scholarly journals LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion

2021 ◽  
Author(s):  
Kaixu Bai ◽  
Ke Li ◽  
Mingliang Ma ◽  
Kaitao Li ◽  
Zhengqiang Li ◽  
...  
Keyword(s):  
Environmental Management ◽  
High Resolution ◽  
Data Fusion ◽  
Air Pollutants ◽  
Big Data Analytics ◽  
Quality Data ◽  
Haze Pollution ◽  
Pm2.5 And Pm10

Abstract. Developing a big data analytics framework for generating a Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor flow based data fusion method, a gap-free aerosol optical depth (AOD) dataset with daily 1-km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from MODIS aboard Terra were reconstructed based on a set of AOD data tensors acquired from satellites, numerical analysis, and in situ air quality data via integrative efforts of spatial pattern recognition for high dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in a good agreement with in situ AOD observations from AERONET, with R of 0.91 and RMSE equaling to 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R of 0.95 and 0.94 and RMSE of 12.03 and 19.56 μg m−3, respectively. Overall, the LGHAP provides a suite of long-term gap free gridded maps with high-resolution to better examine aerosol changes in China over the past two decades, from which three distinct variation periods of haze pollution were revealed in China. Additionally, the proportion of population exposed to unhealthy PM2.5 was increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then drastically reduced to 34.03 % in 2020. Overall, the generated LGHAP aerosol dataset has a great potential to trigger multidisciplinary applications in earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets can be publicly accessed at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Meanwhile, monthly and annual mean datasets can be found at https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, Matlab, R, and IDL codes were also provided to help users read and visualize these data.

scholarly journals Long-Term Exposure to Fine and Coarse Particulate Matter and COVID-19 Incidence and Mortality Rate in Chile during 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7409
Author(s):  
Macarena Valdés Salgado ◽  
Pamela Smith ◽  
Mariel Opazo ◽  
Nicolás Huneeus
Keyword(s):  
Mortality Rate ◽  
Incidence Rate ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Transport Model ◽  
Surface Concentration ◽  
Mortality Rates ◽  
Incidence And Mortality ◽  
Pm2.5 And Pm10

Background: Several countries have documented the relationship between long-term exposure to air pollutants and epidemiological indicators of the COVID-19 pandemic, such as incidence and mortality. This study aims to explore the association between air pollutants, such as PM2.5 and PM10, and the incidence and mortality rates of COVID-19 during 2020. Methods: The incidence and mortality rates were estimated using the COVID-19 cases and deaths from the Chilean Ministry of Science, and the population size was obtained from the Chilean Institute of Statistics. A chemistry transport model was used to estimate the annual mean surface concentration of PM2.5 and PM10 in a period before the current pandemic. Negative binomial regressions were used to associate the epidemiological information with pollutant concentrations while considering demographic and social confounders. Results: For each microgram per cubic meter, the incidence rate increased by 1.3% regarding PM2.5 and 0.9% regarding PM10. There was no statistically significant relationship between the COVID-19 mortality rate and PM2.5 or PM10. Conclusions: The adjusted regression models showed that the COVID-19 incidence rate was significantly associated with chronic exposure to PM2.5 and PM10, even after adjusting for other variables.

scholarly journals Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 336
Author(s):  
Feiyang Zhang ◽  
Guangxing Wang ◽  
Yueming Hu ◽  
Liancheng Chen ◽  
A-xing Zhu
Keyword(s):  
High Resolution ◽  
Monitoring System ◽  
Data Transmission ◽  
Remote Sensing Data ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Quality Data ◽  
Conventional System ◽  
Sensing Data

Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.

Microfluidic passive samplers for in situ collection of live aquatic protists

2014 ◽  
Vol 6 (20) ◽  
pp. 8350-8357 ◽  
Author(s):  
Grant M. Bouchillon ◽  
Jessica Furrer Chau ◽  
George B. McManus ◽  
Leslie M. Shor
Keyword(s):  
High Resolution ◽  
Passive Samplers ◽  
Aquatic Habitats ◽  
Long Term Observation ◽  
In Situ Collection

Examples of microfluidic passive samplers for collecting live protists from aquatic habitats. The samplers allow high-resolution, long-term observation of unstained protists by concentrating and isolating them in nanoliter-scale galleries.

scholarly journals Long-Term Exposure to Air Pollution and The Risk of Developing Sudden Sensorineural Hearing Loss

