哈尔滨市大气污染与气象因素的相关性分析

为了研究哈尔滨市大气污染特征以及气象要素对大气污染的影响,对哈尔滨市2013年采暖期及非采暖期内4种大气污染物(二氧化硫SO2、二氧化氮NO2、可吸入颗粒物PM10、细颗粒物PM2.5)日均浓度分布特征以及日均浓度与部分地面气象要素(风速、气温、气压、相对湿度)相关性进行研究.提出哈尔滨市4种大气污染物日均浓度均符合对数正态分布.采暖期和非采暖期内4种大气污染物浓度与地面气象要素的相关性存在显著差异.采暖期内,4种污染物浓度与风速显著负相关,与风速相关系数最高达-0.639;与气压和相对湿度正相关.非采暖期内,4种大气污染物均与相对湿度呈负相关,相关系数为-0.5左右,与其他3种气象要素相关性普遍不高.全年4种污染物中仅有SO2与气温呈较好负相关,相关系数为-0.4.     

Absorption of Gaseous Air Pollutants By a Standardized Plant Canopy

Concentration profiles for hydrogen fluoride(HF), sulfur dioxide(SO₂), ozone (O₃), nitrogen dioxide(NO₂), and nitric oxide(NO) generated in a standardized alfalfa canopy are presented. Wind, light, temperature, and carbon dioxide(CO₂) profiles, canopy pollutant uptake rates, and canopy structural data are also given. Canopy pollutant concentration profile characteristics were studied to evaluate the relative potentials for major air pollutants to penetrate into canopies. The study was conducted in an environmental growth chamber equipped to control automatically environmental conditions and monitor continuously gas exchange rates. HF, SO₂, and NO₂ profiles suggested that these gases were removed efficiently by the upper portion of the canopy as well as by the immediate subsurface vegetation. The steady state HF profile showed the greatest displacement within the canopy. The NO profile was displaced the least. The uptake rate of NO by plants was apparently too slow in comparison with gas transport and mixing within the canopy to affect the internal profile substantially. O₃ appeared to be readily deposited on the surface tissues, but the deeper tissues in the canopy had less effect on the concentration profile. Data are also presented to show the relationship between NO₂ concentration within the canopy and changes in the air concentration above the vegetation. The results indicated that gas transport between the atmosphere and canopy interior was rapid. The data presented should be of current interest to agriculturists, researchers, administrators, and environmental planners concerned with effects of air pollutants on plants and on the fate of pollutants in the microenvironment.     

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NO_x concentration in the air as a function of traffic count (T_r) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NO_x measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.     

北京空气重污染红色预警期间污染物与气象因子特征

揭示空气重污染红色预警期间污染物与气象因子的变化特征对空气质量预报和污染减排措施评估具有重要参考价值。利用大气污染和气象观测资料,研究了北京2015年11-12月空气重污染红色预警时期污染物浓度、气候特征及气象因子对空气质量影响。结果表明,PM2.5在大气颗粒物中占有较大比重,为首要空气污染物;在重污染峰值时段,城郊PM2.5与PM10比值（R）相差不大,可达0.9以上,空气呈均匀混合的高PM2.5浓度特征,而空气质量较好时城区R值明显高于郊区;研究时段气候特征与历史同期相比有明显差异,其中平均风速偏小19%,平均气温偏高0.23 ℃,气温日较差减小,而多次小型降水增加了空气湿度,导致相对湿度值偏高40%,垂直方向上的逆温层或等温层则加剧了空气重污染的形成和发展,重污染过程中的红色预警措施明显降低了颗粒物浓度;风速与污染物浓度呈指数相关,城郊风速分别低于2.0和2.5 m·s-1时,空气质量较差、污染物浓度随风速升高快速下降,而当城郊风速大于2.0和2.5 m·s-1时变化特征则相反;相对湿度与污染物浓度呈幂相关,相对湿度在65%左右为空气质量特征发生变化的转折点;由于气温日较差存在季节变化,其与空气质量相关关系不太显著。     

Analysis of an anomalous case of high air pollution concentration in Turin after a foehn event

