多种神经网络在华北西部区域城市空气质量预测中的应用

据华北西部区域4个主要城市2003—2012年API日报数据和相应时段的地面气象要素数据,利用4种(BP、Elman、T-S模糊、小波)神经网络构建预测模型并预测相应城市大气环境质量.研究结果显示,4种模型在可靠性、预测精度方面均可满足应用要求可用于实际预测;具有动态反馈能力的Elman神经网络的预测精度以及泛用性要优于具静态馈能力的其他3种网络模型,说明动态神经网络更适用于城市大气环境质量预测.4种神经网络的决策权重大小及其排序虽各不相同,但体现出相似规律性,日最低气温、日均气压、前日API对输出数据的影响较大,说明逆温现象引发的持续性、区域性污染是该地区主要环境问题.     

Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables

There are many different air pollution indexes which represent the global urban air pollution situation. The daily index studied here is also highly correlated with meteorological variables and this index is capable of identifying those variables that significantly affect the air pollution. The index is connected with attention levels of NO₂, CO and O₃ concentrations. The attention levels are fixed by a law proposed by the Italian Ministries of Health and Environment. The relation of that index with some meteorological variables is analysed by the linear multiple partial correlation statistical method. Florence, Milan and Vicence were selected to show the correlation among the air pollution index and the daily thermic excursion, the previous day's air pollution index and the wind speed. During the January–March period the correlation coefficient reaches 0.85 at Milan. The deterministic methods of forecasting air pollution concentrations show very high evaluation errors and are applied on limited areas around the observation stations, as opposed to the whole urban areas. The global air pollution, instead of the concentrations at specific observation stations, allows the evaluation of the level of the sanitary risk regarding the whole urban population.     

How to select the best tree planting locations to enhance air pollution removal in the MillionTreesNYC initiative

Highest priority zones for tree planting within New York City were selected by using a planting priority index developed combining three main indicators: pollution concentration, population density and low canopy cover. This new tree population was projected through time to estimate potential air quality and carbon benefits. Those trees will likely remove more than 10 000 tons of air pollutants and a maximum of 1500 tons of carbon over the next 100 years given a 4% annual mortality rate. Cumulative carbon storage will be reduced through time as carbon loss through tree mortality outweighs carbon accumulation through tree growth. Model projections are strongly affected by mortality rate whose uncertainties limit estimations accuracy. Increasing mortality rate from 4 to 8% per year produce a significant decrease in the total pollution removal over a 100 year period from 11 000 tons to 3000 tons.     

Airborne particulate matter from livestock production systems: A review of an air pollution problem

Livestock housing is an important source of emissions of particulate matter (PM). High concentrations of PM can threaten the environment, as well as the health and welfare of humans and animals. Particulate matter in livestock houses is mainly coarse, primary in origin, and organic; it can adsorb and contain gases, odorous compounds, and micro-organisms, which can enhance its biological effect. Levels of PM in livestock houses are high, influenced by kind of housing and feeding, animal type, and environmental factors. Improved knowledge on particle morphology, primarily size, composition, levels, and the factors influencing these can be useful to identify and quantify sources of PM more accurately, to evaluate their effects, and to propose adequate abatement strategies in livestock houses. This paper reviews the state-of-the-art of PM in and from livestock production systems. Future research to characterize and control PM in livestock houses is discussed.     

Relationship between atmospheric pollution processes and synoptic pressure patterns in northern China

The air pollution index (API) sequences in 10 cities in northern China and the synoptic pressure patterns during autumn and winter from 2002 to 2006 were analyzed with diagnostic and statistical methods. The results showed that the air qualities in northern China had a prominent correlation with the pressure systems. It revealed that (a) the increasing phase of API was associated with high pressure and the successive low pressure, (b) the preceding part of front (i.e. the retral part of low pressure) was associated with the maximum of API values during a cycle of air pollution process, (c) the pressure systems with high gradient led to the decrease of API, and (d) the synoptic pressure patterns and their evolvements were the main causes of regional air pollution processes. These relations can be used to analyze the variation characteristics and mechanism of regional atmosphere pollution process, and provide important basis for the qualitative prediction, control, and management of regional air pollution problems.     

