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.     

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.     

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。     

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.     

Identification of air pollutant sampling period using horizontal dilution potential

A new model is proposed for estimating horizontal dilution potential of an area using wind data. The mean wind speed and wind direction variation are used as a measure of linear and angular spread of pollutant in the atmosphere. The methodology is applied to monitored hourly wind data for each month of 1 year for wind data collected at Vadodara, Gujarat and monthly dilution potential is estimated. It is found that there is a gradual variation of horizontal dilution potential over a year with limited dilution during post monsoon period i.e., October and November and a high dilution in pre monsoon period i.e., May and June. This information can be used to design air quality sampling network and duration of sampling for source apportionment study. Air pollutant sampling during high dilution period can be carried out for identifying urban and rural dust and wind blown dust from mining activity. Air pollutant sampling during low dilution period can be carried out for capturing large amount of particulate matter from anthropogenic sources like elevated stack of furnace.     

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.     

Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

The monthly maximum of the 24-h average time-series data of ambient air quality-sulphur dioxide (SO(2)), nitrogen dioxide (NO(2)) and suspended particulate matter (SPM) concentration monitored at the six National Ambient Air Quality Monitoring (NAAQM) stations in Delhi, was analysed using Box-Jenkins modelling approach (Box et al. 1994). Univariate linear stochastic models were developed to examine the degree of prediction possible for situations where only the past record of pollutant data are available. In all, 18 models were developed, three for each station for each of the respective pollutant. The model evaluation statistics suggest that considerably satisfactory real-time forecasts of pollution concentrations can be generated using the Box-Jenkins approach. The developed models can be used to provide short-term, real-time forecasts of extreme air pollution concentrations for the Air Quality Control Region (AQCR) of Delhi City, India.     

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.     

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。     

海口市臭氧污染特征

基于2013—2015年海口市4个空气质量自动监测站点数据,结合气象资料,分析了海口市O_3的污染特征。结果表明:海口市O_3总体优良,优良天数比例为99.4%,污染天数均为轻度污染;在良和污染天数中,O_3作为首要污染物的天数占40%,超过其他5项污染物占比。海口市10月O_3浓度最高。O_3月均浓度与温度呈负相关关系,同时与风向有密切关系:5—8月气温较高,以南风为主,O_3浓度较低;1月北风频率较高,易受外来污染传输作用,O_3浓度相对较高。O_3超标日以东北风为主,日变化并未呈现单峰型特征,12:00—22:00时段O_3浓度在10%范围内小幅变化。台风外围型和北方冷高压底部型是造成海口市O_3超标的2类典型天气形势。     

美国加州南岸地区空气质量监测系统运行管理与借鉴

借鉴加州南岸空气质量监测管理经验（特别是运行管理模式）对于现阶段我国城市环境空气质量监测管理具有极高的参考价值。简要介绍了加州南岸空气质量管理局（ SCAQMD）的空气质量监测现状、监测网络布局、颗粒物采样方法和相关质量管理体系。对现行的环境空气质量指数、管理架构和PM2?5考核方法进行了综合比较，建议从4个方面借鉴SCAQMD经验：试行“空气质量管理区”模式；开展专项研究网络建设；逐步开展手工监测采样和颗粒物化学组分分析；提升数据挖掘水平，服务管理决策。     

Methodology for designing air quality monitoring networks: I. Theoretical aspects

An objective methodology is presented for determining the number and disposition of ambient air quality stations in a monitoring network for the primary purpose of compliance with air quality standards. The methodolgy utilizes a data base with real or simulated data from an air quality dispersion model for application with a two-step process for ascertaining the optimal monitoring network. In the first step, the air quality patterns in the data base are collapsed into a single composite pattern through a figure-of-merit (FOM) concept. The most desirable locations are ranked and identified using the resultant FOM fields. In the second step the network configuration is determined on the basis of the concept of spheres of influence (SOI) developed from cutoff values of spatial correlation coefficients between potential monitoring sites and adjacent locations. The minimum number of required stations is then determined by deletion of lower-ranked stations whose SOIs overlap. The criteria can be set to provide coverage of less than some fixed, user-provided percentage of the coverage of tha SOIs of the higher ranked stations and for some desired level of minimum detection capability of concentration fluctuations.The methodology is applied in a companion paper (McElroy et al., 1986) to the Las Vegas, Nevada, metropolitan area for the pollutant carbon monoxide.Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through Contract No. 68-03-2446 to Systems Applications, Inc., it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

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。     

基于OPAQ的城市空气质量预报系统研究

空气质量预测在国内的关注度日益提高,传统的空气质量预测系统通常运用数值化学传输模型,利用物理方程来计算污染物的扩散、沉降和化学反应。而化学传输模型的预测准确性很大程度上需要依赖详细的污染源排放信息和气象模型的输出结果。基于统计模型的OPAQ空气质量预报业务系统,采用人工神经网络算法,可预测各污染物的日均值或日最大值。并对北京空气质量预报的结果进行了评价,OPAQ空气质量预报业务系统对空气质量预测的准确性较高,能够利用较低的计算资源得到较为准确的预测结果。与数值预报相比,OPAQ空气质量预报业务系统不需要大量的基础数据作为输入,可弥补数值预报的不足,并成为数值预报的有力补充。     

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.     

