环境空气质量新标准对郑州和开封空气质量评价的影响

利用河南省环境空气质量新标准实施一期城市(郑州、开封)2013年的环境空气自动监测数据,分析了实施《环境空气质量标准》(GB 3095—2012)对这2个城市空气质量评价的影响。研究发现,若采用新标准,郑州、开封污染物年均浓度超标情况加重;城市空气质量达标率从51%、71%下降到35%和31%,下降了16和40个百分点;PM2.5的纳入是空气质量达标率下降的最主要原因,大于NO2标准的收严和CO、O3纳入评价指标的影响,其超标率分别为58%(郑州)和63%(开封);且PM2.5取代PM10成为郑州、开封的首要污染物,其作为首要污染物的比例均大于70%。     

遂宁市2013年大气污染特征及成因

根据遂宁市近年的环境空气监测数据,系统研究了2013年大气污染特征和2007—2013年的变化趋势,分析了颗粒物的组成和主要来源,并与周边地区大气环境监测结果进行了横向比较。研究结果表明,2013年遂宁市环境空气质量达标率为88.1%,大气中SO2、NO2和PM10的年均质量浓度分别为31、30、94μg/m3,空气质量达到二级标准,但劣于往年水平,大气中首要污染物是PM10,夏季和秋季的空气质量明显优于冬季和春季;遂宁市空气质量的变化趋势与周边地区一致,呈现出一定的区域性污染特征,但明显优于周边地区;进一步从5个方面阐述了遂宁市2013年空气质量下降的原因;根据遂宁市大气污染现状,提出了大气防控措施和建议。     

太原市2013年环境空气质量变化特征分析

为全面了解太原市的环境空气质量状况及污染物的时间变化规律,本文选取太原市8个站点2013年1月至12月六种主要污染物(SO2、NO2、PM10、CO、O3、PM2.5)为期一年的监测数据,评价该市2013年环境空气质量的总体情况,研究各污染物在不同时间尺度的变化特征及相互之间的关系。结果表明,2013年太原市环境空气质量以良和轻度污染为主,首要污染物主要为PM2.5和PM10;SO2、NO2、PM10、CO和PM2.5的小时变化规律较为一致,都呈现"双峰型"的变化特征,O3则呈显著的"单峰型"变化规律;上述六种污染物具有明显的季节变化特征,SO2的浓度峰值主要集中在供暖期,NO2和O3—8h浓度在夏季要高于其他季节,PM10的浓度峰值出现在春季(3月)和秋季(10月),CO的浓度峰值集中在11月和12月,PM2.5的浓度峰值主要集中在冬季;相关性分析结果表明,SO2、NO2、PM10、CO、PM2.5浓度日均值在全年各时段均具有很好的正相关性,化石燃料的燃烧可能是上述污染物质的共同来源。     

上海市路边环境空气黑碳气溶胶污染特征及影响因素研究

为研究上海市路边环境空气黑碳(BC)的污染特征,采用连续监测方法对2016年1月至2018年12月上海市路边环境空气BC浓度进行了监测,并同步监测了气象因子,分析了BC的时间变化特征,探讨了气象因素对BC的影响以及不同空气质量等级下BC浓度水平。结果表明:2016、2017、2018年上海市路边环境空气BC年均质量浓度分别为(2 908±2 189)、(2 959±2 224)、(2 824±2 002) ng/m3,呈现出下降趋势;2016、2017、2018年BC与PM2.5年均质量浓度比分别为9. 30%、9. 20%、9. 50%;BC季节变化特征明显,整体表现为春夏高、秋冬低的特点;昼夜变化特征均呈现出双峰分布,第一个峰值均出现在06:00,第二个峰值均出现在16:00-19:00,且第一个峰值高于第二个峰值。气象因素对BC有一定影响,在降水、相对湿度低以及非静风条件下BC浓度较低。随着上海市空气质量由好转差,上海市路边环境空气BC浓度均呈现上升趋势,空气质量为良、轻度污染、中度污染、重度污染时路边环境空气BC平均浓度分别较空气质量为优时增加了0. 38、0. 96、1. 61、1. 96倍。     

