scholarly journals Trends of inorganic and organic aerosols and precursor gases in Europe: insights from the EURODELTA multi-model experiment over the 1990–2010 period

2019 ◽  
Author(s):  
Giancarlo Ciarelli ◽  
Mark R. Theobald ◽  
Marta G. Vivanco ◽  
Matthias Beekmann ◽  
Wenche Aas ◽  
...  
Keyword(s):  
Air Quality ◽  
Comprehensive Evaluation ◽  
Quality Data ◽  
Relative Reduction ◽  
Emission Data ◽  
Chemical Aging ◽  
Modeling Experiment ◽  
Phase Precursor ◽  
Total Ammonium

Abstract. In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for twenty-one continuous years in Europe using emission scenarios prepared by IIASA/GAINS and corresponding year-by-year meteorology derived from ERA-interim global reanalysis. For this study, long-term observations of particle sulfate (SO42−), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to validate the model results. The trends analysis was performed for the full twenty-one years (referred to as PT), but also for two 11-year sub-periods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e. 1990–2000, with a 64–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade, P2, the models estimated a mean relative reduction in SO2 concentrations of about 34–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19–23 % (range of all the models) during the P1 period, and 12 % and between 22–26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42− concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations, and with good performances also during the P2 and PT periods. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42− concentrations compared with its gas-phase precursor (i.e. SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3−) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3− during the P1 period, which was mainly attributed to an increased availability of “free-ammonia”. By contrast, trends in modeled HNO3 and NO3− concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, under-predicted by all models, with worst performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic NMVOCs precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emission and monoterpene emissions during the 1990–2010 period over Fennoscandia and Eastern European regions (i.e. around 14–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic under-estimation of the modeled SOA fractions of between a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.

scholarly journals Trends of inorganic and organic aerosols and precursor gases in Europe: insights from the EURODELTA multi-model experiment over the 1990–2010 period

2019 ◽  
Vol 12 (12) ◽  
pp. 4923-4954 ◽  
Author(s):  
Giancarlo Ciarelli ◽  
Mark R. Theobald ◽  
Marta G. Vivanco ◽  
Matthias Beekmann ◽  
Wenche Aas ◽  
...  
Keyword(s):  
Air Quality ◽  
Comprehensive Evaluation ◽  
Quality Data ◽  
Relative Reduction ◽  
International Institute ◽  
Emission Data ◽  
Modeling Experiment ◽  
Phase Precursor ◽  
Total Ammonium

