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Environmental Effect Condition (Air Temperature) of Aerosols on Gross Primary Productivity of Vegetation
DOI: 10.12677/IJE.2020.92027, PDF, HTML, XML, 下载: 341  浏览: 1,479

Abstract: In this study, WRF Chem model is used to drive the reanalysis data of ECMWF, China mul-ti-resolution emission inventory sharing platform (MEIC) anthropogenic emission data is used as anthropogenic emission data, and North China Plain is used as the research area. The research time is April 6, 2016. A group of comparative tests is set up to simulate whether there is anthropogenic emission shadow or not. In response to the environment, the environmental factors with or without aerosol environment are brought into the vegetation photosynthesis model (VPM) to obtain the gross primary productivity (GPP) in both environments. The main result of this paper is that aerosol causes the average temperature to drop in most periods of the study area, and the average temperature to rise slightly in a small part of the study area due to the absorption of hot aerosol. From the analysis of regional scale, the relationship between aerosol environmental effect and aerosol is affected by downward radiation and aerosol species, which results in the temperature can’t change synchronously with PM 2.5 concentration, and the phenomenon of temperature rise appears in some regions. During the day, the average temperature decreased by 0.350%~2.667%. Its environmental effect led to a decrease of 0.03%~2.55% in the average GPP of the whole study area on April 6, 2016. From the regional scale analysis, although the aerosol environmental effect (air temperature) is mainly cooling, there is regional warming, resulting in the highest increase of GPP in some areas by more than 5%, but the GPP in the area itself is less than 1 g C m−2∙day−1, and its absolute value of change is very small. The absolute value of GPP decreased from 0.05 g C m−2∙day−1~0.2 g C m−2∙day−1 in Shandong Province, Jiangsu Province, Henan Province and Anhui Province, and increased from 0.05 g C m−2∙day−1~0.1 g C m−2∙day−1 in Northern Jiangsu Province.

1. 引言

2. 试验设计与研究区域介绍

3. 数据介绍

3.1. ECMWF数据

3.2. 人为源数据

3.3. MODIS数据

Table 1. MCD12Q1 classification scheme

4. 模型介绍

4.1. WRF-Chem介绍

WRF-Chem模式作为新一代中尺度预报模式，是在WRF模式基础上引进了化学模块，实现了气象模块和化学模块的完全在线耦合。该模式考虑了大气污染物的平流输送、湍流扩散、干湿沉降、气相化学、气溶胶形成和光解率等过程，可用于雾霾天气过程的数值模拟 [29]。

4.2. VPM模型介绍

VPM模型是基于光能利用率原理建立的植被生产力模型。该模型具有结构简单，模拟精度高的特点，目前已被广泛运用于区域和全球尺度生态系统生产力的估算研究中 [30] [31] [32]。VPM的驱动变量包含：遥感数据为驱动变量和涡度相关通量数据。模型的原理为，利用光合有效辖射(absorbed photosynthetic active radiation APAR)与植被吸收的光能利用率相乘获得GPP。植被冠层由非光合有效植被(nonphotosynthetic vegetation, NPV)和光合有效植被(photosynthetically active vegetation, PAV)两部分组成VPM模型，而相应地，冠层利用的PAR比例( ${\text{FPAR}}_{\text{canopy}}$ )也由两个部分组成：

${\text{FPAR}}_{\text{canopy}}={\text{FPAR}}_{\text{PAV}}+{\text{FPAR}}_{\text{NPV}}$ (4.1)

$\text{GPP}={\epsilon }_{g}×{\text{FPAR}}_{\text{PAV}}×\text{PAR}$ (4.2)

PAR为光合有效福射； ${\text{FPAR}}_{\text{PAV}}$ 为绿色叶片吸收光合有效辐射的比率。模型中光能利用率( ${\epsilon }_{g}$ )受温度限制因子( ${T}_{\text{scalar}}$ ) [33] 和最大光能利用率( ${\epsilon }_{0}$ )、水分限制因子( ${W}_{\text{scalar}}$ )以及物候( ${P}_{\text{scalar}}$ )的函数 [34] 影响。公式如下：

${\epsilon }_{g}={\epsilon }_{0}×{T}_{\text{scalar}}×{W}_{\text{scalar}}×{P}_{\text{scalar}}$ (4.3)

