Recommendations on the use of satellite remote-sensing data for urban air quality

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.     

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Increasing the Use of Earth Science Data and Models in Air Quality Management

In 2010, the U.S. National Aeronautics and Space Administration (NASA) initiated the Air Quality Applied Science Team (AQAST) as a 5-year, $17.5-million award with 19 principal investigators. AQAST aims to increase the use of Earth science products in air quality-related research and to help meet air quality managers’ information needs. We conducted a Web-based survey and a limited number of follow-up interviews to investigate federal, state, tribal, and local air quality managers’ perspectives on usefulness of Earth science data and models, and on the impact AQAST has had. The air quality managers we surveyed identified meeting the National Ambient Air Quality Standards for ozone and particulate matter, emissions from mobile sources, and interstate air pollution transport as top challenges in need of improved information. Most survey respondents viewed inadequate coverage or frequency of satellite observations, data uncertainty, and lack of staff time or resources as barriers to increased use of satellite data by their organizations. Managers who have been involved with AQAST indicated that the program has helped build awareness of NASA Earth science products, and assisted their organizations with retrieval and interpretation of satellite data and with application of global chemistry and climate models. AQAST has also helped build a network between researchers and air quality managers with potential for further collaborations.

Implications: NASA’s Air Quality Applied Science Team (AQAST) aims to increase the use of satellite data and global chemistry and climate models for air quality management purposes, by supporting research and tool development projects of interest to both groups. Our survey and interviews of air quality managers indicate they found value in many AQAST projects and particularly appreciated the connections to the research community that the program facilitated. Managers expressed interest in receiving continued support for their organizations’ use of satellite data, including assistance in retrieving and interpreting data from future geostationary platforms meant to provide more frequent coverage for air quality and other applications.     

Lagrangian sampling of 3-D air quality model results for regional transport contributions to sulfate aerosol concentrations at Baltimore,MD, in summer 2004

We use ensemble-mean Lagrangian sampling of a 3-D Eulerian air quality model, CMAQ, together with ground-based ambient monitors data from several air monitoring networks and satellite (MODIS) observations to provide source apportionment and regional transport vs. local contributions to sulfate aerosol and PM_2.5 concentrations at Baltimore, MD, for summer 2004. The Lagrangian method provides estimates of the chemical and physical evolution of air arriving in the daytime boundary layer at Baltimore. Study results indicate a dominant role for regional transport contributions on those days when sulfate air pollution is highest in Baltimore, with a principal transport pathway from the Ohio River Valley (ORV) through southern Pennsylvania and Maryland, consistent with earlier studies. Thus, reductions in sulfur emissions from the ORV under the EPA's Clean Air Interstate Rule may be expected to improve particulate air quality in Baltimore during summer. The Lagrangian sampling of CMAQ offers an inexpensive and complimentary approach to traditional methods of source apportionment based on multivariate observational data analysis, and air quality model emissions separation. This study serves as a prototype for the method applied to Baltimore. EPA is establishing a system to allow air quality planners to readily produce and access equivalent results for locations of their choice.     

The application of satellite remote sensing to coastal management in Singapore

This paper reviews the application of satellite remote sensing to management of Singapore's coastal environment. Remotely sensed data have been used for marine habitat mapping, water quality monitoring, ship and ship-wake detection, oil spill detection, red tide monitoring, and mapping of reclamation activities. While these applications clearly cover most of the range of opportunities for use of remotely sensed data in the coastal zone, there is still a need for more complete baseline studies of natural resources and habitats, and monitoring of the impacts of development on the coastal and marine environment. There is also a requirement for more management-oriented research and continued development and revision of the available datasets. Integration and exchange of information between management agencies and research groups is also an important aspect of sustainable management of Singapore's coastal environment and marine resources.     

Human health risks in megacities due to air pollution

This study evaluates the health risks in megacities in terms of mortality and morbidity due to air pollution. A new spreadsheet model, Risk of Mortality/Morbidity due to Air Pollution (Ri-MAP), is used to estimate the excess numbers of deaths and illnesses. By adopting the World Health Organization (WHO) guideline concentrations for the air pollutants SO₂, NO₂ and total suspended particles (TSP), concentration-response relationships and a population attributable-risk proportion concept are employed. Results suggest that some megacities like Los Angeles, New York, Osaka Kobe, Sao Paulo and Tokyo have very low excess cases in total mortality from these pollutants. In contrast, the approximate numbers of cases is highest in Karachi (15,000/yr) characterized by a very high concentration of total TSP (～670 μg m^?3). Dhaka (7000/yr), Beijing (5500/yr), Karachi (5200/yr), Cairo (5000/yr) and Delhi (3500/yr) rank highest with cardiovascular mortality. The morbidity (hospital admissions) due to Chronic Obstructive Pulmonary Disease (COPD) follows the tendency of cardiovascular mortality. Dhaka and Karachi lead the rankings, having about 2100/yr excess cases, while Osaka-Kobe (～20/yr) and Sao Paulo (～50/yr) are at the low end of all megacities considered. Since air pollution is increasing in many megacities, and our database of measured pollutants is limited to the period up to 2000 and does not include all relevant components (e.g. O₃), these numbers should be interpreted as lower limits. South Asian megacities most urgently need improvement of air quality to prevent excess mortality and morbidity due to exceptionally high levels of air pollution. The risk estimates obtained from Ri-MAP present a realistic baseline evaluation for the consequences of ambient air pollution in comparison to simple air quality indices, and can be expanded and improved in parallel with the development of air pollution monitoring networks.     

