成都市空气质量状况研究

根据PM2.5数据网提供的成都市2013年9月到2014年10月的数据,对空气质量指数(AQI)和空气质量分指数(IAQI)进行定量化研究。结果表明,AQI的年均值为115,属于轻度污染,空气质量优良天数为187天,占全年的51%。四季空气质量状况由好到差是夏季秋季春季冬季。空气质量白天优于夜晚,O3最高值出现在15∶00时,其他污染物浓度最高出现在23∶00,16∶00—18∶00各种污染物的浓度较低,为运动的最佳时间。PM2.5和PM10的相关系数为0.975,其年平均值分别为82.33μg/m3和126.65μg/m3,在日均值样本中超标率分别达43%和30%,NO2超标率为9%,SO2和CO无超标。     

株洲市2013—2018年空气质量时空分布特征

利用2013—2018年株洲市空气自动监测站点PM2.5、PM10、O3、SO2、NO2和CO的逐日监测数据,分析该地区近6 a空气质量的时空变化特征。研究发现:株洲市空气质量整体显著性提高,2018年达标率达到78.9%,重污染日天数逐年降低且持续时间缩短。O3污染问题日益突出,尤其是5月—10月易发生超标。空气质量空间差异明显,郊区达标率较高,工业区和市中心达标率相对较低。污染物超标情况区域特征明显,工业区SO2和NO2超标天数相对较多,市中心O3超标日明显多于其他区域。     

降水对西宁市城市空气污染的影响分析

文章依据西宁市2001~2009年主要污染因子PM10、SO2、NO2浓度日数据,从月(或季年)平均值、有降水(微量、微量以上)平均值、无降水平均值出发,研究西宁市PM10、SO2、NO2在有降水、无降水等不同情况下月、季节和年度变化特征,同时分析降水对PM10、SO2、NO2浓度变化的影响和关系。统计分析结果表明:西宁市城市PM10、SO2、NO2无降水日浓度均值最大,月均值其次,有降水日浓度值最小。三者浓度值在夏秋季最低。SO2的浓度均值2006年出现最小值,最大值出现在2003年;NO2浓度均值2006~2009年间呈上升趋势;PM10浓度均值2002年最低,2007年达到最高。     

四川省大气污染空间分异特征研究

利用GIS空间统计工具分析四川省3项大气污染物SO2、NO2和PM10浓度的空间分异特征。污染物在东北-西南方向上分布最为分散,污染重心与几何中心偏离较多,不均衡性较大。全局来看,NO2和SO2浓度的空间自相关性较强,属于聚集模式;PM10浓度的空间自相关性较弱,属于随机分布模式。局部相关性分析表明,大部分地市的大气污染物之间相关性较弱,SO2和NO2具有相似的自相关分布,PM10局地自相关性与其他两项污染物的差异较大。通过分析2001~2010年间污染重心的变化轨迹,可知10年间污染重心向西南方向偏移,向低纬度、低经度方向移动,其中SO2偏移量最大。2001年和2010年浓度变化率分析,发现全省各地市PM10削减力度明显,SO2局部地区有较大的削减,NO2仅有23.81%的地市有所削减,整体削减程度较小。     

大庆市雾霾形成原因分析及其生态城市建设对策

以大庆市为研究对象,分析了2014—2016年大庆市环境空气质量特征及雾霾成因,并提出了生态城市建设对策。结果表明:2014—2016年大庆市SO2、NO2年均质量浓度均达到环境空气质量标准一级浓度限值,PM2.5浓度超过二级浓度限值3.8%~26.0%,2016年大庆市环境空气质量达标天数和重污染天数分别占全年的89.9%和1.9%。产业和能源结构不合理、大气氧化性强、外来源传输贡献等是造成大庆市雾霾的主要原因,因此建议从工业行业搬迁、推进节能技术进步等方面建设大庆生态城市。     

陕西省空气质量时空差异研究

通过2016年12月~2017年5月陕西省10个地级市6项常规大气污染物逐小时监测数据,分析陕西省冬春季大气污染现状和时空分布规律。结果表明:(1)冬季关中地区空气质量最差,陕南、陕北地区较好,进入春季后,关中地区空气质量有显著提高;(2)冬季延安市SO2浓度异常高于其它城市,各市冬春季大气污染以颗粒物污染为主;(3)关中城市全天颗粒物污染较重,温度变化对O3小时浓度变化影响最显著,各市O3污染高频时段集中在午后。建议关中地区应加强冬季大气颗粒型污染物浓度控制,延安市应加强对SO2污染物排放的监管。     

2017～2018年荆州城区空气质量分布及重污染天气特征分析

利用2017年1月至2018年12月荆州城区3个国家环境空气质量监测点的污染物监测数据和荆州国家气象站地面气象要素观测数据,采用统计学方法,对荆州城区空气质量分布及重污染过程天气特征进行了初步分析,结果表明:空气污染综合指数冬季最高,春秋季次之,夏季最低;PM2.5、PM10、 CO和NO2的浓度在上午和傍晚至上半夜其...     