A case of unusually high concentrations of some urban air pollutants (CO, NO, SO₂) in Turin (Northwestern Italy) is presented. This episode occurred during the late afternoon of 7 November 1995. This day was characterised by a strong northern foehn event in the morning, which followed the passage of a cold front. The analysis has been carried out by means of data derived from the urban and suburban air quality monitoring networks, of meteorological data (wind velocity, temperature and relative humidity) from the same network and from other networks in the region, and of the output of the numerical model LAMBO (operatively used by the Regional Meteorological Service of Emilia Romagna). The analysis of meteorological data appears to be in good correlation with the distribution of the air pollutant concentrations; in particular, the high concentrations observed in the late afternoon are due to a strong thin inversion layer near the ground, developing immediately after the sudden end of the foehn episode.     

春节期间太原市环境空气质量变化

对包含春节节日的2014年1月16日至2月15日太原市环境空气中O3、NO2、CO、SO2、PM2.5、PM10的质量浓度监测数据进行了统计分析,并就春节期间燃放烟花爆竹及气象因素对空气质量产生的影响利用Pearson分析展开相关研究。结果表明：烟花爆竹集中燃放阶段,O3的质量浓度日变化基本不受其直接影响、呈单峰型,其他污染因子由双峰型变化为明显的三峰型,PM2.5、PM10污染突出,PM10增加速率高于PM2.5,PM2.5/PM10的3个谷值与污染因子的3个峰值相对应;分析气温、相对湿度、风速对CO、PM2.5、PM10的影响均在0.01显著水平上相关,但对于烟花爆竹集中燃放污染水平,气温、相对湿度对其影响不大,风速对其影响较为突出,风向与其有明显的相关性。     

Vegetation: A Sink for Atmospheric Pollutants

The possibility of vegetation being an important sink for gaseous air pollutants was investigated. Plant pollutant uptake measurements were made utilizing a typical vegetation canopy and chambers that were designed specifically for gaseous exchange studies. The data indicate that an alfalfa canopy removed gases from the atmosphere in the following order: hydrogen fluoride (HF) > sulfur dioxide (SO₂) > chlorine (Cl₂) > nitrogen dioxide (NO₂) > ozone (O₃) > peroxyacetyl nitrate (PAN) > nitric oxide (NO) > carbon monoxide (CO). The absorption rate of NO was low, and no absorption of CO could be detected with the methods used. In the typical ambient concentration range uptake increased linearly with increasing concentration except for O₃ and Cl₂ which caused partial stomatal closure at the higher concentrations. Wind velocity above the plants, height of the canopy, and light intensity were shown to affect the pollutant removal rate. A relationship between the absorption rate and solubility of the pollutant in water was also shown. It was concluded that vegetation may be an important sink for many gaseous air pollutants.     

Impacts of some meteorological parameters on SO2 and TSP concentrations in Erzurum, Turkey

The air pollution is the one of the most important environmental problems in Erzurum, situated in the eastern of Turkey, during winter periods. The unfavorable climate as well as the city’s topography, and inappropriate urbanization cause serious air pollution problems. The air pollutant concentrations in a city have a close relationship with its meteorological parameters. In the present study, the relationship between daily average total suspended particulate (TSP) and sulphur dioxide (SO₂) concentrations with meteorological factors, such as wind speed, temperature, relative humidity, pressure and precipitation, in 1995–2002 winter seasons was statistically analyzed using the stepwise multiple linear regression analysis. According to the results obtained through analysis, higher TSP and SO₂ concentrations are strongly related to colder temperatures, lower wind speed, higher pressure system and weakly lower precipitation and higher relative humidity. The statistical models of SO₂ and TSP including meteorological parameters gave R² of 0.74 and 0.88, respectively. Furthermore, the correlation between the previous day’s SO₂, TSP concentrations and actual concentrations of these pollutants on that day was investigated and found as 0.84 and 0.53, respectively. In order to develop this model, previous day’s SO₂ and TSP concentrations were added to the equations. The new model for SO₂ enhanced considerably (R² = 0.92), but for TSP new model was not enhanced (R² = 0.89).     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Air ventilation impacts of the “wall effect” resulting from the alignment of high-rise buildings

