基于后向轨迹模式的北京市PM_(2.5)来源分布及传输特征探讨

近年来中国经历了数次大范围雾霾天气,北京等多个城市更是遭遇连续雾霾。造成雾霾天气的主要污染物PM_(2.5)又称细颗粒物。为了进一步治理北京雾霾,为制定政策提供依据,须了解北京地区PM_(2.5)的来源。本文基于后向轨迹模式并结合PM2.5浓度计算了2015年9月1日0:00至2016年8月31日23:00以北京为起始点,向后推算48小时的轨迹,并结合轨迹聚类分析法、潜在源贡献因子法(PSCF)、浓度权重轨迹分析法(CWT)等,探讨北京地区PM_(2.5)的来源。结果表明:模拟的后向轨迹经过聚类分析可分为6类,其中来自内蒙古西部的轨迹最多,来自西北、北西北方向的轨迹次之,来自西西北方向且在京津冀地区停留一段时间的轨迹占比最小,来自河北、山东、河南的交接地区及河北的沿海地区的轨迹占比也较小。其中来自内蒙古西部地区及河北、山东、河南交界地区的两类轨迹对北京的空气质量有较大的影响,是北京PM_(2.5)污染的主要潜在源区;来自北西北方向及河北的沿海地区两类轨迹的气团最为清洁,为北京带来良好的天气;来自西北及西西北方向的部分轨迹对应的PM_(2.5)浓度严重超标,说明来自此方向的气团对北京的空气质量也有一定的影响。     

社交媒体的环境关注能改善空气质量吗？

随着移动互联网的快速发展,社交媒体日益成为公众表达对重污染天气等环境问题关注的工具。本研究以微博为例,探讨了社交媒体反映的环境关注对城市PM_2.5浓度水平的影响以及内在机制。研究发现,微博上环境关注的空间分布显示出向高等级和高污染城市聚集的倾向,PM_2.5浓度水平高的地区集中在煤炭资源或重工业集聚的地区。空间回归模型的结果证实了社交媒体的环境关注能显著降低PM_2.5浓度水平,层级较高的城市及创新能力较强的城市能够更好地回应社交媒体的环境关注,并有效降低城市的PM_2.5浓度水平。本研究表明,社交媒体的环境关注已经成为环境治理中的一种重要力量,在分析环境问题时应该充分考虑新兴社交媒体平台的影响。     

深圳大气污染特征模拟及环境容量估算

本文根据深圳市8 个监测站点2013 年的逐日PM₁₀ 和PM_2.5 浓度监测数据、气象数据,统计风向、风速、稳定度联合频率等,利用污染物在大气中输送扩散模式,由实测的浓度值反推出污染物的产生量或排放量的方法,重点分析龙华新区PM₁₀ 和PM_2.5 的污染特征,并依据环境目标值,估算该区域剩余环境容量。研究结果表明,龙华新区全年盛行东风、南风,其风频分别为16.7%、13.2%,风速约为1.6m/s,PM₁₀、PM_2.5 浓度均呈现出季节性变化,秋、冬季浓度值较高,尤其在10 月到次年1 月份,其排放强度主要受本地污染源的影响。除此以外,其西、北部的污染源对其污染物浓度有一定的影响。新区PM₁₀ 和PM_2.5 的剩余环境容量均呈现负值,尤其以PM_2.5 最为突出,须大力加强减排控制,以达到环境目标值     

我国大气环境管理历程与展望

随着大气环境问题从煤烟型向以PM_2.5和O₃为特征的区域复合型污染演变，我国大气污染控制模式从以污染物排放浓度控制为核心、以污染物排放总量控制为核心逐渐走向以大气环境质量改善为核心。特别是近几年，全国各地在空气质量管理、科学精准治污等领域开展一系列积极的探索与实践，取得了显著成效。本文系统回顾了近50年来我国不同阶段大气环境管理工作的特点，重点梳理了2013年《大气污染防治行动计划》实施以来，空气质量管理经验与成绩，结合减污降碳总体部署对我国2035年“美丽中国建设目标基本实现”时的空气质量进行了展望，从PM_2.5与O₃协同控制的角度出发，提出了“十四五”期间我国大气环境管理的总体思路。     

