Children spend over 6 h a day in schools and have higher asthma morbidity from school environmental exposures. The present study aims to determine indoor and outdoor possible sources affecting indoor ...PM2.5 in classrooms. Weeklong indoor PM2.5 samples were collected from 32 inner-city schools from a Northeastern U.S. community during three seasons (fall, winter and spring) during the years 2009 to 2013. Concurrently, daily outdoor PM2.5 samples were taken at a central monitoring site located at a median distance of 4974 m (range 1065–11,592 m) from the schools. Classroom indoor concentrations of PM2.5 (an average of 5.2 μg/m3) were lower than outdoors (an average of 6.5 μg/m3), and these averages were in the lower range compared to the findings in other schools' studies. The USEPA PMF model was applied to the PM2.5 components measured simultaneously from classroom indoor and outdoor to estimate the source apportionment. The major sources (contributions) identified across all seasons of indoor PM2.5 were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM2.5 across all seasons were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). Secondary pollution was the greatest contributor to indoor and outdoor PM2.5 over all three seasons, with the highest contribution during spring with 53% to indoor PM2.5 and 45% to outdoor PM2.5. Lower contributions of this source during fall and winter are most likely attributed to less infiltration indoors. In contrast, the indoor contribution of motor vehicles source was highest in the fall (29%) and winter (25%), which was presumably categorized by a local source. From the relationship between indoor-to-outdoor sulfur ratios and each source contribution, we also estimated the local and regional influence on indoor PM2.5 concentration. Overall, the observed differences to indoor PM2.5 are related to seasonality, and the distinct characteristics and behavior of each classroom/school.
•Relative source contributions of indoor and outdoor PM2.5 were determined for inner-city school classrooms;•Four outdoor sources and two indoor sources were identified as contributors to indoor PM2.5 concentrations, and;•Regional sources were the greatest contributor to indoor and outdoor PM2.5 in all seasons.
Radon (Rn) is a natural and toxic radioactive gas that accumulates indoors, mainly in low-ventilated underground floors and basements. Several factors make prediction of indoor Rn exposure in ...enclosed spaces challenging. In this study, we investigated the influence of soil, geology, topography, atmospheric variables, radiation, urbanization, community economic well-being, and monthly and yearly variations on indoor Rn concentrations. We analyzed 7,515 monthly indoor Rn measurements in 623 zip codes from two U.S. States (Michigan and Minnesota) during 2005-2018 using a random forest model. Using Shapley Additive exPlanations (SHAP) values we investigated the contribution of each factor using variable importance and partial dependence plots. Factors that predict indoor Rn differed between states, with topographical, geological and soil composition being most influential. Cross-validated Pearson correlation between predictions and measurements was 0.68 (RMSE = 47.8 Bq/m
3
) in Minnesota, and 0.67 (RMSE = 52.5 Bq/m
3
) in Michigan. Our results underline the importance of soil structure for radon exposure, presumably due to strapped Rn in soil. The differences across states also suggest that Rn studies performing model development should consider geographical variables, along with other factors. As indoor Rn levels are multifactorial, an understanding of the factors that influence its emanation and build up indoors will help better assess spatial and temporal variations, which will be useful to improve prevention and mitigation control strategies.
Implications: Radon exposure has become a year-round problem as people spend most of their time indoors. In North America, radon exposure is increasing over time and awareness related to its health effects remains low in the general population. Several factors make prediction of indoor radon exposure in enclosed spaces challenging. In this study, we used random forest to investigate the influence of factors on indoor radon in the Midwest United States. We found that topography, geology, and soil composition were the most influential factors on indoor radon levels. These results will help better assess spatial and temporal variations, which will further help better prevention and mitigation control strategies.
