Ambient air quality levels in Tehran, Iran, from 1988 to 1993

The objectives of this study were to describe trends in ambient air quality in Tehran between 1988 and 1993, to determine if these levels exceeded the World Health Organization (WHO) guidelines, and to discuss possible health effects related to exposure for these particular pollutants. Data were acquired from Iran's Environmental Protection Agency (IEPA) and the Ministry of Health (MH). These agencies operate five automated ambient air monitoring stations located in areas with heavy traffic. Daily samples of SO₂, NO₂, CO, total suspended particulate matter (TSM), and hydrocarbons (HC) were collected to provide 24 hour averages for each pollutant. Every three months, mean concentrations were reported to IEPA. Composite samples from all five stations were stored in a databank operated by IEPA. The ambient air quality guidelines were obtained from WHO reports. Statistical analysis was carried out using a regression model, which was designed to fit the air pollution data and take into account missing data. The results showed that there was a statistically significant upward trend in air pollution levels for all of the measured pollutants, except NO2, during the years 1988 to 1993. WHO guidelines were routinely and substantially exceeded by all pollutants except TSM. These findings suggest that as the population continues to grow, and increasing numbers of motor vehicles are driven in Tehran, there is concern for the health effects that may result from exposure to these pollutants.     

Deconstruction of annoyance due to air pollution by multiple correspondence analyses

Annoyance caused by air pollution is a matter of public health as it can cause stress and ill-health and affect quality of life, among other burdens. The aim of this study is to apply the multiple correspondence analyses (MCA) technique as a differential tooling to explore relationships between variables that can influence peoples’ behaviour concerning annoyance caused by air pollution. Data were collected through a survey on air pollution, environmental issues and quality of life. Face-to-face survey studies were conducted in two industrialized urban areas (Vitoria in Brazil and Dunkirk in France). These two regions were chosen as their inhabitants often report feeling annoyed by air pollution, and both regions have similar industrial characteristics. The results showed a progressive correspondence between levels of annoyance and other active variables in the “air pollution” factor group: as the levels of annoyance increased, the levels of the other qualitative variables (importance of air quality, perceived exposure to industrial risk, assessment of air quality, perceived air pollution) also increased. Respondents who reported feeling annoyed by air pollution also thought that air quality was very important and were very concerned about exposure to industrial risks. Furthermore, they often assessed air quality as horrible, and they could frequently perceive air pollution by dust, odours and decreased visibility. The results also showed a statistically significant association between occurrence of allergies and high levels of annoyance.

     

Comparative health impact assessment of local and regional particulate air pollutants in Scandinavia

The ongoing program Clean Air for Europe (CAFE) is an initiative from the EU Commission to establish a coordinated effort to reach better air quality in the EU. The focus is on particulate matter as it has been shown to have large impact on human health. CAFE requested that WHO make a review of the latest findings on air pollutants and health to facilitate assessments of the different air pollutants and their health effects. The WHO review project on health aspects of air pollution in Europe confirmed that exposure to particulate matter (PM), despite the lower levels we face today, still poses a significant risk to human health. Using the recommended uniform risk coefficients for health impact assessment of PM, regardless of sources, premature mortality related to long-range transported anthropogenic particles has been estimated to be about 3500 deaths per year for the Swedish population, corresponding to a reduction in life expectancy of up to about seven months. The influence of local sources is more difficult to estimate due to large uncertainties when linking available risk coefficients to exposure data, but the estimates indicate about 1800 deaths brought forward each year with a life expectancy reduction of about 2-3 months. However, some sectors of the population are exposed to quite high locally induced concentrations and are likely to suffer excessive reductions in life expectancy. Since the literature increasingly supports assumptions that combustion related particles are associated with higher relative risks, further studies may shift the focus for abatement strategies. CAFE sets out to establish a general cost effective abatement strategy for atmospheric particles. Our results, based on studies of background exposure, show that long-range transported sulfate rich particles dominate the health effects of PM in Sweden. The same results would be found for the whole of Scandinavia and many countries influenced by transboundary air pollution. However, several health studies, including epidemiological studies with a finer spatial resolution, indicate that engine exhaust particles are more damaging to health than other particles. These contradictory findings must be understood and source specific risk estimates have to be established by expert bodies, otherwise it will not be possible to find the most cost effective abatement strategy for Europe. We are not happy with today's situation where every strategy to reduce PM concentrations is estimated to have the same impact per unit change in the mass concentration. Obviously there is a striking need to introduce more specific exposure variables and a higher geographical resolution in epidemiology as well as in health impact assessments.     

