An assessment of the impact of climate change on air quality at two UK sites

Possible effects of climate change on air quality are studied for two urban sites in the UK, London and Glasgow. Hourly meteorological data were obtained from climate simulations for two periods representing the current climate and a plausible late 21st century climate. Of the meteorological quantities relevant to air quality, significant changes were found in temperature, specific humidity, wind speed, wind direction, cloud cover, solar radiation, surface sensible heat flux and precipitation. Using these data, dispersion estimates were made for a variety of single sources and some significant changes in environmental impact were found in the future climate. In addition, estimates for future background concentrations of NO_x, NO₂, ozone and PM₁₀ upwind of London and Glasgow were made using the meteorological data in a statistical model. These showed falls in NO_x and increases in ozone for London, while a fall in NO₂ was the largest percentage change for Glasgow. Other changes were small. With these background estimates, annual-average concentrations of NO_x, NO₂, ozone and PM₁₀ were estimated within the two urban areas. For London, results averaged over a number of sites showed a fall in NO_x and a rise in ozone, but only small changes in NO₂ and PM₁₀. For Glasgow, the changes in all four chemical species were small. Large-scale background ozone values from a global chemical transport model are also presented. These show a decrease in background ozone due to climate change. To assess the net impact of both large scale and local processes will require models which treat all relevant scales.     

Determinants of fine particle (PM2.5) personal exposure levels in transport microenvironments,London, UK

A series of field studies were carried out in London, UK, during 1999–2000 in which over 400 fine particle (PM_2.5) personal exposure level measurements were taken for journeys in bicycle, bus, car and underground rail transport microenvironments. This was the first comprehensive PM_2.5 personal exposure study of transport users. Both a fixed-route multi-transport mode study and a study of cyclists’ commuter journeys were undertaken. Subsequent to these field studies regression modelling of possible influencing factors of these exposure levels was carried out. Meteorological variables, traffic density, mode and route were considered; the relationships of personal exposure levels with fixed site monitor (FSM) concentrations, and of the FSM concentrations with the potential predictor variables, were also investigated. This analysis of the determinants of transport user exposure to PM_2.5 in London, UK, showed that wind speed had a significant influence on personal exposure levels, though explained only up to 20% of the variability of road transport user exposure levels. The occurrence of higher wind speeds was strongly associated with a decrease in personal exposure levels; a 1.5–2.0 fold difference in exposure level concentrations was estimated between the 10th and 90th percentiles of wind speed. Route was a significant factor, whilst mode was not a significant factor in the street microenvironment (between bicycle, bus and car modes); models incorporating route and mode, as well as wind speed, explained approximately 35% of the variability in PM_2.5 exposure levels. Personal exposure levels were reasonably correlated with urban background FSM concentrations, for fixed-route road mode (bicycle, bus and car) exposure level concentrations, r=0.27 (p<0.01) and for commuter cyclists’ exposure level concentrations r=0.58 (p<0.01).     

Behavior of particulate matter during high concentration episodes in Seoul

The behavior of particulate matter (PM) during high-concentration episodes was investigated using monitoring data from Guui station, a comprehensive air monitoring station in Seoul, Korea, from January 2008 to March 2010. Five non-Asian dust (ND) episodes and two Asian dust (AD) episodes of high PM concentrations were selected for the study. During the ND episode, primary air pollutants accumulated due to low wind speeds, and PM_2.5 increased along with most other air pollutants. Particles larger than PM_2.5 were also high since these particles were generated by vehicular traffic rather than wind erosion. During strong AD episodes, PM_10–2.5 primarily increased and gaseous primary air pollutants decreased under high wind speeds. However, even during the AD episode, PM_2.5 and gaseous primary air pollutants increased when the effects of AD were weak and wind speeds were low. This study corroborates that accumulation of air pollutants due to a drop in surface wind speed plays an important role in short-term high-concentration occurrences. However, low wind speeds could not be directly linked to local emissions because a significant portion of accumulated air pollutants resulted from long-range transport.     

Prediction of Ambient PM10 and Toxic Metals Using Artificial Neural Networks

Abstract

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particu-late matter (PM₁₀) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM₁₀ and toxic metals quite accurately.     

