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Wheeler, Amanda; Williams, I.D.; Beaumont, Richard A.; Hamilton, R.

doi:10.1023/a:1006447807980

Cited by 50 publications

(5 citation statements)

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“…Wheeler et al measured in the U.K. mean PM 10 winter concentrations of 80 µg/m 3 [28], while average indoor 8-h PM 10 levels < 50 µg/m 3 were found in Detroit [29]. Noteworthy is that during re-suspension phenomena caused by human presence and activities the re-suspended particles (primarily coarse particles and PM 10 of crustal origin) can exhibit densities markedly higher than PM 2.5 (primarily elementary and organic carbon), which was used in our study to correct all the PM optical data.…”

Section: Discussionmentioning

confidence: 99%

Airborne Particulate Matter in School Classrooms of Northern Italy

Rovelli

Cattaneo

Nuzzi

et al. 2014

IJERPH

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Indoor size-fractioned particulate matter (PM) was measured in seven schools in Milan, to characterize their concentration levels in classrooms, compare the measured concentrations with the recommended guideline values, and provide a preliminary assessment of the efficacy of the intervention measures, based on the guidelines developed by the Italian Ministry of Healthand applied to mitigate exposure to undesirable air pollutants. Indoor sampling was performed from Monday morning to Friday afternoon in three classrooms of each school and was repeated in winter 2011–2012 and 2012–2013. Simultaneously, PM2.5 samples were also collected outdoors. Two different photometers were used to collect the PM continuous data, which were corrected a posteriori using simultaneous gravimetric PM2.5 measurements. Furthermore, the concentrations of carbon dioxide (CO2) were monitored and used to determine the Air Exchange Rates in the classrooms. The results revealed poor IAQ in the school environment. In several cases, the PM2.5 and PM10 24 h concentrations exceeded the 24 h guideline values established by the World Health Organization (WHO). In addition, the indoor CO2 levels often surpassed the CO2 ASHRAE Standard. Our findings confirmed that important indoor sources (human movements, personal clouds, cleaning activities) emitted coarse particles, markedly increasing the measured PM during school hours. In general, the mean PM2.5 indoor concentrations were lower than the average outdoor PM2.5 levels, with I/O ratios generally <1. Fine PM was less affected by indoor sources, exerting a major impact on the PM1–2.5 fraction. Over half of the indoor fine particles were estimated to originate from outdoors. To a first approximation, the intervention proposed to reduce indoor particle levels did not seem to significantly influence the indoor fine PM concentrations. Conversely, the frequent opening of doors and windows appeared to significantly contribute to the reduction of the average indoor CO2 levels.

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Section: Discussionmentioning

confidence: 99%

Airborne Particulate Matter in School Classrooms of Northern Italy

Rovelli

Cattaneo

Nuzzi

et al. 2014

IJERPH

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“…Concentrations of particulate matter at monitoring stations A6 and A7 were inversely related to the temporal trend of other monitoring stations during certain periods as a result of a change in the domestic activities near these monitoring stations at the time. The strong correlation between SPM and RPM indicates that the concentration of RPM, which is the main concern for human health affects, 6,12 would be useful for benchmarking the RPM concentration without field measurement for any opencast coal mining area with similar conditions, by knowing the SPM concentration. Coal transportation was the main source of SPM generation as reported by various researchers.…”

Section: Discussionmentioning

confidence: 99%

“…9 These air pollutants reduce air quality and this ultimately affects people, flora and fauna in and around mining areas. [10][11][12][13] The major air pollutants produced by opencast mining are suspended particulate matter and respirable particulate matter, 9,14 which is in contrast to vehicular emissions where lead and gaseous pollutants are the major concern. 15,16 The environmental impact of coal mining areas must be assessed by detailed studies of air quality.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variations of SPM, RPM, SO2 and NOx concentrations in an opencast coal mining areaElectronic supplementary information (ESI) available: Five figures showing polynomial regression analyses of monthly averages of RPM concentrations for the residential and industrial areas during a one year period, spatial distribution of seasonal RPM concentrations over the study area for winter, summer, autumn and rainy seasons, and variations of SO2 and NOx concentrations in the study area with annual

Chaulya

2004

J. Environ. Monitor.

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A study for assessment and management of air quality was carried out in the Ib Valley area of the Ib Valley coalfield in Orissa state, India. The 24 h average concentrations of suspended particulate matter (SPM), respirable particulate matter (RPM), sulfur dioxide (SO(2)) and oxides of nitrogen (NO(x)) were determined at regular intervals throughout one year at twelve monitoring stations in residential areas and six monitoring stations in mining/industrial areas. The 24 h average SPM and RPM concentrations were 124.6-390.3 microg m(-3) and 25.9-119.9 microg m(-3) in residential areas, and were 146.3-845.2 microg m(-3) and 45.5-290.5 microg m(-3) in industrial areas. During the study period, 24 h and annual average SPM and RPM concentrations exceeded the respective standards set in the Indian national ambient air quality standard (NAAQS) protocol as well as USEPA, EU, WHO and World Bank standards at most of the residential and industrial areas. However, concentrations of SO(2)(annual average: 24.6-36.1 microg m(-3) and 24 h average: 17.0-46.3 microg m(-3)) and NO(x)(annual average: 23.6-40.9 microg m(-3) and 24 h average: 18.3-53.6 microg m(-3)) were well within the prescribed limit of the NAAQS and international standards in both residential and industrial areas. The temporal variations of SPM and RPM fitted polynomial trends well and on average in the mining area 31.91% of the SPM was RPM. The linear regression correlation coefficients between SPM and RPM and between NO(x) and SO(2) were 0.94 (+/-0.04) and 0.66 (+/-0.10), respectively. The optimum interpolation technique, kriging, determined that maximal concentrations of SPM and RPM occurred within the mining site. Highest concentrations of particulate matter were observed during the winter season followed by summer, autumn and rainy seasons. An action plan is formulated for effective control of air pollution at source, and mitigative measures should include implementation of green belts around the sensitive areas where the concentration of air pollutants exceeds the standard limit.

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“…The PSO algorithm implements six steps for optimal searching as the following procedure. In which, the velocity and local positions are continuously updated using Equations (9) and (10), for sharing their experience.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…This dust is cause-related to lung, respiratory, and ocular diseases [3,8]. The fauna and flora of the surrounding area are also affected by the dust caused by open-pit mining operations [9,10]. In open-pit mines, many activities raise PM 10 dust, such as transporting, drilling-blasting, loading/unloading, and dumping [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

et al. 2019

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Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.

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Cited by 50 publications

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Airborne Particulate Matter in School Classrooms of Northern Italy

Airborne Particulate Matter in School Classrooms of Northern Italy

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

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