重庆市主城区大气PM_（10）来源的混合模型解析研究
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摘要
可吸入颗粒物质(PM10)是衡量大气质量的重要指标,也是大气环境中危害人类健康的主要物质。因此,摸清PM10的来源和定量解析出各污染源的贡献比率(分担率),即对PM10进行来源解析研究,对制定切实可行的PM10控制措施具有重要的指导意义。
为了给重庆市防尘工作提供科学依据和技术支持,本文对重庆市主城大气PM10来源进行解析研究;在解析过程中,采用因子分析法(FA)确定主要污染源,将化学质量平衡(CMB)模型和遗传算法(GA)模型混合成为一种混合算法模型应用于来源解析上,为来源解析技术方法的优化提供一些参考。主要得出如下结论:
①重庆市主城PM10的质量浓度有着明显的季节性和区域性变化特征:各采样点冬季PM10的浓度均要高于夏季,PM10的浓度分布遵循工业综合区>商业区>学校和住宅区>旅游风景区的变化规律。
②重庆市主城PM10中各组分的质量浓度有着较为明显的季节性和区域性变化特征:冬季PM10中各组分浓度高于夏季,其中无机元素中Si、S和Al,碳组分中的OC以及水溶性离子组分中的SO42-、NO3-和NH4+升幅较为明显;区域性分布上一般工商业发达、人口集中、交通密集的地区的组分浓度较高。
③夏冬两季主城区PM10的OC/EC平均值达到了5.53,其中冬季OC/EC的比值要高于夏季,说明重庆市主城的次生有机碳(SOC)的污染较为严重,采用最小比值法对重庆市主城SOC的浓度进行估算,结果表明重庆主城夏冬两季SOC浓度的平均值达到10.56μg/m3,占OC的59.87%。NO3-/SO42-的比值很低,说明重庆市PM10中水溶性离子污染主要来自于固定污染源。NH4+、NO3-和SO42-三者有着较好的正相关性,而且从回归方程得出,重庆市大气颗粒物中小颗粒物中主要的物种可能为NH4HSO4、(NH4)2SO4、NH4NO3等。
④采用因子分析法将重庆市主城大气PM10的主要来源归纳为二次粒子、扬尘、建筑材料尘、钢铁尘和交通尘5个污染因子,并初步解析出它们对PM10的相对贡献率分别为46.59%、29.32%、8.02%、11.41%和5.16%。结合实际管理需要最终将重庆市主城大气PM10的主要污染源确定为扬尘(施工扬尘和道路扬尘)、建筑材料尘、燃煤烟尘、机动车尾气尘、钢铁冶炼尘和二次粒子(二次硫酸盐、二次硝酸盐和次生有机碳),进一步完善了PM10污染源的分类体系。
⑤通过引入实际数据进行运算,并使用目前广泛使用的CMB模型进行了验证,结果证明,采用的混合模型算法在来源解析的应用上具有良好的精确性,能够解决一定程度的源类共线性问题。
⑥采用混合模型对重庆市主城大气PM10的主要来源进行解析,结果显示二次粒子、道路扬尘、建筑材料尘、施工扬尘、燃煤烟尘、机动车尾气尘和钢铁尘对重庆市主城PM10的分担率分别为34.45%,23.41%,10.74%,10.44%,8.45%,7.94%和6.12%。可见重庆市主城PM10主要受二次粒子、扬尘(道路扬尘和施工扬尘)、建筑材料尘的制约,其分担率之和达到79.04%,其中二次粒子中大部分来自于燃煤烟尘和机动车尾气尘排放物在空气中的二次反应,因此重庆市主城PM10防治工作的重点应放在扬尘、建筑尘、燃煤尘和机动车尾气尘的控制上。
Inhalable particulate matter (PM10) is an important index for atmospheric environmental quality; is also harmful for human health. So identifying the sources of PM10 and calculating the contribution rate of PM10, namely conducting the source apportionment of PM10, is of great significance for the establishment of the practical measures to control PM10.
The source apportionment of PM10 was conducted in Chongqing for providing scientific basis and technical support for the preventing dust work of Chongqing. In the process, factor analysis (FA) model was used to identify the sources of PM10, and the hybrid model, which was created with the mix of chemical mass balance (CMB) model and genetic algorithm (GA) model, is used for souce apportionment to provide some references for the technical methods. The main conclusions are as follows:
(1) The mass concentrations of PM10 in Chongqing were characteristic of seasonal and regional variety. The concentration of PM10 every sampling site in winter was higher than in summer. The order of concentrations from high to low was: Intergrated Industrial Parks, Sowntowns, Residential Areas and Tourist Districts.
