The Australian Air Quality Forecasting System: The Use of Green Scenarios of Motor Vehicle Usage as an Educational Tool

Abstract

The Australian Air Quality Forecasting System (AAQFS) is one of several newly emerging, high-resolution, numerical air quality forecasting systems. The system is briefly described. A public education application of the air quality impact of motor vehicle usage is explored by computing the concentration and dosage of particulate matter less than 10 µm in aerodynamic diameter (PM₁₀) for a commuter traveling to work between Geelong and Melbourne, Victoria, Australia, under “business-as-usual” and “green” scenarios. This application could be routinely incorporated into systems like AAQFS. Two methodologies for calculating the dosage are described: one for operational use and one for more detailed applications. The Clean Air Research Programme-Personal Exposure Study in Melbourne provides support for this operational methodology. The more detailed methodology is illustrated using a system for predicting concentrations due to near-road emissions of PM₁₀ andapplied in Sydney.     

Evaluation of human exposure to ambient PM10 in the metropolitan area of Mexico City using a GIS-based methodology.

The main goal of this study was to evaluate the magnitude of outdoor exposure to fine particulate matter (PM10) potentially experienced by the population of metropolitan Mexico City. With the use of a geographic information system (GIS), spatially resolved PM10 distributions were generated and linked to the local population. The PM10 concentration exceeded the 24-hr air quality standard of 150 microg/m3 on 16% of the days, and the annual air quality standard of 50 microg/m3 was exceeded by almost twice its value in some places. The basic methodology described in this paper integrates spatial demographic and air quality databases, allowing the evaluation of various air pollution reduction scenarios. Achieving the annual air quality standard would represent a reduction in the annual arithmetic average concentration of 14 microg/m3 for the typical inhabitant. Human exposure to particulate matter (PM) has been associated with mortality and morbidity in Mexico City; reducing the concentration levels of this pollutant would represent a reduction in mortality and morbidity and the associated cost of such impacts. This methodology is critical to assessing the potential benefits of the current initiative to improve air quality implemented by the Environmental Metropolitan Commission of Mexico City.     

Determination of air quality zones in Turkey

In this study, the particulate matter (with an aerodynamic diameter <10 microm; PM10) profile of Turkey with data from the air quality monitoring stations located throughout the country was used. The number of stations (119) was reduced to 55 after a missing data treatment for statistical analyses. First, a classification method was developed based on ongoing national and international (European Commission directives) legislations to categorize air zones into six groups, from a "Very Clear Air Zone" to a "Polluted Air Zone". Then, a Geographic Information System (GIS)-based interpolation technique and statistical analyses (correlation analysis and factor analysis) were used to generate PM10 pollution profiles of the annual heating time and nonheating time periods. Finally, the coherent air pollution management zones of Turkey, based on air quality criteria and measured data using a GIS-based model supported by statistical analyses, were suggested. Based on the analysis, four hot spots were identified: (i) the eastern part of the Black Sea region; (ii) the northeastern part of inland Anatolia; (iii) the western part of Northeastern Anatolia; and (vi) the eastern part of Turkey. The possible reasons for the elevated PM10 levels are discussed using topographic, climatologic, land use, and energy utilization parameters. Finally, the suggested air zones were compared with the administrative air zones, which were newly developed by the Turkish Ministry of Environment and Forestry, to evaluate the level of agreement between the two.     

Characterization of particulate matter for three sites in Kuwait

Many studies have shown strong associations between particulate matter (PM) levels and a variety of health outcomes, leading to changes in air quality standards in many regions, especially the United States and Europe. Kuwait, a desert country located on the Persian Gulf, has a large petroleum industry with associated industrial and urban land uses. It was marked by environmental destruction from the 1990 Iraqi invasion and subsequent oil fires. A detailed particle characterization study was conducted over 12 months in 2004-2005 at three sites simultaneously with an additional 6 months at one of the sites. Two sites were in urban areas (central and southern) and one in a remote desert location (northern). This paper reports the concentrations of particles less than 10 microm in diameter (PM10) and fine PM (PM2.5), as well as fine particle nitrate, sulfate, elemental carbon (EC), organic carbon (OC), and elements measured at the three sites. Mean annual concentrations for PM10 ranged from 66 to 93 microg/m3 across the three sites, exceeding the World Health Organization (WHO) air quality guidelines for PM10 of 20 microg/m3. The arithmetic mean PM2.5 concentrations varied from 38 and 37 microg/m3 at the central and southern sites, respectively, to 31 microg/m3 at the northern site. All sites had mean PM2.5 concentrations more than double the U.S. National Ambient Air Quality Standard (NAAQS) for PM2.5. Coarse particles comprised 50-60% of PM10. The high levels of PM10 and large fraction of coarse particles comprising PM10 are partially explained by the resuspension of dust and soil from the desert crust. However, EC, OC, and most of the elements were significantly higher at the urbanized sites, compared with the more remote northern site, indicating significant pollutant contributions from local mobile and stationary sources. The particulate levels in this study are high enough to generate substantial health impacts and present opportunities for improving public health by reducing airborne PM.     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.     