浙江省火电行业主要污染物减排环境效益研究

利用美国环保局开发的新一代空气质量模式系统Models-3,对2010 年浙江省大气污染物进行数值模拟研究,并对SO₂、NO₂ 和PM₁₀ 等大气污染物浓度的模拟结果与监测结果进行了对比分析。结果表明,Models-3 系统能较好地反映浙江省的大气污染情况,模拟值与观测值的变化趋势具有较好的一致性,说明该模式能较好地进行大气污染物的分布特征和变化规律的研究。为定量分析电力行业多污染物协同控制与区域复合型大气污染之间的定量关系,评估不同控制情景下的环境质量效益,应用CMAQ 空气质量模型分别对2010 年基准排放情景和火电行业主要污染物(SO₂、NO_x 和烟尘)的三套减排控制情景,评价环境空气污染物SO₂、NO_x、PM_2.5 和PM₁₀ 状况进行模拟,评价污染物减排的环境效益。三套方案下PM_2.5 浓度下降比例分别为0.53%、0.55% 和0.57%,其中重点区域浓度下降比例分别为4.36%、4.38%和5.9%;PM₁₀ 浓度下降比例分别为0.40%、0.42% 和0.65%,重点区域下降比例分别为3.42%、5.82% 和8.16%;NO_x 浓度下降比例分别为12.4% 和13.3%,重点区域下降比例分别为13.8% 和16.9%;SO₂ 下降比例平均为11.24%,其中重点区域下降比例为14.4%。SO₂、NO_x 和烟(粉)尘协同减排对于浙江地区特别是重点区域的环境空气质量改善有着显著意义。     

国家《大气污染防治行动计划》健康效益评估

为了提高空气质量改善进程,2013 年国家发布了《大气污染防治行动计划》。本文基于空气污染与健康效益评估模型(BenMAP),对人口分布资料、大气污染与人体健康影响的暴露反应关系等进行了本土化修正,采用“支付意愿法”与“疾病成本法”相结合的方法,系统评估了《大气污染防治行动计划》实施后,PM_2.5 污染变化引起的环境健康效益。研究结果表明,《大气污染防治行动计划》的实施将在一定程度上降低PM_2.5 环境浓度,改善环境空气质量。如果《大气污染防治行动计划》空气质量目标全面实现,可以避免城镇8.9 万居民的过早死亡,减少12 万人次住院治疗以及941 万人次的门诊和急诊病例,实现的全国健康效益约为867 亿元/ 年,说明了《大气污染防治行动计划》实施的健康有益性。本文对政府部门开展污染损失评估及制定环境健康政策具有重要的参考价值。     

利用HYSPLIT分析沈阳市大气污染物时空变化特征

为了解沈阳市大气污染物传输路径和浓度变化规律，利用NCEP气象再分析资料与HYSPLIT模式结合，计算了不同季节抵达沈阳市的气团轨迹，并通过聚类分析方法得到每个季度气团轨迹的代表性传输路径，并将其与对应污染物浓度数值结合。结果表明：夏季，偏南方向的轨迹1气团对应O₃浓度最高，为90.59μg/m³，而来自西北方向的轨迹2气团，其AQI数值较低，为42.59，显示较清洁的空气质量。冬季，来自北偏西方向的轨迹2气团，其PM₁₀浓度最高，为97.28μg/m³，而来自北偏西方向的轨迹3气团，其AQI数值最高，达到100.5，表明污染较为严重。除O₃外，冬季各项污染物平均浓度均高于其他季节。     

