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Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-res.lution satellite-derived and ground-measured PM2.5 data

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019年 233卷 530-542页

作者： bai, Kaixu Ma, Mingliang Chang, Ni-bin Gao, Wei East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Atmospheric fine particulate matters (PM2.5) have raised global concerns because of their markedly adverse effects on public health and environmental quality. In parallel with technological variations and social changes in the evolving industrialization pathways in China, there is an acute need to evaluate the long-term spatio-temporal trend of PM2.5 concentrations across China after years of elevation. Toward this end, an integrated high-res.lution satellite-derived (1998-2016) and ground-measured (2015-2017) PM2.5 data base was applied. Satellite-derived annual mean PM2.5 grids were firstly validated via comparison with collocated surface in situ PM2.5 measurements and were then used for trend analyses. The estimated linear trends from gridded PM2.5 data indicated that PM2.5 concentrations in China increased mainly before 2008 and have decreased since then, with prominent decreases observed primarily in south China. To corroborate the satellite-based PM2.5 trend estimations, surface in situ PM2.5 measurements from the period from 2015 to 2017 were applied to further evaluate the decreasing rate after 2014, at which time the Chinese "Air Pollution Prevention and Control Action Plan" was enforced. The res.lts revealed that the national mean PM(2.5)concentrations decreased by about 6.5 mu g/m(3) from 2015 to 2017, with prominent decreases (by a rate of 5-10 mu g/m(3) per year) observed primarily associated with largePM(2.5) concentrations in Central China, North China, Northeast China, and East China during the period from October to December. Our systematic trend assessment provides a deepened understanding of PM2.5 variations across China in the past few years in association with the newly promoted action plan and offers a brief guideline for relevant policy making in the future.

关键词： Air quality PM2.5 Fine particulate matter Emission control Trend analysis

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Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives

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CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2020年第16期50卷 1640-1725页

作者： Wei, Xiaoli Chang, Ni-bin bai, Kaixu Gao, Wei East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai Peoples R China East China Normal Univ Sch Geog Sci Shanghai Peoples R China Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

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 interes. 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-res.lution 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 res.lting 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, pres.nts the challenges with res.ect to the derivation of high-res.lution AOD, and highlights perspectives regarding the possible improvements of satellite-based AOD estimation.

关键词： aerosol optical depth particulate matter air quality inverse modeling remote sensing

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Seasonal grassland productivity forecast for the US Great Plains using Grass-Cast

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ECOSPHERE 2020年第11期11卷

作者： Hartman, Melannie D. Parton, William J. Derner, Justin D. Schulte, Darin K. Smith, William K. Peck, Dannele E. Day, Ken A. Del Grosso, Stephen J. Lutz, Susan Fuchs, brian A. Chen, Maosi Gao, Wei Colorado State Univ Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Nat Resource Ecol Lab USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA USDA ARS Rangeland Resources & Syst Res Unit Cheyenne WY 82009 USA Univ Arizona Sch Nat Resources & Environm Tucson AZ 85721 USA USDA Northern Plains Climate Hub Ft Collins CO 80526 USA Dept Environm & Sci Climate Variabil Unit Div Sci & Technol Brisbane Qld Australia USDA ARS Soil Management & Sugar Beet Res Unit Ft Collins CO 80526 USA Univ Nebraska Lincoln Natl Drought Mitigat Ctr Lincoln NE 68583 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge-trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season aboveground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)-combined with ecosystem modeling and seasonal precipitation forecasts-to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in larger-scale decision-making-such as where forage res.urces for grazing might be more plentiful if a rancher's own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we pres.nt the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-of-growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP res.lts to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Althou

关键词： actual evapotranspiration adaptive management DayCent model decision‐ making forecast Grass‐ Cast grassland production Great Plains NDVI rangelands

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Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2019年 154卷 10-27页

作者： Zhao, Qing Ma, Guanyu Wang, Qiang Yang, Tianhang Liu, Min Gao, Wei Falabella, Francesco Mastro, Pietro Pepe, Antonio East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Minist Land & Resources Key Lab Land Subsidence Monitoring & Prevent Shanghai 200072 Peoples R China Shanghai Inst Geol Survey Shanghai 200072 Peoples R China East China Normal Univ Chongming ECO Inst Shanghai 200241 Peoples R China Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA Natl Res Council CNR Italy Inst Electromagnet Sensing Environm IREA 328 Diocleziano I-80124 Naples Italy Univ Basilicata Sch Engn Viale Ateneo Lucano I-85100 Potenza Italy

