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 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 NanoSen-AQM: From Sensors to Users

2020 ◽  
Vol 16 (04) ◽  
pp. 51
Author(s):  
Pedro Lucas ◽  
Jorge Silva ◽  
Filipe Araujo ◽  
Catarina Silva ◽  
Paulo Gil ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Easy Access ◽  
The Public ◽  
Nano Sensors ◽  
The Cost ◽  
Air Quality Data ◽  
Monitoring Platform

With the raising of environmental concerns regarding pollution, interest in monitoring air quality is increasing. However, air pollution data is mostly originated from a limited number of government-owned sensors, which can only capture a small fraction of reality. Improving air quality coverage in-volves reducing the cost of sensors and making data widely available to the public. To this end, the NanoSen-AQM project proposes the usage of low-cost nano-sensors as the basis for an air quality monitoring platform, capa-ble of collecting, aggregating, processing, storing, and displaying air quality data. Being an end-to-end system, the platform allows sensor owners to manage their sensors, as well as define calibration functions, that can im-prove data reliability. The public can visualize sensor data in a map, define specific clusters (groups of sensors) as favorites and set alerts in the event of bad air quality in certain sensors. The NanoSen-AQM platform provides easy access to air quality data, with the aim of improving public health.

scholarly journals Understanding Public Response to Air Quality Using Tweet Analysis

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986765
Author(s):  
Supraja Gurajala ◽  
Suresh Dhaniyala ◽  
Jeanna N. Matthews
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Machine Learning Algorithms ◽  
Mitigation Measures ◽  
Quality Data ◽  
Public Health Issue ◽  
New Delhi ◽  
Public Response ◽  
Pollution Levels ◽  
Adverse Health Effects

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.

scholarly journals Long-Term Trends of Air Pollution at National Atmospheric Observatory Košetice (ACTRIS, EMEP, GAW)

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 537
Author(s):  
Milan Váňa ◽  
Adéla Holubová Smejkalová ◽  
Jaroslava Svobodová ◽  
Pavel Machálek
Keyword(s):  
Czech Republic ◽  
Air Quality ◽  
Kendall Test ◽  
Significant Drop ◽  
The Czech Republic ◽  
Pollution Levels ◽  
Volatile Organic ◽  
International Projects ◽  
Long Term Trends

The National Atmospheric Observatory Košetice operated by the Czech Hydrometeorological Institute was established in 1988 as a station specializing in air quality monitoring at the background scale. The observatory is located in the free area outside of the settlement and represents the Czech Republic in various international projects. The objective of the present study is to detect the long-term trends of air quality at the background scale of the Czech Republic. The statistical method used for trend analysis is based on the nonparametric Mann–Kendall test. Generally, the results show that the fundamental drop in emission of basic air pollutants was reflected in the significant decrease in pollution levels. A most significant drop was detected for sulphur. No trend was found for NO2 in 1990–2012, but a visibly decreasing tendency was registered in the last 7 years. A slightly decreasing trend was registered for O3 in the whole period, but a slightly increasing tendency was found after 2006. More importantly, the number of episodes exceeding the target value for human health dropped significantly. The reduction of volatile organic compounds (VOCs) emissions was reflected in a statistically significant decrease of concentrations. Only isoprene, which is of natural origin, displays an inverse trend. Concentrations of elemental carbon (EC) and organic carbon (OC) dropped since 2010, but only for EC is the trend statistically significant.

scholarly journals Brick Sector and Air Quality: An Integrated Assessment towards 2020 Challenge of Environment Development

2021 ◽  
Vol 19 (2) ◽  
pp. 153-164
Author(s):  
Shazia Pervaiz ◽  
◽  
Muhammad Ameer Nawaz Akram ◽  
Filza Zafar Khan ◽  
Kanwal Javid ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Winter Season ◽  
Environmental Parameters ◽  
Control Measures ◽  
Pollution Levels ◽  
Brick Kilns ◽  
Central Regions ◽  
The Government ◽  
Set Up

Brick sector is a mainstay of the urban economy of Punjab. The traditional technology of brick making emits a lot of toxic gases and smoke particulates into air. Hence, the Government of the Punjab, Pakistan announced a ban on low technology brick kiln operations during winter season by the end of December 2020. Initially, the existing set up of brick kilns and air pollution levels were evaluated before and during lockdown period using spatial application. Further, environmental parameters such as aerosols, carbon monoxide, ozone, sulfur dioxide and carbon dioxide were determined to analyze the air quality, including metrological factors. Results of the study exhibited that the upper and central regions of Punjab are the major hubs of brick kilns. So, the level of air quality was inconsistent in the study period due to the existence of large mushrooms of brick kilns. Further, despite lockdown the highest concentration of carbon monoxide was recorded in the eastern side of the province, such as Kasur, Lahore, and Sheikhupura. The level of aerosols also fluctuated and shifted its trends in the central and southern part of the province. While SO2 and CO2 level declined and revealed a satisfactory level of air quality during shutdown. On the other hand, no significant relation to metrological factors, such as rain, is involved in the pollution reduction. Conclusively, the findings of the present study encourage the government agencies to realign the stringent control measures to improve the quality of air in the winter months using the experience of quarantine in 2020.

scholarly journals Research on Air Quality Analysis Based on Cluster Methods

2021 ◽  
Vol 898 (1) ◽  
pp. 012024
Author(s):  
Zhaoni Li ◽  
Jian Zheng
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Quality Analysis ◽  
Quality Data ◽  
Ambient Air Quality ◽  
Republic Of China ◽  
Pollution Levels ◽  
Analysis Process ◽  
Cluster Methods ◽  
The Impact

Abstract Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.

scholarly journals Evidence of air quality data misreporting in China: An impulse indicator saturation model comparison of local government-reported and U.S. embassy-reported PM2.5 concentrations (2015–2017)

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249063
Author(s):  
Jesse S. Turiel ◽  
Robert K. Kaufmann
Keyword(s):  
Air Quality ◽  
Local Government ◽  
Model Comparison ◽  
Quality Data ◽  
Random Chance ◽  
Pollution Levels ◽  
Reported Data ◽  
Impulse Indicator Saturation ◽  
Air Quality Data ◽  
Monitoring Stations

This paper analyzes hourly PM2.5 measurements from government-controlled and U.S. embassy-controlled monitoring stations in five Chinese cities between January 2015 and June 2017. We compare the two datasets with an impulse indicator saturation technique that identifies hours when the relation between Chinese and U.S. reported data diverges in a statistically significant fashion. These temporary divergences, or impulses, are 1) More frequent than expected by random chance; 2) More positive than expected by random chance; and 3) More likely to occur during hours when air pollution concentrations are high. In other words, relative to U.S.-controlled monitoring stations, government-controlled stations systematically under-report pollution levels when local air quality is poor. These results contrast with the findings of other recent studies, which argue that Chinese air quality data misreporting ended after a series of policy reforms beginning in 2012. Our findings provide evidence that local government misreporting did not end after 2012, but instead continued in a different manner. These results suggest that Chinese air quality data, while still useful, should not be taken entirely at face value.

scholarly journals Changes in air-pollution related behaviour measured by Google Trends Search Volume Index in response to reported air quality in Poland

2021 ◽  
Author(s):  
Wojciech Nazar ◽  
Katarzyna Plata-Nazar
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Winter Season ◽  
Google Trends ◽  
Volume Index ◽  
Quality Data ◽  
Coefficient Of Determination ◽  
Pollution Levels ◽  
Search Volume ◽  
Air Quality Data

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

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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