大气传输对连云港城区细颗粒物浓度的影响

本研究基于HYSPLIT后向轨迹模式,结合GDAS气象数据和空气质量数据,利用聚类分析、潜在源分析及浓度权重轨迹分析等多种手段探讨2015年连云港市不同季节气流轨迹特征及PM_(2.5)的潜在来源。结果显示,连云港气流轨迹季节变化特征明显,秋冬气流轨迹主要以西北气流为主,春夏季以东南和西南气流轨迹为主。连云港PM_(2.5)输送方向则以西北方向长距离传输和周边省市的近距离传输为主,相对清洁的轨迹主要受到海洋性气团的影响。潜在源及浓度权重轨迹分析结果表明,污染物主要沿西北和西南路径影响连云港PM_(2.5)浓度。连云港春季潜在源区集中在江苏、山东全境、安徽、浙江、河南大部分地区、山西、河北西南地区,夏季潜在源区主要集中在江苏、安徽、江西及湖南等区域,秋季集中在河北、山东、河南、安徽中北部区域及湖北中部区域,冬季集中在以山东为中心,包含河北、河南、安徽北部、江苏等在内的区域,以及上海、浙北及湖北东等多个地区。     

后向轨迹模式在攀枝花市PM_(2.5)来源分析研究中的应用

利用后向轨迹模式计算2014年1月1日~2014年12月31日以攀枝花市为起始点的后向轨迹,并结合攀枝花市PM_(2.5)的实测浓度数据,通过聚类分析法、潜在源贡献因子法(PSCF)和浓度权重轨迹分析法(CWT)研究不同来源区域对攀枝花市PM_(2.5)浓度的贡献影响。结果表明:攀枝花市的西南区域与云南省的交界地区最有可能是攀枝花市PM_(2.5)的贡献源区。攀枝花市西南区域本地排放的PM_(2.5)污染物及其前体物和来自攀枝花市西南方向的气团对攀枝花市的PM_(2.5)浓度影响最大。     

基于产业链分析的长三角地区CO2与大气污染物排放研究

长三角地区作为我国大气污染较为严重区域之一,如何在保持经济增长的同时减少CO₂与大气污染物的排放已成为一个重要挑战。本研究基于2007年与2012年长三角区域间投入产出表,定量分析了长三角地区省市间贸易引致的二氧化碳和大气污染物排放转移特征和变化趋势。同时,运用产业关联系数法,从前向关联与后向关联双重视角分析了长三角地区减缓CO₂和大气污染物排放的关键行业。研究结果表明,长三角的SO₂、PM_2.5排放总量表现为消费端大于生产端,CO₂、NO_x排放总量表现为生产端大于消费端。安徽省总体呈现为长三角地区贸易的SO₂、NO_x与PM_2.5排放净调出地,而上海与浙江表现为多数污染物排放净调入地。CO₂与大气污染物协同前向减排的关键行业为江苏省、浙江省和安徽省的电力、热力的生产和供应业,安徽省的煤炭开采和洗选业等,可以通过生产端技术革新和能源结构优化来促进减排;CO₂与大气污染物后向协同减排的关键行业为江苏省、浙江省和安徽省的建筑业等,对于这些行业,调整消费结构是有效的减排措施。为更好地制定长三角地区减排与污染防治政策,应当综合考虑行业减排、协同减排等,以确保经济持续增长的同时达到减排目标。     

The direct influence of ship traffic on atmospheric PM2.5, PM10 and PAH in Venice

The direct influence of ship traffic on atmospheric levels of coarse and fine particulate matter (PM_2.5, PM₁₀) and fifteen polycyclic aromatic hydrocarbons (PAHs) has been estimated in the urban area of Venice. Data analysis has been performed on results collected at three sites over the summer, when ship traffic is at a maximum. Results indicate that monitoring of the PM daily concentrations is not sufficiently detailed for the evaluation of this contribution, even though it could be useful for specific markers such as PAHs. Therefore a new methodology, based on high temporal resolution measurements coupled with wind direction information and the database of ship passages of the Harbour Authority of Venice has been developed. The sampling sites were monitored with optical detectors (DustTrack^® and Mie pDR-1200) operating at a high temporal resolution (20s and 1s respectively) for PM_2.5 and PM₁₀. PAH in the particulate and gas phases were recovered from quartz fibre filters and polyurethane foam plugs using pressurised solvent extraction, the extracts were then analysed by gas chromatography- high-resolution mass spectrometry. Our results shows that the direct contribution of ships traffic to PAHs in the gas phase is 10% while the contribution to PM_2.5 and to PM₁₀ is from 1% up to 8%.     