Ground deformation is one of the most significant challenges faced by many coastal mega-cities, with major societal and economic impacts. In this context, the possibility to monitor the temporal evolution of the ground subsidence processes, which may also last for several years, is of great relevance. This goal can be obtained by applying differential SAR interferometry (DInSAR) techniques to sequences of multiple-satellite synthetic aperture radar (SAR) images. Moreover, the growing availability of large archives of SAR images collected by different SAR instruments nowadays leads to the need of developing new data merging techniques, which may take profit from the complementary information recoverable from every single set of data. In this work, a novel data merging approach for the generation of long-tem ground displacement time-series, based on the use of the modified Quantile-Quantile Adjustment (MQQA) algorithm, is proposed. Specifically, the methodology has successfully been applied to study the long-term evolution of the ground subsidence occurred in the coastal area of Shanghai from February 2007 to April 2017. A cross-comparison analysis between DInSAR and ground truth data has also been carried out, showing that the average root mean square error (RMSE) between the obtained displacement time-series and available ground truth data is of about 3?mm. This outcome confirms the validity of the novel DInSAR-based MQQA combination method.

关键词： InSAR Ground deformation Multi-pass interferometry Data merging bias adjustment

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Spatial interpolation of surface ozone observations using deep learning 15

Spatial interpolation of surface ozone observations using de...

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Conference on Remote Sensing and Modeling of Ecosystems for Sustainability XV

作者： Chen, Maosi Sun, Zhibin Davis, John M. Liu, Chaoshun Gao, Wei Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai Peoples R China Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

ISBN: (纸本)9781510621060

Surface ozone can trigger many health problems for human (e.g. coughing, bronchitis, emphysema, and asthma), especially for children and the elderly. It also has harmful effects on plants (e.g. chlorosis, necrosis, and yield reduction). The United State (U.S.) Environmental Protection Agency (EPA) has been monitoring surface ozone concentrations across the U.S. since 1980s. However, their stations are sparsely distributed and mainly in urban areas. Evaluation of surface ozone effects at any given locations in the U.S. requires.spatial interpolation of ozone observations. In this study, we implemented two traditional spatial interpolation methods (i.e. triangulation-based linear interpolation and geostatistics-based method). One limitation of these two methods is their reliance on single-scene observations in constructing the spatial relationship, which is prone to influence of noisy observations and has large uncertainty. Deep learning, on the other hand, is capable of simulating common patterns (including complex spatial patterns) from a large amount of training samples. Therefore, we also implemented three deep learning algorithms for the spatial interpolation problem: mixture model network (MoNet), Convolutional Neural Network for Graphs (ChebNet), and Recurrent Neural Network (RNN). The training and validation data of this study are the 2016 EPA hourly surface ozone observations within +/- 3-degree box centered at the billings, Oklahoma station (usda uv-b monitoring and res.arch program). The res.lts showed that among the five methods, RNN and MoNet outperformed the two traditional spatial interpolation methods and RNN has the lowest validation error (mean absolute error: 2.82 ppb;standard deviation: 2.76 ppb). Finally, we used the integrated gradients method to analyze the attribution of RNN inputs on the surface ozone prediction. The res.lts showed that surface ozone observation is the most important input feature followed by distance and absolute locations (i

关键词： Surface ozone spatial interpolation Spatial Process Model Convolutional Neural Network for Graphs (ChebNet) mixture model network (MoNet) Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM)

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Quantifying impacts of crop res.due burning in the North China Plain on summertime tropospheric ozone over East Asia

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ATMOSPHERIC ENVIRONMENT 2018年 194卷 14-30页

作者： Ma, Mingliang bai, Kaixu Qiao, Fengxue Shi, Runhe Gao, Wei East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China USDA Nat Resource Ecol Lab UV B Monitoring & Res Program Ft Collins CO USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

