Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households.

The effects of indoor air pollution on human health have drawn increasing attention among the scientific community as individuals spend most of their time indoors. However, indoor air sampling is labor-intensive and costly, which limits the ability to study the adverse health effects related to indoor air pollutants. To overcome this challenge, many researchers have attempted to predict indoor exposures based on outdoor pollutant concentrations, home characteristics, and weather parameters. Typically, these models require knowledge of the infiltration factor, which indicates the fraction of ambient particles that penetrates indoors. For estimating indoor fine particulate matter (PM2.5) exposure, a common approach is to use the indoor-to-outdoor sulfur ratio (Sindoor/Soutdoor) as a proxy of the infiltration factor. The objective of this study was to develop a robust model that estimates Sindoor/Soutdoor for individual households that can be incorporated into models to predict indoor PM2.5 and black carbon (BC) concentrations. Overall, our model adequately estimated Sindoor/Soutdoor with an out-of-sample by home-season R2 of 0.89. Estimated Sindoor/Soutdoor reflected behaviors that influence particle infiltration, including window opening, use of forced air heating, and air purifier. Sulfur ratio-adjusted models predicted indoor PM2.5 and BC with high precision, with out-of-sample R2 values of 0.79 and 0.76, respectively.

Developing particle emission inventories using remote sensing (PEIRS).

Information regarding the magnitude and distribution of PM2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time-consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially resolved emission inventories for PM2.5. This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeastern United States during the period 2002-2013 using high-resolution 1 km × 1 km aerosol optical depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R2 = 0.66-0.71, CV = 17.7-20%). Predicted emissions are found to correlate with land use parameters, suggesting that our method can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively. IMPLICATIONS: We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM2.5 emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

Trends and spatial patterns of fine-resolution aerosol optical depth-derived PM2.5 emissions in the Northeast United States from 2002 to 2013.

Clarifying the trends in quantity, location, and causes of PM2.5 (particulate matter with an aerodynamic diameter <2.5 µm) emission changes is critical for evaluating and improving emission control strategies and reduce the risk posed to human health. According to the National Emissions Inventory (NEI) released by the U.S. Environmental Protection Agency (EPA), a general downward trend in PM2.5 emissions has been observed in the United States over the past decade. Although this trend is representative at the national level, it lacks the precision to locate emission hotspots at a finer scale. Moreover, the changes reported in the NEI are likely confounded by periodic modification of inventory methods, and imprecision for area sources. In this regard, it is imperative to acquire emission inventories with as much spatial and temporal details as possible to further our knowledge of particle emissions, exposure levels, and associated health risks. In this study, we employed the PEIRS (Particle Emission Inventory using Remote Sensing) approach (Tang et al., 2016) predict triennial-averaged emissions at 1 km × 1 km resolution across the Northeast United States from 2002 to 2013. Notably, the PEIRS approach is able to capture both primary emission and secondary formation of PM2.5. Regional emission trends were evaluated using quantile regression, and source-oriented trends were modeled with land use regression. The analysis found a regional decrease in PM2.5 emissions of 3.3 tons/yr/km2 (18%) over the 12-yr period. Furthermore, the rate of emission change at the extremes of the emission distribution was significantly different than that of the mean. Both quantile regression and spatial trends imply that the majority of the reduction in PM2.5 emissions was attributable to highly developed spaces such as metropolitan areas and important traffic corridors. This urban-rural disparity was particularly apparent during the cold season. Indirect evidence suggested that the emission decline during the warm season is primarily attributed to less secondary particle formation. These findings warrant closer investigation of the impact of seasonality on PM2.5 emissions. IMPLICATIONS: Emission trend analysis provides crucial information for evaluating and enhancing the efficacies of emission control strategies as well as studying air pollution associated health risks. In this study, the patterns and trends of year-round and seasonal PM2.5 emission over the Northeast United States are presented at a spatial resolution of 1 km × 1 km for the period of 2002-2012.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.