Development of visibility forecasting modeling framework for the Lower Fraser Valley of British Columbia using Canada’s Regional Air Quality Deterministic Prediction System

Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hr lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction more than twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulfate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on postprocessed air quality forecasts without additional statistical modeling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode, illustrated that the statistical prototypes were able to provide timely and skillful visibility forecasts with lead time up to 48 hr.

Implications: This study describes the development of a visibility forecasting modeling framework, which leverages the existing air quality and meteorological forecasts from Canada’s operational Regional Air Quality Deterministic Prediction System. The main applications include tourism and recreation planning, input into air quality management programs, and educational outreach. Visibility forecasts, when supplemented with the existing air quality and health based forecasts, can assist jurisdictions to anticipate the visual air quality impacts as perceived by the public, which can potentially assist in formulating the appropriate air quality bulletins and recommendations.     

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM_2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new “Deep Blue” retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM_2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented.

Implications: Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM_2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.     

A modeling study of coarse particulate matter pollution in Beijing: regional source contributions and control implications for the 2008 Summer Olympics

In the last 10 yr, Beijing has made a great effort to improve its air quality. However, it is still suffering from regional coarse particulate matter (PM10) pollution that could be a challenge to the promise of clean air during the 2008 Olympics. To provide scientific guidance on regional air pollution control, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) air quality modeling system was used to investigate the contributions of emission sources outside the Beijing area to pollution levels in Beijing. The contributions to the PM10 concentrations in Beijing were assessed for the following sources: power plants, industry, domestic sources, transportation, agriculture, and biomass open burning. In January, it is estimated that on average 22% of the PM10 concentrations can be attributed to outside sources, of which domestic and industrial sources contributed 37 and 31%, respectively. In August, as much as 40% of the PM10 concentrations came from regional sources, of which approximately 41% came from industry and 31% from power plants. However, the synchronous analysis of the hourly concentrations, regional contributions, and wind vectors indicates that in the heaviest pollution periods the local emission sources play a more important role. The implications are that long-term control strategies should be based on regional-scale collaborations, and that emission abatement of local sources may be more effective in lowering the PM10 concentration levels on the heavy pollution days. Better air quality can be attained during the Olympics by placing effective emission controls on the local sources in Beijing and by controlling emissions from industry and power plants in the surrounding regions.     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010

This paper gives an overview of the set up, methodology and the obtained results of the CityDelta (phase 1 and 2) project. In the context of the Clean Air For Europe programme of the European Commission, the CityDelta project was designed to evaluate the impact of emission-reduction strategies on air quality at the European continental scale and in European cities. Ozone and particulate matter (PM) are the main components that have been studied. To achieve this goal, a model intercomparison study was organized with the participation of more than 20 modelling groups with a large number of modelling configurations. Two following main topics can be identified in the project. First, in order to evaluate their strengths and weaknesses, the participating models were evaluated against observations in a control year (1999). An accompanying paper will discuss in detail this evaluation aspect for four European cities. The second topic is the actual evaluation of the impact of emission reductions on levels of ozone and PM, with particular attention to the differences between large-scale and fine-scale models. An accompanying paper will discuss this point in detail. In this overview paper the main input to the intercomparison is described as well as the use of the ensemble approach. Finally, attention is given to the policy relevant issue on how to implement the urban air quality signal into large-scale air quality models through the use of functional relationships.     

