Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

BACKGROUND

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error.

OBJECTIVE

To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits.

METHODS

We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics.

RESULTS

Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging).

SIGNIFICANCE

The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies.

IMPACT STATEMENT

This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India

COVID-19 pandemic has forced to lockdown entire India starting from 24th March 2020 to 14th April 2020 (first phase), extended up to 3rd May 2020 (second phase), and further extended up to 17th May 2020 (third phase) with limited relaxation in non-hotspot areas. This strict lockdown has severely curtailed human activity across India. Here, aerosol concentrations of particular matters (PM) i.e., PM10, PM2.5, carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), ammonia (NH3) and ozone (O3), and associated temperature fluctuation in four megacities (Delhi, Mumbai, Kolkata, and Chennai) from different regions of India were investigated. In this pandemic period, air temperature of Delhi, Kolkata, Mumbai and Chennai has decreased about 3 °C, 2.5 °C, 2 °C and 2 °C respectively. Compared to previous years and pre-lockdown period, air pollutants level and aerosol concentration (-41.91%, -37.13%, -54.94% and -46.79% respectively for Delhi, Mumbai, Kolkata and Chennai) in these four megacities has improved drastically during this lockdown period. Emission of PM2.5 has experienced the highest decrease in these megacities, which directly shows the positive impact of restricted vehicular movement. Restricted emissions produce encouraging results in terms of urban air quality and temperature, which may encourage policymakers to consider it in terms of environmental sustainability.

Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990-2019: A Space-Time Bayesian Neural Network Downscaler

Long-term exposure to ambient ozone (O₃) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O₃ concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R² = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R² = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R² = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O₃ are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O₃ are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O₃ tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O₃ exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O₃ database with multiple metrics fills in the literature gap for long-term surface O₃ exposure tracing.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), fine particulate matter (PM_2.5), and three speciated PM_2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO₄)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO₂ and PM_2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM_2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO₂, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Spatially and temporally resolved aerosol data are essential for conducting air quality studies and assessing the health effects associated with exposure to air pollution. As these data are often expensive to acquire and time consuming to estimate, computationally efficient methods are desirable. When coarse-scale data or imagery are available, fine-scale data can be generated through downscaling methods. We developed an Artificial Neural Network Sequential Downscaling Method (ASDM) with Transfer Learning Enhancement (ASDMTE) to translate time-series data from coarse- to fine-scale while maintaining between-scale empirical associations as well as inherent within-scale correlations. Using assimilated aerosol optical depth (AOD) from the GEOS-5 Nature Run (G5NR) (2 years, daily, 7 km resolution) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (20 years, daily, 50 km resolution), coupled with elevation (1 km resolution), we demonstrate the downscaling capability of ASDM and ASDMTE and compare their performances against a deep learning downscaling method, Super Resolution Deep Residual Network (SRDRN), and a traditional statistical downscaling framework called dissever ASDM/ASDMTE utilizes empirical between-scale associations, and accounts for within-scale temporal associations in the fine-scale data. In addition, within-scale temporal associations in the coarse-scale data are integrated into the ASDMTE model through the use of transfer learning to enhance downscaling performance. These features enable ASDM/ASDMTE to be trained on short periods of data yet achieve a good downscaling performance on a longer time-series. Among all the test sets, ASDM and ASDMTE had mean maximum image-wise R2 of 0.735 and 0.758, respectively, while SRDRN, dissever GAM and dissever LM had mean maximum image-wise R2 of 0.313, 0.106 and 0.095, respectively.

Have any effect of COVID-19 lockdown on environmental sustainability? A study from most polluted metropolitan area of India

The long-term lockdown due to COVID-19 has beneficial impact on the natural environment. India has enforced a lockdown on 24th March 2020 and was subsequently extended in various phases. The lockdown due to the sudden spurt of the COVID-19 pandemic has shown a significant decline in concentration of air pollutants across India. The present article dealt with scenarios of air quality concentration of air pollutants, and effect on climatic variability during the COVID-19 lockdown period in Kolkata Metropolitan Area, India. The result showed that the air pollutants are significantly reduced and the air quality index (AQI) was improved during the lockdown months. Aerosol concentrations decreased by - 54.94% from the period of pre-lockdown. The major air pollutants like particulate matters (PM_2.5, PM₁₀), sulphur dioxide (SO₂), carbon monoxide (CO) and Ozone (O₃) were observed the maximum reduction ( - 40 to  - 60%) in the COVID-19 lockdown period. The AQI has been improved by 54.94% in the lockdown period. On the other hand, Sen's slope rank and the Mann-Kendal trend test showed the daily decreased of air pollutants rate is  - 0.051 to  - 1.586 μg /m3. The increasing trend of daily minimum, average, and maximum temperature from the month of March to May in this year (2020s) are 0.091, 0.118, and 0.106 °C which is lowest than the 2016s to 2019s trend. Therefore, this research has an enormous opportunity to explain the effects of the lockdown on air quality and climate variability, and it can also be helpful for policymakers and decision-makers to enact appropriate measures to control air pollution.