Crop res.due burning has been proved to have negative impacts on regional atmospheric environment. In this study, an evidence-based statistical modeling framework was established to quantify potential impacts of crop res.due burning in the North China Plain (NCP) on summertime tropospheric ozone increase over East Asia during 2004-2016. To assess the intensity of crop res.due burning, fire occurrence counts derived from the MODerate-res.lution Imaging Spectroradiometer onboard the Terra and Aqua satellites were used as a proxy. Additionally, another six factors were employed as potent explanatory variables. Maximum covariance analysis was first applied to decouple spatiotemporal interactions between tropospheric ozone and each explanatory variable. based on the decoupled modes, multivariate linear regres.ion (MLR) and artificial neural network (ANN) were used to establish statistical relationships between tropospheric ozone and contributing factors, res.ectively. The res.lts indicate that the ANN-based modeling scheme enables to approximate the observed tropospheric ozone variations better than MLR. Further investigations reveal that the summertime crop res.due burning in the NCP is the predominant factor contributing to the observed additive tropospheric ozone increases over East Asia, yielding extra 8% tropospheric ozone elevation on average in June. Moreover, uv radiation and wind also played critical roles in modulating the observed tropospheric ozone variations therein. In general, the critical role of crop res.due burning over the NCP in modulating summertime tropospheric ozone increase over East Asia have been well demonstrated based on the proposed evidenced-based modeling framework.

关键词： Crop res.due burning Tropospheric ozone North China Plain (NCP) Statistical modeling Ozone pollution

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NILU-uv multi-filter radiometer total ozone columns: Comparison with satellite observations over Thessaloniki, Greece

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SCIENCE OF THE TOTAL ENVIRONMENT 2017年 590卷 92-106页

作者： Zempila, Melina Maria Taylor, Michael Koukouli, Maria Elissavet Lerot, Christophe Fragkos, Konstantinos Fountoulakis, Ilias bais, Alkiviadis balis, Dimitrios van Roozendael, Michel Colorado State Univ USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA Aristotle Univ Thessaloniki Lab Atmospher Phys POB 149 Thessaloniki 54124 Greece BIRA IASB Belgian Inst Space Aeron Brussels Belgium

This study aims to construct and validate a neural network (NN) model for the production of high frequency (similar to 1 min) ground-based estimates of total ozone column (TOC) at a mid-latitude uv and ozone monitoring station in the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki (LAP/AUTh) for the years 2005-2014. In the first stage of model development, similar to 30,000 records of coincident solar uv spectral irradiance measurements from a Norsk Institutt for Luftforskning (NILU)-uv multi-filter radiometer and TOC measurements from a co-located brewer spectroradiometer are used to train a NN to learn the nonlinear functional relation between the irradiances and TOC. The model is then subjected to sensitivity analysis and validation. Close agreement is obtained (R-2 = 0.94, RMSE = 821 DU and bias = -0.15 DU relative to the brewer) for the training data in the correlation of NN estimates on brewer derived TOC with 95% of the coincident data differing by less than 13 DU. In the second stage of development, a long time series (>= 1 million records) of high frequency (similar to 1 min) NILU-uv ground-based measurements are pres.nted as inputs to the NN model to generate high frequency TOC estimates. The advantage of the NN model is that it is not site dependent and is applicable to any NILU input data lying within the range of the training data. GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura and GOME2/MetOp-ATOC records are then used to perform a precise cross-validation analysis and comparison with the NILU TOC estimates over Thessaloniki. All 4 satellite TOC dataset are retrieved using the COME Direct Fitting algorithm, version 3 (GODFIT_v3), for reasons of consistency. The NILU TOC estimates within +/- 30 min of the overpass times agree well with the satellite TOC retrievals with coefficient of determination in the range 0.88 <= R-2 <= 0.90 for all sky conditions and 0.95 <= R-2 <= 0.96 for clear sky conditions. The mean fractional differenc

关键词： Total ozone column NIW-uv multi -filter radiometer brewer spectrophotometer Neural network GOME/ERS-2 SCIAMACHY/Envisat OMI/Aura GOME2/MetOpA

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Using Input Feature Information to Improve Ultraviolet Retrieval in Neural Networks 14

Using Input Feature Information to Improve Ultraviolet Retri...

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Conference on Remote Sensing and Modeling of Ecosystems for Sustainability XIV

作者： Sun, Zhibin Chang, Ni-bin Gao, Wei Chen, Maosi Zempila, Melina Colorado State Univ USDA Nat Resource Ecol Lab UV B Monitoring & Res Program Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA

ISBN: (数字)9781510612686

ISBN: (纸本)9781510612686;9781510612679

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features.of several important physical factors in retrieving surface ultraviolet (uv) are extracted. Extreme learning machine (ELM) is used to retrieve the surface uv of 2014 in the continental United States, using the extracted features.as inputs. The res.lts conclude that more input weights can improve the learning capacities of neural networks.

