scholarly journals Reduced Middle East Air Pollution Linked to Societal Disruption

Eos ◽  
2015 ◽  
Vol 96 ◽  
Author(s):  
JoAnna Wendel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Middle East ◽  
Satellite Data ◽  
Armed Conflict ◽  
Economic Distress ◽  
The Individual

Invasions, armed conflict, sanctions, and economic distress correlate with cleaner air in high-resolution satellite data that reveal air quality at the individual city level.

scholarly journals Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations

2020 ◽  
Vol 20 (15) ◽  
pp. 9281-9310 ◽  
Author(s):  
Alexander Ukhov ◽  
Suleiman Mostamandi ◽  
Arlindo da Silva ◽  
Johannes Flemming ◽  
Yasser Alshehri ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Middle East ◽  
Atmospheric Correction ◽  
World Health ◽  
So2 Emissions ◽  
Anthropogenic Aerosol ◽  
So2 Emission ◽  
Air Quality Forecast

Abstract. Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&amp;DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO2 emissions in WRF-Chem are based on the novel OMI-HTAP SO2 emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMS-OA (0.65–0.87), followed by MODIS-DB&amp;DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&amp;DT AOD product. MAIAC has the highest correlation (R=0.8), followed by MERRA-2 (R=0.66), CAMS-OA (R=0.65), and WRF-Chem (R=0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&amp;DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO2 emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈75 %–95 %) compared to other aerosol types. Near and downwind from the SO2 emission sources, nondust aerosols (primarily sulfate) contribute up to 30 % to PM2.5. The contribution of sea salt to PM in coastal regions can reach 5 %. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM10 and PM2.5 (particles with diameters less than 10 and 2.5 µm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the nondust component to PM2.5 is <25 %, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

scholarly journals Ensemble Forecasts of Air Quality in Eastern China – Part 2. Evaluation of the MarcoPolo-Panda Prediction System, Version 1

2018 ◽  
Author(s):  
Anna Katinka Petersen ◽  
Guy P. Brasseur ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
Michael Gauss ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Observational Data ◽  
Prediction System ◽  
Model Ensemble ◽  
Individual Model ◽  
Forecasting System ◽  
The Individual ◽  
Pollution Events ◽  
Statistical Indicators

Abstract. An operational multi-model forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multi-model ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multi-model ensemble predictions for the next 72 hours are displayed as hourly outputs on a publicly accessible web site (www.marcopolo-panda.eu). In this paper, the performance of the predictions system (individual models and the multi-model ensemble) for the first operational year (April 2016 until June 2017) has been analysed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate a) the seasonal behavior, b) the geographical distribution and c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide alert warning (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

scholarly journals Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

2015 ◽  
Vol XL-7/W3 ◽  
pp. 187-193 ◽  
Author(s):  
A. Fernandes ◽  
M. Riffler ◽  
J. Ferreira ◽  
S. Wunderle ◽  
C. Borrego ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Satellite Data ◽  
Spatial Variations ◽  
Transport Processes ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Vertical Column ◽  
Air Pollution Modelling ◽  
Spatial Coverage

Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx - Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. <br><br> CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

Abstract P002: Long Term Exposure to Outdoor Air Pollution and Cardiovascular Health

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Sarah Singh ◽  
Courtney Pilkerton ◽  
Adam Christian ◽  
Thomas K Bias ◽  
Stephanie J Frisbee
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Air Pollutants ◽  
Cardiovascular Health ◽  
Heart Association ◽  
Health Concern ◽  
Behavioral Risk ◽  
The Individual

BACKGROUND: Although the link between air pollution and cardiovascular disease has been controversial in recent decades, it remains a top global health concern. Most studies have assessed only the relationship between pollutant concentrations and morbidity or mortality in populous cities. In this study, we investigated the association of long term exposure to major air pollutants with current cardiovascular health. This outcome was a measure of health rather than disease, as measured by the Cardiovascular Health Index (CVHI) developed by the American Heart Association. METHODS: We analyzed 2011 data from 3007 counties across the US using Behavioral Risk Factor Surveillance System and Area Health Resources File. Air Quality Index (AQI) for five major pollutants from 2001-2011; Ozone, Sulfur dioxide and Carbon monoxide and Fine particulate matter (aerodynamic diameter of 10 and ≤2.5 μm) were obtained from the EPA Air Quality System database. Categories were based on the 11-year average pollutant AQI level and using Jenks optimization method; persistently good, variant and persistently bad. Associations between categories and the mean CVHI were evaluated using Poisson regression models adjusting for age, sex, race/ethnicity and socioeconomic status at the individual and population level. RESULTS: PM2.5 was most frequently measured (938 counties) and carbon monoxide least frequently (224 counties). Correlations between pollutants were moderate and significant (p<0.0001), ranging from r=0.30 between CO and Oz to r=0.52 between SD and PM2.5. Four pollutants had 11-year average AQI levels significantly associated with increased mean CVHI score of individuals. Living in a county categorized as ‘persistently good’ or ‘variant’ AQI levels for ozone is significantly associated with an estimated 3% increase in CVHI (95% CI 0.1% - 5.0%) as compared to living in a county of ‘persistently bad’ AQI levels. In addition, living in a county of only ‘persistently good’ AQI levels for PM2.5 is significantly associated with an estimated 5% increase in CVHI (95% CI 3% - 9%) as compared to living in a county of ‘persistently bad’ AQI levels. Inverse relationships existed for both PM10 and carbon monoxide. CONCLUSIONS: It is difficult to tease apart the independent effects of individual air pollutants on health as humans are exposed to a mixture of gases. However we have shown that at the individual level, there is an association between long term exposure to air pollution and its effects on current cardiovascular health. Further research is needed to determine whether these effects exist at varying levels of subject characteristics.