Abstract. In the framework of the EURODELTA-Trends (EDT) modeling experiment, several chemical transport models (CTMs) were applied for the 1990–2010 period to investigate air quality changes in Europe as well as the capability of the models to reproduce observed long-term air quality trends. Five CTMs have provided modeled air quality data for 21 continuous years in Europe using emission scenarios prepared by the International Institute for Applied Systems Analysis/Greenhouse Gas – Air Pollution Interactions and Synergies (IIASA/GAINS) and corresponding year-by-year meteorology derived from ERA-Interim global reanalysis. For this study, long-term observations of particle sulfate (SO42-), total nitrate (TNO3), total ammonium (TNHx) as well as sulfur dioxide (SO2) and nitrogen dioxide (NO2) for multiple sites in Europe were used to evaluate the model results. The trend analysis was performed for the full 21 years (referred to as PT) but also for two 11-year subperiods: 1990–2000 (referred to as P1) and 2000–2010 (referred to as P2). The experiment revealed that the models were able to reproduce the faster decline in observed SO2 concentrations during the first decade, i.e., 1990–2000, with a 64 %–76 % mean relative reduction in SO2 concentrations indicated by the EDT experiment (range of all the models) versus an 82 % mean relative reduction in observed concentrations. During the second decade (P2), the models estimated a mean relative reduction in SO2 concentrations of about 34 %–54 %, which was also in line with that observed (47 %). Comparisons of observed and modeled NO2 trends revealed a mean relative decrease of 25 % and between 19 % and 23 % (range of all the models) during the P1 period, and 12 % and between 22 % and 26 % (range of all the models) during the P2 period, respectively. Comparisons of observed and modeled trends in SO42- concentrations during the P1 period indicated that the models were able to reproduce the observed trends at most of the sites, with a 42 %–54 % mean relative reduction indicated by the EDT experiment (range of all models) versus a 57 % mean relative reduction in observed concentrations and with good performance also during the P2 and PT periods, even though all the models overpredicted the number of statistically significant decreasing trends during the P2 period. Moreover, especially during the P1 period, both modeled and observational data indicated smaller reductions in SO42- concentrations compared with their gas-phase precursor (i.e., SO2), which could be mainly attributed to increased oxidant levels and pH-dependent cloud chemistry. An analysis of the trends in TNO3 concentrations indicated a 28 %–39 % and 29 % mean relative reduction in TNO3 concentrations for the full period for model data (range of all the models) and observations, respectively. Further analysis of the trends in modeled HNO3 and particle nitrate (NO3-) concentrations revealed that the relative reduction in HNO3 was larger than that for NO3- during the P1 period, which was mainly attributed to an increased availability of “free ammonia”. By contrast, trends in modeled HNO3 and NO3- concentrations were more comparable during the P2 period. Also, trends of TNHx concentrations were, in general, underpredicted by all models, with worse performance for the P1 period than for P2. Trends in modeled anthropogenic and biogenic secondary organic aerosol (ASOA and BSOA) concentrations together with the trends in available emissions of biogenic volatile organic compounds (BVOCs) were also investigated. A strong decrease in ASOA was indicated by all the models, following the reduction in anthropogenic non-methane VOC (NMVOC) precursors. Biogenic emission data provided by the modeling teams indicated a few areas with statistically significant increase in isoprene emissions and monoterpene emissions during the 1990–2010 period over Fennoscandia and eastern European regions (i.e., around 14 %–27 %), which was mainly attributed to the increase of surface temperature. However, the modeled BSOA concentrations did not linearly follow the increase in biogenic emissions. Finally, a comprehensive evaluation against positive matrix factorization (PMF) data, available during the second period (P2) at various European sites, revealed a systematic underestimation of the modeled SOA fractions of a factor of 3 to 11, on average, most likely because of missing SOA precursors and formation pathways, with reduced biases for the models that accounted for chemical aging of semi-volatile SOA components in the atmosphere.

scholarly journals The Diamond League athletic series: does the air quality sparkle?

2021 ◽  
Author(s):  
James R. Hodgson ◽  
Lee Chapman ◽  
Francis D. Pope
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Performance Metrics ◽  
Urban Air Pollution ◽  
Quality Data ◽  
Performance Variation ◽  
Short And Long Term ◽  
Respiratory Conditions ◽  
Race Performance

AbstractUrban air pollution can have negative short- and long-term impacts on health, including cardiovascular, neurological, immune system and developmental damage. The irritant qualities of pollutants such as ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM) can cause respiratory and cardiovascular distress, which can be heightened during physical activity and particularly so for those with respiratory conditions such as asthma. Previously, research has only examined marathon run outcomes or running under laboratory settings. This study focuses on elite 5-km athletes performing in international events at nine locations. Local meteorological and air quality data are used in conjunction with race performance metrics from the Diamond League Athletics series to determine the extent to which elite competitors are influenced during maximal sustained efforts in real-world conditions. The findings from this study suggest that local meteorological variables (temperature, wind speed and relative humidity) and air quality (ozone and particulate matter) have an impact on athletic performance. Variation between finishing times at different race locations can also be explained by the local meteorology and air quality conditions seen during races.

