Using ground-based observations and satellite retrievals to constrain urban-to-regional-scale air quality chemical transport modeling

The overarching goal of this research is to improve urban- and regional-scale air quality modeling for health risk assessment using a combination of ground-station and satellite-based measurements. The integration of near-surface air pollution concentrations, emissions-based air quality model simulations, and satellite observations of column-integrated quantities will improve the accuracy of exposure metrics and promote the appropriate use of satellite data over extended areas for long time periods, especially where ground-based air quality measurement networks are limited or lacking. In the broader sense, this information will help public health scientists, policy makers, and monitoring agencies to research and implement better control strategies and regulations. The first phase of this study (Friberg et al., 2016) demonstrated and assessed a systematic and practical approach to fusing surface-network measurements with chemical transport model (CTM) simulations to produce daily concentrations for five air pollutants of primary origin (NO2, NOx, CO, SO2, and EC), and seven secondary pollutants (O3, PM10 mass, PM2.5 mass, SO4, NH4, NO3, and OC) for use in cross-sectional epidemiological studies. A second study (Friberg et al., 2017) assessed the ability of the data fusion method to produce daily concentrations across five metropolitan areas from 2002 to 2008. In addition to the variety of pollutant types, the five cities represent a range of meteorological conditions, background aerosol conditions, population densities, and sampling-station distributions. Among the pollutant types, the primary pollutants tend to be more heterogeneously distributed over the urban regions than the secondary ones. The resulting daily spatial field estimates of air pollutant concentrations and associated correlations were not only consistent with observations, emissions, and meteorology, but substantially improved CTM-derived results in areas without observations, for most pollutants and all cities. The data fusion improved daily metrics across all pollutants with the greatest improvements occurring for O3 and PM2.5. The final study (Friberg et al., 2017, to be submitted) demonstrated and assessed an optimization technique, expanding upon the surface-station-model fusion technique, to estimate ambient PM2.5 mass and associated chemically speciated concentrations for potential use in longitudinal epidemiological studies. The newest method constrains surface PM2.5 and chemical-component CTM results, using both ground-station data to anchor speciated, near-surface aerosol concentrations, and total column aerosol optical depth (AOD). When the mid-visible AOD is high, the retrieved AOD from the Multi-angle Imaging SpectroRadiometer (MISR) Research Aerosol retrieval algorithm along with qualitative, column-effective aerosol type observations helped constrain the CTM over extended regions. The retrieved AOD had a horizontal resolution of 275m. The method was applied over a case study area in the San Joaquin Valley of California during NASA’s DISCOVER-AQ field campaign in this region, on days when there was good satellite coverage and considerable suborbital data for validation of the approach. The accuracy of estimated concentrations and evaluation of the latest MISR aerosol retrieval algorithm ability to typify urban AOD, aerosol mixtures, and aerosol airmasses were examined by comparing the results with speciated ground observations and standard model fitting statistics. The results indicate that on days with high AOD and adequate observing conditions, satellite retrievals improve simulated spatial distributions of PM2.5 and chemical component concentrations.Ph.D

Landscape and Environmental Factors Influencing Stage Persistence and Abundance of the Bamboo Mosquito, <i>Tripteroides bambusa</i> (Diptera: Culicidae), across an Altitudinal Gradient

The bamboo mosquito, Tripteroides bambusa (Yamada) (Diptera: Culicidae), is a common insect across East Asia. Several studies have looked at the ecology of Tr. bambusa developmental stages separately, but little is known about the factors associated with the persistence (how often) and abundance (how many individuals) of Tr. bambusa stages simultaneously studied across a heterogeneous landscape. Here, we ask what environmental and landscape factors are associated with the persistence and abundance of Tr. bambusa stages across the altitudinal gradient of Mt. Konpira, Nagasaki City, Japan. During a season-long study we counted 8065 (7297 4th instar larvae, 670 pupae and 98 adults) Tr. bambusa mosquitoes. We found that persistence and abundance patterns were not associated among stages, with the exception of large (4th instar) and small (1st to 3rd instars) larvae persistence, which were positively correlated. We also found that relative humidity was associated with the persistence of Tr. bambusa aquatic stages, being positively associated with large and small larvae, but negatively with pupae. Similarly, landscape aspect changed from positive to negative the sign of its association with Tr. bambusa pupae and adults, highlighting that environmental associations change with life stage. Meanwhile, Tr. bambusa abundance patterns were negatively impacted by more variable microenvironments, as measured by the negative impacts of kurtosis and standard deviation (SD) of environmental variables, indicating Tr. bambusa thrives in stable environments, suggesting this mosquito species has a finely grained response to environmental changes