关键词： Ultraviolet retrieval Input feature Neural network Extreme learning machine

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Intercomparison of CALIOP, MODIS, and AERONET aerosol optical depth over China during the past decade

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2018年第21期39卷 7251-7275页

作者： Liu, Chaoshun Shen, Xianxia Gao, Wei East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai Peoples R China East China Normal Univ Sch Geog Sci Shanghai Peoples R China Colorado State Univ Nat Resource Ecol Lab USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA

Aerosol optical depth (AOD) data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were intercompared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as space-based Moderate res.lution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2015. This article aims to evaluate CALIOP daytime AOD using MODIS and AERONET AODs. Comparing the AOD between CALIOP and AERONET in different regions over China using quality control flags to screen the AOD data, we find that CALIOP AOD is generally lower than AERONET AOD especially at optical depths over 0.4 likely due to differences in the cloud screening algorithms and general retrieval uncertainty. Comparison between CALIOP AOD and MODIS AOD res.lts show that the overall spatio-temporal distribution of CALIOP AOD and MODIS AOD is basically consistent. As for the spatial distribution, both data sets show several high-value regions and low-value regions in China. CALIOP is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data sets show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. A long-term linear trend analysis based on the domain averaged monthly mean CALIOP and MODIS AOD shows agreement among CALIOP and MODIS for the trends over the 10-year period in four regions examined. The trends in AOD derived from CALIOP and MODIS indicate a decline in aerosol loading in China since 2006. It is found from frequency comparison that CALIOP and MODIS AOD generally exhibit a degree of correlation over China. Statistical frequency analysis shows that CALIOP AOD frequency distribution shows a higher peak than MODIS AOD when AOD < 0.4. For the most part, mean MODIS AOD is higher than mean CALIOP AOD. Evaluation of CALIOP AOD retrievals provides the prospect for application of CALIOP data. T

关键词： Quality control

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Photodegradation accelerates ecosystem N cycling in a simulated California grassland

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ECOSPHERE 2018年第8期9卷

作者： Asao, Shinichi Parton, William J. Chen, Maosi Gao, Wei Colorado State Univ USDA UV B Monitoring & Res Program Nat Resource Ecol Lab Ft Collins CO 80523 USA Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA Australian Natl Univ Res Sch Biol ARC Ctr Excellence Plant Energy Biol Bldg 134 Canberra ACT 2601 Australia

Photodegradation accelerates litter decay in arid grasslands where plant growth and litter decay are strongly controlled by precipitation and evapotranspiration. However, the effects of photodegradation on ecosystem C and N dynamics are not well understood. We examined the effects using an ecosystem biogeochemical model DayCent-uv with photodegradation explicitly repres.nted and validated. The model was parameterized for a California grassland where photodegradation was documented to release CO2 from litter. The model was parameterized with an inverse modeling approach using an extensive data set of six years of daily observed carbon and water gas exchange (gross primary production, ecosystem res.iration, and evapotranspiration), soil temperature, and soil moisture. DayCent-uv correctly simulated the seasonal patterns of the observed gas exchange and closely simulated the inter-annual variation in the gas exchange and biomass production rates. The simulations suggested that the inter-annual variation is driven more by actual evapotranspiration than by precipitation because a large portion of precipitation is lost as runoff during wet years. Photodegradation in DayCent-uv accelerated C and N cycling, decreasing system C and N by 9.2% and 9.5% and C and N res.dence times by 9.4% and 18.2%. Accelerated N cycling made a greater fraction of system N available for plants, increasing net N mineralization and plant production for a given amount of system N. Increased net N mineralization was due to decreased immobilization by microbes in the aboveground organic matter. Photodegradation did not alter the control on plant production by evapotranspiration. These res.lts suggest that at the ecosystem level, the central effect of photodegradation is to suppres. microbial activity. We conclude that photodegradation accelerates N cycling at the expense of microbes in this grassland, making it more efficient in supporting plant growth for a given amount of N in the system.

关键词： biogeochemistry C and N cycling DayCent ecosystem modeling grassland litter decomposition photodecay photodegradation solar radiation ultraviolet radiation

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