scholarly journals Evaulation of a Model for Urban Vegetation Barriers’ Effects on Air Pollution

2021 ◽  
Author(s):  
Ray F. Nassar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Urban Areas ◽  
Ground Level ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Global Issue ◽  
Population Ratio ◽  
Carbon Dioxide Co2 ◽  
Total Volatile Organic Compounds

Air pollution is a serious global issue, responsible for approximately one in every nine deaths each year, ranking it among the greatest environmental hazards to human health. It is of particular concern in urban areas, where elevated pollutant concentrations and potential sufferers converge. Over one half of the world’s population presently lives in urban areas, and the urban population ratio is expected to reach 68% by 2050. Common air pollutants include particulate matter (PM), sulphur dioxide (SO2), ground-level ozone (O3), nitrogen oxide (NOx) and carbon monoxide (CO). While elevated rates of air pollution pose serious health risks for humans, outdoor plants can help reduce the harmful effects of air pollution by filtering and purifying the air around us.In this project Common Ivy, Aster and Miniature Andromeda plants were evaluated for air pollutant mitigation. In this study we developed a vegetation barrier model with the plant located in the middle of the greenhouse box, and air pollutant was sprayed on one side of the plant. Dispersion patterns of sprayed pollutants were tested with and without vegetation barrier. Measurements of carbon dioxide (CO2), Formaldehyde (HCHO), Total Volatile Organic Compounds (TVOC), and Particulate Matter (PM2.5/PM10) were taken before spraying, then at 0 and 30 minutes after spraying, using both monitors.The results show mitigation rates (in 177 ft3 of air after 30 min): for TVOC the minimum reduction is 5 mg/m3; for HCHO, 1 mg/m3; for CO2, 2000 ppm; for PM2.5, 2000 ug/m3; and for PM10 it was 1000 ug/m3.

scholarly journals COVID-19 lockdowns cause global air pollution declines

2020 ◽  
Vol 117 (32) ◽  
pp. 18984-18990 ◽  
Author(s):  
Zander S. Venter ◽  
Kristin Aunan ◽  
Sourangsu Chowdhury ◽  
Jos Lelieveld
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Economic Activities ◽  
Long Distance ◽  
Mobility Data ◽  
Fine Particulate

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: −2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOxchemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing “business as usual” air pollutant emissions from economic activities. Explore trends here:https://nina.earthengine.app/view/lockdown-pollution.

scholarly journals Multitemporal analysis with statistical models: influence of the atmospheric condition on urban concentrations of particulate matter

2022 ◽  
Vol 2159 (1) ◽  
pp. 012003
Author(s):  
L Rodríguez-Garavito ◽  
K J Romero-Corredor ◽  
C A Zafra-Mejía
Keyword(s):  
Particulate Matter ◽  
Statistical Models ◽  
Urban Areas ◽  
Moving Average ◽  
Atmospheric Condition ◽  
Air Pollutant ◽  
Atmospheric Instability ◽  
Pollutant Concentrations ◽  
Multitemporal Analysis ◽  
Air Pollutant Concentrations

Abstract This paper shows a multitemporal analysis with autoregressive integrated moving average models of the influence of atmospheric condition on concentrations of particulate matter ≤ 10 µm in Bogotá city, Colombia. Information was collected from six monitoring stations distributed throughout the city. The study period was nine years. Autoregressive component of the models suggests that urban areas with greater atmospheric instability show a lower hourly persistence of particulate matter (one hour) compared to urban areas with lower atmospheric instability (two hours). Moving average component of the models hints those urban areas with greater atmospheric instability show greater hourly variability in particulate matter concentrations (5-10 hours). The models also suggest that a high degree of air pollution decreases the temporal influence of the atmospheric condition on particulate matter concentrations; in this case, the temporal behavior of particulate matter possibly depends on the urban emission sources of this pollutant rather than on the existing atmospheric condition. This study is relevant to deepen the knowledge in relation to the following aspects of atmospheric physics: The use of statistical models for the time series analysis of atmospheric condition, and the analysis by statistical models of the influence of atmospheric condition on air pollutant concentrations.

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

2017 ◽  
Vol 9 (3) ◽  
pp. 13-35 ◽  
Author(s):  
Lazaros Iliadis ◽  
Vardis-Dimitris Anezakis ◽  
Konstantinos Demertzis ◽  
Georgios Mallinis
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Urban Areas ◽  
Respiratory Diseases ◽  
Air Pollutant ◽  
Chi Square ◽  
Self Organizing Maps ◽  
Pollutant Concentrations ◽  
Chi Square Test ◽  
The Impact

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

scholarly journals Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Wind Shear ◽  
Dispersion Model ◽  
Near Field ◽  
Ground Level ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Mobile Sources ◽  
Input Variables

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

scholarly journals Regional Energy, CO2, and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2119 ◽  
Author(s):  
Ying Li ◽  
Yung-Ho Chiu ◽  
Liang Lu
Keyword(s):  
Particulate Matter ◽  
Energy Consumption ◽  
Air Quality ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Significant Difference ◽  
Desirable Output ◽  
Reduce Emissions ◽  
Carbon Dioxide Co2

Rapid economic development has resulted in a significant increase in energy consumption and pollution such as carbon dioxide (CO2), particulate matter (PM2.5), particulate matter 10 (PM10), SO2, and NO2 emissions, which can cause cardiovascular and respiratory diseases. Therefore, to ensure a sustainable future, it is essential to improve economic efficiency and reduce emissions. Using a Meta-frontier Non-radial Directional Distance Function model, this study took energy consumption, the labor force, and fixed asset investments as the inputs, Gross domestic product (GDP) as the desirable output, and CO2 and the Air Quality Index (AQI) scores as the undesirable outputs to assess energy efficiency and air pollutant index efficiency scores in China from 2013–2016 and to identify the areas in which improvements was necessary. It was found that there was a large gap between the western and eastern cities in China. A comparison of the CO2 and AQI in 31 Chinese cities showed a significant difference in the CO2 emissions and AQI efficiency scores, with the lower scoring cities being mainly concentrated in China’s western region. It was therefore concluded that China needs to pay greater attention to the differences in the economic levels, stages of social development, and energy structures in the western cities when developing appropriately focused improvement plans.

scholarly journals Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria

2021 ◽  
pp. 63-73
Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor
Keyword(s):  
Air Pollution ◽  
Temporal Variability ◽  
Niger Delta ◽  
Air Pollutants ◽  
Temporal Variations ◽  
Time Of Day ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Air Pollutant Concentrations

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

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