A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Exposure to fine particulate matter (PM_2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM_2.5 in South Africa due to the lack of high-resolution PM_2.5 exposure estimates. We developed a random forest model to estimate daily PM_2.5 concentrations at 1 km² resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM_2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played important roles in PM_2.5 modeling. The cross-validation R² and Root Mean Square Error of our model was 0.80 and 9.40 μg/m³, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM_2.5 levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM_2.5 concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM_2.5 standards, PM_2.5 concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM_2.5 model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.

Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA

Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO₂, SO₂, O₃, PM₁₀, and PM_2.5 species EC, OC, NO₃, NH₄, SO₄) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM_2.5 species EC (R² = 0.64), OC (R² = 0.75), NH₄ (R² = 0.84), NO₃ (R2 = 0.73), and SO₄ (R² = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM_2.5 species and several gases at this spatial and temporal resolution.

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

Statistical downscaling with spatial misalignment: Application to wildland fire PM2.5 concentration forecasting

Fine particulate matter, PM_2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM_2.5 air pollution in the US. Forecasters use numerical models to predict PM_2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments

Wildland fire is a major emission source of fine particulate matter (PM_2.5), which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM_2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM_2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM_2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In a case study of the eastern U.S. in 2014, we evaluated the calibration performance using three cross-validation methods, which consistently indicated that the prediction accuracy was improved with an R² of 0.47-0.64. In a health impact study based on the wildland fire-specific PM_2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM_2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.

Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations

Ambient exposure to fine particulate matter (PM_2.5) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM_2.5 concentrations nationwide, while the lack of surface monitoring data before 2013 have created difficulties in historical PM_2.5 exposure estimates. Hindcast approaches using statistical models or chemical transport models (CTMs) were developed to overcome this limitation, while those approaches still suffer from incomplete daily coverage due to missing AOD data or limited accuracy due to uncertainties of CTMs. Here we developed a new machine learning (ML) model with high-dimensional expansion (HD-expansion) of numerous predictors (including AOD and other satellite covariates, meteorological variables and CTM simulations). Through comprehensive characterization of the nonlinear effects of, and interactions among different predictors, the HD-expansion parameterized the association between PM_2.5 and AOD as a nonlinear function of space and time covariates (e.g., planetary boundary layer height and relative humidity). In this way, the PM_2.5-AOD association can vary spatiotemporally. We trained the model with data from 2013 to 2016 and evaluated its performance using annually-iterated cross-validation, which iteratively held out the in-situ observations for a whole calendar year (as testing data) to examine the predictions from a model trained by the rest of the observations. Our estimates were found to be in good agreement with in-situ observations, with correlation coefficients (R²) of 0.61, 0.68, and 0.75 for daily, monthly and annual averages, respectively. To interpolate the missing predictions due to incomplete AOD data, we incorporated a generalized additive model into the ML model. The two-stage estimates of PM_2.5 sacrificed the prediction accuracy on a daily timescale (R² = 0.55), but achieved complete spatiotemporal coverage and improved the accuracy of monthly (R² = 0.71) and annual (R² = 0.77) averages. The model was then used to predict daily PM_2.5 concentrations during 2000-2016 across China and estimate long-term trends in PM_2.5 for the period. We found that population-weighted concentrations of PM_2.5 significantly increased, by 2.10 (95% confidence interval (CI): 1.74, 2.46) μg/m³/year during 2000-2007, and rapidly decreased by 4.51 (3.12, 5.90) μg/m³/year during 2013-2016. In this study, we produced AOD-based estimates of historical PM_2.5 with complete spatiotemporal coverage, which were evidenced as accurate, particularly in middle and long term. The products could support large-scale epidemiological studies and risk assessments of ambient PM_2.5 in China and can be accessed via the website (http://www.meicmodel.org/dataset-phd.html).

Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data

High-spatiotemporal-resolution PM2.5 data are critical to assessing the impacts of prolonged exposure to PM2.5 on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM2.5, providing an effective way to reveal spatiotemporal variations of PM2.5 across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM2.5 measurements were fused to estimate daily ground-level PM2.5 concentrations in Beijing for 2013–2017 at 1 km resolution through a linear mixed effect model (LMEM). The results showed a good agreement between the estimated and measured PM2.5 and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM2.5 level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM2.5 level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM2.5 concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM2.5 data products we generated can be used to assess the long-term impacts of PM2.5 on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas.