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

scholarly journals Role of the atmospheric pollution in the Covid-19 outbreak risk in Italy

2020 ◽  
Author(s):  
Daniele Fattorini ◽  
Francesco Regoli
Keyword(s):  
Air Quality ◽  
Atmospheric Pollution ◽  
Inflammatory Responses ◽  
Integrated Approach ◽  
Atmospheric Pollutants ◽  
Quality Data ◽  
Geographical Differences ◽  
Italian Regions ◽  
Italian Provinces

AbstractBackgroundAfter the initial outbreak in China, the diffusion in Italy of SARS-CoV-2 is exhibiting a clear regional trend with Northern areas being the most affected in terms of both frequency and severity of cases. Among multiple factors possibly involved in such geographical differences, a role has been hypothesized for atmospheric pollution.ObjectivesWe provide additional evidence on the possible influence of air quality, particularly in terms of chronicity of exposure on the spread viral infection in Italian regions.MethodsActual data on to COVID-19 outbreak in Italian provinces and corresponding long-term air quality evaluations, were obtained from Italian and European agencies, elaborated and tested for possible interactions.DiscussionOur elaborations reveal that, beside concentrations, the chronicity of exposure may influence the anomalous variability of SARS-CoV-2 in Italy. Data on distribution of atmospheric pollutants (NO2, O3, PM2.5 and PM10) in Italian regions during the last 4 years, days exceeding regulatory limits, and years of the last decade (2010-2019) in which the limits have been exceeded for at least 35 days, confirmed that Northern Italy has been constantly exposed to chronic air pollution. Long-term air-quality data significantly correlated with cases of Covid-19 in up to 71 Italian provinces (updated 6 April) providing further evidence that chronic exposure to atmospheric contamination may represent a favourable context for the spread of the virus. Pro-inflammatory responses and high incidence of respiratory and cardiac affections are well known, while the capability of this coronavirus to bind particulate matters remains to be established. Atmospheric and environmental pollution should be considered as part of an integrated approach for sustainable development, human health protection and prevention of epidemic spreads.

MOPGA/Make Air Quality Great Again: AfriqAir and solution-oriented approaches to improving air quality in the Global South

2021 ◽  
Author(s):  
Michael R Giordano ◽  
Julien Bahino ◽  
Matthias Beekmann ◽  
Ramachandran Subramanian ◽  
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Pollution Levels ◽  
Premature Deaths ◽  
Network Building ◽  
Global Organization ◽  
Data Gap ◽  
Set Up ◽  
The Cost

<div> <div> <p>Air pollution is responsible for seven million premature deaths each year, linked to numerous cardiovascular and other diseases. Both monitoring pollution levels and identifying sources is necessary to reduce overall exposure. Many parts of Africa suffer from extreme pollution levels, but the cost of traditional air quality monitoring leads to a significant data gap, which also hinders the development of local capacity to do these tasks. In order to overcome these obstacles, the “Make Air Quality Great Again” (MAQGA) project was funded by the French Agence nationale de la recherché (ANR) under the MOPGA program. The MAQGA project in turn set up the AfriqAir consortium, a global organization that brings together air quality scientists and researchers interested in using air quality data to tackle air quality problems in Africa. Now entering its third year of existence, the consortium has made real strides in increasing the number of air quality monitors in Africa as well as building capacity with local researchers and partners across the continent. This presentation will provide a recap of what the consortium has achieved with ANR and MOPGA support, how we have persevered through the COVID-19 pandemic, and our plans for the immediate and long-term futures. This presentation will cover the scientific gains made by connecting African air quality researchers as well as the successes aided by the network building that AfriqAir has facilitated. </p> </div> </div>

scholarly journals Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4157 ◽  
Author(s):  
Shengjing Sun ◽  
Xiaochen Zheng ◽  
Javier Villalba-Díez ◽  
Joaquín Ordieres-Meré
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Indoor Air Quality ◽  
Monitoring System ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Data Monitoring ◽  
Quality Data ◽  
Term Monitoring

Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people’s comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.

scholarly journals An air quality management system for Cyprus

2013 ◽  
Vol 12 (1) ◽  
pp. 92-98
Keyword(s):  
Air Quality ◽  
Quality Management ◽  
Management System ◽  
Quality Management System ◽  
Quality Data ◽  
Emission Data ◽  
Model Calculations ◽  
Emissions Inventory ◽  
Major Activity ◽  
Republic Of Cyprus

