Effect of Meteorological Parameters on Fine and Coarse Particulate Matter Mass Concentration in a Coal-Mining Area in Zonguldak, Turkey

Tecer, Lokman Hakan; Süren, Pınar; Alagha, Omar; Karaca, Ferhat; Tuncel, Gürdal

doi:10.3155/1047-3289.58.4.543

Cited by 99 publications

(42 citation statements)

References 27 publications

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“…Being the fraction of PM 10 , PM 2.5 is also high and showed higher correlation (Table 8). Similar correlations were found in the mining areas of Northern European countries (Tecer et al 2008). API was found to be highly correlated with PM 10 and PM 2.5 , which is indicative of the contribution of PM in API increment.…”

Section: Particulate Matter Concentrationssupporting

confidence: 76%

Meteorology drives ambient air quality in a valley: a case of Sukinda chromite mine, one among the ten most polluted areas in the world

Mishra

Pradhan

Prusty

et al. 2016

Environ Monit Assess

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The ambient air quality (AAQ) assessment was undertaken in Sukinda Valley, the chromite hub of India. The possible correlations of meteorological variables with different air quality parameters (PM10, PM2.5, SO2, NO2 and CO) were examined. Being the fourth most polluted area in the globe, Sukinda Valley has always been under attention of researchers, for hexavalent chromium contamination of water. The monitoring was carried out from December 2013 through May 2014 at six strategic locations in the residential and commercial areas around the mining cluster of Sukinda Valley considering the guidelines of Central Pollution Control Board (CPCB). In addition, meteorological parameters viz., temperature, relative humidity, wind speed, wind direction and rainfall, were also monitored. The air quality data were subjected to a general linear model (GLM) coupled with one-way analysis of variance (ANOVA) test for testing the significant difference in the concentration of various parameters among seasons and stations. Further, a two-tailed Pearson's correlation test helped in understanding the influence of meteorological parameters on dispersion of pollutants in the area. All the monitored air quality parameters varied significantly among the monitoring stations suggesting (i) the distance of sampling location to the mine site and other allied activities, (ii) landscape features and topography and (iii) meteorological parameters to be the forcing functions. The area was highly polluted with particulate matters, and in most of the cases, the PM level exceeded the National Ambient Air Quality Standards (NAAQS). The meteorological parameters seemed to play a major role in the dispersion of pollutants around the mine clusters. The role of wind direction, wind speed and temperature was apparent in dispersion of the particulate matters from their source of generation to the surrounding residential and commercial areas of the mine.

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Section: Particulate Matter Concentrationssupporting

confidence: 76%

Meteorology drives ambient air quality in a valley: a case of Sukinda chromite mine, one among the ten most polluted areas in the world

Mishra

Pradhan

Prusty

et al. 2016

Environ Monit Assess

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“…Mbengue et al (2014) identified a bimodal distribution for an urbantraffic site, with peaks centered on 0.5 and 2.5 µm, whereas a trimodal distribution with a third maximum at 8 µm was only observed downwind of industrial emissions. Also Tecer et al (2008) attributed the most important anthropogenic PM 2.5-10 sources to the coal processing and mining industries located close and upwind to the sampling location.…”

Section: Ambient Pm Mass Concentration Size-distribution and Temporamentioning

confidence: 99%

“…For data treatment and analysis, results were differentiated into two main periods: the non-heating season (May 2015-October 15 th 2015) and the heating season (October 19 th 2015-March 2016), as is often performed in studies on atmospheric pollution (Tecer et al, 2008;Schwarz et al, 2012). For convenience, the separation between these periods was made in accordance with the switching on of the household heating systems during October 2015, as programmed by the Regional Directive of Lombardy.…”

Section: Statistical Analysis and Data Treatmentmentioning

confidence: 99%

Mass Concentration and Size-Distribution of Atmospheric Particulate Matter in an Urban Environment

Rovelli

Cattaneo

Borghi

et al. 2017

Aerosol Air Qual. Res.

