Minimizing human health effects of urban air pollution through quantification and control of motor vehicular carbon monoxide (CO) in Lahore

Impact of urban air pollution has variety of focuses such as urban ecology, human health, economy, etc. But human health is always given priority. Air pollution is threat to the lives of people living in big cities of Pakistan. In Lahore only there die 1,250 people annually because of air pollution. A strong correlation exists between urban air pollution and human health in Lahore. Growth of COPD is highest among other air pollution borne diseases. Existing mass transit system (one of driving forces behind motor vehicular emission) in Lahore due to frequent stoppages, entering and exit in flow of traffic causes excess discharge of motor vehicular carbon monoxide (CO) which is a hazardous to human health. Quantification and enumeration of this discharge is essential for environmental management. The paper is an attempt to highlight human health effects of urban air pollution through correlation and regression analysis. Further it is focused upon quantifying excess motor vehicular carbon monoxide through application of simplified mobile emission model. In light of results emission control measures are recommended.     

Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions

Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.     

气体中毒窒息事故的环境应急监测

以株洲市攸县某矿业公司"5·7"重大中毒窒息事故的环境应急监测为例，详细阐述了该事故开展应急监测的全过程。针对锁定污染因子、查找污染源、判断污染团的扩散趋势以及监控敏感点的安全等监测目的设计监测方案。通过监测数据分析确定事故特征因子为CO，其周边环境空气中浓度随着与抽风口距离的增加而快速下降，监测到附近居民区环境空气质量未受事故影响。     

A Simple Model of Pollutant Concentrations in a Street Canyon

A single compartment model has been constructed for predicting hourly concentrations of pollutant concentrations arising from vehicular emissions within a typical street canyon. The model takes account of traffic densities and composition to estimate pollutant emissions within the model compartment. Meteorological data on wind speed and direction are used to define the exchanges of pollutants between the compartment and the surrounding air. A parameter is also included to describe the exchange in calm conditions. The pollutant concentrations are then estimated from a steady state mass balance equation for the compartment, assuming conservation of pollutants. The model was applied to the prediction of carbon monoxide concentrations in Hope Street, Glasgow. Model parameters were fitted using field measurements, together with concurrent meteorological data and traffic flows estimated from traffic census data for Hope Street. The model accounted well for the observed variations in carbon monoxide. It was found that the model parameters varied seasonally, perhaps due to differences in atmospheric stability which have not so far been included in the model formulation.     

Black carbon concentrations in a goods-movement neighborhood of Philadelphia,PA

Communities along the Delaware River in Philadelphia, USA such as Port Richmond, are subject to traffic associated with goods movement to and from port facilities and local industry. Air pollution associated with this traffic poses an environmental health concern in this and other urban areas. Our study measures black carbon (BC) in Port Richmond and examines its relationship to expected sources such as truck traffic. We used a participatory sampling method to conduct 1-min measurements over 8-hour periods at 14 homes, a school and park, during a 4-week period in June 2012. Measurements over 9 sampling days had a 30-min average BC concentration of 1.2 μg m^?3, and a maximum of 12 μg m^?3. Statistical analyses showed some effect of traffic, but greater association between BC concentrations and weather and time of day. BC concentrations varied more by location than by day, and distance to traffic arterials and interstate freeway was a predictor of this variance. While our dataset is limited by number and variety of observations, major findings indicate that BC concentration varies more by location than by day, there is a decrease in median BC concentration with increased distance from an interstate highway, and an expected effect of diesel traffic on average daily BC concentrations. Our findings are an important step towards understanding patterns and determinants of BC concentration in communities colocated with major ports. Our study also demonstrates that participatory methods in air pollution monitoring can help increase awareness of local air pollution levels.     