Multi-LEO Satellite Stereo Winds

The stereo-winds method follows trackable atmospheric cloud features from multiple viewing perspectives over multiple times, generally involving multiple satellite platforms. Multi-temporal observations provide information about the wind velocity and the observed parallax between viewing perspectives provides information about the height. The stereo-winds method requires no prior assumptions about the thermal profile of the atmosphere to assign a wind height, since the height of the tracked feature is directly determined from the viewing geometry. The method is well developed for pairs of Geostationary (GEO) satellites and a GEO paired with a Low Earth Orbiting (LEO) satellite. However, neither GEO-GEO nor GEO-LEO configurations provide coverage of the poles. In this paper, we develop the stereo-winds method for multi-LEO configurations, to extend coverage from pole to pole. The most promising multi-LEO constellation studied consists of Terra/MODIS and Sentinel-3/SLSTR. Stereo-wind products are validated using clear-sky terrain measurements, spaceborne LiDAR, and reanalysis winds for winter and summer over both poles. Applications of multi-LEO polar stereo winds range from polar atmospheric circulation to nighttime cloud identification. Low cloud detection during polar nighttime is extremely challenging for satellite remote sensing. The stereo-winds method can improve polar cloud observations in otherwise challenging conditions

Constraining Chemical Transport PM2.5 Modeling Outputs Using Surface Monitor Measurements and Satellite Retrievals: Application over the San Joaquin Valley

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM(2.5), its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM(2.5) fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM(2.5), 0.88 and 0.65 for NO3(), 0.78 and 0.23 for SO4(2), 1.00 and 1.01 for NH4(+), 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM(2.5) improve by 30% and 13%, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4(2) cross-validation values showed the largest spatial and spatiotemporal R(2) improvement, with a 43% increase. Assessing this physical technique in a well-instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent

Trade, uneven development and people in motion : Used territories and the initial spread of COVID-19 in Mesoamerica and the Caribbean

Mesoamerica and the Caribbean form a region comprised by middle- and low-income countries affected by the COVID-19 pandemic differently. Here, we ask whether the spread of COVID-19, measured using early epidemic growth rates (r), reproduction numbers (Rt), accumulated cases, and deaths, is influenced by how the ‘used territories’ across the regions have been differently shaped by uneven development, human movement and trade differences. Using an econometric approach, we found that trade openness increased cases and deaths, while the number of international cities connected at main airports increased r, cases and deaths. Similarly, increases in concentration of imports, a sign of uneven development, coincided with increases in early epidemic growth and deaths. These results suggest that countries whose used territory was defined by a less uneven development were less likely to show exacerbated COVID-19 patterns of transmission. Health outcomes were worst in more trade-dependent countries, even after controlling for the impact of transmission prevention and mitigation policies, highlighting how structural effects of economic integration in used territories were associated with the initial COVID-19 spread in Mesoamerica and the Caribbean.Arts, Faculty ofGeography, Department ofNon UBCReviewedOpen access funding provided by the UBC Open Access Fund for Humanities and Social Sciences Research.FacultyResearche

COVID-19 basic reproduction number and assessment of initial suppression policies in Costa Rica

SARS-COV-2 is the most recent from a series of emerging pathogens stressing national health systems. Initially restricted to Hubei province in China, COVID-19, the disease caused by SARS-COV-2 has become pandemic, reaching almost every nation on our planet. Here, we present an estimate of the Basic Reproduction Number (R0) for this disease based on confirmed cases recorded during the initial 30 days of transmission. Based on local transmission data for the six initial days of transmission, we estimated (mean ± SE) R0 = 2.58 ± 2.43. R0 was reduced by up to 56% to R0 = 1.12 ± 0.02 following suppression measures in place by April 4th, 2020. Independent estimates for the time-varying reproduction number (Rt) based on the serial interval distribution estimated for China showed that after 30 days, Rt decreased reaching a value of 0.914 ± 0.104 on April 4th, 2020. In this study, we also describe the suppression strategies in place in Costa Rica and compare their impacts with those implemented in Panamá and Uruguay, provided these three middle-income countries have similar demographic and economic indicators. However, these three countries have structurally different health systems and have resorted to different suppression measures against COVID-19. We compare the early epidemic growth curves in the three countries using an exponential deceleration model. We found the lowest epidemic growth rate in Costa Rica, followed by Panamá and then Uruguay, while the highest deceleration was observed in Uruguay, followed by Costa Rica and Panamá. We discuss how the unified, universal healthcare system of Costa Rica has been vital to successfully manage the early stage of the COVID-19 epidemic and call for the developments of precision public health tools to further improve epidemic health surveillance in Costa Rica

Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach

Abstract Background Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas. Methods Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5–18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone. Results The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification. Conclusions Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity

Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data Fusion-Based Estimates and Uncertainties

Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical transport-modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associatedcorrelations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM (sub10), PM (sub 2.5), and five PM (sub 2.5) components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, andmeteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM (sub 2.5). Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R-squared equal to 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM (sub 2.5)), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM (sub 10)). Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra urban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone