scholarly journals Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities

2020 ◽  
Author(s):  
Ying Zhu ◽  
Jia Chen ◽  
Xiao Bi ◽  
Gerrit Kuhlmann ◽  
Ka Lok Chan ◽  
...  
Keyword(s):  
Air Quality ◽  
Nitrogen Dioxide ◽  
Satellite Data ◽  
World Health ◽  
Horizontal Distance ◽  
Street Level ◽  
The World ◽  
Mobile Measurements ◽  
The Impact ◽  
Monitoring Stations

Abstract. In many cities around the world the overall air quality is improving, but at the same time nitrogen dioxide (NO2) trends show stagnating values and in many cases could not be reduced below air quality standards recommended by the World Health Organization (WHO). Many large cities have built monitoring stations to continuously measure different air pollutants. While most stations follow defined rules in terms of measurement height and distance to traffic emissions, the question remains, how representative are those point measurements for the city-wide air quality. The question of the spatial coverage of a point measurement is important because it defines the area of influence and coverage of monitoring networks, determines how to assimilate monitoring data into model simulations or compare to satellite data with a coarser resolution, and is essential to assess the impact of the acquired data on public health. In order to answer this question, we combined different measurement data sets consisting of path averaging remote sensing data and in-situ point measurements in stationary and mobile setups from a measurement campaign that took place in Munich, Germany in June and July 2016. We developed an algorithm to strip temporal diversity and spatial patterns in order to construct a consistent NO2 pollution map for Munich. Continuous long-path differential optical absorption spectroscopy (LP DOAS) measurements were complemented with mobile cavity-enhanced (CE) DOAS, chemiluminescence (CL) and cavity attenuated phase shift (CAPS) instruments and were compared to monitoring stations and satellite data. In order to generate a consistent composite map, the LP DOAS diurnal cycle has been used to normalize for the time of the day dependency of the source patterns, so that spatial and temporal patterns can be analyzed separately. The resulting concentration map visualizes pollution hot spots at traffic junctions and tunnel exits in Munich, providing insights into the strong spatial variations. On the other hand, this database is beneficial to the urban planning and the design of control measures of environment pollution. Directly comparing on-street mobile measurements in the vicinity of monitoring stations resulted in a difference of 48 %. For the extrapolation of the monitoring station data to street level, we determined the influence of the measuring height and distance to the street. We found that a measuring height of 4m, at which the Munich monitoring stations measure, results in 16 % lower average concentrations than a measuring height of 1.5 m, which is the height of the inlet of our mobile measurements and a typical pedestrian breathing height. The horizontal distance of most stations to the center of the street of about 6 m also results in an average reduction of 13 % compared to street level concentration. A difference of 21 % in the NO2 concentrations remained, which could be an indication that city-wide measurements are needed for capturing the full range and variability of concentrations for assessing pollutant exposure and air quality in cities.

scholarly journals Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities

2020 ◽  
Vol 20 (21) ◽  
pp. 13241-13251
Author(s):  
Ying Zhu ◽  
Jia Chen ◽  
Xiao Bi ◽  
Gerrit Kuhlmann ◽  
Ka Lok Chan ◽  
...  
Keyword(s):  
Air Quality ◽  
Nitrogen Dioxide ◽  
Satellite Data ◽  
World Health ◽  
Horizontal Distance ◽  
Street Level ◽  
The World ◽  
Mobile Measurements ◽  
The Impact ◽  
Monitoring Stations

Abstract. In many cities around the world the overall air quality is improving, but at the same time nitrogen dioxide (NO2) trends show stagnating values and in many cases could not be reduced below air quality standards recommended by the World Health Organization (WHO). Many large cities have built monitoring stations to continuously measure different air pollutants. While most stations follow defined rules in terms of measurement height and distance to traffic emissions, the question remains of how representative are those point measurements for the city-wide air quality. The question of the spatial coverage of a point measurement is important because it defines the area of influence and coverage of monitoring networks, determines how to assimilate monitoring data into model simulations or compare to satellite data with a coarser resolution, and is essential to assess the impact of the acquired data on public health. In order to answer this question, we combined different measurement data sets consisting of path-averaging remote sensing data and in situ point measurements in stationary and mobile setups from a measurement campaign that took place in Munich, Germany, in June and July 2016. We developed an algorithm to strip temporal from spatial patterns in order to construct a consistent NO2 pollution map for Munich. Continuous long-path differential optical absorption spectroscopy (LP DOAS) measurements were complemented with mobile cavity-enhanced (CE) DOAS, chemiluminescence (CL) and cavity attenuated phase shift (CAPS) instruments and were compared to monitoring stations and satellite data. In order to generate a consistent composite map, the LP DOAS diurnal cycle has been used to normalize for the time of the day dependency of the source patterns, so that spatial and temporal patterns can be analyzed separately. The resulting concentration map visualizes pollution hot spots at traffic junctions and tunnel exits in Munich, providing insights into the strong spatial variations. On the other hand, this database is beneficial to the urban planning and the design of control measures of environment pollution. Directly comparing on-street mobile measurements in the vicinity of monitoring stations resulted in a difference of 48 %. For the extrapolation of the monitoring station data to street level, we determined the influence of the measuring height and distance to the street. We found that a measuring height of 4 m, at which the Munich monitoring stations measure, results in 16 % lower average concentrations than a measuring height of 1.5 m, which is the height of the inlet of our mobile measurements and a typical pedestrian breathing height. The horizontal distance of most stations to the center of the street of about 6 m also results in an average reduction of 13 % compared to street level concentration. A difference of 21 % in the NO2 concentrations remained, which could be an indication that city-wide measurements are needed for capturing the full range and variability of concentrations for assessing pollutant exposure and air quality in cities.

scholarly journals The impact of COVID-19-induced lockdowns during spring 2020 on nitrogen dioxide levels over major American counties

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
James Poetzscher ◽  
Rima J. Isaifan
Keyword(s):  
United States ◽  
Nitrogen Dioxide ◽  
Satellite Data ◽  
Total Population ◽  
The United States ◽  
Time Frame ◽  
Significant Decline ◽  
Industrial Activities ◽  
The World ◽  
The Impact

COVID-19 has quickly spread throughout the world, infecting and killing millions of people. In an effort to contain the spread of the virus, many governments implemented stringent lockdown measures. These lockdown restrictions, coupled with social distancing, severely curtailed transportation and industrial activities, which are the primary drivers of nitrogen oxides emissions. This study investigates whether lockdown orders in the United States have impacted tropospheric nitrogen dioxide (NO2) levels by evaluating 12 major counties with a total population of 38 million. For each county, Sentinel-5P satellite data were obtained and analyzed to determine NO2 column concentrations during the pre-lockdown, peak lockdown, and loosening lockdown periods in 2020. Then, NO2 levels were compared during these three periods to the same time frame in 2019. Our results show that the lockdowns in the 12 major U.S. counties analyzed led to a significant decline in NO2 levels, with an average reduction of 28.7% (±14.6%) and 17.6% (±10.9%) during peak lockdown and loosening lockdown periods, respectively.

scholarly journals Analysis of air pollution exposure in the area of Iasi county – a warning signal for lung health and the risk of developing COPD

Pneumologia ◽  
2021 ◽  
Vol 69 (3) ◽  
pp. 166-173
Author(s):  
Ioana Buculei ◽  
Mona-Elisabeta Dobrin ◽  
Anda Tesloianu ◽  
Cristina Vicol ◽  
Radu-Adrian Crișan Dabija ◽  
...  
Keyword(s):  
Risk Factors ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
World Health ◽  
Annual Reports ◽  
Chronic Obstructive ◽  
The World ◽  
High Concentrations ◽  
The Impact