Ambient air quality and asthma cases in Niğde, Turkey

Urban air quality is one of the key factors affecting human health. Turkey has transformed itself into an urban society over the last 30 years. At the same time, air pollution has become a serious impairment to health in many urban areas in the country. This is due to many reasons. In this study, a nonparametric evaluation was conducted of health effects that are triggered by urban air pollution. Ni?de, the city which is the administrative centre of Nigde province was chosen of the effects of air pollution since, like many central Turkish cities, it is situated on a valley where atmospheric inversion occurs. In this paper, the relationship between ambient urban air quality, namely PM₁₀ and sulphur dioxide (SO₂), and human health, specifically asthma, during the winter season is examined. Air pollution data and asthma cases from 2006 to 2010 are covered in this study. The results of our study indicate that total asthma cases reported in Nigde between 2008 and 2010 were highly dependent on ambient SO₂ concentration. More asthma cases were recorded when 30 μg?m^?3 or higher SO₂ was present in the ambient air than those recorded under cleaner ambient air conditions. Moreover, it was determined that in Nigde in 2010, asthma cases reported in males aged between 45 and 64 were closely correlated with ambient SO₂ (α?=?0.05).     

Air Quality in Selected Megacities

Abstract

About half of the world's population now lives in urban areas because of the opportunity for a better quality of life. Many of these urban centers are expanding rapidly, leading to the growth of megacities, which are often defined as metropolitan areas with populations exceeding 10 million inhabitants. These concentrations of people and activity are exerting increasing stress on the natural environment, with impacts at urban, regional and global levels. In recent decades, air pollution has become one of the most important problems of megacities. Initially, the main air pollutants of concern were sulfur compounds, which were generated mostly by burning coal. Today, photochemical smog—induced primarily from traffic, but also from industrial activities, power generation, and solvents—has become the main source of concern for air quality, while sulfur is still a major problem in many cities of the developing world. Air pollution has serious impacts on public health, causes urban and regional haze, and has the potential to contribute significantly to global climate change. Yet, with appropriate planning megacities can efficiently address their air quality problems through measures such as application of new emission control technologies and development of mass transit systems.

This review is focused on nine urban centers, chosen as case studies to assess air quality from distinct perspectives: from cities in the industrialized nations to cities in the developing world. This review considers not only megacities, but also urban centers with somewhat smaller populations, for while each city—its problems, resources, and outlook—is unique, the need for a holistic approach to complex environmental problems is the same. There is no single strategy to reduce air pollution in megacities; a mix of policy measures will be needed to improve air quality. Experience shows that strong political will coupled with public dialogue is essential to effectively implement the regulations required to address air quality.     

A spatial multicriteria model for determining air pollution at sample locations

Atmospheric pollution in urban centers has been one of the main causes of human illness related to the respiratory and circulatory system. Efficient monitoring of air quality is a source of information for environmental management and public health. This study investigates the spatial patterns of atmospheric pollution using a spatial multicriteria model that helps target locations for air pollution monitoring sites. The main objective was to identify high-priority areas for measuring human exposures to air pollutants as they relate to emission sources. The method proved to be viable and flexible in its application to various areas.

Implications:?Spatial multicriteria models provide a tool for air pollution management in urban areas. Analytic hierarchy process (AHP) modeling can help with the process of prioritizing monitoring site locations and minimizing costs.     

Air quality in Yanbu,Saudi Arabia

Yanbu, on the Red Sea, is an affluent Saudi Arabian industrial city of modest size. Substantial effort has been spent to balance environmental quality, especially air pollution, and industrial development. We have analyzed six years of observations of criteria pollutants O₃, SO₂, particles (PM_2.5 and PM₁₀) and the known ozone precursors—volatile organic compounds (VOCs) and nitrogen oxides (NO_x). The results suggest frequent VOC-limited conditions in which ozone concentrations increase with decreasing NO_x and with increasing VOCs when NO_x is plentiful. For the remaining circumstances ozone has a complex non-linear relationship with the VOCs. The interactions between these factors at Yanbu cause measurable impacts on air pollution including the weekend effect in which ozone concentrations stay the same or even increase despite significantly lower emissions of the precursors on the weekends. Air pollution was lower during the Eids (al-Fitr and al-Adha), Ramadan and the Hajj periods. During Ramadan, there were substantial night time emissions as the cycle everyday living is almost reversed between night and day. The exceedances of air pollution standards were evaluated using criteria from the U.S. Environmental Protection Agency (EPA), World Health Organization (WHO), the Saudi Presidency of Meteorology and Environment (PME) and the Royal Commission Environmental Regulations (RCER). The latter are stricter standards set just for Yanbu and Jubail. For the fine particles (PM_2.5), an analysis of the winds showed a major impact from desert dust. This effect had to be taken into account but still left many occasions when standards were exceeded. Fewer exceedances were found for SO₂, and fewer still for ozone. The paper presents a comprehensive view of air quality at this isolated desert urban environment.

Implications: Frequent VOC-limited conditions are found at Yanbu in Saudi Arabia that increase ozone pollution if NO_x is are reduced. In this desert environment, increased nightlife produces the highest levels of VOCs and NO_x at night rather than the day. The effects increase during Ramadan. Fine particles peak twice a day—the morning peak is caused by traffic and increases with decreasing wind, potentially representing health concerns, but the larger afternoon peak is caused by the wind, and it increases with increasing wind speeds. These features suggest that exposure to pollutants must be redefined for such an environment.     

环境空气质量自动监测站的管理与维护

随着工业化、城镇化的深入推进,二氧化硫、氮氧化物、烟粉尘和挥发性有机物等各类污染物排放到环境中,致使中国大气受到严重污染,给人体的健康、动植物的生长、发育和繁殖等带来负面的影响。为实时监测环境空气质量,建立环境空气质量自动监测站逐渐成为大气污染防治的主要手段。文中以环境空气质量自动监测站为研究对象,提出环境空气质量自动监测站管理与维护面临的问题,探讨相应的解决措施,以期为环境空气质量自动监测站的管理与维护提供参考依据。     

Review of Singapore's air quality and greenhouse gas emissions: current situation and opportunities

Singapore has many environmental accomplishments to its credit. Accessible data on air quality indicates that all criteria pollutants satisfy both U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality standards and guidelines, respectively. The exception is PM2.5 (particles with an aerodynamic diameter < or = 2.5 microm), which is not currently considered a criteria pollutant in Singapore but may potentially be the major local air pollution problem and cause for health concern. Levels of other airborne pollutants as well as their physical and chemical processes associated with local formation, transformation, dispersion, and deposition are not known. According to available emission inventories, Singapore contribution to the total atmospheric pollution and carbon budget at the regional and global scales is small. Emissions per unit gross domestic product (GDP) are low compared with other countries, although Singapore's per-capita GDP and per-capita emissions are among the highest in the world. Some information is available on health effects, but the impacts on the ecosystem and the complex interactions of air pollution and climate change at a regional level are also unknown. This article reviews existing available information on atmospheric pollution and greenhouse gas emissions and proposes a multipollutant approach to greenhouse gas mitigation and local air quality. Singapore, by reducing its per-capita emissions, increasing the availability of information (e.g., through regularly publishing hourly and/or daily PM2.5 concentrations) and developing a research agenda in this area, would likely be seen to be a model of a high-density, livable, and sustainable city in Southeast Asia and other tropical regions worldwide.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Perceptions of air pollution during the work-related commute by adults in Queensland,Australia

