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.     

空气质量预报在世界互联网大会中的应用

为支持世界互联网大会期间大气污染管控工作，利用人工结合数值模式预报的方式在第二届到第五届世界互联网大会期间开展空气质量预报工作。多模式系统中WRF-CMAQ对乌镇及浙北区域大气污染变化的趋势模拟最好，2016—2018年对AQI日均值模拟的平均分数偏差（MFB）和平均分数误差（MFE）分别为-1.3%~1.6%和24.3%~28.3%。会前48 h、72 h和96 h空气质量等级预报准确率分别为37.5%~83.3%、33.3%~90.0%和0~89.9%。会议期间乌镇的AQI日均值48 h预报准确率为33.3%~100%，等级预报准确率为66.7%~100%。与日常空气质量预报不同，会议期间预报还应重点关注大气污染过程，如有污染可能性，需要给出污染过程的起始时间、持续范围和浓度峰值等情况及其关键时间节点，有针对性地提出大气污染管控的措施建议，为会议期间空气质量保障提供技术支撑。     

An air quality balance index estimating the total amount of air pollutants at ground level

A new index named Air Quality Balance Index (AQBI), which is able to characterise the amount of pollution level in a selected area, is proposed. This index is a function of the ratios between pollutant concentration values and their standards; it aims at identifying all situations in which there is a possible environmental risk even when several pollutants are below their limit values but air quality is reduced. AQBI is evaluated by using a high-resolution three-dimensional dispersion model: the air concentration for each substance is computed starting from detailed emissions sources: point, line and area emissions hourly modulated. This model is driven with accurate meteorological data from ground stations and remote sensing systems providing vertical profiles of temperature and wind; these data are integrated with wind and temperature profiles at higher altitudes obtained by a Local Area Model. The outputs of the dispersion model are compared with pollutant concentrations provided by measuring stations, in order to recalibrate emission data. A three-dimensional high resolution grid of AQBI data is evaluated for an industrial area close to Alessandria (Northern Italy), assessing air quality and environmental conditions. Performance of AQBI is compared with the Air Quality Index (AQI) developed by the U.S. Environmental Protection Agency. AQBI, computed taking into account all pollutants, is able to point out situations not evidenced by AQI, based on a preset limited number of substances; therefore, AQBI is a good tool for evaluating the air quality either in urban and in industrial areas. The AQBI values at ground level, in selected points, are in agreement with in situ observations.     

Evaluation of environmental impacts of Integrated Industrial Estate��Pantnagar through application of air and water quality indices

Large-scale industrialization, population inflow, and rapid urbanization coupled with unfavorable meteorological conditions often induce significant degradation of urban environment. In order to assess the extent of environmental impacts due to establishment of the Integrated Industrial Estate??Pantnagar (IIE-Pantnagar), ambient air and groundwater were monitored from June 2007 to May 2008. Collected baseline information was normalized and interpreted with the application of air (AQI) and water quality indices (WQI). Among the pre-identified air pollutants, suspended particulate matter was found to be the principal culprit to deteriorate ambient air quality, with a maximum annual concentration of 418.5 ??g/m³. Monthly average concentrations of respirable particulate matter (aerodynamic diameter < 10 ??m) also persist at a critical level with an annual maximum of 207.3 ??g/m³. A segmented linear function with maximum operator concept was used to compute AQI, and the developed index was found well suitable to demonstrate temporal variations of ambient air quality. The computed AQI value for the selected study region varied from moderate (97.0) to very poor pollution level (309.2) in respect to developed air quality standards. Furthermore, an integrated WQI was developed comprising 9 parameters, and among all the 10 pre-identified locations, the average groundwater quality was found acceptable in terms of Indian drinking water standards. The maximum WQI (70.6) was found at the Kichha Railway Station during summer months, revealing moderate pollution load. Industrial discharge from IIE-Pantnagar coupled with other industrial setup may hold responsible for such kind of degradation of water quality. In contrast, WQI computed at Rudrapur City demonstrate minimum (15.0?C22.1) pollution load. For 95% of the monitoring period, the computed WQI was found acceptable for all selected locations with few exceptions. The application of WQI to assess temporal variations in groundwater quality was therefore found satisfactory.     