气象因素对成都市主要大气污染物影响的非参数分析

通过获取2014年成都市大气污染物和气象因素的在线监测数据,分析大气污染物的时间变化规律,并采用Spearman秩相关系数来分析污染物逐日浓度和气象因素的相关性。结果显示,SO_2、NO_2、CO、PM_(10)和PM_(2.5)夏季浓度最低,冬季最高,O_3则夏季最高,冬季最低;受秸秆露天焚烧影响,5月各污染物浓度均出现一个小高峰。温度和风速均与O_3显著正相关,与其他污染物显著负相关;大气压则相反;相对湿度与各污染物均显著负相关,但在严重污染时与颗粒物显著正相关。     

城市景观格局与大气颗粒物污染的关系研究

随着城市化和工业化发展,大气颗粒物对城市空气环境造成了严重污染,选取西安市作为研究区,分析不同时间和空间尺度下城市景观格局与大气颗粒物污染的关系。通过GIS软件、Fragstats4.2软件和SPSS软件对西安市2014年土地利用数据和2014年整年的大气颗粒物监测数据进行分析,结果显示,西安市土地利用类型分布较为集中;西安市大气颗粒物浓度的空间分布特征为浓度从市中心至城郊呈梯度递减趋势,在整体上呈现西北高于东南的倾向,大气颗粒物污染物浓度峰值基本都集中出现在建设用地范围内(PM10:102.7μg/m^3;PM2.5:99.7μg/m^3);相关性结果证实了城市景观格局与大气颗粒物浓度的空间分布特征。研究结果为中国西部内陆城市大气颗粒物污染控制、土地利用规划以及生态建设提供参考。     

2015-2018年衡阳市城区环境空气污染特征分析

本文利用2015~2018年衡阳市城区空气质量监测数据,分析环境空气污染特征及变化趋势。研究结果表明:2015~2018年衡阳市城区环境空气以PM2.5、PM10、O3污染为主,其中PM2.5和PM10年均值浓度呈下降趋势,O3年均值浓度呈不规则波动变化;年内时空变化上,O3呈现夏、秋季浓度高和冬、春季浓度低的特点,PM2.5和PM10呈现出夏季浓度低、冬季浓度高的特点。针对衡阳市空气污染物特征,提出了衡阳市城区空气质量改善的对策建议。     

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。     

Impact of COVID-19 lockdown on the elemental profile of PM10 present in the ambient aerosol of an educational institute in Kolkata,India

Reduction in air pollution level was prime observation during COVID-19 lockdown globally. Here, the study was conducted to assess the impact of lockdown on the elemental profile of PM₁₀ in ambient aerosol to quantify the elemental variation. To quantify the variation, phase-wise sampling of air pollutants was carried out using the gravimetric method for PM₁₀, while NO₂ and SO₂ were estimated through the chemiluminescence and fluorescent spectrometric method respectively. The elemental constituents of PM₁₀ were carried out using an Inductively Coupled Plasma Optical Emission Spectrometer and their source apportionment was carried out using the Positive Matrix Factorization model. The results showed that PM₁₀, NO₂ and SO₂ reduced by 86.97%, 83.38%, and 88.60% respectively during the lockdown sampling phase. The highest mean elemental concentration reduction was found in Mn (97.47%) during the lockdown. The inter-correlation among the pollutants exhibited a significant association indicating that they originate from the same source. The metals like Mn and Cu were found at a higher concentration during the lockdown phase corresponding to vehicular emissions. The comparative analysis of the elemental profile of PM₁₀ concluded that the lockdown effectuated in reduction of the majority of elements present in an aerosol enveloping metropolitan like Kolkata.     

