无锡市MODIS气溶胶光学厚度与PM_(2.5)质量浓度的相关性分析

将MODIS数据反演得出的气溶胶光学厚度与无锡市区实测得到的PM2.5质量浓度进行相关性分析,结果两者的直接相关性较低,相关系数为0.283 4。气溶胶光学厚度经垂直分布和湿度修正后,两者相关性显著提高,相关系数为0.565 9。虽然修正过程存在误差,相关性未达预期程度,但该方法得到的气溶胶光学厚度可作为PM2.5监测的有效补充。     

西宁冬季PM2.5和PM10手工与自动监测现状研究

为研究PM_(2.5)和PM_(10)手工与自动监测仪器在高海拔地区的适用性,于2014年12月至2015年1月(冬季)在青海省西宁市开展了为期34 d的监测比对实验。PM监测数据表明:手工监测数据之间都有差异,除了受监测滤膜种类的影响,还存在监测仪器间的系统误差。石英滤膜的PM监测数据都高于聚丙烯滤膜,尤其是PM_(2.5)更为明显,偏高近1/4;石英滤膜与聚丙烯滤膜的PM监测数据具有较好的相关性,PM_(10)监测数据的相关系数为0.97。自动监测数据之间进行了同期比对研究,发现TEOM1405DF(微振荡天平法)和APM-2(β射线法)的PM监测值较低,BAM-1020(β射线法)的PM监测值最高;而Grimm(光散射法)的PM监测值居中。BAM1020配备动态加热系统(DHS),其PM监测数据比没有配备DHS的APM-2偏高40%。基于PM监测比对研究,建议在空气污染严重时加密对各监测仪器的运行维护,并加强长期观测以全面评估PM监测。     

临汾市大气污染物的时间分布特征与源区分析

多年来，临汾市多次名列我国生态环境部公布的空气质量最差的重点城市之列，对其大气污染的时间分布特征和潜在源区进行分析对其环境管理与污染防治具有重要意义。利用2015—2019年临汾市5个国控空气环境质量监测站点的6种空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据和气象观测数据，使用HYSPLIT模型研究了该市空气污染物的时间变化特征、轨迹输送特征和可能的来源。结果表明，PM_2.5和PM₁₀的年均浓度均超过了《环境空气质量标准》(GB 3095—2012)Ⅱ级标准，SO₂仅在2016—2017年超过该标准，其余3种污染物的年均浓度均低于该标准。6种污染物2015—2019年的月均浓度的变化特征表现为O₃浓度呈以6、7月为中心的近似正态分布，SO₂、NO₂和CO以及PM_2.5和PM₁₀浓...     

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.     

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...     

中国主要城市疫情管控期间大气环境改善效应研究

利用我国31个省会(省府)城市、直辖市站点大气污染物数据,对全国主要城市2020年新冠疫情管控期间以及复工复产后的大气污染状况进行统计学分析。结果表明:叠加疫情管控影响,相比往年,2020年春节假期前后全国主要城市整体上PM₁₀、NO₂、SO₂、CO平均质量浓度降幅分别达到22.46%、60.13%、13.71%、17.64%;疫情管控期间全国主要城市PM_2.5与PM₁₀偏相关系数为0.952,PM_2.5与SO₂、NO₂、CO的偏相关系数分别为0.705、0.791、0.831。复工复产初期相较疫情管控期间仅有NO₂平均质量浓度上升;随着复工复产进程深入,PM₁₀、SO₂、NO₂平均质量浓度则均有大幅度上升。采暖区SO₂和CO平均质量浓度在疫情管控期分别为非采暖区的2.6倍及1.6倍,两大区域在复工复产后各大气污染物质量浓度变化情况有所差异,也反映出采暖区与非采暖区的大气污染情况的不同。     