A 3D aerosol and visibility information system for urban areas using remote sensing and GIS

Currently, the depiction of urban air quality at boundary layer scale uses modelled climatic and land cover data. However, such models are difficult to verify, and only low to moderate accuracy may be achieved due to the complexity of the input data required and the reliance on assumptions about dispersion patterns. The provision of comprehensive air quality data to urban residents in city districts, at a level of detail commensurate with other Location-Based Services (LBS) which are time- and place-sensitive, has therefore not been possible. A method for urban air quality monitoring over cities at boundary layer scale, other than by the use of air quality models is presented here. The system presented uses empirical Aerosol Optical Thickness (AOT) data in near-real time, combining AOT data from AERONET with aerosol vertical profiles computed from twice-daily MODIS satellite images at 500 m resolution, to give three dimensional (3D) air quality data over the urban landscape. There has been no previous attempt to project the horizontal spatial distribution of aerosols from satellite image pixels into a vertical dimension to give a spatially comprehensive three dimensional record of air quality. The paper describes the sources and accuracy of the AOT data input to the system as well as its storage and retrieval on a Geographic Information System (GIS) platform, to provide air quality and visibility information according to user query at any 3D geographical location, including individual buildings or building floor.     

Comparative statistical study of hourly precipitation determined by radar-based Stage IV and ground-based methods in the North Central United States

Detailed hourly precipitation data are required for long-range modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants using the CALPUFF model. In sparsely populated areas such as the north central United States, ground-based precipitation measurement stations may be too widely spaced to offer a complete and accurate spatial representation of hourly precipitation within a modeling domain. The availability of remotely sensed precipitation data by satellite and the National Weather Service array of next-generation radars (NEXRAD) deployed nationally provide an opportunity to improve on the paucity of data for these areas. Before adopting a new method of precipitation estimation in a modeling protocol, it should be compared with the ground-based precipitation measurements, which are currently relied upon for modeling purposes. This paper presents a statistical comparison between hourly precipitation measurements for the years 2006 through 2008 at 25 ground-based stations in the north central United States and radar-based precipitation measurements available from the National Center for Environmental Predictions (NCEP) as Stage IV data at the nearest grid cell to each selected precipitation station. It was found that the statistical agreement between the two methods depends strongly on whether the ground-based hourly precipitation is measured to within 0.1 in/hr or to within 0.01 in/hr. The results of the statistical comparison indicate that it would be more accurate to use gridded Stage IV precipitation data in a gridded dispersion model for a long-range simulation, than to rely on precipitation data interpolated between widely scattered rain gauges.

Implications:

The current reliance on ground-based rain gauges for precipitation events and hourly data for modeling of dispersion and wet deposition of particulate matter and water-soluble pollutants results in potentially large discontinuity in data coverage and the need to extrapolate data between monitoring stations. The use of radar-based precipitation data, which is available for the entire continental United States and nearby areas, would resolve these data gaps and provide a complete and accurate spatial representation of hourly precipitation within a large modeling domain.     

The Estimation of Personal Exposures to Air Pollutants for a Community-Based Study of Health Effects in Asthmatics—Design and Results of Air Monitoring

In order to provide reliable pollutant and meteorological exposure estimates for an epidemiological study of asthmatics residing in two Houston neighborhoods, a dedicated three-tier air monitoring system was established. This consisted of fixed site ambient air monitoring at the center of each study area, a mobile van performing simultaneous indoor and outdoor measurements at selected residences of study participants, and a limited amount of direct personal monitoring for half of the participants. Monitored pollutants Included all criteria pollutant gases, as well as aeroallergens, aldehydes, TSP, and IP. Laboratory analyses provided concentrations of sulfate, nitrate, and trace elements. Continuous measurements of several meteorological parameters also were obtained. Intensive quality assurance and data validation efforts resulted in a high percentage of valid data for most pollutants. Ozone was the only measured pollutant that exceeded the NAAQS during the six-month (May to October) study period. The monitoring scheme allowed important pollutant concentration differences to be detected between day and night, between Indoors and outdoors, and among various indoor environments. The use of these monitoring data in combination with personal activity and household characteristics data to generate estimates of personal exposures for the epidemiological analysis will be described in a subsequent paper.     