Photochemical pollution indicators—an analysis of 12 European monitoring stations

Indicators were devised to classify air pollution monitoring sites according to the type of expected photochemical pollution. The indicators are based on measured ozone volume fractions, the most frequently monitored component of photochemical pollution, and in particular on two contributions: one due to the ratio of daily maximum-to-minimum ozone volume fractions and the other to observed peak values. The two contributions regarded as independent are logically connected by “and” and therefore mathematically combined by multiplication. The criterion of classification is mainly described by the mentioned ratio and incidences of ozone volume fractions exceeding the limit of 80 ppb. Twelve monitoring stations within the European network (Cooperative programme for monitoring and evaluation of long-range transmission of air pollutants in Europe, EMEP) were classified according to this indicator predicting what ozone levels can be expected at the particular sites during the growth season (April through September) into three groups: clean, medium, and polluted, based on the data for the 7 years (1997 to 2003).     

Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand

This study explored the use of satellite data to monitor carbon monoxide (CO) and particulate matter (PM) in Northern Thailand during the dry season when forest fires are known to be an important cause of air pollution. Satellite data, including Measurement of Pollution in the Troposphere (MOPITT) CO, Moderate Resolution Imaging Spectroradiometer aerosol optical depth (MODIS AOD), and MODIS fire hotspots, were analyzed with air pollution data measured at nine automatic air quality monitoring stations in the study area for February–April months of 2008–2010. The correlation analysis showed that daily CO and PM with size below 10 μm (PM₁₀) were associated with the forest fire hotspot counts, especially in the rural areas with the maximum correlation coefficient (R) of 0.59 for CO and 0.65 for PM₁₀. The correlations between MODIS AOD and PM₁₀, between MOPITT CO and CO, and between MODIS AOD and MOPITT CO were also analyzed, confirming the association between these variables. Two forest fire episodes were selected, and the dispersion of pollution plumes was studied using the MOPITT CO total column and MODIS AOD data, together with the surface wind vectors. The results showed consistency between the plume dispersion, locations of dense hotspots, ground monitoring data, and prevalent winds. The satellite data were shown to be useful in monitoring the regional transport of forest fire plumes.     

Exposure of motorcycle,car and bus commuters to carbon monoxide on a main road in the Tel Aviv metropolitan area,Israel

Short-term personal exposure of passengers in different types of motor vehicles to carbon monoxide was investigated in an intensively used main road in Israel’s Tel Aviv metropolitan area. According to monitoring stations of the Ministry for Environmental Protection (MEP), concentrations of carbon monoxide (CO) along the road, at a height of 3 m above pedestrian level, in the Tel Aviv metropolitan area, are currently very low. However, these measurements do not reflect the actual exposure of commuters, which were the main objective of this study. Four vehicle types/travel modes were investigated: private cars with closed windows, private cars with open windows, motorcycles, and buses. The commuter CO average exposure was the accumulative exposure divided by the duration of the sampling taken along the route, for each type of vehicles. The results showed that commuters in cars with closed windows were exposed to the highest mean CO level, 27.2 ppm, for a period of 38 min; those in a car with open windows, to 19.7 ppm for 38 min; motorcycle riders, to 12.8 ppm, for 17 min; and bus users were exposed to the lowest mean pollution level, of only 3.6 ppm, for 25 min. Thus, CO values of 1 to 3 ppm, as measured at an MEP adjacent monitoring station, may indicate the exposure to CO pollution of area residents, but do not represent the actual exposure of commuters on the congested main road.     

Monitoring and Assessment of Exhaust Emission in Bangkok Street Air

Measurement of the exhaust emission from gasoline-powered motor vehicles in Bangkok were performed on chassis dynamometer. A fleet of 10 vehicles of different model, years and manufacturers were selected to measure the air pollutants in the exhaust effluent. The study revealed that the carbon monoxide and hydrocarbon emissions averaged 32.3–64.2 and 1.82–2.98 g km^–1, respectively, for 1990–1992 cars and decreased to 17.8–40.71 and 0.75–1.88 g km^–1, respectively, for 1994–1995 cars. A monitoring program for air pollutant concentrations in ambient air was also conducted to evaluate the air pollution problems in Bangkok arising from vehicle exhaust emission. Four air sampling stations were strategically established to cover the Bangkok Metropolitan Region (BMR). Composite air samples in this study area were collected during the day/night times and weekday/weekend. The average concentrations of suspended particulate matter, carbon monoxide, and nitrogen dioxide in Bangkok street air were found to be 0.65 mg/m³ (24 hr ave.), 19.02 mg/m³ (8 hr ave.) and 0.021 mg/m³ (1 hr ave.), respectively. The average concentrations of benzene and toluene in the ambient air of the study area were found to be 15.07–50.20 and 25.76–130.95 g/mf³, respectively, for 8 hr average. These results indicated that there was a significant increase in air pollutant emissions with increasing car mileage and model year. Subsequent analysis of data showed that there were only 20% of the test vehicles complied to approved emission standard. The finding also revealed that there was a correlation between the average air pollutant concentrations with average traffic speed in each traffic zone of the Bangkok Metropolitan Region (BMR).     