scholarly journals A very high-resolution assessment and modelling of urban air quality

2019 ◽  
Author(s):  
Tobias Wolf ◽  
Lasse H. Pettersson ◽  
Igor Esau
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Landscape Heterogeneity ◽  
Urban Air Quality ◽  
City Centre ◽  
Urban Air ◽  
Large Eddy Simulation Model ◽  
The City ◽  
Very High

Abstract. Urban air quality is one of the most prominent environmental concerns for a modern city dweller. Accurate monitoring of air quality is difficult due to intrinsic urban landscape heterogeneity and superposition of multiple polluting sources. Existing approaches often do not provide the necessary spatial details and peak concentrations of pollutants, especially at larger distances from measuring stations. A more advanced approach is needed. This study presents a very high-resolution air quality assessment with the large-eddy simulation model PALM. This fully three-dimensional primitive-equation hydro-dynamical model resolves both structural details of the complex urban surface and turbulent eddies larger than 10 m in size. We ran a set of 9 meteorological scenarios in order to assess the dispersion of pollutants in Bergen, a middle-sized Norwegian city embedded in a coastal valley. This set of scenarios represents typically observed conditions with high air pollution from nitrogen dioxide (NO2) and particulate matter (PM2.5). The modelling methodology helped to identify pathways and patterns of air pollution caused by the three main local air pollution sources in the city. These are road vehicle traffic, domestic house heating with wood-burning fireplaces and ships docked in the harbour area next to the city centre. The study produced vulnerability maps, highlighting the most impacted districts for each scenario.

scholarly journals Use of Satellite Data for Air Pollution Modeling in Bulgaria

Earth ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 586-604
Author(s):  
Emilia Georgieva ◽  
Dimiter Syrakov ◽  
Dimiter Atanassov ◽  
Tatiana Spassova ◽  
Maria Dimitrova ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Satellite Data ◽  
Quality Management System ◽  
Weather Forecast ◽  
Modeling Systems ◽  
Quality Modeling ◽  
Pollution Modeling ◽  
Dust Events ◽  
The City

Air pollution continues to be of concern for Bulgarian cities, mainly due to particulate matter of aerodynamic diameter smaller than 10 μm (PM10). There is public and expert interest in the improvement of two operational air quality modeling systems: the Bulgarian Chemical Weather Forecast System (BgCWFS) and the Local Air Quality Management System (LAQMS) for the city of Plovdiv. The aim of the study is to investigate the effects of satellite data assimilation in BgCWFS on surface concentrations over Bulgaria (resolution 9 km), to downscale BgCWFS output to LAQMS (resolution 250 m), and to examine effects on PM10 in Plovdiv. Data from the Global Ozone Monitoring Experiment-2 (GOME-2) (MetOP satellites) for aerosols, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were assimilated in BgCWFS using objective analysis. Simulation experiments with and without satellite data were conducted for a summer and a winter month. The comparison to surface observations in the country showed improvement of results when using satellite data, especially in the summer due to mineral dust events captured by satellites. The decrease in the normalized mean bias (NMB) over the two months was 43% (PM10) and 73% (SO2). The LAQMS estimated background contributions to PM10 in the city as 32%. The absolute NMB by LAQMS decreased by 38%.

scholarly journals Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 85
Author(s):  
Laura Gladson ◽  
Nicolas Garcia ◽  
Jianzhao Bi ◽  
Yang Liu ◽  
Hyung Joo Lee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Air Quality ◽  
High Resolution ◽  
Quality Management ◽  
Atmospheric Correction ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Air Quality Management ◽  
Spatial Coverage

Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.

scholarly journals Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment

2021 ◽  
Vol 13 (23) ◽  
pp. 4769
Author(s):  
Andrii Shelestov ◽  
Hanna Yailymova ◽  
Bohdan Yailymov ◽  
Nataliia Kussul
Keyword(s):  
European Union ◽  
Air Quality ◽  
High Resolution ◽  
Satellite Data ◽  
City Planning ◽  
Smart Cities ◽  
Population Data ◽  
European Cities ◽  
Monitoring Service ◽  
The City

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries.

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