北京市高海拔地区百花山空气质量状况及与市区的差异性研究

对北京市远郊百花山(海拔1300 m)2007—2017年大气常规6项污染物数据进行了分析,并与代表市区的国控站点均值数据进行了比较。研究发现,百花山SO2、CO、PM2.5浓度为国控站点浓度的35.5%~35.7%,NO2、PM 10、O 3浓度分别为国控站点浓度的14.0%、41.5%、185.5%。11年间,百花山6项常规污染物浓度逐年降低。2013—2017年,百花山PM2.5浓度年均降速为11.4%,低于国控站点13.3%的年均改善水平。百花山和国控站点在污染物季节变化趋势上基本一致,秋季颗粒物浓度差异最大,春季差异最小。百花山6项污染物的日变化峰谷比值为1.21~1.44,其差异小于国控站点。各项污染物浓度在18:00出现峰值,认为主要受城区远距离传输影响。2013—2017年,百花山共出现5个PM2.5重污染天,5级以上重污染小时数为442 h,国控站点有2%的重污染小时与百花山同步。     

沈阳市冬季环境空气质量统计预报模型建立及应用

利用沈阳市2013年1—2月大气自动监测数据和同期气象资料,选取19项预报因子,采用逐步回归方法建立了沈阳市冬季环境空气质量统计预报模型,预报项目包括细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)日均浓度及臭氧(O3)日最大8 h平均浓度。2013年11月至2014年1月,应用该模型并结合人为经验修订,开展了沈阳市环境空气质量预报工作,预报结果与实测结果的对比验证结果表明,环境空气预测结果级别准确率达到79.1%,首要污染物准确率为73.6%。     

江苏制定空气质量新标准实施方案

江苏省环保厅日前制定了《江苏省空气质量新标准实施方案》(以下简称《方案》)。《方案》明确要求,2013年实现县级以上省控空气自动站均具备PM2.5、O3、CO等指标的监测能力,形成覆盖市县、满足实施新标准评价要求的空气质量监测网络。《方案》对空气质量监测站点的建设范围和监测项目也作了细化要求。《方案》指出,要在2012年完成13个省辖城市     

环境空气质量新旧评价体系及评价结果差异

在分析环境空气质量评价体系的变化的基础上,通过分析实际监测数据说明评价体系变化对评价结果的影响。结果表明:由于NO2和PM10的年平均二级浓度限值收严,用新标准评价全国325个地级以上城市2012年的达标情况,将有164个城市由达标变为不达标;参照《环境空气质量评价技术规范(试行)》评价2012年度地级以上城市的达标情况,达标率由91.4%降为41.8%;不同时段O3的1 h平均浓度值对计算其8 h滑动平均浓度值的贡献不同,可能引起8 h滑动平均浓度值计算结果的差异;由于在计算实时空气质量指数时PM10和PM2.5使用24 h滑动平均浓度值,当空气质量突变时会出现评价结果与实际污染状况不符的情况。     

2015—2017年江苏省秋、冬季空气质量状况

采用2015—2017年秋、冬季江苏省环境空气质量监测数据,从空气质量优良(达标)率、首要污染物、主要污染物浓度分析空气质量现状及特点。结果表明,江苏省秋、冬季空气质量优良(达标)率在60%左右,其中沿海地区空气质量达标率最高(71.1%),西北地区达标率最差(52.2%)。污染日的首要污染物主要为PM 2.5,占比高达91.5%。ρ(PM2.5)/ρ(PM 10)存在地区差异,江苏西北地区扬尘源贡献较大,江苏南部地区的二次颗粒物贡献更明显。ρ(NO2)/ρ(SO2)逐年持续升高,表明大气污染类型从燃煤性污染转变为复合型污染。     

基于高斯烟羽模型的热电厂对空气质量影响案例分析

基于高斯烟羽模型的输入频率分布模型(IFDM)系统,模拟了2019年4—6月河北省南部平原地区某热电厂对临近2个省控监测站点空气质量的贡献。结果表明,该热电厂4—6月对2个省控监测站点NO2、SO2、PM10、PM2. 5的平均贡献值分别为2. 0,3. 2和4. 4μg/m3,1. 9,3. 1和3. 5μg/m3,0. 45,0. 7和0. 85 g/m3,0. 25,0. 45和0. 55μg/m3。6月份该热电厂对2个省控监测站点各项污染物贡献值最大,与其西南风出现频率较高相吻合。     

喀什市2022年环境空气质量特征分析

为摸清喀什市环境空气质量变化特征,为管理部门进行污染精准管控提供科学参考,基于喀什市2022年环境空气自动监测数据开展分析研究。结果表明:2022年喀什市二氧化硫、二氧化氮、臭氧、一氧化碳年均质量浓度均优于国家二级标准。超标污染物主要为可吸入颗粒物和细颗粒物。喀什市细颗粒物、可吸入颗粒物浓度呈春、秋、冬季高,夏季低的季节特征。     