New indicator approaches for effective urban air quality management

Measurements of urban air quality at monitoring stations in developed countries have frequently involved the criteria gaseous pollutants, particulates, hazardous air pollutants, perceived air quality and relevant meteorological conditions. Large numbers of indicators have therefore been established to quantify emissions, concentrations and environmental and human health impacts of each of these groups of substances. To simplify the data for management, several indicators have been grouped together to form urban air quality indices but the weightings of individual variables is contentious. In industrialising and developing countries, data may be limited and traditional air pollutant indicators cannot often be constructed. The emphasis therefore has to be placed on the development of policy-relevant indicators, such as Response Indicators that reflect different policy principles for regulating air pollutant emissions. Indices that quantify the air quality management capabilities and capacities at the city level provide further useful decision-relevant tools. Four sets of indices, namely, 1. air quality measurement capacity, 2. data assessment and availability, 3. emissions estimates, and 4. management enabling capabilities, and a composite index to evaluate air quality management capability, were constructed and applied to 80 cities. The indices revealed that management capability varied widely between the cities. In some of the cities, existing national knowledge on urban air quality could have been more effectively used for management. It was concluded that for effective urban air quality management, a greater emphasis should be given, not just to monitoring and data capture programmes, but to the development of indicators and indices that empower decision-makers to initiate management response strategies. Over-reliance on restricted, predetermined sets of traditional air quality indicators should be avoided.     

The impact of spatial correlation and incommensurability on model evaluation

Standard evaluations of air quality models rely heavily on a direct comparison of monitoring data matched with the model output for the grid cell containing the monitor's location. While such techniques may be adequate for some applications, conclusions are limited by such factors as the sparseness of the available observations (limiting the number of grid cells at which the model can be evaluated) and the incommensurability between volume-averages and pointwise observations. We examine several sets of simulations to illustrate the effect of incommensurability in a variety of cases distinguished by the type and extent of spatial correlation present. Block kriging, a statistical method which can be used to address the issue, is then demonstrated using the simulations. Lastly, we apply this method to actual data and discuss the practical importance of understanding the impact of spatial correlation structure and incommensurability.     

Workplace and Environmental Air Contaminant Concentrations Measured by Open Path Fourier Transform Infrared Spectroscopy: A Statistical Process Control Technique to Detect Changes from Normal Operating Conditions

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions.

Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.     

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.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

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.     

An empirical relationship between PM(2.5) and aerosol optical depth in Delhi Metropolitan

Atmospheric remote sensing offers a unique opportunity to compute indirect estimates of air quality, which are critically important for the management and surveillance of air quality in megacities of developing countries, particularly in India and China, which have experienced elevated concentration of air pollution but lack adequate spatial-temporal coverage of air pollution monitoring. This article examines the relationship between aerosol optical depth (AOD) estimated from satellite data at 5 km spatial resolution and the mass of fine particles ≤2.5 μm in aerodynamic diameter (PM(2.5)) monitored on the ground in Delhi Metropolitan where a series of environmental laws have been instituted in recent years.PM(2.5) monitored at 113 sites were collocated by time and space with the AOD computed using the data from Moderate Resolution Imaging Spectroradiometer (MODIS onboard the Terra satellite). MODIS data were acquired from NASA's Goddard Space Flight Center Earth Sciences Distributed Active Archive Center (DAAC). Our analysis shows a significant positive association between AOD and PM(2.5). After controlling for weather conditions, a 1% change in AOD explains 0.52±0.202% and 0.39±0.15% change in PM(2.5) monitored within ±45 and 150 min intervals of AOD data. This relationship will be used to estimate air quality surface for previous years, which will allow us to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.     

Airborne Survey of Major Air Basins in California

An instrumented aircraft has been used to study photochemical air pollution in the State of California. Simultaneous measurements of the most important chemical constituents (ozone, total oxidant, hydrocarbons, and nitrogen oxides, as well as several meteorological variables) were made. State-of-the-art measurement techniques and sampling procedures are discussed. Data from flights over the South Coast Air Basin, the San Francisco Bay area, the Salinas Valley, and the Pacific Ocean within 200 miles of the California coast are presented. Pollutants were found to be concentrated in distinct layers up to at least 18,000 feet. In many of these layers, the pollutant concentrations were much higher than at ground level. Furthermore, the presence of stable air very effectively inhibits the dilution of air masses for distances of 30 miles or more. Very low levels of ozone were recorded over the Pacific Ocean and measurements relating to air mass aging were made. The background ozone level for the South Coast Air Basin is estimated to be 0.03 ppm. These findings bring into question the validity of the present practice of depending solely on data from ground-based monitoring stations for predictive models.     

Review of Statistics of Extreme Values with Applications to Air Quality Data

In the field of air pollution control, the rare event is often of more significance than the common event. This is evidenced by the content of air quality standards which define acceptable upper limits of air pollution concentrations and acceptable frequencies with which such concentrations can be exceeded. The principles of extreme value statistics provide important tools for analyzing air quality data in an appropriately significant context.

Part II of the paper presents applications of the theory to air quality data. First, application is made to decisions regarding the length of air monitoring experiments and the length of data records for dis-person analyses. The theory is then applied to the analysis of long term air pollution data collected by the South Coast Air Pollution Control District. The interrelations between extremes from monthly and annual samples are noted and are shown to be consistent with theory.