AIMC Topic: Environmental Monitoring

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Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system.

Environmental monitoring and assessment
This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main ...

Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant.

Environmental monitoring and assessment
In this study, artificial neural networks (ANNs) including feed forward back propagation neural network (FFBP-NN) and the radial basis function neural network (RBF-NN) were applied to predict daily sewage sludge quantity in wastewater treatment plant...

An ensemble long short-term memory neural network for hourly PM concentration forecasting.

Chemosphere
To protect public health by providing an early warning, PM concentration forecasting is an essential and effective work. In this paper, an ensemble long short-term memory neural network (E-LSTM) is proposed for hourly PM concentration forecasting. Th...

Hydrochemical Analysis and Fuzzy Logic Method for Evaluation of Groundwater Quality in the North Chengdu Plain, China.

International journal of environmental research and public health
Groundwater is a major water resource in the North Chengdu Plain, China. The research objective is to determine the quality and suitability of groundwater for drinking purposes within the vicinity of a shallow, unconsolidated aquifer of Quaternary ag...

Gradients of three coastal environments off the South China Sea and their impacts on the dynamics of heterotrophic microbial communities.

The Science of the total environment
Heterotrophic fungus-like marine protists are recognized to contribute significantly to the coastal carbon cycling largely due to their high biomass and ability to decompose recalcitrant organic matter. Yet, little is known about their dynamics at po...

Determination of 17 potential endocrine-disrupting chemicals in human saliva by dispersive liquid-liquid microextraction and liquid chromatography-tandem mass spectrometry.

Talanta
Endocrine-disrupting chemicals are a group of emerging contaminants that alters the function of the endocrine system, causing possible adverse health effects. In this study, a dispersive liquid-liquid microextraction method coupled with liquid chroma...

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...