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Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors' emissions, and Earth's crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Fine particulate matter with an aerodynamic diameter less than 2.5 µm ( P M 2.5 ) profoundly affects environmental systems, human health and economic structures. Multi-source data and advanced machine or deep-learning methods have provided a new chance for estimating the P M 2.5 concentrations at a high spatiotemporal resolution. In this paper, the Random Forest (RF) algorithm was applied to estimate hourly P M 2.5 of the North China area (Beijing–Tianjin–Hebei, BTH) based on the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) aerosol optical depth (AOD) products. To improve the estimation of P M 2.5 concentration across large areas, we construct a method for co-weighting the environmental similarity and the geographical distances by using an attention mechanism so that it can efficiently characterize the influence of spatial–temporal information hidden in adjacent ground monitoring sites. In experiment results, the hourly P M 2.5 estimates are well correlated with ground measurements in BTH, with a coefficient of determination ( R 2 ) of 0.887, a root-mean-square error (RMSE) of 18.31 μ g/ m 3 , and a mean absolute error (MAE) of 11.17 µg/ m 3 , indicating good model performance. In addition, this paper makes a comprehensive analysis of the effectiveness of multi-source data in the estimation process, in this way, to simplify the model structure and improve the estimation efficiency of the model while ensuring its accuracy. [ABSTRACT FROM AUTHOR]

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process. [ABSTRACT FROM AUTHOR]

During the "Parade Blue" period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the "Parade Blue" period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the "Parade Blue" period, and the effectiveness of the source control strategy was proved. [ABSTRACT FROM AUTHOR]

Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to predict hourly air pollutant concentrations over longer periods of time while ensuring their accuracy. In this paper, an air pollutant concentration prediction method based on improved inverse distance interpolation and Informer model (EIDW-Informer), and hour-by-hour prediction of PM2.5, NO2, and O3 concentrations in Taiyuan, China is carried out. In this study, historical data from seven environmental monitoring stations in Taiyuan City were used to build multidimensional environmental vectors and calculate the similarity between sample points. Then, the missing values in the dataset were interpolated according to the similarity and distance weights, and the long series prediction was performed by Informer. The experimental results show that the EIDW-Informer method has advantages in hour-by-hour prediction compared to LSTM, CNN-LSTM, and Attention-LSTM models, which improves by 20%, 27%, and 43% on 1 h, 8 h, and 72 h time scales, respectively. [ABSTRACT FROM AUTHOR]

PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of "high in the south and low in the north" to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing. [ABSTRACT FROM AUTHOR]

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The "CEEMDAN-FE" decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model's performance in predicting the real-time concentrations of PM2.5. [ABSTRACT FROM AUTHOR]

While the district of Mae Moh, Thailand has been well known for its atmospheric pollution associated with coal power production, economic assessment of demand for improved air quality has not been conducted to date. This study estimated local residents' individual and aggregate willingness to pay (WTP) for mitigation of atmospheric pollution in Mae Moh using the contingent valuation method (CVM), and analyzed the factors associated with the individual WTP using the bivariate tobit and double-hurdle regression techniques. Primary data were collected through face-to-face interviews with a stratified sample of 200 residents. The hypothetical scenarios used in the CVM module were 50% and 80% mitigation of atmospheric concentrations of major pollutants. The weighted average WTP was found to be THB 251.3 and 307.9 per annum (USD 8.4 and 10.3) for the 50% and 80% reduction scenarios, respectively. The aggregate WTP for the entire population of Mae Moh was THB 10,008,733 and 12,264,761 per annum (USD 336,294 and 412,096), respectively. Education, occupation type, income, expenses, satisfaction with ambient quality, and perceived sources of pollution had significant associations with the individual WTP. The paper concludes by discussing policy implications for atmospheric pollution management and avenues for future research. [ABSTRACT FROM AUTHOR]

