An approach for determining air pollution monitoring sites

EC Directive 2008/50/CE on ambient air quality and cleaner air for Europe specifies the obligations of Member States regarding fixed measurements of atmospheric pollutants in areas where maximum concentration levels exceed the lower assessment threshold. However, indications as to the siting of atmospheric monitoring stations are less precise and sometimes confusing. Campaigns to measure benzene and other volatile organic compounds in Murcia in 2007 and 2008 revealed that the areas where measurements coincided with the mean concentration for the whole city were always practically the same. Consequently, we propose a method for siting atmospheric monitoring stations in cities for which emission sources remain steady throughout the year, as is the case for cities in most southern European countries, where the most important emission source is traffic. The method is particularly useful for towns in which only one fixed measuring point is compulsory and should help local authorities in making correct decisions in this context.     

Urban air pollution modelling and measurements of boundary layer height

An urban field trial has been undertaken with the aim of assessing the performance of the boundary layer height (BLH) determination of two models: the Met Office Unified Model (UM) and a Gaussian-type plume model, ADMS. Pulsed Doppler lidar data were used to measure mixing layer height and cloud base heights for a variety of meteorological conditions over a 3 week period in July 2003. In this work, the daily growth and decay of the BLH from the lidar data and model simulations for 5 days are compared. The results show that although the UM can do a good job of reproducing the boundary layer growth, there are occasions where the BLH is overestimated by 30–100%. Within dispersion models it is the BLH that effectively limits the height to which pollution disperses, so these results have very important implications for pollution dispersion modelling. The results show that correct development of the boundary layer in the UM is critically dependant on morning cloud cover. The ADMS model is used routinely by local authorities in the UK for local air-quality forecasting. The ADMS model was run under three settings; an ‘urban’ roughness, a ‘rural’ roughness and a ‘transition’ roughness. In all cases, the ‘urban’ setting over estimated the BLH and is clearly a poor predictor of urban BLH. The ‘transition’ setting, which distinguishes between the meteorological data input site and the dispersion modelling site, gave the best results under the well mixed conditions of the trial.     

Comparison of spatiotemporal distribution patterns of NO2 between four different types of air quality monitoring stations

The concentration data of nitrogen dioxide (NO(2)), obtained from four different types of air quality monitoring (AQM) stations in Korea (i.e., urban traffic (A), urban background (B), suburban background (C), and rural background (D)), were explored to evaluate the fundamental facets of its distribution and behavior. As there are many distinctions between these four types of AQM stations, the observed NO(2) values were clearly distinguished from each other. It is found that the average NO(2) concentrations from all A stations exhibit notably high values within the range of 24.8 (Gwangju) to 54.6 ppb (Seoul), while those of all B stations change from 19.6 (Ulsan) to 34.7 ppb (Seoul). Similarly, large differences were also observed from NO(2) values measured between C and D type stations. The NO(2) values of the former were from 16.5 (Jeonbuk) to 30.2 ppb (Gyunggi), while the latter from 4.3 (Gyeongbuk) to 8.7 ppb (Gyunggi). Although their annual patterns are rather complicated to explain, the results by and large reflected the changes in the conditions of the surrounding environment. When the results are compared across seasons, most stations (A, B, and D types) tend to exhibit their maximum values in the winter followed by spring, fall, and summer. The results of this study confirm that the distribution patterns of NO(2) are fairly sensitive enough to reflect the basic characteristics of its source processes in association with such factors as the intensity of anthropogenic activity or population density.     

福州市城市化进程与大气污染关系研究

以福州市为研究区域,选取福州市1996—2012年的时间序列数据,将大气污染物浓度作为被解释变量,城市化水平作为解释变量,分别设定二次曲线模型和三次曲线模型,通过比较PM10、SO2、NO2浓度与城市化水平的二次曲线和三次曲线模型的回归拟合效果,得出福州市大气污染与城市化水平之间的演化规律。结果表明,1996—2012年,福州市城市化水平平稳上升,城市的大气污染程度与城市化水平之间的关系并不完全符合"倒U型"的环境库兹涅茨曲线,不同的污染物类型具有不同的演化规律。PM10、SO2与城市化水平呈"倒N型"的曲线关系;NO2与城市化水平之间呈现"倒U型"曲线关系。由于研究的时间序列较短,因而研究结果可能只是真实曲线的一部分,福州市的大气污染与城市化水平之间的关系还有待进一步研究。     

Interaction between air pollution and meteorologicalparameters in Erzurum, Turkey

In recent years, the rapid increase in population density has caused increases in the consumption of fuel, and the outdoor air quality has deteriorated in the crowded urban areas of Turkey. Erzurum, a city in the eastern part of Turkey, is influenced by air pollutants such as SO₂ and suspended particles. It is known that, in general, the air pollution concentrations have a close relationship with meteorological factors. In this study, the relationship between outdoor air quality data and meteorological factors, such as wind speed, rainfall, temperature, sunshine hours and relative humidity, is statistically analysed, using the code SPSS. According to the results obtained through multiple linear regression analysis, there are moderate levels of correlation between SO₂ and particle concentrations and meteorological factors in Erzurum.     