The objective of this study is to investigate the air ventilation impacts of the so called “wall effect” caused by the alignment of high-rise buildings in complex building clusters. The research method employs the numerical algorithm of computational fluid dynamics (CFD – FLUENT) to simulate the steady-state wind field in a typical Hong Kong urban setting and investigate pollutant dispersion inside the street canyon utilizing a pollutant transport model. The model settings of validation study were accomplished by comparing the simulation wind field around a single building block to wind tunnel data. The results revealed that our model simulation is fairly close to the wind tunnel measurements. In this paper, a typical dense building distribution in Hong Kong with 2 incident wind directions (0° and 22.5°) is studied. Two performance indicators are used to quantify the air ventilation impacts, namely the velocity ratio (VR) and the retention time (T_r) of pollutants at the street level. The results indicated that the velocity ratio at 2 m above ground was reduced 40% and retention time of pollutants increased 80% inside the street canyon when high-rise buildings with 4 times height of the street canyon were aligned as a “wall” upstream. While this reduction of air ventilation was anticipated, the magnitude is significant and this result clearly has important implications for building and urban planning.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM₁₀ and PM_2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO₂, PM₁₀, NO₂, CO, and O₃; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ng?m^?3, surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10^?6) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98?×?10^?7 in PM₁₀ and 1.06?×?10^?6 in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO₂, NO₂, PM₁₀, CO, and solar radiation had positive correlation with PAHs concentrations, while O₃, temperature, relative humidity, and wind speed were negatively correlated.     

Interactions of Environmental Factors on the Sensitivity of Plants to Air Pollution

Before potential damage to vegetation can be adequately forecast, even after an air pollution alert has been placed in effect, a clear understanding of the interactions of environment on plant sensitivity must be ascertained. This involves detailed study of single factors and then multiple factors using the phytotoxicants in question. Factors studied or suggested include light (quality, intensity, and duration), temperature, carbon dioxide concentration, humidity, wind, soil moisture, soil aeration, nutrient levels, and soil texture. This paper presents a review of the work relating plant injury to specific air pollutants as conditioned by several environmental conditions supported by research on the interactions of ozone with these environmental conditions.     

Enhancing mixed toluene and formaldehyde pollutant removal by Zamioculcas zamiifolia combined with Sansevieria trifasciata and its CO2 emission

Indoor air pollutants comprise both polar and non-polar volatile organic compounds (VOCs). Indoor potted plants are well known for their innate ability to improve indoor air quality (IAQ) by detoxification of indoor air pollutants. In this study, a combination of two different plant species comprising a C3 plant (Zamioculcas zamiifolia) and a crassulacean acid metabolism (CAM) plant (Sansevieria trifasciata) was used to remove polar and non-polar VOCs and minimize CO₂ emission from the chamber. Z. zamiifolia and S. trifasciata, when combined, were able to remove more than 95% of pollutants within 48 h and could do so for six consecutive pollutant’s exposure cycles. The CO₂ concentration was reduced from 410 down to 160 ppm inside the chamber. Our results showed that using plant growth medium rather than soil had a positive effect on decreasing CO₂. We also re-affirmed the role of formaldehyde dehydrogenase in the detoxification and metabolism of formaldehyde and that exposure of plants to pollutants enhances the activity of this enzyme in the shoots of both Z. zamiifolia and S. trifasciata. Overall, a mixed plant of Z. zamiifolia and S. trifasciata was more efficient at removing mixed pollutants and reducing CO₂ than individual plants.