基于GIS的唐山地区大气污染物时空分布研究

应用数据统计和ArcGIS对北方重工业城市唐山地区2014年14个县(区)18个空气自动监测站的数据进行时空分布特征分析,监测的污染物为PM_(10)、SO_2、NO_2、PM_(2.5)、O_3、CO共6项。利用ArcGIS对各个自动监测站污染物数据建立网格模型,采用反距离权重法分别对年均、采暖期、非采暖期的环境空气质量综合指数和6项污染因子浓度的空间分布进行估算,直观比较了污染物在不同时期内的空间分布状况。结果表明,空气质量时间分布较为明显,非采暖期明显好于采暖期。同时,计算出每个网格单元污染指数的标准偏差,结合气象气候、地形地势、工业发展等情况,分析得出北部山区、市中心区附近区域空气质量波动较大。为区域大气污染有针对性的综合防治、联防联控及污染物区域削减计划打下数据基础。     

基于聚类分析的颗粒物监测网络优化研究

为了优化香港环境监测网络,收集香港14个监测站2011年1月1日至2015年11月30日的颗粒物PM_(2.5)、PM_(10)的小时数据进行统计分析。对PM_(2.5)进行聚类,并利用日均浓度变化图进行验证,结果表明,可将监测站分为4类(A、B、C、D类),A类位于城市郊区,B类则位于港口附近,且A、B类的PM_(2.5)日变化特征均呈现双峰型分布,峰值分别出现在09:00和21:00。对PM_(10)进行类似分析结果表明,监测站同样可以分为4类,A类位于九龙区,B类则位于港口附近,而且A、B类的PM_(10)日变化双峰分别出现在11:00和20:00左右。说明污染源头及地形的相似致使某些监测站颗粒物浓度的变化出现相同的趋势,导致监测设备的浪费和管理的冗余。建议建立更高效的空气管理系统,将冗余设备转移到其他地区,扩大空气监控区域。对PM_(2.5)/PM_(10)聚类结果表明,将监测站分为4类,B类均属于路边站,C类则位于居民区。同时还发现同类监测站PM_(2.5)/PM_(10)数值变化相同,并且可以用其中一个站的PM_(2.5)和PM_(10)浓度及另一个站的PM_(2.5)或PM_(10)浓度预测PM_(2.5)或PM_(10)浓度,为优化监测资源提供了一种新的思路。     

青海高原一次沙尘重污染天气成因分析

利用常规观测的卫星云图资料、地面资料、探空资料、地面污染物监测数据,结合拉格朗日粒子扩散模型(LPDM)污染源溯源方法,对2018年2月青海高原一次沙尘重污染天气的主要成因以及沙尘传输特征进行了分析。结果表明:此次重污染天气受高空低槽东移影响,在300~700 hPa形成了强烈的辐散下沉,槽后的高空急流随之东移。在其东移过程中,受高空急流动量下传及偏北气流中的冷空气共同作用,青海东部出现了大风沙尘天气。边界层中逆温层的存在是此次污染天气持续的重要原因之一,加之未出现明显降水,不利于大气污染物的扩散。通过运用LPDM对此次污染天气的运动轨迹进行分析来看,气团影响的模拟高度层距离地面100 m,气团层趋势一致。研究区地处青藏高原,海拔较高,0~100 m高度的气团足迹可以反映出PM 10污染气团的输送路径。同时,0~100 m是主要的人为源排放空间,也是对人类活动影响较大的区域。气团足迹与PM 10浓度的变化趋势一致,即青海东部沙尘污染主要是由河西走廊沙尘倒灌进入青海东部导致,这与天气学分析结果一致。     

空气自动监测中PM2.5与PM10 “倒挂”现象特征及原因

采用不同原理的自动监测仪器在不同季节同时测定PM2.5与PM10,对所得数据中的PM2.5与PM10"倒挂"现象进行分析。结果表明,当PM10采用振荡天平法时,PM2.5与PM10的"倒挂"率较高;冬季和夏季"倒挂"现象发生率明显高于其他季节;造成PM2.5与PM10"倒挂"的原因主要有监测过程中的随机误差,PM2.5与PM10的监测方法原理不同,监测方法之间存在显著差异等。     

Assessment of indoor and outdoor particulate air pollution at an urban background site in Iran