无锡市国控环境空气自动监测站点周边裸土分布高分辨率遥感监测

以无锡市为研究区,使用过境时间相近的哨兵2号Sentinel-2和Landsat8影像,综合使用NDSI、NDISI、MNDWI、LST等指数进行决策树分类,获得10 m高空间分辨率的土地利用分类结果和裸土分布,裸土提取精度达到94.13%。统计了无锡市与8个国控环境空气自动监测站点1、2、3 km缓冲区范围内的裸土分布情况,并与各站点监测的大气颗粒物浓度进行相关性分析。结果表明,国控环境空气自动监测站点周边裸土面积对颗粒物浓度有较大影响,其中对PM₁₀浓度的影响明显大于PM_2.5;相比于1 km和3 km,2 km缓冲区范围内的裸土面积对PM₁₀浓度的影响最大,建议环境管理部门重点关注无锡地区国控监测站点周边2 km范围内的裸土扬尘源分布情况。     

Assessment of Ambient Air Quality in Urban Centres of Haryana (India) in Relation to Different Anthropogenic Activities and Health Risks

Considering the mounting evidences of the effects of air pollution on health, the present study was undertaken to assess the ambient air quality status in the fast growing urban centres of Haryana state, India. The samples were collected for total suspended particulate matter (TSPM), respirable suspended particulate matter (PM₁₀), sulfur dioxide (SO₂), and oxides of nitrogen (NO₂) during different seasons from 8 districts of Haryana during January, 1999 to September, 2000. The four types of sampling sites with different anthropogenic activities i.e. residential, sensitive, commercial and industrial were identified in each city. The ambient air concentration of TSPM and PM₁₀ observed was well above the prescribed standards at almost all the sites. The average ambient air concentrations of SO₂ and NO₂ were found below the permissible limits at all the centres. Comparatively higher concentration of SO₂ was observed during winter seasons, which seems to be related with the enhanced combustion of fuel for space heating and relatively stable atmospheric conditions. Air Quality Index (AQI) prepared for these cities shows that residential, sensitive and commercial areas were moderately to severely polluted which is a cause of concern for the residents of these cities. The high levels of TSPM and SO₂ especially in winter are of major health concern because of their synergistic action. The data from Hisar city reveals a significant increase in the total number of hospital visits/admissions of the patients with acute respiratory diseases during winter season when the level of air pollutants was high.     

Air Quality Status of an Open Pit Mining Area in India

This investigation presents the assessment of ambient air quality carried out at an open pit coal mining area in Orissa state of India. The 24-h average concentrations of suspended particulate matter (SPM), respirable particulate matter (RPM, particles of less than 10 m aerodynamic diameter), sulphur dioxide (SO₂) and oxides of nitrogen (NO_x) were determined at regular interval throughout one year at 13 monitoring stations in residential area and four stations in mining/industrial area. During the study period, the 24-h and annual average SPM and RPM concentrations exceeded the respective standards set in the Indian ambient air quality standard (NAAQS) protocol in most of the residential and industrial areas. However, the 24-h and annual average concentrations of SO₂ and NO_x were well within the prescribed limit of the NAAQS in both residential and industrial areas. A management strategy is formulated for effective control of particulate matter at source and other mitigative measures are recommended including implementation of green belts around the sensitive areas.     

Receptor model-based source apportionment of particulate pollution in Hyderabad, India

Air quality in Hyderabad, India, often exceeds the national ambient air quality standards, especially for particulate matter (PM), which, in 2010, averaged 82.2?±?24.6, 96.2?±?12.1, and 64.3?±?21.2 μg/m³ of PM₁₀, at commercial, industrial, and residential monitoring stations, respectively, exceeding the national ambient standard of 60 μg/m³. In 2005, following an ordinance passed by the Supreme Court of India, a source apportionment study was conducted to quantify source contributions to PM pollution in Hyderabad, using the chemical mass balance (version 8.2) receptor model for 180 ambient samples collected at three stations for PM₁₀ and PM_2.5 size fractions for three seasons. The receptor modeling results indicated that the PM₁₀ pollution is dominated by the direct vehicular exhaust and road dust (more than 60 %). PM_2.5 with higher propensity to enter the human respiratory tracks, has mixed sources of vehicle exhaust, industrial coal combustion, garbage burning, and secondary PM. In order to improve the air quality in the city, these findings demonstrate the need to control emissions from all known sources and particularly focus on the low-hanging fruits like road dust and waste burning, while the technological and institutional advancements in the transport and industrial sectors are bound to enhance efficiencies. Andhra Pradesh Pollution Control Board utilized these results to prepare an air pollution control action plan for the city.     