博鳌亚洲论坛2018年年会期间空气质量预报保障技术研究

为了做好博鳌亚洲论坛2018年年会期间的空气质量保障工作,通过综合分析2015-2017年同期的监测数据及其气象条件,依托空气质量多模式数值预报系统,以专家组现场会商研判的方式开展了空气质量预报保障工作。结果显示,海口市、三亚市和博鳌镇在会议期间空气质量保持优良,每日AQI预报结果在±10的准确率为72. 2%,有效支撑了博论坛期间空气质量保障工作。会议期间文昌区域站和五指山背景站出现超标现象,超标污染物均为O3,外来污染传输是今后博鳌论坛保障中需重点关注的关键影响因素。     

建立空气质量综合评价指数的探讨

分析了空气污染指数(API)系统在评价空气污染水平中的不足,提出了在原API系统基础上进行指标拓展,增加能见度和PM2.5评价指标,建立空气质量综合评价指数（AQI）;分析了扬州市区PM2.5和能见度在雾霾日及正常日的百分位分布,确定了PM2.5和能见度分级值,并利用扬州市区环境空气质量监测数据进行了验证。     

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.     

长三角空气质量区域预报准确性评估方法研究

根据长三角空气质量区域预报工作的实际需要,对分区文字预报和落区图预报两种方式分别制定了不同的空气质量指数级别预报准确性评估方法。分区文字预报根据设定的预报准确性判定方法计算预报评分,落区图预报按区域内预报准确城市占比进行准确率统计。分区文字预报结果显示,2017年长三角区域的预报准确天数占比为62.2%,预报评分为70.2,区域预报评估效果良好。落区图预报评估结果显示,预报级别偏差具有地域性差异,安徽北部、江苏北部和江西中北部预报等级偏高,长三角中南部沿海城市预报等级偏低。该套评估方法可为区域空气质量预报偏差成因分析提供依据,为区域预报工作的改进提供定量参考。     

环渤海地区空气质量时空变化特征及动态预测

基于环渤海地区2017—2021年各城市空气质量指数(AQI)、污染物浓度与社会经济数据,利用数理统计、克里金插值法对环渤海地区AQI与污染物浓度的时空变化特征进行分析,运用皮尔逊相关性分析方法探讨AQI与污染物浓度、社会经济因素的相关关系,采用时间序列预测模型对2022年6月—2023年12月空气质量及污染物浓度进行预测。结果表明:环渤海地区AQI及污染物浓度大致呈逐年降低的趋势。AQI的逐月变化呈"W"形,O₃浓度的年内变化呈倒"V"形,其余污染物则呈现与O₃相反的变化趋势。AQI大致呈现西南高、东北低的空间分布特点,而污染物浓度分布具有明显的空间差异。环渤海地区5个代表性城市的AQI类别以良好为主,冬季首要污染物主要为PM_2.5、PM₁₀,夏季首要污染物以O₃为主。人口数量是影响AQI的主要因素,城市园林绿地面积对AQI具有一定影响。预测结果显示,未来环渤海地区AQI、主要污染物浓度(O₃除外)均呈现出随时间的推移逐渐下降的变化趋势。     

基于CFSv2的延伸期空气质量数值预报技术及效果评估

介绍了基于CFSv2产品开展空气质量数值模拟的技术方法以及基于该技术的成都市延伸期空气质量预报系统,评价了2019年9月25日至12月25日的预报结果,以2019年12月为例分析了系统对具体污染过程的预报能力。结果表明,系统AQI预报准确率为36.67%,等级预报准确率为68.93%,"时段1"的预报效果优于其余时段,但"时段2"和"时段3"在污染物浓度水平上仍然具有较高的参考价值。系统在PM2.5浓度、气温和气压的变化趋势上提供21 d左右的参考。后续工作中,将针对CFSv2产品开展气象模型参数方案优选,并尝试结合基于GFS产品驱动的常规业务数值预报产品为延伸期空气质量预报提供污染物浓度初始场,减少误差传递,从而提高延伸期空气质量预报产品的准确性和可用性。     

校园空气微生物和悬浮物污染评价及相关性分析

在对广西师范大学育才校区教学区、宿舍区、运动场和交通区大气微生物和总悬浮颗粒物(TSP)进行监测的基础上,评价与分析了育才校区大气污染状况。依照中国科学院生态中心推荐使用的空气微生物评价标准,育才校区大气微生物污染轻微,其中交通区和运动场大气微生物含量相对较高,但都能达到清洁空气的标准;大气总悬浮颗粒物污染下午较严重,多数测定值超过了国家环境空气质量标准中的二级标准限值,而上午的测定值相对较低;四个功能区中TSP浓度值为交通区>运动场>教学区>宿舍区。上午空气中TSP浓度与细菌总数具有显著相关性,与霉菌总数相关关系不显著;下午TSP测定值与空气中细菌总数和霉菌总数的相关关系均不显著;校园空气中细菌总数和霉菌总数为极显著相关。     