2020 ◽  
Author(s):  
Stella Chin-Shaw Tsai ◽  
Yi-Chao Hsu ◽  
Jung-Nien Lai ◽  
Ruey-Hwang Chou ◽  
Hueng-Chuen Fan ◽  
...  
Keyword(s):  
Air Pollution ◽  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Air Pollutants ◽  
Sudden Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Quality Data ◽  
Increased Risk ◽  
High Level

Abstract Background The association between exposure to air pollution and sudden sensorineural hearing loss (SSNHL) has not been extensively discussed in the literature. Therefore, we conducted this nationwide study to evaluate the risk of SSNHL in Taiwanese residents with exposure to air pollution.Methods We enrolled subjects aged older than 20 years with no history of SSNHL from 1998 to 2010, and followed up until developing SSNHL, withdrawn from the National Health Insurance program, and the end of the database (2011/12/31). The air quality data are managed by Taiwan Environmental Protection Administration. The annual concentrations of PM2.5, SO2, CO, NO, and NO2 from 1998 to 2010 were classified into the three levels according to tertiles. We calculated the annual average of pollutants from baseline until the end of the study, and classified into tertiles. The adjusted hazard ratio (aHR) was estimated by using the multivariate Cox proportional hazard model.Results When considered continuous air pollutants concentration, subjects who exposed with higher concentration of CO (aHR=2.19, 95%CI=1.52-3.15), NO (aHR=1.02, 95%CI=1.01-1.03), and NO2 (aHR=1.02, 95%CI=1.01-1.04) developing significant higher risk of SSNHL.When classified air pollutants concentration into low, moderate and high level by tertiles, and selected low level as reference, patients exposed with moderate (aHR=1.58, 95%CI=1.21-2.06) or high level (aHR=1.32, 95%CI=1.00-1.74) of PM2.5 showed significant higher risk of developing SSNHL.Conclusion This study indicated an increased risk of SSNHL in residents with long-term exposure to air pollution. Nevertheless, further experimental and clinical studies are needed to validate the study findings.Disclosure statementAll authors declare that there is no conflicts of interest. This study was approved in part by Taiwan Ministry of Health and Welfare Clinical Trial and Research Center of Excellence (MOHW105-TDU-B-212-133019). The committee agree to waive the requirement for consent.

scholarly journals Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

2021 ◽  
Vol 15 (9) ◽  
pp. 4261-4279
Author(s):  
Xiaodan Wu ◽  
Kathrin Naegeli ◽  
Valentina Premier ◽  
Carlo Marin ◽  
Dujuan Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Land Cover ◽  
Snow Cover ◽  
Area Coverage ◽  
Slope Aspect ◽  
Hindu Kush ◽  
Very High

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

scholarly journals In-situ Real-time Field Imaging and Monitoring of Leaf Stomata by High-resolution Portable Microscope

2019 ◽  
Author(s):  
Prashant Purwar ◽  
Junghoon Lee
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Stomatal Opening ◽  
Objective Lens ◽  
Lower Epidermis ◽  
Plant Hydraulics ◽  
Small Depth ◽  
Field Imaging

ABSTRACTStomata, functionally specialized micrometer-sized pores on the epidermis of leaves (mainly on the lower epidermis), control the flow of gases and water between the interior of the plant and atmosphere. Real-time monitoring of stomatal dynamics can be used for predicting the plant hydraulics, photosensitivity, and gas exchanges effectively. To date, several techniques offer the direct or indirect measurement of stomatal dynamics, yet none offer real-time, long-term persistent measurement of multiple stomal apertures simultaneously of an intact leaf in a field under natural conditions. Here, we report a high-resolution portable microscope-based technique for in situ real-time field imaging and monitoring of stomata. Our technique is capable of analyzing and quantifying the multiple lower epidermis stomal pore dynamics simultaneously and does not require any physical or chemical manipulation of a leaf. An upward facing objective lens in our portable microscope allows the imaging of lower epidermis stomatal opening of a leaf while upper epidermis being exposed to the natural environment. Small depth of field (~ 1.3 μm) of a high-magnifying objection lens assists in focusing the stomatal plane in highly non-planar tomato leaf having a high density of trichome (hair-like structures). For long-term monitoring, the leaf is fixed mechanically by a novel designed leaf holder providing freedom to expose the upper epidermis to the sunlight and lower epidermis to the wind simultaneously. In our study, a direct relation between the stomatal opening and the intensity of sunlight illuminating on the upper epidermis has been observed in real-time. In addition, real-time porosity of leaf (ratio between the areas of stomatal opening to the area of a leaf) and stomatal aspect ratio (ratio between the major axis and minor axis of stomatal opening) along with stomatal density have been quantified.

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