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Environmental Monitoring

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Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.

Chemosphere
People have been suffering from air pollution for a decade in China, especially from PM (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-dr...

PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.

PloS one
Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and...

Prenatal exposure to persistent organic pollutants in Northern Tanzania and their distribution between breast milk, maternal blood, placenta and cord blood.

Environmental research
Human exposure to persistent organic pollutants (POPs) begins during pregnancy and may cause adverse health effects in the fetus or later in life. The present study aimed to assess prenatal POPs exposure to Tanzanian infants and evaluate the distribu...

Spatiotemporal continuous estimates of PM concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.

Environment international
Ambient exposure to fine particulate matter (PM) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM concentrat...

Particulate Matter Exposure of Passengers at Bus Stations: A Review.

International journal of environmental research and public health
This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM...

Using a stacked-autoencoder neural network model to estimate sea state bias for a radar altimeter.

PloS one
This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training data...

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Trends in microbiology
Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement ...

Shallow convective mixing promotes massive Noctiluca scintillans bloom in the northeastern Arabian Sea.

Marine pollution bulletin
The northeastern Arabian Sea (NEAS) experiences convective mixing during winter, but this mixing does not reach up to the silicicline, resulting in the limited supply of silicate (Si) compared to nitrate (N) and phosphate (P) to the mixed layer (ML) ...

Marine litter accumulation along the Bulgarian Black Sea coast: Categories and predominance.

Waste management (New York, N.Y.)
Quantitative assessment of marine litter (ML) along the Bulgarian Black Sea coastline was presented. ML surveys were conducted every season in a total of 8 beach monitoring sites during 2015-2016. Eight main categories of material were determined, ba...

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BMC ecology
BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple envi...