The relationship between indoor and outdoor particulate air pollution was investigated at an urban background site on the Payambar Azam Campus of Mazandaran University of Medical Sciences in Sari, Northern Iran. The concentration of particulate matter sized with a diameter less than 1 μm (PM_1.0), 2.5 μm (PM_2.5), and 10 μm (PM₁₀) was evaluated at 5 outdoor and 12 indoor locations. Indoor sites included classrooms, corridors, and office sites in four university buildings. Outdoor PM concentrations were characterized at five locations around the university campus. Indoor and outdoor PM measurements (1-min resolution) were conducted in parallel during weekday mornings and afternoons. No difference found between indoor PM₁₀ (50.1 ± 32.1 μg/m³) and outdoor PM₁₀ concentrations (46.5 ± 26.0 μg/m³), indoor PM_2.5 (22.6 ± 17.4 μg/m³) and outdoor PM_2.5 concentration (22.2 ± 15.4 μg/m³), or indoor PM_1.0 (14.5 ± 13.4 μg/m³) and outdoor mean PM_1.0 concentrations (14.2 ± 12.3 μg/m³). Despite these similar concentrations, no correlations were found between outdoor and indoor PM levels. The present findings are not only of importance for the potential health effects of particulate air pollution on people who spend their daytime over a period of several hours in closed and confined spaces located at a university campus but also can inform regulatory about the improvement of indoor air quality, especially in developing countries.     

Evaluation of the Trend of Air Quality in Taipei, Taiwan from 1994 to 2003

The data collected from the five air quality monitoring stations established by the Taiwan Environmental Protection Administration (TEPA) in Taipei City were analyzed to assess the changes in air quality. The analyses reveal that the air quality in Taipei City improved over the last decade from 1994 to 2003, as evidenced by the significant downward trends of the various primary air pollutant concentrations, such as CO, NO_X, SO₂, and PM₁₀. An air pollution fee was collected by TEPA in 1995, and several air pollution control measures were likewise taken to improve the air quality in Taiwan. However, although the extreme daily maximum O₃ concentrations occurred more frequently in earlier years and showed a downward trend, its moderately high concentrations increased annually in recent years. It implied that after the reduction of various primary pollutant concentrations, the effective reduction of O₃ pollution still remains an important issue.     

Particulate matter concentration in ambient air and its effects on lung functions among residents in the National Capital Region, India

The World Health Organization has estimated that air pollution is responsible for 1.4 % of all deaths and 0.8 % of disability-adjusted life years. NOIDA, located at the National Capital Region, India, was declared as one of the critically air-polluted areas by the Central Pollution Control Board of the Government of India. Studies on the relationship of reduction in lung functions of residents living in areas with higher concentrations of particulate matter (PM) in ambient air were inconclusive since the subjects of most of the studies are hospital admission cases. Very few studies, including one from India, have shown the relationship of PM concentration and its effects of lung functions in the same location. Hence, a cross-sectional study was undertaken to study the effect of particulate matter concentration in ambient air on the lung functions of residents living in a critically air-polluted area in India. PM concentrations in ambient air (PM_1, PM_2.5) were monitored at residential locations and identified locations with higher (NOIDA) and lower concentrations (Gurgaon). Lung function tests (FEV₁, PEFR) were conducted using a spirometer in 757 residents. Both air monitoring and lung function tests were conducted on the same day. Significant negative linear relationship exists between higher concentrations of PM₁ with reduced FEV₁ and increased concentrations of PM_2.5 with reduced PEFR and FEV₁. The study shows that reductions in lung functions (PEFR and FEV₁) can be attributed to higher particulate matter concentrations in ambient air. Decline in airflow obstruction in subjects exposed to high PM concentrations can be attributed to the fibrogenic response and associated airway wall remodeling. The study suggests the intervention of policy makers and stake holders to take necessary steps to reduce the emissions of PM concentrations, especially PM_1, PM_2.5, which can lead to serious respiratory health concerns in residents.     

宜都市PM2.5与O3季节性分布特征及潜在源区分析

为了解宜都市PM_2.5与O₃的污染特征及潜在来源,利用宜都市2020年3月至2022年2月在线监测数据及气象数据,对宜都市PM_2.5与O₃质量浓度变化特征、气象影响因素及潜在源区进行了分析,结果表明:宜都市PM_2.5质量浓度冬高夏低,日变化呈双峰特征,O₃质量浓度夏高冬低,日变化呈单峰特征。高湿、静稳的气象条件以及较强偏北风作用下的区域污染传输对PM_2.5污染有重要影响,高温以及中湿度对O₃污染过程有重要作用。春、夏、秋季偏南方向气流轨迹占主导,且携带较高的污染物浓度,冬季来自湖北东北及西南方向的气流占比较高且携带的PM_2.5浓度较高;宜都市PM_2.5、O₃的潜在源区具有季节性差异,总体来看,主要分布在河南南部、湖北东部及湖南的北部区域。     

西藏自治区“十二五”和“十三五”环境空气质量变化特征

"十二五"以来中国先后颁布了一系列大气污染防治政策并实施相应的大气污染治理措施以提高环境空气质量。为研究西藏自治区"十二五"(2011-2015年)和"十三五"(2016 -2020年)时期环境空气质量变化状况,评估大气污染防治措施实施效果,笔者对2011-2020年西藏自治区7个城市(地区)主要大气污染物浓度和优良天数比例变化进行分析。结果表明:随着一系列大气污染防治措施的实施,西藏自治区环境空气质量显著提升,与"十二五"时期相比,"十三五"时期西藏自治区年平均优良天数比例由97.5%±2.0%提升至99.3%±0.4%,SO₂、 NO₂、 PM₁₀、 PM_2.5浓度年均值和CO 浓度第95百分位数均呈下降趋势,其中CO、PM₁₀和PM_2.5浓度下降幅度较大,O₃日最大8h滑动平均值第90百分位数略有上升。西藏自治区环境空气质量与人口、经济发展程度呈负相关关系。受污染源排放、气象条件和区域传输等因素影响,西藏自治区O₃浓度春、夏季较高,而其余污染物浓度冬季较高。     

Integrated assessment of brick kiln emission impacts on air quality

This paper presents monitoring results of daily brick kiln stack emission and the derived emission factors. Emission of individual air pollutant varied significantly during a firing batch (7 days) and between kilns. Average emission factors per 1,000 bricks were 6.35–12.3 kg of CO, 0.52–5.9 kg of SO₂ and 0.64–1.4 kg of particulate matter (PM). PM emission size distribution in the stack plume was determined using a modified cascade impactor. Obtained emission factors and PM size distribution data were used in simulation study using the Industrial Source Complex Short-Term (ISCST3) dispersion model. The model performance was successfully evaluated for the local conditions using the simultaneous ambient monitoring data in 2006 and 2007. SO₂ was the most critical pollutant, exceeding the hourly National Ambient Air Quality Standards over 63 km² out of the 100-km² modelled domain in the base case. Impacts of different emission scenarios on the ambient air quality (SO₂, PM, CO, PM dry deposition flux) were assessed.     

烟花爆竹禁燃对春节期间东北地区空气质量影响评估

利用2015—2017年春节期间东北地区主要大气污染物(PM_(10)、PM_(2.5)、SO_2、NO_2、CO和O3)质量浓度监测资料及相应气象因子(温度、湿度、风速和气压)观测资料,分析了春节期间烟花爆竹禁燃对东北地区空气质量的影响。结果表明:随着东北地区主要城市禁燃力度的增强,空气质量逐年提升,PM_(2.5)和SO_2浓度逐年大幅度下降。禁燃可明显降低城区PM_(2.5)浓度,而由于春节期间污染源整体减少,城区和城郊监测点PM_(2.5)浓度值差异减小。烟花爆竹对PM_(10)和PM_(2.5)浓度影响高于对气体污染物SO_2、NO_2和CO的影响。此外,气象条件对东北地区春节期间禁燃改善空气质量的效果也有明显影响。因此,结合春节期间的气象条件,在东北地区实施禁燃政策动态调整非常必要。     