The relationship between atmospheric pollutant emissions and fuel qualities of inland vessels in Jiangsu Province,China

Air pollution caused by ship exhaust emission is receiving more and more attention. The physical and chemical properties of fuels, such as sulfur content and PAHs content, potentially had a significant influence on air pollutant emissions from inland vessels. In order to investigate the effects of fuel qualities on atmospheric pollutant emissions systematically, a series of experiments was conducted based on the method of actual ship testing. As a result, SO2, PM and NOx emission rates all increased with the increase of main engine rotating speed under cruise mode, while PM and NOx emission factors were inversely proportional to the main engine rotating speed. Moreover, SO2 emission factor changed little with the increase of the main engine rotating speed. In summary, the fuel-dependent specific emission of SO2 was a direct reflection of the sulfur content in fuel. The PM emission increased with the increase of sulfur content and PAHs content in fuel. However, fuel qualities impacted little on NOx emissions from inland vessels because of NOx formation mechanisms and conditions.

Implications: Ship activity is considered to be the third largest source of air pollution in China. In particular, air pollutants emitted from ships in river ports and waterways have a direct impact on regional air quality and pose threat on the health of local residents owing to high pollutants concentration and poor air diffusion. The study on the relationship between air pollutant emissions and fuel quality of inland vessels can provide foundational data for local authority to formulate reasonable and appropriate policies for reducing atmospheric pollution due to inland vessels.     

A new methodology development for the regulatory forecasting of PM10. Application in the Greater Athens Area,Greece

The paper introduces a new methodology for the prediction of daily PM10 concentrations, in line with the regulatory framework introduced through the EU Directive 2008/50/EC. The proposed approach is based on the efficient utilisation of the data collected over short time intervals (hourly) rather than the daily values used to derive the daily regulatory threshold. It is sufficiently simple and easily applicable in operational forecasting systems with the ability to accept as inputs both historical data and exogenous paraeters, such as meteorological variables. The application of the proposed methodology is demonstrated using data from five monitoring stations of air pollutants located in Athens, over a five year period (2000–2004) as well as compatible meteorological data from the NCEP (National Centers for Environmental Protection). A set of different models have been tested at the same time to reveal the effectiveness of the proposed approach, both univariate and multivariate, and linear and non-linear models. The analysis of all examined datasets has shown conclusive evidence that the introduction of the newly developed procedure which utilises data collected over a shorter horizon can significantly increase the forecasting ability of any developed model using daily historic PM10 data, under all examined metrics.     

Forecasting human exposure to atmospheric pollutants in Portugal – A modelling approach

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure.To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM₁₀ (<10 μg m^?3 h), but for O₃, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.     

Evaluation of Human Exposure to Ambient PM10 in the Metropolitan Area of Mexico City Using a GIS-Based Methodology

ABSTRACT

The main goal of this study was to evaluate the magnitude of outdoor exposure to fine particulate matter (PM₁₀) potentially experienced by the population of metropolitan Mexico City. With the use of a geographic information system (GIS), spatially resolved PM₁₀ distributions were generated and linked to the local population. The PM₁₀ concentration exceeded the 24-hr air quality standard of 150 μg/m³ on 16% of the days, and the annual air quality standard of 50 μg/m³ was exceeded by almost twice its value in some places. The basic methodology described in this paper integrates spatial demographic and air quality databases, allowing the evaluation of various air pollution reduction scenarios. Achieving the annual air quality standard would represent a reduction in the annual arithmetic average concentration of 14 μg/m³ for the typical inhabitant. Human exposure to particulate matter (PM) has been associated with mortality and morbidity in Mexico City; reducing the concentration levels of this pollutant would represent a reduction in mortality and morbidity and the associated cost of such impacts. This methodology is critical to assessing the potential benefits of the current initiative to improve air quality implemented by the Environmental Metropolitan Commission of Mexico City.     

Modeling Pareto efficient PM10 control policies in Northern Italy to reduce health effects

High PM10 concentrations can cause human health problems, both related to short-term and long-term exposure to particles. In this work the impact of efficient PM10 control problems in Northern Italy is assessed by means of a two-stage methodology. In the first stage a multi-objective optimization approach is applied. The multi-objective problem defines two control objectives (the emission reduction costs and the air quality index) to be minimized varying the decision variables (precursor emission reductions). The solution of the multi-objective problem is the Pareto efficient PM10 control policies. In the second stage, the ExternE methodology is applied to estimate health impacts and external costs for the efficient emission reduction scenarios computed in the first stage. The methodology has been applied over Lombardia region, one of the most polluted areas in Europe.     