     

Process analysis of a regional air pollution episode over Pearl River Delta Region,China, using the MM5-CMAQ model

This study focuses on the influences of a warm high-pressure meteorological system on aerosol pollutants, employing the simulations by the Models-3/CMAQ system and the observations collected during October 10–12, 2004, over the Pearl River Delta (PRD) region. The results show that the spatial distributions of air pollutants are generally circular near Guangzhou and Foshan, which are cities with high emissions rates. The primary pollutant is particulate matter (PM) over the PRD. MM5 shows reasonable performance for major meteorological variables (i.e., temperature, relative humidity, wind direction) with normalized mean biases (NMB) of 4.5–38.8% and for their time series. CMAQ can capture one peak of all air pollutant concentrations on October 11, but misses other peaks. The CMAQ model systematically underpredicts the mass concentrations of all air pollutants. Compared with chemical observations, SO₂ and O₃ are predicted well with a correlation coefficient of 0.70 and 0.65. PM_2.5 and NO are significantly underpredicted with an NMB of 43% and 90%, respectively. The process analysis results show that the emission, dry deposition, horizontal transport, and vertical transport are four main processes affecting air pollutants. The contributions of each physical process are different for the various pollutants. The most important process for PM₁₀ is dry deposition, and for NO_x it is transport. The contributions of horizontal and vertical transport processes vary during the period, but these two processes mostly contribute to the removal of air pollutants at Guangzhou city, whose emissions are high. For this high-pressure case, the contributions of the various processes show high correlations in cities with the similar geographical attributes. According to the statistical results, cities in the PRD region are divided into four groups with different features. The contributions from local and nonlocal emission sources are discussed in different groups.

Implications: The characteristics of aerosol pollution episodes are intensively studied in this work using the high-resolution modeling system MM5/SMOKE/CMAQ, with special efforts on examining the contributions of different physical and chemical processes to air concentrations for each city over the PRD region by a process analysis method, so as to provide a scientific basis for understanding the formation mechanism of regional aerosol pollution under the high-pressure system over PRD.     

等效排气筒有效高度计算方法的比选及其应用于大气污染预测的可行性

目前,等效排气筒多用于大气污染物总量控制,其预测精度和范围的不明晰限制了其在污染物运移扩散领域的进一步应用。基于《大气污染物综合排放标准》,依据所预测的范围和浓度精度对8种典型等效计算方法进行了比选,并验证了将等效排气筒用于不同工况下污染物运移扩散预测的可行性。改进的有效高度等效算法 (源强加权算术平均法) 综合考虑了不同高度和源强参数特征,以2个排放同种污染物的相邻排气筒为例,所计算的高斯模式下等效后下风向污染物浓度场总体分布趋势与等效前叠加计算结果一致,且预测精度优于《大气污染物综合排放标准》中提出的均方根平均法和其他等效算法。对不同风速条件下 (1.5~4.5 m·s⁻¹) 等效前后下风向污染物浓度场分布计算比较,发现即使风速改变仍可保证较高的最大落地浓度预测精度 (−6.87%~−2.21%),特别是风速较大时其预测精度更高 (达到−2.21%) 。这验证了该方法的有效性和稳定性。本研究探讨的源强加权算术平均值算法,进一步提升了等效排气筒相关参数计算的合理性,并拓展了其在大气预测评价中的应用。     

The effect of humidity and state of water surfaces on deposition of aerosol particles onto a water surface

Deposition processes of particles with dry diameter larger than about 10 μm are dominated by gravitational settling, while molecular diffusion and Brownian motion predominate the deposition processes of particles smaller than 0.1 μm in dry diameter. Many air pollution derived elements exhibit characteristics common to sub-micron particles. The objective of the present study is to examine the effects of meteorological conditions within the turbulent transfer layer on the deposition velocity of particles with dry diameter between 0.1 and 1 μm. It is for these sub-micron particles that particle growth by condensation in the deposition layer, the broken water surface effect and the enhanced transfer process due to atmospheric turbulence in the turbulent transfer layer play important roles in controlling the particle deposition velocity. Results of the present study show that the `dry air’ assumption of Williams’ model is unrealistic. Effects of ambient air relative humidity and water surface temperature cannot be ignored in determining the deposition velocity over a water surface. Neglecting effects of ambient air relative humidity and water surface temperature will result in defining atmospheric stability incorrectly. It is found that the largest effect of air relative humidity on deposition velocity occurs at an air–water temperature difference corresponding to the point of `displaced neutral stability'. For a given wind speed of U=5 m s⁻¹ the additive effects of water surface temperature, T_w, changes from 5 to 25°C and ambient air relative humidity variations from 85 to 60%, respectively, lead to a maximum difference in v_d of about 20%. For a higher wind speed of 10 m s⁻¹, however, the corresponding change in v_d reduces to less than 5%. This is further confirmation that wind speed is one of the strongest variables that governs the magnitude of v_d. The present study also found that the broken surface transfer coefficient, k_bs, given as a multiple of the smooth surface transfer coefficient, k_ss, is physically more meaningful than assigning it a constant value independent of particle size. The method used in this study requires only a single level of atmospheric data coupled with the surface temperature measurement. The present method is applicable for determining deposition velocity not only at the conventional measurement height of 10 m but also at any other heights that are different from the measurement height.     

