Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe

An impact related daily air quality index (DAQx), calculated for 15 air quality monitoring stations (traffic, background, and industry) in Belgium, France, Germany and Luxembourg, was compared to mesoscale atmospheric patterns between 2001 and 2007. Meteorological conditions were described by the Hess and Brezowsky synoptic weather classification system and gridded data of the EU FP6 ENSEMBLES project of total precipitation and mean surface temperature. DAQx values indicate sufficient to poor air quality in the urban area of Brussels and at urban traffic stations, as well as satisfactory air quality at the background stations. The air quality index refers to more than 90% to the presence of high PM₁₀, O₃ and NO₂ concentrations. SO₂ and CO play only a minor role. The investigation of weather regimes indicates that zonal and mixed cyclonic circulation regimes are associated with better air quality than meridional and anticyclonic weather regimes. In general, weather regimes with high daily precipitation lead to better air quality than dryer air masses because of lower contribution of PM₁₀ to the air quality index. A trend analysis of weather regimes from 1978 to 2007 shows significant (α = 0.05) positive trends for weather classes associated with lower PM₁₀ concentrations. The results of a case study at a German station examining the relationship between PM₁₀ concentrations and local meteorological quantities (wind speed and precipitation) confirm the results of the regional analysis.     

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO₂) concentrations for health exposure studies. NO₂ is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO₂ concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.     

Interaction between air pollution and meteorological parameters 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.     

Evaluating urban PM10 pollution benefit induced by street cleaning activities

Despite their burden in urban particulate air pollution, road traffic non-exhaust emissions are often uncontrolled and information about the effectiveness of mitigation measures on paved roads is still scarce. The present study is aimed to evaluate the effectiveness of mechanical sweeping/water flushing treatments in mitigating urban road dust resuspension and to quantify the real benefit in terms of ambient PM₁₀ concentrations. To this aim a specific campaign was carried out in a heavily trafficked central road of Barcelona (Spain), a Mediterranean city suffering from a traffic-related pollution, both for a high car density and a frequent lack of precipitation. Several street washings were performed by means of mechanical sweepers and pressure water during night in all traffic lanes and sidewalks. PM₁₀ levels were simultaneously compared with four reference urban background air quality stations to interpret any meteorological variability. At the downwind measurement site, PM₁₀ concentrations registered a mean daily decrease of 8.8 μg m^?3 during the 24 h after street washing treatments. However 3.7–4.9 μg m^?3 of such decrease were due to the meteorological variability detected at the upwind site, as well as at two of the reference sites. This reveals that an effective decrease of 4–5 μg m^?3 (7–10%) can be related to street washing efficiency. Mitigation of road dust resuspension was confirmed by investigating the chemical composition of airborne-PM₁₀ filters. Concentrations of Cu, Sb, Fe and mineral matter decrease significantly with respect to concentrations of elemental carbon, used as tracer for exhaust diesel emissions. High efficiency of street washing in reducing road dust loads was found by performing periodic samplings both on the treated and the untreated areas.     

Analysis and evaluation of selected local-scale PM10 air pollution episodes in four European cities: Helsinki,London, Milan and Oslo

We have analysed in detail four selected episodes involving substantially high concentrations of PM₁₀ that occurred in Oslo on 4–10 January 2003, in Helsinki on 3–14 April 2002, in London on 18–27 February 2003 and in Milan on 14–19 December 1998. We have also utilised a more extensive dataset containing relevant information regarding 21 episodes from seven cities in six countries. The four episodes analysed in detail were recently occurring cases that were at least partly caused by various local emission sources. In particular, we have addressed the evolution of the measured concentrations in terms of the measured, meteorologically pre-processed and predicted (using numerical weather prediction models and a meso-scale meteorological model) meteorological variables. All the four episodes addressed were associated with areas of high pressure (Oslo, Helsinki and London) or a high-pressure ridge (Milan). The best meteorological prediction variables were found to be the temporal evolution of the temperature inversions and atmospheric stability and, in some of the cases, wind speed. Strong ground-based or slightly elevated temperature inversions prevailed in the course of the episodes in Oslo, Helsinki and Milan, and there was a slight ground-based inversion also in London; their occurrence coinciding with the highest PM₁₀ concentrations. The same result was also obtained by considering an additional set of seven PM₁₀ episodes from the larger dataset. The inversions in Oslo and Milan were mainly caused by the advection of warmer air above a relatively colder surface, and that in Helsinki by radiation cooling of snow-covered ground. It was also found that a low wind speed is not necessarily a good indicator of episodes; this is the case, e.g., in the Po valley, due to the frequently occurring calm and low wind speed conditions there.     

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.     

