Daily accumulation of marine litter in a clam culture area in Can Gio District, Ho Chi Minh City, Vietnam.

The health of humans, the economy, and the marine ecology are all seriously threatened by marine litter. Therefore, quantifying the scope of the issue is gaining more and more attention. Studying beach litter accumulation is one of the approaches to investigating its flows into the marine environment.

Hyperspectral sensing of heavy metals in soil by integrating AI and UAV technology.

Given the limitation of conventional soil pollution monitoring through mapping which is a costly, time-consuming work, the study aims to establish an image recognition model to identify the source of pollution automatically. The study choses a contaminated land and then use a non-destructive instrument that can quickly and effectively measure the content of heavy metals. A two concentration prediction models of Ni, Cu, Zn, Cr, Pb, As, Cd, and Hg using hyperspectral imaging were developed, Decision Tree and Back Propagation Neural Network, in combination of particle swarm optimization employed for optimization algorithm.

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series.

Generation patterns and consumer behavior of single-use plastic towards plastic-free university campuses.

This study was conducted to estimate the generation of single-use plastics (SUPs) and elucidate consumer behavior towards a plastic-free university. The results show that the consumption rate of plastic bottles was the highest at 1.39 g per student per day (g.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control.

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention.

Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling.

Sand and dust storms in arid and semiarid regions deteriorate regional air quality and threaten public health security. To quantify the negative effects of river dust on regional air quality, this study selected the estuary areas located in central Taiwan as a case study and proposed an integrated framework to measure the fugitive emission of dust from riverbeds with the aid of satellite remote sensing and wind tunnel test, together with the concentrations of particulate matter with a diameter of <10 μm (PM) around the river system by using The Air Pollution Model. Additionally, the effects of 25 types of meteorological conditions on the health risk due to exposure to dust were evaluated near the estuary areas.