Spatiotemporal Associations between PM2.5 and SO2 as well as NO2 in China from 2015 to 2018

Given the critical roles of nitrates and sulfates in fine particulate matter (PM2.5) formation, we examined spatiotemporal associations between PM2.5 and sulfur dioxide (SO2) as well as nitrogen dioxide (NO2) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO2 and NO2 to PM2.5 was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM2.5 and SO2 decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM2.5 and SO2 loadings therein. The statistically significant coupled MCA mode between PM2.5 and SO2 indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing–Tianjin–Hebei region. Further statistical modeling practices revealed that the observed PM2.5 variations in northern China could be explained largely by SO2 rather than NO2 therein, given the estimated relatively high importance of SO2. In general, the evidence-based results in this study indicate a strong linkage between PM2.5 and SO2 in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China

Multisensor Data Fusion And Machine Learning For Environmental Remote Sensing

Combining versatile data sets from multiple satellite sensors with advanced thematic information retrieval is a powerful way for studying complex earth systems. The book Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing offers complete understanding of the basic scientific principles needed to perform image processing, gap filling, data merging, data fusion, machine learning, and feature extraction. Written by two experts in remote sensing, the book presents the required basic concepts, tools, algorithms, platforms, and technology hubs toward advanced integration. By merging and fusing data sets collected from different satellite sensors with common features, we are enabled to utilize the strength of each satellite sensor to the maximum extent. The inclusion of machine learning or data mining techniques to aid in feature extraction after gap filling, data merging and/or data fusion further empowers earth observation, leading to confirm the whole is greater than the sum of its parts. Contemporary applications discussed in this book make all essential knowledge seamlessly integrated by an interdisciplinary manner. These case-based engineering practices uniquely illustrate how to improve such an emerging field of importance to cope with the most challenging real-world environmental monitoring issues

Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy

Quantification Of Relative Contribution Of Antarctic Ozone Depletion To Increased Austral Extratropical Precipitation During 1979-2013

Attributing the observed climate changes to relevant forcing factors is critical to predicting future climate change scenarios. Precipitation observations in the Southern Hemisphere indicate an apparent moistening pattern over the extratropics during the time period 1979 to 2013. To investigate the predominant forcing factor in triggering such an observed wetting climate pattern, precipitation responses to four climatic forcing factors, including Antarctic ozone, water vapor, sea surface temperature (SST), and carbon dioxide, were assessed quantitatively in sequence through an inductive approach. Coupled time-space patterns between the observed austral extratropical precipitation and each climatic forcing factor were firstly diagnosed by using the maximum covariance analysis (MCA). With the derived time series from each coupled MCA modes, statistical relationships were established between extratropical precipitation variations and each climatic forcing factor by using the extreme learning machine. Based on these established statistical relationships, sensitivity tests were conducted to estimate precipitation responses to each climatic forcing factor quantitatively. Quantified differential contribution with respect to those climatic forcing factors may explain why the observed austral extratropical moistening pattern is primarily driven by the Antarctic ozone depletion, while mildly modulated by the cooling effect of equatorial Pacific SST and the increased greenhouse gases, respectively

Smart Information Reconstruction Via Time-Space-Spectrum Continuum For Cloud Removal In Satellite Images

Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of 7.4 E-04 sr-1 between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications

Spatial and Temporal Variabilities of PM2.5 Concentrations in China Using Functional Data Analysis

As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally

Spectral Information Adaptation And Synthesis Scheme For Merging Cross-Mission Ocean Color Reflectance Observations From Modis And Viirs

Obtaining a full clear view of coastal bays, estuaries, lakes, and inland waters is challenging with single satellite sensor observations due to cloud impacts. Cross-mission sensors provide the synergistic opportunity to improve spatial and temporal coverage by merging their observations; however, discrepancies originating from the instrumental, algorithmic, and temporal differences should be eliminated before merging. This paper presents the Spectral Information Adaptation and Synthesis Scheme (SIASS) for generating cross-mission consistent ocean color reflectance by merging 2012-2015 observations from Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite over Lake Nicaragua in Central America, where the cloud impact is salient. The SIASS is able to not only eliminate incompatibilities for matchup bands but also reconstruct spectral information for mismatched bands among sensors. Statistics indicate that the average monthly coverage of a merged ocean color reflectance product over Lake Nicaragua is nearly twice that of any single-sensor observation. Results show that SIASS significantly improves consistency among cross-mission sensors by mitigating prominent discrepancies. In addition, reconstructed spectral information for those mismatched bands help preserve more spectral characteristics needed to better monitor and understand the dynamic aquatic environment. The final implementation of SIASS to map the chlorophyll-aconcentration demonstrates the efficacy of SIASS in bias correction and consistency improvement. In general, SIASS can be applied to remove cross-mission discrepancies among sensors to improve the overall consistency

Statistical Bias Correction For Creating Coherent Total Ozone Record From Omi And Omps Observations

A long-term coherent total column ozone (TCO) record is essential to ozone layer variability assessment, especially the detection of early signs of ozone recovery after years of depletion. Because of differences in satellite platforms and instruments design, calibration, and retrieval algorithms, however, significant cross-mission biases are observed between multiple sensor TCO observations in the common time-space domain. To attain a coherent TCO record, observed cross-mission biases should be accurately addressed prior to the data-merging scheme. In this study, a modified statistical bias correction method was proposed based on the quantile-quantile adjustment to remove apparent cross-mission TCO biases between the Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS). To evaluate the effectiveness of this modified algorithm, the overall inconsistency (OI), a unique time-series similarity measure, was proposed to quantify the improvements of consistency (or similarity) between cross-mission TCO time series data before and after bias correction. Common observations during the overlapped time period of 2012-2015 were used to characterize the systematic bias between OMPS and OMI through the modified bias correction method. TCO observations from OMI during 2004-2015 were then projected to the OMPS level by removing associated cross-mission biases. This modified bias correction scheme significantly improved the overall consistency, with an average improvement of 90% during the overlapped time period at the global scale. In addition to the evaluation of consistency improvements before and after bias correction, impacts of cross-mission biases on long-term trend estimations were also investigated. Comparisons of derived trends from the merged TCO time series before and after bias correction across 38 ground-based stations indicate that cross-mission biases not only affect magnitudes of estimated trends, but also result in different phases of trends. Further comparisons of estimated seasonal TCO trends before and after bias correction at the global scale suggest that trends derived from the bias-corrected time series are more accurate than those without bias correction. Overall, the bias correction scheme developed in this study is essential for preparing an accurate long-term TCO record representative of trend analysis to support future assessment of ozone recovery at the global scale

Integrating Multisensor Satellite Data Merging And Image Reconstruction In Support Of Machine Learning For Better Water Quality Management

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed “cross-mission data merging and image reconstruction with machine learning” (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies