scholarly journals Ambient Air Quality, Pollutant Behavior, and Distribution Pattern in Rabigh City Using an Air Dispersion Model

2021 ◽  
Vol 22 (1) ◽  
pp. 1-12
Author(s):  
Aljahdali Mohammed Othman
Keyword(s):  
Air Quality ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Dispersion Model ◽  
Heavy Traffic ◽  
Winter Season ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Atmospheric Pollutants ◽  
Anthropogenic Sources

The rise in industrial development and modern technology is one of the major causes of atmospheric pollution, which negatively affects human health. In this study, meteorological conditions and atmospheric pollution dispersion in Rabigh city and its catchments were analyzed using measured data and an air quality dispersion model. The Hybrid Single-Particle Lagrangian Integrated Trajectory model was used to simulate the dispersion of atmospheric pollutants. A dataset from 2018 was analyzed to clarify the seasonal distributions of atmospheric pollutant concentrations in Rabigh and other areas (Thuwal and Khulais). A significant variation in atmospheric pollutants was recorded across the seasons, which may be caused by changes in meteorological conditions. Variations in other anthropogenic sources related to high population density or heavy traffic in the nearby road may also be involved in these fluctuations. Predictions indicated that pollutants would impact the Thuwal area (>50 μg m−3) and Khulais (>35 μg m−3) during the winter season and affect Thuwal (>20 μg m−3) and Rabigh (>20 μg m−3) during the fall season. The concentrations of pollutants were mostly negatively correlated with wind speed, except for carbon monoxide. We established variations in the seasonal concentration of pollutants and the effect of meteorological conditions on atmospheric pollutants for the year 2018 in the study area. Policymakers and stakeholders must provide solutions to mitigate the environmental effect of atmospheric pollution in Rabigh city, Thuwal, and Khulais for the health of inhabitants.

scholarly journals Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Akash Saxena ◽  
Shalini Shekhawat
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Industrial Development ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Mathematical Framework ◽  
Primary Concern

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

Day-to-day variations in paddy-residue burning and residential heating emissions control aerosol pollution peaks in rural north-west India

2021 ◽  
Author(s):  
Harshita Pawar ◽  
Baerbel Sinha
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Learning Algorithm ◽  
Low Cost ◽  
Winter Season ◽  
Ambient Air ◽  
Rural Site ◽  
North West ◽  
Daily Average ◽  
Residential Heating

<p>November onwards, the poor air quality over north-west India is blamed on the large-scale paddy residue burning in Punjab and Haryana. However, the emission strength of this source remains poorly constrained due to the lack of ground-based measurements within the rural source regions. In this study, we report the particulate matter (PM) levels at Nadampur, a rural site in the Sangrur district of Punjab that witnesses rampant paddy residue burning, using the Airveda low-cost PM sensors from October to December 2019. The raw PM measurements from the sensor were corrected using the Random Forest machine learning algorithm. The daily average PM<sub>10</sub> and PM<sub>2.5</sub> mass concentration at Nadampur correlated well  (r > 0.7) with the daily sum of VIIRS fire counts. Agricultural activities, including paddy residue burning and harvesting operations, contributed less than 40% to the overall PM loading, even in the peak burning period at Nadampur. We show that the increased residential heating emissions in the winter season have a profound and currently neglected impact on ambient air quality. A dip in the daily average temperature by 1 ºC increased the daily emission of PM<sub>10</sub> by 6.3 tonnes and that of PM<sub>2.5</sub> by 5.8 tonnes. Overall, paddy harvest, local and regional paddy residue burning, residential heating emissions, ventilation, and wet scavenging could explain 79% of the variations in PM<sub>10</sub> and 85% of the variations in PM<sub>2.5</sub>. Day to day variations in PM emissions from residential heating in response to the ambient temperature must be incorporated into emission inventories and models for accurate air quality forecasts.</p>