The new Air Quality Directive (2008/50/EC) encourages the introduction of modelling as a necessary tool for air quality assessment and management. Towards this aim, a new Air Quality Management System (AQMS) has been developed and installed in the Department of Labour Inspection of the Republic of Cyprus. The core of the system handles the compilation of an emissions inventory that includes data from all major activity sectors and functions on the basis of a continuous update of the emissions database. Emission data are then fed into an advanced air quality modelling system that simulates concentration fields for all the major air pollutants over the island of Cyprus. The AQMS comprises of two operational modules, providing hourly nowcasting and daily forecasting of the air quality status, implemented as an integrated model system that performs nested grid meteorological and photochemical simulations. Hourly air quality data from nine measuring stations are continuously assimilated into these model calculations. A third operational module provides the capability of an interactive configuration of custom emission scenarios and corresponding model runs covering user-defined domains of interest. The system provides an advanced user interface, which is realised as a web-based application providing access to model results from any computer with an internet connection and a web browser.

scholarly journals The use of tunnel concentration profile data to determine the ratio of NO<sub>2</sub>/NO<sub>x</sub> directly emitted from vehicles

2005 ◽  
Vol 5 (6) ◽  
pp. 12723-12740 ◽  
Author(s):  
X. Yao ◽  
N. T. Lau ◽  
C. K. Chan ◽  
M. Fang
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Quality Data ◽  
Concentration Profiles ◽  
Profile Data ◽  
Annual Average ◽  
Air Quality Data ◽  
Monitoring Platform

Abstract. Recently, it is reported that primary vehicular NO2/NOx ratio to be 10–30% and primary vehicular NO2 has raised much interest and concern in the control of NO2 in urban areas. In this study, primary vehicular NO2/NOx ratio in Hong Kong was investigated based on intensive long tunnel (3.7–4 km in length) experiments where concentration profiles of air pollutants along the entire lengths of the tunnels were obtained. Long tunnels were selected because of the inherent low O3 concentrations in the partially enclosed environment. In addition the concentrations of pollutants from vehicles are high. Thus, the NO2 measured inside long tunnels would be more representative of the primary NO2 emitted by vehicles and contribution due to atmospheric transformation would be limited. This dataset was supported by a long-term on-road air quality dataset (June 2002–August 2003). Both datasets were obtained using the Mobile Real-time Air Monitoring Platform (MAP). The primary on-road vehicular NO2/NOx ratio was less than 2%, detected in the mid sections of tunnels investigated, where O3 concentration was at a minimum. In sections of the tunnels (entrance and exit) where O3 concentrations were relatively high, the NO2/NOx ratio could be as high as 19%. Long-term (annual average) on-road air quality data in open air yielded NO2/NOx ratios up to 28%. Thus, it is apparent that directly emitted NO2 from vehicles is not significant in atmospheric NO2 concentration. A simple model was used to segregate the contribution of background NO2 and transformed NO2 measured in vehicle plumes.

Ambient Air Quality Assessment via Comprehensive Evaluation through Automatic Weight Determining

2014 ◽  
Vol 955-959 ◽  
pp. 1764-1767
Author(s):  
Dai Ying Li
Keyword(s):  
Mathematical Model ◽  
Air Quality ◽  
Quality Evaluation ◽  
Comprehensive Evaluation ◽  
Ambient Air ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Air Quality Assessment ◽  
Viable Approach ◽  
The Mathematical Model

A mathematical model is built for air quality evaluation, which has taken PM2.5, PM10, SO2, NO2, CO and O3six parameters into consideration. The comprehensive air quality evaluation is carried out via standard deviation method and principle component analysis method; automatic weight determining has been discussed. Correlations of the factors are considered and a preprocess procedure is issued to eliminate the effects. Ambient air quality data of eleven cities are taken as an example and the comprehensive evaluation results are compared, which shows the mathematical model provides a viable approach to environmental assessment.

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