Table 5 - available via license: CC BY
Content may be subject to copyright.
Source publication
There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) 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 sp...
Similar publications
Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filter...
Groundwater is an important part of water storage and one of the important sources of agricultural irrigation, urban living, and industrial water use. The recent launch of Gravity Recovery and Climate Experiment (GRACE) Satellite has provided a new way for studying large-scale water storage. The application of GRACE in local water resources has bee...
Climate change intensifies longstanding tensions over groundwater sustainability and equity of access among users. Though private land ownership is a primary mechanism for accessing groundwater in many regions, few studies have systematically examined the extent to which farmland markets transform groundwater access patterns over time. This study b...
Agricultural bioenergy utilization relies on crop and livestock production, favouring an integrated crop–livestock–bioenergy production model. Yet the integrated system’s exact contribution to mitigating various environmental burdens from the crop production system and livestock production system remains unclear. Here we inventory the environmental...
Rescaling digital images for display on various devices, while simultaneously removing noise, has increasingly become a focus of attention. However, limited research has been done on a unified framework that can efficiently perform both tasks. In response, we propose INDIRECT (INvertible and Discrete noisy Image Rescaling with Enhancement from Case...
Citations
... Satellite-based estimates of surface PM 2.5 concentrations are continually advancing (van Donkelaar et al., 2021) and can guide exposure-reducing actions to protect public health (Anenberg et al., 2020;Holloway et al., 2021). Surface PM 2.5 is estimated from satellites by relating satellite observations of columnar aerosol optical depth (AOD) to ground-level PM 2.5 concentrations with ground-based monitors and/or atmospheric chemistry models (Di et al., 2019;Diao et al., 2019;Hammer et al., 2020;Wang et al., 2018;. These satellite-based data sets have been used to quantify health impacts of PM 2.5 (e.g., Anenberg et al., 2018;Diao et al., 2019), identify racial and socioeconomic disparities in PM 2.5 exposure (Castillo et al., 2021;Kerr et al., 2022;Liu et al., 2021), and estimate smoke exposure during wildfire events (e.g., Geng et al., 2018;Rappold et al., 2011). ...
Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national‐level alert‐day identification relies predominately on surface monitor networks and forecasters. Satellite‐based estimates of surface air quality have rapidly advanced and have the capability to inform exposure‐reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite‐based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 μg m⁻³) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person‐alerts (alert days × population) in 2020 over polar‐orbiting satellite data. We apply pre‐existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person‐alerts may lead to 1,200 (800–1,500) or 54% more averted PM2.5‐attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8–17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.
... Albeit remaining CNNs have made getting through applications in PC vision, scarcely any examinations on lingering profound organizations for relapse are accounted for, likely because of the absence of ideal organization geography and accessibility of thick example information for profound organization preparing [22]. ...
... By exploiting the POLDER land aerosol inversion algorithm based on existing assumptions (such as aerosol models of the LUT and surface effects, etc.), it was confirmed that the polarization retrieval is sensitive to small particles and is therefore better than MODIS (Deuz e et al., 2001;Br eon et al., 2002;Br eon & Maignan, 2016;Vachon et al., 2004;Antezana, Massey, Ruiz, & Stojanoff, 2007;Fan, Chen, Lin, Han, & Goloub, 2009). Moreover, forward computations are valid based on the BRDF model, such as the Hyper-Angular Rainbow Polarimeter CubeSat, and therefore the POLDER AOD is reliable for estimating the POLDER sensitivity to aerosol size (Fan et al., 2009;Cheng, Gu, Yu, & Tian, 2010;Tanr e et al., 2011;Li, Li, & Zha, 2018;Wang, Hu, et al., 2018). Analysis of the cloud contamination helps improve AOD retrieval accuracy (Stap, Hasekamp, & Grey et al. (2006) Ocean and land ATSR-2, AATSR Over land, MODIS land surface BRDF model is used to determine a priori surface reflectance Over the sea a surface reflectance model is used A multiple view angle retrieval algorithm named Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) is used Land and dark water ...
... Most importantly, the MCD19 products have finer spatial resolution than the others. The availability of a long-term AOD record across the globe has increased our understanding of the mechanism of air pollution (Kloog et al., 2004;Di et al., 2016;Tang, Coull, Schwartz, Di, & Koutrakis, 2017;Just et al., 2015;Wang, Hu, et al., 2018;Xiao et al., 2017;Chanpimol, Seamon, Hernandez, Harris-Love, & Blackman, 2017) and AOD relationships with meteorological systems . The long-term time series of MODIS AOD data make it possible to analyze climate change and radiative forcing (Yu et al., 2006;Kumar, Srivastava, & Kumari, 2007;Remer et al., 2008;Li, Yuan, et al., 2009;Guleria, Kuniyal, & Dhyani, 2012;Paasonen et al., 2013;Lodhi, Beegum, & Singh, 2013;Gautam et al., 2013;Mao et al., 2014;Soni, Payra, & Verma, 2018;Zhang, Ma, et al., 2018;Boiyo, Kumar, Zhao, & Bao, 2017). ...