Monitoring particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The model introduced meteorological variable, column water vapor (CWV), which has as the same resolution as satellite-derived aerosol optical thickness (AOT), to enhance PM2.5 estimation accuracy by considering spatiotemporal consistency of CWV and AOT. The model was implemented to urban agglomeration of Chengdu Plain during 2015. The results show that model accuracy has been improved significantly compared to linear regression model (R2 = 0.49), with R2 of 0.81 and root mean squared prediction error (RMSPE) of 15.47 μg/m3, mean prediction error (MPE) of 11.09 μg/m3, and effectively revealed the characteristics of spatiotemporal variations PM2.5 level across the study area: The PM2.5 level is higher in the central and southern areas with dense population, while it is lower in the northwest and southwest mountain areas; and the PM2.5 level is higher during autumn and winter, while it is lower during spring and summer. The product data in this paper are valuable for local government pollution monitoring, public health research, and urban air quality control. [ABSTRACT FROM AUTHOR]

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei's PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers. [ABSTRACT FROM AUTHOR]

Studies on air quality frequently adopt clustering, in particular the k-means technique, owing to its simplicity, ease of implementation and efficiency. The aim of the present paper was the assessment of air quality in a winter season (December–February) in the conditions of temperature inversion using the k-means method, representing a non-hierarchical algorithm of cluster analysis. The air quality was assessed on the basis of the concentrations of particulate matter (PM10, PM2.5). The studies were conducted in four winter seasons (2015/16, 2016/17, 2017/18, 2019/20) in Wrocław (Poland). As a result of the application of the v-fold cross test, six clusters for each fraction of PM were identified. Even though the analysis covers only four winter seasons, the applied method has unequivocally revealed that the characteristics of surface-based (SBI) and elevated inversions (ELI) affect the concentration level of both fractions of particulate matter. In the case of PM10, the average lowest daily concentration (15.5 µg·m−3) was recorded in the conditions of approx. 205 m in thickness, 0.5 °C intensity of the SBI and at the height of the base of the ELI at approx. 1700 m a.g.l., a thickness of 148 m and an intensity of 1.2 °C. In turn, the average highest concentration (136 µg·m−3) was recorded at a thickness of SBI of approx. 400 m and an intensity of 1.4 °C. Such high concentration occurred when the lowest location of ELI formed at 764 m a.g.l. with a thickness of 308 m and an intensity of 0.96 °C. A marked role of the thickness of the SBI and ELI as well as the height of the base of the lowest location of ELI was also manifested with respect to PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Beijing, the capital city of China, has achieved remarkable progress in terms of an improvement in air quality under strict control policies in the past 10 years from various sources. In this paper, the characteristics of fine particulate matter (PM2.5) and O3 in January 2013 and 2018 in Beijing are discussed on the basis of daily sample analysis and hourly monitoring data. It was found that the PM2.5 pollution for the month of January in Beijing has been greatly curbed. The SO42− concentration and proportion of PM2.5 decreased, while the proportions of NO3− and NH4+ increased. Organic matter represented the major component during the two periods with the proportions of 31.7% ± 8.2% and 31.4% ± 9.8%. The results of the Hybrid Single Particle Lagrangian Integrated Trajectory (Hysplit) model and Potential Source Contribution Function (PSCF) method showed that air mass from southern nearby regions accounted for 34% and 10% in 2013 and 2018, respectively, which was closely related to the pollution period. Thus, the input direction of air mass in January 2018 was more conducive to the diffusion of pollutants. Modeling results of the Weather Research and Forecasting model (WRF) coupled with Comprehensive Air Quality Model Extensions (CAMx) indicated that the contribution of industry sources to PM2.5 and O3 decreased from 2013 to 2018, while mobile sources increased. This was mainly due to the different control policies on various emission sources. In terms of O3 sources, more control measurements should be taken on volatile organic compounds (VOCs) due to its prominent effect on O3 concentration in both periods. The reduction in emissions and the meteorological conditions both contributed effectively to the sharp decrease in PM2.5 concentration. However, the change in weather conditions had the greater impact on the decrease in PM2.5 concentration, while the reduction in emissions was weakened as a function of this change. [ABSTRACT FROM AUTHOR]

