Modelling of the urban concentrations of PM&lt;sub&gt;2.5&lt;/sub&gt; on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects

Abstract. Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM2.5) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s), compared with the results for more recent years (2000s and early 2010s). The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for the assessment of health effects associated with air pollution.

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Modelling of the urban concentrations of PM<sub>2.5</sub> on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects

10.5194/acp-2017-968 ◽

2018 ◽

Author(s):

Jaakko Kukkonen ◽

Leena Kangas ◽

Mari Kauhaniemi ◽

Mikhail Sofiev ◽

Mia Aarnio ◽

...

Keyword(s):

High Resolution ◽

Health Effects ◽

Dispersion Model ◽

Fine Particulate Matter ◽

Health Impact ◽

Atmospheric Composition ◽

Local Source ◽

Small Scale ◽

Regional Background

Abstract. Reliable and self-consistent data on air quality is needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban scale concentrations of fine particulate matter (PM2.5) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. These high resolution computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple source Gaussian dispersion model UDM-FMI. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The modelled concentrations of PM2.5 agreed fairly well or well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980's and 1990's), compared with the results for more recent years (2000's and early 2010's). The local vehicular emissions were about five-folds higher in the 1980's, compared with the emissions during the latest considered years. However, the local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980's; these have since decreased to about to a half of the highest values. However, there is only a very slight decreasing trend of the PM2.5 concentrations during the last decade. Regional background is the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular suspension emissions have increased. The study provides long-term, high-resolution concentration databases on regional and urban scales that have been used for the assessment of health effects.

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Long-term exposure to air pollutants from multiple sources and mortality in an industrial area: a cohort study

Occupational and Environmental Medicine ◽

10.1136/oemed-2018-105059 ◽

2018 ◽

Vol 76 (1) ◽

pp. 48-57 ◽

Cited By ~ 6

Author(s):

Lisa Bauleo ◽

Simone Bucci ◽

Chiara Antonucci ◽

Roberto Sozzi ◽

Marina Davoli ◽

...

Keyword(s):

Health Effects ◽

Air Pollutants ◽

Dispersion Model ◽

Neurological Diseases ◽

Industrial Area ◽

Multiple Sources ◽

Potential Health ◽

Dichotomous Variable

Background and aimsResidents near industrial areas are exposed to several toxins from various sources and the assessment of the health effects is difficult. The area of Civitavecchia (Italy) has several sources of environmental contamination with potential health effects. We evaluated the association between exposure to pollutants from multiple sources and mortality in a cohort of people living in the area.MethodsAll residents of the area in 1996 were enrolled (from municipal registers) and followed until 2013. Long-term exposures to emissions from industrial sources (PM10) and traffic (NOx) at the residential addresses were assessed using a dispersion model. Residence close to the harbour was also considered. Cox survival analysis was conducted including a linear term for industrial PM10 and NOx exposure and a dichotomous variable to indicate residence within 500 m of the harbour. Age, sex, calendar period, occupation and area-based socioeconomic position (SEP) were considered (HRs, 95% CI).Results71 362 people were enrolled (52% female, 43% low SEP) and 14 844 died during the follow-up. We found an association between industrial PM10 and mortality from non-accidental causes (HR=1.06, 95% CI 1.01 to 1.12), all cancers (HR=1.11, 95% CI 1.01 to 1.21) and cardiac diseases (HR=1.12, 95% CI 1.01 to 1.23). We also found an association between NOx exposure from traffic and mortality from all cancers (HR=1.13, 95% CI 1.01 to 1.26) and neurological diseases (HR=1.50, 95% CI 1.01 to 2.20). Living near the harbour was associated with higher mortality from lung cancer (HR=1.31, 95% CI 1.04 to 1.66) and neurological diseases (HR=1.51, 95% CI 1.05 to 2.18).ConclusionsEstimated exposures to different pollution sources in this area were independently associated with several mortality outcomes while adjusting for occupation and socioeconomic status.

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Comparison of measured residential black carbon levels outdoors and indoors with fixed-site monitoring data and with dispersion modelling

Environmental Science and Pollution Research ◽

10.1007/s11356-020-12134-8 ◽

2020 ◽

Author(s):

Olena Gruzieva ◽

Antonios Georgelis ◽

Niklas Andersson ◽

Tom Bellander ◽

Christer Johansson ◽

...