Effects of air pollution on rice yield in the Pakistan Punjab

A study using open-top chambers ventilated with ambient or charcoal filtered air in the vicinity of Lahore, Pakistan demonstrated reductions of 42% and 37% in the grain yield of two cultivars of rice (Oryza sativa L.). This yield reduction was primarily due to the reduction in mean panicle number per plant, although significant effects of filtration on 1000 grain weight and the number of filled grains per panicle were also detected. The 6-h daily mean O(3) concentrations were only 10-20 nl litre(-1) during the monsoon season, but increased to 30-55 nl litre(-1) later in the growing season, while the mean NO(2) concentration during the experiment was 12 nl litre(-1). On the basis of experience in North America and Japan, the reductions in yield in the present study are substantially greater than might be predicted. The reasons for this discrepancy are discussed, together with the implications for effects on rice yield in other regions of south and south-east Asia.     

Assessment of traffic-related air pollution in the urban streets before and during the 2008 Beijing Olympic Games traffic control period

In order to investigate the air quality and the abatement of traffic-related pollution during the 2008 Olympic Games, we select 12 avenues in the urban area of Beijing to calculate the concentrations of PM₁₀, CO, NO₂ and O₃ before and during the Olympic traffic controlling days, with the OSPM model.Through comparing the modeled results with the measurement results on a representative street, the OSPM model is validated as sufficient to predict the average concentrations of these pollutants at street level, and also reflects their daily variations well, i.e. CO presents the similar double peaks as the traffic flow, PM₁₀ concentration is influenced by other sources. Meanwhile, the model predicts O₃ to stay less during the daytime and ascend in the night, just opposite to NO₂, which reveals the impact of photochemical reactions. In addition, the predicted concentrations on the windward side often exceed the leeward side, indicating the impact of the special street shape, as well as the wind.The comparison between the predicted street concentrations before and during the Olympic traffic control period shows that the overall on-road air quality was improved effectively, due to the 32.3% traffic flow reduction. The concentrations of PM₁₀, CO and NO₂ have reduced from 142.6 μg m⁻³, 3.02 mg m⁻³ and 118.7 μg m⁻³ to 102.0 μg m⁻³, 2.43 mg m⁻³ and 104.1 μg m⁻³. However, the different pollutants show diverse changes after the traffic control. PM₁₀ decreases most, and the reduction effect focusing on the first half-day even clears the morning peak, whereas CO and NO₂ have even reductions to minify the daily fluctuations on the whole. Opposite to the other pollutants, ozone shows an increase of concentration. The average reduction rate of PM₁₀, CO, NO₂ and O₃ are respectively 28%, 19.3%, 12.3% and −25.2%. Furthermore, the streets in east, west, south and north areas present different air quality improvements, probably induced by the varied background pollution in different regions around Beijing, along with the impact of wind force. This finding suggests the pollution control in the surrounding regions, not only in the urban area.     

上海市霾污染判别指标体系初步研究

随着全球变暖及工业污染排放的蔓延式发展,由空气污染带来的区域能见度恶化越来越成为中国东部经济发达地区所面临的严重区域性问题,常出现大范围的雾霾天气,造成区域空气质量的恶化及能见度水平的下降.基于对能见度及相对湿度、颗粒物浓度和组分的观测分析及影响能见度的相对湿度和空气污染因子的理论分析,提出了环保范畴内的霾污染概念,并制定了包括能见度、颗粒物浓度及其组分的上海市霾污染判别指标体系,基于颗粒物组分观测数据,对相对湿度因子进行了参数化.根据所提出的指标体系,通过上海市某点位一年观测数据的跟踪分析发现,根据霾污染判别指标体系计算得到的霾污染天数与气象学意义上的霾天数具有很好的对应关系,且新的判别指标体系能很好地区分霾污染和浮尘过程.     