Aerosol optical depth (AOD) is widely recognized as a critical indicator in understanding atmospheric physics and regional air quality because of its capability for quantifying aerosol loading in the atmosphere. Retrieving AOD from space-borne sensors’ observations has become the primary technique for monitoring aerosol loading on a large scale. There is currently a renewed interest in designing new satellite sensors and developing more advanced retrieval algorithms to measure AOD from space in order to better quantify concentrations of particulate matters (PMs) for advanced air quality management, environmental health assessment, and climate change studies. However, retrieving high-resolution AOD at varying scales is still a challenging task due to the low signal-to-noise ratio in sensing, algorithmic synthesis constraints, downscaling issues, and data gaps resulting from adverse impacts such as cloud contamination. Current state-of-the-art technologies still do not permit delicate urban-scale environmental health studies based on appropriate AOD-PMs relationships. This review paper provides a holistic view of the major advances in AOD measurements, elucidates the limitations of current AOD products, presents the challenges with respect to the derivation of high-resolution AOD, and highlights perspectives regarding the possible improvements of satellite-based AOD estimation.
Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectivesAll authors
Xiaoli Wei, Ni-Bin Chang , Kaixu Bai & Wei Gao
https://doi.org/10.1080/10643389.2019.1665944
Published online
04 November 2019
... An expository strategy for show determination and they take after a long-running process to get the foremost precise demonstration. At long, it is worth saying that modern approaches that combine machine learning methods with the utilize of air pollution information to get it and make strides forecasts on air contamination [10]. ...
From the past few decades, it has been observed that the urbanization and industrialization are expanding in the developed nations and are confronting the overwhelming air contamination issue. The citizens and governments have experienced and expressed the increasingly concerned regarding the impact of air pollution affecting human health and proposed sustainable development for overriding air pollution issues across the worldwide. The outcome of modern industrialization contains the liquid droplets, solid particles and gas molecules and is spreading in the atmospheric air. The heavy concentration of particulate matter of size PM10 and PM2.5 is seriously caused adverse health effect. Through the determination of particulate matter concentration in atmospheric air for the betterment of human being well in primary importance. In this paper machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017. These models were compared with the existing traditional models and perform better in predictive performance. The performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R²).
... In Newport City, contaminated air gets to be hurtful to numerous human lives and particularly fifty percent of the kids are unhealthy influenced due to the contaminated air [3]. The genuine of the relationship between the particulate matter and other pollutant factors Such as PM 2.5 , SO 2 , NO x , CO, PM 10 , O 3 , etc. Appears the level of air pollution and the particulate matter level in air pollution. PM 2.5 particle size is less than 2.5 micrograms within the across which has been connected to numerous unfavorable well being impacts, counting cardiovascular and respiratory morbidity. ...
... An expository strategy for show determination and they take after a long-running process to get the foremost precise demonstrate. At long, it is worth saying modern approaches that combine machine learning methods with the utilize of air pollution information to get it and make strides forecasts on air contamination [10]. ...
... They found that the coefficient of judgments (R 2 ) of cross validation (CV) were 0.79 and 0.72 for PM 10 and PM 2.5 , separately, illustrating that the AOD-based demonstrate can be connected in a little range with the strenuous territory and parched climate conditions. Connected Direct determination Imaging Spectroradiometer (MODIS) AOD to gauge ground-level PM 10 concentrations amid 2000 to 2009 in Lombardy, North Italy. They detailed a tall execution of AOD-based estimation show (R 2 = 0.787, year to year variety 0.738 to 0.818) and declared that AOD-based estimation show could be a solid strategy to assess PM 10 concentration in a region with complex geographic and climate designs [11]. ...
... While we have focused our discussion here on AOD, the most widely used metric for ambient 5 particulates, there are other remote sensing instruments that can expand the value of satellite-6 based information (some of these are already in use in the data products presented here). For coverage, which can be used to detect aerosols present above the surface ( Ford and Heald, 2013), 10 and to correct model biases in the vertical profiles of aerosol extinction when quality controlled 11 retrievals are available ( Geng et al., 2015;van Donkelaar et al., 2013;van Donkelaar et al., 2016;12 Li et al., 2015). ...
... When choosing the methods for data fusion or 16 interpolation, users often need to weigh between the advantages and disadvantages of different 17 methods. One may choose the efficient calculation of the Bayesian statistical downscaling 18method for lower spatial resolution (such as 12 km resolution inWang et al. (2018)), while 19 others may choose the more computational expensive methods that combine CTM, satellite and 20 ground monitors(van Donkelaar et al., 2019), or machine learning techniques (Hu et al., 2017; 21 Reid et al., 2015; Di et al., 2019). Methods that incorporate dispersion modeling may also be 22 useful in some applications (Scheffe et al., 2016; Ahangar et al., 2019). ...
Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are: ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.
... CTM simulations can be combined with air quality observations through data fusion (DF) methods to other air quality data to correct for the biases associated with the model outputs [20,21]. Models can be fused through mathematical methods such as statistical interpolation or machine learning approaches [22][23][24][25][26][27][28]. The data fused with CTM outputs can range from satellite, observational, land use, or meteorology data. ...
Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3−, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.
... The method also neglects nonlinear interactions in chemistry when applied for combinations of emission reduction cases. Furthermore, the approach projects PM 2.5 at monitoring sites, but the epidemiologic studies that inform health impact assessments are increasingly based on spatial concentration fields rather than concentrations at discrete monitoring sites (e.g., Crouse et al., 2015;Di et al., 2017a;Di et al., 2017b;Shi et al., 2016 Many hybrid methods have been developed in recent years to create PM 2.5 spatial fields by combining information from monitoring and other sources such as air quality models, satellites, and land-use data (e.g., Beckerman et al., 2013;Berrocal et al., 2012;Di et al., 2016;Friberg et al., 2016;Hu et al., 2017;Keller et al., 2015;Kim et al., 2017;Kloog et al., 2014;Lv et al., 2016;van Donkelaar et al., 2015;Wang et al., 2016;Wang et al., 2018). Predictions from these methods agree reasonably well with withheld observations in cross validation (CV) assessments. ...
... For instance, R 2 is frequently low for sites in the western U.S. where terrain is complex and wildfire emissions strongly influence PM 2.5 concentrations (Fig. S2). Previous studies have also reported challenges in modeling PM 2.5 in the western U.S. for these reasons (Di et al., 2016;Geng et al., 2018;Hu et al., 2017;Wang et al., 2018). Finally, the spatial prediction models performed well in areas where monitor siting is dense. ...
PM 2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGMs) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM 2.5 spatial fields to correspond with just meeting the PM 2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM 2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM 2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM 2.5 tends to be most responsive to primary PM 2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO 2 emission changes. On average, PM 2.5 is more responsive to changes in anthropogenic primary PM 2.5 emissions than NOx and SO 2 emissions in the contiguous U.S.
... Many hybrid methods have been developed in recent years to create PM 2.5 spatial fields by combining information from monitoring and other sources such as air quality models, satellites, and land-use data (e.g., Beckerman et al., 2013;Berrocal et al., 2012;Di et al., 2016;Friberg et al., 2016;Hu et al., 2017;Keller et al., 2015;Kim et al., 2017;Kloog et al., 2014;Lv et al., 2016;van Donkelaar et al., 2015;Wang et al., 2016;Wang et al., 2018). Predictions from these methods agree reasonably well with withheld observations in cross validation (CV) assessments. ...
... For instance, R 2 is frequently low for sites in the western U.S. where terrain is complex and wildfire emissions strongly influence PM 2.5 concentrations ( Figure S2). Previous studies have also reported challenges in modeling PM 2.5 in the western U.S. for these reasons (Di et al., 2016;Geng et al., 2018;Hu et al., 2017;Wang et al., 2018). Finally, the spatial prediction models performed well in areas where monitor siting is dense. ...
To estimate PM2.5 concentrations, many parametric regression models have been developed, while non-parametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating Aerosol Optical Depth (AOD) data, meteorological fields, and land use variables to estimate daily 24-hour averaged ground level PM2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross validation (CV) R2 value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 µg/m3, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM2.5 measurements increase CV R2 by ~0.02 and~0.06, respectively, indicating their significant contributions to prediction accuracy. Two different variable importance measures both indicate that the convolutional layer for nearby PM2.5 measurements and AOD values are among the most important predictor variables for the training process.
The link between agriculture and air pollution is well‐established, as are the benefits of the US Department of Agriculture's Conservation Reserve Program (CRP). However, little research has linked CRP to air quality directly. This study aims to address this gap by modeling the relationship between CRP and fine particulate matter (PM2.5) concentrations at the county level from 2001 to 2016. Several econometric models are estimated with panel data while controlling for drought, population, and wildfire. Results show that CRP has a statistically significant negative effect on PM2.5 concentrations. Using estimates from this model, we project an avoided 1,353 deaths, 1,687 deaths, and 3,022 deaths nationally in 2008 relative to three different counterfactual scenarios: all CRP acreage placed under cultivation, increased drought, and a combination of the first two. The value of the avoided mortality is estimated to be $9.5 billion, $11.8 billion, and $21.2 billion, respectively. These findings provide evidence that CRP may generate economic gains in terms of avoided mortality, well above the cost of the program.