A Review of Recent Reports Dealing with the Greenhouse Effect of Atmospheric Carbon Dioxide

Abstract

A statistical study on the behavior of ground-level O₃ concentration in different regions of a large urban area was carried out, with emphasis on pollutant gas concentrations and meteorological variables. The study was based on data generated by a network of measuring stations distributed throughout the São Paulo Metropolitan Area, in regions with different characteristics of traffic and economic activities. The combined application of principal component analysis and clustering techniques to data collected from 1997 until 2000 has led to the identification of implicit relationships between variables that have been associated with dominant processes related to O₃ formation in different locations. Similarities between different regions of the city have also been detected and associated with local characteristics. The results indicate that the application of such statistical techniques to data collected in large urban areas enables the grouping of different regions according to their behavior in terms of O₃ levels, as well as the identification of dominant processes in each group. These techniques are thus important in the planning of air pollution policies, especially in the case of O₃ , a pollutant that is not directly related to pollution levels alone.     

Rapid urban growth,land-use changes and air pollution in Santiago,Chile

This paper is a contribution to the understanding of the topoclimatic and environmental geography of the basin where Santiago — one of the most polluted Latin American city – is located. In the first part, land-use change is analysed looking at the climatic transformation caused by the rapid transit from natural semiarid surface to urban areas. In the second part, seasonal weather and daily cycles of slope winds and the available ventilation are described trying to relate those patterns with the spatial distribution of air pollution. A combination of meteorological, geographical and cultural factors explain extreme air pollution events: meteorologically, Santiago is under permanent subsidence inversion layers. Geographically, the city is located in a closed basin surrounded by mountains. Culturally, the urban area has the highest population concentration (40% of the national total), industries (near 70% of the total) and vehicles, which are the main sources of smog. The urban and suburban transport system is based on a large number of buses (diesel) and private cars, both experiencing a rapid growth from the past few years. The city and specially the transport system generates high emissions of pollutant, but the natural semiarid deforested soils and slopes are also important sources. The local wind system can explain the differential spatial distribution on the concentration of air pollutants in the city and its periphery. In winter (rain season) concentrations of particulate matter are higher at the centre and the SW part of the city. The andean piedmont area (E part of the city) shows minimum values, suggesting major ventilation effects of slope and valley winds. Ozone exceeds air quality standards in summer (dry season) at all sites in the centre and periphery. However, the O₃-concentrations are higher on preferred residential areas located at the piedmont area (E part of the city), suggesting air pollution transport effects. Currently, there is no consideration of these local climatic features in the process of urban planning.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

Air pollution modelling in Erzurum city

Various techniques have been applied to air pollution modelling. In this study, a model was developed by applying a continuity equation to the air control volume over Erzurum city centre. Model parameters were calculated by regression analysis, using SO2 and particle concentrations taken in 1989-1990 and 1990-1991 winter seasons. Multiple correlation coefficients, R2, for all analyses were of the same order of 97%. According to this model, daily polluting concentrations are not only influenced by daily meterological parameters but also by the values of previous days. The proposed model allows estimation of daily polluting concentrations by means of meteorological data.     

Air pollution in the Krusne Hory region,Czech Republic during the 1990s

Emissions from the Black Triangle Region were considered to be the major source of air pollution problems in Europe during the 1990s. This discussion reviews the changes in emissions and pollution concentrations in the Krusne Hory Region (Czech Republic) in the winter half of the year during most of the past decade, and describes the relationships with meteorology. Sulfur dioxide (SO₂) is used as the example pollutant. The results show a decrease in pollution concentrations since 1996, as air pollution control and management strategies for important point sources take effect. The winter of 1995–1996 was especially harsh in the number of pollution episodes. Correlations between SO₂ and meteorological parameters are inconsistent. Wind direction provides the best relationship at monitoring stations along the Krusne Hory Plateau, with wind speed and temperature more variable depending on month and location. For the valley stations, higher SO₂ concentrations are strongly related to colder temperatures, higher relative humidities, and lower wind speeds. A case study during the winter of 1995–1996 (November 9–15) illustrated the importance of synoptic high pressure and a low-level inversion in minimizing plume dispersion from point sources. Specific sources of SO₂ affecting each station could thus be identified.     

Contribution of transboundary air pollution to ionic concentrations in fog in the Kinki Region of Japan

To estimate the contribution of transboundary transported air pollutants from other Asian countries to Japan in ionic concentrations in fog water in March 2005, the Community Multiscale Air Quality (CMAQ) modeling system was utilized with meteorological fields produced by the 5th generation Mesoscale Model (MM5). For meteorological predictions, the model well reproduced the surface meteorological variables, particularly temperature and humidity, and generally captured fog occurrence. For chemical predictions, most of the model-predicted monthly mean concentrations were approximately within a factor of 2 of the observations, indicating that the model well simulated the long-range atmospheric transport from the Asian Continent to Japan. For SO₄^2?, NO₃^? and NH₄⁺, the contribution rates of the transboundary air pollution in the Kinki Region of Japan ranged from 69 to 82% for aerosols, from 47 to 87% for ionic concentrations in rain, and from 55 to 79% for ionic concentrations in fog. The study found that the transboundary air pollution also affected ionic concentrations in fog as well as aerosol concentrations and ionic concentrations in rain.     

