2013—2022年全国环境空气质量变化特征

基于2013—2022年全国339个地级及以上城市环境空气质量监测数据,分析了10年来环境空气质量变化特征。结果表明,2013—2022年全国环境空气质量持续改善,74个重点城市ρ（PM_2.5）从2013年的68μg/m³降至2022年的29μg/m³,降幅为57.4%;一次排放污染物ρ（SO₂）和ρ（NO₂）10年降幅分别为71.9%和27.6%,ρ（CO）自2015年以来下降42.1%;全国ρ（O₃）波动变化,受高温干旱影响,2019和2022年ρ（O₃）为148和145μg/m³,其他年份ρ（O₃）在137~139μg/m³波动。全国74个重点城市优良天数比例从2013年的65.7%上升至2022年的83.0%,相当于10年间每个城市优良天数增加65d;重污染天数比例从2013年的7.9%下降至2022年的0.6%,重污染天数减少90%。2020—2022年,90%以上的非沙尘重污染天出现在秋冬季（11—12月和1—2月）,夏秋季O₃超标问题对优良天数比例影响显著,建议进一步强化重点区域污染联防联控与重点城市大气污染防治,加强多污染物协同治理,推进空气质量持续改善。     

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。     

拉萨市环境空气质量与气象特征分析

分析了拉萨市环境空气质量及其拉萨市的地理位置、地形地貌和气候特征的关系。研究结果表明 ,自然沙尘是拉萨市环境空气质量的主要自然源。月降尘量与降水、大风等有明显的相关关系。加快拉萨地区生态环境建设 ,增加植被覆盖率 ,减少沙源是改善拉萨市环境空气质量的关键     

2013—2019年青岛市空气质量变化特征分析

为掌握滨海城市环境空气质量变化特征,为污染精准管控和打赢蓝天保卫战提供科学参考,以沿海重要中心城市青岛市为研究区域,基于青岛市9个国控空气监测点位监测结果,对2013—2019年青岛市6项空气污染物浓度监测结果进行分析,总结归纳青岛市空气质量时间变化特征。结果表明：2019年,青岛市空气质量超标,超标指标为颗粒物;青岛市2014年空气质量最差,2018年空气质量最好;2013—2019年,青岛市O₃浓度总体呈上升趋势,其余5项污染物浓度呈下降趋势;青岛市环境空气主要污染物是PM_2.5,其次是O₃;青岛市空气质量冬季差于其他季节,春节期间烟花爆竹燃放等人为活动使空气质量变差。     

金昌市环境空气质量评价及污染特征分析

对１９９７年金昌市大气（２４ｈ连续采样）环境空气质量进行了评价，简单分析了空气污染特征及原因，以便为大气污染防治提供依据。     

攀枝花市西区环境空气质量变化趋势及污染特征分析

选取攀枝花市西区2006-2016年度环境空气质量数据,运用Spearman秩相关系数分析环境空气质量变化,综合比较了西区在全国和四川省各城市的环境空气质量排名,分类比较了与西区排名相近的四个工业城市各指标每月及年均浓度值,得出西区近11年环境空气质量变化趋势及污染特征。研究结果表明,攀枝花市西区环境空气质量改善明显,但二氧化硫和一氧化碳仍处在较高浓度水平,臭氧已经成为影响空气质量达标率的主要污染物。     

北方沙尘对四川盆地环境空气质量影响和特征分析

利用沙尘天气资料和颗粒物浓度、激光雷达监测数据以及后向轨迹,分析浮尘天气的气象特征及传输路径,构建了浮尘天气对四川盆地各城市大气环境质量影响的量化指标,并利用该量化指标分析了浮尘天气对四川盆地各城市大气环境的影响,得出2013—2015年浮尘对四川盆地PM_(10)年均质量浓度的贡献,2013—2015年约为4.82、1.00、0.56μg/m~3;浮尘对PM_(10)年均质量浓度的贡献川西区域最大,川东北区域次之,川中区域最小,其贡献量分别为3.5、2.2、1.4μg/m~3。同时还进一步分析浮尘对区域PM_(10)影响差异的原因。     

