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。     

基于分形求和模型的克拉玛依市空气质量评价

以克拉玛依市4个区2012年的大气自动监测数据为样本，基于分形求和模型，分析大气污染物的分布特征，利用分维数确定污染物浓度分布的随机程度，计算 SO2、NO2、PM10的大气环境背景值与标准值，确定适合于评价区域的ORAQI指数计算公式，并与 API指数作对比。ORAQI指数计算结果显示，克拉玛依市全年环境空气质量基本呈现“U”字形变化，春夏季大气质量好于秋冬季，全年空气质量有明显的季节变化，4个区中克拉玛依区空气质量相对较差，乌尔禾区空气质量最好。相对于 API指数的均匀分布结果，ORAQI指数具有更好的次要污染物体现能力，可以综合体现所评价的各项污染因子的贡献。     

Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessment

Epidemiological studies typically use monitored air pollution data from a single station or as averaged data from several stations to estimate population exposure. In industrialized urban areas, this approach may present critical issues due to the spatial complexities of air pollutants which are emitted by different sources. This study focused on the city of Taranto, which is one of the most highly industrialized cities in southern Italy. Epidemiological studies have revealed several critical situations in this area, in terms of mortality excess and short-term health effects of air pollution. The aims of this paper are to study the variability of air pollutants in the city of Taranto and to interpret the results in relation to the applicability of the data in assessing population exposure. Meteorological and pollution data (SO2, NO2, PM10), measured simultaneously and continuously during the period 2006–2010 in five air quality stations, were analyzed. Relative and absolute spatial concentration variations were investigated by means of statistical indexes. Results show significant differences among stations. The highest correlation between stations was observed for PM10 concentrations, while critical values were found for NO2. The worst values were observed for the SO2 series. The high values of 90th percentile of differences between pairs of monitoring sites for the three pollutants index suggest that mean concentrations differ by large amounts from site to site. The overall analysis supports the hypothesis that various parts of the city are differently affected by the different emission sources, depending on meteorological conditions. In particular, analysis revealed that the influence of the industrial site may be primarily identified with the series of SO2 data which exhibit higher mean concentration values and positive correlations with wind intensity when the monitoring station is downwind from the industrial site. Results suggest evaluating the population exposure to air pollutants in industrialized cities by taking into account the possible zones of influence of different emission sources. More research is needed to identify an indicator, which ought to be a synthesis of several pollutants, and take into account the meteorological variables.     

An Analysis of the Annual and Seasonal Trends of Air Quality Index of Delhi

The Air Quality Index (AQI) is an index for reporting daily air quality. A study on the annual and seasonal variations of Air Quality Index over a period of 9 years (1996-2004) based on daily averaged concentration data of criteria air pollutants has been conducted for Delhi. An attempt has been made to quantify the changes in the AQI on annual and seasonal (winter, summer, monsoon and post monsoon) basis for 9 years. Measurements for the seven monitoring sites (Nizamuddin, Ashok Vihar, Shahzada Baug, Shahadara, Janakpuri, Sirifort and ITO) in Delhi were analysed and trends were also compared amongst these sites. Maximum Operator Function method was used to compute the Air Quality Index of the above areas and percentage variations in different severity class is discussed which provides in depth analysis of the trends. The best air quality was depicted by Shahzada Baug followed by Shahdara, both of these were classified as industrial areas indicating that policy measures relating to the industries in the city during past years have helped in improving the air quality. The air quality in other areas have improved slightly in the span of nine years but still remains critical indicating continued rigorous efforts in this direction. Increased traffic density seems to have resulted into the worst air quality at ITO in the city amongst all the monitoring stations. There is a shift for the worst AQI in the city from winter to summer season in a time span of these nine years. Change of season for worst AQI from Winter to Summer may also be likely due to increased photochemical reactions playing major role with change in the nature of emissions imposed due to different control measures such as CNG implementation, significant shift to LPG in domestic sector etc. calling for a detailed study, those which started after the year 2000. After the year 2000, there is a significant increase in the Nitrogen-dioxide (NO(2)) concentration at all stations. ITO which has shown continuous exponential increase in pollution levels has first time showed a declining AQI trend in the year 2004 and one of the contributing factors could have been the Delhi metro (initiated in 2002) passing through congested neighbouring areas causing traffic decongestion here. In general, the areas which are farthest from metro route viz., Siri-fort, Nizamuddin, Janakpuri etc. did not record declining AQI in 2003 onwards as happened with stations closer to Metro route such as Ashok Vihar and ITO. An attempt has been made to quantify the reasons that lead to the changes in the values of the AQI.     

Analyzing SO 2 concentrations and wind directions during a short monitoring campaign at a site far from the industrial pole of La Plata, Argentina

This article presents and discusses SO2 air quality concentrations (ppbv) together with wind velocities and directions measurements carried out between September 1st and December 21st 2005 at a site located 8.5 km away from the Industrial Pole of La Plata area. As the city and its surroundings have no official monitoring network, the current work enlarges the air quality information available from the zone and sets some initial considerations to the future siting of monitoring stations. The statistical analysis of the data was performed using techniques of tests for outliers and trends, dissimilarity measures and robust regression. In relation to SO2 concentrations, low values were found during this short campaign considering daily averages (with a maximum of 8.5 ppbv) and hourly averages (with a maximum of 25.9 ppbv); World Health Organization guidelines were never surpassed. Nevertheless, a strong dependence between wind directions carrying air pollutants from the Industrial Pole and hourly concentration peaks were found. Due to low monthly SO2 concentrations and because a decreasing time trend was found, the authors propose, as an example, the implementation of an alternative discontinuous method to the continuous analyzer used in the current campaign. Our results state that sampling every 7 days at 13:00-13:59 hours (local time) would be enough to get representative values of the air quality. As a general remark it is possible to highlight that longer and systematic studies should be encouraged to confirm the seasonal wind pattern and to evaluate the air quality.     

Air Quality Status of Visakhapatnam (India) – Indices Basis

Industrial development in Visakhapatnam is conspicuous to urban agglomeration and the city is located in a topographical bowl formed by two-hill ranges. A major portion of the city is within the bowl area wherein most of the industrial and commercial activities are existing and lies within a distance of 10 km from the shore of the Bay of Bengal. Due to the peculiar geographic location of city, wind movement is either eastern or western and is engulfed within the hill ranges. Hence, there is a possibility of buildup of air pollution levels within the city. Due to gravity of prevailing situation, air quality status of Visakhapatnam on indices basis is analyzed using a non-linear equation for variable parameters i.e. Suspended particulate matter (SPM). Sulfur dioxide (SO2) and Oxides of nitrogen (NO(x)), which are main criteria pollutants in India. For current analysis seasonal air quality data is used, which indicates SPM values in winter at most of the sites and in summer at few sites are exceeding the prescribed standards. Calculated indices reveal that, in winter as well as in summer, most of the locations experienced poor or bad air quality, which is mainly due to higher concentration of SPM and certain extent of SO2 values. Application of Oak Ridge Air Quality Index (ORAQI) type equations (non-linear) are helpful for air quality management plan in the region on long-term basis and it has been also observed that there are certain lapses of weightage assignment for individual pollutant in application.     

