显降低。

 Figure Option 图7 2015年1月26日观测的西安上空气溶胶消光与后向散射系数随高度变化廓线Fig.7 Vertical profiles of aerosol extinction coefficient and backscatter coefficient performed at Xi’ an on 26 January 2015

 Figure Option 图8 1.0, 1.47以及2.1 km三个不同高度处的气溶胶粒子体积谱分布Fig.8 Particle size distributions at different heights of 1.0, 1.47 and 2.1 km respectively

 Figure Option 图9 2015年1月26日雾霾天气条件下的质量消光效率和质量浓度廓线Fig.9 Vertical profiles of mass extinction efficiency (a) and mass concentration (b) on 26 January 2015

3 结 论

The authors have declared that no competing interests exist.

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