Air quality biomonitoring through pollen viability of Fabaceae

In this study, pollen viability and germination of three plant species, Cercis siliquastrum L., Robinia pseudoacacia L., and Spartium junceum L., belonging to the Fabaceae family, was evaluated in sites with different intensity of road traffic, constantly monitored with continuous analysers for air pollutants (carbon monoxide (CO), sulphur dioxide (SO₂), and nitrogen dioxide (NO₂)) by the Municipality of Catania. Two sites, in which road traffic was absent, were selected, too. The percentages of viable pollen by 2,3,5-trypheniltetrazolium chloride (TTC) test ranged from 59.0 to 90.2 % in C. siliquastrum, from 61.5 to 83.5 % in S. junceum and from 67.5 to 84.3 % in R. pseudoacacia. The percentages of germination varied from 41.0 to 72.7 % in C. siliquastrum, from 42.0 to 64.7 % in S. junceum and from 38.3 to 66.3 % in R. pseudoacacia. The highest percentages of viable pollens were found in no-road traffic stations by either TTC or germination tests, while the lowest values were detected in a site characterised by heavy road traffic. In the monitored period (2007–2009), pollen viability, germinability and tube length of C. siliquastrum resulted in a significant negative correlation to CO, SO₂ and NO₂, whereas data from TTC and germination tests on S. junceum and R. pseudoacacia pollens were not well correlated to air pollutants. The results showed that pollen viability, germination and tube growth in C. siliquastrum were affected by air pollution. S. junceum and R. pseudoacacia were not very influenced by air pollutants, suggesting a different pollen sensitivity of these species.     

上海市交通环境空气质量监测与发展探讨

机动车的快速增长引发了严重的车辆尾气污染问题.通过分析上海市交通环境空气质量现状,评估污染物浓度改善效果,回顾交通环境空气质量监测发展的进程,建议借鉴国外交通环境空气质量监测网络的经验,加快完善上海的交通环境空气质量监测网络.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

The Impact of Road Traffic in Madrid Community by Using a Nested Gaussian-Mesoscale Air Quality Model (GAMA)

In this contribution we show the integration of a mesoscale air quality model OPANA with the ISCST3 Gaussian model (EPA) in order to analyze the impact of different emission sources and particularly the traffic emission into the different gridboxes which define the OPANA Eulerian structure. The application is done over the Madrid (Spain) regional area with 80 × 100 km and gridboxes of about 5 km. Thousands of Gaussian runs over interested gridboxes are executed in order to simulate the traffic emissions from each gridbox. Each mobile unit is represented by a Gaussian point emitter. Input meteorological variables for the ISCST3 are taken from the OPANA mesoscale air quality model. Results shows that it is possible to model the impact of traffic emissions over each gridbox. A short comparison with air quality monitoring in each gridbox is also shown.     

