Remote Sensing

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19 pages, 12714 KiB

Open AccessArticle

Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment

by Andrii Shelestov, Hanna Yailymova, Bohdan Yailymov and Nataliia Kussul

Remote Sens. 2021, 13(23), 4769; https://doi.org/10.3390/rs13234769 - 25 Nov 2021

Cited by 15 | Viewed by 4145

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building [...] Read more.

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM_2.5 and PM₁₀) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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19 pages, 31131 KiB

Open AccessArticle

Estimation of PM_2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

by Xingdi Chen, Peng Kong, Peng Jiang and Yanlan Wu

Remote Sens. 2021, 13(22), 4545; https://doi.org/10.3390/rs13224545 - 12 Nov 2021

Cited by 7 | Viewed by 2712

Abstract

Directly establishing the relationship between satellite data and PM_2.5 concentration through deep learning methods for PM_2.5 concentration estimation is an important means for estimating regional PM_2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, [...] Read more.

Directly establishing the relationship between satellite data and PM_2.5 concentration through deep learning methods for PM_2.5 concentration estimation is an important means for estimating regional PM_2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM_2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM_2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM_2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R² of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m⁻³, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R² of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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21 pages, 3872 KiB

Open AccessArticle

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM_2.5 Temporal and Spatial Distributions

by Johana M. Carmona, Pawan Gupta, Diego F. Lozano-García, Ana Y. Vanoye, Iván Y. Hernández-Paniagua and Alberto Mendoza

Remote Sens. 2021, 13(16), 3102; https://doi.org/10.3390/rs13163102 - 6 Aug 2021

Cited by 9 | Viewed by 4039

Abstract

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5), mainly in urban areas with low air quality monitor density. [...] Read more.

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM_2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM_2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m⁻³. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m⁻³. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM_2.5 distributions in an urban area with low air quality monitor density. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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20 pages, 8656 KiB

Open AccessArticle

Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI

by Erik Crosman

Remote Sens. 2021, 13(5), 896; https://doi.org/10.3390/rs13050896 - 27 Feb 2021

Cited by 13 | Viewed by 3966

Abstract

The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH₄) columns to meteorological flow patterns over the [...] Read more.

The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH₄) columns to meteorological flow patterns over the Permian Basin, the largest oil and second-largest natural gas producing region in the United States. Over a two-year period (1 December 2018–1 December 2020), the largest average CH₄ enhancements were observed near and to the north and west of the primary emission regions. Four case study periods—two with moderate westerly winds associated with passing weather disturbances (8–15 March 2019 and 1 April–10 May 2019) and two other periods dominated by high pressure and low wind speeds (16–23 March 2019 and 24 September–9 October 2020)—were analyzed to better understand meteorological drivers of the variability in CH₄. Meteorological observations and analyses combined with TROPOMI observations suggest that weakened transport out of the Basin during low wind speed periods contributes to CH₄ enhancements throughout the Basin, while valley and slope flows may explain the observed western expansion of the Permian Basin CH₄ anomaly. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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20 pages, 4635 KiB

Open AccessArticle

Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM_2.5 Prediction

by Behzad Kianian, Yang Liu and Howard H. Chang

Remote Sens. 2021, 13(1), 126; https://doi.org/10.3390/rs13010126 - 1 Jan 2021

Cited by 34 | Viewed by 4752

Abstract

A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM_2.5 estimation, despite significant gaps in coverage. We analyze [...] Read more.

A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM_2.5 estimation, despite significant gaps in coverage. We analyze PM_2.5 and AOD from July 2011 in the contiguous United States. We examine two methods to aid in gap-filling AOD: (1) lattice kriging, a spatial statistical method adapted to handle large amounts data, and (2) random forest, a tree-based machine learning method. First, we evaluate each model’s performance in the spatial prediction of AOD, and we additionally consider ensemble methods for combining the predictors. In order to accurately assess the predictive performance of these methods, we construct spatially clustered holdouts to mimic the observed patterns of missing data. Finally, we assess whether gap-filling AOD through one of the proposed ensemble methods can improve prediction of PM_2.5 in a random forest model. Our results suggest that ensemble methods of combining lattice kriging and random forest can improve AOD gap-filling. Based on summary metrics of performance, PM_2.5 predictions based on random forest models were largely similar regardless of the inclusion of gap-filled AOD, but there was some variability in daily model predictions. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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20 pages, 4989 KiB

Open AccessArticle

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

by Kun Wang, Wen Wang, Weijia Wang, Xiaoqun Jiang, Tao Yu and Pubu Ciren

Remote Sens. 2020, 12(5), 790; https://doi.org/10.3390/rs12050790 - 1 Mar 2020

Cited by 6 | Viewed by 3295

Abstract

Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify [...] Read more.

Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM₁₀, PM_2.5, O₃, NO₂, SO₂ and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25° × 0.25°, show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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19 pages, 6111 KiB

Open AccessArticle

Retrieval of Fine-Resolution Aerosol Optical Depth (AOD) in Semiarid Urban Areas Using Landsat Data: A Case Study in Urumqi, NW China

by Xiangyue Chen, Jianli Ding, Jingzhe Wang, Xiangyu Ge, Mayira Raxidin, Jing Liang, Xiaoxiao Chen, Zipeng Zhang, Xiaoyi Cao and Yue Ding

Remote Sens. 2020, 12(3), 467; https://doi.org/10.3390/rs12030467 - 2 Feb 2020

Cited by 23 | Viewed by 4879

Abstract

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional [...] Read more.

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional air pollution and is often characterized by significant spatiotemporal dynamics. However, detailed local AOD information is ambiguous at best due to limited monitoring techniques. Currently, the availability of abundant satellite data and constantly updated AOD extraction algorithms offer unprecedented perspectives for high-resolution AOD extraction and long-time series analysis. This study, based on the long-term sequence MOD09A1 data from 2010 to 2018 and lookup table generation, uses the improved deep blue algorithm (DB) to conduct fine-resolution (500 m) AOD (at 550 nm wavelength) remote sensing (RS) estimation on Landsat TM/OLI data from the Urumqi region, analyzes the spatiotemporal AOD variation characteristics in Urumqi and combines gray relational analysis (GRA) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze AOD influence factors and simulate pollutant propagation trajectories in representative periods. The results demonstrate that the improved DB algorithm has a high inversion accuracy for continuous AOD inversion at a high spatial resolution in urban areas. The spatial AOD distribution in Urumqi declines from urban to suburban areas, and higher AODs are concentrated in cities and along roads. Among these areas, Xinshi District has the highest AOD, and Urumqi County has the lowest AOD. The seasonal AOD variation characteristics are distinct, and the AOD order is spring (0.411) > summer (0.285) > autumn (0.203), with the largest variation in spring. The average AOD in Urumqi is 0.187, and the interannual variation generally shows an upward trend. However, from 2010 to 2018, AOD first declined gradually and then declined significantly. Thereafter, AOD reached its lowest value in 2015 (0.076), followed by a significant AOD increase, reaching a peak in 2016 (0.354). This shows that coal to natural gas (NG) project implementation in Urumqi promoted the improvement of Urumqi’s atmospheric environment. According to GRA, the temperature has the largest impact on the AOD in Urumqi (0.699). Combined with the HYSPLIT model, it was found that the aerosols observed over Urumqi were associated with long-range transport from Central Asia, and these aerosols can affect the entire northern part of China through long-distance transport. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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22 pages, 5426 KiB

Open AccessArticle

Estimating Spatio-Temporal Variations of PM_2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

by Kainan Zhang, Gerrit de Leeuw, Zhiqiang Yang, Xingfeng Chen, Xiaoli Su and Jiashuang Jiao

Remote Sens. 2019, 11(22), 2679; https://doi.org/10.3390/rs11222679 - 16 Nov 2019

Cited by 39 | Viewed by 4152

Abstract

Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM_2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating [...] Read more.

Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM_2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM_2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R²), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m³, and 12.746 µg/m³, respectively, and the model cross-validation results were 0.703, 1.451 µg/m³, and 15.731 µg/m³, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM_2.5 concentrations in the Guanzhong Basin, and PM_2.5 concentrations vary with the seasons. This method can provide reliable PM_2.5 predictions to reduce the bias of exposure assessment in air pollution and health research. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Air Quality and Health)

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