scholarly journals The influence of COVID-19 preventive measures on the air quality in Abu Dhabi (United Arab Emirates)

2021 ◽  
Author(s):  
Oriol Teixidó ◽  
Aurelio Tobías ◽  
Jordi Massagué ◽  
Ruqaya Mohamed ◽  
Rashed Ekaabi ◽  
...  
Keyword(s):  
Air Quality ◽  
United Arab Emirates ◽  
Preventive Measures ◽  
Dust Storms ◽  
Air Pollutant ◽  
Anthropogenic Sources ◽  
Abu Dhabi ◽  
Quality Data ◽  
Gaseous Pollutants ◽  
Pollutant Concentrations

AbstractThe preventive and cautionary measures taken by the UAE and Abu Dhabi governments to reduce the spread of the coronavirus disease (COVID-19) and promote social distancing have led to a reduction of mobility and a modification of economic and social activities. This paper provides statistical analysis of the air quality data monitored by the Environment Agency – Abu Dhabi (EAD) during the first 10 months of 2020, comparing the different stages of the preventive measures. Ground monitoring data is compared with satellite images and mobility indicators. The study shows a drastic decrease during lockdown in the concentration of the gaseous pollutants analysed (NO2, SO2, CO, and C6H6) that aligns with the results reported in other international cities and metropolitan areas. However, particulate matter (PM10 and PM2.5) averaged concentrations followed a markedly different trend from the gaseous pollutants, indicating a larger influence from natural events (sand and dust storms) and other anthropogenic sources. The ozone (O3) levels increased during the lockdown, showing the complexity of O3 formation. The end of lockdown led to an increase of the mobility and the air pollution; however, air pollutant concentrations remained in lower levels than during the same period of 2019. The results in this study show the large impact of human activities on the quality of air and present an opportunity for policymakers and decision-makers to design stimulus packages to overcome the economic slow-down, with strategies to accelerate the transition to resilient, low-emission economies and societies more connected to the nature that protect human health and the environment.

scholarly journals Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 562
Author(s):  
Jorge Moreda-Piñeiro ◽  
Joel Sánchez-Piñero ◽  
María Fernández-Amado ◽  
Paula Costa-Tomé ◽  
Nuria Gallego-Fernández ◽  
...  
Keyword(s):  
Air Quality ◽  
Road Traffic ◽  
Statistical Significance ◽  
Temporal Trends ◽  
Air Pollutant ◽  
Concentration Data ◽  
Southwest Atlantic ◽  
Pollutant Concentrations ◽  
Air Quality Monitoring Stations ◽  
Almost All

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

scholarly journals Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 76
Author(s):  
Estrella Lucena-Sánchez ◽  
Guido Sciavicco ◽  
Ionel Eduard Stan
Keyword(s):  
Air Quality ◽  
Fundamental Problem ◽  
Multivariate Time Series ◽  
Quality Data ◽  
Data Sets ◽  
Air Quality Modelling ◽  
Regression Problem ◽  
Car Traffic ◽  
Pollutant Concentrations ◽  
Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

An Automated Mobile Laboratory for Continuous Monitoring of Atmospheric Pollutants

1980 ◽  
Vol 194 (1) ◽  
pp. 357-364
Author(s):  
R. M. Harrison ◽  
H. A. McCartney
Keyword(s):  
Continuous Monitoring ◽  
Air Pollutant ◽  
Atmospheric Pollutants ◽  
Gaseous Pollutants ◽  
Mobile Laboratory ◽  
Digital Form ◽  
Pollutant Concentrations ◽  
Particulate Pollutants ◽  
Nitrate Fertilizer ◽  
Wet Chemical

The construction and operation of an automated mobile laboratory for continuous air pollutant monitoring are described. The gaseous pollutants sulphur dioxide, nitric oxide, nitrogen dioxide and ozone are monitored continuously, whilst particulate pollutants are collected for subsequent wet chemical analysis. Gaseous pollutant concentrations together with measurements of wind direction and speed and solar radiation are recorded continuously in both analogue and digital form. The problems inherent in siting and operating the mobile laboratory are discussed and the analysis of monitoring data is illustrated with reference to a recent survey carried out in the vicinity of an ammonium nitrate fertilizer works.