河源市2014-2017年空气污染特征及天气类型分析

利用2014—2017年河源城区环境空气自动监测站数据和气象数据，对期间出现的污染天气过程进行统计，对影响污染的天气类型进行分类。结果表明，2014—2017年，河源城区累计出现污染天气65 d，超标污染物主要为PM_2.5和O₃，O₃超标比例逐年上升成为达标率首要影响因子。PM_2.5易污染天气型中冷高压出海占比最多（38.2%），其次为冷锋前（17.7%）和均压场（14.8%）；O₃易污染天气型中副高控制占比最多（31.0%），其次为副高叠加台风外围（24.1%）和冷高压出海（13.8%）及均压场（13.8%）。河源城区低程度污染（AQI值101～110）占比较大。     

新冠疫情期间无锡市环境空气质量分析

通过对无锡市区环境空气污染物的连续监测,对新冠疫情期间环境空气质量进行研究分析.结果表明,2月疫情期间,无锡市环境空气质量同比与环比均有明显好转,AQI指数在24～80,均为优良天.这可能与全社会生产、生活、活动有极大变动有关,可指导空气污染防治工作的努力方向.从单项指标看,除O3以外的其他5项指标均有不同程度的好转....     

Indoor air in schools and lung function of Austrian school children

The Children's Environment and Health Action Plan for Europe (CEHAPE) of WHO focuses (inter alia) on improving indoor environments where children spend most of their time. At present, only little is known about air pollution in schools and its effect on the lung function of school children. Our project was set up as an Austrian contribution to CEHAPE. In a cross-sectional approach, differences in indoor pollution in nine elementary all-day schools were assessed and 34 of these pollutants were analyzed for a relationship with respiratory health determined by spirometry using a linear regression model. Overall 596 children (aged 6-10 years) were eligible for the study. Spirometry was performed in 433 children. Socio-economic status, area of living (urban/rural), and smoking at home were included in the model as potential confounders with school-related average concentration of air pollutants as the variable of primary interest. A negative association with flow volumes (MEF(75)) was found for formaldehyde in air samples, benzylbutylphthalate and the sum of polybrominated diphenylethers in school dust. FVC and FEV(1) were negatively associated with ethylbenzene and xylenes in air samples and tris(1,3-dichlor-2-propyl)-phosphate on particulates. Although, in general, the quality of school indoor air was not worse than that reported for homes, effects on the respiratory health of children cannot be excluded. A multi-faceted strategy to improve the school environment is needed.     

Air pollution exposure monitoring and estimating. Part I. Integrated air quality monitoring system

This paper presents an integrated exposure monitoring system, based on an expansion of existing air quality monitoring systems using dispersion modelling. The system allows: (1) identifying geographical areas whose inhabitants are most exposed to ambient pollution; (2) identifying how many people in an area are exposed to concentrations of pollution exceeding air quality guidelines; (3) describing the exposure of population subgroups (e.g. children); (4) planning pollution abatement measures and quantifying their effects; (5) establishing risk assessment and management programs, and (6) investigating the short- and long-term effects of both pollutants and pollution sources on public health. The effect of pollution is rarely very large and in order to discover it, exposure estimation must provide data that reflects both spatial and temporal variations. Estimates of pollution exposure are obtained using an integrated approach that combines results of measurements from monitoring programs with dispersion calculations. These values can serve as estimates for individual short-term or long-term exposure. The grouped data allows the expression of ambient pollution concentrations as the spatial distribution of estimates such as the mean or 98th percentile of such compounds as SO2, O3, NO2, PM10 and PM2.5. This integrated approach has been combined into a single software package, AirQUIS.     

长株潭区域一次重度污染过程气象成因

利用PM_2.5质量浓度、地面气象要素、NCEP、ERSST＿V3、GBL等资料,研究了2021年12月7—11日长株潭地区一次重度空气污染过程的特征及成因。结果表明,高空平直环流、无明显槽脊影响,地面弱冷空气活跃是本次重度空气污染过程的主要环流形势特征;地面均压场、小风和升温增湿是此次重度空气污染过程的主要气象要素特征。污染物浓度变化与主导风向和污染通道密切相关,本地风速对混合层的高度、污染物水平扩散影响较大,600～700 hPa逆温层有利于污染物在主导风作用下近距离传输及在低层交换积累。我国中东部污染物积聚是长株潭区域重要的污染来源,长株潭地区存在区域性同步污染现象。低层流入长株潭区域气流轨迹差异及地理条件是长株潭污染物空间分布差异的重要因素。     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

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。     

空气质量AQI实时发布的改进探讨

针对《环境空气质量指数(AQI)技术规定(试行)》(HJ 633-2012)中对空气质量AQI实时发布存在的欠缺,从增加颗粒物1 h浓度的AQI分级浓度限值及颗粒物24 h滑动平均值计算方法改进着手,解决PM2.5和PM10的24 h滑动平均值实时延迟、1 h平均值代替24 h滑动平均值偏高等问题。