Root growth responses of Anagallis arvensis L., primulaceae to air pollution

The root growth response to air pollution in populations of Anagallis arvensis growing about 0.5, 2, 6, 12 and 20 km leeward from a power plant complex varied with the level of pollution, age of the stand and meteorological conditions. The roots were more affected by the pollutants at a young stage and the loss in net primary productivity was proportional to the pollution level. The populations up to 2 km from the source of pollution completed their life cycle quickly. The coal consumption rate at the power plant, relative humidity, wind direction and other environmental factors were found to influence the degree of growth response to air pollution.     

Air Pollution Research Needs: Herbaceous and Ornamental Plants and Agriculturally Generated Pollutants

Effects of various air pollutants on economically important crops and ornamentals have been studied since before the turn of the century.

Summaries of this research on the effects of air pollutants, that have appeared in criteria documents developed by the Environmental Protection Agency, should be reviewed with respect to differences in plant susceptibility found in various regions of the country. These susceptibility differences are associated with variations in both environmental conditions and distribution of pollutants. Research efforts on air pollution injury to vegetation have often been poorly coordinated leaving many gaps in our knowledge. A better assessment of the impact of air pollution on vegetation is required to attain realistic controls for pollutants affecting agriculture. Research areas of major concern include: baseline information on effects of pollutants on agricultural productivity; dose-response information to support predictive mathematical models for acute and chronic studies of growth, yield, and quality effects; effects of pollutants interacting with other pollutants and with insects and plant diseases; mechanisms of pollutant action; genetic changes related to pollutant effects; effects of environmental stresses on plant response to pollutants; evaluation of plants including soil microbes as pollutant sinks; development of techniques to minimize pollutant effects; and, the effects of agricultural chemicals as air pollutants. There is a need for studies that consider the whole plant in its natural environment. Conceptual models interrelating pollutant effects and their interactions and ultimately mathematical models will be needed to develop an intelligent approach to land management. The effects of agriculturally produced pollutants on plants and other receptors must be identified and quantified.     

The co-occurrence of potentially phytotoxic concentrations of various gaseous air pollutants

Studies on impacts of air pollutants on vegetation have focused primarily on individual pollutants: ozone, sulfur dioxide and nitrogen dioxide. The impacts of pollutant combinations have not been extensively studied and there has been no concerted effort to ensure that experimental regimes for combined pollutant exposures are representative of ambient pollutant concentration, frequency, duration and time intervals between events. Most studies concerning the impact of pollutant combinations on vegetation have used concentrations of 0.05 ppm and greater. Therefore, co-occurrence was defined as the simultaneous occurrence of hourly averaged concentrations of 0.05 ppm or greater for pollutant pairs (SO₂/NO₂, O₃/SO₂, or O₃/NO₂). Air quality information from three data bases (EPA-SAROAD, EPRI-SURE and TVA) was analyzed to determine the frequency of co-occurrence for pollutant pairs. Ambient air quality data representing a diverse range of monitoring sites (e.g. rural, remote, city center, urban, near urban, etc.) were used in the analysis. Results showed that at most sites (1) the co-occurrence of two-pollutant mixtures lasted only a few hours per episode, (2) the time interval between episodes was generally large (weeks, sometimes months) and (3) most studies have used more intense exposure regimes than occurred at most monitored sites. When designing vegetation experiments for assessing pollutant mixture effects, it may be desirable to give greater emphasis to sequential patterns of exposure. It is suggested that future work is required before a reliable estimate can be made of the environmental significance of pollutant mixtures on vegetation.