The sensitivity of satellite-based PM2.5 estimates to its inputs: Implications to model development in data-poor regions

Exposure to fine particulate matter (PM_2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air pollution health effects research in the data-poor regions, where pollution levels are often the highest, is still very limited due to the lack of high-quality exposure estimates. To improve our understanding of the desired input datasets for the application of satellite-based PM_2.5 exposure models in data-poor areas, we applied a Bayesian ensemble model in the southeast U.S. that was selected as a representative data-rich region. We designed four groups of sensitivity tests to simulate various data-poor scenarios. The factors considered that would influence the model performance included the temporal sampling frequency of the monitors, the number of ground monitors, the accuracy of the chemical transport model simulation of PM_2.5 concentrations, and different combinations of the additional predictors. While our full model achieved a 10-fold cross-validated (CV) R² of 0.82, we found that when reducing the sampling frequency from the current 1-in-3 day to 1-in-9 day, the CV R² decreased to 0.58, and the predictions could not capture the daily variations of PM_2.5. Half of the current stations (i.e., 30 monitors) could still support a robust model with a CV R² of 0.79. With 20 monitors, the CV R² decreased from 0.71 to 0.55 when 100% additional random errors were added to the original CMAQ simulations. However, with a sufficient number of ground monitors (e.g., 30 monitors), our Bayesian ensemble model had the ability to tolerate CMAQ errors with only a slight decrease in CV R² (from 0.79 to 0.75). With fewer than 15 monitors, our full model collapsed and failed to fit any covariates, while the models with only time-varying variables could still converge even with only five monitors left. A model without the land use parameters lacked fine spatial details in the prediction maps, but could still capture the daily variability of PM_2.5 (CV R² ≥ 0.67) and might support a study of the acute health effects of PM_2.5 exposure.

A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM_2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM_2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM_2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM_2.5 with an R² at 70% and generated reliable annual and seasonal PM_2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM_2.5 exposure assessments and can also quantify the prediction errors.

Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region

Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Satellite-Based Daily PM2.5 Estimates During Fire Seasons in Colorado

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high-resolution PM2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5 concentrations over fire seasons (April to September) in Colorado for 2011-2014. Our model had a 10-fold cross-validated R 2 of 0.66 and root-mean-squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM2.5.

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM_2.5 is a promising way to fill the areas that are not covered by ground PM_2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM_2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM_2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R² = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM_2.5 estimates.

Daily estimation of ground-level PM2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM_2.5) concentrations to evaluate their health effects. The associated estimation accuracy is often reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM_2.5 variations. In this study, we aim to estimate ground-level PM_2.5 concentrations at a fine resolution with improved accuracy by fusing fine-scale satellite and ground observations in the populated and polluted Beijing-Tianjin-Hebei (BTH) area of China in 2014. We employed a Bayesian-based statistical downscaler to model the spatio-temporal linear AOD-PM_2.5 relationships. We used a 3km MODIS AOD product, which was resampled to a 4km resolution in a Lambert conic conformal projection, to assist comparison and fusion with predictions by atmospheric chemistry models. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a good performance in the fitting procedure (R²=0.75) and in the cross validation procedure (R²=0.58 by random method and R²=0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM_2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM_2.5 pollution conditions in BTH. The prediction accuracy of PM_2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM_2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM_2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

Estimation of daily PM10 concentrations in Italy (2006-2012) using finely resolved satellite data, land use variables and meteorology

Health effects of air pollution, especially particulate matter (PM), have been widely investigated. However, most of the studies rely on few monitors located in urban areas for short-term assessments, or land use/dispersion modelling for long-term evaluations, again mostly in cities. Recently, the availability of finely resolved satellite data provides an opportunity to estimate daily concentrations of air pollutants over wide spatio-temporal domains. Italy lacks a robust and validated high resolution spatio-temporally resolved model of particulate matter. The complex topography and the air mixture from both natural and anthropogenic sources are great challenges difficult to be addressed. We combined finely resolved data on Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, ground-level PM₁₀ measurements, land-use variables and meteorological parameters into a four-stage mixed model framework to derive estimates of daily PM₁₀ concentrations at 1-km2 grid over Italy, for the years 2006-2012. We checked performance of our models by applying 10-fold cross-validation (CV) for each year. Our models displayed good fitting, with mean CV-R2=0.65 and little bias (average slope of predicted VS observed PM₁₀=0.99). Out-of-sample predictions were more accurate in Northern Italy (Po valley) and large conurbations (e.g. Rome), for background monitoring stations, and in the winter season. Resulting concentration maps showed highest average PM₁₀ levels in specific areas (Po river valley, main industrial and metropolitan areas) with decreasing trends over time. Our daily predictions of PM₁₀ concentrations across the whole Italy will allow, for the first time, estimation of long-term and short-term effects of air pollution nationwide, even in areas lacking monitoring data.

Evaluating the effectiveness of air quality regulations: A review of accountability studies and frameworks

Assessments of past environmental policies-termed accountability studies-contribute important information to the decision-making process used to review the efficacy of past policies, and subsequently aid in the development of effective new policies. These studies have used a variety of methods that have achieved varying levels of success at linking improvements in air quality and/or health to regulations. The Health Effects Institute defines the air pollution accountability framework as a chain of events that includes the regulation of interest, air quality, exposure/dose, and health outcomes, and suggests that accountability research should address impacts for each of these linkages. Early accountability studies investigated short-term, local regulatory actions (for example, coal use banned city-wide on a specific date or traffic pattern changes made for Olympic Games). Recent studies assessed regulations implemented over longer time and larger spatial scales. Studies on broader scales require accountability research methods that account for effects of confounding factors that increase over time and space. Improved estimates of appropriate baseline levels (sometimes termed "counterfactual"-the expected state in a scenario without an intervention) that account for confounders and uncertainties at each link in the accountability chain will help estimate causality with greater certainty. In the direct accountability framework, researchers link outcomes with regulations using statistical methods that bypass the link-by-link approach of classical accountability. Direct accountability results and methods complement the classical approach. New studies should take advantage of advanced planning for accountability studies, new data sources (such as satellite measurements), and new statistical methods. Evaluation of new methods and data sources is necessary to improve investigations of long-term regulations, and associated uncertainty should be accounted for at each link to provide a confidence estimate of air quality regulation effectiveness. The final step in any accountability is the comparison of results with the proposed benefits of an air quality policy.

IMPLICATIONS

The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.

Spatiotemporal calibration and resolution refinement of output from deterministic models

Spatiotemporal calibration of output from deterministic models is an increasingly popular tool to more accurately and efficiently estimate the true distribution of spatial and temporal processes. Current calibration techniques have focused on a single source of data on observed measurements of the process of interest that are both temporally and spatially dense. Additionally, these methods often calibrate deterministic models available in grid-cell format with pixel sizes small enough that the centroid of the pixel closely approximates the measurement for other points within the pixel. We develop a modeling strategy that allows us to simultaneously incorporate information from two sources of data on observed measurements of the process (that differ in their spatial and temporal resolutions) to calibrate estimates from a deterministic model available on a regular grid. This method not only improves estimates of the pollutant at the grid centroids but also refines the spatial resolution of the grid data. The modeling strategy is illustrated by calibrating and spatially refining daily estimates of ambient nitrogen dioxide concentration over Connecticut for 1994 from the Community Multiscale Air Quality model (temporally dense grid-cell estimates on a large pixel size) using observations from an epidemiologic study (spatially dense and temporally sparse) and Environmental Protection Agency monitoring stations (temporally dense and spatially sparse). Copyright © 2016 John Wiley & Sons, Ltd.

Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China

The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.

High-resolution satellite-based analysis of ground-level PM2.5 for the city of Montreal

Satellite remote sensing offers the opportunity to determine the spatial distribution of aerosol properties and could fill the gap of ground-level observations. Various algorithms have recently been developed in order to retrieve the aerosol optical depth (AOD) at continental scales. However, they are, to some extent, subject to coarse spatial resolutions which are not appropriate for intraurban scales as usually needed in health studies. This paper presents an improved AOD retrieval algorithm for satellite instrument MODIS at 1-km resolution for intraurban scales. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles and local scale factors obtained from the GEOS-Chem model simulation. The developed method has been applied to retrieve the AODs and to evaluate the ground-level PM2.5 over the city of Montreal, Canada for 2009 on daily, monthly and annual scales. The daily and monthly results are compared with the monitoring values with correlations R(2) ranging from 0.86 to 0.93. Especially, the annual mean PM2.5 concentrations are in good agreement with the measurement values at all monitoring stations (r=0.96, slope=1.0132 ± 0.0025, intercept=0.5739 ± 0.0013). This illustrates that the developed AOD retrieval algorithm can be used to retrieve AODs at a higher spatial resolution than previous studies to further derive the regional full coverage PM2.5 results at finer spatial and temporal scales. The study results are useful in health risk assessment across this region.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data

Estimates of exposure to PM2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM2.5 and PM10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM2.5 and PM10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R2 values of 0.79 and 0.72 for PM10 and PM2.5, respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM2.5 and PM10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM2.5 and PM10 in Israel, which could be used in the future for epidemiological studies.

Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City

Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

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.