Indoor air pollutants are ranked in the top five environmental risks to public health. As the time spent indoors is large enough and increasing over time, we were interested in understanding sources, ...transport, physicochemical characteristics, influential factors, and trends to enhance our understanding of exposures in the indoor environment.In Chapter 2, we analyzed PM2.5 and its components to determine indoor and outdoor possible sources affecting indoor PM2.5 in classrooms. PM2.5 mass and its components were collected from 32 inner-city schools in the Northeastern U.S. from 2009-2013. We applied the USEPA PMF to the PM2.5 components to estimate the source apportionment both indoors and outdoors. Classroom indoor concentrations of PM2.5 (an average of 5.2 μg/m3) were lower than outdoors (an average of 6.5 μg/m3). The major sources (contributions) of indoor PM2.5 were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM2.5 were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). In Chapter 3, we were interested to find influential factors of radon levels in residential environments from different geographical areas. We analyzed factors from the soil, geology, topography, atmospheric variables, radiation, urbanization, community economic well-being, and monthly and year variations using random forest. We analyzed 802 zip codes from Massachusetts, Michigan, and Minnesota during 2005-2018. Factors that predict radon varied across the states, due possibly to a different soil composition in the states. Cross-validated R2 between predictions and measurements was 0.68 (RMSE = 47.81 Bq/m3) in Minnesota, 0.67, (RMSE = 52.61 Bq/m3) in Michigan, and 0.41 (RMSE = 52.57 Bq/m3) in Massachusetts.In Chapter 4, we assessed whether proximity to shale gas development areas affect indoor radon levels. We analyzed 35,442 monthly indoor radon observations from Pennsylvania between 2001-2015 using linear mixed effects model, with a random intercept for zip code. We found that shale gas development via hydraulic fracturing was associated with an increase in downwind indoor radon levels slope = 1.93 (± 0.94); p-value 0.0393. In addition, our analysis revealed that the contribution of having 100 wells in 1 km will increase indoor radon levels by 185 Bq/m3 (5 pCi/L).
Children spend over 6 h a day in schools and have higher asthma morbidity from school environmental exposures. The present study aims to determine indoor and outdoor possible sources affecting indoor ...PM
in classrooms. Weeklong indoor PM
samples were collected from 32 inner-city schools from a Northeastern U.S. community during three seasons (fall, winter and spring) during the years 2009 to 2013. Concurrently, daily outdoor PM
samples were taken at a central monitoring site located at a median distance of 4974 m (range 1065-11,592 m) from the schools. Classroom indoor concentrations of PM
(an average of 5.2 μg/m
) were lower than outdoors (an average of 6.5 μg/m
), and these averages were in the lower range compared to the findings in other schools' studies. The USEPA PMF model was applied to the PM
components measured simultaneously from classroom indoor and outdoor to estimate the source apportionment. The major sources (contributions) identified across all seasons of indoor PM
were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM
across all seasons were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). Secondary pollution was the greatest contributor to indoor and outdoor PM
over all three seasons, with the highest contribution during spring with 53% to indoor PM
and 45% to outdoor PM
. Lower contributions of this source during fall and winter are most likely attributed to less infiltration indoors. In contrast, the indoor contribution of motor vehicles source was highest in the fall (29%) and winter (25%), which was presumably categorized by a local source. From the relationship between indoor-to-outdoor sulfur ratios and each source contribution, we also estimated the local and regional influence on indoor PM
concentration. Overall, the observed differences to indoor PM
are related to seasonality, and the distinct characteristics and behavior of each classroom/school.
This work presents the results of a local empirical model that describes the behavior of the ionospheric F2 region peak. The model was developed using nearly 25 years of incoherent scatter radar ...(ISR) measurements made at the Arecibo Observatory (AO) between 1985 and 2009. The model describes the variability of the F2 peak frequency (foF2) and F2 peak height (hmF2) as a function of local time, season, and solar activity for quiet‐to‐moderate geomagnetic activity conditions (Kp < 4+). Our results show that the solar activity control of hmF2 and foF2 over Arecibo can be better described by a new proxy of the solar flux (F107P), which is presented here. The variation of hmF2 parameter with F107P is virtually linear, and only a small saturation of the foF2 parameter is observed at the highest levels of solar flux. The winter anomaly and asymmetries in the variation of the modeled parameters between equinoxes were detected during the analyses and have been taken into account by the AO model. Comparisons of ISR data with international reference ionosphere (IRI) model predictions indicate that both CCIR and URSI modes overestimate foF2 during the daytime and underestimate it at night. As expected, this underestimation is not observed in the AO model. Our analyses also show that the hmF2 parameter predicted by the IRI modes shows a saturation point, which causes hmF2 to be underestimated at high solar activity. The underestimation increases with higher levels of solar activity. Finally, we also found that IRI predictions of the seasonal variability of foF2 and hmF2 over Arecibo can be improved by using a small correction that varies with solar activity and local time.
Key Points
IRI solar activity correction for hmF2 and foF2 predictions
Climatology of the hmF2 and foF2 over Arecibo
Development of an empirical model of the ionospheric F2 region peak
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