Development of the microbial communities in lake donghu in relation to water quality

There is increasing recognition that protozoa is very useful in monitoring and evaluating water ecological healthy and quality. In order to study the relationship between structure and function of protozoan communities and water qualities, six sampling stations were set on Lake Donghu, a hypereutrophic subtropical Chinese lake. Microbial communities and protists sampling from the six stations was conducted by PFU (Polyurethane foam unit) method. Species number (S), diversity index (DI), percentage of phytomastigophra, community pollution value (CPV), community similarity and heterophy index (HI) were mensurated. The measured indicators of water quality included total phosphorus (TP), dissolved oxygen (DO), Chemical oxygen demand (COD), NH(4)(+), NO(2)(-) and NO(3)(-). Every month water samples from stations I, II, III, IV were chemically analyzed for a whole year, Among the chemically analyzed stations, station I was the most heavily polluted, station II was the next, stations III and IV had similar pollution degrees. The variable tendencies of COD, TP, NH(3), NO(2)(-), NO(3)(-), and DO during the year was approximately coincident among the six stations. Analysis from the community parameters showed that the pollution of station 0 was much more serious than others, and station V was the most slight. Of the community parameters, CPV and HI were sensitive in reflecting the variables of the water quality. Community similarity index was also sensitive in dividing water qualities and the water quality status of different stations could be correctly classified by the cluster analysis. DI could reflect the tendency of water quality gradient, species number and percentage of Phytomastigophora was not obvious in indicating the water quality gradient.     

新冠肺炎疫情期间冀南地区空气质量时空变化特征

为研究新冠肺炎疫情期间冀南地区空气质量变化规律,明确防疫管控措施对空气质量的具体影响及大气污染物排放特征,笔者综合分析了研究区域的常规监测数据和部分大气超级站的PM_2.5组分数据。结果表明:疫情防控重点区域石家庄市、邢台市空气质量整体好转,细颗粒物和一次排放气态污染物浓度下降明显,PM_2.5来源中燃煤、生物质燃烧源占比上升,机动车尾气源占比下降,体现出交通管制、企业限产和道路工地扬尘管理等环保措施的有效性。疫情防控高风险区域石家庄市藁城区出现了明显的NO₂浓度降低、PM_2.5污染好转现象,而O₃浓度显著升高成为新的特征污染物。结果显示,藁城区综合防疫管控举措对本地一次排放污染物起到了明显抑制作用。疫情防控核心区域藁城区增村镇因实行最严格的封村、限行、停产等措施,人为污染源排放"触底",6项监测指标中除O₃浓度同比、环比均大幅度升高外,其他污染物浓度全时段降低,SO₂和CO昼夜差距缩小,环境质量明显优于周边乡镇。分析认为大规模持续化学消杀可能对O₃浓度升高有影响,此问题需要进一步探讨。     

融合拓扑信息与气象信息的空气质量预测网络

通过空气质量监测数据对正在形成或即将到来的空气污染进行预测是一项具有重要意义的工作，而空气质量监测站只能检测其周围一定范围内的空气污染情况。为了衡量整个城市的空气污染情况，获取任意时间、任意位置的空气质量信息，结合交叉注意力机制，提出了一种融合拓扑信息与气象信息的空气质量预测网络(CGMIM)。将西安市空气质量监测数据与气象数据转换为图像拼接起来，作为输入信息。在高阶非线性时空动态神经网络(MIM)的基础上引入注意力机制，并增加拓扑图编码器模块，提高模型提取能力以及对空气质量监测数据中的空间特征的利用率。最后，使用时空损失函数替代传统的均方误差损失函数，提高模型对空间关系的关注。结果表明：CGMIM网络模型能够在准确预测的同时，对位置区域合理填充，能够有效提升空气质量监测数据的空间分辨率。     

Spatial concentration distributions of sulfur dioxide and nitrogen oxides in Patras, Greece, in a winter period