Trends of air pollution in the Fichtelgebirge Mountains, Bavaria

We analyzed 13 years of hourly measurements of SO₂, NO_x, and O₃, at forest ecosystem research sites in SE Germany. A quasi-continuous data record was obtained by combining data sets from two locations. Before interpreting trends in the combined data set, we analyzed if the change of location introduced a systematic bias. We employed autocorrelation functions, Hurst statistics, complexity analysis, and recurrence quantification and found that the partial data sets exhibited no indication of the presence of any bias. For SO₂, we also compared the data from the forest sites with data obtained in nearby cities and also found no indications for any systematic effects. Applying nonparametric trend statistics we found a significant decrease of the SO₂. Most of the observed decrease is due to the reductions of SO₂ emissions in eastern Germany, but reductions in western Germany and the Czech Republic also played important roles. For O₃, we observed a significant increase, the causes of which are unclear from our data alone. No trend was identified for NO_x.     

室内空气污染分析方法研究进展

本文对室内空气污染分析方法进行了详细综述 ,以期对开发室内空气污染分析的新方法及新技术提供参考。     

Ozone, nitric acid, and ammonia air pollution is unhealthy for people and ecosystems in southern Sierra Nevada, California

Two-week average concentrations of ozone (O₃), nitric acid vapor (HNO₃) and ammonia (NH₃) were measured with passive samplers during the 2002 summer season across the central Sierra Nevada Mountains, California, along the San Joaquin River drainage. Elevated concentrations of the pollutants were determined with seasonal means for individual sites ranging between 62 and 88 ppb for O₃, 1.0-3.8 μg m⁻³ for HNO₃, and 2.6-5.2 μg m⁻³ for NH₃. Calculated O₃ exposure indices were very high, reaching SUM00-191 ppm h, SUM60-151 ppm h, and W126-124 ppm h. Calculated nitrogen (N) dry deposition ranged from 1.4 to 15 kg N ha⁻¹ for maximum values, and 0.4-8 kg N ha⁻¹ for minimum values; potentially exceeding Critical Loads (CL) for nutritional N. The U.S., California, and European 8 h O₃ human health standards were exceeded during 104, 108, and 114 days respectively, indicating high risk to humans from ambient O₃.     

Lichens as an integrating tool for monitoring PAH atmospheric deposition: A comparison with soil, air and pine needles

The aim of this study was to validate lichens as biomonitors of PAH atmospheric deposition; for that, an inter-comparison between the PAH profile and concentrations intercepted in lichens with those of air, soil and pine needles was performed. The study was conducted in a petro-industrial area and the results showed that PAH profiles in lichens were similar to those of the air and pine needles, but completely different from those of soils. Lichens accumulated higher PAH concentrations when compared to the other environmental compartments and its concentrations were significantly and linearly correlated with concentrations of PAHs in soil; we showed that a translation of the lichen PAHs concentrations into regulatory standards is possible, fulfilling one of the most important requirements of using lichens as biomonitors. With lichens we were then able to characterize the air PAHs profile of urban, petro-industrial and background areas.     

火电厂大气污染物排放预测模型

根据大气污染物排放浓度变化特点,将无偏GM(1,1)模型与神经网络模型组合,并以矩阵型输入方式替代传统的数列型数据输入方式,得到改进型灰色神经网络模型,称为UGMN模型。接着,采用烟囱入口烟气自动监控系统(CEMS)数据,将模型运用于贵州省某电厂白天及夜间两段时间段内大气污染物排放浓度的模拟与预测。研究结果表明UGMN模型预测精度较好,可以应用于火电厂大气污染物排放浓度预测。     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

Atmospheric pollution in an urban environment by tree bark biomonitoring--part I: trace element analysis

Tree bark has been shown to be a useful biomonitor of past air quality because it accumulates atmospheric particulate matter (PM) in its outermost structure. Trace element concentrations of tree bark of more than 73 trees allow to elucidate the impact of past atmospheric pollution on the urban environment of the cities of Strasbourg and Kehl in the Rhine Valley. Compared to the upper continental crust (UCC) tree barks are strongly enriched in Mn, Ni, Cu, Zn, Cd and Pb. To assess the degree of pollution of the different sites in the cities, a geoaccumulation index I_geo was applied. Global pollution by V, Ni, Cr, Sb, Sn and Pb was observed in barks sampled close to traffic axes. Cr, Mo, Cd pollution principally occurred in the industrial area. A total geoaccumulation index I_GEO-tot was defined; it is based on the total of the investigated elements and allows to evaluate the global pollution of the studied environment by assembling the I_geo indices on a pollution map.     