Installation of platform screen doors and their impact on indoor air quality: Seoul subway trains

In this study, variations of particulate matter (PM) concentrations in subway trains following installation of platform screen doors (PSDs) in the Seoul subway system were investigated. PM samples were collected in the trains on subway lines 1–8 before and after installation of PSDs. It was found that the mean PM₁₀ concentration in the trains after PSDs installation increased significantly by 29.9% compared to that before installation. In particular, the increase of PM₁₀ in line 6 was the highest at 103%. When the relationship between PM₁₀ and PM_2.5 was compared, coefficients of determination (r²) before and after PSDs installations were 0.696 and 0.169, respectively. This suggests that air mixing between the platform and the tunnel after PSDs installation was extremely restricted. In addition, the indoor/outdoor PM₁₀ ratio following PSDs installation increased from 1.32 to 2.97 relative to the period with no installed PSDs. Furthermore, this study revealed that PM levels in subway trains increased significantly after all underground PSDs were put in use. Several potential factors were examined that could result in this PM increase, such as train ventilation systems, operational conditions, passenger volume, subway depth, and the length of underground segments.

ImplicationsPM₁₀ concentrations inside the subway trains increased after PSDs installation. This indicates that air quality in trains was very seriously impacted by PSDs. PM₁₀ levels were also influenced by the tunnel depth and length of the underground segments. To prevent the adverse effect on human health by PM₁₀ emitted from the tunnel, an applicable ventilation system to reduce PM₁₀ is required inside trains and tunnels.     

Multi-criteria analysis for PM10 planning

To implement sound air quality policies, Regulatory Agencies require tools to evaluate outcomes and costs associated to different emission reduction strategies. These tools are even more useful when considering atmospheric PM10 concentrations due to the complex nonlinear processes that affect production and accumulation of the secondary fraction of this pollutant. The approaches presented in the literature (Integrated Assessment Modeling) are mainly cost-benefit and cost-effective analysis. In this work, the formulation of a multi-objective problem to control particulate matter is proposed. The methodology defines: (a) the control objectives (the air quality indicator and the emission reduction cost functions); (b) the decision variables (precursor emission reductions); (c) the problem constraints (maximum feasible technology reductions). The cause-effect relations between air quality indicators and decision variables are identified tuning nonlinear source–receptor models. The multi-objective problem solution provides to the decision maker a set of not-dominated scenarios representing the efficient trade-off between the air quality benefit and the internal costs (emission reduction technology costs). The methodology has been implemented for Northern Italy, often affected by high long-term exposure to PM10. The source–receptor models used in the multi-objective analysis are identified processing long-term simulations of GAMES multiphase modeling system, performed in the framework of CAFE-Citydelta project.     

Windsor, Ontario exposure assessment study: design and methods validation of personal, indoor, and outdoor air pollution monitoring

The Windsor, Ontario Exposure Assessment Study evaluated the contribution of ambient air pollutants to personal and indoor exposures of adults and asthmatic children living in Windsor, Ontario, Canada. In addition, the role of personal, indoor, and outdoor air pollution exposures upon asthmatic children's respiratory health was assessed. Several active and passive sampling methods were applied, or adapted, for personal, indoor, and outdoor residential monitoring of nitrogen dioxide, volatile organic compounds, particulate matter (PM; PM < or = 2.5 microm [PM2.5] and < or = 10 microm [PM10] in aerodynamic diameter), elemental carbon, ultrafine particles, ozone, air exchange rates, allergens in settled dust, and particulate-associated metals. Participants completed five consecutive days of monitoring during the winter and summer of 2005 and 2006. During 2006, in addition to undertaking the air pollution measurements, asthmatic children completed respiratory health measurements (including peak flow meter tests and exhaled breath condensate) and tracked respiratory symptoms in a diary. Extensive quality assurance and quality control steps were implemented, including the collocation of instruments at the National Air Pollution Surveillance site operated by Environment Canada and at the Michigan Department of Environmental Quality site in Allen Park, Detroit, MI. During field sampling, duplicate and blank samples were also completed and these data are reported. In total, 50 adults and 51 asthmatic children were recruited to participate, resulting in 922 participant days of data. When comparing the methods used in the study with standard reference methods, field blanks were low and bias was acceptable, with most methods being within 20% of reference methods. Duplicates were typically within less than 10% of each other, indicating that study results can be used with confidence. This paper covers study design, recruitment, methodology, time activity diary, surveys, and quality assurance and control results for the different methods used.