An inexpensive method to estimate CO and NOx emissions from mobile sources

CO and NO_x measurements from mobile sources at two urban locations in Córdoba City, Argentina, were used to develop a very simple method to estimate emission from these sources. This development was possible because primary urban air pollution in Córdoba comes mostly from mobile sources and because a field measurement campaign was conducted by the city government during 1995–1996 that has allowed us to have a complete and valuable data bank. Air concentrations of CO, NO_x as well as physical, and meteorological variables were measured at two urban sites with two monitoring stations. We compared the measured CO and NO_x air concentration data with the predictions of a method that uses regression analysis to estimate the emission factor from the mobile sources. The agreement is good, considering the simplicity of the approach.     

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.     

The air pollution episode of January 1985 as revealed by background data measured in Hungary

The air pollution level in January 1985 is discussed on the basis of the concentrations of NO₂ and SO₂ measured at three regional background stations in Hungary. For one background air pollution station some aerosol components (SO₄²⁻, NO₃⁻, NH₄⁺) are also included in the study. The data obtained are compared to average values for January. This comparison makes a severe country-wide pollution episode evident. The fossil fuel consumption and emission data as well as the meteorological parameters observed in this month are analyzed and backward trajectories are calculated to gain some information on the causes of this unusual situation.     

Effects of air pollution on lung function and symptoms of asthma, rhinitis and eczema in primary school children

Health effects of ambient air pollution were studied in three groups of schoolchildren living in areas (suburban, urban and urban-traffic) with different air pollution levels in Eski?ehir, Turkey. This study involved 1,880 students aged between 9 and 13 years from 16 public primary schools. This two-season study was conducted from January 2008 through March 2009. Symptoms of asthma, rhinitis and eczema were determined by the International Study of Asthma and Allergies in Childhood questionnaire in 2008. Two lung function tests were performed by each child for summer and winter seasons with simultaneous ambient air measurements of ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) by passive sampling. Effects of air pollution on impaired lung function and symptoms in schoolchildren were estimated by multivariate logistic regression analyses. Girls with impaired lung function (only for the summer season evaluation) were more observed in suburban and urban areas when compared to urban-traffic area ([odds ratio (OR)?=?1.49; 95 % confidence interval (CI) 1.04–2.14] and [OR?=?1.69 (95 % CI 1.06–2.71)] for suburban vs. urban-traffic and urban vs. urban-traffic, respectively). Significant association between ambient ozone concentrations and impaired lung function (for an increase of 10 μg m^?3) was found only for girls for the summer season evaluation [OR?=?1.11 (95 % CI 1.03–1.19)]. No association was found for boys and for the winter season evaluation. No association was found between any of the measured air pollutants and symptoms of current wheeze, current rhinoconjunctivitis and current itchy rash. The results of this study showed that increasing ozone concentrations may cause a sub-acute impairment in lung function of school aged children.     

Urban air pollution monitoring and correlation properties between fixed-site stations

The rich regional air-monitoring network of the Emilia-Romagna region of Italy has been used to quantify the spatial variability of the main pollutants within urban environments and to analyze the correlations between stations. The spatial variability of the concentrations of the majority of pollutants within the city was very high, making it difficult to differentiate and characterize the urban environments and to apply legal limits with uniform criteria. On the other hand, the correlations between the fixed-site monitoring stations were high enough for their data to be retained generally very appropriately for controlling temporal trends. Starting from the high correlation level, a procedure was proposed and tested to derive pollution levels, using short-term measurements, such as passive samplers and mobile-station data. The importance of long-term statistics in urban air pollution mapping was emphasized. Treatment of missing data in time series and quality assurance were indicated as possible fields for applications for the correlation properties.     

Acute effect of daily fine particulate matter pollution on cerebrovascular mortality in Shanghai,China: a population-based time series study

Numerous studies have investigated the impacts of ambient fine particulate matter (PM_2.5) on human health. In this study, we examined the association of daily PM_2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM_2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM_2.5 in Shanghai was high, 55 μg/m³, with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 μg/m³ increase in PM_2.5 concentration in the unadjusted model. However, PM_2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association.

     

Intercomparison of nocturnal mixing height estimate methods for urban air pollution modelling

One of the most important meteorological input parameters for three-dimensional photochemical air pollution models is the mixing height h, which has a strong influence on the shape and intensity of the vertical diffusivity K_z and, as a consequence, on ground-level air concentrations of primary and secondary pollutants. A number of indirect algorithms for the estimate of h in nocturnal, stable conditions, when the mixing is dominated by mechanical turbulence, are reviewed and compared with mixing heights derived from wind (SODAR) and temperature (RASS) profiles measured in the Milan urban area during spring and summer 1996. Mixing heights derived from temperature soundings correlate positively with those derived from wind soundings only when a stable layer is superimposed to a quasi-adiabatic layer, while the correlation is very weak in the presence of a ground-based inversion. In general, indirect algorithms perform very poorly if compared with RASS-based estimates, and reasonably well if compared with SODAR-based estimates. Among the others, Benkley and Schulman (1979, Journal of Applied Meteorology 18, 772–780) method, which makes use of wind speed observed at 10 m height, and Nieuwstadt (1984, Boundary-Layer Meteorology 30, 31–55), which makes use of friction velocity and Monin–Obukhov length, give the best results.