It is well documented that Southern Poland is one of the most polluted areas in Europe due to the highest airborne concentrations of particulate matter (PM). Concentrations of fine particles are especially high in winter. Apart from detailed number concentrations, it is essential to accurately identify and quantify specific particulate pollution sources. Only a few Polish research centers are involved in such experiments—among them is Krakow research group. For the most part, research focuses on collecting 24-h average samples from stationary PM samplers at ambient monitoring sites and quantifying the specific elements and chemical constituents in PM. This approach includes modeling methods that can use the variability in physical and chemical PM characteristics as an input dataset to identify possible sources of the particles. The objective of this paper is to provide research results based on data collected from June 2018 to May 2019 from a single monitoring station at a central urban site. Careful comparison of data obtained prior to a 2019 law prohibiting solid fuel burning in the city of Krakow with data (2019–2020) when a regulation went into effect should indicate progress by noting lower PM levels. This work has shown that the method applied and Krakow results might be of interest to the broader community in regions of high PM concentration. [ABSTRACT FROM AUTHOR]

Accurate air quality modelling is an essential tool, both for strategic assessment (regulation development for emission controls) and for short-term forecasting (enabling warnings to be issued to protect vulnerable members of society when the pollution levels are predicted to be high). Model intercomparison studies are a valuable support to this work, being useful for identifying any issues with air quality models, and benchmarking their performance against international standards, thereby increasing confidence in their predictions. This paper presents the results of a comparison study of six chemical transport models which have been used to simulate short-term hourly to 24 hourly concentrations of fine particulate matter less than and equal to 2.5 µm in diameter (PM2.5) and ozone (O3) for Sydney, Australia. Model performance was evaluated by comparison to air quality measurements made at 16 locations for O3 and 5 locations for PM2.5, during three time periods that coincided with major atmospheric composition measurement campaigns in the region. These major campaigns included daytime measurements of PM2.5 composition, and so model performance for particulate sulfate (SO42−), nitrate (NO3−), ammonium (NH4+) and elemental carbon (EC) was evaluated at one site per modelling period. Domain-wide performance of the models for hourly O3 was good, with models meeting benchmark criteria and reproducing the observed O3 production regime (based on the O3/NOx indicator) at 80% or more of the sites. Nevertheless, model performance was worse at high (and low) O3 percentiles. Domain-wide model performance for 24 h average PM2.5 was more variable, with a general tendency for the models to under-predict PM2.5 concentrations during the summer and over-predict PM2.5 concentrations in the autumn. The modelling intercomparison exercise has led to improvements in the implementation of these models for Sydney and has increased confidence in their skill at reproducing observed atmospheric composition. [ABSTRACT FROM AUTHOR]

The pervasiveness of "stacking" between traditional and clean fuels/stoves is moving the household clean cooking research and policy agenda from programs that are centered on one-fuel or stove, to multi-clean fuel/device interventions that could result in a more effective displacement of traditional biomass stoves. However, there is little recognition and knowledge of the benefits realistic clean-stacking cooking solutions can have on indoor air quality and health. In this paper, particulate matter (PM2.5) and carbon monoxide (CO) concentration levels that are associated with stove-stacking options (Patsari-U-shaped open fire (U-type), Patsari-liquefied petroleum gas (LPG) stove, U-type-LPG, and Patsari-U-type-LPG) common within rural Mexico were evaluated while using a controlled cooking cycle (CCC) from the Purepecha Highlands, which mimics the most common dishes prepared daily within the region as well as their sequential arrangement. The results confirms that the WHO Interim Target-1 (IT1) of 35 μg/m3 for PM2.5 is exceeded whenever woodburning open fires are part of the stacking combination (Patsari-U-type 107 ± 9 μg/m3, U-type-LPG 131 ± 55 μg/m3 and Patsari-U-type-LPG 107 ± 22 μg/m3). However, well-designed and properly operated woodburning chimney stoves, such as the Patsari can meet the IT1, either used exclusively (21 ± 8 μg/m3) or as a "clean stacking" option with LPG (24 ± 5 μg/m3). Given stove stacking patterns, evaluating the health and environmental consequences of stove transitions while assuming the total replacement of traditional fires by clean cooking options will lead to misplaced expectations, and programs should evaluate more realistic "clean-stacking" options. [ABSTRACT FROM AUTHOR]