Abstract Air pollution is a major threat to public health, and the effects of pollution are perceived in all countries of the world, by all social categories, regardless of age. Chronic obstructive pulmonary disease (COPD) has a growing prevalence worldwide and an increasing number of risk factors that exacerbate symptoms and accelerate disease progression. Exposure to air pollution is one of the less studied and less investigated risk factors for COPD. Depending on the size and chemical nature of the pollutant, it can overcome the defence mechanism of the respiratory system and enter the lung tissues, thus causing some respiratory diseases. The World Health Organisation (WHO) reports that six major air pollutants have been identified, namely particulate matter (PM), ground-level ozone, carbon monoxide, sulphur dioxide, nitrogen dioxide and lead. The severe impact of PM exposure is demonstrated by the link between exposure to high concentrations of PM and certain severe diseases such as silicosis, lung cancer, cardiovascular disease and COPD. In Iași County, Romania, air quality measurements are performed by six automatic air quality monitoring stations, and the data obtained are used to create annual reports and these are also available online. Due to the high concentrations of air pollutants, the city of Iași is one of the three topmost polluted cities in Romania. A future assessment on the impact of air pollution on the health of the inhabitants of these cities and the implementation of new methods to improve air quality is needed.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

Impact of hydroxychloroquin/azithromycin protocol on COVID-19 case-fatality rate reduction in Algeria. (Preprint)

2020 ◽  
Author(s):  
Ahmed Youssef Kada
Keyword(s):  
Case Fatality Rate ◽  
Doubling Time ◽  
Case Fatality ◽  
World Health ◽  
Fatality Rate ◽  
Therapeutic Protocol ◽  
First Case ◽  
The World ◽  
The Impact ◽  
At Home

BACKGROUND Covid-19 is an emerging infectious disease like viral zoonosis caused by new coronavirus SARS CoV 2. On December 31, 2019, Wuhan Municipal Health Commission in Hubei province (China) reported cases of pneumonia, the origin of which is a new coronavirus. Rapidly extendable around the world, the World Health Organization (WHO) declares it pandemic on March 11, 2020. This pandemic reaches Algeria on February 25, 2020, date on which the Algerian minister of health, announced the first case of Covid-19, a foreign citizen. From March 1, a cluster is formed in Blida and becomes the epicentre of the coronavirus epidemic in Algeria, its total quarantine is established on March 24, 2020, it will be smoothly alleviated on April 24. A therapeutic protocol based on hydroxychloroquine and azithromycin was put in place on March 23, for complicated cases, it was extended to all the cases confirmed on April 06. OBJECTIVE This study aimed to demonstrate the effectiveness of hydroxychloroquin/azithromycin protocol in Algeria, in particular after its extension to all patients diagnosed COVID-19 positive on RT-PCR test. We were able to illustrate this fact graphically, but not to prove it statistically because the design of our study, indeed in the 7 days which followed generalization of therapeutic protocol, case fatality rate decrease and doubling time increase, thus confirming the impact of wide and early prescription of hydroxychloroquin/azithromycin protocol. METHODS We have analyzed the data collected from press releases and follow-ups published daily by the Ministry of Health, we have studied the possible correlations of these data with certain events or decisions having a possible impact on their development, such as confinement at home and its reduction, the prescription of hydroxychloroquine/azithromycin combination for serious patients and its extension to all positive COVID subjects. Results are presented in graphics, the data collection was closed on 31/05/2020. RESULTS Covid-19 pandemic spreads from February 25, 2020, when a foreign citizen is tested positive, on March 1 a cluster is formed in the city of Blida where sixteen members of the same family are infected during a wedding party. Wilaya of Blida becomes the epicentre of coronavirus epidemic in Algeria and lockdown measures taken, while the number of national cases diagnosed begins to increases In any event, the association of early containment measures combined with a generalized initial treatment for all positive cases, whatever their degree of severity, will have contributed to a reduction in the fatality rate of COVID 19 and a slowing down of its doubling time. CONCLUSIONS In Algeria, the rapid combination of rigorous containment measure at home and early generalized treatment with hydroxychloroquin have demonstrated their effectiveness in terms of morbidity and mortality, the classic measures of social distancing and hygiene will make it possible to perpetuate these results by reducing viral transmission, the only unknown, the reopening procedure which can only be started after being surrounded by precautions aimed at ensuring the understanding of the population. CLINICALTRIAL Algeria, Covid-19, pandemic, hydroxychloroquin, azithromycin, case fatality rate

scholarly journals The Impact of Ultraviolet Radiation on the Aetiology and Development of Uveal Melanoma