An economic and quick methodology for performing a preliminary spatial assessment of a city air quality with the purpose to identify locations and zones susceptible to high pollution levels is proposed. A Patras case-study is selected, regarding the air pollutants of sulfur dioxide (SO₂) and oxides of nitrogen (NO_x). A total number of 451 samples of short duration, of which 225 were randomly picked in morning rush hours and 226 within evening rush hours, were collected from 50 locations of the major Patras area during a year period, when peaks of primary air pollutants usually occur. Concentration measurements at prescribed locations used to statistically calculate spatial average concentrations approximating 1-h mean values with mean probable errors less than 25.9% for SO₂, NO and NO_x and less than 15.5% for NO₂. Then iso-concentration contour diagrams plotted indicate high pollution zones and possibly appropriate locations for continuous or random monitoring according to the European Community (EC) Directives. The 1-h mean concentrations were in good correlation to the corresponding traffic rates and useful relationships are given (0.54 ≤ r ≤ 0.63). In addition, comparisons with data available for other cities, as well as with the limit and guide values provided by the EC and the World Health Organization (WHO) were given. The present data could be useful for the design and optimization of a city network of stations for monitoring air quality, for environmental impact assessments, future reference and comparisons due to city development needs, as well as for validating dispersion models.     

黑龙江省“十三五”时期生态环境质量变化及趋势预测

以黑龙江省生态环境监测网监测结果为基础,总结归纳了"十三五"期间黑龙江省生态环境质量变化特征,并采用随机森林和GM(1,1)预测模型对"十四五"期间黑龙江省生态环境质量状况进行了预测。结果表明:"十三五"期间,黑龙江省环境空气、水环境和声环境质量全面好转。其中,环境空气主要污染物PM_2.5、PM₁₀、SO₂、NO₂和CO的年均质量浓度均呈现出明显下降的趋势,"哈大绥"重点区域PM₁₀、SO₂、NO₂和CO年均质量浓度呈现下降趋势。地表水水质总体呈波动变化趋势,水质状况均为轻度污染。"十四五"期间,黑龙江省生态环境质量将处于稳中向好的趋势。环境空气主要污染物及地表水主要污染指标年均质量浓度均呈现明显的下降趋势,道路交通声环境质量也将得到进一步改善。     

淄博化工园区大气有机污染应急监测与管理对策

针对工业聚集区内多种恶臭混杂的情况，以淄博化工园区为例，研究了大气有机污染突发事件中有机物的应急监测方法，并提出了治理对策。利用便携式气相色谱-质谱联用仪，对重点企业事先监测，掌握各企业特征污染物，作为应急监测查找污染源头的依据；现场分析受污染空气中的化合物组成及浓度，通过查询、比对各企业特征污染物，确认责任排污企业。提出了末端监测处罚治理与对重点污染企业推行清洁生产相结合，应急监测与有机污染物在线监测相结合，建立企业污染源特征组分数据库，加强企业无组织排放监测与监管等治理大气有机污染的建议。     

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。     

An Assessment on Variation of Sulphur Dioxide and Particulate Matter in Erzurum (Turkey)

Sulphur dioxide and PM₁₀ levels are investigated in Erzurum during the periods of 1990–2000 heating season to assess air pollution level. For that reason, emissions of sulphur dioxide and particulate matter were calculated by using consumption of fuels and Turkish emission factors. These emission values were evaluated together with air pollution levels, which were measured at six stations in Erzurum atmosphere during 1990–2000 winter periods. Results reveal that in 1990–1994 heating period, there is an increasing trend in the emissions and air pollution levels over Erzurum, and the air quality limits were not met. The daily 24 h limit (short-term limit) was exceeded 127 days in 1992–1993 winter period. The reason for this increase was found to be the switching to use of low-quality fossil fuels instead of cleaner ones. Results also indicated that there was a considerable decrease in emissions of air pollutants and air pollution levels after 1995. This can be explained by the consumption of more high-quality fossil fuels. The correlation coefficient of SO₂ with PM₁₀ is obtained as r² = 0.85, which is a high value supporting the idea that both pollutants are emitted from the same source.     

Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters

The Tamsui River basin is located in Northern Taiwan and encompasses the most metropolitan city in Taiwan, Taipei City. The Taiwan Environmental Protection Administration (EPA) has established 38 water quality monitoring stations in the Tamsui River basin and performed regular river water quality monitoring for the past two decades. Because of the limited budget of the Taiwan EPA, adjusting the monitoring program while maintaining water quality data is critical. Multivariate analysis methods, such as cluster analysis (CA), factor analysis (FA), and discriminate analysis (DA), are useful tools for the statistically spatial assessment of surface water quality. This study integrated CA, FA, and DA to evaluate the spatial variance of water quality in the metropolitan city of Taipei. Performing CA involved categorizing monitoring stations into three groups: high-, moderate-, and low-pollution areas. In addition, this categorization of monitoring stations was in agreement with that of the assessment that involved using the simple river pollution index. Four latent factors that predominantly influence the river water quality of the Tamsui River basin are assessed using FA: anthropogenic pollution, the nitrification process, seawater intrusion, and geological and weathering processes. We plotted a spatial pattern using the four latent factor scores and identified ten redundant monitoring stations near each upstream station with the same score pattern. We extracted five significant parameters by using DA: total organic carbon, total phosphorus, As, Cu, and nitrate, with spatial variance to differentiate them from the polluted condition of the group obtained by using CA. Finally, this study suggests that the Taiwan EPA can adjust the surface water-monitoring program of the Tamsui River by reducing the monitoring stations to 28 and the measured chemical parameters to five to lower monitoring costs.     

支撑京津冀区域大气污染联防联控的大气监测体系构建

京津冀区域已成为全国大气污染最严重的地区之一,并且呈现出明显的区域性污染特征,加强区域间的环境合作,实施区域联防联控是解决京津冀区域大气污染问题的有效手段。对照《生态环境监测网络建设方案》的要求,目前京津冀区域大气监测体系还存在着监测网络不健全、监测项目覆盖不全面、监测新技术应用不足、质控体系不完善、信息产品供给与公众需求有差距等问题,与京津冀区域大气污染联防联控的要求不相适应。为全面提升京津冀区域大气监测体系对区域联防联控的技术支撑能力,亟需进一步完善京津冀区域的监测网络,增设传输研究、污染监控等特殊监测点位;逐步拓展监测项目,开展颗粒物化学组分和O_3前体物监测;加快遥感监测等立体监测技术在大气监测中的应用,全面分析污染物时空分布特征与传输规律;加强空气质量预报预警能力建设,为重污染天气应对提供技术支持;规范监测事权上收后的全过程质控体系,建立颗粒物标准方法比对和O_3量值传递质控机制;构建环境监测大数据平台,加强数据分析应用与信息公开。     

辛集市"十三五"期间空气质量特征分析

重点对河北省辛集市"十三五"期间整体空气质量变化情况以及影响辛集市优良天数的2个重要参数O₃和PM_2.5的污染规律进行了分析。结果表明,辛集市"十三五"期间空气质量改善明显,优良天数整体增加,污染天数整体减少。O₃浓度及其作为首要污染物出现的天数整体呈现上升趋势,对综合指数的贡献率逐年增加;O₃污染高发期主要集中在4—9月,高值区域分布差距较大,但市区污染持续突出。PM_2.5浓度逐年下降,以PM_2.5为首要污染物的天数逐年减少;PM_2.5浓度季节变化特征整体呈现"秋冬高、春夏低"的分布特点,空间分布呈"南北高、中间低"的污染特征。     

Extending the use of air quality indices to reflect effective population exposure

Although there are tendencies to develop a single common index which would describe an overall air quality status within an area, constructed from a choice of measurements of individual pollutants, indices describing individual pollutants themselves have several potentials which can be used in ways which are not possible with pollutant concentrations. On the case of Belgrade, Serbia, we investigated possibilities of using such indices for comparisons between pollutants, characterization of monitoring sites, and extending their use to include elements of population exposure. A methodology of adjusting the results obtained at monitoring stations located in severe pollution conditions, like street canyons, is proposed and used.     

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。