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM2.5 and PMcoarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R2 and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM2.5 (CV R2 = 0.47, RMSE = 1.5 µg/m3) and PMcoarse (CV R2 = 0.65, RMSE = 8.07 µg/m3). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m3 for PM2.5 and 17.7 µg/m3 for PMcoarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM2.5, and seasonality, type of farmland, and length of roads as drivers of PMcoarse. Predicted PM2.5 was elevated near the US/Mexico border and predicted PMcoarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities. [ABSTRACT FROM AUTHOR]

This paper presents measurements and analyses of the concentrations of black carbon (BC), particle number concentration (PNC), and PM2.5 (≤2.5 μm) while commuting by ferries in Istanbul. In this context, exposures to the mentioned pollutants were estimated for car ferry, fast ferry, and at the piers, and for two travel routes, for a total of 89 trips. BC, PNC, and PM2.5 measurements were simultaneously performed in a ferry and at the piers, and the correlation between pollutant concentrations, meteorological parameters, and environmental factors were analyzed. The mean concentrations for all pollutants in car ferry were lower than the average concentrations in fast ferry. The concentration ratios of fast ferry to car ferry for BC, PNC, and PM2.5 were 6.4, 1.2, and 1.3, respectively. High variability in the concentrations was observed at the piers and in ferry during berthing. The highest mean concentrations (±standard deviation) of BC (14.3 ± 10.1 µg m−3) and PNC (42,005 ± 30,899 pt cm−3) were measured at Yalova pier. The highest mean concentration (±standard deviation) of PM2.5 (26.1 ± 11.5) was measured at Bostancı pier. It was observed that the main external sources of BC, PNC, and PM2.5 at the piers were road transport, residential heating, and shipping activity. There were no significant correlations between BC, PNC, and PM2.5 in fast ferry, while BC was positively correlated with PNC (r = 0.61, p < 0.01) and PM2.5 (r = 0.76, p < 0.01) in car ferry. At the piers, significant relations between pollutants and meteorological variables were observed. It was noticed that there was no significant difference between summer and winter in ferry and at the pier concentrations of BC, PNC, and PM2.5 except for Yenikapı pier and Bakırköy pier. The highest total exposure to PNC and PM2.5 was in car ferry mode, while the highest total exposure to BC was in fast ferry mode. [ABSTRACT FROM AUTHOR]

There is increasing awareness in Australia of the health impacts of poor air quality. A common public concern raised at a number of "roadshow" events as part of the federally funded Clean Air and Urban Landscapes Hub (CAUL) project was whether or not the air quality monitoring network around Sydney was sampling air representative of typical suburban settings. In order to investigate this concern, ambient air quality measurements were made on the roof of a two-storey building in the Sydney suburb of Auburn, to simulate a typical suburban balcony site. Measurements were also taken at a busy roadside and these are discussed in a companion paper (Part 2). Measurements made at the balcony site were compared to data from three proximate regulatory air quality monitoring stations: Chullora, Liverpool and Prospect. During the 16-month measurement campaign, observations of carbon monoxide, oxides of nitrogen, ozone and particulate matter less than 2.5-µm diameter at the simulated urban balcony site were comparable to those at the closest permanent air quality stations. Despite the Auburn site experiencing 10% higher average carbon monoxide amounts than any of the permanent air quality monitoring sites, the oxides of nitrogen were within the range of the permanent sites and the pollutants of greatest concern within Sydney (PM2.5 and ozone) were both lowest at Auburn. Similar diurnal and seasonal cycles were observed between all sites, suggesting common pollutant sources and mechanisms. Therefore, it is concluded that the existing air quality network provides a good representation of typical pollution levels at the Auburn "balcony" site. [ABSTRACT FROM AUTHOR]