The Danish eulerian hemispheric model — a three-dimensional air pollution model used for the arctic

A three-dimensional Eulerian hemispheric air pollution model, the Danish Eulerian Hemispheric Model (DEHM), is in development at the National Environmental Research Institute (NERI). The model has been used to study long-range transport of air pollution in the Northern Hemisphere. The present version of the model includes long-range transport of sulphur dioxide (SO₂) and particulate sulphate (SC₄²⁻. The chemistry in the model is described by a simple linear oxidation of SO₂ to SO₄²⁻, and the wet deposition of SO₂ and SO₄⁻ is estimated based on the amount of precipitation, which is calculated from the contents of liquid cloud water (see Christensen, Air Pollution Modelling and its Applicatioons, Vol. X, pp. 119–127, Vol. XI, pp. 249–256, Plenum press, New York; 1995, Ph.D. thesis, National Environmental Research Institute, Denmark). The model has been used to study the air pollution in the Arctic. Results from yr simulation with an analysis of the results is presented: the model results are verified by comparisons, to measurements not only from the Arctic region but also from Europe and Canada. Some examples of episodes in the Arctic including analysis of the meteorological conditions during the episodes are presented. Finally, the model has been used to estimate the contribution from the different source regions on the northern hemisphere to the Arctic sulphur pollution.     

The impacts of different kinds of dust events on PM10 pollution in northern China

In this study the frequencies of PM₁₀ (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM₁₀ concentration in the high-frequency period is higher with an extent 0.009–0.066 mg m⁻³ than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM₁₀ concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM₁₀ concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM₁₀ is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM₁₀ concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM₁₀ concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM₁₀ concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM₁₀ pollution concentration is usually caused by floating dust.     

Application of an ISCST3 model for predicting urban air pollution in the Izmir metropolitan area

A Gaussian atmospheric dispersion model, Industrial Source Complex Short Term (ISCST3), was used to estimate ground-level concentrations of sulphur dioxide (SO₂) emitted from source categories of industrial and domestic heating in the city of Izmir, Turkey. Predictions were estimated for the year 2000 across a study area of 80 km x 100 km. Statistical analyses were carried out to evaluate the model performance by comparing predicted and observed SO₂ concentrations at four ambient air quality monitoring stations using two main methods root mean square error (RMSE) and an index of agreement (d). The results showed that industry was found as the most air-polluting sector and industries located at outside of the metropolitan area were found to carry important risks for urban air quality. The most polluted area was found at a distance of about 1 km from a major petroleum refinery and a large petrochemical industry.     

西安市PM2.5污染特征及其估测模型

为了研究大气中PM2.5污染特征以及其随时间变化规律,基于西安市2013年1月—2014年4月间SO2、NO2、CO、O3、日最高温度(Tmax)、日最低温度(Tmin)、PM2.5、PM10等因素的监测数据.运用统计学原理和多元回归分析方法,分析了PM2.5的污染特征及相关因素对其产生的贡献度,进一步建立了四季的最优多元回归模型.研究结果表明,西安市年平均质量浓度124.9 μg/m3,四季的平均污染浓度从大到小依次为冬、春、秋、夏;春夏两季贡献较大的为SO2、CO;秋冬两季贡献较大的为NO2、CO;最终建立的模型的相关系数较高,模型很好地拟合了冬春两季PM2.5变化趋势,能较准确地反映了西安市PM2.5的污染特征,具有一定的理论和实用价值.