Enhancing near-road exposure assessment Koutrakis, Petros; Greenbaum, Dan
Journal of the Air & Waste Management Association (1995),
02/2021, Volume:
71, Issue:
2
Journal Article
Weather impacts on the chemical composition of PM2.5 varies significantly over space and time given the diversity of particle components and the complex mechanisms governing particle formation and ...removal. In this study, we employed generalized additive models (GAMs) to estimate weather-associated changes in PM2.5 composition in the US during 1988–2017. We considered seven components of ambient PM2.5, which included elemental carbon (EC), organic carbon (OC), nitrate, sulfate, sodium, ammonium, and silicon. The impact of long-term weather changes on each PM2.5 component was defined in our study as “weather penalty”. During our study period, temperature increased in four regions, including the Industrial Midwest and Northeast during the warm and cold season; and Upper Midwest and West in the cold season. Wind speed decreased in the both seasons. Relative humidity increased in the warm season and decreased in the cold season. The weather changes between 1988 and 2017 were associated with most PM2.5 components during both warm and cold seasons. The direction and the magnitude of the weather penalty varied considerably over the space and season. In the warm season, our findings suggest a nationwide weather penalty for EC, OC, nitrate, sulfate, sodium, ammonium, and silicon of 0.04, 0.21, 0.04, 0.35, −0.01, 0.05, and 0.01 μg/m3, respectively. In the cold season, the estimated total penalty was 0.04, 0.21, 0.06, 0.04, −0.01, −0.02, and 0.02 μg/m3, respectively.
•The West demonstrated the highest weather penalty on nitrate.•Our results indicate minimal influences of weather changes on sulfate in the cold season.•The weather penalties for sodium were negative in all coastal regions.•Weather penalties on silicon were positive in all regions in both warm and cold seasons.
Although meteorological stations provide accurate air temperature observations, their spatial coverage is limited and thus often insufficient for epidemiological studies. Satellite data expand ...spatial coverage, enhancing our ability to estimate near surface air temperature (Ta). However, the derivation of Ta from surface temperature (Ts) measured by satellites is far from being straightforward. In this study, we present a novel approach that incorporates land use regression, meteorological variables and spatial smoothing to first calibrate between Ts and Ta on a daily basis and then predict Ta for days when satellite Ts data were not available. We applied mixed regression models with daily random slopes to calibrate Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with monitored Ta measurements for 2003. Then, we used a generalized additive mixed model with spatial smoothing to estimate Ta in days with missing Ts. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (mean out-of-sample R2=0.946 and R2=0.941 respectively). Furthermore, based on the high quality predictions we investigated the spatial patterns of Ta within the study domain as they relate to urban vs. non-urban land uses.
► We assess minimum air temperature from satellite surface temperature. ► We use a daily calibration approach and general additive models. ► Air temperature was also estimated for days with missing satellite data. ► Our model performance for days with satellite data was excellent (R2=0.946). ► For days without satellite data the model also performed well (R2=0.941).
Display omitted
•A random forest machine learning and a mixed effect models were used.•PM2.5 exposures from 2001 to 2018 for Kuwait and Iraq were assessed.•The study region had very high PM2.5 ...concentrations.
Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001–2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.
Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults.
To determine if exposure to traffic and PM2.5 is associated ...with longitudinal changes in lung function in a population-based cohort in the Northeastern United States, where pollution levels are relatively low.
FEV1 and FVC were measured up to two times between 1995 and 2011 among 6,339 participants of the Framingham Offspring or Third Generation studies. We tested associations between residential proximity to a major roadway and PM2.5 exposure in 2001 (estimated by a land-use model using satellite measurements of aerosol optical thickness) and lung function. We examined differences in average lung function using mixed-effects models and differences in lung function decline using linear regression models. Current smokers were excluded. Models were adjusted for age, sex, height, weight, pack-years, socioeconomic status indicators, cohort, time, season, and weather.
Living less than 100 m from a major roadway was associated with a 23.2 ml (95% confidence interval CI, -44.4 to -1.9) lower FEV1 and a 5.0 ml/yr (95% CI, -9.0 to -0.9) faster decline in FEV1 compared with more than 400 m. Each 2 μg/m(3) increase in average of PM2.5 was associated with a 13.5 ml (95% CI, -26.6 to -0.3) lower FEV1 and a 2.1 ml/yr (95% CI, -4.1 to -0.2) faster decline in FEV1. There were similar associations with FVC. Associations with FEV1/FVC ratio were weak or absent.
Long-term exposure to traffic and PM2.5, at relatively low levels, was associated with lower FEV1 and FVC and an accelerated rate of lung function decline.
Epidemiological investigations regarding temperature influence on human health have focused on mortality rather than morbidity. In addition, most information comes from developed countries despite ...the increasing evidence that climate change will have devastating impacts on disadvantaged populations living in developing countries. In the present study, we assessed the impact of daily temperature range on upper and lower respiratory infections in Cordoba, Argentina, and explored the effect modification of socio-economic factors and influence of airborne particles We found that temperature range is a strong risk factor for admissions due to both upper and lower respiratory infections, particularly in elderly individuals, and that these effects are more pronounced in sub-populations with low education level or in poor living conditions. These results indicate that socio-economic factors are strong modifiers of the association between temperature variability and respiratory morbidity, thus they should be considered in risk assessments.
•Daily temperature range is a strong risk factor for respiratory infections.•Low education level and poor living conditions are strong modifiers of this relationship.•In Cordoba city higher risk for respiratory infections were observed during summertime.
Daily temperature range is a strong risk factor for respiratory infections, particularly for populations with low educational level or poor living conditions.
Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring ...stations of PM2.5 to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1-km(2) resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R(2) values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 μg/m(3) for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas because of the paucity of monitors in rural areas.
Background Although ambient air pollution has been linked to reduced lung function in healthy children, longitudinal analyses of pollution effects in asthmatic patients are lacking. Objective We ...sought to investigate pollution effects in a longitudinal asthma study and effect modification by controller medications. Methods We examined associations of lung function and methacholine responsiveness (PC20 ) with ozone, carbon monoxide (CO), nitrogen dioxide, and sulfur dioxide concentrations in 1003 asthmatic children participating in a 4-year clinical trial. We further investigated whether budesonide and nedocromil modified pollution effects. Daily pollutant concentrations were linked to ZIP/postal code of residence. Linear mixed models tested associations of within-subject pollutant concentrations with FEV1 and forced vital capacity (FVC) percent predicted, FEV1 /FVC ratio, and PC20 , adjusting for seasonality and confounders. Results Same-day and 1-week average CO concentrations were negatively associated with postbronchodilator percent predicted FEV1 (change per interquartile range, −0.33 95% CI, −0.49 to −0.16 and −0.41 95% CI, −0.62 to −0.21, respectively) and FVC (−0.19 95% CI, −0.25 to −0.07 and −0.25 95% CI, −0.43 to −0.07, respectively). Longer-term 4-month CO averages were negatively associated with prebronchodilator percent predicted FEV1 and FVC (−0.36 95% CI, −0.62 to −0.10 and −0.21 95% CI, −0.42 to −0.01, respectively). Four-month averaged CO and ozone concentrations were negatively associated with FEV1 /FVC ratio ( P < .05). Increased 4-month average nitrogen dioxide concentrations were associated with reduced postbronchodilator FEV1 and FVC percent predicted. Long-term exposures to sulfur dioxide were associated with reduced PC20 (percent change per interquartile range, −6% 95% CI, −11% to −1.5%). Treatment augmented the negative short-term CO effect on PC20. Conclusions Air pollution adversely influences lung function and PC20 in asthmatic children. Treatment with controller medications might not protect but rather worsens the effects of CO on PC20 . This clinical trial design evaluates modification of pollution effects by treatment without confounding by indication.
PDF