杭州市环境空气中黑碳质量浓度变化特征

采用2013年环境空气自动监测数据,分析杭州市空气中黑碳质量浓度的变化规律,并对变化特征的产生原因进行探讨。结果表明:黑碳测定年均值为4.10μg/m3,日变化有明显双峰结构,峰值出现在早7时和晚8时左右;从季节看,黑碳质量浓度冬季高(5.20μg/m3)、夏季低(3.00μg/m3);黑碳质量浓度与NO2、CO、PM10、PM2.5显著相关,与O3、风速、气温呈负相关,降水对黑碳的清除作用明显。     

青岛市环境空气臭氧污染特征分析

青岛市是国家环保部确定的臭氧试点监测城市之一。文章结合青岛市市南区东部和四方区空气子站2008—2011年的试点监测数据,从区域差异、时间变化等方面分析了青岛市的臭氧污染特征,结果表明：①二区域臭氧浓度的日分布均呈现“单峰型”,12：00～15：00是一天中臭氧污染最严重的时段;②每月监测累积值市南区东部呈现“双峰型”,四方区呈现“单峰型”;③二区域臭氧污染最突出的月份均为5月;④二区域臭氧平均浓度从高到低季节排序略有差异;⑤2009年二区域臭氧污染最严重,该年四季中春季臭氧污染最为突出。     

长三角与珠三角城市环境空气质量变化对比分析

利用重点城市环境空气质量日报数据中的空气污染指数(简称API),选择上海市和广州市作为代表性城市,研究了长三角和珠三角自2001年至2008年环境空气质量的变化趋势及其影响因素。结果表明,从年际变化看,上海市2002年前空气质量恶化,2002年后空气质量优化;广州市2004年前空气质量恶化,2004年后空气质量优化,较之上海变化趋势转折点存在2年的滞后。从季度变化看,上海市冬季春季秋季夏季,广州市冬季秋季春季夏季。与社会经济发展关系的分析表明,上海市随着社会经济的发展环境空气质量总体呈现好转趋势,广州市随着社会经济的发展环境空气质量总体呈现先恶化再好转的趋势,且经济发展对环境空气质量的影响大于社会发展。     

克拉玛依市空气质量特征分析

为研究克拉玛依市空气质量特征,对2015年克拉玛依市空气自动站监测数据进行分析。结果表明,克拉玛依市空气质量整体较为清洁,优良天数占有效监测天数的91.5%,PM_(2.5)平均浓度为31μg/m~3,PM_(10)平均浓度为64μg/m~3,PM_(2.5)在PM_(10)中占比近半;SO_2和NO_2浓度相对不高,NO_2/SO_2比值为2.4,流动源问题相对突出;对各项污染物的综合指数分担率分析发现,克拉玛依市PM_(10)和PM_(2.5)为综合指数分担率最高的因子,不足四分之一,O_3排第三位,超过五分之一,SO_2最低,仅为3.6%。     

Cluster analysis of Delhi's ambient air quality data

The purpose of this study was to study the spatial patterns of ambient air quality in Delhi in the absence of extensive datasets needed for space-time modeling. A spatial classification was attempted on the basis of ambient air quality data of nine years (1998 is latest year for which published data were available) for three criteria pollutants--nitrogen dioxide, sulfur dioxide, and suspended particulate matter. Monitoring stations take 24-hour samples twice a week. Published monthly average concentration data were used in this study. A hierarchical agglomerative algorithm using the average linkage between groups method and the Euclidean distance metric was used. Cluster analysis indicated that till 1998, by and large, two distinct classes existed. The results of cluster analysis prompted an investigation of systematic biases in the monitored data. No statistically significant differences in the mean concentration of all pollutants were observed between stations belonging to different land-use types (residential and industrial). This fact would be useful, if and when the authorities consider modifying the network or expanding it in Delhi. The results also support the recommendation that Delhi have a uniform standard across all areas. This study has provided a methodology for Indian researchers and practitioners to do an exploratory study of spatial patterns of air pollution and data quality issues in Indian cities using the National Ambient Air Quality Monitoring System data.     

Indoor air quality during renovation actions: a case study

A temporary renovation activity releases considerably high concentrations of particulate matter, viable and non-viable, into air. These pollutants are a potential contributor to unacceptable indoor air quality (IAQ). Particulate matter and its constituents lead, sulfate, nitrate, chloride, ammonium and fungi as well as fungal spores in air were evaluated in a building during renovation action. Suspended dust was recorded at a mean value of 6.1 mg m(-3) which exceeded the Egyptian limit values for indoor air (0.15 mg m(-3)) and occupational environments (5 mg m(-3)). The highest particle frequency (23%) of aerodynamic diameter (dae) was 1.7 microm. Particulate sulfate (SO(4)(2-)), nitrate (NO(3)(-)), chloride (Cl(-)), ammonium (NH(4)(+)) and lead components of suspended dust averaged 2960, 28, 1350, 100 and 13.3 microg m(-3), respectively. Viable fungi associated with suspended dust and that in air averaged 1.11 x 10(6) colony forming unit per gram (cfu g(-1)) and 92 colony forming unit per plate per hour (cfu p(-1) h(-1)), respectively. Cladosporium(33%), Aspergillus(25.6%), Alternaria(11.2%) and Penicillium(6.6%) were the most frequent fungal genera in air, whereas Aspergillus(56.8%), Penicillium(10.3%) and Eurotium(10.3%) were the most common fungal genera associated with suspended dust. The detection of Aureobasidium, Epicoccum, Exophiala, Paecilomyces, Scopulariopsis, Ulocladium and Trichoderma is an indication of moisture-damaged building materials. Alternaria, Aureobasidium, Cladosporium, Scopulariopsis and Nigrospora have dae > 5 microm whereas Aspergillus, Penicillium and Verticillium have dae < 5 microm which are suited to penetrate deeply into lungs. Particulate matter from the working area infiltrates the occupied zones if precautionary measures are inadequate. This may cause deterioration of IAQ, discomfort and acute health problems. Renovation should be carefully designed and managed, in order to minimize degradation of the indoor and outdoor air quality.     

Characterization of ambient air quality during a rice straw burning episode

Spatiotemporal characteristics and impact of ambient air-quality attributed to open burning of rice straw were analyzed and estimated with measured data. Two multivariate analytic methods, factor analysis and cluster analysis, were adopted to analyze the temporal and spatial impact on ambient air-quality during the rice straw burning episode. Temporal features of three scenarios were cited to compare the concentrations for ambient air-quality between the rice straw burning episode and non-episodes over two typical stations by factor analysis. Factor analysis demonstrated that the first rotational component, identified as being highly correlated to the open burning of rice straw, accounts for about 40% of the concentration variance for ambient air-quality. In typical air-quality stations, the average hourly incremental concentrations between the episode and non-episodes were greater than 300 μg m(-3) for PM(10), 1.0 ppm for CO and 35 ppb for NO(2) during the impact of rice straw burning. Factor analysis presented that the first rotated component was highly correlated with several primary pollutants (NO(2), NMHC, PM(10) and CO) during the rice straw burning episode, while every component was only highly correlated with a unique air pollutant during non-episodes. The delineation isopleths indicated that factor analysis could serve as a better method than cluster analysis and provides cross-county cooperation for local governments located in the same separated district during the rice straw burning season. The results of factor analysis revealed that CO is the best index to demonstrate the impact of rice straw burning than the other six air pollutants measured during the episode. Backward trajectory analysis supplied a cause-effect relationship between measured stations and specific rice planted regions during the rice straw burning episode.     