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To investigate the ambient mass concentration, size-distribution and temporal variability of atmospheric particulate matter (PM), a long-term monitoring campaign was undertaken at an urban background site in Como, Northern Italy, from May 2015 to March 2016. A 13-stage Low Pressure Impactor (DLPI) was used for the collection of size-segregated particulates in the 0.028-10 µm size range. The results revealed a good level of agreement between DLPI and a co-located Harvard-type PM 2.5 Impactor, allowing them to be classified as comparable and characterized by a reciprocal predictability. The PM concentration levels varied greatly between the different 5-days monitoring sessions, with higher mean mass concentrations during the heating season. Appreciable seasonal differences were found for particles between 0.15 and 1.60 µm that, on average, registered concentration levels 3.5 times higher during the heating season (mean: 28.2 µg m -3 ; median: 24.4 µg m -3 ) compared to the non-heating season (mean: 8.3 µg m -3 ; median: 7.6 µg m -3 ). No relevant and significant differences were detected for the coarser ranges (> 1.60 µm). Temporal variabilities were influenced by typical PM urban sources (e.g., household heating, traffic), that significantly affected fine and submicrometer particles, and were related to meteorological factors. Ambient air particles exhibited a trimodal distribution: a first and sharp peak more pronounced during the heating season was identified between 0.3 and 0.5 µm and two other slight peaks in the coarse mode were centered on approximately 3 and 8 µm. No relevant differences were found in the shape of the size-distribution between the two investigated seasons. The mean PM 2.5 (22.4 µg m -3 ) and PM 10 (27.7 µg m -3 ) concentrations monitored in the study area exceeded the annual Air Quality Guideline Values (respectively equal to 10 µg m -3 and 20 µg m ) established by the World Health Organization.

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“…Earlier studies (Aneja et al 2012;Hendryx 2009;Tecer et al 2008;Pless-Mulloli et al 2001) show that PM from different mining operations contributes to significant adverse effect on human health in the form of black lung, asthma, cardiovascular diseases and lung cancer. PM generated during mining operations at the workplace (called "bench") disperses in the mine by moving to several benches and contributes to the enhanced concentration at different parts of the mine .…”

Section: Introductionmentioning

confidence: 99%

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

Patra

Gautam

Majumdar

et al. 2015

Air Qual Atmos Health

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Springer Science+Business Media Dordrecht Particulate matter (PM) is a major pollutant in and around opencast mine areas. The problem of degradation of air quality due to opencast mine is more severe than those in underground mine. Prediction of dust concentration must be known to implement control strategies and techniques to control air quality degradation in the workplace environment. Limited studies have reported the dispersion profile and travel time of PM between the benches inside the mine. In this paper, PM concentration has been measured and modeled in Malanjkhand Copper Project (MCP), which is one of the deepest opencast copper mines in India. Meteorological parameters (wind speed, temperature, relative humidity) and PM concentration in seven size ranges (i.e., PM0.23–0.3, PM0.3–0.4, PM0.4–0.5, PM0.5–0.65, PM0.65–0.8, PM0.8–1, and PM1–1.6) have been measured for 8 days. The results of the field study provide an understanding of the dispersion of the PM generated due to mining activities. This research work presents an approach to assess the exposure of enhanced level of PM concentration on mine workers and its variation with depth. The correlations study shows that concentration of PM during its travel from source to surface is associated with depth. Empirical equations are developed to represent relationships between concentrations of PM and depth. Artificial neural network (ANN) model showing the relationship between PM concentration and meteorological parameters has been developed. The performance of the ANN model is evaluated in terms of the correlation coefficient between the real and the forecasted data. The results show strong agreement between the experimental data and the modeled output. The findings of this work are important in understanding fine PM variation inside the mine at the workplace and the associated exposure of mine workers

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Effect of Meteorological Parameters on Fine and Coarse Particulate Matter Mass Concentration in a Coal-Mining Area in Zonguldak, Turkey

Cited by 99 publications

References 27 publications

Meteorology drives ambient air quality in a valley: a case of Sukinda chromite mine, one among the ten most polluted areas in the world

Meteorology drives ambient air quality in a valley: a case of Sukinda chromite mine, one among the ten most polluted areas in the world

Mass Concentration and Size-Distribution of Atmospheric Particulate Matter in an Urban Environment

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

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