中国煤炭消费对PM2.5污染的影响研究

国务院颁布的《大气污染防治行动计划》明确提出制定国家煤炭消费总量中长期控制目标,到2017年,煤炭占能源消费总量比重降低到65%以下,然而煤炭消费对PM_(2.5)污染的贡献到底多大,这是当前亟待研究的科学问题。为定量分析煤炭消费对我国PM_(2.5)污染的影响,本研究首先计算了2012年煤炭消费产生的大气污染物排量,然后利用CAMx空气质量模型,分别采用组分分析法和情景模拟法两种方法研究了煤炭消费对全国PM_(2.5)污染的影响。组分分析法研究表明,煤炭消费对全国PM_(2.5)年均浓度的贡献率约为61%,其中煤炭直接燃烧、煤炭相关行业的贡献率分别约为37%、24%;情景模拟法研究表明,煤炭消费对全国PM_(2.5)年均浓度的贡献率约为56%。因此,我国由于煤炭消费对全国PM_(2.5)年均浓度的贡献率为56%~61%。     

Temporal‐spatial variations of air pollutants in Lanzhou,Gansu Province,China, during the spring–summer periods, 2014–2016

In this article, we analyzed the mass concentrations of particulate matter 2.5 micrometers (µm) or less in size (PM_2.5), particulate matter 10 µm or less in size (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lanzhou, the capital of Gansu province, China. We analyzed monitoring data collected from five air quality monitoring stations during the spring–summer period from 2014 to 2016. Our comparison of contaminant concentrations and average diurnal, daily, monthly, and annual concentrations revealed that the average concentrations of PM_2.5 and PM₁₀ amounted to 128.57 and 46.4 micrograms per cubic meters (µg/m³), respectively, exceeding the Chinese National Ambient Air Quality Standard (NAAQS). We used the Pearson correlation coefficient to establish connections between particulate matter and gaseous pollutants. The results show significant differences in the concentration levels of airborne pollutants. The Pearson correlation coefficient between PM_2.5 and PM₁₀ had the highest coefficient of r = 0.842. A correlation between the two particulate matter sizes (PM_2.5 and PM₁₀) and SO₂ was PM_2.5 and SO₂ r = 0.313; PM₁₀ and SO₂ r = 0.279; and CO and the two particulate matter sizes, PM_2.5 and CO r = 0.304; and PM₁₀ and CO r = 0.203. The average monthly ratio for the study months of PM_2.5 to PM₁₀ was 0.361. In addition, we used the hybrid single particle Lagrangian integrated trajectory model for tracking sources and pathways of the air pollutants in Lanzhou.     

长沙市空气自动站周边区域大气污染物排放源清单

以长沙市空气自动站周边3 km为研究对象,基于统计年鉴和实地调查,获得了该地区2015年储存运输源、废弃物处理源、工艺过程源、化石燃料固定燃烧源、农业源、生物质燃烧源、扬尘源、移动源8个源类的活动水平数据。以大气污染物排放源清单编制技术指南为依据,建立了2015年长沙市空气自动站周边3 km区域NH_3、NO_x、PM_(10)、PM_(2.5)、SO_2、VOCs等6项污染物的源排放清单。结果表明,2015年长沙空气自动站周边3 km内,8类大气污染源排放的NH_3、NO_x、PM_(2.5)、PM_(10)、SO_2、VOCs总量分别为53.65t、4 899.35t、1 846.09t、6 257.75t、989.49t、4 383.31t。NH_3、NO_x、PM_(2.5)、PM_(10)、SO_2、VOCs排放量最大的源分别是农业源、移动源、扬尘源、扬尘源、化石燃料固定燃烧源和移动源,贡献率分别为98.45%、84.24%、60.82%、85.90%、97.33%、49.88%。优化道路交通、减少燃煤、减少建筑工地扬尘排放可促进长沙市空气自动站周边空气质量改善。     

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O₃), fine particulate matter (PM_2.5), and regional haze in the Southeastern United States. O₃ and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO₂, primary carbon (PC), NH₃, NO_x or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case.The sensitivity of summertime O₃ to VOC emissions is small in the Southeast and ground-level NO_x controls are generally more beneficial than elevated NO_x controls (per unit mass of emissions reduced). SO₂ emission reduction is the most beneficial control strategy in reducing summertime PM_2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO₂. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH₃ emissions is an effective strategy to reduce wintertime ammonium nitrate (NO₃NH₄) levels and improve visibility; NO_x emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.     

Development Potentials and Policy Options of Biomass in China

Biomass, one of the most important renewable energies, is playing and will continue to play an important role in the future energy structure of the world. This article aims to analyze the position and role, assess the resource availability, discuss the geographic distribution, market scale and industry development, and present the policy options of biomass in China. The resource availability and geographical distribution of biomass byproducts are assessed in terms of crop residues, manure, forest and wood biomass byproducts, municipal waste and wastewater. The position of biomass use for power generation is just next to hydropower among types of renewable energy in China. The potential quantity of all biomass byproducts energy in 2004 is 3511 Mtce (Mtce is the abbreviation of million tons of coal equivalents and 1 Mtce is equal to10⁶ tce.), while the acquirable quantity is 460 Mtce. Biomass energy plays a critical role in rural regions of China. The geographical distribution and quantity of biomass byproducts resources depends mainly on the relationship between ecological zones and climate conditions. Our estimation shows that the total quantity of crop residues, manure, forest and wood biomass byproducts, municipal waste and wastewater resources are 728, 3926, 2175, 155 and 48240 Mt (million tons), respectively. Crop residues come mainly from the provinces of Henan, Shandong, Heilongjiang, Jilin and Sichuan. All manure is mainly located in the provinces of Henan, Shandong, Sichuan, Hebei and Hunan. Forest and wood biomass byproducts are mainly produced in the provinces or autonomous regions of Tibet, Sichuan, Yunnan, Heilongjiang and Inner Mongolia, while most of municipal waste mainly comes from Guangdong, Shandong, Heilongjiang, Hubei and Jiangsu. Most of wastewater is largely discharged from advanced provinces like Guangdong, Jiangsu, Zhejiang, Shandong and Henan. Biomass byproducts’ energy distribution also varies from province to province in China. Based on the analysis of the market scale and industry development, the article argues that China’s biomass energy industry is still at a very early stage of development and that Feed-in Tariffs (FIT) might be the best policy option for China to promote its development of biomass energy. A successful enforcement of FIT in China needs some policy combination of special capital subsidies, R&D funding, tax incentives and pricing.     

Qualitative assessment on premature human mortality due to the emission of fine particulate matter from the Matarbari coal power plant

This study reports the probability of increased mortality of people within the political border of Bangladesh due to the emission of fine particulate matter with diameters of 2.5 microns or less (PM_2.5) from the Matarbari coal power plant (MCPP). A Gaussian plume dispersion model has been used for this estimation. The PM_2.5 emission rate data are unavailable as the construction of MCPP is still in its initial stage; therefore, the anticipated PM_2.5 emission rate has been estimated based on data from a number of coal‐fired power plants in India and China. To make this study more meaningful, two different emission rates have been considered representing the best‐case and worst‐case scenarios. In both cases, the intake fraction has been found to be 0.12×10^?2, and the value of relative risk varies between 1.134 and 1.374, respectively. Finally, it is estimated that approximately 11.5 million people inside Bangladesh will be exposed to the PM_2.5 emission from MCPP, and between 7,667 and 17,675 people will experience premature death every year.