${T}_{\text{scalar}}=\frac{\left(T-{T}_{\mathrm{min}}\right)\left(T-{T}_{\mathrm{max}}\right)}{\left(T-{T}_{\mathrm{min}}\right)\left(T-{T}_{\mathrm{max}}\right)-{\left(T-{T}_{\text{opt}}\right)}^{2}}$ (4.4)

${W}_{\text{scalar}}$ 表示水分对光能利用率的影响并使用地表水指数(LSWI)进行计算。

${W}_{\text{scalar}}=\frac{1+\text{LSWI}}{1+{\text{LSWI}}_{\mathrm{max}}}$ (4.5)

$\text{FPAR}=\alpha ×\text{EVI}$ (4.6)

$\text{EVI}=2.5×\frac{{\rho }_{\text{nir}}-{\rho }_{\text{red}}}{{\rho }_{\text{nir}}+6×{\rho }_{\text{red}}-7.5×{\rho }_{\text{blue}}+1}$ (4.7)

$\text{LSWI}=\frac{{\rho }_{\text{nir}}-{\rho }_{\text{red}}}{{\rho }_{\text{nir}}+{\rho }_{\text{swir}}}$ (4.8)

Table 2. List of relevant parameters of different biomes

${P}_{\text{scalar}}$ 表征叶龄对光合作用的影响。叶片出现到充分舒展阶段的计算公式为：

${P}_{\text{scalar}}=\frac{1+\text{LSWI}}{2}$ (4.9)

${P}_{\text{scalar}}=1$ (4.10)

5. WRF-Chem模型模拟结果分析

1) 当地的下行短波辐射值高于其他地方，导致PM 2.5浓度相对高值区较低时也能引起较大的降温。

2) 不同种类气溶胶，对气温的影响不同。很多研究表明气溶胶对辐射的直接效应和间接效应影响不同，导致气温受到的影响纯在差异，已经证明不同种类的气溶胶种类如： ${\text{SO}}_{4}^{2-}$${\text{NO}}_{3}^{-}$ 和BC。 ${\text{SO}}_{4}^{2-}$${\text{NO}}_{3}^{-}$ 气溶胶属于散射型气溶胶，能够吸收和散射太阳辐射，从而产生降温效应。黑碳气溶胶作为重要的吸收性气溶胶，对太阳辐射有强烈吸收，从而加热大气。虽然这样能减少到达地表的短波辐射，但是地面气温还是会因为黑碳的吸收效应增加；黑碳气溶胶间接辐射强迫很小并且有时事增温有时事降温，具有很强的不确定性，这主要是由于云的反馈过程比较复杂造成的 [36] [37] [38] [39]。

Figure 1. PM 2.5 average concentration distribution on April 6

Figure 2. Average temperature distribution in daytime on April 6

6. 气溶胶环境效应对区域尺度GPP的影响

Figure 3. GPP in the study area without aerosol influence on April 6

Figure 4. GPP in the study area under the influence of aerosol on April 6

Figure 5. GPP difference in the presence or absence of aerosol on April 6

Figure 6. Percentage change of GPP under the influence of aerosol on April 6

7. 气溶胶环境效应对区域尺度GPP的影响

Figure 7. Air temperature difference in the presence or absence of aerosol on April 6

Figure 8. Percentage change of air temperature with or without aerosol on April 6

1) 从区域尺度分析，气溶胶模拟结果出现两个高值区域分别是北面的燕山山脉，西面为太行山脉沿线。其原因为山体的阻挡与疏导作用。此结果与龙鑫 [40] 等人的研究结果相一致。气溶胶环境效应与气溶胶的关系受下行辐射与气溶胶种类影响，导致气温不能随PM 2.5浓度同步变化，甚至在一些区域出现气温升高的现象。气溶胶环境效应(气温)变化会影响植被气孔导度，光合、呼吸、蒸腾等代谢过程。受气溶胶影响，白天平均气温均有所下降，下降范围为0.350%~2.667%。从而导致2016年4月6日整个研究区平均GPP下降0.03%~2.55%。

2) 气溶胶环境效应(气温)总体以降温为主，但存在区域性增温，导致部分地区GPP最高增加超过5%，但其地区本身GPP小于1 g C m−2∙day−1，其变化绝对值很小。GPP绝对值减少的地区位于山东省，江苏省，河南省，安徽省交界处，减少数值为0.05 g C m−2∙day−1~0.2 g C m−2∙day−1，增加绝对值在苏北地区增加数值为0.05 g C m−2∙day−1~0.1 g C m−2∙day−1