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1.
A measuring campaign was conducted in the street canyon 'Runeberg street' in Helsinki in 1997. Hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOX), nitrogen dioxide (NO2) and ozone (O3) were measured at the street and roof levels, and the relevant hourly meteorological parameters were measured at the roof level. The hourly street level measurements and on-site electronic traffic counts were conducted during the whole year 1997, and roof level measurements were conducted during approximately two months, from 3 March to 30 April in 1997. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NOx, but the model slightly overestimated the measured concentrations of NO2. The database, which contains all measured and predicted data, is available for a further testing of other street canyon dispersion models.  相似文献   

2.
The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO2, NO, O3, SO2 and PM2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO2, NO, O3, SO2 and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.  相似文献   

3.
Air pollutant concentrations from a monitoring campaign in Buenos Aires City, Argentina, are used to investigate the relationships between ambient levels of ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2) as a function of NO x (=NO + NO2). This campaign undertaken by the electricity sector was aimed at elucidating the apportionment of thermal power plants to air quality deterioration. Concentrations of carbon monoxide (CO) and sulphur dioxide (SO2) were also registered. Photo stationary state (PSS) of the NO, NO2, O3 and peroxy radicals species has been analysed. The ‘oxidant’ level concept has been introduced, OX (=O3 + NO2), which varies with the level of NO x . It is shown that this level is made up of NO x -independent and NO x -dependent contributions. The former is a regional contribution that equates the background O3 level, whereas the latter is a local contribution that correlates with the level of primary pollution. Furthermore, the anticorrelation between NO2 and O3 levels, which is a characteristic of the atmospheric photo stationary cycle has been verified.The analysis of the concentration of the primary pollutants CO and NO strongly suggests that the vehicle traffic is the principal source of them. Levels of continuous measurements of SO2 for Buenos Aires City are reported in this work as a complement of previously published results.  相似文献   

4.
The purpose of the present research is to identify the trends in the concentrations of few atmospheric pollutants and meteorological parameters over an urban station Kolkata (22° 32′ N; 88° 20′ E), India, during the period from 2002 to 2011 and subsequently develop models for precise forecast of the concentration of the pollutants and the meteorological parameters over the station Kolkata. The pollutants considered in this study are sulphur dioxide (SO2), nitrogen dioxide (NO2), particulates of size 10-μm diameters (PM10), carbon monoxide (CO) and tropospheric ozone (O3). The meteorological parameters considered are the surface temperature and relative humidity. The Mann–Kendall, non-parametric statistical analysis is implemented to observe the trends in the data series of the selected parameters. A time series approach with autoregressive integrated moving average (ARIMA) modelling is used to provide daily forecast of the parameters with precision. ARIMA models of different categories; ARIMA (1, 1, 1), ARIMA (0, 2, 2) and ARIMA (2, 1, 2) are considered and the skill of each model is estimated and compared in forecasting the concentration of the atmospheric pollutants and meteorological parameters. The results of the study reveal that the ARIMA (0, 2, 2) is the best statistical model for forecasting the daily concentration of pollutants as well as the meteorological parameters over Kolkata. The result is validated with the observation of 2012.  相似文献   

5.
A novel hybrid model has been developed to support the provision of real-time air quality forecasts. Statistical techniques have been applied in parallel with air mass history modelling to provide an efficient and accurate forecasting system with the ability to identify high NO2 events, which tend to be the episodes of most significance in Ireland. Air mass history modelling and k-means clustering are used to identify air mass types that lead to high NO2 levels in Ireland. Trajectory matching techniques allow data associated with these air masses to be partitioned during model development. Non-parametric regression (NPR) has been applied to describe nonlinear variations in concentration levels with wind speed, direction and season and produce a set of linearized factors which, together with other meteorological variables, are employed as inputs to a multiple linear regression. The model uses an innovative integrated approach to combine the NPR with the air mass history modelling results. On validation, a correlation coefficient of 0.75 was obtained, and 91 % of daily maximum (hourly averaged) NO2 predictions were within a factor of two of the measured value. High pollution events were well captured, as indicated by strong agreement between measured and modelled high percentile values. The model requires only simple input data, does not require an emission inventory and utilises very low computational resources. It represents an accurate and efficient means of producing real-time air quality forecasts and, when used in combination with forecaster experience, is a useful tool for identifying periods of poor air quality 24 h in advance. The hybrid approach outlined in this paper can easily be applied to produce high-quality forecasts of both NO2 and additional pollutants at new locations/countries where historical monitoring data are available.  相似文献   