scholarly journals First steps in the development of a possible measurement method to estimate the radon concentration as an indicator of the indoor air quality

2014 ◽  
Vol 29 (suppl.) ◽  
pp. 52-58
Author(s):  
Franz Roessler ◽  
Jai Azzam ◽  
Volker Grimm ◽  
Hans Hingmann ◽  
Tina Orovwighose ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Primary Energy ◽  
Air Pollutant ◽  
Daily Routine ◽  
Air Exchange Rate ◽  
Pollutant Concentrations ◽  
Air Exchange

The energy conservation regulation provides upper limits for the annual primary energy requirements for new buildings and old building renovation. The actions required could accompany a reduction of the air exchange rate and cause a degradation of the indoor air quality. In addition to climate and building specific aspects, the air exchange rate is essentially affected by the residents. Present methods for the estimation of the indoor air quality can only be effected under test conditions, whereby the influence of the residents cannot be considered and so an estimation under daily routine cannot be ensured. In the context of this contribution first steps of a method are presented, that allows an estimation of the progression of the air exchange rate under favourable conditions by using radon as an indicator. Therefore mathematical connections are established that could be affirmed practically in an experimental set-up. So this method could provide a tool that allows the estimation of the progression of the air exchange rate and in a later step the estimation of a correlating progression of air pollutant concentrations without limitations of using the dwelling.

scholarly journals La surveillance de la qualité de l'air dans les Hauts-de-France

2021 ◽  
pp. 045
Author(s):  
Jimmy Leyes ◽  
Laure Roussel
Keyword(s):  
Air Quality ◽  
Air Pollutant ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Non Profit ◽  
The Public ◽  
Pollutant Concentrations ◽  
Ambient Concentrations ◽  
Air Pollutant Concentrations ◽  
Qualité De L’Air

La surveillance réglementaire de la qualité de l'air en France est confiée aux associations régionales agréées de surveillance de la qualité de l'air (Aasqa) telles qu'Atmo Hauts-de-France. Elles s'appuient sur une palette d'outils et leur expertise pour mesurer les polluants dans l'air de leur territoire, alerter les populations en cas d'épisode de pollution, répondre aux exigences réglementaires de surveillance définies au niveau européen, tout en prenant en compte les spécificités régionales. Cet article présente les différents outils utilisés par les Aasqa, et plus particulièrement Atmo Hauts-de-France, pour surveiller et estimer la qualité de l'air. L'association régionale opère ainsi un ensemble de stations de mesures fixes et mobiles pour suivre en continu les concentrations de polluants réglementés ou non sur son territoire, et dispose d'outils de modélisation pour évaluer et prévoir la qualité de l'air en tous points de la région. Cet article présente également certains des paramètres météorologiques qui influencent la qualité de l'air de la région Hauts-de-France, particulièrement concernée par les épisodes de pollution aux particules. Regulatory air quality monitoring in France is performed by government-approved non-profit organisations called AASQAs, one of which is Atmo Hauts-de-France. These organisations rely on decades of accumulated air quality expertise and use several techniques to measure air pollutant concentrations, inform the public when pollutant levels are unhealthy, and comply with E.U. air quality monitoring regulations. This paper gives an overview of the tools used by AASQAs, and more particularly by Atmo Hauts-de-France, to monitor and forecast air quality. The year-round continuous monitoring of air pollutant levels at fixed sites is supplemented by short-term measurements made with fully-equipped vehicles or trailers and by modelling tools that forecast air quality and estimate pollutant levels where there are no measurements. AASQAs study pollutants which ambient concentrations are regulated by European air quality standards as well as other pollutants which are not regulated in this way. This work also discusses some of the meteorological factors, that affect air quality in the region Hauts-de-France, which is heavily impacted by particulate matter pollution.

scholarly journals Long-Term Assessment of Air Quality and Identification of Aerosol Sources at Setúbal, Portugal

2020 ◽  
Vol 17 (15) ◽  
pp. 5447 ◽  
Author(s):  
Alexandra Viana Silva ◽  
Cristina M. Oliveira ◽  
Nuno Canha ◽  
Ana Isabel Miranda ◽  
Susana Marta Almeida
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Traffic ◽  
Anthropogenic Sources ◽  
World Health ◽  
Mitigation Measures ◽  
Guideline Values ◽  
Health Organization