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1700
Author(s):  
Melissa Chalada ◽  
Charmaine A. Ramlogan-Steel ◽  
Bijay P. Dhungel ◽  
Christopher J. Layton ◽  
Jason C. Steel
Keyword(s):  
Risk Factors ◽  
Ultraviolet Radiation ◽  
Uveal Melanoma ◽  
World Health Organisation ◽  
World Health ◽  
Genetic Mutations ◽  
High Incidence ◽  
The World ◽  
The Impact

Uveal melanoma (UM) is currently classified by the World Health Organisation as a melanoma caused by risk factors other than cumulative solar damage. However, factors relating to ultraviolet radiation (UVR) susceptibility such as light-coloured skin and eyes, propensity to burn, and proximity to the equator, frequently correlate with higher risk of UM. These risk factors echo those of the far more common cutaneous melanoma (CM), which is widely accepted to be caused by excessive UVR exposure, suggesting a role of UVR in the development and progression of a proportion of UM. Indeed, this could mean that countries, such as Australia, with high UVR exposure and the highest incidences of CM would represent a similarly high incidence of UM if UVR exposure is truly involved. Most cases of UM lack the typical genetic mutations that are related to UVR damage, although recent evidence in a small minority of cases has shown otherwise. This review therefore reassesses statistical, environmental, anatomical, and physiological evidence for and against the role of UVR in the aetiology of UM.

Mental Health Consequences of Intimate Partner Violence Against Women in El Salvador

2021 ◽  
pp. 107780122097880
Author(s):  
Laura Navarro-Mantas ◽  
Soledad de Lemus ◽  
Jesús L. Megías
Keyword(s):  
Mental Health ◽  
Intimate Partner Violence ◽  
El Salvador ◽  
Violence Against Women ◽  
Partner Violence ◽  
Common Mental Disorders ◽  
Intimate Partner ◽  
World Health ◽  
The World ◽  
The Impact

Violence against women (VAW) is currently one of the main problems in El Salvador, which leads the ranking of femicides in the world. We conducted the first national survey on VAW in El Salvador following the World Health Organization (WHO) methodology, to determine the impact of violence on women’s mental health ( N = 1,274). Women who had experienced intimate partner violence showed significantly poorer mental health and more suicidal ideations. Common mental disorders were significantly associated with the experience of all forms of violence, after adjusting for sociodemographic variables and stressful life experiences. The results are discussed in connection with the primary care protocols and the design of public policies.

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

scholarly journals The Impact of COVID-19 Pandemic and Lockdown Measures on Quality of Life among Italian General Population

2021 ◽  
Vol 10 (2) ◽  
pp. 289 ◽  
Author(s):  
Maria Stella Epifanio ◽  
Federica Andrei ◽  
Giacomo Mancini ◽  
Francesca Agostini ◽  
Marco Andrea Piombo ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Online Survey ◽  
Physical Space ◽  
Sampling Strategy ◽  
World Health ◽  
Snowball Sampling ◽  
World Health Organization Quality ◽  
The World ◽  
The Impact

The COVID-19 pandemic that has hit the world in the year 2020 has put a strain on our ability to cope with events and revolutionized our daily habits. On 9 March, Italy was forced to lockdown to prevent the spread of the infection, with measures including the mandatory closure of schools and nonessential activities, travel restrictions, and the obligation to spend entire weeks in the same physical space. The aim of this study was to assess the impact of the COVID-19 pandemic and lockdown measures on quality of life (QoL) in a large Italian sample, in order to investigate possible differences in QoL levels related to both demographic and pandemic-specific variables. A total of 2251 Italian adults (1665 women, mainly young and middle adults) were recruited via a snowball sampling strategy. Participants were requested to answer to an online survey, which included demographic and COVID-related information items, and the World Health Organization Quality of Life BREF questionnaire (WHOQOL-BREF). The results showed statistically significant differences in QoL depending on a number of variables, including sex, area of residence in Italy, and being diagnosed with a medical/psychiatric condition. To our knowledge, this is the first study to assess QoL during COVID-19 pandemic in Italy, therefore the present findings can offer guidelines regarding which social groups are more vulnerable of a decline in QoL and would benefit of psychological interventions.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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