scholarly journals Assessing the impact of Eskom power plant emissions on ambient air quality over KwaZamokuhle

2019 ◽  
Vol 29 (1) ◽  
Author(s):  
Prince Chidhindi ◽  
Monray D Belelie ◽  
Roelof P Burger ◽  
Gabi Mkhatshwa ◽  
Stuart J Piketh
Keyword(s):  
Air Quality ◽  
Low Income ◽  
Power Plants ◽  
Dispersion Model ◽  
Ambient Air ◽  
Background Concentration ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
The Impact

Coal-fired power plants are considered a major source of criteria air pollutants. The existence of such activities close to densely populated areas has an impact on human health and more generally on the environment. The impact of a pollutant typically depends on its residence time and the existence of background concentration levels. This study evaluates the dispersion of PM2.5, SO2 and NOX emissions from Eskom power plants (Arnot, Hendrina, and Komati) located close to KwaZamokuhle Township. AERMOD was used to assess the contribution of each plant to the air quality of the township. This steady-state dispersion model was used to simulate surface concentrations (1-hour, 24-hour and annual average concentrations) on a 50km domain for 2015-2017. The modelled results together with data obtained from Eskom’s KwaZamokuhle monitoring site were used to estimate the extent to which these power plants contribute to the ambient air quality of KwaZamokuhle Township. The results confirm that the power plants do contribute to concentrations of PM2.5, SO2, and NOx in the ambient air of the township. However, based on a comparison between the modelled and monitored data, it was inferred that power plants are not the only significant source of these criteria pollutants. Evidence from temporal variations in the monitored data shows that domestic burning is likely the major contributor since the variability is more closely associated with burning habits. It is therefore likely that existing regulatory strategies that focus mostly on the industrial sector may not be successful in improving ambient air quality in low-income settlements like KwaZamokuhle.

scholarly journals Ambient Air Quality Assessment with Particular Reference to Particulates in Western part of Jharia Coalfield, India

2015 ◽  
Vol 10 (2) ◽  
pp. 523-528 ◽  
Author(s):  
Gurdeep Singh ◽  
Amarjeet Singh
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Environmental Parameters ◽  
Ambient Air ◽  
Primary Energy ◽  
Underground Mines ◽  
Opencast Mining ◽  
Ambient Air Quality ◽  
Jharia Coalfield

India is in the list of fastest growing countries of the world. India's energy needs are also increasing due to population and industrial growth for improving quality of living style. In India, coal is major input infrastructure industries for example Power plants, Steel plants and Cement industries. India’s 52% of primary energy is coal dependent1. 66% of India's power generation depends upon coal production1. Jharia Coalfield (JCF) is falling in the Lower Gondwana Coalfields of India. The area of the JCF is about 450 km2. It is important for the major supply of precious coking coal required for steel plants in India. It is located in Dhanbad district of Jharkhand state of India, The latitude is 23° 39' to 23° 48' N and longitude is 86° 11' to 86° 27' E for the Jharia coalfield. Based on environmental parameters, all the 103 mines of BCCL have been grouped under 17 Clusters. A cluster consists of a group of mines with mine lease boundary lying in close vicinity and includes-Operating mines, Abandoned/ closed mines and proposed projects.The focused study area is in the western part of the Jharia coalfield is named as Cluster XV group of mines of BCCL consists of four mines, Kharkharee Colliery (UG), Dharmaband Colliery (UG), Madhuband Colliery (UG) and Phularitand Colliery (UG) .The present study was carried out with the objective to measure the ambient air quality of the study area with reference to particulate matter (SPM, PM10 & PM2.5). Ambient air monitoring results have shown that the observe air quality were found within the limit prescribed by MoEF / CPCB. It may due to Underground mines as there are pollution causing lesser activities involved in the UG mining process compared to opencast mining. Implementation of Master plan for Jharia coalfields for environmental management has also improve the air quality in the area10,11.

scholarly journals Air Quality and Human Health among a Low Income Community in the Highveld Priority Area