Characterisation of PM10, PM2.5 and Benzene Soluble Organic Fraction of Particulate Matter in an Urban Area of Kolkata, India

In this study, the relationship between inhalable particulate (PM₁₀), fine particulate (PM_2.5), coarse particles (PM_{2.5 – 10}) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003–2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3–5 m above ground near highly trafficked and congested areas. The 24 h average PM₁₀ and PM_2.5 samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM_2.5 and PM₁₀ were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM₁₀ and PM_2.5 and inverse correlation was observed between particulate matter (PM₁₀ and PM_2.5) and wind speed. Statistical analysis of air quality data shows that PM₁₀ and PM_2.5 are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM₁₀ and PM_2.5 and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM₁₀) and fine particulate (PM_2.5) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM₁₀ (BSM10) and benzene soluble organic fraction of PM_2.5 (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.     

道路绿化带影响大气颗粒物分布实测研究

以福州市西三环快速路某路段为实验靶区，利用微型环境检测仪采集路边细颗粒物(PM_2.5)、亚微米颗粒物(PM_1.0)和黑碳(BC)的空间分布样本，解析其在道路绿化带前后(绿化带前是指干道和辅道外边缘线位置，其他位置均视为绿化带后)的变化特征及原因。结果表明：(1)颗粒物浓度随着采样点远离干道而整体趋于递减，呈现BC>PM_2.5>PM_1.0的衰减率变化特征，且植被稠密的路边环境对应更大的颗粒物浓度降幅。(2)夏季绿化带后的颗粒物浓度降幅高于冬季，冬季绿化带后部分采样点的PM_1.0和PM_2.5浓度甚至有所抬升。在植被茂密的路边环境下，风自干道吹向绿化带情景的路边空气质量介于风自绿化带吹向干道情景和风平行于干道情景之间。(3)BC对交通变化的敏感性高于PM_2.5和PM_1.0,植被茂密的绿化带后的颗粒物浓度降幅会因交通强度的上升而增大。风自干道吹向绿化带时，绿化带对颗粒物的调节作用会随交通源强和季节的变化而不同...     

川东北中心城市南充大气污染态势及其主要成因

基于南充市主城区6项大气污染物浓度数据,研究了2014-2020年南充市的空气质量指数、空气质量指数等级和首要污染物的时序分布。结果表明:随着大气污染防治的开展,南充市大气污染物浓度逐渐下降,出现首要污染物的天数逐年减少,空气质量逐步提高。受污染物节律性影响,空气质量呈现明显的季节差异,冬季空气质量最差,春季次之,夏季污染相对较轻,秋季最轻。首要污染物类型的季节分布特征表现为冬季出现首要污染物天数最多,春季和夏季次之,秋季最少。春、秋、冬季以PM_2.5污染为主,夏季以O₃污染为主。从全年来看,与O₃相比,PM_2.5对空气质量的影响更为突出。在持续控制大气污染物排放总量的同时,精细化协同管控细颗粒物、氮氧化物、挥发性有机物和二氧化硫排放将有助于现阶段的大气污染防治。     

基于不同时间尺度综合指数贡献率的攀枝花市大气污染特征分析

选取2016—2022年攀枝花市环境空气质量监测数据，基于年度、季度、月度、小时时间尺度评价环境空气质量综合指数贡献率。结果表明，攀枝花市污染类型已由传统的单一工业型污染转变为复合型污染，且污染物在不同时间尺度下的分布特征明显。从季度尺度来看，第一季度细颗粒物(PM_2.5)综合指数贡献率较大，第二和第三季度臭氧(O₃)综合指数贡献率较大，第四季度二氧化氮(NO₂)及PM_2.5综合指数贡献率较大。从月度尺度来看，O₃在3—8月的贡献较大，PM_2.5、NO₂、可吸入颗粒物(PM₁₀)、一氧化碳(CO)、二氧化硫(SO₂)在1—2月及11—12月的贡献较大。从小时尺度来看，O₃在13—19点贡献较大，PM₁₀、PM_2.5、CO在8—13点贡献较大，SO₂在9—13点贡献较大，NO₂在8—1...