6.
Development of baseline (air quality) data in Pakistan   总被引:1,自引:0,他引:1  
During 2003–2004, SUPARCO, the Pakistan Space and Upper Atmosphere Research Commission has conducted a year long baseline air quality study in country’s major urban areas (Karachi, Lahore, Quetta, Rawalpindi, Islamabad and Peshawar). The objective of this study was to establish baseline levels and behavior of airborne pollutants in urban centers with temporal and spatial parameters. This study reveals that the highest concentrations of CO were observed at Quetta (14 ppm) while other pollutants like SO2 (52.5 ppb), NO x (60.75 ppb) and O3 (50 ppb) were higher at Lahore compared to other urban centers like Karachi, Peshawar etc. The maximum particulate (TSP) and PM10 levels were observed at Lahore (996 ug/m3 and 368 ug/m3 respectively), Quetta (778 ug/m3, 298 ug/m3) and in Karachi (410 ug/m3, 302 ug/m3). In all major cities the highest levels were recorded at major intersections and variations were directly correlated with traffic density. These pollutants showed highest levels in summer and spring while lowest were observed in winter and monsoon. A data bank has been generated for future planning and air pollution impact studies.  相似文献   

7.
沈阳市冬季环境空气质量统计预报模型建立及应用   总被引:5,自引:3,他引:2  
利用沈阳市2013年1—2月大气自动监测数据和同期气象资料,选取19项预报因子,采用逐步回归方法建立了沈阳市冬季环境空气质量统计预报模型,预报项目包括细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)日均浓度及臭氧(O3)日最大8 h平均浓度。2013年11月至2014年1月,应用该模型并结合人为经验修订,开展了沈阳市环境空气质量预报工作,预报结果与实测结果的对比验证结果表明,环境空气预测结果级别准确率达到79.1%,首要污染物准确率为73.6%。  相似文献   

8.
The analyses presented in this paper include the concentration levels of NO2 and O3 measured during 2 successive years in twin cities (Rawalpindi and Islamabad) of Pakistan from November 2009 to March 2011. NO2 was determined using the passive sampling method, while ozone was determined by Model 400E ozone analyzer. The average NO2 and O3 concentration in twin cities of Pakistan was found to be 44?±?6 and 18.2?±?1.24 ppb, respectively. Results indicate that the concentration of NO2 and O3 show seasonal variations. Results also depict that NO2 and O3 concentration levels are high in areas of intense traffic flow and congestion. Rawalpindi has more elevated levels of NO2 and O3 as compared to the Islamabad due to the narrow roads, enclosing architecture of road network and congestion. Climatic variables also influenced the NO2 and O3 concentration, i.e., temperature is positively related with O3, while negatively related with NO2, relative humidity is directly related with NO2 and inversely related with O3, whereas rainfall show negative association with both NO2 and O3 concentration. Comparing the results with WHO standards reveals that NO2 concentration levels at all the sampling points are above the permissible limit, while ozone concentration is still lower than the WHO standards. Thus, there is a need to take appropriate steps to control these continuously increasing levels of NO2 and O3 before they become a serious hazard for the environment and people living in those areas.  相似文献   

9.
As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NOx and NO2 are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NOx and NO2 concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.  相似文献   

10.
This study addresses the significant effects of both well-known contaminants (particles, gases) and less-studied variables (temperature, humidity) on serious, if relatively common, respiratory and circulatory diseases. The area of study is Lisbon, Portugal, and time series of health outcome (daily admissions in 12 hospitals) and environmental data (daily averages of air temperature, relative humidity, PM10, SO2, NO, NO2, CO, and O3) have been gathered for 1999–2004 to ascertain (1) whether concentrations of air pollutants and levels of temperature and humidity do interfere on human health, as gauged by hospital admissions due to respiratory and circulatory ailments; and (2) whether there is an effect of population age in such admissions. In general terms, statistically significant (p?<?0.001) correlations were found between hospital admissions and temperature, humidity, PM10, and all gaseous pollutants except CO and NO. Age appears to influence respiratory conditions in association with temperature, whereas, for circulatory conditions, such an influence likely involves temperature as well as the gaseous pollutants NO2 and SO2.  相似文献   

11.
In recent years, a significant part of the studies on air pollutants has been devoted to improve statistical techniques for forecasting the values of their concentrations in the atmosphere. Reliable predictions of pollutant trends are essential not only for setting up preventive measures able to avoid risks for human health but also for helping stakeholders to take decision about traffic limitations. In this paper, we present an operating procedure, including both pollutant concentration measurements (CO, SO2, NO2, O3, PM10) and meteorological parameters (hourly data of atmospheric pressure, relative humidity, wind speed), which improves the simple use of neural network for the prediction of pollutant concentration trends by means of the integration of multivariate statistical analysis. In particular, we used principal component analysis in order to define an unconstrained mix of variables able to improve the performance of the model. The developed procedure is particularly suitable for characterizing the investigated phenomena at a local scale.  相似文献   

12.
Ozone, NO2, SO2, CO, PM10 and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O3 and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R 2?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R 2?=?0.82).  相似文献   