Mass concentrations and metals speciation of PM2.5, PM10, and total suspended solids in Oxford,Ohio and comparison with those from metropolitan sites in the Greater Cincinnati region

Ambient aerosols were sampled for a period of 12 months from January to December 2005 in Oxford, OH, which is a relatively small college town approximately 50 miles northwest of Cincinnati. The goal of this study was to compare the mass concentrations and metals speciation of ambient aerosols collected in Oxford, OH with those collected in three urban centers (Cincinnati, Middletown, and Hamilton) in the Greater Cincinnati region. A secondary goal was to compare the effects of meteorological conditions on PM_2.5 and PM₁₀. Three different sampling instruments were used to collect PM_2.5, PM₁₀, and total suspended solids (TSP) in Oxford.It was found that PM_2.5 mass concentrations in Oxford were approximately 10% lower than those measured in Cincinnati, Middletown, and Hamilton. It was also found that the PM_2.5 contributed ∼95% to PM₁₀ and >60% to TSP. Acid extraction of the filters followed by inductively coupled plasma (ICP) was used for metals speciation. It was found that arsenic, antimony, cobalt, and lead were predominantly contained in PM_2.5, whereas cadmium, chromium, iron, nickel, molybdenum, silicon, vanadium, and zinc were found predominantly in PM₁₀. The crustal elements, including aluminum, calcium, magnesium, manganese, and potassium, were found predominantly in TSP. Copper had relatively high concentrations in TSP samples in Oxford, which may be due to emissions from a local industry that manufactures copper-based products. The metals concentrations in Oxford were generally lower than those reported in for Cincinnati and Middletown. However, different analytical methods were used for metals speciation in those cities, which likely contributed significantly to observed differences.It was observed that PM concentrations increased with air temperature and decreased with increasing wind speed. Humidity, precipitation, and wind direction appeared to have very little effect on PM concentrations.     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): Analysis of Short-Term and Episodic Variations in PM2.5 Concentrations Using Hourly Air Monitoring Data

Abstract

One-hour average ambient concentrations of particulate matter (PM) with an aerodynamic diameter <2.5 μm (PM_2.5) were determined in Steubenville, OH, between June 2000 and May 2002 with a tapered element oscillating microbalance (TEOM). Hourly average gaseous copollutant [carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen oxide (NO_x), and ozone (O₃)] concentrations and meteorological conditions also were measured. Although 75% of the 14,682 hourly PM_2.5 concentrations measured during this period were ≤17 μg/m³, concentrations >65 μg/m³ were observed 76 times. On average, PM_2.5 concentrations at Steubenville exhibited a diurnal pattern of higher early morning concentrations and lower afternoon concentrations, similar to the diurnal profiles of CO and NO_x. This pattern was highly variable; however, PM_2.5 concentrations >65 μg/m³ were never observed during the mid-afternoon between 1:00 p.m. and 5:00 p.m. EST. Twenty-two episodes centered on one or more of these elevated concentrations were identified. Five episodes occurred during the months June through August; the maximum PM_2.5 concentration during these episodes was 76.6 μg/m³. Episodes occurring during climatologically cooler months often featured higher peak concentrations (five had maximum concentrations between 95.0 and 139.6 μg/m³), and many exhibited strong covariation between PM_2.5 and CO, NO_x, or SO₂. Case studies suggested that nocturnal surface-based temperature inversions were influential in driving high nighttime concentrations of these species during several cool season episodes, which typically had dramatically lower afternoon concentrations. These findings provide insights that may be useful in the development of PM_2.5 reduction strategies for Steubenville, and suggest that studies assessing possible health effects of PM_2.5 should carefully consider exposure issues related to the intraday timing of PM_2.5 episodes, as well as the potential for toxicological interactions among PM_2.5 and primary gaseous pollutants.     

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.     

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO_x and NO₂ concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO₂ concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO₂ concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO₂ concentrations both at the highest (u⩾6 m s⁻¹) and at the lowest wind speeds (u<2 m s⁻¹). For higher wind speeds, the modelling system overpredicts the measured NO₂ concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.     

An empirical approach for the prediction of daily mean PM10 concentrations

An empirical model has been devised to predict concentrations of PM₁₀ at background and roadside locations in London. Factors to calculate primary PM₁₀ and PM_2.5 concentrations are derived from annual mean NO_X, PM_2.5 and PM₁₀ measurements across London and south east England. These factors are used to calculate daily means for the primary and non-primary PM₁₀ fractions for the London area. The model accurately predicts daily mean PM₁₀ and EU Directive Limit values across a range of sites from kerbside to rural. Predictions of future PM₁₀ can be made using the expected reductions in secondary PM₁₀ and site specific annual mean NO_X predicted from emission inventories and dispersion modelling. The model suggests that the EU Directive Limit values will be exceeded close to many of London's busiest roads, and perhaps at central background sites should there be a repeat of 1996 meteorological conditions during 2005. A repeat of 1997 meteorology conditions during 2005 would lead to the EU Limit Value being exceeded alongside the busiest central London roads only. The model is applicable for London and south east England but the methodology could be applied elsewhere at a city or regional level. The model relies on the currently observed ratio between NO_X and PM₁₀. This ratio has remained constant over the last 4 years but might change in the future. The NO_X:PM₁₀ ratio derived from measurements and used in this model, implies that emission inventories might over estimate primary PM₁₀ by more than 50%.     

Characteristics of vertical profiles and sources of PM2.5, PM10 and carbonaceous species in Beijing

In August 2003 during the anticipated month of the 2008 Beijing Summer Olympic Games, we simultaneously collected PM₁₀ and PM_2.5 samples at 8, 100, 200 and 325 m heights up a meteorological tower and in an urban and a suburban site in Beijing. The samples were analysed for organic carbon (OC) and elemental carbon (EC) contents. Particulate matter (PM) and carbonaceous species pollution in the Beijing region were serious and widespread with 86% of PM_2.5 samples exceeding the daily National Ambient Air Quality Standard of the USA (65 μg m⁻³) and the overall daily average PM₁₀ concentrations of the three surface sites exceeding the Class II National Air Quality Standard of China (150 μg m⁻³). The maximum daily PM_2.5 and PM₁₀ concentrations reached 178.7 and 368.1 μg m⁻³, respectively, while those of OC and EC reached 22.2 and 9.1 μg m⁻³ in PM_2.5 and 30.0 and 13.0 μg m⁻³ in PM₁₀, respectively. PM, especially PM_2.5, OC and EC showed complex vertical distributions and distinct layered structures up the meteorological tower with elevated levels extending to the 100, 200 and 300 m heights. Meteorological evidence suggested that there exist fine atmospheric layers over urban Beijing. These layers were featured by strong temperature inversions close to the surface (<50 m) and more stable conditions aloft. They enhanced the accumulation of pollutants and probably caused the complex vertical distributions of PM and carbonaceous species over urban Beijing. The built-up of PM was accompanied by transport of industrial emissions from the southwest direction of the city. Emissions from road traffic and construction activities as well as secondary organic carbon (SOC) are important sources of PM. High OC/EC ratios (range of 1.8–5.1 for PM_2.5 and 2.0–4.3 for PM₁₀) were found, especially in the higher levels of the meteorological tower suggesting there were substantial productions of SOC in summer Beijing. SOC is estimated to account for at least 33.8% and 28.1% of OC in PM_2.5 and PM₁₀, respectively, with higher percentages at the higher levels of the tower.     

Indoor–outdoor concentrations of RSPM in classroom of a naturally ventilated school building near an urban traffic roadway

A study on indoor–outdoor RSPM (PM₁₀, PM_2.5 and PM_1.0) mass concentration monitoring has been carried out at a classroom of a naturally ventilated school building located near an urban roadway in Delhi City. The monitoring has been planned for a year starting from August 2006 till August 2007, including weekdays (Monday, Wednesday and Friday) and weekends (Saturday and Sunday) from 8:0 a.m. to 2:0 p.m., in order to take into account hourly, daily, weekly, monthly and seasonal variations in pollutant concentrations. Meteorological parameters, including temperature, rH, pressure, wind speed and direction, and traffic parameters, including its type and volume has been monitored simultaneously to relate the concentrations of indoor–outdoor RSPM with them. Ventilation rate has also been estimated to find out its relation with indoor particulate concentrations. The results of the study indicates that RSPM concentrations in classroom exceeds the permissible limits during all monitoring hours of weekdays and weekends in all seasons that may cause potential health hazards to occupants, when exposed. I/O for all sizes of particulates are greater than 1, which implies that building envelop does not provide protection from outdoor pollutants. Further, a significant influence of meteorological parameters, ventilation rate and of traffic has been observed on I/O. Higher I/O for PM₁₀ is indicating the presence of its indoor sources in classroom and their indoor concentrations are strongly influenced by activities of occupants during weekdays.     

Roadside measurements of fine and ultrafine particles at a major road north of Gothenburg

Particle measurements were conducted at a road site 15 km north of the city of Gothenburg for 3 weeks in June 2000. The size distribution between 10 and 368 nm was measured continuously by using a differential mobility particle sizer (DMPS) system. PM_2.5 was sampled on a daily basis with subsequent elemental analysis using EDXRF-spectroscopy. The road is a straight four-lane road with a speed limit of 90 kph. The road passing the site is flat with no elevations where the vehicles run on a steady workload and with constant speed. The traffic intensity is about 20,000 cars per workday and 13,000 vehicles per day during weekends. The diesel fuel used in Sweden is low in sulphur content (<10 ppm) and therefore the diesel vehicles passing the site contribute less to particle emissions in comparison with other studies. A correlation between PM_2.5 and accumulation mode particles (100–368 nm) was observed. However, no significant correlation was found between number concentrations of ultrafine particles (10–100 nm) and PM_2.5 or the accumulation mode number concentration. The particle distribution between 10 and 368 nm showed great dependency on wind speed and wind direction, where the wind speed was the dominant factor for ultrafine (10–100 nm) particle concentrations. The difference in traffic intensity between workday and weekend together with wind data made it possible to single out the traffic contribution to particle emissions and measure the size distribution. The results presented in combination with previous studies show that both PM_2.5 and the mass of accumulation mode particles are bad estimates for ultrafine particles.     

Prediction of daily averaged PM10 concentrations by statistical time-varying model

In this study, a time-varying statistical model, TVAREX, was proposed for daily averaged PM₁₀ concentrations forecasting of coastal cities. It is a Kalman filter based autoregressive model with exogenous inputs depending on selected meteorological properties on the day of prediction. The TVAREX model was evaluated and compared to an ANN model, trained with the Levenberg–Marquardt backpropagation algorithm subjected to the same set of inputs. It was found that the error statistics of the TVAREX model in general were comparable to those of the ANN model, but the TVAREX model was more efficient in capturing the PM₁₀ pollution episodes due to its online nature, therefore having an appealing advantage for implementation.     

A semi-empirical model for urban PM10 concentrations,and its evaluation against data from an urban measurement network

We have developed a model for evaluating the mass-based concentrations of urban particulate matter. The basic model assumption is that local vehicular traffic is responsible for a substantial fraction of the street-level concentrations of both PM₁₀ and NO_x, either due to primary emissions or resuspension from street surfaces. The modelling system utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. We have determined linear relationships between the measured urban PM₁₀ data against those of NO_x in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 3 yr, from 1996 to 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter. The model performance was evaluated against the measured PM₁₀ data from the above-mentioned three stations and from two other stations, using data that was measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM₁₀ vs. NO_x) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM₁₀: the FB values range from −0.05 to +0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. The model was applied in evaluating the yearly average spatial concentration distribution of PM₁₀ in central Helsinki, based on the corresponding modelled NO_x concentrations. With re-evaluation of a few parameters that can be determined empirically, the model could be evaluated, and most probably applied, in other urban areas as well.     

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.     

Long-term trends and characteristics of visibility in two megacities in southwest China: Chengdu and Chongqing

Visibility is a good indicator of air quality because it reflects the combined influences of atmospheric pollutants and synoptic processes. Trends in visibility and relationships with various factors in Chengdu and Chongqing, two megacities in southwest China, were analyzed using daily data from National Climatic Data Center and the Air Pollution Index (API) of the Ministry of Environmental Protection of China. Average annual visibility during the period of 1973–2010 was 8.1 ± 3.9 in Chengdu and 6.2 ± 4.3 km in Chongqing. PM₁₀ dominates the reported primary pollutants in both cities, although concentrations have decreased from a high of 127.9 and 150 µg m³ before 2005 to 100.4 and 93.5 µg m^?3 in Chengdu and Chongqing, respectively. Low average visibility and extremely high levels of PM10 were observed in winter, whereas relative humidity had irregular and weak seasonal variations. Visibility in both cities has deteriorated in comparison to the 1960s and 1970s, mostly due to the elevation of optical depth caused by anthropogenic pollution.

Correlations and principal component analysis (PCA) were undertaken to determine the key factors affecting visibility. Visibility was only moderately correlated with PM₁₀. In Chengdu, visibility displayed weak correlations with various factors, whereas visibility in Chongqing was most strongly related to relative humidity due to the atmospheric particulates in the region containing more hygroscopic components. PCA results further confirmed that high relative humidity and low wind speed increased the occurrence of low visibility events under high PM₁₀ concentrations. Temperature and pressure, as indicators of weather systems, also played important roles in affecting visibility. Mathematical models of visibility prediction indicated that wind speed had the largest coefficients among all meteorological factors, and reductions in PM₁₀ concentration only led to minor improvements in visibility.

Implications: Long-term data indicates that visibility in Chengdu and Chongqing has been lower than 10 km since the 1970s, and the poor visibility primarily results from anthropogenic pollution. Although PM₁₀ concentrations have decreased consistently to around 100 μg m^?3, trends of visibility have shown no improvement but much fluctuation. Correlation and principal component analysis demonstrate that low visibility in Chengdu is influenced by high relative humidity, while in Chongqing the degrading visibility is related with high relative humidity and pressure and low wind speed under a stable weather system. The results are important to understand the widespread haze event in the two megacities of southwest China.     

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.     

Spatial variation of particle number and mass over four European cities

The number of ultrafine particles may be a more health relevant characteristic of ambient particulate matter than the conventionally measured mass. Epidemiological time series studies typically use a central site to characterize human exposure to outdoor air pollution. There is currently very limited information how well measurements at a central site reflect temporal and spatial variation across an urban area for particle number concentrations (PNC).The main objective of the study was to assess the spatial variation of PNC compared to the mass concentration of particles with diameter less than 10 or 2.5 μm (PM₁₀ and PM_2.5).Continuous measurements of PM₁₀, PM_2.5, PNC and soot concentrations were conducted at a central site during October 2002–March 2004 in four cities spread over Europe (Amsterdam, Athens, Birmingham and Helsinki). The same measurements were conducted directly outside 152 homes spread over the metropolitan areas. Each home was monitored during 1 week. We assessed the temporal correlation and the variability of absolute concentrations.For all particle indices, including particle number, temporal correlation of 24-h average concentrations was high. The median correlation for PNC per city ranged between 0.67 and 0.76. For PM_2.5 median correlation ranged between 0.79 and 0.98. The median correlation for hourly average PNC was lower (range 0.56–0.66). Absolute concentration levels varied substantially more within cities for PNC and coarse particles than for PM_2.5. Measurements at the central site reflected the temporal variation of 24-h average concentrations for all particle indices at the selected homes across the urban area. A central site could not assess absolute concentrations across the urban areas for particle number.     

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.     

Temporal variations of atmospheric aerosol in four European urban areas

Purpose

The concentrations of PM₁₀ mass, PM_2.5 mass and particle number were continuously measured for 18 months in urban background locations across Europe to determine the spatial and temporal variability of particulate matter.

Methods

Daily PM₁₀ and PM_2.5 samples were continuously collected from October 2002 to April 2004 in background areas in Helsinki, Athens, Amsterdam and Birmingham. Particle mass was determined using analytical microbalances with precision of 1 ??g. Pre- and post-reflectance measurements were taken using smoke-stain reflectometers. One-minute measurements of particle number were obtained using condensation particle counters.

Results

The 18-month mean PM₁₀ and PM_2.5 mass concentrations ranged from 15.4 ??g/m³ in Helsinki to 56.7 ??g/m³ in Athens and from 9.0 ??g/m³ in Helsinki to 25.0 ??g/m³ in Athens, respectively. Particle number concentrations ranged from 10,091 part/cm³ in Helsinki to 24,180 part/cm³ in Athens with highest levels being measured in winter. Fine particles accounted for more than 60% of PM₁₀ with the exception of Athens where PM_2.5 comprised 43% of PM₁₀. Higher PM mass and number concentrations were measured in winter as compared to summer in all urban areas at a significance level p?Conclusions Significant quantitative and qualitative differences for particle mass across the four urban areas in Europe were observed. These were due to strong local and regional characteristics of particulate pollution sources which contribute to the heterogeneity of health responses. In addition, these findings also bear on the ability of different countries to comply with existing directives and the effectiveness of mitigation policies.