Understanding air pollution in urban areas is crucial to identify mitigation actions that may improve air quality and, consequently, minimize human exposure to air pollutants and their impact. This study aimed to assess the temporal evolution of the air quality in the city of Setúbal (Portugal) during a time period of 10 years (2003–2012), by evaluating seasonal trends of air pollutants (PM10, PM2.5, O3, NO, NO2 and NOx) measured in nine monitoring stations. In order to identify emission sources of particulate matter, PM2.5 and PM2.5–10 were characterized in two different areas (urban traffic and industrial) in winter and summer and, afterwards, source apportionment was performed by means of Positive Matrix Factorization. Overall, the air quality has been improving over the years with a decreasing trend of air pollutant concentration, with the exception of O3. Despite this improvement, levels of PM10, O3 and nitrogen oxides still do not fully comply with the requirements of European legislation, as well as with the guideline values of the World Health Organization (WHO). The main anthropogenic sources contributing to local PM levels were traffic, industry and wood burning, which should be addressed by specific mitigation measures in order to minimize their impact on the local air quality.

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.

scholarly journals Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

2019 ◽  
Vol 19 (12) ◽  
pp. 8209-8228 ◽  
Author(s):  
Min Zhong ◽  
Eri Saikawa ◽  
Alexander Avramov ◽  
Chen Chen ◽  
Boya Sun ◽  
...  
Keyword(s):  
Air Quality ◽  
Vehicle Emissions ◽  
Air Pollutant ◽  
Kathmandu Valley ◽  
So2 Emissions ◽  
Emissions Inventory ◽  
Brick Kilns ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Local Emissions

Abstract. Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emissions (IVE) model with local emissions factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emissions factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. HTAP_v2.2 does not include the brick sector and we found that our sulfur dioxide (SO2) emissions estimates from brick kilns are comparable to 70 % of the total SO2 emissions considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emissions scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model's boundary-layer representation, to capture strong spatial gradients of air pollutant concentrations.

Comparison of Modeled-to-Monitored PM2.5 Exposure Concentrations Resulting from Transportation Emissions in a Near-Road Community

2020 ◽  
Vol 2674 (12) ◽  
pp. 130-143
Author(s):  
Mayra Chavez ◽  
Wen-Whai Li
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Temporal Variations ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Spatial And Temporal Variations ◽  
Quality Data ◽  
Background Concentrations ◽  
Regional Background ◽  
Transportation Emissions

Residents living in near-road communities are exposed to traffic-related air pollutants, which can adversely affect their health. Near-road communities are expected to observe significant spatial and temporal variations in pollutant concentrations. Determining these variations in the surrounding areas can help raise awareness among government agencies of these underserved communities living near highways. This study conducted traffic and air quality measurements along with emission and dispersion modeling of the exposure to transportation emissions of a near-road urban community adjacent to the US 54 highway (US 54), with annual average daily traffic (AADT) of 107,237. The objectives of this study were (i) to develop spatial and temporal patterns of pollutant concentration variation and (ii) to apportion the differences in exposure concentrations to background concentrations and those that are contributed from major highways. It was observed that: (a) particulate matter (PM2.5) in near-road communities is dominated by the regional background concentrations which account for more than 85% of the pollution; and (b) only near-road receptors are affected by the traffic-related air pollutant emissions from major highways while spatial and temporal variations of PM2.5 concentrations in near-road communities are less influenced by local traffic, subsiding rapidly to negligible concentrations at 300 m from the road. Modeled PM2.5 concentrations were compared with monitored data. For better air quality impact assessments, higher quality data such as time-specific traffic volume and fleet information as well as site-specific meteorological data could help yield more accurate concentration predictions. Modeled-to-monitored comparison shows that air quality in near-road communities is dominated by regional background concentrations.

scholarly journals Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritwik Nigam ◽  
Kanvi Pandya ◽  
Alvarinho J. Luis ◽  
Raja Sengupta ◽  
Mahender Kotha
Keyword(s):  
Air Quality ◽  
Meta Analysis ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Western India ◽  
Rapid Reduction ◽  
Positive Effects ◽  
Pollutant Concentrations ◽  
The Impact

AbstractOn January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

Sign in / Sign up

Export Citation Format

Share Document