Measurements and Comparative Air Quality Analysis of a Goat Farm Operation

2019 ◽  
Vol 62 (6) ◽  
pp. 1723-1733
Author(s):  
Arndreya Howard ◽  
Venkata S. V. Botlaguduru ◽  
Hongbo Du ◽  
Raghava R. Kommalapati ◽  
Ziaul Huque
Keyword(s):  
Hydrogen Sulfide ◽  
Air Quality ◽  
Solar Radiation ◽  
Multiple Regression ◽  
Air Pollutants ◽  
Emission Rate ◽  
Particle Diameter ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Goat Farm

Abstract. Air pollutants such as hydrogen sulfide, ammonia, particulate matter (PM10 and PM2.5), methane, and volatile organic compounds (VOCs) are harmful to the respiratory systems of humans and animals. Livestock facilities have been documented as a major source of dangerous air pollutants; however, there is a lack of data on the emissions from goat farms. This study investigated a goat farm in Texas to evaluate the emission levels and determine the correlation of meteorological conditions with these pollutants. Two locations on the goat farm were selected for monitoring: inside a goat barn, and at a manure lagoon. The monitoring campaign was conducted over a 53-day period during winter and summer seasons. Carbon dioxide, ozone, nitrous oxide, ammonia, PM10, PM2.5, hydrogen sulfide, methane, and VOCs were measured to determine hourly average concentrations using chemiluminescent instruments. An analysis of meteorological conditions using multiple regression was conducted to investigate probable correlations between emission rates and characteristic climate data, such as temperature, humidity, barometric pressure, and solar radiation. Particle size distributions of PM10 and PM2.5 were evaluated for the two monitoring locations during the different seasons to determine the typical particle diameter and the impact of season on particle diameter. The highest emission rate of 364.4 ±50 g m-2 d-1 occurred at the manure lagoon for methane, which contributed the most to the overall emissions at this animal operation. The regression results for the manure lagoon had the highest positive correlations for ozone with temperature and solar radiation. The outdoor meteorological conditions had the most significant influence on pollutants at both locations. Therefore, meteorological conditions are instrumental in the intensity of the air pollutants found on animal farms. The particle diameters ranged from 0.1 to 6.0 µm in the goat barn and from 0.3 to 1.0 µm at the manure lagoon. Even though moderate levels of emissions were monitored at this facility, the emissions from the goat farm do not pose a risk to human health and do not significantly impact the ambient air quality when compared to other livestock facilities.HighlightsEmissions from a goat farm were measured inside a goat barn and at a manure lagoon over summer and winter seasons.The highest methane emission rate of 364.4 ±50 g m-2 d-1 occurred at the manure lagoon during summer.Meteorological conditions significantly influenced emissions at both locations, especially for O3 at the manure lagoon.Emissions from goat farm operations are much lower than those from cow, swine, and chicken farm operations. Keywords: Air quality, Ammonia, Emissions, Goat farm, Methane, Multiple regression analysis.

scholarly journals Effectiveness of short term air quality emission controls: A high-resolution model study of Beijing during the APEC period

2019 ◽  
Author(s):  
Tabish Umar Ansari ◽  
Oliver Wild ◽  
Jie Li ◽  
Ting Yang ◽  
Weiqi Xu ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Atmospheric Pollutants ◽  
Small Contribution ◽  
Model Study ◽  
Emission Controls ◽  
Resolution Model ◽  
Current Emission

Abstract. We explore the impacts of emission controls on haze events in Beijing in October–November 2014 using high resolution WRF-Chem simulations. The model reproduces surface temperature and relative humidity profiles over the period well and captures the observed variations in key atmospheric pollutants. We highlight the sensitivity of simulated pollutant levels to meteorological variables and model resolution, and in particular to treatment of turbulent mixing in the planetary boundary layer. We note that simulating particle composition in the region remains a challenge, and we overpredict NH4 and NO3 at the expense of SO4. We find that the emission controls implemented for the APEC Summit period made a relatively small contribution to improved air quality (20–26 %), highlighting the important role played by favourable meteorological conditions over this period. We demonstrate that the same controls applied under less favourable meteorological conditions would have been insufficient to reduce pollutant levels to meet the required standards. Continued application of these controls over the 6-week period considered would only have reduced the number of haze days where daily-mean fine particulate matter exceeds 75 μg m−3 from 15 to 13 days. Our study highlights the limitations of current emission controls and the need for more stringent measures over a wider region during meteorologically stagnant weather.