Exposure estimates for the Houston Area Asthma Study

Procedures for determining exposure extimates for the Houston Area Asthma Study are discussed. Two residential clusters of asthmatics in the Houston area have been studied as part of an overall attempt to assess the health effects of air pollution in the Texas Gulf Coast area. Air pollutant exposure data have been produced according to a three-tier monitoring scheme, i.e. (1) continuous data from centrally-located fixed stations, (2) residential indoor/outdoor measurements, and (3) personal monitoring. This air monitoring network should yield estimates of individual exposures for use in health effect correlations.The fixed sites and mobile continuously monitor ambient levels of ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and meteorological parameters (wind speed, wind direction, temperature, dew point, solar irradiation, and precipitation). Continuous sequential integrated sampling is preformed for total suspended particulates (TSP) and, inhalable particulates (IP) (total of < 2.5m and 2.5–15.0 m) by dichotomous samplers for 24 hr samplers), aldehydes (bubblers), and aeroallergens (intermittent rotorod samplers). In addition to gravimetric determinations, particulate filters are routinely analyzed for sulfate, nitrate, and (every third day) trace elements. Personal monitoring includes ozone, respirable particles, nitrogen dioxide and formaldehyde. A quality assurance/control program that meets specifications established in the EPA Quality Assurance Handbook for air pollution measurement systems was implemented. Examples of results consisting of unvalidated preliminary data from the monitoring for ozone for three participants is presented and approaches to determining exposure estimates are discussed for both an integrated exposure estimate and short time period exposure estimate.(These studies have been funded by the U.S. Environmental Protection Agency through Cooperative Agreement CR 807108010 and Grant R 808738010. Statements made in this article should not be taken as Agency policy.)     

Health risk estimation due to carbon monoxide pollution at different spatial levels in Santiago, Chile

The Metropolitan Region of Santiago has been declared as nonattainment area for carbon monoxide (CO). The data was based on seven air monitoring stations that do not necessarily represent the real population exposure. Usually, a series of coefficients that relates the concentration of pollutants with health effects (betas of the concentration–response equations) are used. For the calculation of these betas, normally, a city average air pollution concentration is used; nevertheless, these betas do not necessarily represent the real risk of exposure that the inhabitants are experimented. For this reason, this study estimates two aspects that are unknown and that are necessary to policymakers in public health decisions. First, estimate the exposure at a lower spatial resolution level and, second, estimate a concentration–response curve that differs spatially and depends on the proportion of susceptible groups. We followed the Air Pollution and Health, European Approach/Spanish Multicenter Study on the Relationship Between Air Pollution and the Mortality methodology for the determination of the short-term health effect due to CO population exposure at the three spatial levels (city, zone, and county). We found that there are different risks in cardiorespiratory mortality in the group over 64 years old associated to an unequal exposure to CO in Santiago city. The higher risks are in counties located to the south–east of the city. These finding confirm the necessity to face, in a different way, the approach when evaluating control strategies and policy decision to control air pollution in Santiago.     

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.     

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。     

The Use of Measurement Programmes and Models to Assess Concentrations Next to Major Roads in Urban Areas

This paper shows that monitoring campaigns in association withmodelling, particularly detailed roadside modelling, are auseful and cost effective method of assessing air quality. Itproposes that an approach based on detailed monitoringassociated with concurrent meteorological data can be used incombination with a roadside model to assess concentrations at other locations near a road and in other weather conditions. Plans for improving air quality can also be assessed in simpleways. The need for complex models based on detailed emissioninventories is avoided.Two examples from streets in London and Paris are presented inwhich measurements of CO, NO_x and volatile organic compoundshave been made. The measurements are interpreted usingcorrelations between pollutant concentrations and with windspeed, and by using the USEPA CALINE4 model. It is demonstratedthat air quality measurements can be used to assess the validityof road traffic emission factors.     

上海市机动车污染实时排放预警系统设计与应用

研究上海市机动车污染的动态排放测算和网格化动态排放清单构建,在实时的交通数据和交通环境监测数据的基础上,结合交通模型、机动车排放清单模型等业务模型和算法,依托大数据存储、可视化和GIS等技术,开发了上海市机动车污染物实时排放预警系统,实现了上海市全市道路的机动车动态排放测算、交通环境政策实施情景模拟和网格化排放清单,更新频率为每30 min一次,包含PM、NOx、CO、SO2、VOCs等污染物和9种车型。系统建成后直接服务于首届中国国际进口博览会,为大气污染排放实时总量跟踪评估、污染源管控措施分析及监测成因分析等提供了有力的实时数据和技术支撑。     

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。