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.

scholarly journals Air Quality during New Year’s Eve: A Biomonitoring Study with Moss

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 975
Author(s):  
Paweł Świsłowski ◽  
Zbigniew Ziembik ◽  
Małgorzata Rajfur
Keyword(s):  
Heavy Metals ◽  
Air Quality ◽  
Atmospheric Aerosol ◽  
Point Sources ◽  
Accumulation Factor ◽  
Active Biomonitoring ◽  
Aerosol Pollution ◽  
New Year’S Eve ◽  
Short And Long Term

Mosses are one of the best bioindicators in the assessment of atmospheric aerosol pollution by heavy metals. Studies using mosses allow both short- and long-term air quality monitoring. The increasing contamination of the environment (including air) is causing a search for new, cheap and effective methods of monitoring its condition. Once such method is the use of mosses in active biomonitoring. The aim of the study was to assess the atmospheric aerosol pollution with selected heavy metals (Ni, Cu, Zn, Cd, Hg and Pb) from the smoke of fireworks used during New Year’s Eve in the years 2019/2020 and 2020/2021. In studies a biomonitoring moss-bag method with moss Pleurozium schreberi (Willd. ex Brid.) Mitt. genus Pleurozium was used. The research was conducted in the town Prószków (5 km in south direction from Opole, opolskie voivodship, Poland). The moss was exposed 14 days before 31 December (from 17 to 30 of December), on New Year’s Eve (31 December and 1 January) and 2 weeks after the New Year (from 2–15 January). Higher concentrations of analysed elements were determined in samples exposed during New Year’s Eve. Increases in concentrations were demonstrated by analysis of the Relative Accumulation Factor (RAF). The results indicate that the use of fireworks during New Year’s Eve causes an increase in air pollution with heavy metals. In addition, it was shown that the COVID-19 induced restrictions during New Year’s Eve 2020 resulted in a reduction of heavy metal content in moss samples and thus in lower atmospheric aerosol pollution with these analytes. The study confirmed moss usefulness in monitoring of atmospheric aerosol pollution from point sources.

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 Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8009
Author(s):  
Abdulmajid Murad ◽  
Frank Alexander Kraemer ◽  
Kerstin Bach ◽  
Gavin Taylor
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Current Data ◽  
Data Driven ◽  
Quality Data ◽  
Empirical Performance ◽  
Adversarial Training ◽  
Scalable Methods

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

scholarly journals Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

2020 ◽  
Vol 10 (6) ◽  
pp. 1953 ◽  
Author(s):  
Songzhou Li ◽  
Gang Xie ◽  
Jinchang Ren ◽  
Lei Guo ◽  
Yunyun Yang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutant ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
The Past ◽  
Lstm Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

Genotoxic Effect of Inhaled Ambient Particulate Matter

2012 ◽  
Vol 18 (S5) ◽  
pp. 25-26
Author(s):  
A. S. Boa-Alma ◽  
G. Ramalhinho ◽  
D. Dias ◽  
M. L. Mathias ◽  
P. A Carvalho ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Micronucleus Test ◽  
Natural Populations ◽  
Micronucleus Assay ◽  
Chromosome Breakage ◽  
Epidemiological Studies ◽  
Ambient Particulate Matter ◽  
Good Tool ◽  
Short And Long Term

In recent years great importance has been given to the adverse effects of particulate matter (PM) in health, and several epidemiological studies correlating the airborne particles with pulmonary injury have been carried out. The adverse effect of short- and long-term expositions to PM can, indeed, cause a variety of effects, from minor effects on the respiratory system to serious oncogenic effects that can lead to precocious death and are considered one of the top environmental public health concerns. Some cytogenetic techniques, as micronucleus assay, allow to detect chromosome breakage and loss by measuring the formation of micronuclei and proved to be a good tool in the evaluation of genotoxic damage induced by PM. Thus genotoxic effects can be evaluated by micronucleus test in order to identify a gradient of potential exposure and to assess environmental monitoring. However, cross investigations involving biomarkers of genotoxicity and electron microscopy PM-induced tracheal and lung damages in natural populations are not common.

scholarly journals High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

2019 ◽  
Author(s):  
Bas Mijling
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Urban Air Pollution ◽  
Heterogeneous Data ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Reference Measurements ◽  
Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

LONG-TERM SIMULATIONS OF PARTICULATE MATTER OVER EUROPE WITH EMPHASIS ON NORTH-RHINE-WESTPHALIA WITH A COMPREHENSIVE AIR QUALITY MODEL SYSTEM

2004 ◽  
Vol 35 ◽  
pp. S767-S768
Author(s):  
S. WURZLER ◽  
J. GEIGER ◽  
U. HARTMANN ◽  
V. HOFFMANN ◽  
U. PFEFFER ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model

scholarly journals Effects of short- and long-term exposures to particulate matter on inflammatory marker levels in the general population

2019 ◽  
Vol 26 (19) ◽  
pp. 19697-19704 ◽  
Author(s):  
Dai-Hua Tsai ◽  
Michael Riediker ◽  
Antoine Berchet ◽  
Fred Paccaud ◽  
Gerard Waeber ◽  
...  
Keyword(s):  
Particulate Matter ◽  
General Population ◽  
Inflammatory Marker ◽  
Short And Long Term

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.

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