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     

京津冀地区重点耗煤行业大气污染物排放清单研究

本研究通过京津冀地区各行业的年度煤炭消费量确定火电行业、钢铁行业和焦化行业为重点耗煤行业,以在线监测数据、污染源调查(现场调研、环评、验收)数据、排放因子数据为基础,自下而上建立了2013年京津冀地区重点耗煤行业大气污染物排放清单,分析研究了SO_2、NO_x和PM_(10)的排放量与污染贡献分布情况,掌握了京津冀地区重点耗煤行业大气污染物排放现状,为大气污染物减排提供数据基础。研究表明,2013年京津冀火电、钢铁焦化行业共排放SO_2 72.35万t、NO_x 131.99万t、PM_(10) 30.36万t。     

The impact of urban growth on regional air quality surrounding the Langat River Basin, Malaysia

Studies of air quality were carried out in the towns of Kajang, Nilai and Banting in the Langat River Basin, southern region of Kuala Lumpur to determine the status and trend of air quality. The determination of air quality was based on several parameters such as suspended solids with diameters less than 10???m (PM₁₀) and gaseous pollutants of sulphur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). Primary concentration data of air pollutants were compiled through fieldwork studies and combined with secondary data obtained from the regular monitoring data as collected by Alam Sekitar Malaysia Sdn. Bhd. (ASMA) on behalf of Malaysian Department of Environment (DOE) at their stations in Kajang and Nilai. Results showed that the average concentrations of PM₁₀, SO₂, NO₂, O_3, and CO at all sampling stations were still below the permissible values recommended by the Malaysian DOE. The level of gaseous pollutants of NO₂, O_3, and CO was recorded at statistically higher levels (p?<?0.05) than values recorded at the control station at Pangsun Recreational Area. These pollutants were suspected to have originated mainly from exhaust systems of motor vehicles. Data for the years 1996 to 2006 as obtained from ASMA showed long-term air quality trends of increasing O₃ and NO₂ concentrations in Kajang whilst concentrations of PM₁₀ recorded at both Kajang and Nilai stations were mostly expected coming from transboundary sources especially biomass burning and the development activities around the study areas.     

《大气污染防治行动计划》实施环境效果模拟

本研究利用2010年污染源普查数据和MEIC排放清单建立全国大气污染物高时空分辨率排放清单,在此基础上利用2012年环境统计数据对其进行修订建立2012年全国大气污染物高时空分辨率排放清单;结合《大气污染防治行动计划》(以下简称《计划》)研究工作,测算了《计划》实施后在污染源综合治理、落后产能淘汰、能源结构调整方面对SO2、NOx、颗粒物、VOCs的减排量,同时对污染物新增量进行了预测,建立了《计划》实施后全国大气污染物高时空分辨率排放清单;利用CMAQ空气质量模型模拟分析了《计划》实施的空气质量改善效果。结果表明:《计划》实施后,将可以减少641万吨SO2、859万吨NOx、547万吨颗粒物(不含扬尘污染控制)、627万吨VOCs,全国、京津冀、长三角及珠三角区域PM2.5年均浓度将分别比2012年下降22.08%、33.99%、23.98%、24.04%。如果《计划》要求全部落实,可以实现空气质量改善目标。     

空气污染物的时空演化及社会经济驱动因素研究——以长江三角洲地区为例

本文以2006—2015年长江三角洲城市群为研究对象,分析该地区不同部门因能源消费而产生的典型污染物排放量,然后利用LMDI模型,对空气污染进行社会经济驱动因素分析。结果表明:该地区CO_2、SO_2、PM_(2.5)与PM_(10)等空气污染物排放量均呈现先快速增长后缓慢减少的趋势,排放的峰值多出现在了2013年,而NO_x则一直保持增长的趋势。其中,电力与工业部门是空气污染物的主要排放源,但对排放量贡献呈减少趋势,生活部门与交通部门污染物排放量则逐步增长,尤其是对PM_(2.5)与PM_(10)排放量的贡献不可忽视。人口与经济增长对污染物排放量起到了正向拉动作用,经济因素的驱动作用最为明显,其效应值呈现先小幅增加后大幅下降的趋势,能源效率与能源结构有抑制作用,其对污染物排放的效应值仅次于经济因素,而能源结构变化的效应很小。     

Environmental quality in India: Application of environmental Kuznets curve and Sustainable Human Development Index

This study explores the trend of selected air pollutants, carbon dioxide (CO₂), nitrogen dioxide NO₂), sulfur dioxide (SO₂) and particulate matter 10 microns in diameter (PM₁₀) in India. Environmental Kuznets curves (EKC), which show the association of the economy and pollution, are fitted for selected Indian states to understand whether EKC in India fits well with the inverted “U” shape. We observed that CO₂ and PM₁₀ are steadily increasing in India. The states of Kerala and Punjab follow the inverted “U” shape of EKC, while Bihar, Uttar Pradesh, West Bengal, and Maharashtra may take decades to reduce pollutants. A new Sustainable Human Development Index calculated in this paper indicates a diverse picture of Indian states, especially in regard to environmental parameters. The government needs to implement stringent, state‐specific laws and regulations to assist in curbing air pollutants. The time has also come to represent the rankings of states in terms of an environmentally inclusive development index.     

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.