The short term adaptation of the autonomic nervous systems (ANS) by type of urban environment and ethnicity.

BACKGROUND: Previous studies examined the effects of urban environments on the Autonomic Nervous System (ANS). These studies measured the effects of environments on Heart Rate Variability (HRV) averaging different time intervals to one value. Yet, the dynamics of change, reflecting the functions and their derivatives that describe the adaptation to the new environments remain unknown. In addition, ethnic differences in the ANS adaptation were not investigated. METHOD: Forty-eight Arab and 24 Jewish women ages 20-35 years, all healthy, non-smokers were recruited by a snowball sample. Both groups were of a similar socioeconomic status and BMI distributions. Using a portable monitor, the HRV response was continuously analyzed for 35 min of sedentary sitting in each of the three environments: a park, a city center and a residential area. LF/HF polynomial function was adapted to describe the dynamic change in each environment for each ethnic group. RESULTS: Green area exposure was associated with 90% immediate change while in built-up areas, the change in HRV is about 40% adaptive (changing gradually). The adaptive process of HRV may stabilize after 15 min in the city center yet not even after 35 min in the residential environment. The total change (immediate + adaptive) reached 24% in city centers and 10% in residential areas. Changes in HRV rates in the park and the city center environments were higher among Arab women as compared to Jewish women but similar between the two groups in the residential area. The distributions of LF/HF in each time cohort were normal, meaning that shifting the focus to analyze functions of change in HRV, opens the possibility to employ analytic methods that assume the normal distribution. CONCLUSIONS: Changing the focus from average levels of HRV to functions of change and their derivatives brings new insight into the understanding of the ANS response to environmental challenges. ANS short term adaptation to different environments is gradual and spans differently both in magnitude of response and latencies between different environments. Importantly, in green areas, the response is immediate unlike the adaptation to urban environments that is significantly more gradual. The ethnic differences in ANS adaptation is also noteworthy. In addition, adaptation proceeesses are normaly distributed in each time cohort suggesting a possible novel ANS index.

Is health-related quality of life 1-year after coronary artery bypass graft surgery associated with living in a greener environment?

INTRODUCTION: Greenery in the residential environment and in the hospital has been associated with improved surgical outcomes and recovery. We investigated the association between the level of residential greenness of patients with coronary disease and their heart disease-related Quality of Life (HRQoL) 1-year after a coronary artery bypass grafting (CABG) surgery. METHODS: Participants in a prospective cohort study who underwent CABG surgery at seven cardiothoracic units throughout Israel during the years 2004-2007 filled in the MacNew HRQoL one day before and one year after surgery. Successful recovery was defined as ≥0.5 increase in the MacNew score between baseline and follow-up. Exposure to residential greenness in 90 m and 300 m buffers around the patient's home was assessed with Linear Spectral Unmixing analysis of Landsat 30 m imagery. RESULTS: The cohort comprised of 861 patients (22% female) with a mean age of 65.5 years, and 59.2% classified as low-income. In the total cohort, higher residential greenness was associated with an improvement in emotional HRQoL (OR = 1.33 (95%CI: 0.99-1.79)), adjusting for demographic and socio-economic factors, living in the periphery/center, presence of diabetes, attending cardiac rehabilitation following surgery, BMI, and change in physical fitness and depression over the 1-year follow-up. Although no association was found between greenness and change in the physical or social subscales, a positive association was specifically observed among the low-income patients for the global HRQoL score, OR = 1.42 (95%CI: 0.97-2.10), as compared to the higher-income patients, p for interaction = 0.03. CONCLUSIONS: Residential greenness is associated with improvement in HRQoL 1-year after CABG surgery, but not the physical and social scales, only in low-income patients. Ensuring greenery in the living environment may act as a social intervention that supports human health and disease recovery.

Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data.

Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and spatial variability of atmospheric particulate matter (aerosol) over the urban area of Córdoba (central Argentina) using over ten years (2003-2015) of high-resolution (1 km) satellite-based retrievals of aerosol optical depth (AOD). This fine resolution is achieved exploiting the capabilities of a recently developed inversion algorithm (Multiangle implementation of atmospheric correction, MAIAC) applied to the MODIS sensor datasets of the NASA-Terra and -Aqua platforms. Results of this investigation show a clear seasonality of AOD over the investigated area. This is found to be shaped by an intricate superposition of aerosol sources, acting over different spatial scales and affecting the region with different yearly cycles. During late winter and spring (August-October), local as well as near- and long-range transported biomass burning (BB) aerosols enhance the Córdoba aerosol load, and AOD levels reach their maximum values (> 0.35 at 0.47µm). The fine AOD spatial resolution allowed to disclose that, in this period, AOD maxima are found in the rural/agricultural area around the city, reaching up to the city boundaries pinpointing that fires of local and near-range origin play a major role in the AOD enhancement. A reverse spatial AOD gradient is found from December to March, the urban area showing AODs 40 to 80% higher than in the city surroundings. In fact, during summer, the columnar aerosol load over the Córdoba region is dominated by local (urban and industrial) sources, likely coupled to secondary processes driven by enhanced radiation and mixing effects within a deeper planetary boundary layer (PBL). With the support of modelled AOD data from the Modern-Era Retrospective Analysis for Research and Application (MERRA), we further investigated into the chemical nature of AOD. The results suggest that mineral dust is also an important aerosol component in Córdoba, with maximum impact from November to February. The use of a long-term dataset finally allowed a preliminary assessment of AOD trends over the Córdoba region. For those months in which local sources and secondary processes were found to dominate the AOD (December to March), we found a positive AOD trend in the Córdoba outskirts, mainly in the areas with maximum urbanization/population growth over the investigated decade. Conversely, a negative AOD trend (up to -0.1 per decade) is observed all over the rural area of Córdoba during the BB season, this being attributed to a decrease of fires both at the local and the continental scale.

Exploring pathways linking greenspace to health: Theoretical and methodological guidance.

BACKGROUND: In a rapidly urbanizing world, many people have little contact with natural environments, which may affect health and well-being. Existing reviews generally conclude that residential greenspace is beneficial to health. However, the processes generating these benefits and how they can be best promoted remain unclear. OBJECTIVES: During an Expert Workshop held in September 2016, the evidence linking greenspace and health was reviewed from a transdisciplinary standpoint, with a particular focus on potential underlying biopsychosocial pathways and how these can be explored and organized to support policy-relevant population health research. DISCUSSIONS: Potential pathways linking greenspace to health are here presented in three domains, which emphasize three general functions of greenspace: reducing harm (e.g. reducing exposure to air pollution, noise and heat), restoring capacities (e.g. attention restoration and physiological stress recovery) and building capacities (e.g. encouraging physical activity and facilitating social cohesion). Interrelations between among the three domains are also noted. Among several recommendations, future studies should: use greenspace and behavioural measures that are relevant to hypothesized pathways; include assessment of presence, access and use of greenspace; use longitudinal, interventional and (quasi)experimental study designs to assess causation; and include low and middle income countries given their absence in the existing literature. Cultural, climatic, geographic and other contextual factors also need further consideration. CONCLUSIONS: While the existing evidence affirms beneficial impacts of greenspace on health, much remains to be learned about the specific pathways and functional form of such relationships, and how these may vary by context, population groups and health outcomes. This Report provides guidance for further epidemiological research with the goal of creating new evidence upon which to develop policy recommendations.

Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure.

BACKGROUND: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however few studies have compared results across different exposure assessment methods. METHODS: We utilized a cohort of 5679 patients who had undergone a cardiac catheterization between 2002-2009 and resided in NC. Exposure to PM2.5 for the year prior to catheterization was estimated using data from air quality monitors (AQS), Community Multiscale Air Quality (CMAQ) fused models at the census tract and 12km spatial resolutions, and satellite-based models at 10km and 1km resolutions. Case status was either a coronary artery disease (CAD) index >23 or a recent myocardial infarction (MI). Logistic regression was used to model odds of having CAD or an MI with each 1-unit (µg/m3) increase in PM2.5, adjusting for sex, race, smoking status, socioeconomic status, and urban/rural status. RESULTS: We found that the elevated odds for CAD>23 and MI were nearly equivalent for all exposure assessment methods. One difference was that data from AQS and the census tract CMAQ showed a rural/urban difference in relative risk, which was not apparent with the satellite or 12km-CMAQ models. CONCLUSIONS: Long-term air pollution exposure was associated with coronary artery disease for both modeled and monitored data.

Association between satellite-based estimates of long-term PM2.5 exposure and coronary artery disease.

BACKGROUND: Epidemiological studies have identified associations between long-term PM2.5 exposure and cardiovascular events, though most have relied on concentrations from central-site air quality monitors. METHODS: We utilized a cohort of 5679 patients who had undergone cardiac catheterization at Duke University between 2002-2009 and resided in North Carolina. We used estimates of daily PM2.5 concentrations for North Carolina during the study period based on satellite derived Aerosol Optical Depth (AOD) measurements and PM2.5 concentrations from ground monitors, which were spatially resolved with a 10×10km resolution, matched to each patient's residential address and averaged for the year prior to catheterization. The Coronary Artery Disease (CAD) index was used to measure severity of CAD; scores >23 represent a hemodynamically significant coronary artery lesion in at least one major coronary vessel. Logistic regression modeled odds of having CAD or an MI with each 1µg/m(3) increase in annual average PM2.5, adjusting for sex, race, smoking status and socioeconomic status. RESULTS: In adjusted models, a 1µg/m(3) increase in annual average PM2.5 was associated with an 11.1% relative increase in the odds of significant CAD (95% CI: 4.0-18.6%) and a 14.2% increase in the odds of having a myocardial infarction (MI) within a year prior (95% CI: 3.7-25.8%). CONCLUSIONS: Satellite-based estimates of long-term PM2.5 exposure were associated with both coronary artery disease (CAD) and incidence of myocardial infarction (MI) in a cohort of cardiac catheterization patients.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

BACKGROUND: The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. METHODS: We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. RESULTS: Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). CONCLUSION: Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES).

UNLABELLED: Although ground-level PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies. IMPLICATIONS: The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 x 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.

Residential greenness and increased physical activity in patients after coronary artery bypass graft surgery.

AIMS: Physical activity is a fundamental component of rehabilitation following coronary artery bypass (CABG) surgery. Proximity to neighbourhood green spaces may encourage physical activity. We investigated the association between residential greenness and exercise-related physical activity post-CABG surgery. METHODS: Participants in a prospective cohort study of 846 patients (78% men) who underwent CABG surgery at seven cardiothoracic units during the time period 2004-2007 were interviewed regarding their physical activity habits one day before and one year after surgery. Exposure to residential neighbourhood greenness (within a 300 m buffer around their place of residence) was measured using the Normalized Difference Vegetative Index. Participation in exercise-related physical activity (yes/no), weekly duration of exercise-related physical activity and the change in exercise-related physical activity between baseline and follow-up were examined for associations with residential greenness, adjusting for socio-demographic factors, propensity score adjusted participation in cardiac rehabilitation and health-related covariates after multiple imputation for missing variables. RESULTS: Living in a higher quartile of residential greenness was associated with a 52% greater odds of being physically active (OR 1.52, 95% CI 1.22-1.90). This association persisted only (OR 1.75, 95% CI 1.35-2.27) among patients who did not participate in cardiac rehabilitation following surgery and was stronger in women (OR 2.38, 95% CI 1.40-4.07) than in men (OR 1.37, 95% CI 1.07-1.75). Participants who lived in greener areas were more likely to increase their post-surgical physical activity than those who lived in less green areas (OR 1.59, 95% CI 1.25-2.01). CONCLUSIONS: Residential greenness appears to be beneficial in increasing exercise-related physical activity in cardiac patients, especially those not particpating in cardiac rehabilitation after CABG surgery.

Remote sensing metrics to assess exposure to residential greenness in epidemiological studies: A population case study from the Eastern Mediterranean.

INTRODUCTION/AIMS: Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. METHODS: NDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. RESULTS: For the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. CONCLUSIONS: LSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification.