BackgroundThis study examines perceived health effect risks from air pollution during the work-related commute by socio-demographic and travel mode variables.MethodsPopulation-representative survey data were collected by telephone from 745 adults from Queensland, Australia. Variables assessed included socio-demographics, usual work travel mode, commute perceptions, and perceived health effects associated with air pollution during the work-related commute.ResultsApproximately 45% of the sample perceived air pollution negatively affected health outcomes when commuting to/from work, yet only 13% recognised air pollution as a major barrier to walking or cycling to/from work. No relationships existed between usual travel mode to/from work and perceived health risks associated with air pollution. Those higher educated or living in major cities were more likely to recognise air pollution harmed their health during their work-related commute when compared to respective referent categories (p ≤ 0.05).ConclusionsRecognition of health risks from air pollution during the work-related commute was high, and awareness did not differ by travel mode. For the majority, air pollution was not a primary barrier for walking or cycling to/from work.     

The effects of air pollution on human mortality: does gender difference matter in African countries?

The relationship between environmental factors and human health has long been a concern among academic researchers. We use two indicators of environmental pollution, namely particulate matter (PM₁₀) and carbon dioxide (CO₂) to examine the effects of poor air quality on human mortality. This study explores an issue that has largely been ignored, particularly in the African literature, where the effect of air pollution on human mortality could be influenced by gender specification. We analyse a panel data from 35 African countries and our result suggests that the elevated levels of PM₁₀ and CO₂ have a significant effect on the increasing mortality rates in infants, under-five children and adults. Although the effect of poor air quality on adults is found to differ between genders, such difference is not statistically significant. We conclude that the air pollution effects, on average, are similar between genders in the African countries.     

The state of ambient air quality in Pakistan—a review

Background and purpose  

Pakistan, during the last decade, has seen an extensive escalation in population growth, urbanization, and industrialization, together with a great increase in motorization and energy use. As a result, a substantial rise has taken place in the types and number of emission sources of various air pollutants. However, due to the lack of air quality management capabilities, the country is suffering from deterioration of air quality. Evidence from various governmental organizations and international bodies has indicated that air pollution is a significant risk to the environment, quality of life, and health of the population. The Government has taken positive steps toward air quality management in the form of the Pakistan Clean Air Program and has recently established a small number of continuous monitoring stations. However, ambient air quality standards have not yet been established. This paper reviews the data being available on the criteria air pollutants: particulate matter (PM), sulfur dioxide, ozone, carbon monoxide, nitrogen dioxide, and lead.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Review of Singapore's air quality and greenhouse gas emissions: Current situation and opportunities

Singapore has many environmental accomplishments to its credit. Accessible data on air quality indicates that all criteria pollutants satisfy both U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality standards and guidelines, respectively. The exception is PM_2.5 (particles with an aerodynamic diameter ≤2.5 μm), which is not currently considered a criteria pollutant in Singapore but may potentially be the major local air pollution problem and cause for health concern. Levels of other airborne pollutants as well as their physical and chemical processes associated with local formation, transformation, dispersion, and deposition are not known. According to available emission inventories, Singapore's contribution to the total atmospheric pollution and carbon budget at the regional and global scales is small. Emissions per unit gross domestic product (GDP) are low compared with other countries, although Singapore's per-capita GDP and per-capita emissions are among the highest in the world. Some information is available on health effects, but the impacts on the ecosystem and the complex interactions of air pollution and climate change at a regional level are also unknown. This article reviews existing available information on atmospheric pollution and greenhouse gas emissions and proposes a multipollutant approach to greenhouse gas mitigation and local air quality. Singapore, by reducing its per-capita emissions, increasing the availability of information (e.g., through regularly publishing hourly and/or daily PM_2.5 concentrations) and developing a research agenda in this area, would likely be seen to be a model of a high-density, livable, and sustainable city in Southeast Asia and other tropical regions worldwide.

Implications Singapore is widely recognized for its environmental achievements and often cited as a model of a high-density, livable, and sustainable city. This article reviews available information with the aim to provide a reference for future scientific research of strategic relevance for Singapore's air quality and greenhouse gas mitigation management under a multipollutant framework. However, the limited publicly accessible data and little scientific information prevent a comprehensive assessment of the local air quality and greenhouse gas emissions. Singapore's dynamic economy and strong profile in advanced science and technological innovation have the potential to enhance the research agenda in this area, which is not yet well developed in tropical cities.     

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.     

Air pollution modeling at road sides using the operational street pollution model--a case study in Hanoi, Vietnam

In many metropolitan areas, traffic is the main source of air pollution. The high concentrations of pollutants in streets have the potential to affect human health. Therefore, estimation of air pollution at the street level is required for health impact assessment. This task has been carried out in many developed countries by a combination of air quality measurements and modeling. This study focuses on how to apply a dispersion model to cities in the developing world, where model input data and data from air quality monitoring stations are limited or of varying quality. This research uses the operational street pollution model (OSPM) developed by the National Environmental Research Institute in Denmark for a case study in Hanoi, the capital of Vietnam. OSPM predictions from five streets were evaluated against air pollution measurements of nitrogen oxides (NO(x)), sulfur dioxide (SO2), carbon monoxide (CO), and benzene (BNZ) that were available from previous studies. Hourly measurements and passive sample measurements collected over 3-week periods were compared with model outputs, applying emission factors from previous studies. In addition, so-called "backward calculations" were performed to adapt the emission factors for Hanoi conditions. The average fleet emission factors estimated can be used for emission calculations at other streets in Hanoi and in other locations in Southeast Asia with similar vehicle types. This study also emphasizes the need to further eliminate uncertainties in input data for the street-scale air pollution modeling in Vietnam, namely by providing reliable emission factors and hourly air pollution measurements of high quality.     

Indoor air quality and health

During the last two decades there has been increasing concern within the scientific community over the effects of indoor air quality on health. Changes in building design devised to improve energy efficiency have meant that modern homes and offices are frequently more airtight than older structures. Furthermore, advances in construction technology have caused a much greater use of synthetic building materials. Whilst these improvements have led to more comfortable buildings with lower running costs, they also provide indoor environments in which contaminants are readily produced and may build up to much higher concentrations than are found outside. This article reviews our current understanding of the relationship between indoor air pollution and health. Indoor pollutants can emanate from a range of sources. The health impacts from indoor exposure to combustion products from heating, cooking, and the smoking of tobacco are examined. Also discussed are the symptoms associated with pollutants emitted from building materials. Of particular importance might be substances known as volatile organic compounds (VOCs), which arise from sources including paints, varnishes, solvents, and preservatives. Furthermore, if the structure of a building begins to deteriorate, exposure to asbestos may be an important risk factor for the chronic respiratory disease mesothelioma. The health effects of inhaled biological particles can be significant, as a large variety of biological materials are present in indoor environments. Their role in inducing illness through immune mechanisms, infectious processes, and direct toxicity is considered. Outdoor sources can be the main contributors to indoor concentrations of some contaminants. Of particular significance is Radon, the radioactive gas that arises from outside, yet only presents a serious health risk when found inside buildings. Radon and its decay products are now recognised as important indoor pollutants, and their effects are explored. This review also considers the phenomenon that has become known as Sick Building Syndrome (SBS), where the occupants of certain affected buildings repeatedly describe a complex range of vague and often subjective health complaints. These are often attributed to poor air quality. However, many cases of SBS provide a valuable insight into the problems faced by investigators attempting to establish causality. We know much less about the health risks from indoor air pollution than we do about those attributable to the contamination of outdoor air. This imbalance must be redressed by the provision of adequate funding, and the development of a strong commitment to action within both the public and private sectors. It is clear that meeting the challenges and resolving the uncertainties associated with air quality problems in the indoor environment will be a considerable undertaking.     

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies—termed accountability studies—contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed “counterfactual”—the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

Implications: The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.