Neighborhood-scale ambient NO2 concentrations using TROPOMI NO2 data: Applications for spatially comprehensive exposure assessment.

This study estimated long-term average ambient NO2 concentrations using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO2 data and land use information at the spatial resolution of 500 m in California for the years 2018-2019. Our satellite-land use regression model demonstrated reasonably high predictive power with cross-validation (CV) R2 = 0.76, mean absolute error (MAE) = 1.95 ppb, and root mean squared error (RMSE) = 2.51 ppb in a comparison between measured and estimated NO2 concentrations. Exploiting the high-resolution NO2 estimates, we further investigated the representativeness of ground NO2 monitors for population exposures and examined the spatial variation of NO2 in relation to parcel-level property data for exposure attributions. The ground NO2 monitors were the most representative of population exposures in Los Angeles and San Diego counties, supported by population-weighted average NO2 concentrations (satellite-derived estimations) similar to arithmetic average NO2 concentrations (ground measurements). On the contrary, the exposure assessment using the ground monitors was the least representative and protective in Humboldt, San Luis Obispo, and Yolo counties with population-weighted average NO2 greater than arithmetic average NO2 by 82.2 % (1.85 ppb), 67.1 % (1.89 ppb), and 58.2 % (2.48 ppb), respectively. In a case study of LA County, we identified comparatively high NO2 concentrations for the property types of food processing facilities and high-density residential complexes (such as high-rise apartments and apartments). This finding provides evidence that these emerging sources may be crucial to mitigate cumulative NO2 exposures and subsequent health risks from a regulatory perspective.

Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.

The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's R >0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9% and 16.5% decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000-2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7% and 9.5% for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes.

Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This is a brief review on using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some example R code for data processing and modeling.

Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 µg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 µg/m3.

Spatial analysis of concentrations of multiple air pollutants using NASA DISCOVER-AQ aircraft measurements: Implications for exposure assessment.

In recent years, multipollutant approaches have been employed to investigate the association with health outcomes to better represent real-world conditions than more traditional analysis that considers a single pollutant. With regard to the exposure assessment of a mixture of air pollutants, it is critical to understand the spatial variability in multipollutant relations in order to assess their potential health implications. In this study, we investigated the spatial relations of multiple pollutant concentrations (i.e., NOx, NOy, black carbon, carbon monoxide, acetaldehyde, formaldehyde, toluene, xylenes/ethylbenzene, ozone, water-soluble organic carbon, and aerosol extinction) observed from the P-3B aircraft in the 2011 NASA field campaign in Baltimore/Washington D.C. areas during July 2011. The between-pollutant Pearson correlations and Z-scores (calculated from log-transformed concentrations) between near-highways and non-highways and between near-urban centers and non-urban centers varied by pollutant pair and space. We found generally lower correlations between NOx and other pollutants for near-highways (average r = 0.36) than for non-highways (average r = 0.41) and also for non-urban centers (average r = 0.37) than for near-urban centers (average r = 0.41). This indicated that the temporal associations between NOx and health outcomes might be less affected by other pollutants, which were also related to same health outcomes, for near-highways and non-urban centers. The analysis of between-pollutant Z-scores showed varying spatial relations for popular traffic-related pollutants with the Z-score differences of 0.43 (NOx-carbon monoxide), 0.29 (NOx-black carbon), and 0.17 (black carbon-carbon monoxide) between near-highways and non-highways. This result exhibited heterogeneous traffic-related pollutant mixtures with the proximity to highways, potentially leading to the diverse extent of health associations. Furthermore, a mixed effects model presented pollutant-specific associations between the concentrations and the proximity to highways and urban centers, showing larger declines for NOx, xylenes/ethylbenzene, toluene, and NOy than those for the pollutants related to secondary pollutant formation. The model also demonstrated the different sensitivity of each pollutant to meteorological parameters, which may modify the spatial and temporal variability in the relations between the pollutants. Our findings provide insights for exposure assessment studies to better understand the cumulative health consequences associated with multiple air pollutants simultaneously.

Monthly analysis of PM ratio characteristics and its relation to AOD.

Airborne particulate matter (PM) is derived from diverse sources-natural and anthropogenic. Climate change processes and remote sensing measurements are affected by the PM properties, which are often lumped into homogeneous size fractions that show spatiotemporal variation. Since different sources are attributed to different geographic locations and show specific spatial and temporal PM patterns, we explored the spatiotemporal characteristics of the PM2.5/PM10 ratio in different areas. Furthermore, we examined the statistical relationships between AERONET aerosol optical depth (AOD) products, satellite-based AOD, and the PM ratio, as well as the specific PM size fractions. PM data from the northeastern United States, from San Joaquin Valley, CA, and from Italy, Israel, and France were analyzed, as well as the spatial and temporal co-measured AOD products obtained from the MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. Our results suggest that when both the AERONET AOD and the AERONET fine-mode AOD are available, the AERONET AOD ratio can be a fair proxy for the ground PM ratio. Therefore, we recommend incorporating the fine-mode AERONET AOD in the calibration of MAIAC. Along with a relatively large variation in the observed PM ratio (especially in the northeastern United States), this shows the need to revisit MAIAC assumptions on aerosol microphysical properties, and perhaps their seasonal variability, which are used to generate the look-up tables and conduct aerosol retrievals. Our results call for further scrutiny of satellite-borne AOD, in particular its errors, limitations, and relation to the vertical aerosol profile and the particle size, shape, and composition distribution. This work is one step of the required analyses to gain better understanding of what the satellite-based AOD represents. IMPLICATIONS: The analysis results recommend incorporating the fine-mode AERONET AOD in MAIAC calibration. Specifically, they indicate the need to revisit MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition.

Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States.

We estimated daily ground-level PM2.5 concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM2.5 data suggested that the PM2.5 predictability could be enhanced by temporally varying PM2.5 and AOD relations at least at a seasonal scale. In this study, we used a mixed effects model that allowed daily variations in DB AOD-PM2.5 relations. Because DB AOD might less effectively represent local source emissions compared to regional ones, we added geographic information system (GIS) predictors into the mixed effects model to further explain PM2.5 concentrations influenced by local sources. A cross validation (CV) mixed effects model revealed reasonably high predictive power for PM2.5 concentrations with R(2) = 0.66. The relations between DB AOD and PM2.5 considerably varied by day, and seasonally varying effects of GIS predictors on PM2.5 suggest season-specific source emissions and atmospheric conditions. This study indicates that DB AOD in combination with land use regression can be particularly useful to generate spatially resolved PM2.5 estimates. This may reduce exposure errors for health effect studies in California. We expect that more detailed PM2.5 concentration patterns can help air quality management plan to meet air quality standards more effectively.