Air-water interactions: The signature of meteorological and air-quality parameters on the chemical characteristics of water produced from the atmosphere.

Atmospheric water is considered an alternative sustainable solution for global water scarcity. We analyzed the effects of meteorological and air-quality parameters on the chemical characteristics of atmospheric water. First, we measured the chemical characteristics of water produced by a unique atmospheric water generator (AWG) apparatus in Tel Aviv, Israel. To examine the complex air-water relationships, we obtained atmospheric data from several sources: adjacent air-quality-monitoring stations, aerosol robotic network (AERONET), aerosol pollution profile using PollyXT lidar, and air back-trajectory simulation (HYSPLIT). We found a strong impact of different pollution sources on the water quality. The integration between HYSPLIT, AERONET and lidar analyses shows that the pathway crossed by the air parcel three days before arrival at the site affected the chemical properties of the produced water. Nearby sea salt aerosols from the Mediterranean were persistently observed in the water (medians: sodium 69 µg/L, chloride ions 120 µg/L), corresponding to lidar identification of a sea-breeze layer (30-50 sr lidar ratio in lower elevation). Seasonal variability in climatic conditions affected the concentration of dust-related elements in the water. During dust-storm events, calcium was the most dominant element (median 900 µg/L). Thus, the chemical characteristics of the water can be considered a "footprint" of both regional, local, and phenological composition of the atmosphere.

Sparse NIR optimization method (SNIRO) to quantify analyte composition with visible (VIS)/near infrared (NIR) spectroscopy (350 nm-2500 nm).

Visual-Near-Infra-Red (VIS/NIR) spectroscopy has led the revolution in high-throughput phenotyping methods used to determine chemical and structural elements of organic materials. In the current state of the art, spectrophotometers used for imaging techniques are either very expensive or too large to be used as a field-operable device. In this study we developed a Sparse NIR Optimization method (SNIRO) that selects a pre-determined number of wavelengths that enable quantification of analytes in a given sample using linear regression. We compared the computed complexity time and the accuracy of SNIRO to Marten's test, to forward selection test and to LASSO all applied to the determination of protein content in corn flour and meat and octane number in diesel using publicly available datasets. In addition, for the first time, we determined the glucose content in the green seaweed Ulva sp., an important feedstock for marine biorefinery. The SNIRO approach can be used as a first step in designing a spectrophotometer that can scan a small number of specific spectral regions, thus decreasing, potentially, production costs and scanner size and enabling the development of field-operable devices for content analysis of complex organic materials.

Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery.

Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R2=0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R2=0.935) than the AT-BT model (R2=0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach.

Thermochemical hydrolysis of macroalgae Ulva for biorefinery: Taguchi robust design method.

Understanding the impact of all process parameters on the efficiency of biomass hydrolysis and on the final yield of products is critical to biorefinery design. Using Taguchi orthogonal arrays experimental design and Partial Least Square Regression, we investigated the impact of change and the comparative significance of thermochemical process temperature, treatment time, %Acid and %Solid load on carbohydrates release from green macroalgae from Ulva genus, a promising biorefinery feedstock. The average density of hydrolysate was determined using a new microelectromechanical optical resonator mass sensor. In addition, using Flux Balance Analysis techniques, we compared the potential fermentation yields of these hydrolysate products using metabolic models of Escherichia coli, Saccharomyces cerevisiae wild type, Saccharomyces cerevisiae RN1016 with xylose isomerase and Clostridium acetobutylicum. We found that %Acid plays the most significant role and treatment time the least significant role in affecting the monosaccharaides released from Ulva biomass. We also found that within the tested range of parameters, hydrolysis with 121 °C, 30 min 2% Acid, 15% Solids could lead to the highest yields of conversion: 54.134-57.500 gr ethanol kg(-1) Ulva dry weight by S. cerevisiae RN1016 with xylose isomerase. Our results support optimized marine algae utilization process design and will enable smart energy harvesting by thermochemical hydrolysis.

Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003-2011.

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.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data.

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

Spatial scales of pollution from variable resolution satellite imaging.

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed.

Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA.

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 R(2)=0.946 and R(2)=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.