洞庭湖区一次重度空气污染过程成因分析

利用AQI和PM_(2. 5)质量浓度、地面气象要素、NCEP、ERSST_V3、GBL等资料,对2016年12月29日至2017年1月5日洞庭湖区一次重度空气污染过程成因进行了分析。结果表明,静稳天气形势下的累积效应和本地持续升温、降压、增湿、小风导致污染物浓度不断增加。本地风速与雨量对污染物浓度产生显著影响。降温前风速明显加大,有利于污染物快速扩散。湿度增加有利于污染物吸湿性增长,但高湿易引起降水有利于污染物的湿清除。此次重度空气污染过程中大气稳定度为中性或稳定,14:00混合层高度逐渐降低且重度空气污染日降至100 m以下。污染物空间分布与主导风向和污染通道密切相关。气流后向轨迹分析表明,洞庭湖区各地气流来源和影响路径差异明显,且存在大范围区域性同步污染现象。北方外来污染源是洞庭湖区重要的污染面源,本地工业污染排放点源和地理条件也是洞庭湖区空气污染物空间分布差异的重要因素。     

Local Air Quality Impacts due to Downwash Around Thermal Power Plants: Numerical Simulations of the Effect of Building Orientation

One of the primary adverse environmental impacts associated with power generation facilities and in particular thermal power plants is local air quality. When these plants are operated at inland areas the dry type cooling towers used may significantly increase ambient concentrations of air pollutants due to the building downwash effect. When one or more buildings in the vicinity of a point source interrupt wind flow, an area of turbulence known as a building wake is created. Pollutants emitted from relatively low level sources can be caught in this turbulence affecting their dispersion. In spite of the fact that natural gas-fired combined-cycle power plants have lower air emission levels compared to other power plants using alternative fossil fuel, they can still create significant local air pollution problems. In this paper, local air quality impacts of a natural gas-fired combined-cycle power plant located in a coastal area are compared with those of another natural gas-fired combined-cycle power plant having identical air emissions but located in an inland area taking into account differences in topography and meteorology. Additionally, a series of scenarios for the inland site have been envisaged to illustrate the importance of plant lay-out configurations paying particular attention to the building downwash effect. Model results showed that different geometrical configurations of the stacks and cooling towers will cause remarkable differences in ambient air pollutant concentrations; thus it is concluded that when selecting a plant site, a detailed site-specific investigation should be conducted in order to achieve the least possible ambient air pollution concentrations with the given emissions.     

Developing an indicator for the chronic health impact of traffic-related pollutant emissions

The goal of this study is to develop an emission based indicator for the health impact of the air pollution caused by traffic. This indicator must make it possible to compare different situations, for example different Urban Travel Plans, or technical innovations. Our work is based on a literature survey of methods for evaluating health impacts and, more particularly, those which relate to the atmospheric pollution caused by transport. We then define a health impact indicator based on the traffic emissions, named IISCEP for Chronic health impact indicator of pollutant emission. Here health is understood in a restricted meaning, excluding well-being. Only primary pollutants can be considered, as the inputs are emission data and an indicator must be simple. The indicator is calculated as the sum of each pollutant emission multiplied by a dispersion and exposition factor and a substance specific toxicity factor taking account of the severity.Last, two examples are shown using the IISCEP: comparison between petrol and diesel vehicles, and Nantes urban district in 2008 vs 2002.Even if it could still be improved, IISCEP is a straightforward indicator which can be used to gauge the chronic effects of inhaling primary pollutants. It can only be used in comparisons, between different scenarios or different technologies. The quality of the emissions data and the choice of the pollutants that are considered are the two essential factors that determine its validity and reliability.     

Multipoint source method in air pollution modelling of cold start emission

The paper presents a new method of air pollution modelling on a micro scale. For estimation of concentration of car exhaust pollutants, each car is treated as an instantaneous moving emission source. This approach enables us to model time and spatial changes of emission, especially during cold and cool start of an engine. These stages of engine work are a source of significant pollution concentration in urban areas. In this work, two models are proposed: one for the estimation of emission after cold start of the engine and another for the prediction of pollutant concentration. The first model (defined for individual exhaust gas pollutants) enables us to calculate the emission as a function of time after the cold or cool start, ambient temperature and average speed of motion. This model uses the HBEFA database. The second mathematical model is developed in order to calculate the pollutant dispersion and concentrations. The finite volume method is applied to discretise the set of partial differential equations describing wind flow and pollutant dispersion in the domain considered. Models presented in this paper can be called short-term models on a small spatial scale. The results of numerical simulation of pollutant emission and dispersion are also presented.     

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.