scholarly journals Numerical Simulation of Meteorological Conditions and Air Quality above Tomsk, West Siberia

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1148
Author(s):  
Alexander Starchenko ◽  
Elena Shelmina ◽  
Lubov Kizhner
Keyword(s):  
Air Quality ◽  
West Siberia ◽  
Ambient Air ◽  
Quality Parameters ◽  
Meteorological Conditions ◽  
Observation Data ◽  
Near Surface ◽  
Calculation Results ◽  
Simulation Results ◽  
Calm Wind

This paper presents the simulation results of meteorological and air quality parameters for the Siberian city of Tomsk predicted by mesoscale meteorological and chemical transport models. Changes in the numerically predicted wind velocity fields, temperature, and concentration of major air pollutants were modelled in detail for the selected dates, when anticyclonic weather with cloud free and calm wind conditions was observed in Tomsk. The simulation results have shown that stable or neutral atmospheric stratification with light wind and low ambient air temperature (−30, −20 °C) are the most unfavorable meteorological conditions leading to the near surface pollutants accumulation. The numerical calculation results were compared with observation data from the Joint Use Center (JUC) “Atmosphere” of V.E. Zuev Institute of Atmospheric Optics (IAO) and showed good agreement.

scholarly journals Dietary antioxidants and environmental stress

2004 ◽  
Vol 63 (4) ◽  
pp. 579-585 ◽  
Author(s):  
Frank J. Kelly
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Air Quality ◽  
Industrial Revolution ◽  
Ambient Air ◽  
Atmospheric Pollutants ◽  
Ambient Air Pollution ◽  
Political Agenda ◽  
Antioxidant Defences ◽  
The 1960S

Air is one of our most important natural resources; however, it is also in the front line for receiving environmental pollution. Air quality decreased markedly following the industrial revolution, but it was not until the great London Smog in 1952 that air quality made it onto the political agenda. The introduction of the Clean Air Act in 1956 led to dramatic decreases in black smoke and SO2 concentrations over the next two decades, as domestic and industrial coal-burning activities ceased. However, as these improvements progressed, a new threat to public health was being released into the air in ever-increasing quantities. Rapid motorisation of society from the 1960s onwards has led to the increased release of atmospheric pollutants such as tiny particles (particulate matter of &10 μm in aerodynamic diameter) and oxides of N, and the generation of the secondary pollutant O3. These primary and secondary traffic-related pollutants have all proved to be major risks factors to public health. Recently, oxidative stress has been identified as a unifying feature underlying the toxic actions of these pollutants. Fortunately, the surface of the lung is covered with a thin layer of fluid containing a range of antioxidants that appear to provide the first line of defence against oxidant pollutants. As diet is the only source of antioxidant micronutrients, a plausible link now exists between the sensitivity to air pollution and the quality of the food eaten. However, many questions remain unanswered in relation to inter-individual sensitivity to ambient air pollution, and extent to which this sensitivity is modified by airway antioxidant defences.

scholarly journals The Effect of the Covid-19 Lockdown on Air Quality in Three Italian Medium-Sized Cities

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1118 ◽  
Author(s):  
Gabriele Donzelli ◽  
Lorenzo Cioni ◽  
Mariagrazia Cancellieri ◽  
Agustin Llopis Morales ◽  
Maria Morales Suárez-Varela
Keyword(s):  
Air Quality ◽  
Heavy Traffic ◽  
Urban Environments ◽  
Meteorological Conditions ◽  
Control Measures ◽  
Source Control ◽  
Vehicular Traffic ◽  
Urban Centers ◽  
Italian Government ◽  
The Impact