Keyword(s):

Time Series ◽

Black Carbon ◽

Health Effects ◽

Dispersion Model ◽

Monitoring Data ◽

Dispersion Modelling ◽

Night Time ◽

Urban Background ◽

Site Monitoring

AbstractEpidemiologic studies on health effects of air pollution usually rely on time-series of ambient monitoring data or on spatially modelled levels. Little is known how well these estimate residential outdoor and indoor levels. We investigated the agreement of measured residential black carbon (BC) levels outdoors and indoors with fixed-site monitoring data and with levels calculated using a Gaussian dispersion model. One-week residential outdoor and indoor BC measurements were conducted for 15 families living in central Stockholm. Time-series from urban background and street-level monitors were compared to these measurements. The observed weekly concentrations were also standardized to reflect annual averages, using urban background levels, and compared spatially to long-term levels as estimated by dispersion modelling. Weekly average outdoor BC level was 472 ng/m3 (range 261–797 ng/m3). The corresponding fixed-site urban background and street levels were 313 and 1039 ng/m3, respectively. Urban background variation explained 50% of the temporal variation in residential outdoor levels averaged over 24 h. Modelled residential long-term outdoor levels were on average comparable with the standardized measured home outdoor levels, and explained 49% of the spatial variability. The median indoor/outdoor ratio across all addresses was 0.79, with no difference between day and night time. Common exposure estimation approaches in the epidemiology of health effects related to BC displayed high validity for residencies in central Stockholm. Urban background monitored levels explained half of the outdoor day-to-day variability at residential addresses. Long-term dispersion modelling explained half of the spatial differences in outdoor levels. Indoor BC concentrations tended to be somewhat lower than outdoor levels.

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Association of Cardiovascular Disease and Long-Term Exposure to Fine Particulate Matter (PM2.5) in the Southeastern United States

Atmosphere ◽

10.3390/atmos12080947 ◽

2021 ◽

Vol 12 (8) ◽

pp. 947

Author(s):

R. Burciaga Valdez ◽

Mohammad Z. Al-Hamdan ◽

Mohammad Tabatabai ◽

Darryl B. Hood ◽

Wansoo Im ◽

...

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Particulate Matter ◽

Health Effects ◽

Fine Particulate Matter ◽

Clinical Risk Factors ◽

Clinical Risk ◽

Fine Particulate ◽

Ambient Levels

There is a well-documented association between ambient fine particulate matter air pollution (PM2.5) and cardiovascular disease (CVD) morbidity and mortality. Exposure to PM2.5 can cause premature death and harmful and chronic health effects such as heart attack, diabetes, and stroke. The Environmental Protection Agency sets annual PM2.5 standards to reduce these negative health effects. Currently above an annual average level of 12.0 µg/m is considered unhealthy. Methods. We examined the association of long-term exposure to PM2.5 and CVD in a cohort of 44,610 individuals who resided in 12 states recruited into the Southern Community Cohort Study (SCCS). The SCCS was designed to recruit Black and White participants who received care from Federally Qualified Health Centers; hence, they represent vulnerable individuals from low-income families across this vast region. This study tests whether SCCS participants who lived in locations exposed to elevated ambient levels of PM2.5 concentrations were more likely to report a history of CVD at enrollment (2002–2009). Remotely sensed satellite data integrated with ground monitoring data provide an assessment of the average annual PM2.5 in urban and rural locations where the SCCS participants resided. We used multilevel logistic regression to estimate the associations between self-reported CVD and exposure to elevated ambient levels of PM2.5. Results. We found a 13.4 percent increase in the odds of reported CVD with exposure to unhealthy levels of PM2.5 exposure at enrollment. The SCCS participants with medical histories of hypertension, hypercholesterolemia, and smoking had, overall, 385 percent higher odds of reported CVD than those without these clinical risk factors. Additionally, Black participants were more likely to live in locations with higher ambient PM2.5 concentrations and report high levels of clinical risk factors, thus, they may be at a greater future risk of CVD. Conclusions: In the SCCS participants, we found a strong relation between exposures to high ambient levels of PM2.5 and self-reported CVD at enrollment.

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The 17-y spatiotemporal trend of PM2.5and its mortality burden in China

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1919641117 ◽

2020 ◽

Vol 117 (41) ◽

pp. 25601-25608

Author(s):

Fengchao Liang ◽

Qingyang Xiao ◽

Keyong Huang ◽

Xueli Yang ◽

Fangchao Liu ◽

...

Keyword(s):

Machine Learning ◽

Health Effects ◽

Adult Population ◽

Fine Particulate Matter ◽

Atmospheric Correction ◽

Adult Mortality ◽

Learning Approaches ◽

Spatiotemporal Trends ◽

Mortality Burden

Investigations on the chronic health effects of fine particulate matter (PM2.5) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM2.5levels, we aimed to estimate high-resolution, long-term PM2.5and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM2.5concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM2.5concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM2.5exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validationR2of 0.93 at the monthly level. For the time period outside the model training window, predictionR2values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM2.5exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM2.5estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.

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Economic analysis of health effects from forest fires

Canadian Journal of Forest Research ◽

10.1139/x05-293 ◽

2006 ◽

Vol 36 (4) ◽

pp. 868-877 ◽

Cited By ~ 43

Author(s):

R Rittmaster ◽

W L Adamowicz ◽

B Amiro ◽

R T Pelletier

Keyword(s):

At Risk ◽

Health Effects ◽

Forest Fires ◽

Dispersion Model ◽

Fine Particulate Matter ◽

Epidemiological Studies ◽

Air Dispersion ◽

Air Dispersion Model ◽

In Fire

Epidemiological studies have shown that high levels of fine particulate matter (PM) are correlated with adverse human health effects. Approximately one-third of PM emissions in Canada originate from forest fires. However, air quality concerns are not typically included in resource allocation decisions in fire management. In this paper we examine the economic magnitude of these health concerns and compare them to other costs of forest fires using the 2001 fire in Chisholm, Alberta, as a case study. We construct an empirical air dispersion model to estimate the concentration of PM arising from the fire. Benefit transfer methods were used to determine the health impacts associated with elevated PM from the fire and to value these impacts. The economic impacts appear to be substantial, second only to timber losses. The approaches used in this case study can be extended to construct a map that identifies the values at risk from health effects. The use of monetary values of these impacts helps in comparison and aggregation of the values at risk.

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Subpixel Characterization of HIRS Spectral Radiances Using Cloud Properties from AVHRR

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0187.1 ◽

2016 ◽

Vol 33 (7) ◽

pp. 1519-1538 ◽

Cited By ~ 3

Author(s):

Paul W. Staten ◽

Brian H. Kahn ◽

Mathias M. Schreier ◽

Andrew K. Heidinger

Keyword(s):

High Resolution ◽

Global Scale ◽

Small Scale ◽

Spectral Brightness ◽

Cloud Type ◽

Brightness Temperatures ◽

Cloud Properties ◽

Ocean Region ◽

Intersatellite Calibration

AbstractThis paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures (Tb) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and Tb statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the Tb histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of Tb biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed Tb well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS–AVHRR observing record.

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Emissions of methane in Europe inferred by total column measurements

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-3963-2019 ◽

2019 ◽

Vol 19 (6) ◽

pp. 3963-3980 ◽

Cited By ~ 2

Author(s):

Debra Wunch ◽

Dylan B. A. Jones ◽

Geoffrey C. Toon ◽

Nicholas M. Deutscher ◽

Frank Hase ◽

...

Keyword(s):

Carbon Monoxide ◽

High Resolution ◽

State Of The Art ◽

Methane Emissions ◽

Atmospheric Composition ◽

Emission Inventories ◽

Atmospheric Research ◽

Spatial Grid ◽

Total Column

Abstract. Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methane emissions contribute to the discrepancy between our inferred emissions and those from the inventories.

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High Resolution Sequence Stratigraphy of the Natih Formation (Cenomanian/Turonian) in Northern Oman: Distribution of Source Rocks and Reservoir Facies

GeoArabia ◽

10.2113/geoarabia010165 ◽

1996 ◽

Vol 1 (1) ◽

pp. 65-91

Author(s):

Frans S.P. van Buchem ◽

Philippe Razin ◽

Peter W. Homewood ◽

Jean M. Philip ◽

Gregor P. Eberli ◽

...

Keyword(s):

High Resolution ◽

Arabian Peninsula ◽

Carbonate Platform ◽

Hierarchical Organization ◽

Source Rocks ◽

Small Scale ◽

Depositional Sequences ◽

Accommodation Space ◽

Facies Associations

ABSTRACT The Cenomanian of the Arabian Peninsula comprises a carbonate platform setting with rudists, characterized by gradual lateral facies changes including the interfingering of carbonate reservoirs (Natih and Mishrif formations) and source rocks. In order to be more predictive with regard to the distribution and the geometrical aspects of the reservoirs and source rocks, a high resolution sequence stratigraphic study has been carried out in the Adam Foothills of Northern Oman. Based on detailed field sections a correlation scheme covering a transect of 100 kilometers (km) has been established. Three orders of stacked depositional sequences have been found based on the reoccurrence of facies. During long-term increase of accommodation the depositional environment was separated in basinal and platform facies. In contrast, during longer term sea level fall, i.e. long-term decrease of accommodation space, prograding shelfal units extended platform facies over a large part of the basin. The most heterogeneous facies associations are found in times of minimal accommodation space, when incisions and subaerial exposure produce lateral variable strata (e.g. top Natih E). The organic matter is found at the base of two of the three longer term (3rd order) depositional sequences. The organic carbon is contained in marl-limestone couplets (small-scale cyclicity) with a high abundance of oysters and monospecific brachiopod faunas (coquinas). Rudists are found in the progradational part of these sequences, and occur mostly as reworked rudstone layers in meter to decimeter scale, high frequency cycles. The detailed regional correlation depends on the identification of medium- to small-scale (4th to 5th order) depositional sequences which are bounded by regional shifts of the facies belts. The distinct hierarchical organization of the depositional sequences in the Cenomanian, and the relative stability at that time of the Arabian Peninsula, implies a strong correlation potential and thus a broad regional similarity of the architecture of the petroleum systems at that time.

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SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

Atmospheric Measurement Techniques ◽

10.5194/amt-8-505-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 505-521 ◽

Cited By ~ 41

Author(s):

G. Snider ◽

C. L. Weagle ◽

R. V. Martin ◽

A. van Donkelaar ◽

K. Conrad ◽

...

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Health Effects ◽

Satellite Remote Sensing ◽

Fine Particulate Matter ◽

Ground Level ◽

Water Soluble ◽

Global Network ◽

Spatial Coverage

Abstract. Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.

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