The design of cost-effective air quality monitoring networks

A statistical measure of information content is used as a means for assessing the effectiveness of a particular monitoring network configuration. This measure is directly related to the precision with which the network can estimate the level of air quality in the region. Each potential site in the network can contribute to an increase in the overall precision of the estimate. The amount of increase is determined by the degree of dependence, displayed by observed pollutant concentrations, between potential sites.A statistical procedure is used to combine the observed pollution dynamics and the measurement network in order to assess network effectiveness. An iterative application of this procedure leads to the design of the most effective network subject to a specified budget constraint.     

The potential of biomonitoring of air quality using leaf characteristics of white willow (Salix alba L.)

In this study, we assess the potential of white willow (Salix alba L.) as bioindicator for monitoring of air quality. Therefore, shoot biomass, specific leaf area, stomatal density, stomatal pore surface, and stomatal resistance were assessed from leaves of stem cuttings. The stem cuttings were introduced in two regions in Belgium with a relatively high and a relatively low level of air pollution, i.e., Antwerp city and Zoersel, respectively. In each of these regions, nine sampling points were selected. At each sampling point, three stem cuttings of white willow were planted in potting soil. Shoot biomass and specific leaf area were not significantly different between Antwerp city and Zoersel. Microclimatic differences between the sampling points may have been more important to plant growth than differences in air quality. However, stomatal pore surface and stomatal resistance of white willow were significantly different between Zoersel and Antwerp city. Stomatal pore surface was 20% lower in Antwerp city due to a significant reduction in both stomatal length (?11%) and stomatal width (?14%). Stomatal resistance at the adaxial leaf surface was 17% higher in Antwerp city because of the reduction in stomatal pore surface. Based on these results, we conclude that stomatal characteristics of white willow are potentially useful indicators for air quality.     

Estimating air quality impacts

LEONARD ORTOLANO has written a textbook, Environmental Planning and Decision Making, that is ideal for introducing undergraduates to the field of environmental planning. His three chapters on environmental impact assessment (from 16 chapters overall) present a range of impact assessment methods in a helpful decision-making context. Specific techniques for considering biological factors in planning, evaluating the visual qualities of the environment, assessing noise impacts, assessing air quality impacts, and assessing impacts on water resources are also covered in five additional chapters. Professional planners and engineers should find this introduction to the approaches employed by specialists of various kinds helpful. With the permission of the publisher, John Wiley and Sons, we here reprint Chapter 15, “Estimating Air Quality Impacts,” from Ortolano's excellent book. This straightforward presentation of a six-step process for establishing the air quality impacts of proposed development projects is a model of clarity.     

Study of temporal variation in ambient air quality during Diwali festival in India

The variation in air quality was assessed from the ambient concentrations of various air pollutants [total suspended particle (TSP), particulate matter ≤10 μm (PM₁₀), SO₂, and NO₂] for pre-Diwali, Diwali festival, post-Diwali, and foggy day (October, November, and December), Delhi (India), from 2002 to 2007. The extensive use of fireworks was found to be related to short-term variation in air quality. During the festival, TSP is almost of the same order as compared to the concentration at an industrial site in Delhi in all the years. However, the concentrations of PM₁₀, SO₂, and NO₂ increased two to six times during the Diwali period when compared to the data reported for an industrial site. Similar trend was observed when the concentrations of pollutants were compared with values obtained for a typical foggy day each year in December. The levels of these pollutants observed during Diwali were found to be higher due to adverse meteorological conditions, i.e., decrease in 24 h average mixing height, temperature, and wind speed. The trend analysis shows that TSP, PM₁₀, NO₂, and SO₂ concentration increased just before Diwali and reached to a maximum concentration on the day of the festival. The values gradually decreased after the festival. On Diwali day, 24-h values for TSP and PM₁₀ in all the years from 2002 to 2007 and for NO₂ in 2004 and 2007 were found to be higher than prescribed limits of National Ambient Air Quality Standards and exceptionally high (3.6 times) for PM₁₀ in 2007. These results indicate that fireworks during the Diwali festival affected the ambient air quality adversely due to emission and accumulation of TSP, PM₁₀, SO₂, and NO_2.