Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor E.

doi:10.1080/10962247.2017.1415236

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…In particular, dust concentration from drilling activities spreads up to 80-100 m from the source, where the heavier fractions of dust settle [44]. For this reason, some authors suggest that the proportion of fine dust tends to increase with increasing distance from the source [49], as PM 10 can travel up to 100 km (or more) and stay in the atmosphere for days, whereas PM 2.5 can travel as far as thousands of kilometres, staying in the air for weeks. Coarse particulates usually deposit quickly and within short distances of the source [3].…”

Section: Technical Variables Distancementioning

confidence: 99%

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Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Duarte

Branco

Rodrigues

et al. 2022

Safety

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Anthropogenic activity is related to several environmental imbalances, including dust. Particulate matter can also hinder humans with numerous health consequences, such as asthma, cancer, and pneumoconiosis. With a particular focus on mineral dust, this review is intended to determine in which circumstances occupational exposure occurs in the mining and earthmoving industries. Research followed the guidelines provided by the preferred reporting items for systematic review and meta-analysis protocols and its extension for scoping reviews. Of the 8993 records identified, only 24 passed both exclusion and inclusion criteria. Within the pool of results, it was possible to identify the following variables related to dust exposure: job-related (activity, job category, and site), engineering (equipment, transport system), technical (distance), and physical (season and weather) variables. Due to the significant variance in protocol settings, it was challenging to perform a general analysis, resulting in a study-by-study approach. The most significant conclusion of this study is not related to the setting of occupational exposure, although it derives from it. The necessity of adopting standard procedures for data collection, independent of research objective, was demonstrated within the context of occupational exposure to mineral dust.

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Section: Technical Variables Distancementioning

confidence: 99%

“…Despite the experimental protocol of each paper, only 15 out of the 24 papers referred to dust-particle size [6,22,24,25,28,31,[43][44][45]48,49,51,52,55,56]. The remaining papers did not specify this parameter, despite mentioning "respirable dust" [41,47,53], "respirable and inhalable" [42,54], or just "dust" [32,50].…”

mentioning

confidence: 99%

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Duarte

Branco

Rodrigues

et al. 2022

Safety

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“…Compared with other countries, the concentration of PM 10 in major Australian cities is relatively low ( Liu et al, 2019a ). The Australian National Pollutant Inventory (NPI) has identified open-cut coal mines as the most significant source of PM emissions, with approximately 25% of industrial emissions arising from coal mining activities ( Richardson et al, 2018 ). Furthermore a recent study, has shown the PM 10 concentrations in Australia are higher in coal mining towns than non-mining urban and rural regions ( Hendryx et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Surrounding Moranbah are several large, operational, open-cut coal mines (see Fig. 1 ), which provide a potential source of dust to the local population ( Alvarado et al, 2015 ; Richardson et al, 2018 ; Ghose and Majee, 2007 ; National Pollutant Inventory (NPI), 2020 ) (see Supplementary Fig. 1b ).…”

Section: Introductionmentioning

confidence: 99%

Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah

Mallet

2021

Atmospheric Pollution Research

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The impacts of poor air quality on human health are becoming more apparent. Businesses and governments are implementing technologies and policies in order to improve air quality. Despite this the PM 10 air quality in the mining town of Moranbah, Australia, has worsened since measurements commenced in 2011. The annual average PM 10 concentrations during 2012, 2017, 2018 and 2019 have all exceeded the Australian National Environmental Protection Measure's standard, and there has been an increase in the frequency of exceedances of the daily standard. The average annual increase in PM 10 was 1.2 0.5 μg per year between 2011 and 2019 and has been 2.5 1.2 μg per year since 2014. The cause of this has not previously been established. Here, two machine learning algorithms (gradient boosted regression and random forest) have been implemented to model and then meteorologically normalise PM 10 mass concentrations measured in Moranbah. The best performing model, using the random forest algorithm, was able to explain 59% of the variance in PM 10 using a range of meteorological, environmental and temporal variables as predictors. An increasing trend after normalising for these factors was found of 0.6 0.5 μg per year since 2011 and 1.7 0.3 μg per year since 2014. These results indicate that more than half of the increase in PM 10 is due to a rise in local emissions in the region. The remainder of the rise in PM 10 was found to be due to a decrease of soil water content in the surrounding region, which can facilitate higher dust emissions. Whether the presence of open-cut coal mines exacerbated the role of soil water content is unclear. Although fires can have drastic effects on the local air quality, changes in fire patterns are not responsible for the rising trend. PM 10 composition measurements or more detailed data relating to local sources is still needed to better isolate these emissions. Nonetheless, this study highlights the need and potential for action by industry and government to improve the air quality and reduce health risks for the nearby population.

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“…Another example is the Sparse Matrix Operator Kerner Emissions Modeling System (SMOKE), developed by the Center for Environmental Modeling for Policy Development (CEMPD), which is based on RatePerStart (RPS) emission rates [15]. However, the results from the emission models could be wrong even if representative source profiles are used, and thus a contradiction in the empirical evidence for ground-level monitoring could appear [16][17][18][19][20][21][22]. On the other hand, measures of PM 2.5 from monitoring stations on the surface could be used in statistical models under a dispersion modelling approach.…”

Section: Introductionmentioning

confidence: 99%

Compositional Spatio-Temporal PM2.5 Modelling in Wildfires

Sánchez-Balseca

Foguet

2021

Atmosphere

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Wildfires are natural ecological processes that generate high levels of fine particulate matter (PM2.5) that are dispersed into the atmosphere. PM2.5 could be a potential health problem due to its size. Having adequate numerical models to predict the spatial and temporal distribution of PM2.5 helps to mitigate the impact on human health. The compositional data approach is widely used in the environmental sciences and concentration analyses (parts of a whole). This numerical approach in the modelling process avoids one common statistical problem: the spurious correlation. PM2.5 is a part of the atmospheric composition. In this way, this study developed an hourly spatio-temporal PM2.5 model based on the dynamic linear modelling framework (DLM) with a compositional approach. The results of the model are extended using a Gaussian–Mattern field. The modelling of PM2.5 using a compositional approach presented adequate quality model indices (NSE = 0.82, RMSE = 0.23, and a Pearson correlation coefficient of 0.91); however, the correlation range showed a slightly lower value than the conventional/traditional approach. The proposed method could be used in spatial prediction in places without monitoring stations.

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Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates

Cited by 14 publications

References 11 publications

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Occupational Exposure to Mineral Dust in Mining and Earthmoving Works: A Scoping Review

Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah

Compositional Spatio-Temporal PM2.5 Modelling in Wildfires

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