(2) The component mass concentrations of PM10 were characteristic of seasonal and regional variety. The component concentrations of PM10 in winter were higher than summer, especially the increase of Si, S and Al in inorganic elements, organic carbon and SO42-, NO3- and NH4+ in water-solubility ions were more obvious. In the developed areas that have lots of industries, commerces, population and traffics, the component concentrations of PM10 were generally higher than in other areas.
(3) The average value of OC/EC was 5.53, which in winter was higher than in summer. This showed that the pollution of secondary organic carbon (SOC) was serious. The concentration of SOC was 10.56μg/m3 and 59.87% of OC, which was calculated by the minimum method. The value of NO3-/SO42- was low, and it showed that the water-solubility ions mainly came from stationary pollution source. There was good correlation among NH4+, NO3- and SO42-. According to the linear regression equations, the main components of the atmospheric small particles were NH4HSO4, (NH4)2SO4, NH4NO3, and so on.
(4) Secondary particles, fugitive dust, cement dust, steel dust and traffic dust the five pollution factors of PM10 in Chongqing were identified by FA model, and their relative contribution rates were 46.59%, 29.32%, 8.02%, 11.41% and 5.16%. At last the main pollution sources of PM10 in Chongqing were identified as fugitive dust (including soil dust and road dust), cement dust, coal smoke dust, mobile exhaust gas dust, steel dust and secondary particles (including secondary sulfate, secondary nitrate and secondary organic carbon) to perfect the pollution sources classification system of PM10.
(5) According to the practical database operation and the test used by CMB model, the hybrid model had good accuracy, and could solve the problem of excessive source collinearity to a certain extent with its analytical results reliable.
(6) The contribution rates of secondary particles, road dust, cement dust, soil dust, coal smoke dust, mobile exhaust gas dust and steel dust in Chongqing were 34.45%,23.41%,10.74%,10.44%,8.45%,7.94%和6.12% calculated by the hybrid model. It showed that the pollution of PM10 was mainly affected by secondary particulates, fugitive dust and cement dust with the sum of their contribution rates 79.04%. Most of secondary come from the atmospheric secondary reaction conducted by emissions of coal smoke dust and mobile exhaust gas dust, so the PM10 provention and control work should focus on the control of fugitive dust, construction dust, burning coal dust and mobile mobile exhaust gas dust.
为了给重庆市防尘工作提供科学依据和技术支持,本文对重庆市主城大气PM10来源进行解析研究;在解析过程中,采用因子分析法(FA)确定主要污染源,将化学质量平衡(CMB)模型和遗传算法(GA)模型混合成为一种混合算法模型应用于来源解析上,为来源解析技术方法的优化提供一些参考。主要得出如下结论:
①重庆市主城PM10的质量浓度有着明显的季节性和区域性变化特征:各采样点冬季PM10的浓度均要高于夏季,PM10的浓度分布遵循工业综合区>商业区>学校和住宅区>旅游风景区的变化规律。
②重庆市主城PM10中各组分的质量浓度有着较为明显的季节性和区域性变化特征:冬季PM10中各组分浓度高于夏季,其中无机元素中Si、S和Al,碳组分中的OC以及水溶性离子组分中的SO42-、NO3-和NH4+升幅较为明显;区域性分布上一般工商业发达、人口集中、交通密集的地区的组分浓度较高。
③夏冬两季主城区PM10的OC/EC平均值达到了5.53,其中冬季OC/EC的比值要高于夏季,说明重庆市主城的次生有机碳(SOC)的污染较为严重,采用最小比值法对重庆市主城SOC的浓度进行估算,结果表明重庆主城夏冬两季SOC浓度的平均值达到10.56μg/m3,占OC的59.87%。NO3-/SO42-的比值很低,说明重庆市PM10中水溶性离子污染主要来自于固定污染源。NH4+、NO3-和SO42-三者有着较好的正相关性,而且从回归方程得出,重庆市大气颗粒物中小颗粒物中主要的物种可能为NH4HSO4、(NH4)2SO4、NH4NO3等。
④采用因子分析法将重庆市主城大气PM10的主要来源归纳为二次粒子、扬尘、建筑材料尘、钢铁尘和交通尘5个污染因子,并初步解析出它们对PM10的相对贡献率分别为46.59%、29.32%、8.02%、11.41%和5.16%。结合实际管理需要最终将重庆市主城大气PM10的主要污染源确定为扬尘(施工扬尘和道路扬尘)、建筑材料尘、燃煤烟尘、机动车尾气尘、钢铁冶炼尘和二次粒子(二次硫酸盐、二次硝酸盐和次生有机碳),进一步完善了PM10污染源的分类体系。
⑤通过引入实际数据进行运算,并使用目前广泛使用的CMB模型进行了验证,结果证明,采用的混合模型算法在来源解析的应用上具有良好的精确性,能够解决一定程度的源类共线性问题。
⑥采用混合模型对重庆市主城大气PM10的主要来源进行解析,结果显示二次粒子、道路扬尘、建筑材料尘、施工扬尘、燃煤烟尘、机动车尾气尘和钢铁尘对重庆市主城PM10的分担率分别为34.45%,23.41%,10.74%,10.44%,8.45%,7.94%和6.12%。可见重庆市主城PM10主要受二次粒子、扬尘(道路扬尘和施工扬尘)、建筑材料尘的制约,其分担率之和达到79.04%,其中二次粒子中大部分来自于燃煤烟尘和机动车尾气尘排放物在空气中的二次反应,因此重庆市主城PM10防治工作的重点应放在扬尘、建筑尘、燃煤尘和机动车尾气尘的控制上。
Inhalable particulate matter (PM10) is an important index for atmospheric environmental quality; is also harmful for human health. So identifying the sources of PM10 and calculating the contribution rate of PM10, namely conducting the source apportionment of PM10, is of great significance for the establishment of the practical measures to control PM10.
The source apportionment of PM10 was conducted in Chongqing for providing scientific basis and technical support for the preventing dust work of Chongqing. In the process, factor analysis (FA) model was used to identify the sources of PM10, and the hybrid model, which was created with the mix of chemical mass balance (CMB) model and genetic algorithm (GA) model, is used for souce apportionment to provide some references for the technical methods. The main conclusions are as follows:
(1) The mass concentrations of PM10 in Chongqing were characteristic of seasonal and regional variety. The concentration of PM10 every sampling site in winter was higher than in summer. The order of concentrations from high to low was: Intergrated Industrial Parks, Sowntowns, Residential Areas and Tourist Districts.
(2) The component mass concentrations of PM10 were characteristic of seasonal and regional variety. The component concentrations of PM10 in winter were higher than summer, especially the increase of Si, S and Al in inorganic elements, organic carbon and SO42-, NO3- and NH4+ in water-solubility ions were more obvious. In the developed areas that have lots of industries, commerces, population and traffics, the component concentrations of PM10 were generally higher than in other areas.
(3) The average value of OC/EC was 5.53, which in winter was higher than in summer. This showed that the pollution of secondary organic carbon (SOC) was serious. The concentration of SOC was 10.56μg/m3 and 59.87% of OC, which was calculated by the minimum method. The value of NO3-/SO42- was low, and it showed that the water-solubility ions mainly came from stationary pollution source. There was good correlation among NH4+, NO3- and SO42-. According to the linear regression equations, the main components of the atmospheric small particles were NH4HSO4, (NH4)2SO4, NH4NO3, and so on.
(4) Secondary particles, fugitive dust, cement dust, steel dust and traffic dust the five pollution factors of PM10 in Chongqing were identified by FA model, and their relative contribution rates were 46.59%, 29.32%, 8.02%, 11.41% and 5.16%. At last the main pollution sources of PM10 in Chongqing were identified as fugitive dust (including soil dust and road dust), cement dust, coal smoke dust, mobile exhaust gas dust, steel dust and secondary particles (including secondary sulfate, secondary nitrate and secondary organic carbon) to perfect the pollution sources classification system of PM10.
(5) According to the practical database operation and the test used by CMB model, the hybrid model had good accuracy, and could solve the problem of excessive source collinearity to a certain extent with its analytical results reliable.
(6) The contribution rates of secondary particles, road dust, cement dust, soil dust, coal smoke dust, mobile exhaust gas dust and steel dust in Chongqing were 34.45%,23.41%,10.74%,10.44%,8.45%,7.94%和6.12% calculated by the hybrid model. It showed that the pollution of PM10 was mainly affected by secondary particulates, fugitive dust and cement dust with the sum of their contribution rates 79.04%. Most of secondary come from the atmospheric secondary reaction conducted by emissions of coal smoke dust and mobile exhaust gas dust, so the PM10 provention and control work should focus on the control of fugitive dust, construction dust, burning coal dust and mobile mobile exhaust gas dust.
引文
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