2011 ◽  
Vol 20 (1) ◽  
Author(s):  
C.Y Wright ◽  
R Oosthuizen ◽  
J John ◽  
R.M Garland ◽  
P Albers ◽  
...  
Keyword(s):  
South Africa ◽  
Air Pollution ◽  
Air Quality ◽  
Human Health ◽  
Low Income ◽  
Power Plants ◽  
Ambient Air ◽  
Agricultural Activity ◽  
Priority Area ◽  
The Impact

Human exposure to poor air quality is linked to adverse health effects. The largest burden of air pollution-related diseases is in developing countries where air pollution levels are also among the highest in the world. In South Africa, two geographic areas, the Vaal Triangle and the Highveld, have been identified for air quality managementinterventions to ensure compliance with National Air Quality Management Standards and to control potential harmful air pollution impacts on human health. The Highveld Priority Area (HPA) is characterised by intense mining, coal-fired power plants, industries, including iron and steel manufacturing, chemical plants, agricultural activity, motor vehicles and domestic fuel burning. Apart from two previous studies, no respiratory health studies have been carried out in the HPA. This paper describes the results of a recent, comprehensive study of ambient air quality, potential exposure to air pollution and air-related human health among a low income community living in the HPA in order to better understand the impact of air pollution on human health in South Africa.

scholarly journals Ambient air quality of Katra Town (J&K): A Study with Reference to atmospheric particulates

2013 ◽  
Vol 14 (1&2) ◽  
pp. 113-119
Author(s):  
Anil K. Raina ◽  
Anita Sharma
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Monsoon Season ◽  
Ambient Air ◽  
Ambient Air Quality ◽  
Residential Areas ◽  
Monsoon Period ◽  
Post Monsoon ◽  
The Impact

The monitoring of ambient air quality of Katra (one of the important town of Jammu from economic as well as religious point of view), at selected locations of residential areas, commercial areas and traffic crossings with respect to particulate matter (both respirable and non-respirable) has been conducted for a period of two years i.e. July 2010 – June 2012. Large variations in ambient particulate matter concentrations have been observed throughout the study period. Seasonally, the particulate matter exhibited low values during monsoon period and high values during post-monsoon period. The concentration of particulates (both respirable and non-respirable) in post monsoon season exceeded the concentrations than that of other seasons at most of the sites, thereby signifying the impact of local factors on pollutant concentrations, besides the impact of meteorological factors. Higher concentrations have been recorded in the year 2011-2012 as compared to 2010-2011 at all the sites except traffic crossings.

scholarly journals Investigation of Impacts of Mining and Transportation Activities on Ambient Air Quality of Dhanbad City

2015 ◽  
Vol 10 (3) ◽  
pp. 1022-1028 ◽  
Author(s):  
Sridevi Jena ◽  
Atahar Perwez ◽  
Gurdeep Singh ◽  
Ashok Dubey
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Mining Area ◽  
Ambient Air Quality ◽  
Substantial Impact ◽  
Pollution Levels ◽  
Significant Seasonal Variation ◽  
Transportation Routes ◽  
The Impact

The present study was intended to emphasize the assessment of ambient air quality of Dhanbad city with respect to PM10, PM2.5, SO2 and NOX concentrations, in order to investigate the impact of mining and transportation activities. From the monitoring and analysis at four selected monitoring stations during winter and summer seasons, significant spatial variation in pollutant (PM10, PM2.5, SO2 and NOX) concentrations is quite evident. The concentrations of PM10 were observed highest in mining area (at Dhansar PS; 291 µg/m3), whereas the PM2.5 the concentrations were observed higher along traffic routes (especially, at Bank More; 218 µg/m3). Higher concentratios of PM10 in mining area indicates the substantial impact of dust emanated from mining and associated activities on air quality. Whereas, the higher PM2.5 concentration along the transportation routes shows the influence of transportation activities on the airshed of the area. The significant seasonal variation in pollution levels is also apparent, as the concentrations of every pollutant were observed higher during the winter, than the summer season, at all sites. The mean concentration levels of PM10 and PM2.5 were observed 267 µg/m3, 173 µg/m3 and 234 µg/m3, 108 µg/m3 during winter and summer seasons, respectively. From the calculated values of air quality index, it is evident that Dhansar PS and Bank More are most polluted sites and PM10 is the most alarming pollutant in the area under investigation.