13.
A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO2) and nitrogen dioxide (NO2). Delhi, which is one of the most polluted cities in the world, is also going through the implementation phase of the control policies. Ambient air quality data monitored during 2000 to 2003, at 10 sites in Delhi, were analyzed to assess the impact of implementation of these measures, specifically fuel change in vehicles. This paper presents the impact of policy measures on ambient air quality levels and also the source apportionment. CO and NO2 concentration levels in ambient air are found to be associated with the mobile sources. The temporal variation of air quality data shows the significant effect of shift to CNG (Compressed Natural Gas) in vehicles.  相似文献   

14.
Delhi is one of the many megacities struggling with punishing levels of pollution from industrial, residential, and transportation sources. Over the years, pollution abatement in Delhi has become an important constituent of state policies. In the past one decade a lot of policies and regulations have been implemented which have had a noticeable effect on pollution levels. In this context, air quality models provide a powerful tool to study the impact of development plans on the expected air pollution levels and thus aid the regulating and planning authorities in decision-making process. In air quality modeling, emissions in the modeling domain at regular interval are one of the most important inputs. From the annual emission data of over a decade (1990–2000), emission inventory is prepared for the megacity Delhi. Four criteria pollutants namely, CO, SO2, PM, and NO x are considered and a gridded emission inventory over Delhi has been prepared taking into account land use pattern, population density, traffic density, industrial areas, etc. A top down approach is used for this purpose. Emission isopleths are drawn and annual emission patterns are discussed mainly for the years 1990, 1996 and 2000. Primary and secondary areas of emission hotspots are identified and emission variations discussed during the study period. Validation of estimated values is desired from the available data. There is a direct relationship of pollution levels and emission strength in a given area. Hence, an attempt has been made to validate the emission inventory for all criteria pollutants by analyzing emissions in various sampling zones with the ambient pollution levels. For validation purpose, the geographical region encompassing the study area (Delhi) has been divided into seven emission zones as per the air quality monitoring stations using Voronoi polygon concept. Dispersion modeling is also used for continuous elevated sources to have the contributing emissions at the ground level to facilitate validation. A good correlation between emission estimates and concentration has been found. Correlation coefficient of 0.82, 0.77, 0.58 and 0.68 for CO, SO2, PM and NO x respectively shows a reasonably satisfactory performance of the present estimates.  相似文献   

15.
Public transport in Delhi was amended by the Supreme Court of India to use Compressed Natural Gas (CNG) instead of diesel or petrol. After the implementation of CNG since April 2001, Delhi has the highest fraction of CNG-run public vehicles in the world and most of them were introduced within 20 months. In the present study, the concentrations of various criteria air pollutants (SPM, PM10, CO, SO2 and NOx) and organic pollutants such as benzene, toluene, xylene (BTX) and polycyclic aromatic hydrocarbons (PAHs) were assessed before and after the implementation of CNG. A decreasing trend was found for PAHs, SO2 and CO concentrations, while the NOx level was increased in comparison to those before the implementation of CNG. Further, SPM, PM10, and BTX concentrations showed no significant change after the implementation of CNG. However, the BTX concentration demonstrated a clear relation with the benzene content of gasoline. In addition to the impact of the introduction of CNG the daily variation in PAHs levels was also studied and the PAHs concentrations were observed to be relatively high between 10 pm to 6 am, which gives a proof of a relation with the limited day entry and movement of heavy vehicles in Delhi.  相似文献   

16.
Gaseous emissions from the fluidised bed combustion of chickenlitter were monitored and recorded. Emission data was used tocreate a dispersion model for a proposed site on a poultry farmin Limerick, Ireland. Variables within the combustion unit notonly influenced combustion but also emission levels ofpollutants such as SO2 and NOx. CO emissions decreased withuse of the correct ratio between fluidising and secondary air,the staging of secondary air and the degree of turbulence withwhich the secondary air is introduced. Dispersion modelling ofactual combustion at a proposed poultry unit predicted thatground level concentrations for the set of emissions data wouldbe below the limits and guidelines set by air quality standards.This was true for both and line source. Line sourceconcentration levels were less than those for point source.  相似文献   

17.
18.
The concentrations of criteria air pollutants such as CO, NOx (NO + NO2), SO2 and PM were measured in the period of May 2001 and April 2003 in the city of Bursa, Turkey. The average concentrations for this period were 1115±1600 μg/m3, 29±50 μg/m3, 51±24 μg/m3, 79±65 μg/m3, 40±35 μg/m3, 98±220 μg/m3, for CO, NO, NO2, NOx, SO2 and PM, respectively. Temporal changes in concentrations were analyzed using meteorological factors. Correlations among pollutant concentrations and meteorological parameters showed weak relations nearly in all data. Lower concentrations were observed in the summer months while higher concentrations were measured in the winter months. The increase in winter concentrations was probably due to residential heating. Pollutants were associated with each other in order to have information about their origin. NOx/SO2 ratio was also examined to bring out the source origin contributing on air pollution (i.e., traffic or stationary).  相似文献   

19.
The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO2 pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO2 annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NOx emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO2 concentration is above the annual limit value.  相似文献   

20.
The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.  相似文献   

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