scholarly journals Quantifying Fugitive Dust Emissions From Limestone Quarries : Data Selection And Uncertainty Assessment

Author(s):  
Aaron B. Weinstock

Accurate quantification of fugitive dust emissions from quarries helps maintain the integrity of the National Pollutant Release Inventory. Emissions from unpaved roads, material handling, and storage piles at three quarries were calculated using the AP-42 emissions factor method, and the error of using 30-year climate averages, the uncertainty introduced by typical material properties, and the limited availability of climate data were addressed. Using daily and hourly data predicted unpaved roads emissions between 38.95% and 43.50% higher, materials handling emissions 15.31% lower to 18.64% higher, and storage pile emissions 12.48% to 37.50% lower than calculations using 30-yer averages. Employing Monte Carlo simulation, the confidence intervals attributable to typical material properties ranged from 87.50% below to 650% above the mean. Krige-interpolated climate data showed potential for being more accurate that observations at the nearest station. Using site-specific, temporally-relevant data and assessing uncertainty promotes calculations that better match the goals of the inventory.


2021 ◽  
Author(s):  
Aaron B. Weinstock

Accurate quantification of fugitive dust emissions from quarries helps maintain the integrity of the National Pollutant Release Inventory. Emissions from unpaved roads, material handling, and storage piles at three quarries were calculated using the AP-42 emissions factor method, and the error of using 30-year climate averages, the uncertainty introduced by typical material properties, and the limited availability of climate data were addressed. Using daily and hourly data predicted unpaved roads emissions between 38.95% and 43.50% higher, materials handling emissions 15.31% lower to 18.64% higher, and storage pile emissions 12.48% to 37.50% lower than calculations using 30-yer averages. Employing Monte Carlo simulation, the confidence intervals attributable to typical material properties ranged from 87.50% below to 650% above the mean. Krige-interpolated climate data showed potential for being more accurate that observations at the nearest station. Using site-specific, temporally-relevant data and assessing uncertainty promotes calculations that better match the goals of the inventory.



1984 ◽  
Vol 3 (1) ◽  
pp. 33-40
Author(s):  
David Stein ◽  
Glenn Crow


1976 ◽  
Vol 10 (10) ◽  
pp. 1046-1048 ◽  
Author(s):  
Rodney I. J. Dyck ◽  
James J. Stukel


2007 ◽  
Vol 144 (1-3) ◽  
pp. 93-103 ◽  
Author(s):  
Pamela E. Padgett ◽  
Dexter Meadows ◽  
Ellen Eubanks ◽  
William E. Ryan


Author(s):  
William Wrennall ◽  
Herbert Tuttle


Author(s):  
Sandra Sorte ◽  
Myriam Lopes ◽  
Vera Rodrigues ◽  
Joana Leitão ◽  
Alexandra Monteiro ◽  
...  


2020 ◽  
Vol 143 (1-2) ◽  
pp. 241-265
Author(s):  
Christina Görner ◽  
Johannes Franke ◽  
Rico Kronenberg ◽  
Olaf Hellmuth ◽  
Christian Bernhofer

AbstractThe algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical records. EDM is designed to disaggregate daily means/sums of several climate elements at once, here temperature (T), precipitation (P), sunshine duration (SD), relative humidity (rH), and wind speed (WS), while conserving physical consistency over all disaggregated elements. Since weather conditions and hence the diurnal cycles of climate elements depend on the weather pattern, a selection approach including objective weather patterns (OWP) was developed. The OWP serve as an additional criterion to filter the most similar day. For a case study, EDM was applied to the daily climate data of the stations Dresden and Fichtelberg (Saxony, Germany). The EDM results agree well with the observed data, maintaining their statistics. Hourly results fit better for climate elements with homogenous diurnal cycles, e.g., T with very high correlations of up to 0.99. In contrast, the hourly results of the SD and the WS provide correlations up to 0.79. EDM tends to overestimate heavy precipitation rates, e.g., by up to 15% for Dresden and 26% for Fichtelberg, potentially due to, e.g., the smaller data pool for such events, and the equal-weighted impact of P in the ED calculation. The OWPs lead to somewhat improved results for all climate elements in terms of similar climate conditions of the basic stations. Finally, the performance of EDM is compared with the disaggregation tool MELODIST (Förster et al. 2015). Both tools deliver comparable and well corresponding results. All analyses of the generated hourly data show that EDM is a very robust and flexible model that can be applied to any climate station. Since EDM can disaggregate daily data of climate projections, future research should address whether the model is capable to respect and (re)produce future climate trends. Further, possible improvements by including the flow direction and future OWPs should be investigated, also with regard to reduce the overestimation of heavy rainfall rates.





2015 ◽  
Vol 25 (5) ◽  
pp. 551-569 ◽  
Author(s):  
Eleftheria Chalvatzaki ◽  
Thodoros Glytsos ◽  
Mihalis Lazaridis


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