Preparation and Validation of Gridded Emission Inventory of Criteria Air Pollutants and Identification of Emission Hotspots for Megacity Delhi

Delhi is one of the many megacities struggling with punishing levels of pollution from industrial, residential, and transportation sources. Over the years, pollution abatement in Delhi has become an important constituent of state policies. In the past one decade a lot of policies and regulations have been implemented which have had a noticeable effect on pollution levels. In this context, air quality models provide a powerful tool to study the impact of development plans on the expected air pollution levels and thus aid the regulating and planning authorities in decision-making process. In air quality modeling, emissions in the modeling domain at regular interval are one of the most important inputs. From the annual emission data of over a decade (1990–2000), emission inventory is prepared for the megacity Delhi. Four criteria pollutants namely, CO, SO₂, PM, and NO _x are considered and a gridded emission inventory over Delhi has been prepared taking into account land use pattern, population density, traffic density, industrial areas, etc. A top down approach is used for this purpose. Emission isopleths are drawn and annual emission patterns are discussed mainly for the years 1990, 1996 and 2000. Primary and secondary areas of emission hotspots are identified and emission variations discussed during the study period. Validation of estimated values is desired from the available data. There is a direct relationship of pollution levels and emission strength in a given area. Hence, an attempt has been made to validate the emission inventory for all criteria pollutants by analyzing emissions in various sampling zones with the ambient pollution levels. For validation purpose, the geographical region encompassing the study area (Delhi) has been divided into seven emission zones as per the air quality monitoring stations using Voronoi polygon concept. Dispersion modeling is also used for continuous elevated sources to have the contributing emissions at the ground level to facilitate validation. A good correlation between emission estimates and concentration has been found. Correlation coefficient of 0.82, 0.77, 0.58 and 0.68 for CO, SO₂, PM and NO _x respectively shows a reasonably satisfactory performance of the present estimates.     

2014—2018年洛阳市大气污染变化特征及影响因素研究

研究采用空气质量指数法对2014—2018年洛阳市大气污染变化特征进行了分析,构建了空气污染物浓度的影响指标体系,采用灰色关联法研究了空气污染物浓度与影响因子之间的关联度,得到了影响空气污染物浓度的主要指标因子,并提出了改善洛阳市空气质量的措施。结果表明:洛阳市空气质量指数类别主要为良和轻度污染。2014—2018年空气质量为优良的天数主要出现在春季、夏季和秋季,重度污染和严重污染主要出现在冬季。2018年PM₁₀、PM_2.5、NO₂、SO₂和CO这5项污染物浓度随时间变化呈"V"型,污染主要集中在1—5月和11—12月。O₃浓度随时间变化呈倒"V"型,污染主要集中在4—9月。研究期内PM_2.5、PM₁₀和O₃是主要污染物。市区总人口、工业(综合)能源消耗量、人均生产总值、城市机动车总数、城市房屋施工面积、人均公园绿地面积、建成区绿化覆盖率和一般工业固体废物产生量等8项指标因子与PM_2.5、PM₁₀和O₃的浓度表现出高关联度或较高关联度。     

Seasonal variation in ambient PM mass and number concentrations (case study: Tehran, Iran)

Tehran, the capital city of Iran, is an important industrial and commercial center. This city is one of the worst cities in the world in terms of air pollution, which is mostly due to mobile sources rather than stationary sources. Particulate matter (PM), which is a complex mixture of extremely small particles and liquid droplets, is considered as an important source of air pollution in Tehran. In this study, our objective was to study PM₁₀, PM_2.5, and PM_1.0 mass and number concentrations and find the correlations of these two parameters in the west-central parts of Tehran during two consecutive warm and cold seasons. The particles collected from five stations were analyzed for their mass and number simultaneously by a laser-based Grimm dust monitor. In general, it was found that the accumulation of the PM in this region is more in the cold season. PM₁₀ mass concentration increases almost twofold and PM_2.5 and PM_1.0 almost three times in this season. The mean number concentration of the particles (0.3–20 μm) was found to be almost 4.8 times in the cold season. It was also noticed that the average dimensions of the particles decrease in that season.