Despite the societal and economic impacts of the COVID-19 pandemic, the lockdown measures put in place by the Italian government provided an unprecedented opportunity to increase our knowledge of the effect transportation and industry-related emissions have on the air quality in our cities. This study assessed the effect of reduced emissions during the lockdown period, due to COVID-19, on air quality in three Italian cities, Florence, Pisa, and Lucca. For this study, we compared the concentration of particulate matter PM10, PM2.5, NO2, and O3 measured during the lockdown period, with values obtained in the same period of 2019. Our results show no evidence of a direct relationship between the lockdown measures implemented and PM reduction in urban centers, except in areas with heavy traffic. Consistent with recently published studies, we did, however, observe a significant decrease in NO2 concentrations among all the air-monitoring stations for each city in this study. Finally, O3 levels remained unchanged during the lockdown period. Of note, there were slight variations in the meteorological conditions for the same periods of different years. Our results suggest a need for further studies on the impact of vehicular traffic and industrial activities on PM air pollution, including adopting holistic source-control measures for improved air quality in urban environments.

scholarly journals Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

2019 ◽  
Vol 19 (17) ◽  
pp. 11303-11314 ◽  
Author(s):  
Tuan V. Vu ◽  
Zongbo Shi ◽  
Jing Cheng ◽  
Qiang Zhang ◽  
Kebin He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Action Plan ◽  
Ambient Air ◽  
Meteorological Conditions ◽  
Assessment Process ◽  
Pollutant Emissions ◽  
Ambient Air Quality ◽  
Clean Air ◽  
The Impact

Abstract. A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5<60 µg m−3) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013–2017), the primary emission controls required by the action plan have led to significant reductions in PM2.5, PM10, NO2, SO2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

scholarly journals Air Pollution and Lung Capacity of People Living around the Cement Industry

2016 ◽  
Vol 2 (2) ◽  
pp. 76-83
Author(s):  
Erwin Azizi Jayadipraja ◽  
Anwar Daud ◽  
Alimuddin Hamzah Assegaf ◽  
Maming
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Quality Standard ◽  
Cement Industry ◽  
Anthropogenic Sources ◽  
Dust Particles ◽  
Ambient Air Quality ◽  
Cross Sectional ◽  
Lung Capacity

Backgrounds: A cement industry is one of anthropogenic sources of air pollution. In polluting the air, the industry creates some dust particles, nitrogen oxide (NO2), sulfur oxide (SO2), and carbon monoxide (CO).Research Purpose: The research aims at finding out the ambient air quality around a cement industry and relating it with the lung capacity of people living around the area.Methodology: This research uses cross sectional studies by measuring the ambient air quality in the morning, noon, and evening in four different settlements within 3 km from the cement industry. The measurement is then correlated with the FEV1 and FVC of lung capacity of people living around the area.Result: Of all four locations, three have ambient air quality (PM2.5 = 109.47 µg/Nm3, TSP = 454.7 µg/Nm3) that surpass the quality standard (PM2.5 = 65 µg/Nm3, TSP = 230 µg/Nm3). Of 241 respondents, the average level of FVC and FEV1 is respectively 1.9352 liter (SD: 0.45578) and 1.7486 liter (SD: 0.43874). Furthermore, the level of PM2.5 in the morning and at noon is respectively p=0.009 and p=0.003; the level of TSP in the morning and at noon is respectively p=0.003 and p=0.01; the level of NO2 in the morning is p=0.006; the level of SO2 in the morning, at noon and in the evening is respectively p=0.000, p=0.022, and p=0.000; and the level of CO in the morning, at noon and in the evening is respectively p=0.003, p=0.015, and p=0.024. Those levels are associated with the level of respondents’ FEV1. Moreover, the level of TSP in the morning is p=0.024; the level of SO2 in the morning and in the evening is p=0.007. These levels relate to the level of respondents’ FVC.Keywords: FVC, FEV1, CO, NO2, SO2, TSP, PM2.5, cement industry. 

scholarly journals VISUALISING URBAN AIR QUALITY USING AERMOD, CALPUFF AND CFD MODELS: A CRITICAL REVIEW

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 355-363
Author(s):  
N. Ridzuan ◽  
U. Ujang ◽  
S. Azri ◽  
T. L. Choon
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Air Pollutants ◽  
Dispersion Model ◽  
Ambient Air ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Modelling ◽  
Modeling Tools ◽  
Wide Range

Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.

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