scholarly journals Impact of COVID-19 induced lockdown and unlock down phases on the ambient air quality of Delhi, capital city of India

Urban Climate ◽  
2021 ◽  
pp. 100945
Author(s):  
Mayank Pandey ◽  
M.P. George ◽  
R.K. Gupta ◽  
Deepak Gusain ◽  
Atul Dwivedi
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Capital City ◽  
Ambient Air Quality

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

scholarly journals Exposure Assessment of Mercury and Its Compounds by Dispersion Modeling: A Case Study in the Sea of Japan Coastal Area

2011 ◽  
Vol 4 ◽  
pp. ASWR.S6551
Author(s):  
Khandakar Habib Al Razi ◽  
Moritomi Hiroshi ◽  
Kambara Shinji
Keyword(s):  
Air Quality ◽  
Sea Of Japan ◽  
Coastal Area ◽  
Concentration Distribution ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Background Concentration ◽  
The Sea Of Japan ◽  
Atmospheric Concentration ◽  
The Impact

In Japan, mercury and its compounds were categorized as hazardous air pollutants in 1996 and are on the list of “Substances Requiring Priority Action” published by the Central Environmental Council of Japan. The Air Quality Management Division of the Environmental Bureau, Ministry of the Environment, Japan, selected the current annual mean environmental air quality standard for mercury and its compounds of 0.04 μg/m3. Long-term exposure to mercury and its compounds can have a carcinogenic effect, inducing eg, Minamata disease. This study evaluates the impact of mercury emissions on air quality in the coastal area of the Sea of Japan. Average yearly emission of mercury from an elevated point source in this area with background concentration and one-year meteorological data were used to predict the ground level concentration of mercury. The annual mean concentration distribution of mercury and its compounds were calculated for the middle part of Honshu Island, which served as a background level of mercury concentration for the coastal are of the Sea of Japan. To estimate the concentration of mercury and its compounds in air of the local area, two different simulation models have been used. The first is the National Institute of Advanced Science and Technology Atmospheric Dispersion Model for Exposure and Risk Assessment (AIST-ADMER) that estimates regional atmospheric concentration and distribution. The second is the Ministry of Economy, Trade and Industry Low Rise Industrial Source Dispersion Model (METI-LIS) that estimates the atmospheric concentration distribution in the vicinity of facilities.

Ambient Air Quality Monitoring Studies in Four Specific Location of Tamilnadu, India

2019 ◽  
pp. 677-683
Author(s):  
Mageshkumar P ◽  
Ramesh S ◽  
Angu Senthil K
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
High Volume ◽  
Ambient Air ◽  
Quality Parameters ◽  
Air Quality Index ◽  
Gaseous Pollutants ◽  
Ambient Air Quality ◽  
Air Sampler

A comprehensive study on the air quality was carried out in four locations namely, Tiruchengode Bus Stand, K.S.R College Campus, Pallipalayam Bus Stop and Erode Government Hospital to assess the prevailing quality of air. Ambient air sampling was carried out in four locations using a high volume air sampler and the mass concentrations of PM10, PM2.5, SO2, NOX and CO were measured. The analyzed quality parameters were compared with the values suggested by National Ambient Air Quality Standards (NAAQS). Air quality index was also calculated for the gaseous pollutants and for Particulate Matters. It was found that PM10 concentration exceeds the threshold limits in all the measured locations. The higher vehicular density is one of the main reasons for the higher concentrations of these gaseous pollutants. The air quality index results show that the selected locations come under moderate air pollution.

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