Urban atmospheric pollution: personal exposure versus fixed monitoring station measurements

We initiated the PETER (pedestrian environmental traffic pollutant exposure research) project to investigate pedestrians' exposure to traffic related atmospheric pollutants, based on data obtained with the collaboration of selected categories of pedestrian urban workers. We investigated relations between roadside personal exposure levels of volatile aromatic hydrocarbons (including benzene) and particulate matter <10 microm (PM10) among traffic police (n = 126) and parking wardens (n = 50) working in downtown Bologna, Italy. Data were collected from workshifts throughout four 1-week periods in different seasons of 2000-2001. For benzene and PM10, comparisons were made with measurements by fixed monitoring stations, and influence of localized traffic intensity and meteorological parameters was examined. Roadside personal exposure to benzene correlated more strongly with other volatile aromatic hydrocarbons (toluene, xylenes and ethylbenzene) than with PM10. Benzene and PM10 personal exposure levels were higher than fixed monitoring station values (both p<0.0001). At multivariate analysis, benzene and PM10 data from fixed monitoring stations both correlated with meteorological variables, and were also influenced by localized traffic intensity. Plausibly because of the downtown canyon-like streets, weather conditions (during a period of drought) only marginally affected benzene personal exposure, and moderately affected PM10 personal exposure. These findings reinforce the concept that urban atmospheric pollution data from fixed air monitoring stations cannot automatically be taken as indications of roadside exposures.     

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.     

Objective analysis of air pollution monitoring network data; spatial interpolation and network density

To obtain meaningful interpretations of the results of fixed air pollution monitoring stations, quantitative information on the spatial representativity of these results is of crucial importance. On the basis of data of the Dutch Air Pollution Monitoring Network a study was undertaken to estimate the absolute and relative errors which are involved in extrapolating the results beyond the actual spatial argument of measurement. In the first place, three interpolation techniques, optimum interpolation, eigenvector interpolation and so called distance-density interpolation, were compared for SO₂, NO, NO₂ and O₃. The differences between the results of these techniques proved to be small. It was further concluded that the interpolation errors and the associated persistence in space and time, as given by mutual correlations, should be specified with respect to pure space- or space-time variability. In the second place, the interpolation errors were generalized to other network densities on the basis of the optimum interpolation scheme and empirical observations. From the resulting relation between interpolation error and network density it was concluded that the SO₂-network of 108 stations over the measurement area of 150 × 220 km² results in relative errors of 20 %, small enough to detect mesoscale transports downwind of major source areas. The effect of sampling error, i.e., the small scale influences of local sources combined with the effect of measurement error, appeared to be of overriding importance in the efficiency of reconstruction of pollutant concentration fields at a given confidence level from monitoring network data.     

Relationships between indoor and outdoor air pollution by carcinogenic PAHs and PCBs

PAHs and PCBs were collected simultaneously indoors and outdoors at eight non-smoking homes located in four buildings in high-traffic areas of Rome. The purpose was to evaluate the relevance of indoor air in contributing to the overall exposure of the urban population. The vertical distribution was also investigated by collecting outdoor samples at both road and roof level, and indoor samples in both a high and a low floor flat of each building. At one coal-heated building, samples were collected during both the heating and the non-heating season. No evident PAH source was present indoors. Indoor and outdoor daily concentrations of benzo[a]pyrene (BaP) ranged, respectively, 0.1–4.6 ng m⁻³ and 0.7–2.3 ng m⁻³. With the heating on, indoor PAH concentrations equalled or exceeded those outdoors, with BaP indoor/outdoor ratios up to 4; during the warm season, ratios decreased to 0.2–0.6. Indoor PAHs at the low floors exceeded the high-floor ones when the heating was off (vehicle exhausts being the dominant source), while being equal or lower with the heating on; the vertical gradient of indoor PAHs between different floors was within a factor of 2. Outdoor PAHs at roof level were 20–70% of those at road level, which in turn exceeded those at the medium-traffic station up to a factor of 4. The outdoor concentrations of Σ6 indicator PCBs ranged 0.1–1.6 ng m⁻³. Indoor PCB concentrations exceeded those outdoors by an approximate factor of 2–50. No vertical gradient was observed. The results indicated that indoor air may contribute to the overall exposure to PAHs and PCBs more than the urban air. They were also consistent with recent findings suggesting that indoor air can be a relevant source of PCBs for outdoor air.     

Contemporary threats and air pollution

It is now well understood that air pollution produces significant adverse health effects in the general public and over the past 60 years, there have been on-going efforts to reduce the emitted pollutants and their resulting health effects. There are now shifting patterns of industrialization with many heavily polluting industries moving from developed countries with increasingly stringent air quality standards to the developing world. However, even in decreasing concentrations of pollutants, health effects remain important possibly as a result of changes in the nature of the pollutants as new chemicals are produced and as other causes of mortality and morbidity are reduced. In addition, there is now the potential for deliberate introduction of toxic air pollutants by local armed conflicts and terrorists. Thus, there are new challenges to understand the role of the atmospheric environment on public health in this time of changing economic and demographic conditions overlaid with the willingness to indirectly attack governments and other established entities through direct attacks on the general public.     

Spatial modeling for air pollution monitoring network design: example of residential woodsmoke

The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia. The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data. Second, fireplace density and a compound topographic index based on hydrological flow regimes are used to enhance the emissions surface. Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies. Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations.