121. Use of Employee Exposure Monitoring Data to Estimate Ambient Air Emissions

1999 ◽  
Author(s):  
J. Brown ◽  
M. Malachowski
Keyword(s):  
Ambient Air ◽  
Monitoring Data ◽  
Air Emissions ◽  
Exposure Monitoring

Comparison of regional air dispersion simulation and ambient air monitoring data for the soil fumigant 1,3-dichloropropene

2016 ◽  
Vol 569-570 ◽  
pp. 603-610 ◽  
Author(s):  
I.J. van Wesenbeeck ◽  
S.A. Cryer ◽  
O. de Cirugeda Helle ◽  
C. Li ◽  
J.H. Driver
Keyword(s):  
Ambient Air ◽  
Air Monitoring ◽  
Monitoring Data ◽  
Soil Fumigant ◽  
Air Dispersion ◽  
Ambient Air Monitoring

scholarly journals Occupational exposure monitoring data collection, storage, and use among state-based and private workers' compensation insurers

2018 ◽  
Vol 15 (6) ◽  
pp. 502-509 ◽  
Author(s):  
Taylor M. Shockey ◽  
Kelsey R. Babik ◽  
Steven J. Wurzelbacher ◽  
Libby L. Moore ◽  
Michael S. Bisesi
Keyword(s):  
Occupational Exposure ◽  
Data Collection ◽  
Monitoring Data ◽  
Workers Compensation ◽  
Exposure Monitoring

scholarly journals Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa

2021 ◽  
Vol 18 (7) ◽  
pp. 3374
Author(s):  
Oluwaseyi Olalekan Arowosegbe ◽  
Martin Röösli ◽  
Nino Künzli ◽  
Apolline Saucy ◽  
Temitope Christina Adebayo-Ojo ◽  
...  
Keyword(s):  
South Africa ◽  
Air Pollution ◽  
Ground Level ◽  
Ambient Air ◽  
Pollution Monitoring ◽  
Monitoring Data ◽  
Ambient Air Pollution ◽  
Site Specific ◽  
The Impact ◽  
Daily Pm10

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.

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